Radiomics is widely used to assist in clinical decision-making, disease diagnosis, and treatment planning for various target organs, including the breast. Recent advances in large language models (LLMs) have helped enhance radiomics analysis.

Herein, we sought to improve radiomics analysis by incorporating LLM-learned clinical knowledge, to classify benign and malignant tumors in breast mammography. We extracted radiomics features from the mammograms based on the region of interest and retained the features related to the target task. Using prompt engineering, we devised an input sequence that reflected the selected features and the target task. The input sequence was fed to the chosen LLM (LLaMA variant), which was fine-tuned using low-rank adaptation to enhance radiomics features. This was then evaluated on two mammogram datasets (VinDr-Mammo and INbreast) against conventional baselines.

The enhanced radiomics-based method performed better than baselines using conventional radiomics features tested on two mammogram datasets, achieving accuracies of 0.671 for the VinDr-Mammo dataset and 0.839 for the INbreast dataset. Conventional radiomics models require retraining from scratch for an unseen dataset using a new set of features. In contrast, the model developed in this study effectively reused the common features between the training and unseen datasets by explicitly linking feature names with feature values, leading to extensible learning across datasets. Our method performed better than the baseline method in this retraining setting using an unseen dataset.

Our method, one of the first to incorporate LLM into radiomics, has the potential to improve radiomics analysis.

The aim of this study was to thoroughly analyze the reproducibility of radiomics feature extraction across three Image Biomarker Standardization Initiative (IBSI)-compliant platforms using a digital phantom for benchmarking. It uncovers high consistency among common features while also pointing out the necessity for standardization in computational algorithms and mathematical definitions due to unique platform-specific features.

We selected three widely used radiomics platforms: LIFEx, Computational Environment for Radiological Research (CERR), and PyRadiomics. Using the IBSI digital phantom, we performed a comparative analysis to extract and benchmark radiomics features. The study design included testing each platform's ability to consistently reproduce radiomics features, with statistical analyses to assess the variability and agreement among the platforms.

The results indicated varying levels of feature reproducibility across the platforms. Although some features showed high consistency, others varied significantly, highlighting the need for standardized computational algorithms. Specifically, LIFEx and PyRadiomics performed consistently well across many features, whereas CERR showed greater variability in certain feature categories than LIFEx and PyRadiomics.

The study findings highlight the need for harmonized feature calculation methods to enhance the reliability and clinical usefulness of radiomics. Additionally, this study recommends incorporating clinical data and establishing benchmarking procedures in future studies to enhance the role of radiomics in personalized medicine.

To investigate magnetic resonance imaging (MRI)-based radiomics for predicting renal function response for patients treated for atherosclerotic renal artery stenosis (ARAS) by endoluminal means.

A cohort of 146 ARAS patients who underwent stenting was analyzed, with retrospective training and prospective validation groups delineated based on the treatment timing. Patients were categorized into benefit and no-benefit groups based on postoperative renal function during follow-up. Optimal radiomics labels were selected from regions of interest (ROIs) including the stenotic side and both kidneys. The nomogram combined optimal radiomics signatures with independent clinical factors using multivariable logistic regression. Shapley Additive exPlanations (SHAP), decision curve analysis (DCA), the net reclassification index (NRI), and the total integrated discrimination index (IDI) were conducted to determine the clinical usefulness of the nomogram.

Split renal function of the stenotic side and diabetes emerged as independent clinical predictors. A nomogram, incorporating these clinical factors and radiomics features from the stenotic side and both kidneys, achieved area under the curve (AUCs) of 0.927 (0.861-0.979) and 0.904 (0.819-0.972) in the training and test groups, respectively, for predicting benefits. The clinical-radiomics model significantly improved diagnostic performance (p = 0.001 and p = 0.011 for the training and test groups, respectively). DCA, NRI, and IDI analyses suggested the nomogram's superiority. SHAP analysis highlighted the radiomics feature from stenotic side kidney as the most critical predictive feature.

Both MRI radiomics and clinical factors may be valuable in pre-treatment counseling of ARAS patients who may benefit from endovascular treatment.

Radiomics allows for the quantification of medical images and facilitates precision medicine. Many radiomic features derived from computed tomography (CT) are sensitive to variations across scanners, reconstruction settings, and acquisition protocols. In this phantom study, eight different CT reconstruction parameters were varied to explore image- and feature-level harmonization approaches to improve tissue classification.

Varying reconstructions of an anthropomorphic radiopaque phantom containing three lesion categories (metastasis, hemangioma, and benign cyst) and normal liver tissue were used for evaluating two harmonization methods and their combination: (i) generative adversarial networks (GANs) at the image level; (ii) ComBat at the feature level, and (iii) a combination of (i) and (ii). A total of 93 texture and intensity features were extracted from each tissue class before and after image-level harmonization and were also harmonized at the feature level. Reproducibility and stability were assessed via the Concordance Correlation Coefficient (CCC) and pairwise comparisons using paired stability tests. The ability of features to discriminate between tissue classes was assessed by measuring the area under the receiver operating characteristic curve. The global reproducibility and discriminative power were assessed by averaging over the entire dataset and across all tissue types.

ComBat improved reproducibility by 31.58% and stability by 5.24%, while GAN increased reproducibility by 8% it reduced stability by 4.33%. Classification analysis revealed that ComBat increased average AUC by 15.19%, whereas GAN decreased AUC by 2.56%.

While GAN qualitatively enhances image harmonization, ComBat provides superior statistical improvements in feature stability and classification performance, highlighting the importance of robust feature-level harmonization in radiomics.

The aim of this study was to map multicollinearity of the radiomic feature set in five independent [18F]FDG-PET cohorts with different tumour types and identify generalizable non-redundant features.

Five [18F]FDG-PET radiomic cohorts were analysed: non-small cell lung carcinomas (N = 35), pheochromocytomas and paragangliomas (N = 40), head and neck squamous cell carcinomas (N = 54), [18F]FDG-positive thyroid nodules with indeterminate cytology (N = 84), and gastric carcinomas (N = 206). Lesions were delineated, and 105 radiomic features were extracted using PyRradiomics. In every cohort, Spearman's rank correlation coefficient (ρ) matrices of features were calculated to determine which features showed (very) strong (ρ > 0.7 and ρ > 0.9) correlations with any other feature in all five cohorts. Cluster analysis of an averaged correlation matrix for all cohorts was performed at a threshold of ρ = 0.7 and ρ = 0.9. For each cluster, a representative, non-redundant feature was selected.

Seventy-two and 90 out of 105 features showed a (very) strong correlation with another feature in the correlation matrix in all five cohorts. Cluster analysis resulted in 35 and 15 non-redundant features at thresholds of ρ = 0.9 and ρ = 0.7, including 6 and 3 shape features, 4 and 2 intensity features, and 25 and 10 texture features, respectively. Seventy or 90 redundant features could be omitted at these thresholds, respectively.

At least two-thirds of the radiomic feature set could be omitted because of strong multicollinearity in multiple independent cohorts. More redundant features could be identified using a less conservative threshold. Future research should indicate whether multicollinearity of the radiomic feature set is similar for other radiopharmaceuticals and imaging modalities.

Question Radiomic feature sets contain many strongly correlating features, which results in statistical challenges. Findings Analysis of the correlation matrices showed that the same radiomic features were strongly correlated in five independent [18F]FDG-PET cohorts with different tumour types. Clinical relevance At least two-thirds of the radiomic feature set could be omitted, because of strong multicollinearity. More redundant features could be identified using a less conservative threshold.

Perineural invasion (PNI) is an independent risk factor for poor prognosis in gastric cancer (GC) patients. This study aimed to develop and validate predictive models based on CT imaging and clinical features to predict PNI status in GC patients.

This retrospective study included 291 GC patients (229 in the training cohort and 62 in the validation cohort) who underwent gastrectomy between January 2020 and August 2022. The clinical data and preoperative abdominal contrast-enhanced computed tomography (CECT) images were collected. Radiomics features were extracted from the venous phase of CECT images. The intraclass correlation coefficient (ICC), Pearson correlation coefficient, and t-test were applied for radiomics feature selection. The random forest algorithm was used to construct a radiomics signature and calculate the radiomics feature score (Rad-score). A hybrid model was built by aggregating the Rad-score and clinical predictors. The area under the receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to evaluate the prediction performance of the radiomics, clinical, and hybrid models.

A total of 994 radiomics features were extracted from the venous phase images of each patient. Finally, 5 radiomics features were selected and used to construct a radiomics signature. The hybrid model demonstrated strong predictive ability for PNI, with AUCs of 0.833 (95% CI: 0.779-0.887) and 0.806 (95% CI: 0.628-0.983) in the training and validation cohorts, respectively. The DCA showed that the hybrid model had good clinical utility.

We established three models, and the hybrid model that combined the Rad-score and clinical predictors had a high potential for predicting PNI in GC patients.

Radiomics is a health technology that has the potential to extract clinically meaningful biomarkers from standard of care imaging. Despite a wealth of exploratory analysis performed on scans acquired from patients with lung cancer and existing guidelines describing some of the key steps, no radiomic-based biomarker has been widely accepted. This is primarily due to limitations with methodology, data analysis, and interpretation of the available studies. There is currently a lack of guidance relating to the entire radiomic workflow from study design to critical appraisal. This guide, written with early career lung cancer researchers, describes a more complete radiomic workflow. Lung cancer image analysis is the focus due to some of the unique challenges encountered such as patient movement from breathing. The guide will focus on CT imaging as these are the most common scans performed on patients with lung cancer. The aim of this article is to support the production of high-quality research that has the potential to positively impact outcome of patients with lung cancer.

Radiomics-based machine learning (ML) models of amino acid positron emission tomography (PET) images have shown efficiency in glioma prediction tasks. However, their clinical impact on physician interpretation remains limited. This study investigated whether an explainable radiomics model modifies nuclear physicians' assessment of glioma aggressiveness at diagnosis.

Patients underwent dynamic 6-[18F]fluoro-L-DOPA PET acquisition. With a 75%/25% split for training (n = 63) and test sets (n = 22), an ensemble ML model was trained using radiomics features extracted from static/dynamic parametric PET images to classify lesion aggressiveness. Three explainable ML methods-Local Interpretable Model-agnostic Explanations (LIME), Anchor, and SHapley Additive exPlanations (SHAP)-generated patient-specific explanations. Eighteen physicians from eight institutions evaluated the test samples. During the first phase, physicians analyzed the 22 cases exclusively through magnetic resonance and static/dynamic PET images, acquired within a maximum interval of 30 days. In the second phase, the same physicians reevaluated the same cases (n = 22), using all available data, including the radiomics model predictions and explanations.

Eighty-five patients (54[39-62] years old, 41 women) were selected. In the second phase, physicians demonstrated a significant improvement in diagnostic accuracy compared to the first phase (0.775 [0.750-0.802] vs. 0.717 [0.694-0.737], p = 0.007). The explainable radiomics model augmented physician agreement, with a 22.72% increase in Fleiss's kappa, and significantly enhanced physician confidence (p < 0.001). Among all physicians, Anchor and SHAP showed efficacy in 75% and 72% of cases, respectively, outperforming LIME (p ≤ 0.001).

Our results highlight the potential of an explainable radiomics model using amino acid PET scans as a diagnostic support to assist physicians in identifying glioma aggressiveness.

Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in cancer research, offering the ability to process huge data rapidly and make precise therapeutic decisions. Over the last decade, AI, particularly deep learning (DL) and machine learning (ML), has significantly enhanced cancer prediction, diagnosis, and treatment by leveraging algorithms such as convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs). These technologies provide reliable, efficient solutions for managing aggressive diseases like cancer, which have high recurrence and mortality rates. This review prospective highlights the applications of AI in oncology, a long with FDA-approved technologies like EFAI RTSuite CT HN-Segmentation System, Quantib Prostate, and Paige Prostate, and explore their role in advancing cancer detection, personalized care, and treatment. Furthermore, we also explored broader applications of AI in healthcare, addressing challenges, limitations, regulatory considerations, and ethical implications. By presenting these advancements, we underscore AI's potential to revolutionize cancer care, management and treatment.

Metabolic reprogramming is a key contributor to cancer therapeutic resistance. Peroxisomes are highly metabolic organelles essential for lipid metabolism and reactive oxygen species (ROS) turnover. Recent studies pointed out that targeting peroxisomal genes could be a promising strategy for treating therapy-resistant cells. However, the role of peroxisomes in CRC chemoresistance remains largely unexplored. This study aimed to investigate the function of peroxisomes in CRC chemoresistance and uncover the underlying mechanisms. Our results showed that the protein level of peroxisome marker PMP70 was strongly correlated with oxaliplatin (LOHP)-treated tumor recurrence in CRC. LOHP was confirmed to induce pexophagy in CRC cells, whereas LOHP-resistant cells maintained stable peroxisome levels and resisted this selective autophagy. Moreover, depletion of PMP70 significantly reduced the viability of resistant CRC cells in response to LOHP, both in vitro and in vivo. Mechanistically, PMP70 acted as a potential protector against excessive lipid peroxidation (LPO) in PMP70High and LOHP-resistant CRC cells. Additionally, PMP70-depleted cells exhibited an altered metabolic profile, characterized by reduced neutral lipids, fewer lipid droplets (LDs), and cell cycle arrest, indicating that PMP70 downregulation resulted in metabolic impairment. In conclusion, our study unveiled the pivotal role of PMP70-mediated peroxisomal functions in conferring chemoresistance, particularly in the context of LOHP resistance in CRC.

To construct and validate an interpretable machine learning (ML) radiomics model derived from multiparametric magnetic resonance imaging (MRI) images to differentiate between luminal and non-luminal breast cancer (BC) subtypes.

This study enrolled 1098 BC participants from four medical centers, categorized into a training cohort (n = 580) and validation cohorts 1-3 (n = 252, 89, and 177, respectively). Multiparametric MRI-based radiomics features, including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) imaging, were extracted. Five ML algorithms were applied to develop various radiomics models, from which the best performing model was identified. A ML-based combined model including optimal radiomics features and clinical predictors was constructed, with performance assessed through receiver operating characteristic (ROC) analysis. The Shapley additive explanation (SHAP) method was utilized to assess model interpretability.

Tumor size and MR-reported lymph node status were chosen as significant clinical variables. Thirteen radiomics features were identified from multiparametric MRI images. The extreme gradient boosting (XGBoost) radiomics model performed the best, achieving area under the curves (AUCs) of 0.941, 0.903, 0.862, and 0.894 across training and validation cohorts 1-3, respectively. The XGBoost combined model showed favorable discriminative power, with AUCs of 0.956, 0.912, 0.894, and 0.906 in training and validation cohorts 1-3, respectively. The SHAP visualization facilitated global interpretation, identifying "ADC_wavelet-HLH_glszm_ZoneEntropy" and "DCE_wavelet-HLL_gldm_DependenceVariance" as the most significant features for the model's predictions.

The XGBoost combined model derived from multiparametric MRI may proficiently differentiate between luminal and non-luminal BC and aid in treatment decision-making.

An interpretable machine learning radiomics model can preoperatively predict luminal and non-luminal subtypes in breast cancer, thereby aiding therapeutic decision-making.

Predicting tumor biomarkers with high precision is essential for improving the diagnostic accuracy and developing more effective treatment strategies. This paper proposes a machine learning model that utilizes CT images and biopsy whole slide images (WSI) to classify mesothelin expression levels in pancreatic cancer. By combining multimodal learning and stochastic configuration networks, a radiopathomics mesothelin-prediction system named RPMSNet is developed. The system extracts radiomic and pathomic features from CT images and WSI, respectively, and sends them into stochastic configuration networks for the final prediction. Compared to traditional radiomics or pathomics, this system has the capability to capture more comprehensive image features, providing a multidimensional insight into tissue characteristics. The experiments and analyses demonstrate the accuracy and effectiveness of the system, with an area under the curve of 81.03%, an accuracy of 73.67%, a sensitivity of 71.54%, a precision of 76.78%, and a F1-score of 72.61%, surpassing both single-modality and dual-modality models. RPMSNet highlights its potential for early diagnosis and personalized treatment in precision medicine.

To identify and classify submucosal tumors by building and validating a radiomics model with gastrointestinal endoscopic ultrasonography (EUS) images.

A total of 144 patients diagnosed with submucosal tumors through gastrointestinal EUS were collected between January 2019 and October 2020. There are 1952 radiomic features extracted from each patient's EUS images. The statistical test and the customized least absolute shrinkage and selection operator regression were used for feature selection. Subsequently, an extremely randomized trees algorithm was utilized to construct a robust radiomics classification model specifically tailored for gastrointestinal EUS images. The performance of the model was measured by evaluating the area under the receiver operating characteristic curve.

The radiomics model comprised 30 selected features that showed good discrimination performance in the validation cohorts. During validation, the area under the receiver operating characteristic curve was calculated as 0.9203 and the mean value after 10-fold cross-validation was 0.9260, indicating excellent stability and calibration. These results confirm the clinical utility of the model.

Utilizing the dataset provided curated from gastrointestinal EUS examinations at our collaborating hospital, we have developed a well-performing radiomics model. It can be used for personalized and non-invasive prediction of the type of submucosal tumors, providing physicians with aid for early treatment and management of tumor progression.

Prognostic modeling in head and neck cancers (HNC) has advanced with the integration of clinical factors and radiomic data from CT and MRI scans. However, previous reviews have not systematically evaluated the predictive performance of these models across different oncological endpoints or assessed factors affecting their generalizability. This study aims to fill this gap by providing a comprehensive analysis of prognostic models in HNC.

Our systematic review and meta-analysis sourced data from PubMed, Embase, and Web of Science until August 30, 2023, shortlisting 16 studies. We concentrated on studies detailing HNC prognosis prediction through radiomics, which transparently tabulated performance metrics of c-index and utilized external validation sets. We excluded studies employing imaging techniques other than CT or MRI. Study quality was assessed using the QUIPS and RQS tools. Our meta-analysis comprised the radiomics prognosis model on all validation datasets, overall survival prediction with radiomics on all validation datasets, and overall survival prediction integrating clinical and radiomics data on external validation sets. All assessments adopted a random effects model. The research has been registered under CRD42023459049.

When evaluating by distinct endpoints, marked differences were observed. Delving deeper into the complexities of overall survival prediction, variables such as incorporation of clinical features and an enlarged training set were identified as major enhancers of the model's performance. Evaluating exclusively on external validation cohorts, purely clinical models demonstrated a prognostic strength of pooled 0.69 c-index for overall survival, in contrast to the 0.68 pooled c-index achieved by models rooted in radiomics. Combining both approaches elevated the pooled c-index to 0.76. It was clear that a blend of an expanded training dataset and features selected, coupled with the diversity in CT and MRI equipment and model counts, are pivotal in fortifying the model's resilience.

This systematic review and meta-analysis demonstrate that combining clinical and radiomic features significantly improves the predictive performance of prognostic models for overall survival in HNC. By systematically evaluating various endpoints and identifying key factors influencing model generalizability, our study fills a critical gap in the literature. These findings provide valuable insights for developing more accurate and personalized prognostic tools in HNC, guiding future research and enhancing clinical decision-making.

Background: This study aims to investigate stability and reproducibility of radiomics biomarkers for adrenal lesion characterization across different software packages. Methods: Unenhanced CT images from patients with adrenal tumors were analyzed. Radiomic features were extracted using SOPHIA Radiomics and SIBEX software. The datasets underwent Z-score normalization. Statistical comparisons were made using two-sample t-tests and Spearman correlation coefficients. Three classification models-Logistic Regression, Linear Discriminant Analysis, and Linear Support Vector Machine-were trained on the datasets. Model performance was evaluated using accuracy, precision, recall, F1 score, and ROC curves. Feature importance and the statistical significance of model performance differences were also analyzed. Results: The t-test results showed no significant differences in the radiomic features extracted by SOPHIA and SIBEX (p-values all equal to 1.0). Spearman correlation coefficients were high for most features, suggesting a strong similarity between the two software tools. Classification models generally performed better on the SOPHIA dataset, with higher accuracy and precision. Feature importance analysis identified "Quadratic mean" and "Strength" as consistently influential features. Paired t-tests indicated significant differences in accuracy and precision, while Wilcoxon signed-rank tests did not find significant differences across all performance metrics. Conclusions: Radiomic features extracted by SOPHIA and SIBEX are comparable, but slight variations in model performance highlight the need for standardized extraction protocols and fine-tuning of predictive features. The study underscores the importance of ensuring the stability and reproducibility of radiomics features for reliable clinical application in adrenal lesion characterization.

Proliferation activity mapping is crucial for the guidance of first biopsy and treatment evaluation of gliomas due to the highly heterogenous nature of glioma tumor. Here we propose and demonstrate an ease-of-use way of in vivo spatiotemporal mapping of proliferation activity by simply tracking transmembrane water dynamics with magnetic resonance imaging (MRI). Specifically, we demonstrated that proliferation activity can accelerate the transmembrane water transport in glioma cells. Method: The transmembrane water-efflux rate (k io) measured by water-exchange dynamic contrast-enhanced (DCE) MRI. Immunofluorescence, immunohistochemistry, and immunocytochemistry staining were used to validate results obtained from the in vivo imaging studies. Results: In glioma cell cultures, k io precisely followed the dynamic changes of proliferation activity in growth cycles and response to temozolomide (TMZ) treatment. In both animal glioma model and human glioma, k io linearly and strongly correlated with the spatial heterogeneity of intra-tumoral proliferation activity. More importantly, proliferation activity predicted by the single MRI parameter k io is much more accurate than those predicted by state-of-the-art methods using multimodal standard MRIs and advanced machine learning. Upregulated aquaporin 4 (AQP4) expression were observed in most proliferating glioma cells and the knockout of AQP4 could largely slow down proliferation activity, suggesting AQP4 is the potential molecule connecting MRI-k io with proliferation activity. Conclusion: This study provides an ease-of-use, accurate, and non-invasive imaging method for the spatiotemporal monitoring of proliferation activity in glioma.

To evaluate the value of radiomics based on ultrasonography in differentiating follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA) and construct a tool for preoperative noninvasive predicting FTC and FTA.

The clinical data and ultrasound images of 389 patients diagnosed with FTC or FTA postoperatively were retrospectively analyzed at 3 institutions from January 2017 to December 2023. Patients in our hospital were randomly assigned in a 7:3 ratio to training cohort and validation cohort. External test cohort consisted of data collected from other 2 hospitals. Radiomics features were used to develop models based on different machine learning classifiers. A combined model was developed combining radiomics features with clinical characteristics and a nomogram was depicted. The performance of the models was assessed by area under the receiver operating characteristic curve (AUC), calibration curve and decision curve.

Radiomics model based on random forest showed best performance in discriminating FTC and FTA, with AUCs 0.880 (95% confidence interval [CI]: 0.8290-0.9308), 0.871 (95% CI: 0.7690-0.9734), and 0.821 (95% CI: 0.7036-0.9389) in training, validation, and test cohort, respectively. The combined model presented better efficacy comparing with clinical model and radiomics model, with AUCs 0.883 (95% CI: 0.8359-0.9295), 0.874 (95% CI: 0.7873-0.9615), and 0.876 (0.7809-0.9714) in training, validation, and test cohort, respectively. The calibration curves suggested good consistency and decision curves showed the highest overall clinical benefit for the combined model.

Ultrasound radiomics model based on random forest is feasible to differentiate FTC and FTA, and the combined model is an intuitively noninvasive tool for FTC and FTA preoperative identification.

The aim of this work is to compare different machine learning models for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer using radiomics features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

The study included 55 patients with breast cancer, among whom 18 achieved pCR and 37 did not respond completely to NAC (non-pCR). After some pre-processing steps, 1446 features were extracted and corrected for batch effects using ComBat. Five machine learning algorithms, namely random forest (RF), decision tree (DT), logistic regression (LR), k-nearest neighbors (k-NN), and extreme gradient boosting (XGB), were evaluated using area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score as classification metrics. A Leave-Group-Out cross validation (LGOCV) was applied in the outer loop.

RF and DT models exhibited the highest performances compared to the other algorithms. DT achieved an accuracy of 0.96 ± 0.07, and RF achieved 0.95 ± 0.05. The AUC values for RF and DT were 0.98 ± 0.06 and 0.94 ± 0.07, respectively. LR and k-NN demonstrated lower performance across all metrics, while XGB showed competitive results but slightly lower than RF and DT.

This study demonstrates the potential of radiomics and machine learning for predicting pCR to NAC in breast cancer. RF and DT models proved to be the most effective in capturing underlying patterns in radiomics data. Further research is required to validate and strengthen the proposed approach and explore its applicability in diverse radiomics datasets and clinical scenarios.

This study aims to refine a radiomics-based diagnostic approach for detecting neonatal respiratory distress syndrome (NRDS) and examines the influence of rib suppression on the diagnostic precision of radiomics models using neonatal chest X-ray (CXR) images. A total of 138 CXR images were collected in this study. The data was partitioned into training and validation subsets based on chronological order. We applied rib suppression to the CXR images and extracted and analyzed radiomic features from lung regions both before and after rib suppression. This approach was designed to identify NRDS, develop radiomics models, and assess the impact of rib suppression on model performance. To establish these radiomics models, six machine learning models were utilized in the study. The performance was evaluated using the area under the receiver operating characteristic curve (AUC). On the validation set, the models demonstrated significant improvements after rib suppression. Specifically, the Gradient Boosting Machine (GBM) achieved an AUC of 0.781 post-suppression compared to 0.556 pre-suppression. Notably, Linear Discriminant Analysis (LDA) and Logistic Regression (LR) performed particularly well when combining features from both scenarios, achieving AUCs of 0.762 and 0.756. The results indicate the feasibility of developing radiomics models for diagnosing NRDS and highlight the enhancement in model performance due to rib suppression. This study provides a promising new method for the imaging diagnosis and prognosis evaluation of neonatal respiratory distress syndrome, showcasing the potential of radiomics in pediatric imaging.

This study aims to develop and validate a nomogram that combines traditional ultrasound radiomics features with clinical parameters to assess early intracranial hypertension (IH) following primary decompressive craniectomy (DC) in patients with severe traumatic brain injury (TBI). The study incorporates the Shapley Additive Explanations (SHAP) method to interpret the radiomics model.

This study included 199 patients with severe TBI (training cohort: n = 159; testing cohort: n = 40). Postoperative ultrasound images of the optic nerve sheath (ONS) were obtained at 6 and 18 h after DC. Based on invasive intracranial pressure (ICPi) measurements, patients were grouped according to threshold values of 15 mmHg and 20 mmHg. Radiomics features were extracted from ONS images, and feature selection methods were applied to construct predictive models using logistic regression (LR), support vector machine (SVM), random forest (RF), and K-Nearest Neighbors (KNN). Clinical-ultrasound variables were incorporated into the model through univariate and multivariate logistic regression. A combined nomogram was developed by integrating radiomics features with clinical-ultrasound variables, and its diagnostic performance was evaluated using Receiver Operating Characteristic (ROC) curve analysis and decision curve analysis (DCA). The SHAP method was adopted to explain the prediction models.

Among the machine learning models, the LR model demonstrated superior predictive efficiency and robustness at threshold values of 15 mmHg and 20 mmHg. At a threshold of 20 mmHg, the AUC values for the training and testing cohorts were 0.803 and 0.735 for the clinical model, 0.908 and 0.891 for the radiomics model, and 0.918 and 0.902 for the nomogram model, respectively. Similarly, at a threshold of 15 mmHg, the AUC values were consistent across models: 0.803 and 0.735 for the clinical model, 0.908 and 0.891 for the radiomics model, and 0.918 and 0.902 for the nomogram model. Notably, the nomogram model outperformed the clinical model. Decision curve analysis (DCA) further confirmed a higher net benefit for predicting intracranial hypertension across all models.

The nomogram model, which integrates both clinical-semantic and radiomics features, demonstrated strong performance in predicting intracranial hypertension across different threshold values. It shows promise for enhancing non-invasive ICP monitoring and supporting individualized therapeutic strategies.

Missed or delayed diagnosis of occult fractures of tibial plateau may cause adverse effects on patients. The objective of this study was to evaluate the diagnostic performance of texture analysis (TA) of knee joint radiographs combined with machine learning (ML) in identifying patients at risk of tibial plateau occult fractures.

A total of 169 patients with negative fracture on knee X-ray films from 2018 to 2022 who were diagnosed with occult tibial plateau fractures or no fractures by subsequent magnetic resonance imaging (MRI) examination were retrospectively enrolled. The X-ray images of the patient's knee joint were used for texture feature extraction. A total of 9 ML feature selection methods (including 6 mainstream methods and 3 methods provided by MaZda software) combined with 3 classification methods were used to build the best diagnostic model. The performance of each model was evaluated by accuracy, F1-value, and area under the curve (AUC).

The least absolute shrinkage and selection operator (LASSO) method had the best performance of the 6 mainstream methods, with an accuracy of 0.81, an F1 value of 0.80, and an AUC of 0.920, all of which were higher than those of the other five methods (accuracy range: 0.65-0.80, F1 score range: 0.61-0.79, AUC range: 0.722-0.895). Among the three feature selection models in MaZda software, the most ideal method for accuracy measurement was the MI method, reaching 0.77. In the measurement of the F1 value and AUC, MaZda's best method was Fisher, reaching 0.78 and 0.888, respectively. All indicators were lower than those of the LASSO method. The combination of LASSO and support vector machine (SVM) yielded the best classification performance, while the performance of the combination of LASSO and logistic regression was slightly inferior, but the difference was not statistically significant.

TA of knee joint radiography combined with ML has achieved high performance in identifying patients at risk of occult fractures of the tibial plateau. Considering both the model performance and computational complexity, the LASSO feature selection method combined with the logistic regression classifier yielded the best classification performance in this process.

Radiological imaging is a cornerstone in the clinical workup of lung diseases. Radiomics represents a significant advancement in clinical lung imaging, offering a powerful tool to complement traditional qualitative image analysis. Radiomic features are quantitative and computationally describe shape, intensity, texture and wavelet characteristics from medical images that can uncover detailed and often subtle information that goes beyond the visual capabilities of radiological examiners. By extracting this quantitative information, radiomics can provide deep insights into the pathophysiology of lung diseases and support clinical decision-making as well as personalised medicine approaches. In this educational review, we provide a step-by-step guide to radiomics-based medical image analysis, discussing the technical challenges and pitfalls, and outline the potential clinical applications of radiomics in diagnosing, prognosticating and evaluating treatment responses in respiratory medicine.

Recent studies in the field of lung cancer have emphasized the important role of body composition, particularly fatty tissue, as a prognostic factor. However, there is still a lack of practice in combining fatty tissue to discriminate benign and malignant pulmonary nodules.

This study proposes a deep learning (DL) approach to explore the potential predictive value of dual imaging markers, including intrathoracic fat (ITF), in patients with pulmonary nodules.

We enrolled 1321 patients with pulmonary nodules from three centers. Image feature extraction was performed on computed tomography (CT) images of pulmonary nodules and ITF by DL, multimodal information was used to discriminate benign and malignant in patients with pulmonary nodules.

Here, the areas under the receiver operating characteristic curve (AUC) of the model for ITF combined with pulmonary nodules were 0.910(95 % confidence interval [CI]: 0.870-0.950, P = 0.016), 0.922(95 % CI: 0.883-0.960, P = 0.037) and 0.899(95 % CI: 0.849-0.949, P = 0.033) in the internal test cohort, external test cohort1 and external test cohort2, respectively, which were significantly better than the model for pulmonary nodules. Intrathoracic fat index (ITFI) emerged as an independent influencing factor for benign and malignant in patients with pulmonary nodules, correlating with a 9.4 % decrease in the risk of malignancy for each additional unit.

This study demonstrates the potential auxiliary predictive value of ITF as a noninvasive imaging biomarker in assessing pulmonary nodules.

To investigate and authenticate the effectiveness of various radiomics models in distinguishing between benign and malignant BI-RADS 4A lesions.

A total of 936 patients with pathologically confirmed 4A lesions were included in the study (training cohort: n = 655; test cohort: n = 281). Radiomic features were derived from greyscale US images. Following dimensionality reduction and feature selection, radiomics models were developed using logistic regression (LR), support vector machine (SVM), random forest (RF), eXtreme gradient boosting (XGBoost) and multilayer perceptron (MLP) algorithms. Univariate and multivariable logistic regression analyses were employed to investigate clinical-radiological characteristics and determine variables for creating a clinical model. Five combined models integrating radiomic and clinical parameters were constructed by using each algorithm, and comparison with radiologists' performance was performed. SHapley Additive exPlanations (SHAP) approach was used to elucidate the radiomic model by ranking the significance of features based on their contribution to the evaluation.

A total of 1561 radiomic features were extracted. Thirty-six features were deemed significant by dimensionality reduction and selection. The radiomic models showed good performance with AUCs of 0.829-0.945 in training cohort; and 0.805-0.857 in test cohort. The combined model developed by using LR showed the best performance (AUC, training cohort: 0.909; test cohort: 0.905), which is superior to radiologists' performance. Decision curve analysis (DCA) of this combined model indicated better clinical efficacy than clinical and radiomic models.

The combined model integrating radiomic and clinical features demonstrated excellent performance in differentiating between benign and malignant 4A lesions. It may offer a non-invasive and efficient approach to aid in clinical decision-making.

The purpose of this study was to investigate the dosiomics features of the interplay between CT density and dose distribution in lung SBRT plans, and to develop a model to predict treatment failure following lung SBRT treatment.

A retrospective study was conducted involving 179 lung cancer patients treated with SBRT at the University of Nebraska Medical Center (UNMC) between October 2007 and June 2022. Features from the CT image, Biological Effective Dose (BED) and five interaction matrices between CT and BED were extracted using radiomics mathematics. Our in-house feature selection pipeline was utilized to evaluate and rank features based on their importance and redundancy, with only the selected non-redundant features being used for predictive modeling. We randomly selected 151 cases and 28 cases as training and test datasets. Four different models were trained utilizing the Balanced Random Forest framework on the same training dataset to differentiate between failure and non-failure cases. These four models utilized the same number of selected features extracted from CT-only, BED-only, a combination of CT and BED, and a composite of CT and BED including their interaction matrices, respectively.

The cohort included 125 non-failure cases and 54 failure cases, with a median follow-up time of 34.4 months. We selected the top 17 important and non-redundant features (with the Pearsons's coefficient < 0.5) in each model. When evaluated on the same independent test set, the four models-CT features-only, BED features-only, a combination of CT and BED features, and a composite model including features from CT and BED that includes their interaction matrices-achieved AUC values of 0.56, 0.75, 0.73, and 0.82, respectively, with corresponding accuracies of 0.61, 0.79, 0.71, and 0.79. The composite model demonstrated the highest AUC and accuracy, indicating that incorporating interactions between CT and BED reveals more predictive capabilities in distinguishing between failure and non-failure cases.

The dosiomics model integrating the interaction between CT and Dose can effectively predict treatment failure following lung SBRT treatment and may serve as a useful tool to proactively evaluate and select lung SBRT treatment plans to reduce treatment failure in the future.

This study aimed to investigate the performance of multiparametric magnetic resonance imaging (mpMRI) radiomic feature-based machine learning (ML) models in classifying the Gleason grade group (GG) of prostate cancer.

In this retrospective study, a total of 203 patients with histopathologically confirmed prostate cancer who underwent mpMRI before prostate biopsy were included. After manual segmentation, radiomic features (RFs) were extracted from T2-weighted, apparent diffusion coefficient, and high b-value diffusion-weighted magnetic resonance imaging (DWMRI). Patients were split into training sets and testing sets according to a ratio of 8:2. A pipeline considering combinations of two feature selection (FS) methods and six ML classifiers was developed and evaluated. The performance of models was assessed using the accuracy, sensitivity, precision, F1-measure, and the area under curve (AUC).

On high b-value DWMRI-derived features, a combination of FS method recursive feature elimination (RFE) and classifier random forest achieved the highest performance for classification of prostate cancer into five GGs, with 97.0% accuracy, 98.0% sensitivity, 98.0% precision, and 97.0% F1-measure. The method also achieved an average AUC for GG of 98%.

Preoperative mpMRI radiomic analysis based on ML, as a noninvasive approach, showed good performance for classification of prostate cancer into five GGs.

Herein, radiomic models based on preoperative mpMRI and ML were developed to classify prostate cancer into 5 GGs. Our study provides evidence that analysis of quantitative RFs extracted from high b-value DWMRI images based on a combination of FS method RFE and classifier random forest can be applied for multiclass grading of prostate cancer with an accuracy of 97.0%.

This study aimed to assess the radiomic features of computed tomography (CT) and magnetic resonance imaging (MRI) of the bladder wall before radiotherapy using machine learning (ML) methods to predict bladder radiotoxicity in patients with prostate cancer.

This study enrolled 70 patients with pathologically confirmed prostate cancer who were candidates for radiation therapy (RT). CT and MRI of the bladder wall before radiotherapy were used to extract radiomic features. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. Algorithms such as Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and K-Nearest Neighbors (KNN) have been used to develop models based on radiomic, dosimetry, and clinical parameters. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and accuracy were used to analyze the predictive power of all models.

The RF and LR models based on the radiomic features of MRI and clinical/dosimetry parameters with an AUC of 0.95 and 0.93, and an accuracy of 86% and 86%, respectively, had the highest performance in the prediction of bladder radiation toxicity.

This study showed that, firstly, CT and MRI radiomic features of the bladder wall before treatment could be used to predict bladder radiotoxicity. Second, MRI is better than CT in predicting bladder toxicity caused by radiation. And thirdly, the performance of the predictive models based on the combination of radiomic, clinical, and dosimetry characteristics was improved.

Manual delineation of volumes of interest (VOIs) by experts is considered the gold-standard method in radiomics analysis. However, it suffers from inter- and intra-operator variability. A quantitative assessment of the impact of variations in these delineations on the performance of the radiomics predictors is required to develop robust radiomics based prediction models. In this study, we developed radiomics models for the prediction of pathological complete response to neoadjuvant chemotherapy in patients with two different breast cancer subtypes based on contrast-enhanced magnetic resonance imaging acquired prior to treatment (baseline MRI scans). Different mathematical operations such as erosion, smoothing, dilation, randomization, and ellipse fitting were applied to the original VOIs delineated by experts to simulate variations of segmentation masks. The effects of such VOI modifications on various steps of the radiomics workflow, including feature extraction, feature selection, and prediction performance, were evaluated. Using manual tumor VOIs and radiomics features extracted from baseline MRI scans, an AUC of up to 0.96 and 0.89 was achieved for human epidermal growth receptor 2 positive and triple-negative breast cancer, respectively. For smoothing and erosion, VOIs yielded the highest number of robust features and the best prediction performance, while ellipse fitting and dilation lead to the lowest robustness and prediction performance for both breast cancer subtypes. At most 28% of the selected features were similar to manual VOIs when different VOI delineation data were used. Differences in VOI delineation affect different steps of radiomics analysis, and their quantification is therefore important for development of standardized radiomics research.

Low-dose computed tomography (LDCT) screening has shown promise in reducing lung cancer mortality; however, it suffers from high false positive rates and a scarcity of available annotated datasets. To overcome these challenges, we propose a novel approach using synthetic LDCT images generated from standard-dose CT (SDCT) scans from the LIDC-IDRI dataset. Our objective is to develop and validate an interpretable radiomics-based model for distinguishing likely benign from likely malignant pulmonary nodules.

From a total of 1010 CT images (695 SDCTs and 315 LDCTs), we degraded SDCTs in the sinogram domain and obtained 1950 nodules as the training set. The 675 nodules from the LDCTs were stratified into 50%-50% partitions for validation and testing. Radiomic features were extracted from nodules, and three feature sets were assessed using: a) only shape and size (SS) features, b) all features but SS features, and c) all features. A systematic pipeline was developed to optimize the feature set and evaluate multiple machine learning models. Models were trained using degraded SDCT, validated and tested on the LDCT nodules.

Training a logistic regression model using three SS features yielded the most promising results, achieving on the test set mean balanced accuracy, sensitivity, specificity, and AUC-ROC scores of 0.81, 0.76, 0.85, and 0.87, respectively.

Our study demonstrates the feasibility and effectiveness of using synthetic LDCT images for developing a relatively accurate radiomics-based model in lung nodule classification. This approach addresses challenges associated with LDCT screening, offering potential implications for improving lung cancer detection and reducing false positives.

To conduct a computational investigation to explore the influence of clinical reference uncertainty on magnetic resonance imaging (MRI) radiomics feature selection, modelling, and performance. This study used two sets of publicly available prostate cancer MRI = radiomics data (Dataset 1: n = 260; Dataset 2: n = 100) with Gleason score clinical references. Each dataset was divided into training and holdout testing datasets at a ratio of 7:3 and analysed independently. The clinical references of the training set were permuted at different levels (increments of 5%) and repeated 20 times. Four feature selection algorithms and two classifiers were used to construct the models. Cross-validation was employed for training, while a separate hold-out testing set was used for evaluation. The Jaccard similarity coefficient was used to evaluate feature selection, while the area under the curve (AUC) and accuracy were used to assess model performance. An analysis of variance test with Bonferroni correction was conducted to compare the metrics of each model. The consistency of the feature selection performance decreased substantially with the clinical reference permutation. AUCs of the trained models with permutation particularly after 20% were significantly lower (Dataset 1 (with ≥ 20% permutation): 0.67, and Dataset 2 (≥ 20% permutation): 0.74), compared to the AUC of models without permutation (Dataset 1: 0.94, Dataset 2: 0.97). The performances of the models were also associated with larger uncertainties and an increasing number of permuted clinical references. Clinical reference uncertainty can substantially influence MRI radiomic feature selection and modelling. The high accuracy of clinical references should be helpful in building reliable and robust radiomic models. Careful interpretation of the model performance is necessary, particularly for high-dimensional data.

Feature extraction and selection from medical data are the basis of radiomics and image biomarker discovery for various architectures, including convolutional neural networks (CNNs). We herein describe the typical radiomics steps and the components of a CNN for both deep feature extraction and end-to-end approaches. We discuss the curse of dimensionality, along with dimensionality reduction techniques. Despite the outstanding performance of deep learning (DL) approaches, the use of handcrafted features instead of deep learned features needs to be considered for each specific study. Dataset size is a key factor: large-scale datasets with low sample diversity could lead to overfitting; limited sample sizes can provide unstable models. The dataset must be representative of all the "facets" of the clinical phenomenon/disease investigated. The access to high-performance computational resources from graphics processing units is another key factor, especially for the training phase of deep architectures. The advantages of multi-institutional federated/collaborative learning are described. When large language models are used, high stability is needed to avoid catastrophic forgetting in complex domain-specific tasks. We highlight that non-DL approaches provide model explainability superior to that provided by DL approaches. To implement explainability, the need for explainable AI arises, also through post hoc mechanisms. RELEVANCE STATEMENT: This work aims to provide the key concepts for processing the imaging features to extract reliable and robust image biomarkers. KEY POINTS: The key concepts for processing the imaging features to extract reliable and robust image biomarkers are provided. The main differences between radiomics and representation learning approaches are highlighted. The advantages and disadvantages of handcrafted versus learned features are given without losing sight of the clinical purpose of artificial intelligence models.

Radiomics, which involves the conversion of digital images into high-dimensional data, has been used in oncological studies since 2012. We analyzed the publications that had been conducted on this subject using bibliometric and visual methods to expound the hotpots and future trends regarding radiomics in evaluating lymph node status in oncology.

Documents published between 2012 and 2023, updated to August 1, 2024, were searched using the Scopus database. VOSviewer, R Package, and Microsoft Excel were used for visualization.

A total of 898 original articles and reviews written in English and be related to radiomics for evaluating lymph node status in oncology, published between 2015 and 2023, were retrieved. A significant increase in the number of publications was observed, with an annual growth rate of 100.77%. The publications predominantly originated from three countries, with China leading in the number of publications and citations. Fudan University was the most contributing affiliation, followed by Sun Yat-sen University and Southern Medical University, all of which were from China. Tian J. from the Chinese Academy of Sciences contributed the most within 5885 authors. In addition, Frontiers in Oncology had the most publications and transcended other journals in recent 4 years. Moreover, the keywords co-occurrence suggested that the interplay of "radiomics" and "lymph node metastasis," as well as "major clinical study" were the predominant topics, furthermore, the focused topics shifted from revealing the diagnosis of cancers to exploring the deep learning-based prediction of lymph node metastasis, suggesting the combination of artificial intelligence research would develop in the future.

The present bibliometric and visual analysis described an approximately continuous trend of increasing publications related to radiomics in evaluating lymph node status in oncology and revealed that it could serve as an efficient tool for personalized diagnosis and treatment guidance in clinical patients, and combined artificial intelligence should be further considered in the future.

There is a paucity of research using different machine learning algorithms for distinguishing between adrenal metastases and benign tumors in lung cancer patients with adrenal indeterminate nodules based on plain and biphasic-enhanced CT radiomics.

This study retrospectively enrolled 292 lung cancer patients with adrenal indeterminate nodules (training dataset, 205 (benign, 96; metastases, 109); testing dataset, 87 (benign, 42; metastases, 45)). Radiomics features were extracted from the plain, arterial, and portal CT images, respectively. The independent risk radiomics features selected by least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression (LR) were used to construct the single-phase and combined-phase radiomics models, respectively, by support vector machine (SVM), decision tree (DT), random forest (RF), and LR. The independent clinical-pathological and radiological risk factors for predicting adrenal metastases selected by using univariate and multivariate LR were used to develop the traditional model. The optimal model was selected by ROC curve, and the models' clinical values were estimated by decision curve analysis (DCA).

In the testing dataset, all SVM radiomics models showed the best robustness and efficiency, and then RF, LR, and DT models. The combined radiomics model had the best ability in predicting adrenal metastases (AUC=0.938), and then the plain (AUC=0.935), arterial (AUC=0.870), and portal radiomics model (AUC=0.851). Besides, compared to clinical-pathological-radiological model (AUC=0.870), the discriminatory capability of the plain and combined radiomics model were further improved. All radiomics models had good calibration curves and DCA showed the plain and combined radiomics models had more optimal clinical efficacy compared to other models, with the combined radiomics model having the largest net benefit.

The combined SVM radiomics model can non-invasively and efficiently predict adrenal metastatic nodules in lung cancer patients. In addition, the plain radiomics model with high predictive performance provides a convenient and accurate new method for patients with contraindications in enhanced CT.

Crohn's disease is a severe chronic and relapsing inflammatory bowel disease. Although contrast-enhanced computed tomography enterography is commonly used to evaluate crohn's disease, its imaging findings are often nonspecific and can overlap with other bowel diseases. Recent studies have explored the application of radiomics-based machine learning algorithms to aid in the diagnosis of medical images. This study aims to develop a non-invasive method for detecting bowel lesions associated with Crohn's disease using CT enterography radiomics and machine learning algorithms.

A total of 139 patients with pathologically confirmed Crohn's disease were retrospectively enrolled in this study. Radiomics features were extracted from both arterial- and venous-phase CT enterography images, representing both bowel lesions with Crohn's disease and segments of normal bowel. A machine learning classification system was constructed by combining six selected radiomics features with eight classification algorithms. The models were trained using leave-one-out cross-validation and evaluated for accuracy.

The classification model demonstrated robust performance and high accuracy, with an area under the curve of 0.938 and 0.961 for the arterial- and venous-phase images, respectively. The model achieved an accuracy of 0.938 for arterial-phase images and 0.961 for venous-phase images.

This study successfully identified a radiomics machine learning method that effectively differentiates Crohn's disease bowel lesions from normal bowel segments. Further studies with larger sample sizes and external cohorts are needed to validate these findings.

Radiomics-based analyses encompass multiple steps, leading to ambiguity regarding the optimal approaches for enhancing model performance. This study compares the effect of several feature selection methods, machine learning (ML) classifiers, and sources of radiomic features, on models' performance for the diagnosis of clinically significant prostate cancer (csPCa) from bi-parametric MRI.

Two multi-centric datasets, with 465 and 204 patients each, were used to extract 1246 radiomic features per patient and MRI sequence. Ten feature selection methods, such as Boruta, mRMRe, ReliefF, recursive feature elimination (RFE), random forest (RF) variable importance, L1-lasso, etc., four ML classifiers, namely SVM, RF, LASSO, and boosted generalized linear model (GLM), and three sets of radiomics features, derived from T2w images, ADC maps, and their combination, were used to develop predictive models of csPCa. Their performance was evaluated in a nested cross-validation and externally, using seven performance metrics.

In total, 480 models were developed. In nested cross-validation, the best model combined Boruta with Boosted GLM (AUC = 0.71, F1 = 0.76). In external validation, the best model combined L1-lasso with boosted GLM (AUC = 0.71, F1 = 0.47). Overall, Boruta, RFE, L1-lasso, and RF variable importance were the top-performing feature selection methods, while the choice of ML classifier didn't significantly affect the results. The ADC-derived features showed the highest discriminatory power with T2w-derived features being less informative, while their combination did not lead to improved performance.

The choice of feature selection method and the source of radiomic features have a profound effect on the models' performance for csPCa diagnosis.

This work may guide future radiomic research, paving the way for the development of more effective and reliable radiomic models; not only for advancing prostate cancer diagnostic strategies, but also for informing broader applications of radiomics in different medical contexts.

Radiomics is a growing field that can still be optimized. Feature selection method impacts radiomics models' performance more than ML algorithms. Best feature selection methods: RFE, LASSO, RF, and Boruta. ADC-derived radiomic features yield more robust models compared to T2w-derived radiomic features.

The BRCA1-associated protein 1 (BAP1) gene is of great interest because somatic (BAP1) mutations are the most common alteration associated with pleural mesothelioma (PM). Further, germline mutation of the BAP1 gene has been linked to the development of PM. This study aimed to explore the potential of radiomics on computed tomography scans to identify somatic BAP1 gene mutations and assess the feasibility of radiomics in future research in identifying germline mutations.

A cohort of 149 patients with PM and known somatic BAP1 mutation status was collected, and a previously published deep learning model was used to first automatically segment the tumor, followed by radiologist modifications. Image preprocessing was performed, and texture features were extracted from the segmented tumor regions. The top features were selected and used to train 18 separate machine learning models using leave-one-out cross-validation (LOOCV). The performance of the models in distinguishing between BAP1-mutated (BAP1+) and BAP1 wild-type (BAP1-) tumors was evaluated using the receiver operating characteristic area under the curve (ROC AUC).

A decision tree classifier achieved the highest overall AUC value of 0.69 (95% confidence interval: 0.60 and 0.77). The features selected most frequently through the LOOCV were all second-order (gray-level co-occurrence or gray-level size zone matrices) and were extracted from images with an applied transformation.

This proof-of-concept work demonstrated the potential of radiomics to differentiate among BAP1+/- in patients with PM. Future work will extend these methods to the assessment of germline BAP1 mutation status through image analysis for improved patient prognostication.

Stereotactic body radiation therapy (SBRT) is currently the alternative for inoperable early-stage and oligometastatic non-small cell lung cancer (NSCLC) patients. While most patients are good responders among this specific group, some patients do not experience the benefits of this treatment. Even though physicians use clinical variables and semantic radiological features to make treatment decisions, medical images contain a wealth of personalized pathophysiological information that can be extracted and used for clinical decision support systems. In the form of radiomics features, details unique to each patient's medical scans can be utilized to create predictive models and to identify biomarking signatures. Then, these tools and indices can predict treatment outcomes and categorize patients to the most optimal treatment regimen. A conceptual review of relevant topics centered around the identification and development of radiomic-based biomarkers for SBRT-treated NSCLC was conducted. To begin with, an overview of the nature and management of non-small cell lung cancer was provided. To continue, biomarkers were defined in the context of cancer care. Then, the uses of stereotactic body radiation therapy in the treatment of NSCLC were further explained. Finally, the study of radiomics was discussed, and the uses and limitations of radiomic features and ML for SBRT-treated NSCLC were expanded upon. Radiomics-based biomarkers and predictive algorithmic models can potentially improve the SBRT treatment of early-stage and oligometastatic NSCLC by providing personalized support systems to healthcare professionals. While many institutions are attempting to optimize their biomarkers and AI-based tools for clinical use, additional prospective studies are needed to properly ensure their efficacy. As such, the improvements made in the field of personalized medicine are promising.

New machine learning methods and techniques are frequently introduced in radiomics, but they are often tested on a single dataset, which makes it challenging to assess their true benefit. Currently, there is a lack of a larger, publicly accessible dataset collection on which such assessments could be performed. In this study, a collection of radiomics datasets with binary outcomes in tabular form was curated to allow benchmarking of machine learning methods and techniques.

A variety of journals and online sources were searched to identify tabular radiomics data with binary outcomes, which were then compiled into a homogeneous data collection that is easily accessible via Python. To illustrate the utility of the dataset collection, it was applied to investigate whether feature decorrelation prior to feature selection could improve predictive performance in a radiomics pipeline.

A total of 50 radiomic datasets were collected, with sample sizes ranging from 51 to 969 and 101 to 11165 features. Using this data, it was observed that decorrelating features did not yield any significant improvement on average.

A large collection of datasets, easily accessible via Python, suitable for benchmarking and evaluating new machine learning techniques and methods was curated. Its utility was exemplified by demonstrating that feature decorrelation prior to feature selection does not, on average, lead to significant performance gains and could be omitted, thereby increasing the robustness and reliability of the radiomics pipeline.

The rapidly evolving field of radiomics has shown that radiomic features are able to capture characteristics of both tumor and normal tissue that can be used to make accurate and clinically relevant predictions. In the present study we sought to determine if radiomic features can characterize the adverse effects caused by normal tissue injury as well as identify if human embryonic stem cell (hESC) derived extracellular vesicle (EV) treatment can resolve certain adverse complications. A cohort of 72 mice (n = 12 per treatment group) were exposed to X-ray radiation to the whole lung (3 × 8 Gy) or to the apex of the right lung (3 × 12 Gy), immediately followed by retro-orbital injection of EVs. Cone-Beam Computed Tomography images were acquired before and 2 weeks after treatment. In total, 851 radiomic features were extracted from the whole lungs and < 20 features were selected to train and validate a series of random forest classification models trained to predict radiation status, EV status and treatment group. It was found that all three classification models achieved significantly high prediction accuracies on a validation subset of the dataset (AUCs of 0.91, 0.86 and 0.80 respectively). In the locally irradiated lung, a significant difference between irradiated and unirradiated groups as well as an EV sparing effect were observed in several radiomic features that were not seen in the unirradiated lung (including wavelet-LLH Kurtosis, wavelet HLL Large Area High Gray Level Emphasis, and Gray Level Non-Uniformity). Additionally, a radiation difference was not observed in a secondary comparison cohort, but there was no impact of imaging machine parameters on the radiomic signature of unirradiated mice. Our data demonstrate that radiomics has the potential to identify radiation-induced lung injury and could be applied to predict therapeutic efficacy at early timepoints.

Accurate assessment of N staging in patients with non-small cell lung cancer (NSCLC) is critical for the development of effective treatment plans, the optimization of therapeutic strategies, and the enhancement of patient survival rates. This study proposes a hybrid model based on 3D convolutional neural networks (CNNs) and transformers for predicting the N-staging and survival rates of NSCLC patients within the NSCLC radiogenomics and Nsclc-radiomics datasets. The model achieved accuracies of 0.805, 0.828, and 0.819 for the training, validation, and testing sets, respectively. By leveraging the strengths of CNNs in local feature extraction and the superior performance of transformers in global information modeling, the model significantly enhances predictive accuracy and efficacy. A comparative analysis with traditional CNN and transformer architectures demonstrates that the CNN-transformer hybrid model outperforms N-staging predictions. Furthermore, this study extracts the one-year survival rate as a feature and employs the Lasso-Cox model for survival predictions at various time intervals (1, 3, 5, and 7 years), with all survival prediction p-values being less than 0.05, illustrating the time-dependent nature of survival analysis. The application of time-dependent ROC curves further validates the model's accuracy and reliability for survival predictions. Overall, this research provides innovative methodologies and new insights for the early diagnosis and prognostic evaluation of NSCLC.