Review Open Access
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Aug 28, 2021; 27(32): 5306-5321
Published online Aug 28, 2021. doi: 10.3748/wjg.v27.i32.5306
Radiomics and machine learning applications in rectal cancer: Current update and future perspectives
Arnaldo Stanzione, Francesco Verde, Valeria Romeo, Francesca Boccadifuoco, Pier Paolo Mainenti, Simone Maurea
Arnaldo Stanzione, Francesco Verde, Valeria Romeo, Francesca Boccadifuoco, Simone Maurea, Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
Pier Paolo Mainenti, Institute of Biostructures and Bioimaging, National Council of Research, Napoli 80131, Italy
ORCID number: Arnaldo Stanzione (0000-0002-7905-5789); Francesco Verde (0000-0002-9823-4678); Valeria Romeo (0000-0002-1603-6396); Francesca Boccadifuoco (0000-0001-6003-362X); Pier Paolo Mainenti (0000-0003-3592-808X); Simone Maurea (0000-0002-8269-3765).
Author contributions: Stanzione A, Verde F, Romeo V, Boccadifuoco F, Mainenti PP, and Maurea S equally contributed to this paper with conception and design of the study, literature review and analysis, drafting and critical revision and editing, and final approval of the final version.
Conflict-of-interest statement: The authors declare that they have no conflicting interests.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Valeria Romeo, MD, Academic Research, Doctor, Research Fellow, Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Sergio Pansini 5, Naples 80131, Italy. valeria.romeo@unina.it
Received: January 27, 2021
Peer-review started: January 27, 2021
First decision: March 7, 2021
Revised: March 13, 2021
Accepted: July 22, 2021
Article in press: July 22, 2021
Published online: August 28, 2021

Abstract

The high incidence of rectal cancer in both sexes makes it one of the most common tumors, with significant morbidity and mortality rates. To define the best treatment option and optimize patient outcome, several rectal cancer biological variables must be evaluated. Currently, medical imaging plays a crucial role in the characterization of this disease, and it often requires a multimodal approach. Magnetic resonance imaging is the first-choice imaging modality for local staging and restaging and can be used to detect high-risk prognostic factors. Computed tomography is widely adopted for the detection of distant metastases. However, conventional imaging has recognized limitations, and many rectal cancer characteristics remain assessable only after surgery and histopathology evaluation. There is a growing interest in artificial intelligence applications in medicine, and imaging is by no means an exception. The introduction of radiomics, which allows the extraction of quantitative features that reflect tumor heterogeneity, allows the mining of data in medical images and paved the way for the identification of potential new imaging biomarkers. To manage such a huge amount of data, the use of machine learning algorithms has been proposed. Indeed, without prior explicit programming, they can be employed to build prediction models to support clinical decision making. In this review, current applications and future perspectives of artificial intelligence in medical imaging of rectal cancer are presented, with an imaging modality-based approach and a keen eye on unsolved issues. The results are promising, but the road ahead for translation in clinical practice is rather long.

Key Words: Rectal cancer, Radiomics, Radiogenomics, Artificial intelligence, Machine learning, Deep learning

Core Tip: Rectal cancer is a common malignancy requiring a multidisciplinary approach to ensure the best clinical management. Diagnostic imaging has contributed to increased survival rates and provided crucial information on the course of rectal cancer patients. Artificial intelligence, and in particular radiomics and machine learning, are promising techniques that could further enhance the value of medical imaging, allowing the building of decision support tools based on quantitative data. We herein present and discuss the potential role of artificial intelligence in rectal cancer applied to different medical imaging modalities.



INTRODUCTION

In 2020, more than 40,000 cases of rectal cancer (RC) were expected in the United States alone, with a higher incidence in men than in women and a median age at diagnosis of 63 years[1]. However, over the past years there has been an improvement in RC management associated with a reduction of mortality and higher survival rates, mainly related to earlier diagnosis and more effective treatment[2]. While endoscopy represents the gold standard for RC diagnosis, there are several factors to be considered that influence prognosis and therapeutic strategy, including local tumor extent (T), lymph nodes status (N) and presence of distant metastases (M)[3]. Indeed, radical surgery with curative intent (i.e. total mesorectal excision, TME) is recommended as a first-line strategy in patients with locally confined disease after neoadjuvant chemoradiotherapy (nCRT) for locally advanced RC (LARC). Metastatic patients, on the other hand, usually undergo systemic therapies such as chemotherapy, targeted therapy, or immunotherapy[4,5]. Diagnostic imaging plays a crucial role for pretreatment disease staging, with a multimodal approach commonly being necessary[6]. Magnetic resonance imaging (MRI) is regarded as the most valuable imaging modality for primary loco-regional staging of RC and restaging after nCRT[7,8]. Computed tomography (CT) scans are routinely performed to detect distant metastases, with the most common metastatic sites being the liver and lungs[2]. Currently, hybrid imaging by positron emission tomography/CT (PET/CT) could provide useful prognostic data for RC, even if its role still remains to be defined[6,9]. Likewise, the potential of simultaneously acquired PET and MRI still has to be explored[10]. However, conventional image assessment has recognized limitations that are driving the research towards the identification and validation of novel strategies to further increase the value of diagnostic imaging[11-13]. In this setting, a post processing quantitative technique known as radiomics appears particularly promising, with encouraging evidence collected in recent years[14,15]. Radiomics has been frequently and successfully coupled with artificial intelligence (AI), and in particular machine learning (ML) approaches in the field of oncologic imaging[16-19]. This review aims to introduce readers to the concepts of radiomics and ML and to present the state-of-the-art of RC radiomics-ML applications, with an imaging modality-based approach, highlighting their strengths and drawbacks.

RADIOMICS AND ML: WHAT, WHY, AND HOW

Trying to quantify what is visually assessed in medical imaging is a rather difficult task, and radiologists have traditionally provided qualitative information and semi-quantitative data in their reports[20]. However, this leads to a large amount of unused data remaining hidden in medical images[21]. Furthermore, semantic descriptors of cancer imaging phenotype (e.g., “central necrosis”, “irregular margin”, and “diffusely heterogeneous”) are prone to poor intra- and interobserver reliability, experience dependent, and might not significantly reflect actual tumor biology[22]. Indeed, tumors are not considered to be homogeneous entities but instead composed of various cell clones with biologically relevant differences[23]. Radiomics allows the conversion of images into mineable data with the high-throughput extraction of quantitative parameters (i.e. radiomics features) that capture the heterogeneities and provide important information on cancer phenotype[24]. Radiomics is a multistep process beginning with image acquisition and followed by image segmentation, which is the two- or three-dimensional delineation of the region of interest (ROI), usually represented by the primary tumor. Image segmentation can be manual, performed by a human operator; semiautomatic, performed by AI and manually adjusted; or automatic, exclusively performed by AI[25] . Subsequently, hundreds of radiomics features can be extracted from the ROI using specifically designed formulae conveying different information, including shape, first-order (based on the distribution of pixel intensities), second and higher-order features (accounting for pixel intensities spatial distribution)[26]. Correlating radiomics features to the outcomes of interest is the endpoint of radiomics, and many believe it could open the gateway to precision medicine[27,28]. However, such a huge amount of data can be more easily handled by AI rather than traditional statistical methods[21]. Indeed, ML is a branch of AI focused on algorithms that can be trained for a task they were not specifically programmed to perform[29]. The algorithms are essentially used for classification problems, with the main oncologic imaging application being decision support in various settings that include detection, characterization, and monitoring[30-32]. To properly train an ML algorithm, “the curse of dimensionality,” which is a set of issues arising when working with a number of features much higher than the patient population must be avoided. Feature reduction can be achieved in several ways that may also be combined to achieve better results[33,34]. Indeed, an excessive number of features increases the chances of finding nongeneralizable correlations (i.e. overfitting). On the other hand, complex relationships might need more features to build a proper prediction model[35]. Finally, trained ML classifiers need to be tested to verify generalizability on external data not used in the training process and possibly provided by different institutions[36]. A kind of ML algorithm called deep learning (DL), based on neural networks (NN), does not necessarily require image segmentation and learns autonomously the best features for performing data classification[37]. A brief description of the most commonly applied ML algorithms in RC radiomics can be found in Table 1.

Table 1 Overview of the most widely adopted machine learning algorithms in rectal cancer imaging.
Algorithm name
Description
Random forestAn ensemble method that combines multiple decision trees (a class of predictive learning models used in supervised ML) to obtain more accurate results for classification and regression tasks
Support vector machineA linear approach used mainly for classification problems with the aim to find the best hyper plane which most accurately separate input data into two classes
Logistic regressionA classifier used to obtain the best fitting model for the relationship between multiple predictor variables and a dichotomous outcome
LASSOA regularized regression method that performs both variable selection and regularization in order to optimally fit the resulting generalized statistical model
Naive BayesA classifier relying on the Bayes Theorem to model the probability of an outcome based on the strong (naive) independence assumptions between the features data
Quadratic discriminant analysisA subtype of Dimensionality Reduction Algorithms that turn high-dimensional data into to low-dimensional data retaining the most significant features of original data for the prediction of the class label
ANNA subgroup of ML composed of neuronal-like multi-layered networks allowing to automatically extract features without prior labelling and perform complex operations
CNNAs subset of ANN containing multiple computational hidden layers that filter and compute high-dimensional data to enhance the learning of high-level tasks (deep learning)
RADIOMICS AND ML APPLICATIONS IN RC: MRI

Thanks to its superb contrast resolution, MRI plays a pivotal role in the diagnostic pathway of RC patients, particularly for primary local staging and restaging after treatment[38]. Indeed, in addition to T and N staging, MRI provides valuable information such as the circumferential resection margin, defined as the minimum distance between the tumor and the mesorectal fascia, as well as extramural venous invasion (EMVI), an independent negative prognostic factor for RC[39,40]. In the following paragraphs, radiomics and ML approaches proposed to further increase the value of MRI in the assessment of RC are described.

Staging

Currently, MRI represents the first-choice imaging modality for determining RC local extent. However, the assessment of T stage is a challenging task, and staging failures often occur in the differentiation between T2 in which the tumor involves the muscularis propria and T3, in which the tumor involves perirectal tissue beyond the muscularis propria[41]. Decision support tools based on MRI radiomics and ML might be able to aid radiologists in this endeavor[42-44]. Using multilayer perceptron, a DL model powered by T2-weighted (T2w) radiomics features from pretreatment MRI, Ma et al[42] were able to discriminate between patients with T1 or T2 and those with T3 or T4 RC with 76% sensitivity and 74% specificity. Similar results were found using diffusion-weighted imaging (DWI) to extract radiomics features in a recent investigation on 115 patients. A logistic regression (LR) algorithm reached a sensitivity of 79% and a specificity of 74% for the same classification problem[43]. Finally, an LR model built with T2w images, both with and without fa-suppression, radiomics features achieved a sensitivity of 88% and specificity of 61% for classifying T1-2 vs T3-4 in a group of 174 patients[44].

MRI is also considered the imaging gold standard for the assessment of lymph node involvement in RC, but it suffers from a relatively low specificity, with potential negative implications on patient outcome[45]. Indeed, the management of patients with different nodal status is a highly debated and complex topic[46]. Radiomics has been proposed as a feasible solution to enhance the accuracy of MRI for N staging in RC patients[47]. In a recent retrospective single-center study in 152 patients, T2w radiomics were coupled to a random forest (RF) algorithm to create an ML classifier that was able to discriminate N0 from N1-2 patients with a sensitivity of 79% and a specificity of 72%[42]. Once again, similar results (81% sensitivity and 68% specificity) were found with LR and a different ML model derived from DWI radiomics features[43]. In both studies, pretreatment MRI scans were used, and the primary tumor was segmented. With a different approach, Zhu et al[48] extracted collective radiomics features from all noticeable lymph nodes on T2w images acquired before and after nCRT in patients with LARC; the LR model was trained to predict pathological node status after nCRT with a group of 143 patients, and had a sensitivity of 95% and a specificity of 60% in the validation cohort of 72 patients. The sensitivity was slightly lower and the specificity slightly higher than those reached by radiologist in the same patient cohorts (100% and 43%). Notwithstanding the specificity insufficient for clinical needs, such models might be useful tools for radiologists in the assessment of N stage in RC.

Finally, the identification of distant metastases in RC patients usually relies on imaging modalities other than MRI. Nevertheless, it should be mentioned that radiomics of the primary tumor was able to provide valuable information for the prediction of synchronous (already present at the time of diagnosis) or metachronous (developed after treatment) liver metastases[49-51] as well as synchronous metastases to other sites[52]. With specific regard to metachronous liver metastases, radiomics of T2w and post-contrast T1-weighted dynamic contrast enhanced (DCE) images were combined to build two ML predictive models, a support vector machine (SVM) and LR, with cross-validation in 108 patients[50]. The LR algorithm had the best performance, but not significantly better than SVM, with 83% sensitivity and 76% specificity, confirming the potential of radiomics and ML for the identification of RC patients who will develop liver metastases after treatment.

Predicting response to nCRT in patients with LARC

While TME should follow nCRT in patients with LARC, the role of surgery in patients with a complete response to nCRT is currently debated, and a “watch and wait” strategy has been proposed[53]. Indeed, patients who achieve a pathological complete response (pCR) after nCRT have better long-term outcomes compared with non-pCR patients, and could therefore be managed differently[54]. Unfortunately, pCR cannot be accurately predicted before surgery by conventional evaluation of MR images[55]. Recently, several radiomics features extracted from T2w, DWI, and DCE sequences have been investigated as possible imaging biomarkers for pCR prediction, with promising results[56-58]. The main studies that aimed to build classification models using ML algorithms for preoperative prediction of pCR after nCRT are shown in Table 2. Overall, the performance of the different models is encouraging. While a trend can be observed, with lower values found in those studies that validated the model in an external dataset and thus with the better chances of high generalizability, it is difficult to draw a final conclusion from the available evidence[59,60]. Most studies focused on MRI scans acquired before nCRT had started, extracting radiomics features from highly available sequences (i.e. T2w). The ideal approach exploits an advantage of radiomics that allows developing predictive models using medical images as they are acquired in the clinical routine[61]. On the other hand, each of the retrospective studies presented its own model, with a certain degree of heterogeneity that does not facilitate translation into clinical practice. An overview of the main studies proposing MRI radiomics and ML algorithms for the prediction of nCRT outcomes other than pCR is reported in Table 3. In those studies, the ML models were generally designed to classify patients into two groups (i.e. good and poor responders to nCRT), with one study prospectively designed but lacking external validation[62].

Table 2 Key characteristics of the main studies using radiomics and machine learning algorithms on magnetic resonance images to predict pathologic complete response after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer.
Ref.
Study design (n of sites)
Number of patients
Definition of pCR
MRI field strength (n of scanners)
MRI timing
MRI sequence
ML algorithm
Data powering algorithm
Validation
Performance (AUC)
Antunes et al[59], 2020Retrospective (3)104TRG 0 according to AJCC1.5 and 3 T (> 10)Pre-nCRTT2wRFRadiomics featuresExternal validation0.71
Ferrari et al[106], 2019 Retrospective (1)55TRG 4 according to Dowrak-Rodel3 T (1)Pre-, mid- and post-nCRTT2wRFRadiomics featuresInternal validation (train/test split)0.86
Horvat et al[107], 2018Retrospective (11)114ypT0N01,5 and 3 T (4)Post-nCRTT2wRFRadiomics featuresInternal validation (cross-validation)0.93
Nie et al[108], 2016Retrospective (1)48ypT0N03 T (1)Pre-nCRTT2w, DWI, pre and post-contrast T1wANNRadiomics featuresInternal validation (cross-validation)0.84
Petkovska et al[109], 2020 Retrospective (11)1022ypT0N01,5 and 3 T (4)Pre-nCRTT2wSVMRadiomics and semantic featuresInternal validation (train/test split)0.75
Shaish et al[110], 2020 Retrospective (2)132ypT0N01,5 and 3 T (multiple3)Pre-nCRTT2wLRRadiomics featuresInternal validation (train/test split)0.80
Shi et al[111], 2019 Retrospective (1)51TRG 0 according to Ryan3 T (1)Pre- and mid-Ncrt4T2w, DWI, pre- and post-contrast T1wCNNRadiomics featuresInternal validation (cross-validation)0.83
van Griethuysen et al[60], 2019Retrospective (2)133ypT0/TRG1 according to Mandard1,5 T (3)Pre-nCRTT2w and DWILRRadiomics featuresExternal validation0.77
Yi et al[112], 2019Retrospective (1)134ypT0N01,5 and 3 T (2)Pre-nCRTT2wSVMRadiomics, clinical and semantic featuresInternal validation (train/test split)0.88
Table 3 Key characteristics of the main studies using radiomics and machine learning algorithms on magnetic resonance images to predict outcome other than pathologic complete response after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer.
Ref.
Study design (n of sites)
Number of patients
Prediction task
CT phase (n of CT scanner)
Segmentation method
ML algorithm
Data powering algorithm
Validation
Performance
Bibault et al[85], 2018Retrospective (3)99pCR after nCRTUnenhanced (3)Manual – 3DDNNRadiomics and clinical featuresInternal validation (cross-validation)AUC: 0.72
Hamerla et al[86], 2019Retrospective (1)169pCR after nCRTUnenhanced (1)Manual – 3DRFRadiomics featuresInternal validation (cross-validation)Accuracy: 0.87
Yuan et al[87], 2020Retrospective (1)91pCR after nCRTUnenhanced (1)Manual – 3DRFRadiomics featuresInternal validation (train/validation split)Accuracy: 0.84
Wu et al[90], 2019 Retrospective (1)102MSI statusVenous phase - DECT (2)Manual - 3 2D ROIs for lesionLRRadiomics featuresInternal validation (train/validation /test split)AUC: 0.87
Fan et al[91], 2019Retrospective (1)100MSI statusPortal venous phase (2) Semiautomatic – 3DNBRadiomics featuresInternal validation (cross-validation)AUC: 0.75
Wu et al[92], 2020Retrospective (1)173KRAS mutationPortal venous phase (3)Manual + DL – single 2D ROILRRadiomics featuresInternal validation (train/test split)C-index: 0.83
Wang et al[94], 2019Retrospective (1)411Prediction of survivalUnenhanced (1)Manual – 3D10-F CVRadiomics and clinical featuresInternal validation (cross-validation)C-index: 0.73

Additionally, recent studies explored the feasibility of radiomics nomograms, based on the combination of a radiomics signature and either a pretreatment MRI T stage[63] or a post treatment tumor length[64], to predict pCR to nCRT. In particular, Liu et al[64] built and validated a radiomics signature in LARC patients using T2w in 152 and DWI images in 70 the T2w and DWI images were acquired both before and after nCRT. An SVM ML algorithm incorporating signatures and post treatment tumor length in a nomogram was able to reach a final diagnostic accuracy of 94% in the prediction of pCR. Finally, Wang et al[65] developed a radiomics signature to classify good responders and poor responders to nCRT with an LR ML algorithm and radiomics features from T2w, DWI, and DCE sequences. When combined in a nomogram with MRI T stage and circumferential resection margin as well as apparent diffusion coefficient values, they were able to predict a good response with a sensitivity of 71% and a specificity of 88%.

Genotyping

Radiogenomics aims to correlate imaging features of a disease with its genotypic characteristics and represents the next step in a radiology-pathology correlation[66]. Radiomics and radiogenomics are not equivalent, and both qualitative and quantitative imaging features can be used for radiogenomic analysis, with quantitative data having promising associations with genetic mutations in RC[67]. Among the negative genetic prognostic factors in RC, KRAS mutations are associated with poor response to epidermal growth factor receptor-targeted antibodies[68] and an increased risk of developing distant metastases[69]. In a recent multicenter study by Cui et al[70], three classifiers (decision tree, SVM and LR) powered by T2w-based radiomics features were trained to predict KRAS mutations in data from 213 patients and validated in both an internal cohort of 91 patients and external cohort of 86. The SVM obtained the greatest area under the receiver operating characteristic curve (AUC) in the training dataset (0.72), which was substantially confirmed in the internal (AUC = 0.68) as well as external (AUC = 0.71) validation cohorts. The finding supports the potential generalizability of such models. Interestingly, in the same study, no associations were found between KRAS status and baseline clinical and histopathological data. More optimistic results were recently published using a decision tree classifier (AUC = 0.88) by a different study group[71], but the sample size was substantially smaller (60 patients) and the model was not externally validated. Finally, T2w-based radiomics have been also paired with DL, with an artificial NN discriminating between patients with or without KRAS mutations and a classification error of 13%[72].

Assessing high-risk histopathological variables

Several histopathological characteristics, EMVI, differentiation degree, and perineural invasion (PNI) for example, are associated with poor clinical outcome and need to be considered in the risk stratification of patients with RC. It is fair to assume that a reliable pretreatment evaluation of these high-risk variables would ease the transition toward precision medicine[5]. ML classifiers applied to MRI radiomics features have been recently explored in this setting[73-75]. As previously highlighted, MRI can be used to identify EMVI; however, its sensitivity is not as high as desirable[76]. To overcome current MRI limitations, Yu et al[73] built a nomogram based on both a DCE MRI radiomics signature and clinical data, finding that it outperformed conventional quantitative perfusion parameters such as Ktrans in the prediction of EMVI, with a sensitivity of 88.9% and a specificity of 78.3% in the validation cohort.

Well-differentiated tumors are associated with better outcomes of RC patients[77]. In a large cohort of 345 patients retrospectively enrolled at a single institution, Meng et al[74] explored the performance of three ML classifiers, RF, SVM, and least absolute shrinkage, and selection operator (LASSO) to identify well-differentiated RC. Radiomics features were extracted from multiple MRI sequences, including T2w, DWI, and DCE. The LASSO algorithm had the best performance, with an AUC of 0.72 in the validation dataset. Finally, PNI, the tumor spreading along the nerve sheath, is a histopathological factor known to be associated with poor prognosis[78]. Using T2w radiomics and AI, Chen et al[79] developed a nomogram to predict the presence of PNI in RC patients (AUC = 0.85). A decision curve analysis confirmed the clinical utility of their nomogram, but the sample size of only seven PNI-positive patients in the test dataset requires validation of this preliminary findings in larger datasets.

RADIOMICS AND ML APPLICATIONS IN RC: CT

In the management of RC, CT is commonly used as the initial staging modality, allowing accurate nodal and metastases staging and target volume delineation before radiation therapy in patients with LARC[6]. Conversely, the role of CT in RC pretreatment local staging as well as restaging after nCRT is limited because of its intrinsically lower contrast resolution compared with MRI[80,81]. Nevertheless, much effort has been directed toward the use of CT data beyond clinical indications, with the aim of developing CT-based radiomics signatures reflecting tumor heterogeneity[82]. CT images contain robust volumetric data that are highly reproducible across patients and are an ideal source of data to feed AI systems[83,84]. In that perspective, ML models that can find correlations of RC CT radiomics features that can be used to predict outcomes such as complete response to nCRT in LARC patients, genetic profiles, overall survival, and segmentation (Table 4).

Table 4 Key characteristics of the main studies using radiomics and machine learning algorithms on computed tomography for v prediction tasks.
Ref.Study design (n of sites)Number of patientsPrediction taskCT phase (n of CT scanner)Segmentation methodML algorithmData powering algorithmValidationPerformance
Bibault et al[85], 2018Retrospective (3)99pCR after nCRTUnenhanced (3)Manual – 3DDNNRadiomics and clinical featuresInternal validation (cross-validation)AUC: 0.72
Hamerla et al[86], 2019Retrospective (1)169pCR after nCRTUnenhanced (1)Manual – 3DRFRadiomics featuresInternal validation (cross-validation)Accuracy: 0.87
Yuan et al[87], 2020Retrospective (1)91pCR after nCRTUnenhanced (1)Manual – 3DRFRadiomics featuresInternal validation (train/validation split)Accuracy: 0.84
Wu et al[90], 2019Retrospective (1)102MSI statusVenous phase - DECT (2)Manual - 3 2D ROIs for lesionLRRadiomics featuresInternal validation (train/validation /test split)AUC: 0.87
Fan et al[91], 2019Retrospective (1)100MSI statusPortal venous phase (2)Semiautomatic – 3DNBRadiomics featuresInternal validation (cross-validation)AUC: 0.75
Wu et al[92], 2020Retrospective (1)173KRAS mutationPortal venous phase (3)Manual + DL – single 2D ROILRRadiomics featuresInternal validation (train/test split)C-index: 0.83
Wang et al[94], 2019Retrospective (1)411Prediction of survivalUnenhanced (1)Manual – 3D10-F CVRadiomics and clinical featuresInternal validation (cross-validation)C-index: 0.73
Predicting response to nCRT in patients with LARC

Bibault et al[85] explored the reliability of deep NN (DNN) integrating clinical features (T stage) and robust radiomics CT-based features in assessing the pCR to nCRT in a multicenter cohort of patients with LARC. The DNN model predicted pCR with an accuracy of 80% compared with 69.5% achieved with an LR model using only the TNM stage and an SVM model with the same parameters as the DNN that had an accuracy of 71.58%. Similarly, Hamerla et al[86] reported an accuracy of 87% for prediction of pCR after nCRT using an ML algorithm and CT radiomics data, but they noted that the model was not generalizable because of bias introduced by an imbalanced distribution of the minority class (pCR: 13% and non-pCR = 87%) in the study population. In another study, Yuan et al[87] tested and compared different ML algorithms using robust CT-based radiomics features significantly correlated with pCR. The best performing model was an RF with an accuracy of 83.9% in the test population. Interestingly, these studies used radiomics features extracted from unenhanced CT scans used for radiotherapy planning. The process highlights the potential value of nonroutine CT data for pretreatment risk stratification.

Genotyping

Recent studies have shown encouraging results with regard to the high predictive ability of AI-radiomics CT-based models of the biologic behavior of RC, in terms of microsatellite instability (MSI) status and KRAS gene mutations, which are considered significant molecular markers of improved prognosis and adjuvant therapy[11,88,89]. Wu et al[90] developed a pretreatment predictive model of MSI status in RC using ML radiomics features extracted from venous phase images of iodine-based material decomposition with dual-energy CT (DECT). Performance of the model was tested on images acquired with a different DECT scanner, and achieving a diagnostic accuracy of 79%. The result suggests a possible link between iodine DECT images and augmented tumor vascularization. In a preliminary retrospective study, Fan et al[91] found that an ML model combining clinical and CT radiomics features had a better classification performance for MSI status (AUC = 0.75) in stage II RC patients than models using only clinical features (AUC = 0.60) or only radiomics features (AUC = 0.70).

Another research group[92] investigated the performance of a model that used handcrafted radiomics signatures combined with those in a DL algorithm. The combined model was able to the discriminate patients with mutant or the wild-type KRAS with a sensitivity of 80% and a specificity of 72% in the validation cohort, thus showing a good predictive performance.

Prognosis

An active field of AI oncology-related research is the discovery of new clinical and imaging tumor biomarkers that are correlated with prognosis, with the goal of developing accurate predictive models of treatment response based on personalized tumor profiles[93]. Wang et al[94] explored the use of CT-based ML models powered by clinical and radiomics features to assess the prognostic outcomes of LARC patients treated with nCRT. Radiomics features were extracted from nonenhanced CT images used for planning the treatment of 411 LARC patients. Images analyzed by unsupervised ML did not find a relationship between the clinical and radiomics features. A supervised ML model with embedded radiomics and clinical parameters had an improved overall survival prediction in the testing set and a c-index of 0.73 which was significantly better (P = 0.044) than the performance of the model using only clinical factors (c-index = 0.67).

RADIOMICS AND ML APPLICATIONS IN RC: MULTIMODAL AND HYBRID IMAGING

The advantage of multimodality and hybrid imaging in oncology is mainly related to the combined evaluation of anatomical and functional tumor characteristics. Radiomics and ML could further increase the potential value of the techniques[95]. However, the number of studies evaluating RC is still limited, and the role of multimodal radiomics and ML models has mainly been investigated for the prediction of response to nCRT in patients with LARC[96,97]. In a single-center study in 169 patients, Shen et al[96] developed an RF model based on baseline PET/CT images that accurately predicted pCR to nCRT in LARC patients, with a sensitivity of 81.8% and a specificity of 97.3%. Another study confirmed the feasibility of combining pretreatment MRI data from T2w sequences and PET radiomics features to build a prediction model able to identify responders or nonresponders. ML algorithms were used for semiautomatic segmentation of the primary tumor in both sets of images[97]. The final LR model had a sensitivity of 86% and specificity of 83%. Beyond nuclear medicine, Li et al[98] described a multimodal radiomics-based nomogram with features extracted from baseline MRI and CT images, which better performed better than individual imaging techniques in the prediction of response to nCRT. Although multimodal radiomics for RC is in its infancy, the encouraging preliminary reports support the idea that it could allow an even more comprehensive assessment of tumor characteristics compared with individual images.

CURRENT LIMITATIONS AND FUTURE PERSPECTIVES

The available evidence confirms that AI is a feasible tool to broaden the spectrum of information that medical imaging can provide for the management of RC patients. Nevertheless, there is a risk that negative results are not published because of publication bias[99]. Furthermore, what could theoretically be done is not ready for clinical practice at present. Indeed, there are many exploratory studies and very few confirmatory ones to support the use of one radiomics-ML model over another. A possible solution to the problems of verifying generalizability and comparing the performance of different models proposed for the same prediction task might be the use of open-source data[100]. Indeed, a publicly available large dataset from multiple institutions could serve as a common benchmark to verify whether the available models can reproduce previous results while we wait for well-designed prospective clinical trials that will overcome the limitations of retrospective studies. Currently, there is a great interest in public imaging datasets, but their quality might be heterogeneous[101]. It should also be considered that significant variations in radiomics and ML pipelines make it difficult to compare studies. Adherence to shared guidelines for AI study design is this highly advisable[102]. Another issue of concern that could prevent widespread adoption of radiomics-ML prediction models is manual segmentation. It is often necessary, but is a time-consuming procedure that requires automatization. However, AI could also solve that problem. Recent studies have described the use of DL for fully automated segmentation of RC on both CT and MR images, with encouraging accuracy and computational time results[103,104]. Several of the radiomics-ML models described in this review had promising accuracy, but it should be noted that the potential clinical utility of such models depends on multiple factors, such as their added value in comparison with current gold standards, the cost-effectiveness of their implementation, and their actual impact on clinical practice. Decision curve analysis might be helpful in the analysis[34]. Finally, a recent study found that the overall quality of radiomics studies in oncology is below the desired standards, suggesting that most of the problems identified in the field of RC radiomics are shared among the studies involving different types of cancer[105].

CONCLUSION

Medical images contain mineable data with great potential. AI appears to be a convenient tool to harness their value for RC management. AI in imaging can support physicians in the transition toward precision medicine for RC patients, but there is still a long road ahead and it is time to start moving to the next step. Robust prospective multicenter studies and clinical trials are needed to confirm the clinical implications of this new methodology.

Footnotes

Manuscript source: Invited manuscript

Specialty type: Radiology, nuclear medicine and medical imaging

Country/Territory of origin: Italy

Peer-review report’s scientific quality classification

Grade A (Excellent): A, A

Grade B (Very good): B

Grade C (Good): 0

Grade D (Fair): 0

Grade E (Poor): 0

P-Reviewer: Alves A, De Ridder M S-Editor: Liu M L-Editor: Filipodia P-Editor: Liu JH

References
1.  Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70:7-30.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 6298]  [Cited by in F6Publishing: 4430]  [Article Influence: 6298.0]  [Reference Citation Analysis (0)]
2.  Dekker E, Tanis PJ, Vleugels JLA, Kasi PM, Wallace MB. Colorectal cancer. Lancet. 2019;394:1467-1480.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 564]  [Cited by in F6Publishing: 296]  [Article Influence: 282.0]  [Reference Citation Analysis (0)]
3.  Keller DS, Berho M, Perez RO, Wexner SD, Chand M. The multidisciplinary management of rectal cancer. Nat Rev Gastroenterol Hepatol. 2020;17:414-429.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 11]  [Cited by in F6Publishing: 8]  [Article Influence: 11.0]  [Reference Citation Analysis (0)]
4.  1 Benson AB, Venook AP, Al-Hawary MM, Arain MA, Chen YJ, Ciombor KK, Cohen S, Cooper HS, Deming D, Garrido-Laguna I, Grem JL, Gunn A, Hoffe S, Hubbard J, Hunt S, Kirilcuk N, Krishnamurthi S, Messersmith WA, Meyerhardt J, Miller ED, Mulcahy MF, Nurkin S, Overman MJ, Parikh A, Patel H, Pedersen K, Saltz L, Schneider C, Shibata D, Skibber JM, Sofocleous CT, Stoffel EM, Stotsky-Himelfarb E, Willett CG, Johnson-Chilla A, Gurski LA. NCCN Guidelines Insights: Rectal Cancer, Version 6.2020. J Natl Compr Canc Netw. 2020;18:806-815.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 32]  [Cited by in F6Publishing: 25]  [Article Influence: 32.0]  [Reference Citation Analysis (0)]
5.  Glynne-Jones R, Wyrwicz L, Tiret E, Brown G, Rödel C, Cervantes A, Arnold D;  ESMO Guidelines Committee. Rectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2017;28:iv22-iv40.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 477]  [Cited by in F6Publishing: 261]  [Article Influence: 159.0]  [Reference Citation Analysis (0)]
6.  Heo SH, Kim JW, Shin SS, Jeong YY, Kang HK. Multimodal imaging evaluation in staging of rectal cancer. World J Gastroenterol. 2014;20:4244-4255.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in CrossRef: 35]  [Cited by in F6Publishing: 20]  [Article Influence: 5.8]  [Reference Citation Analysis (0)]
7.  Horvat N, Carlos Tavares Rocha C, Clemente Oliveira B, Petkovska I, Gollub MJ. MRI of Rectal Cancer: Tumor Staging, Imaging Techniques, and Management. Radiographics. 2019;39:367-387.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 55]  [Cited by in F6Publishing: 39]  [Article Influence: 27.5]  [Reference Citation Analysis (0)]
8.  Beets-Tan RGH, Lambregts DMJ, Maas M, Bipat S, Barbaro B, Curvo-Semedo L, Fenlon HM, Gollub MJ, Gourtsoyianni S, Halligan S, Hoeffel C, Kim SH, Laghi A, Maier A, Rafaelsen SR, Stoker J, Taylor SA, Torkzad MR, Blomqvist L. Magnetic resonance imaging for clinical management of rectal cancer: Updated recommendations from the 2016 European Society of Gastrointestinal and Abdominal Radiology (ESGAR) consensus meeting. Eur Radiol. 2018;28:1465-1475.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 207]  [Cited by in F6Publishing: 107]  [Article Influence: 51.8]  [Reference Citation Analysis (0)]
9.  Maffione AM, Montesi G, Caroli P, Colletti PM, Rubello D, Matteucci F. Is It Time to Introduce PET/CT in Rectal Cancer Guidelines? Clin Nucl Med. 2020;45:611-617.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 3]  [Cited by in F6Publishing: 1]  [Article Influence: 3.0]  [Reference Citation Analysis (0)]
10.  Catalano OA, Lee SI, Parente C, Cauley C, Furtado FS, Striar R, Soricelli A, Salvatore M, Li Y, Umutlu L, Cañamaque LG, Groshar D, Mahmood U, Blaszkowsky LS, Ryan DP, Clark JW, Wo J, Hong TS, Kunitake H, Bordeianou L, Berger D, Ricciardi R, Rosen B. Improving staging of rectal cancer in the pelvis: the role of PET/MRI. Eur J Nucl Med Mol Imaging. 2021;48:1235-1245.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 7]  [Cited by in F6Publishing: 4]  [Article Influence: 7.0]  [Reference Citation Analysis (0)]
11.  Mainenti PP, Stanzione A, Guarino S, Romeo V, Ugga L, Romano F, Storto G, Maurea S, Brunetti A. Colorectal cancer: Parametric evaluation of morphological, functional and molecular tomographic imaging. World J Gastroenterol. 2019;25:5233-5256.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in CrossRef: 9]  [Cited by in F6Publishing: 5]  [Article Influence: 9.0]  [Reference Citation Analysis (0)]
12.  Mainenti PP, Pizzuti LM, Segreto S, Comerci M, Fronzo S, Romano F, Crisci V, Smaldone M, Laccetti E, Storto G, Alfano B, Maurea S, Salvatore M, Pace L. Diffusion volume (DV) measurement in endometrial and cervical cancer: A new MRI parameter in the evaluation of the tumor grading and the risk classification. Eur J Radiol. 2016;85:113-124.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 22]  [Cited by in F6Publishing: 8]  [Article Influence: 3.7]  [Reference Citation Analysis (0)]
13.  Romeo V, Cuocolo R, Ricciardi C, Ugga L, Cocozza S, Verde F, Stanzione A, Napolitano V, Russo D, Improta G, Elefante A, Staibano S, Brunetti A. Prediction of Tumor Grade and Nodal Status in Oropharyngeal and Oral Cavity Squamous-cell Carcinoma Using a Radiomic Approach. Anticancer Res. 2020;40:271-280.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 23]  [Cited by in F6Publishing: 11]  [Article Influence: 23.0]  [Reference Citation Analysis (0)]
14.  Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016;278:563-577.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 2333]  [Cited by in F6Publishing: 1381]  [Article Influence: 388.8]  [Reference Citation Analysis (0)]
15.  Horvat N, Bates DDB, Petkovska I. Novel imaging techniques of rectal cancer: what do radiomics and radiogenomics have to offer? Abdom Radiol (NY). 2019;44:3764-3774.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 24]  [Cited by in F6Publishing: 18]  [Article Influence: 24.0]  [Reference Citation Analysis (0)]
16.  Cuocolo R, Cipullo MB, Stanzione A, Romeo V, Green R, Cantoni V, Ponsiglione A, Ugga L, Imbriaco M. Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis. Eur Radiol. 2020;30:6877-6887.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 12]  [Cited by in F6Publishing: 9]  [Article Influence: 12.0]  [Reference Citation Analysis (0)]
17.  Comelli A, Bignardi S, Stefano A, Russo G, Sabini MG, Ippolito M, Yezzi A. Development of a new fully three-dimensional methodology for tumours delineation in functional images. Comput Biol Med. 2020;120:103701.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 16]  [Cited by in F6Publishing: 6]  [Article Influence: 16.0]  [Reference Citation Analysis (0)]
18.  Stanzione A, Cuocolo R, Del Grosso R, Nardiello A, Romeo V, Travaglino A, Raffone A, Bifulco G, Zullo F, Insabato L, Maurea S, Mainenti PP. Deep Myometrial Infiltration of Endometrial Cancer on MRI: A Radiomics-Powered Machine Learning Pilot Study. Acad Radiol. 2021;28:737-744.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 8]  [Cited by in F6Publishing: 6]  [Article Influence: 8.0]  [Reference Citation Analysis (0)]
19.  Stanzione A, Ricciardi C, Cuocolo R, Romeo V, Petrone J, Sarnataro M, Mainenti PP, Improta G, De Rosa F, Insabato L, Brunetti A, Maurea S. MRI Radiomics for the Prediction of Fuhrman Grade in Clear Cell Renal Cell Carcinoma: a Machine Learning Exploratory Study. J Digit Imaging. 2020;33:879-887.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 15]  [Cited by in F6Publishing: 6]  [Article Influence: 15.0]  [Reference Citation Analysis (0)]
20.  Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, Mak RH, Tamimi RM, Tempany CM, Swanton C, Hoffmann U, Schwartz LH, Gillies RJ, Huang RY, Aerts HJWL. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin. 2019;69:127-157.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 132]  [Cited by in F6Publishing: 123]  [Article Influence: 66.0]  [Reference Citation Analysis (0)]
21.  Koçak B, Durmaz EŞ, Ateş E, Kılıçkesmez Ö. Radiomics with artificial intelligence: a practical guide for beginners. Diagn Interv Radiol. 2019;25:485-495.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 53]  [Cited by in F6Publishing: 33]  [Article Influence: 53.0]  [Reference Citation Analysis (0)]
22.  Yip SS, Aerts HJ. Applications and limitations of radiomics. Phys Med Biol. 2016;61:R150-R166.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 397]  [Cited by in F6Publishing: 153]  [Article Influence: 79.4]  [Reference Citation Analysis (0)]
23.  Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1769]  [Cited by in F6Publishing: 1188]  [Article Influence: 252.7]  [Reference Citation Analysis (0)]
24.  Nougaret S, Tibermacine H, Tardieu M, Sala E. Radiomics: an Introductory Guide to What It May Foretell. Curr Oncol Rep. 2019;21:70.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 9]  [Cited by in F6Publishing: 7]  [Article Influence: 4.5]  [Reference Citation Analysis (0)]
25.  Avanzo M, Stancanello J, El Naqa I. Beyond imaging: The promise of radiomics. Phys Med. 2017;38:122-139.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 168]  [Cited by in F6Publishing: 87]  [Article Influence: 42.0]  [Reference Citation Analysis (0)]
26.  Rogers W, Thulasi Seetha S, Refaee TAG, Lieverse RIY, Granzier RWY, Ibrahim A, Keek SA, Sanduleanu S, Primakov SP, Beuque MPL, Marcus D, van der Wiel AMA, Zerka F, Oberije CJG, van Timmeren JE, Woodruff HC, Lambin P. Radiomics: from qualitative to quantitative imaging. Br J Radiol. 2020;93:20190948.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 32]  [Cited by in F6Publishing: 21]  [Article Influence: 32.0]  [Reference Citation Analysis (0)]
27.  Aerts HJ. The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review. JAMA Oncol. 2016;2:1636-1642.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 230]  [Cited by in F6Publishing: 156]  [Article Influence: 57.5]  [Reference Citation Analysis (0)]
28.  Ibrahim A, Primakov S, Beuque M, Woodruff HC, Halilaj I, Wu G, Refaee T, Granzier R, Widaatalla Y, Hustinx R, Mottaghy FM, Lambin P. Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework. Methods. 2021;188:20-29.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 12]  [Cited by in F6Publishing: 12]  [Article Influence: 12.0]  [Reference Citation Analysis (0)]
29.  Cuocolo R, Caruso M, Perillo T, Ugga L, Petretta M. Machine Learning in oncology: A clinical appraisal. Cancer Lett. 2020;481:55-62.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 31]  [Cited by in F6Publishing: 23]  [Article Influence: 31.0]  [Reference Citation Analysis (0)]
30.  Giger ML. Machine Learning in Medical Imaging. J Am Coll Radiol. 2018;15:512-520.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 166]  [Cited by in F6Publishing: 70]  [Article Influence: 55.3]  [Reference Citation Analysis (0)]
31.  Choy G, Khalilzadeh O, Michalski M, Do S, Samir AE, Pianykh OS, Geis JR, Pandharipande PV, Brink JA, Dreyer KJ. Current Applications and Future Impact of Machine Learning in Radiology. Radiology. 2018;288:318-328.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 236]  [Cited by in F6Publishing: 117]  [Article Influence: 78.7]  [Reference Citation Analysis (0)]
32.  Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18:500-510.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 608]  [Cited by in F6Publishing: 295]  [Article Influence: 304.0]  [Reference Citation Analysis (0)]
33.  Tseng HH, Wei L, Cui S, Luo Y, Ten Haken RK, El Naqa I. Machine Learning and Imaging Informatics in Oncology. Oncology. 2020;98:344-362.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 15]  [Cited by in F6Publishing: 8]  [Article Influence: 5.0]  [Reference Citation Analysis (0)]
34.  Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue RTHM, Even AJG, Jochems A, van Wijk Y, Woodruff H, van Soest J, Lustberg T, Roelofs E, van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749-762.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1063]  [Cited by in F6Publishing: 653]  [Article Influence: 265.8]  [Reference Citation Analysis (0)]
35.  Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine Learning for Medical Imaging. Radiographics. 2017;37:505-515.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 443]  [Cited by in F6Publishing: 205]  [Article Influence: 110.8]  [Reference Citation Analysis (0)]
36.  Mayerhoefer ME, Materka A, Langs G, Häggström I, Szczypiński P, Gibbs P, Cook G. Introduction to Radiomics. J Nucl Med. 2020;61:488-495.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 51]  [Cited by in F6Publishing: 28]  [Article Influence: 51.0]  [Reference Citation Analysis (0)]
37.  Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, Kadoury S, Tang A. Deep Learning: A Primer for Radiologists. Radiographics. 2017;37:2113-2131.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 344]  [Cited by in F6Publishing: 178]  [Article Influence: 114.7]  [Reference Citation Analysis (0)]
38.  Kalisz KR, Enzerra MD, Paspulati RM. MRI Evaluation of the Response of Rectal Cancer to Neoadjuvant Chemoradiation Therapy. Radiographics. 2019;39:538-556.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 20]  [Cited by in F6Publishing: 15]  [Article Influence: 20.0]  [Reference Citation Analysis (0)]
39.  Gaertner WB, Kwaan MR, Madoff RD, Melton GB. Rectal cancer: An evidence-based update for primary care providers. World J Gastroenterol. 2015;21:7659-7671.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in CrossRef: 25]  [Cited by in F6Publishing: 10]  [Article Influence: 5.0]  [Reference Citation Analysis (0)]
40.  Ale Ali H, Kirsch R, Razaz S, Jhaveri A, Thipphavong S, Kennedy ED, Jhaveri KS. Extramural venous invasion in rectal cancer: overview of imaging, histopathology, and clinical implications. Abdom Radiol (NY). 2019;44:1-10.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 18]  [Cited by in F6Publishing: 11]  [Article Influence: 18.0]  [Reference Citation Analysis (0)]
41.  Curvo-Semedo L. Rectal Cancer: Staging. Magn Reson Imaging Clin N Am. 2020;28:105-115.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 8]  [Cited by in F6Publishing: 6]  [Article Influence: 4.0]  [Reference Citation Analysis (0)]
42.  Ma X, Shen F, Jia Y, Xia Y, Li Q, Lu J. MRI-based radiomics of rectal cancer: preoperative assessment of the pathological features. BMC Med Imaging. 2019;19:86.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 20]  [Cited by in F6Publishing: 17]  [Article Influence: 10.0]  [Reference Citation Analysis (0)]
43.  Yin JD, Song LR, Lu HC, Zheng X. Prediction of different stages of rectal cancer: Texture analysis based on diffusion-weighted images and apparent diffusion coefficient maps. World J Gastroenterol. 2020;26:2082-2096.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in CrossRef: 9]  [Cited by in F6Publishing: 6]  [Article Influence: 9.0]  [Reference Citation Analysis (0)]
44.  Lu HC, Wang F, Yin JD. Texture Analysis Based on Sagittal Fat-Suppression and Transverse T2-Weighted Magnetic Resonance Imaging for Determining Local Invasion of Rectal Cancer. Front Oncol. 2020;10:1476.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 2]  [Cited by in F6Publishing: 2]  [Article Influence: 2.0]  [Reference Citation Analysis (0)]
45.  Nougaret S, Jhaveri K, Kassam Z, Lall C, Kim DH. Rectal cancer MR staging: pearls and pitfalls at baseline examination. Abdom Radiol (NY). 2019;44:3536-3548.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 12]  [Cited by in F6Publishing: 8]  [Article Influence: 12.0]  [Reference Citation Analysis (0)]
46.  Christou N, Meyer J, Toso C, Ris F, Buchs NC. Lateral lymph node dissection for low rectal cancer: Is it necessary? World J Gastroenterol. 2019;25:4294-4299.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in CrossRef: 4]  [Cited by in F6Publishing: 2]  [Article Influence: 4.0]  [Reference Citation Analysis (0)]
47.  Yang L, Liu D, Fang X, Wang Z, Xing Y, Ma L, Wu B. Rectal cancer: can T2WI histogram of the primary tumor help predict the existence of lymph node metastasis? Eur Radiol. 2019;29:6469-6476.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 21]  [Cited by in F6Publishing: 18]  [Article Influence: 10.5]  [Reference Citation Analysis (0)]
48.  Zhu H, Zhang X, Li X, Shi Y, Zhu H, Sun Y. Prediction of pathological nodal stage of locally advanced rectal cancer by collective features of multiple lymph nodes in magnetic resonance images before and after neoadjuvant chemoradiotherapy. Chin J Cancer Res. 2019;31:984-992.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 3]  [Cited by in F6Publishing: 3]  [Article Influence: 1.5]  [Reference Citation Analysis (0)]
49.  Shu Z, Fang S, Ding Z, Mao D, Cai R, Chen Y, Pang P, Gong X. MRI-based Radiomics nomogram to detect primary rectal cancer with synchronous liver metastases. Sci Rep. 2019;9:3374.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 20]  [Cited by in F6Publishing: 15]  [Article Influence: 10.0]  [Reference Citation Analysis (0)]
50.  Liang M, Cai Z, Zhang H, Huang C, Meng Y, Zhao L, Li D, Ma X, Zhao X. Machine Learning-based Analysis of Rectal Cancer MRI Radiomics for Prediction of Metachronous Liver Metastasis. Acad Radiol. 2019;26:1495-1504.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 17]  [Cited by in F6Publishing: 12]  [Article Influence: 8.5]  [Reference Citation Analysis (0)]
51.  Liu M, Ma X, Shen F, Xia Y, Jia Y, Lu J. MRI-based radiomics nomogram to predict synchronous liver metastasis in primary rectal cancer patients. Cancer Med. 2020;9:5155-5163.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 7]  [Cited by in F6Publishing: 8]  [Article Influence: 7.0]  [Reference Citation Analysis (0)]
52.  Liu H, Zhang C, Wang L, Luo R, Li J, Zheng H, Yin Q, Zhang Z, Duan S, Li X, Wang D. MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer. Eur Radiol. 2019;29:4418-4426.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 33]  [Cited by in F6Publishing: 23]  [Article Influence: 11.0]  [Reference Citation Analysis (0)]
53.  López-Campos F, Martín-Martín M, Fornell-Pérez R, García-Pérez JC, Die-Trill J, Fuentes-Mateos R, López-Durán S, Domínguez-Rullán J, Ferreiro R, Riquelme-Oliveira A, Hervás-Morón A, Couñago F. Watch and wait approach in rectal cancer: Current controversies and future directions. World J Gastroenterol. 2020;26:4218-4239.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in CrossRef: 8]  [Cited by in F6Publishing: 5]  [Article Influence: 8.0]  [Reference Citation Analysis (0)]
54.  Maas M, Nelemans PJ, Valentini V, Das P, Rödel C, Kuo LJ, Calvo FA, García-Aguilar J, Glynne-Jones R, Haustermans K, Mohiuddin M, Pucciarelli S, Small W Jr, Suárez J, Theodoropoulos G, Biondo S, Beets-Tan RG, Beets GL. Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: a pooled analysis of individual patient data. Lancet Oncol. 2010;11:835-844.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1035]  [Cited by in F6Publishing: 317]  [Article Influence: 94.1]  [Reference Citation Analysis (0)]
55.  Sclafani F, Brown G, Cunningham D, Wotherspoon A, Mendes LST, Balyasnikova S, Evans J, Peckitt C, Begum R, Tait D, Tabernero J, Glimelius B, Roselló S, Thomas J, Oates J, Chau I. Comparison between MRI and pathology in the assessment of tumour regression grade in rectal cancer. Br J Cancer. 2017;117:1478-1485.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 55]  [Cited by in F6Publishing: 33]  [Article Influence: 13.8]  [Reference Citation Analysis (0)]
56.  Aker M, Ganeshan B, Afaq A, Wan S, Groves AM, Arulampalam T. Magnetic Resonance Texture Analysis in Identifying Complete Pathological Response to Neoadjuvant Treatment in Locally Advanced Rectal Cancer. Dis Colon Rectum. 2019;62:163-170.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 19]  [Cited by in F6Publishing: 13]  [Article Influence: 9.5]  [Reference Citation Analysis (0)]
57.  Yang L, Qiu M, Xia C, Li Z, Wang Z, Zhou X, Wu B. Value of High-Resolution DWI in Combination With Texture Analysis for the Evaluation of Tumor Response After Preoperative Chemoradiotherapy for Locally Advanced Rectal Cancer. AJR Am J Roentgenol. 2019;1-8.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 7]  [Cited by in F6Publishing: 4]  [Article Influence: 3.5]  [Reference Citation Analysis (0)]
58.  Zou HH, Yu J, Wei Y, Wu JF, Xu Q. Response to neoadjuvant chemoradiotherapy for locally advanced rectum cancer: Texture analysis of dynamic contrast-enhanced MRI. J Magn Reson Imaging. 2019;49:885-893.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 11]  [Cited by in F6Publishing: 9]  [Article Influence: 3.7]  [Reference Citation Analysis (0)]
59.  Antunes JT, Ofshteyn A, Bera K, Wang EY, Brady JT, Willis JE, Friedman KA, Marderstein EL, Kalady MF, Stein SL, Purysko AS, Paspulati R, Gollamudi J, Madabhushi A, Viswanath SE. Radiomic Features of Primary Rectal Cancers on Baseline T2 -Weighted MRI Are Associated With Pathologic Complete Response to Neoadjuvant Chemoradiation: A Multisite Study. J Magn Reson Imaging. 2020;52:1531-1541.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 17]  [Cited by in F6Publishing: 13]  [Article Influence: 17.0]  [Reference Citation Analysis (0)]
60.  van Griethuysen JJM, Lambregts DMJ, Trebeschi S, Lahaye MJ, Bakers FCH, Vliegen RFA, Beets GL, Aerts HJWL, Beets-Tan RGH. Radiomics performs comparable to morphologic assessment by expert radiologists for prediction of response to neoadjuvant chemoradiotherapy on baseline staging MRI in rectal cancer. Abdom Radiol (NY). 2020;45:632-643.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 14]  [Cited by in F6Publishing: 9]  [Article Influence: 14.0]  [Reference Citation Analysis (0)]
61.  Tiwari P, Verma R. The Pursuit of Generalizability to Enable Clinical Translation of Radiomics. Radiol Artif Intell. 2021;3:e200227.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1]  [Cited by in F6Publishing: 1]  [Article Influence: 1.0]  [Reference Citation Analysis (0)]
62.  Shayesteh SP, Alikhassi A, Fard Esfahani A, Miraie M, Geramifar P, Bitarafan-Rajabi A, Haddad P. Neo-adjuvant chemoradiotherapy response prediction using MRI based ensemble learning method in rectal cancer patients. Phys Med. 2019;62:111-119.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 18]  [Cited by in F6Publishing: 15]  [Article Influence: 9.0]  [Reference Citation Analysis (0)]
63.  Cui Y, Yang X, Shi Z, Yang Z, Du X, Zhao Z, Cheng X. Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Eur Radiol. 2019;29:1211-1220.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 75]  [Cited by in F6Publishing: 54]  [Article Influence: 25.0]  [Reference Citation Analysis (0)]
64.  Liu Z, Zhang XY, Shi YJ, Wang L, Zhu HT, Tang Z, Wang S, Li XT, Tian J, Sun YS. Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer. Clin Cancer Res. 2017;23:7253-7262.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 194]  [Cited by in F6Publishing: 94]  [Article Influence: 48.5]  [Reference Citation Analysis (0)]
65.  Wang J, Liu X, Hu B, Gao Y, Chen J, Li J. Development and validation of an MRI-based radiomic nomogram to distinguish between good and poor responders in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiotherapy. Abdom Radiol (NY). 2021;46:1805-1815.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 5]  [Cited by in F6Publishing: 4]  [Article Influence: 5.0]  [Reference Citation Analysis (0)]
66.  Pinker K, Shitano F, Sala E, Do RK, Young RJ, Wibmer AG, Hricak H, Sutton EJ, Morris EA. Background, current role, and potential applications of radiogenomics. J Magn Reson Imaging. 2018;47:604-620.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 71]  [Cited by in F6Publishing: 36]  [Article Influence: 17.8]  [Reference Citation Analysis (0)]
67.  Horvat N, Veeraraghavan H, Pelossof RA, Fernandes MC, Arora A, Khan M, Marco M, Cheng CT, Gonen M, Golia Pernicka JS, Gollub MJ, Garcia-Aguillar J, Petkovska I. Radiogenomics of rectal adenocarcinoma in the era of precision medicine: A pilot study of associations between qualitative and quantitative MRI imaging features and genetic mutations. Eur J Radiol. 2019;113:174-181.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 14]  [Cited by in F6Publishing: 10]  [Article Influence: 7.0]  [Reference Citation Analysis (0)]
68.  Sorich MJ, Wiese MD, Rowland A, Kichenadasse G, McKinnon RA, Karapetis CS. Extended RAS mutations and anti-EGFR monoclonal antibody survival benefit in metastatic colorectal cancer: a meta-analysis of randomized, controlled trials. Ann Oncol. 2015;26:13-21.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 295]  [Cited by in F6Publishing: 178]  [Article Influence: 42.1]  [Reference Citation Analysis (0)]
69.  Zhu K, Yan H, Wang R, Zhu H, Meng X, Xu X, Dou X, Chen D. Mutations of KRAS and PIK3CA as independent predictors of distant metastases in colorectal cancer. Med Oncol. 2014;31:16.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 13]  [Cited by in F6Publishing: 9]  [Article Influence: 1.9]  [Reference Citation Analysis (0)]
70.  Cui Y, Liu H, Ren J, Du X, Xin L, Li D, Yang X, Wang D. Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer. Eur Radiol. 2020;30:1948-1958.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 23]  [Cited by in F6Publishing: 19]  [Article Influence: 23.0]  [Reference Citation Analysis (0)]
71.  Oh JE, Kim MJ, Lee J, Hur BY, Kim B, Kim DY, Baek JY, Chang HJ, Park SC, Oh JH, Cho SA, Sohn DK. Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer. Cancer Res Treat. 2020;52:51-59.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 28]  [Cited by in F6Publishing: 17]  [Article Influence: 14.0]  [Reference Citation Analysis (0)]
72.  Xu Y, Xu Q, Ma Y, Duan J, Zhang H, Liu T, Li L, Sun H, Shi K, Xie S, Wang W. Characterizing MRI features of rectal cancers with different KRAS status. BMC Cancer. 2019;19:1111.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 10]  [Cited by in F6Publishing: 6]  [Article Influence: 5.0]  [Reference Citation Analysis (0)]
73.  Yu X, Song W, Guo D, Liu H, Zhang H, He X, Song J, Zhou J, Liu X. Preoperative Prediction of Extramural Venous Invasion in Rectal Cancer: Comparison of the Diagnostic Efficacy of Radiomics Models and Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging. Front Oncol. 2020;10:459.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 10]  [Cited by in F6Publishing: 9]  [Article Influence: 10.0]  [Reference Citation Analysis (0)]
74.  Meng X, Xia W, Xie P, Zhang R, Li W, Wang M, Xiong F, Liu Y, Fan X, Xie Y, Wan X, Zhu K, Shan H, Wang L, Gao X. Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer. Eur Radiol. 2019;29:3200-3209.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 38]  [Cited by in F6Publishing: 31]  [Article Influence: 12.7]  [Reference Citation Analysis (1)]
75.  He B, Ji T, Zhang H, Zhu Y, Shu R, Zhao W, Wang K. MRI-based radiomics signature for tumor grading of rectal carcinoma using random forest model. J Cell Physiol. 2019;234:20501-20509.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 8]  [Cited by in F6Publishing: 5]  [Article Influence: 4.0]  [Reference Citation Analysis (0)]
76.  Kim TH, Woo S, Han S, Suh CH, Vargas HA. The Diagnostic Performance of MRI for Detection of Extramural Venous Invasion in Colorectal Cancer: A Systematic Review and Meta-Analysis of the Literature. AJR Am J Roentgenol. 2019;213:575-585.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 10]  [Cited by in F6Publishing: 4]  [Article Influence: 5.0]  [Reference Citation Analysis (0)]
77.  Xiao H, Yoon YS, Hong SM, Roh SA, Cho DH, Yu CS, Kim JC. Poorly differentiated colorectal cancers: correlation of microsatellite instability with clinicopathologic features and survival. Am J Clin Pathol. 2013;140:341-347.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 33]  [Cited by in F6Publishing: 9]  [Article Influence: 4.1]  [Reference Citation Analysis (0)]
78.  Dhadda AS, Bessell EM, Scholefield J, Dickinson P, Zaitoun AM. Mandard tumour regression grade, perineural invasion, circumferential resection margin and post-chemoradiation nodal status strongly predict outcome in locally advanced rectal cancer treated with preoperative chemoradiotherapy. Clin Oncol (R Coll Radiol). 2014;26:197-202.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 20]  [Cited by in F6Publishing: 11]  [Article Influence: 2.9]  [Reference Citation Analysis (0)]
79.  Chen J, Chen Y, Zheng D, Pang P, Zhang H, Zheng X, Liao J. Pretreatment MR-based radiomics nomogram as potential imaging biomarker for individualized assessment of perineural invasion status in rectal cancer. Abdom Radiol (NY). 2021;46:847-857.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 2]  [Cited by in F6Publishing: 3]  [Article Influence: 2.0]  [Reference Citation Analysis (0)]
80.  García-Figueiras R, Baleato-González S, Padhani AR, Luna-Alcalá A, Marhuenda A, Vilanova JC, Osorio-Vázquez I, Martínez-de-Alegría A, Gómez-Caamaño A. Advanced Imaging Techniques in Evaluation of Colorectal Cancer. Radiographics. 2018;38:740-765.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 19]  [Cited by in F6Publishing: 15]  [Article Influence: 6.3]  [Reference Citation Analysis (0)]
81.  Delli Pizzi A, Basilico R, Cianci R, Seccia B, Timpani M, Tavoletta A, Caposiena D, Faricelli B, Gabrielli D, Caulo M. Rectal cancer MRI: protocols, signs and future perspectives radiologists should consider in everyday clinical practice. Insights Imaging. 2018;9:405-412.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 19]  [Cited by in F6Publishing: 11]  [Article Influence: 6.3]  [Reference Citation Analysis (0)]
82.  Eloyan A, Yue MS, Khachatryan D. Tumor heterogeneity estimation for radiomics in cancer. Stat Med. 2020;39:4704-4723.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 2]  [Cited by in F6Publishing: 2]  [Article Influence: 2.0]  [Reference Citation Analysis (0)]
83.  Park BW, Kim JK, Heo C, Park KJ. Reliability of CT radiomic features reflecting tumour heterogeneity according to image quality and image processing parameters. Sci Rep. 2020;10:3852.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 9]  [Cited by in F6Publishing: 7]  [Article Influence: 9.0]  [Reference Citation Analysis (0)]
84.  Haibe-Kains B, Adam GA, Hosny A, Khodakarami F;  Massive Analysis Quality Control (MAQC) Society Board of Directors; Waldron L, Wang B, McIntosh C, Goldenberg A, Kundaje A, Greene CS, Broderick T, Hoffman MM, Leek JT, Korthauer K, Huber W, Brazma A, Pineau J, Tibshirani R, Hastie T, Ioannidis JPA, Quackenbush J, Aerts HJWL. Transparency and reproducibility in artificial intelligence. Nature. 2020;586:E14-E16.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 37]  [Cited by in F6Publishing: 14]  [Article Influence: 37.0]  [Reference Citation Analysis (0)]
85.  Bibault JE, Giraud P, Housset M, Durdux C, Taieb J, Berger A, Coriat R, Chaussade S, Dousset B, Nordlinger B, Burgun A. Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci Rep. 2018;8:12611.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 52]  [Cited by in F6Publishing: 40]  [Article Influence: 17.3]  [Reference Citation Analysis (0)]
86.  Hamerla G, Meyer HJ, Hambsch P, Wolf U, Kuhnt T, Hoffmann KT, Surov A. Radiomics Model Based on Non-Contrast CT Shows No Predictive Power for Complete Pathological Response in Locally Advanced Rectal Cancer. Cancers (Basel). 2019;11:1680.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 9]  [Cited by in F6Publishing: 6]  [Article Influence: 4.5]  [Reference Citation Analysis (0)]
87.  Yuan Z, Frazer M, Zhang GG, Latifi K, Moros EG, Feygelman V, Felder S, Sanchez J, Dessureault S, Imanirad I, Kim RD, Harrison LB, Hoffe SE, Frakes JM. CT-based radiomic features to predict pathological response in rectal cancer: A retrospective cohort study. J Med Imaging Radiat Oncol. 2020;64:444-449.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 3]  [Cited by in F6Publishing: 4]  [Article Influence: 3.0]  [Reference Citation Analysis (0)]
88.  Li K, Luo H, Huang L, Zhu X. Microsatellite instability: a review of what the oncologist should know. Cancer Cell Int. 2020;20:16.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 57]  [Cited by in F6Publishing: 37]  [Article Influence: 57.0]  [Reference Citation Analysis (0)]
89.  Li W, Li H, Liu R, Yang X, Gao Y, Niu Y, Geng J, Xue Y, Jin X, You Q, Meng H. Comprehensive Analysis of the Relationship Between RAS and RAF Mutations and MSI Status of Colorectal Cancer in Northeastern China. Cell Physiol Biochem. 2018;50:1496-1509.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 5]  [Cited by in F6Publishing: 7]  [Article Influence: 1.7]  [Reference Citation Analysis (0)]
90.  Wu J, Zhang Q, Zhao Y, Liu Y, Chen A, Li X, Wu T, Li J, Guo Y, Liu A. Radiomics Analysis of Iodine-Based Material Decomposition Images With Dual-Energy Computed Tomography Imaging for Preoperatively Predicting Microsatellite Instability Status in Colorectal Cancer. Front Oncol. 2019;9:1250.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 15]  [Cited by in F6Publishing: 13]  [Article Influence: 7.5]  [Reference Citation Analysis (0)]
91.  Fan S, Li X, Cui X, Zheng L, Ren X, Ma W, Ye Z. Computed Tomography-Based Radiomic Features Could Potentially Predict Microsatellite Instability Status in Stage II Colorectal Cancer: A Preliminary Study. Acad Radiol. 2019;26:1633-1640.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 16]  [Cited by in F6Publishing: 15]  [Article Influence: 8.0]  [Reference Citation Analysis (0)]
92.  Wu X, Li Y, Chen X, Huang Y, He L, Zhao K, Huang X, Zhang W, Dong M, Huang J, Xia T, Liang C, Liu Z. Deep Learning Features Improve the Performance of a Radiomics Signature for Predicting KRAS Status in Patients with Colorectal Cancer. Acad Radiol. 2020;27:e254-e262.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 12]  [Cited by in F6Publishing: 8]  [Article Influence: 12.0]  [Reference Citation Analysis (0)]
93.  Verde F, Romeo V, Stanzione A, Maurea S. Current trends of artificial intelligence in cancer imaging. Artif Intell Med Imaging. 2020;1:87-93.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in CrossRef: 1]  [Cited by in F6Publishing: 1]  [Article Influence: 1.0]  [Reference Citation Analysis (0)]
94.  Wang J, Shen L, Zhong H, Zhou Z, Hu P, Gan J, Luo R, Hu W, Zhang Z. Radiomics features on radiotherapy treatment planning CT can predict patient survival in locally advanced rectal cancer patients. Sci Rep. 2019;9:15346.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 8]  [Cited by in F6Publishing: 6]  [Article Influence: 4.0]  [Reference Citation Analysis (0)]
95.  Wei L, Osman S, Hatt M, El Naqa I. Machine learning for radiomics-based multimodality and multiparametric modeling. Q J Nucl Med Mol Imaging. 2019;63:323-338.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 14]  [Cited by in F6Publishing: 3]  [Article Influence: 7.0]  [Reference Citation Analysis (0)]
96.  Shen WC, Chen SW, Wu KC, Lee PY, Feng CL, Hsieh TC, Yen KY, Kao CH. Predicting pathological complete response in rectal cancer after chemoradiotherapy with a random forest using 18F-fluorodeoxyglucose positron emission tomography and computed tomography radiomics. Ann Transl Med. 2020;8:207.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 6]  [Cited by in F6Publishing: 7]  [Article Influence: 6.0]  [Reference Citation Analysis (0)]
97.  Giannini V, Mazzetti S, Bertotto I, Chiarenza C, Cauda S, Delmastro E, Bracco C, Di Dia A, Leone F, Medico E, Pisacane A, Ribero D, Stasi M, Regge D. Predicting locally advanced rectal cancer response to neoadjuvant therapy with 18F-FDG PET and MRI radiomics features. Eur J Nucl Med Mol Imaging. 2019;46:878-888.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 53]  [Cited by in F6Publishing: 36]  [Article Influence: 26.5]  [Reference Citation Analysis (0)]
98.  Li ZY, Wang XD, Li M, Liu XJ, Ye Z, Song B, Yuan F, Yuan Y, Xia CC, Zhang X, Li Q. Multi-modal radiomics model to predict treatment response to neoadjuvant chemotherapy for locally advanced rectal cancer. World J Gastroenterol. 2020;26:2388-2402.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in CrossRef: 8]  [Cited by in F6Publishing: 7]  [Article Influence: 8.0]  [Reference Citation Analysis (0)]
99.  Buvat I, Orlhac F. The Dark Side of Radiomics: On the Paramount Importance of Publishing Negative Results. J Nucl Med. 2019;60:1543-1544.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 21]  [Cited by in F6Publishing: 12]  [Article Influence: 10.5]  [Reference Citation Analysis (0)]
100.  van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging-"how-to" guide and critical reflection. Insights Imaging. 2020;11:91.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 41]  [Cited by in F6Publishing: 27]  [Article Influence: 41.0]  [Reference Citation Analysis (0)]
101.  Oakden-Rayner L. Exploring Large-scale Public Medical Image Datasets. Acad Radiol. 2020;27:106-112.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 41]  [Cited by in F6Publishing: 23]  [Article Influence: 20.5]  [Reference Citation Analysis (0)]
102.  Mongan J, Moy L, Kahn CE Jr. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. Radiol Artif Intell. 2020;2:e200029.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 64]  [Cited by in F6Publishing: 35]  [Article Influence: 64.0]  [Reference Citation Analysis (0)]
103.  Men K, Boimel P, Janopaul-Naylor J, Zhong H, Huang M, Geng H, Cheng C, Fan Y, Plastaras JP, Ben-Josef E, Xiao Y. Cascaded atrous convolution and spatial pyramid pooling for more accurate tumor target segmentation for rectal cancer radiotherapy. Phys Med Biol. 2018;63:185016.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 22]  [Cited by in F6Publishing: 11]  [Article Influence: 7.3]  [Reference Citation Analysis (0)]
104.  Trebeschi S, van Griethuysen JJM, Lambregts DMJ, Lahaye MJ, Parmar C, Bakers FCH, Peters NHGM, Beets-Tan RGH, Aerts HJWL. Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR. Sci Rep. 2017;7:5301.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 111]  [Cited by in F6Publishing: 70]  [Article Influence: 27.8]  [Reference Citation Analysis (0)]
105.  Park JE, Kim D, Kim HS, Park SY, Kim JY, Cho SJ, Shin JH, Kim JH. Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement. Eur Radiol. 2020;30:523-536.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 54]  [Cited by in F6Publishing: 39]  [Article Influence: 27.0]  [Reference Citation Analysis (0)]
106.  Ferrari R, Mancini-Terracciano C, Voena C, Rengo M, Zerunian M, Ciardiello A, Grasso S, Mare' V, Paramatti R, Russomando A, Santacesaria R, Satta A, Solfaroli Camillocci E, Faccini R, Laghi A. MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer. Eur J Radiol. 2019;118:1-9.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 25]  [Cited by in F6Publishing: 17]  [Article Influence: 12.5]  [Reference Citation Analysis (0)]
107.  Horvat N, Veeraraghavan H, Khan M, Blazic I, Zheng J, Capanu M, Sala E, Garcia-Aguilar J, Gollub MJ, Petkovska I. MR Imaging of Rectal Cancer: Radiomics Analysis to Assess Treatment Response after Neoadjuvant Therapy. Radiology. 2018;287:833-843.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 116]  [Cited by in F6Publishing: 85]  [Article Influence: 38.7]  [Reference Citation Analysis (0)]
108.  Nie K, Shi L, Chen Q, Hu X, Jabbour SK, Yue N, Niu T, Sun X. Rectal Cancer: Assessment of Neoadjuvant Chemoradiation Outcome based on Radiomics of Multiparametric MRI. Clin Cancer Res. 2016;22:5256-5264.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 192]  [Cited by in F6Publishing: 92]  [Article Influence: 38.4]  [Reference Citation Analysis (0)]
109.  Petkovska I, Tixier F, Ortiz EJ, Golia Pernicka JS, Paroder V, Bates DD, Horvat N, Fuqua J, Schilsky J, Gollub MJ, Garcia-Aguilar J, Veeraraghavan H. Clinical utility of radiomics at baseline rectal MRI to predict complete response of rectal cancer after chemoradiation therapy. Abdom Radiol (NY). 2020;45:3608-3617.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 13]  [Cited by in F6Publishing: 10]  [Article Influence: 13.0]  [Reference Citation Analysis (0)]
110.  Shaish H, Aukerman A, Vanguri R, Spinelli A, Armenta P, Jambawalikar S, Makkar J, Bentley-Hibbert S, Del Portillo A, Kiran R, Monti L, Bonifacio C, Kirienko M, Gardner KL, Schwartz L, Keller D. Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter study. Eur Radiol. 2020;30:6263-6273.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 12]  [Cited by in F6Publishing: 10]  [Article Influence: 12.0]  [Reference Citation Analysis (0)]
111.  Shi L, Zhang Y, Nie K, Sun X, Niu T, Yue N, Kwong T, Chang P, Chow D, Chen JH, Su MY. Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI. Magn Reson Imaging. 2019;61:33-40.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 35]  [Cited by in F6Publishing: 29]  [Article Influence: 17.5]  [Reference Citation Analysis (0)]
112.  Yi X, Pei Q, Zhang Y, Zhu H, Wang Z, Chen C, Li Q, Long X, Tan F, Zhou Z, Liu W, Li C, Zhou Y, Song X, Li Y, Liao W, Li X, Sun L, Pei H, Zee C, Chen BT. MRI-Based Radiomics Predicts Tumor Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer. Front Oncol. 2019;9:552.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 23]  [Cited by in F6Publishing: 17]  [Article Influence: 11.5]  [Reference Citation Analysis (0)]
113.  Alvarez-Jimenez C, Antunes JT, Talasila N, Bera K, Brady JT, Gollamudi J, Marderstein E, Kalady MF, Purysko A, Willis JE, Stein S, Friedman K, Paspulati R, Delaney CP, Romero E, Madabhushi A, Viswanath SE. Radiomic Texture and Shape Descriptors of the Rectal Environment on Post-Chemoradiation T2-Weighted MRI are Associated with Pathologic Tumor Stage Regression in Rectal Cancers: A Retrospective, Multi-Institution Study. Cancers (Basel). 2020;12:2027.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 6]  [Cited by in F6Publishing: 4]  [Article Influence: 6.0]  [Reference Citation Analysis (0)]
114.  Bulens P, Couwenberg A, Intven M, Debucquoy A, Vandecaveye V, Van Cutsem E, D'Hoore A, Wolthuis A, Mukherjee P, Gevaert O, Haustermans K. Predicting the tumor response to chemoradiotherapy for rectal cancer: Model development and external validation using MRI radiomics. Radiother Oncol. 2020;142:246-252.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 17]  [Cited by in F6Publishing: 13]  [Article Influence: 8.5]  [Reference Citation Analysis (0)]
115.  Yang C, Jiang ZK, Liu LH, Zeng MS. Pre-treatment ADC image-based random forest classifier for identifying resistant rectal adenocarcinoma to neoadjuvant chemoradiotherapy. Int J Colorectal Dis. 2020;35:101-107.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 10]  [Cited by in F6Publishing: 8]  [Article Influence: 5.0]  [Reference Citation Analysis (0)]
116.  Zhu HT, Zhang XY, Shi YJ, Li XT, Sun YS. A Deep Learning Model to Predict the Response to Neoadjuvant Chemoradiotherapy by the Pretreatment Apparent Diffusion Coefficient Images of Locally Advanced Rectal Cancer. Front Oncol. 2020;10:574337.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 4]  [Cited by in F6Publishing: 3]  [Article Influence: 4.0]  [Reference Citation Analysis (0)]