• X-ray computed
• liver neoplasms
• margins of excision
• radiomics
• surgical oncology
• tomography

• #2019-23822 Italian Association for Cancer Research

• Humans
• Tumor Microenvironment
• Multiparametric Magnetic Resonance Imaging/methods
• Neoplasms/diagnostic imaging
• Neoplasms/pathology

• Artificial Intelligence
• Cholangiocarcinoma
• Computed Tomography
• Deep Learning
• Diagnosis
• Hepatocellular carcinoma
• Liver metastases
• Machine Learning
• Magnetic resonance imaging
• Prognosis
• Radiomics
• Texture analysis

• Humans
• Radiomics
• Tomography, X-Ray Computed
• Liver Neoplasms/diagnostic imaging
• Liver Neoplasms/pathology
• Radiography
• Magnetic Resonance Imaging

• Computed tomography
• Malignant ovarian tumor
• Radiomics nomogram

• Female
• Humans
• Nomograms
• Ovarian Neoplasms/diagnostic imaging
• Radiomics
• Retrospective Studies
• Tomography, X-Ray Computed

• Guike AD20238096 Guangxi Clinical Research Center for Medical Imaging Construction
• 2018GXNSFBA138048 Natural Science Foundation of Guangxi
• 46/0230220001C Postdoctoral Special Fund
• 46/0230520007X Postdoctoral Research Start-up Fund

• Gadolinium
• Gadoxetate disodium
• Gd-EOB-DTPA
• Hepatobiliary contrast agents
• Liver metastases
• Liver specific contrast agents
• Magnetic resonance imaging

• Humans
• Contrast Media
• Gadolinium DTPA
• Liver Neoplasms/pathology
• Magnetic Resonance Imaging/methods
• Liver/diagnostic imaging
• Liver/pathology

• Liver
• Magnetic resonance imaging
• Radiomics
• Reproducibility of results
• Software

• Humans
• Reproducibility of Results
• Retrospective Studies
• Liver
• Abdomen
• Magnetic Resonance Imaging

• Charité - Universitätsmedizin Berlin (3093)

• Crohn’s disease
• Infliximab
• Magnetic resonance enterography
• Radiomics

• Humans
• Infliximab/therapeutic use
• Crohn Disease/diagnostic imaging
• Crohn Disease/drug therapy
• Gastrointestinal Agents/pharmacology
• Gastrointestinal Agents/therapeutic use
• Treatment Outcome
• Logistic Models
• Retrospective Studies

• 81770545 National Natural Science Foundation of China

• artificial intelligence
• colorectal cancer
• deep learning
• machine learning
• radiomics

• Colorectal cancer metastases
• Computed tomography
• Magnetic resonance imaging
• Parametric imaging
• Positron emission tomography
• Prediction response

• Humans
• Colorectal Neoplasms/diagnostic imaging
• Colorectal Neoplasms/therapy
• Colorectal Neoplasms/pathology
• Tomography, X-Ray Computed/methods
• Magnetic Resonance Imaging
• Diffusion Magnetic Resonance Imaging
• Liver Neoplasms/therapy
• Liver Neoplasms/drug therapy

• CT
• colorectal cancer
• liver metastases
• radiomics
• texture features

• BFP/GRG/ICT/21/010 The Research Council (TRC)

• Computed tomography
• Lateral pelvic lymph node
• Rectal cancer
• Texture analysis

• Liver metastasis
• Magnetic resonance imaging
• Radiomics

• And molecular prognostic factors
• Chemotherapy
• Clinical
• Liver limited colorectal metastases
• Liver surgery
• Pathological
• Radiological staging

• Chemotherapy, Adjuvant
• Colorectal Neoplasms/drug therapy
• Hepatectomy
• Humans
• Liver Neoplasms/diagnostic imaging
• Liver Neoplasms/drug therapy
• Liver Neoplasms/surgery
• Neoadjuvant Therapy

To assess Radiomics and Machine Learning Analysis in Liver Colon and Rectal Cancer Metastases (CRLM) Growth Pattern, we evaluated, retrospectively, a training set of 51 patients with 121 liver metastases and an external validation set of 30 patients with a single lesion. All patients were subjected to MRI studies in pre-surgical setting. For each segmented volume of interest (VOI), 851 radiomics features were extracted using PyRadiomics package. Nonparametric test, univariate, linear regression analysis and patter recognition approaches were performed. The best results to discriminate expansive versus infiltrative front of tumor growth with the highest accuracy and AUC at univariate analysis were obtained by the wavelet_LHH_glrlm_ShortRunLowGray Level Emphasis from portal phase of contrast study. With regard to linear regression model, this increased the performance obtained respect to the univariate analysis for each sequence except that for EOB-phase sequence. The best results were obtained by a linear regression model of 15 significant features extracted by the T2-W SPACE sequence. Furthermore, using pattern recognition approaches, the diagnostic performance to discriminate the expansive versus infiltrative front of tumor growth increased again and the best classifier was a weighted KNN trained with the 9 significant metrics extracted from the portal phase of contrast study, with an accuracy of 92% on training set and of 91% on validation set. In the present study, we have demonstrated as Radiomics and Machine Learning Analysis, based on EOB-MRI study, allow to identify several biomarkers that permit to recognise the different Growth Patterns in CRLM.

• Morphological features
• Radiogenomics
• Radiomics
• Textural analysis

• Liver metastasis
• Magnetic resonance imaging
• Outcome prediction
• Pattern recognition
• Radiomics

The objective of the study was to assess the radiomic features obtained by computed tomography (CT) examination as prognostic biomarkers in patients with colorectal liver metastases, in order to predict histopathological outcomes following liver resection. We obtained good performance considering the single significant textural metric in the identification of the front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type, and in the detection of recurrences.

Purpose: We aimed to assess the efficacy of radiomic features extracted by computed tomography (CT) in predicting histopathological outcomes following liver resection in colorectal liver metastases patients, evaluating recurrence, mutational status, histopathological characteristics (mucinous), and surgical resection margin. Methods: This retrospectively approved study included a training set and an external validation set. The internal training set included 49 patients with a median age of 60 years and 119 liver colorectal metastases. The validation cohort consisted of 28 patients with single liver colorectal metastasis and a median age of 61 years. Radiomic features were extracted using PyRadiomics on CT portal phase. Nonparametric Kruskal–Wallis tests, intraclass correlation, receiver operating characteristic (ROC) analyses, linear regression modeling, and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. Results: The median value of intraclass correlation coefficients for the features was 0.92 (range 0.87–0.96). The best performance in discriminating expansive versus infiltrative front of tumor growth was wavelet_HHL_glcm_Imc2, with an accuracy of 79%, a sensitivity of 84%, and a specificity of 67%. The best performance in discriminating expansive versus tumor budding was wavelet_LLL_firstorder_Mean, with an accuracy of 86%, a sensitivity of 91%, and a specificity of 65%. The best performance in differentiating the mucinous type of tumor was original_firstorder_RobustMeanAbsoluteDeviation, with an accuracy of 88%, a sensitivity of 42%, and a specificity of 100%. The best performance in identifying tumor recurrence was the wavelet_HLH_glcm_Idmn, with an accuracy of 85%, a sensitivity of 81%, and a specificity of 88%. The best linear regression model was obtained with the identification of recurrence considering the linear combination of the 16 significant textural metrics (accuracy of 97%, sensitivity of 94%, and specificity of 98%). The best performance for each outcome was reached using KNN as a classifier with an accuracy greater than 86% in the training and validation sets for each classification problem; the best results were obtained with the identification of tumor front growth considering the seven significant textural features (accuracy of 97%, sensitivity of 90%, and specificity of 100%). Conclusions: This study confirmed the capacity of radiomics data to identify several prognostic features that may affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.

• computed tomography
• liver metastases
• prediction of histopathological outcomes
• radiomics analysis

• Cholangiocarcinoma
• Hepatocellular carcinoma
• Machine learnings
• Radiomics
• Texture analysis

The aim of this study was to assess the efficacy of radiomics features obtained by EOB-MRI phase in order to predict clinical outcomes following liver resection in Colorectal Liver Metastases Patients, and evaluate recurrence, mutational status, pathological characteristic (mucinous) and surgical resection margin. Ours results confirmed the capacity of radiomics to identify, as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach. These results were confirmed by external validation dataset. We obtained a good performance considering the single textural significant metric in the identification of front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type and in the detection of recurrences.

The aim of this study was to assess the efficacy of radiomics features obtained by EOB-MRI phase in order to predict clinical outcomes following liver resection in Colorectal Liver Metastases Patients, and evaluate recurrence, mutational status, pathological characteristic (mucinous) and surgical resection margin. This retrospective analysis was approved by the local Ethical Committee board of National Cancer of Naples, IRCCS “Fondazione Pascale”. Radiological databases were interrogated from January 2018 to May 2021 in order to select patients with liver metastases with pathological proof and EOB-MRI study in pre-surgical setting. The cohort of patients included a training set (51 patients with 61 years of median age and 121 liver metastases) and an external validation set (30 patients with single lesion with 60 years of median age). For each segmented volume of interest by 2 expert radiologists, 851 radiomics features were extracted as median values using PyRadiomics. non-parametric test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. The best predictor to discriminate expansive versus infiltrative front of tumor growth was HLH_glcm_MaximumProbability extraxted on VIBE_FA30 with an accuracy of 84%, a sensitivity of 83%, and a specificity of 82%. The best predictor to discriminate tumor budding was Inverse Variance obtained by the original GLCM matrix extraxted on VIBE_FA30 with an accuracy of 89%, a sensitivity of 96% and a specificity of 65%. The best predictor to differentiate the mucinous type of tumor was the HHL_glszm_ZoneVariance extraxted on VIBE_FA30 with an accuracy of 85%, a sensitivity of 46% and a specificity of 95%. The best predictor to identify tumor recurrence was the LHL_glcm_Correlation extraxted on VIBE_FA30 with an accuracy of 86%, a sensitivity of 52% and a specificity of 97%. The best linear regression model was obtained in the identification of the tumor growth front considering the height textural significant metrics by VIBE_FA10 (an accuracy of 89%; sensitivity of 93% and a specificity of 82%). Considering significant texture metrics tested with pattern recognition approaches, the best performance for each outcome was reached by a KNN in the identification of recurrence with the 3 textural significant features extracted by VIBE_FA10 (AUC of 91%, an accuracy of 93%; sensitivity of 99% and a specificity of 77%). Ours results confirmed the capacity of radiomics to identify as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.

• Liver metastasis
• Magnetic Resonance Imaging
• artificial intelligence
• radiomics

The objective of the study was to evaluate the radiomics features obtained by contrast MRI studies as prognostic biomarkers in colorectal liver metastases patients to predict clinical outcomes following liver resection. We demonstrated a good performance considering the single textural significant metric in the identification of front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type and in the detection of recurrences. Moreover, considering linear regression models or neural network classifiers in a multivariate approach was possible to increase the performance in terms of accuracy, sensitivity, and specificity.

Purpose: To assess radiomics features efficacy obtained by arterial and portal MRI phase in the prediction of clinical outcomes in the colorectal liver metastases patients, evaluating recurrence, mutational status, pathological characteristic (mucinous and tumor budding) and surgical resection margin. Methods: This retrospective analysis was approved by the local Ethical Committee board, and radiological databases were used to select patients with colorectal liver metastases with pathological proof and MRI study in a pre-surgical setting after neoadjuvant chemotherapy. The cohort of patients included a training set (51 patients with 61 years of median age and 121 liver metastases) and an external validation set (30 patients with single lesion with 60 years of median age). For each segmented volume of interest on MRI by two expert radiologists, 851 radiomics features were extracted as median values using the PyRadiomics tool. Non-parametric Kruskal-Wallis test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. Results: The best predictor to discriminate expansive versus infiltrative tumor growth front was wavelet_LHH_glrlm_ShortRunLowGrayLevelEmphasis extracted on portal phase with accuracy of 82%, sensitivity of 84%, and specificity of 77%. The best predictor to discriminate tumor budding was wavelet_LLH_firstorder_10Percentile extracted on portal phase with accuracy of 92%, a sensitivity of 96%, and a specificity of 81%. The best predictor to differentiate the mucinous type of tumor was the wavelet_LLL_glcm_ClusterTendency extracted on portal phase with accuracy of 88%, a sensitivity of 38%, and a specificity of 100%. The best predictor to identify the recurrence was the wavelet_HLH_ngtdm_Complexity extracted on arterial phase with accuracy of 90%, a sensitivity of 71%, and a specificity of 95%. The best linear regression model was obtained in the identification of mucinous type considering the 13 textural significant metrics extracted by arterial phase (accuracy of 94%, sensitivity of 77% and a specificity of 99%). The best results were obtained in the identification of tumor budding with the eleven textural significant features extracted by arterial phase using a KNN (accuracy of 95%, sensitivity of 84%, and a specificity of 99%). Conclusions: Our results confirmed the capacity of radiomics to identify as biomarkers and several prognostic features that could affect the treatment choice in patients with liver metastases in order to obtain a more personalized approach.

• colorectal liver metastasis
• magnetic resonance imaging
• outcome prediction
• pattern recognition
• radiomics

Artificial intelligence in radiology has the potential to assist with the diagnosis, prognostication and therapeutic response prediction of various cancers. A few studies have reported that texture analysis can be helpful in predicting the response to chemotherapy for colorectal liver metastases, however, the results have varied. Necrotic metastases were not clearly excluded in these studies and in most studies the full range of texture analysis features were not evaluated. This study was designed to determine if the computed tomography (CT) texture analysis results of non-necrotic colorectal liver metastases differ from previous reports. A larger range of texture features were also evaluated to identify potential new biomarkers.

To identify potential new imaging biomarkers with CT texture analysis which can predict the response to first-line cytotoxic chemotherapy in non-necrotic colorectal liver metastases (CRLMs).

Patients who presented with CRLMs from 2012 to 2020 were retrospectively selected on the institutional radiology information system of our private radiology practice. The inclusion criteria were non-necrotic CRLMs with a minimum size of 10 mm (diagnosed on archived 1.25 mm portal venous phase CT scans) which were treated with standard first-line cytotoxic chemotherapy (FOLFOX, FOLFIRI, FOLFOXIRI, CAPE-OX, CAPE-IRI or capecitabine). The final study cohort consisted of 29 patients. The treatment response of the CRLMs was classified according to the RECIST 1.1 criteria. By means of CT texture analysis, various first and second order texture features were extracted from a single non-necrotic target CRLM in each responding and non-responding patient. Associations between features and response to chemotherapy were assessed by logistic regression models. The prognostic accuracy of selected features was evaluated by using the area under the curve.

There were 15 responders (partial response) and 14 non-responders (7 stable and 7 with progressive disease). The responders presented with a higher number of CRLMs (P = 0.05). In univariable analysis, eight texture features of the responding CRLMs were associated with treatment response, but due to strong correlations among some of the features, only two features, namely minimum histogram gradient intensity and long run low grey level emphasis, were included in the multiple analysis. The area under the receiver operating characteristic curve of the multiple model was 0.80 (95%CI: 0.64 to 0.96), with a sensitivity of 0.73 (95%CI: 0.48 to 0.89) and a specificity of 0.79 (95%CI: 0.52 to 0.92).

Eight first and second order texture features, but particularly minimum histogram gradient intensity and long run low grey level emphasis are significantly correlated with treatment response in non-necrotic CRLMs.

• Colorectal cancer
• Liver metastases
• Radiomics
• Computed tomography texture analysis
• Response assessment

• colorectal cancer
• gene
• liver metastasis
• prognosis
• radiomics
• treatment

• colon cancer
• early detection
• liver metastasis
• metastatic niche
• radiogenomics
• radiomics

• PI17/01847; PI20/00602; CB16/12/00273; Instituto de Salud Carlos III

• Cavernous angioma with symptomatic hemorrhage
• MR perfusion
• MR permeability
• biomarkers
• machine learning

Objective: The present study aimed to explore the application value of magnetic resonance imaging (MRI) histograms with multiple sequences in the preoperative differential diagnosis of endometrial stromal sarcoma (ESS) and degenerative hysteromyoma (DH).

Methods: The clinical and preoperative MRI data of 20 patients with pathologically confirmed ESS and 24 patients with pathologically confirmed DH were retrospectively analyzed, forming the two study groups. Mazda software was used to select the MRI layer with the largest tumor diameter in T2WI, the apparent diffusion coefficient (ADC), and enhanced T1WI (T₁CE) images. The region of interest (ROI) was outlined for gray-scale histogram analysis. Nine parameters—the mean, variance, kurtosis, skewness, 1st percentile, 10th percentile, 50th percentile, 90th percentile, and 99th percentile—were obtained for intergroup analysis, and the receiver operating curves (ROCs) were plotted to analyze the differential diagnostic efficacy for each parameter.

Results: In the T2WI histogram, the differences between the two groups in seven of the parameters (mean, skewness, 1st percentile, 10th percentile, 50th percentile, 90th percentile, and 99th percentile) were statistically significant (P < 0.05). In the ADC histogram, the differences between the two groups in three of the parameters (skewness, 10th percentile, and 50th percentile) were statistically significant (P < 0.05). In the T₁CE histogram, no significant differences were found between the two groups in any of the parameters (all P > 0.05). Of the nine parameters, the 50th percentile was found to have the best diagnostic efficacy. In the T2WI histogram, ROC curve analysis of the 50th percentile yielded the best area under the ROC curve (AUC; 0.742), sensitivity of 70%, and specificity of 83.3%. In the ADC histogram, ROC curve analysis of the 50th percentile yielded the best area under the ROC curve (AUC; 0.783), sensitivity of 81%, and specificity of 76.9%.

Conclusion: The parameters of the mean, 10th percentile and 50th percentile in the T2WI histogram have good diagnostic efficacy, providing new methods and ideas for clinical diagnosis.

• degenerative hysteromyoma
• endometrial stromal sarcoma
• histogram analysis
• magnetic resonance imaging
• preoperative differential diagnosis

• Chemotherapy response
• Colorectal liver metastases
• Contrast-enhanced pattern
• Hepatobiliary phase
• Magnetic resonance imaging

• Antineoplastic Combined Chemotherapy Protocols
• Capecitabine
• Colorectal Neoplasms/diagnostic imaging
• Colorectal Neoplasms/drug therapy
• Contrast Media
• Gadolinium DTPA
• Humans
• Liver
• Liver Neoplasms/diagnostic imaging
• Liver Neoplasms/drug therapy
• Magnetic Resonance Imaging
• Oxaloacetates
• Retrospective Studies

• Advanced analytics
• Artificial intelligence
• Diagnostic imaging
• Gastrointestinal cancer
• Radiomics
• Treatment response

• Artificial Intelligence
• Gastrointestinal Neoplasms/diagnostic imaging
• Gastrointestinal Neoplasms/therapy
• Humans

• MRI
• delta-radiomics
• machine learning
• rectal cancer
• treatment response

• Advanced image analytics
• Colorectal liver metastases
• Radiomics

• colorectal liver metastases
• deep learning
• magnetic resonance imaging
• tumor regression grade

• Aged
• Algorithms
• Cohort Studies
• Colorectal Neoplasms/diagnostic imaging
• Colorectal Neoplasms/drug therapy
• Colorectal Neoplasms/mortality
• Colorectal Neoplasms/pathology
• Computer Simulation
• Deep Learning
• Female
• Hepatectomy
• Humans
• Image Processing, Computer-Assisted
• Liver Neoplasms/diagnostic imaging
• Liver Neoplasms/drug therapy
• Liver Neoplasms/mortality
• Liver Neoplasms/secondary
• Male
• Middle Aged
• Neoplasm Staging
• Prognosis
• Retrospective Studies
• Survival Analysis
• Treatment Outcome

• Magnetic resonance imaging
• Nomogram
• Peripheral nerve
• Radiomics
• Rectal cancer

• Biomarkers
• Humans
• Magnetic Resonance Imaging
• Magnetic Resonance Spectroscopy
• Nomograms
• Rectal Neoplasms/diagnostic imaging
• Retrospective Studies

• Malignant nodes
• Rectal cancer
• Texture analysis
• Tumor deposit

• Extranodal Extension
• Humans
• Lymph Nodes/diagnostic imaging
• Magnetic Resonance Imaging
• Rectal Neoplasms/diagnostic imaging
• Retrospective Studies

• Aged
• Bayes Theorem
• Colorectal Neoplasms/pathology
• Contrast Media
• Female
• Gadolinium DTPA
• Humans
• Image Enhancement/methods
• Liver Neoplasms/diagnostic imaging
• Liver Neoplasms/secondary
• Magnetic Resonance Imaging/methods
• Male
• Prognosis
• Retrospective Studies
• Sensitivity and Specificity

• chemotherapy
• colorectal liver metastases
• contrast-enhanced multidetector computed tomography
• deep learning
• radiomics

• Artificial intelligence
• Metastasis
• Neoadjuvant chemotherapy
• Quantitative imaging analysis
• Response

Patients with liver metastases can be scheduled for different therapies (e.g., chemotherapy, surgery, radiotherapy, and ablation). The choice of the most appropriate treatment should rely on adequate understanding of tumor biology and prediction of survival, but reliable biomarkers are lacking. Radiomics is an innovative approach to medical imaging: it identifies invisible-to-the-human-eye radiological patterns that can predict tumor aggressiveness and patients outcome. We reviewed the available literature to elucidate the role of radiomics in patients with liver metastases. Thirty-two papers were analyzed, mostly (56%) concerning metastases from colorectal cancer. Even if available studies are still preliminary, radiomics provided effective prediction of response to chemotherapy and of survival, allowing more accurate and earlier prediction than standard predictors. Entropy and homogeneity were the radiomic features with the strongest clinical impact. In the next few years, radiomics is expected to give a consistent contribution to the precision medicine approach to patients with liver metastases.

Multidisciplinary management of patients with liver metastases (LM) requires a precision medicine approach, based on adequate profiling of tumor biology and robust biomarkers. Radiomics, defined as the high-throughput identification, analysis, and translational applications of radiological textural features, could fulfill this need. The present review aims to elucidate the contribution of radiomic analyses to the management of patients with LM. We performed a systematic review of the literature through the most relevant databases and web sources. English language original articles published before June 2020 and concerning radiomics of LM extracted from CT, MRI, or PET-CT were considered. Thirty-two papers were identified. Baseline higher entropy and lower homogeneity of LM were associated with better survival and higher chemotherapy response rates. A decrease in entropy and an increase in homogeneity after chemotherapy correlated with radiological tumor response. Entropy and homogeneity were also highly predictive of tumor regression grade. In comparison with RECIST criteria, radiomic features provided an earlier prediction of response to chemotherapy. Lastly, texture analyses could differentiate LM from other liver tumors. The commonest limitations of studies were small sample size, retrospective design, lack of validation datasets, and unavailability of univocal cut-off values of radiomic features. In conclusion, radiomics can potentially contribute to the precision medicine approach to patients with LM, but interdisciplinarity, standardization, and adequate software tools are needed to translate the anticipated potentialities into clinical practice.

• computer-assisted diagnosis
• gray level matrices
• liver metastases
• overall and recurrence-free survival
• radiomics
• response to chemotherapy
• texture analysis

• Colorectal cancer
• Histological growth patterns
• Liver metastases
• Prediction

• Hepatocellular carcinoma (HCC)
• cholangiocarcinoma
• magnetic resonance imaging (MRI)
• postoperative survival
• radiomics

• Endometrial carcinoma
• Liver pathology
• MR imaging
• Pancreatic carcinoma
• Prostate cancer
• Rectal carcinoma
• Renal cell carcinoma
• Texture analysis

• colorectal cancer
• computed tomography
• magnetic resonance imaging
• radiomics