Development of a Radiomics-Based Machine Learning Model Utilizing CT-Guided Biopsy Images: Virtual Biopsy of Thoracic Cancers

BackgroundDuring the diagnosis and treatment of non–small cell lung cancer (NSCLC), detecting the risk of its recurrence in an early phase is still challenging. Recent studies have investigated the radiomics-based machine learning (ML) models for detecting the risk of recurrence in NSCLC. However, there is still insufficient systematic evidence to prove its efficiency.ObjectiveThis study is designed to systematically evaluate the effectiveness of radiomics-based ML in predicting the risk of recurrence in NSCLC, aiming to provide evidence-based support for the subsequent development of scoring tools to forecast recurrence risk.MethodsFor acquiring research on radiomics-based models for forecasting the risk of recurrence in NSCLC, Cochrane Library, Web of Science, PubMed, and Embase were systematically retrieved, up to October 24, 2025. Studies on analyzing the recurrence of NSCLC using radiomics-based ML were included, while those in which only texture analysis was conducted or radiomics-based ML was not constructed were excluded. The Radiomics Quality Score (RQS) was used to appraise the eligible studies. Subgroup analyses were conducted according to the variables of the model, the background of treatment, the stage of lung cancer, and the pathological type.ResultsUltimately, 30 eligible studies in total were included, covering 7964 patients with NSCLC. According to the meta-analysis, the c-index of radiomics-based ML models for forecasting the risk of recurrence in NSCLC was 0.850 (95% CI 0.834‐0.866, 95% prediction interval [PI] 0.623‐1.004) in the training set. Specifically, the pooled c-index was 0.876 (95% CI 0.853‐0.900) among the patients receiving the stereotactic body radiation therapy and 0.825 (95% CI 0.804‐0.848) among those who received surgeries combined with other adjuvant treatment regimens. The c-index of the radiomics-based ML models combined with clinical features for forecasting the risk of recurrence in NSCLC was 0.833 (95% CI 0.822‐0.854, 95% PI 0.717‐0.945) in the training set. In contrast, the c-index of radiomics-based ML models for forecasting the risk of recurrence in NSCLC was 0.878 (95% CI 0.854‐0.902, 95% PI 0.681‐1.000) in the validation set. The c-index of radiomics-based ML models combined with clinical features for forecasting the risk of recurrence in NSCLC was 0.854 (95% CI 0.830‐0.878, 95% PI 0.655‐0.992) in the validation set. The average RQS across the included studies was 27.4%, revealing methodological limitations and an absence of standardization.ConclusionsThis study is the first to confirm that radiomics-based ML models effectively predict the risk of recurrence in NSCLC. This study provides evidence-based support for the subsequent development or updating of radiomics-based ML models. However, the current methodological application of radiomics remains concerning. Therefore, in the future, research should standardize the workflow for implementing radiomics-based ML and incorporate multicenter imaging data to enhance its generalizability.

To explore whether radiomics-based machine learning (ML) models could outperform conventional diagnostic methods at identifying vulnerable lesions on coronary computed tomographic angiography (CCTA). In this retrospective study, 36 heart transplant recipients with coronary heart disease (CAD) and end-stage heart failure were included. Pathological cross-section samples of 350 plaques were collected and coregistered to patients' preoperative CCTA images. A total of 1184 radiomic features were extracted from CCTA images. Through feature selection and stratified fivefold cross-validation, we derived eight radiomics-based ML models for lesion vulnerability prediction. An independent set of 196 plaques from another 8 CAD patients who underwent heart transplants was collected to validate radiomics-based ML models' diagnostic accuracy against conventional CCTA feature-based diagnosis (presence of at least 2 high-risk plaque features). The performance of the prediction models was assessed by the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CI). The training group used to develop radiomics-based ML models contained 200/350 (57.1%) vulnerable plaques and the external validation group was composed of 67.3% (132/196) vulnerable plaques. The radiomics-based ML model based on eight radiomic features showed excellent cross-validation diagnostic accuracy (AUC: 0.900 ± 0.033). In the validation group, diagnosis based on conventional CCTA features demonstrated moderate performance (AUC: 0.656 [95% CI: 0.593 -0.718]), while the radiomics-based ML model showed higher diagnostic ability (0.782 [95% CI: 0.710 -0.846]). Radiomics-based ML models showed better diagnostic ability than the conventional CCTA features at assessing coronary plaque vulnerability. • CCTA has great potential in the diagnosis of vulnerable coronary artery lesions. • Radiomics model built through CCTA could discriminate coronary vulnerable lesions in good diagnostic ability. • Radiomics model could improve the ability of vulnerability diagnosis against traditional CCTA method, sensitivity especially.

BackgroundPancreatic ductal adenocarcinoma (PDAC) and mass-forming pancreatitis (MFP) share similar clinical, laboratory, and imaging features, making accurate diagnosis challenging. Nevertheless, PDAC is highly malignant with a poor prognosis, whereas MFP is an inflammatory condition typically responding well to medical or interventional therapies. Some investigators have explored radiomics-based machine learning (ML) models for distinguishing PDAC from MFP. However, systematic evidence supporting the feasibility of these models is insufficient, presenting a notable challenge for clinical application.ObjectiveThis study intended to review the diagnostic performance of radiomics-based ML models in differentiating PDAC from MFP, summarize the methodological quality of the included studies, and provide evidence-based guidance for optimizing radiomics-based ML models and advancing their clinical use.MethodsPubMed, Embase, Cochrane, and Web of Science were searched for relevant studies up to June 29, 2024. Eligible studies comprised English cohort, case-control, or cross-sectional designs that applied fully developed radiomics-based ML models—including traditional and deep radiomics—to differentiate PDAC from MFP, while also reporting their diagnostic performance. Studies without full text, limited to image segmentation, or insufficient outcome metrics were excluded. Methodological quality was appraised by means of the radiomics quality score. Since the limited applicability of QUADAS-2 in radiomics-based ML studies, the risk of bias was not formally assessed. Pooled sensitivity, specificity, area under the curve of summary receiver operating characteristics (SROC), likelihood ratios, and diagnostic odds ratio were estimated through a bivariate mixed-effects model. Results were presented with forest plots, SROC curves, and Fagan’s nomogram. Subgroup analysis was performed to appraise the diagnostic performance of radiomics-based ML models across various imaging modalities, including computed tomography (CT), magnetic resonance imaging, positron emission tomography-CT, and endoscopic ultrasound.ResultsThis meta-analysis included 24 studies with 14,406 cases, including 7635 PDAC cases. All studies adopted a case-control design, with 5 conducted across multiple centers. Most studies used CT as the primary imaging modality. The radiomics quality score scores ranged from 5 points (14%) to 17 points (47%), with an average score of 9 (25%). The radiomics-based ML models demonstrated high diagnostic performance. Based on the independent validation sets, the pooled sensitivity, specificity, area under the curve of SROC, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.92 (95% CI 0.91‐0.94), 0.90 (95% CI 0.85‐0.94), 0.94 (95% CI 0.74‐0.99), 9.3 (95% CI 6.0‐14.2), 0.08 (95% CI 0.07‐0.11), and 110 (95% CI 62‐194), respectively.ConclusionsRadiomics-based ML models demonstrate high diagnostic accuracy in differentiating PDAC from MFP, underscoring their potential as noninvasive tools for clinical decision-making. Nonetheless, the overall methodological quality was moderate due to limitations in external validation, standardized protocols, and reproducibility. These findings support the promise of radiomics in clinical diagnostics while highlighting the need for more rigorous, multicenter research to enhance model generalizability and clinical applicability.

Qualitative observer-based and quantitative radiomics-based analyses of T1w contrast-enhanced magnetic resonance imaging (T1w-CE MRI) have both been shown to predict the outcomes of brain metastasis (BM) stereotactic radiosurgery (SRS). Comparison of these methods and interpretation of radiomics-based machine learning (ML) models remains limited. To address this need, we collected a dataset of n = 123 BMs from 99 patients including 12 clinical features, 107 pre-treatment T1w-CE MRI radiomic features, and BM post-SRS progression scores. A previously published outcome model using SRS dose prescription and five-way BM qualitative appearance scoring was evaluated. We found high qualitative scoring interobserver variability across five observers that negatively impacted the model’s risk stratification. Radiomics-based ML models trained to replicate the qualitative scoring did so with high accuracy (bootstrap-corrected AUC = 0.84–0.94), but risk stratification using these replicated qualitative scores remained poor. Radiomics-based ML models trained to directly predict post-SRS progression offered enhanced risk stratification (Kaplan–Meier rank-sum p = 0.0003) compared to using qualitative appearance. The qualitative appearance scoring enabled interpretation of the progression radiomics-based ML model, with necrotic BMs and a subset of heterogeneous BMs predicted as being at high-risk of post-SRS progression, in agreement with current radiobiological understanding. Our study’s results show that while radiomics-based SRS outcome models out-perform qualitative appearance analysis, qualitative appearance still provides critical insight into ML model operation.

ObjectiveTo develop a computed tomography (CT) radiomics-based interpretable machine learning (ML) model to preoperatively predict human epidermal growth factor receptor 2 (HER2) status in bladder cancer (BCa) with multicenter validation.MethodsIn this retrospective study, 207 patients with pathologically confirmed BCa were enrolled and divided into the training set (n = 154) and test set (n = 53). Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most discriminative features in the training set. Five radiomics-based ML models, namely logistic regression (LR), support vector machine (SVM), k-nearest neighbors (KNN), eXtreme Gradient Boosting (XGBoost) and random forest (RF), were developed. The predictive performance of established ML models was evaluated by the area under the receiver operating characteristic curve (AUC). The Shapley additive explanation (SHAP) was used to analyze the interpretability of ML models.ResultsA total of 1218 radiomics features were extracted from the nephrographic phase CT images, and 11 features were filtered for constructing ML models. In the test set, the AUCs of LR, SVM, KNN, XGBoost, and RF were 0.803, 0.709, 0.679, 0.794, and 0.815, with corresponding accuracies of 71.7%, 69.8%, 60.4%, 75.5%, and 75.5%, respectively. RF was identified as the optimal classifier. SHAP analysis showed that texture features (gray level size zone matrix and gray level co-occurrence matrix) were significant predictors of HER2 status.ConclusionsThe radiomics-based interpretable ML model provides a noninvasive tool to predict the HER2 status of BCa with satisfactory discriminatory performance.Critical relevance statementAn interpretable radiomics-based machine learning model can preoperatively predict HER2 status in bladder cancer, potentially aiding in the clinical decision-making process.Key PointsThe CT radiomics model could identify HER2 status in bladder cancer.The random forest model showed a more robust and accurate performance.The model demonstrated favorable interpretability through SHAP method.Graphical

Medulloblastomas are a major cause of cancer-related mortality in the pediatric population. Four molecular groups have been identified, and these molecular groups drive risk stratification, prognostic modeling, and the development of novel treatment modalities. It has been demonstrated that radiomics-based machine learning (ML) models are effective at predicting the diagnosis, molecular class, and grades of CNS tumors. To assess radiomics-based ML models' diagnostic performance in predicting medulloblastoma subgroups and the methodological quality of the studies. A comprehensive literature search was performed on PubMed; the last search was conducted on 1 May 2022. Studies that predicted all four medulloblastoma subgroups in patients with histopathologically confirmed medulloblastoma and reporting area under the curve (AUC) values were included in the study. The quality assessments were conducted according to the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Checklist for Artificial Intelligence in Medical Imaging (CLAIM). A meta-analysis of radiomics-based ML studies' diagnostic performance for the preoperative evaluation of medulloblastoma subgrouping was performed. Five studies were included in this meta-analysis. Regarding patient selection, two studies indicated an unclear risk of bias according to the QUADAS-2. The five studies had an average CLAIM score and compliance score of 23.2 and 0.57, respectively. The meta-analysis showed pooled AUCs of 0.88, 0.82, 0.83, and 0.88 for WNT, SHH, group 3, and group 4 for classification, respectively. Radiomics-based ML studies have good classification performance in predicting medulloblastoma subgroups, with AUCs >0.80 in every subgroup. To be applied to clinical practice, they need methodological quality improvement and stability.

BackgroundOccult pleural dissemination (PD) in non-small cell lung cancer (NSCLC) patients is likely to be missed on computed tomography (CT) scans, associated with poor survival, and generally contraindicated for radical surgery. This study aimed to develop and compare the performance of radiomics-based machine learning (ML), deep learning (DL), and fusion models to preoperatively identify occult PD in NSCLC patients.Materials and methodsA total of 326 NSCLC patients from three Chinese high-volume medical centers (2016–2023) were retrospectively collected and divided into training (n = 216), internal test (n = 54), and external test (n = 56) cohorts. Ten radiomics-based ML models and eight DL models were trained using CT images at the maximum cross-sectional slice of the primary tumor. Moreover, another two fusion models (prefusion and postfusion) were developed using feature-based and decision-based methods. The receiver operating characteristic curve (ROC) and area under the curve (AUC) were mainly used to compare the predictive performance of the models.ResultsThe GBM (AUC: 0.821) and DenseNet121 (AUC: 0.764) models achieved the highest AUC among ML and DL models in the external test cohorts, respectively. The postfusion model, integrating the output probabilities from GBM and DenseNet121 models, showed superior performance (AUC: 0.828–0.978) compared to the prefusion model (AUC: 0.817–0.877). Moreover, the postfusion model demonstrated the highest degree of sensitivity (82.1–97.2%) among all models across the three cohorts.ConclusionsThe postfusion model, which integrates radiomics-based ML and DL models, can serve as a sensitive diagnostic tool to predict occult PD in NSCLC patients, thereby helping to avoid unnecessary surgeries.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12885-025-15121-9.

This research is based on a literature analysis of 30 (thirty) relevant journals on the topic of conflict resolution education as part of multicultural education in application and is associated with the development of a project-based learning-based learning design thinking learning model to foster students in civics learning by improving the ability to solve everyday critical-critical problem-based problems in the surrounding environment. The analysis of the journal resulted in two main concepts studied, namely multicultural learning and educational models. development of a design thinking learning model as a way to foster students' critical thinking in learning activities analysing multicultural conflict issues in civic education learning. The analysis method uses systematic literature review by sorting out the selected journals to be discussed and producing the stages of application of the design thinking model to make ideas and reprise the selected work through presentations and independent assessments by teachers as facilitators and developers of learning models. This model aims to prevent the occurrence of potential negative conflicts among students in the future due to increasingly complex social issues.

This article is intended to find solutions related to the lack of adequate history learning to strengthen ethnic identity and maintain social harmony in a multicultural society through the development of a local history learning model based on the value clarification technique (VCT). The learning model is achieved through research and development using the Plomp model. The learning model goes through an expert validation process, including experts in local history teaching materials, experts in value education, experts in ethnic identity and Saminism, and experts in developing learning models. Furthermore, the learning model was tested on 30 students as participants with a pre-experimental pre-and post-tests one group design. The data collection technique was carried out through the distribution of questionnaires. Data analysis was carried out using descriptive statistics and inferential analysis was performed using a t-test. The results of the t-test showed a significant strengthening of ethnic identity after the trial. The local history learning model based on the value clarification technique (VCT) can strengthen ethnic identity and maintain social harmony. The development of this learning model is possible if history lecturers have experience related to relevant ethnic studies.

The role of religion in human life is very important because it relates to a person's spiritual life with his belief in God. The role of religion is to be a guide in the effort to create a meaningful, peaceful and dignified life. Education is an effort so that humans can develop their potential through the learning process and or other ways that are known and recognized by the community. The development of contextual learning models that make students the center of learning in class can increase student interest in PAK subjects and other subjects because they do not rely on conventional methods, namely lecture methods that are generally mnoton and boring. In this study the design used is classroom action research. The data analysis technique used in this study is the first analysis technique with student participation in learning activities. Data on student participation were analyzed using descriptive statistics with percentage techniques. Second through learning outcomes. Data on learning outcomes obtained from the pretest and posttest results were analyzed by comparing the percentage and average results at the end of each cycle. The research resulted in the following conclusions: The development of contextual-based learning models has a positive impact on the potential of students as active, creative and innovative learning centers and teachers as facilitators, managers and mentors who can motivate learning and student achievement and competence and are able to motivate to interested in learning Christian Religious Education, especially at SD HKBP Maranatha 2019/2020. Secondly, based on existing studies, research and papers, the development of learning models through a contextual approach shows significant learning outcomes that are better than conventional learning models both quantitatively and qualitatively on PAK subject students.

Radiomics versus Conventional Assessment to Identify Symptomatic Participants at Carotid Computed Tomography Angiography

Background Crohn’s disease (CD) is a chronic inflammatory bowel disease, with infliximab (IFX) commonly used for treatment. However, no clinically applicable model currently exists to predict the response of patients with CD to IFX therapy. Given the strong association between sarcopenia and IFX treatment outcomes, this study developed computerized tomography radiomics-based machine learning (ML) models, utilizing psoas muscle volume as a proxy for skeletal muscle mass, to predict the response of patients with CD to IFX therapy. Methods In this retrospective study, patients with CD from two institutions were recruited between January 2010 and January 2023, following stringent inclusion and exclusion criteria. Regions of interest were delineated using 3D Slicer software, and radiomics features were extracted with the Pyradiomics package in Python. Z score standardization and independent sample t test were applied to identify optimal predictive features, which were then utilized in seven ML algorithms for training and validation. Model performance was assessed through receiver-operating characteristic curves, precision–recall curves, and calibration curve analyses, evaluating accuracy and clinical applicability. Binary logistic regression was employed to identify predictors of IFX treatment response. Results A total of 134 patients were included, divided into a training cohort (n = 84) and a validation cohort (n = 50). Twenty differential radiomics features were selected for integration into the ML models. All models demonstrated strong predictive performance in the validation cohort, with a mean area under the curve of 0.849. The eXtreme Gradient Boosting algorithm outperformed others, achieving an area under the curve of 0.910. Conclusion Psoas computerized tomography radiomics-based ML models effectively predict the response of patients with CD to IFX therapy, with the eXtreme Gradient Boosting model exhibiting superior performance.

Ultrasound Radiomics-Based Machine Learning and SHapley Additive exPlanations Method Predicting Pathological Prognostic Stage in Breast Cancer: A Bicentric and Validation Study.

BackgroundNeoadjuvant immunotherapy has been shown to improve survival in patients with gastric cancer. This study sought to develop and validate a radiomics-based machine learning (ML) model for patients with locally advanced gastric cancer (LAGC), specifically to predict whether patients will achieve a major pathological response (MPR) following neoadjuvant immunotherapy. With its predictive capabilities, this tool shows promise for enhancing clinical decision-making processes in the future.MethodsThis study utilized a multicenter cohort design, retrospectively gathering clinical data and computed tomography (CT) images from 268 patients diagnosed with advanced gastric cancer who underwent neoadjuvant immunotherapy between January 2019 and December 2023 from two medical centers. Radiomic features were extracted from CT images, and a multi-step feature selection procedure was applied to identify the top 20 representative features. Nine ML algorithms were implemented to build prediction models, with the optimal algorithm selected for the final prediction model. The hyperparameters of the chosen model were fine-tuned using Bayesian optimization and grid search. The performance of the model was evaluated using several metrics, including the area under the curve (AUC), accuracy, and Cohen’s kappa coefficient.ResultsThree cohorts were included in this study: the development cohort (DC, n = 86), the internal validation cohort (IVC, n = 59), and the external validation cohort (EVC, n = 52). Nine ML models were developed using DC cases. Among these, an optimized Bayesian-LightGBM model, demonstrated robust predictive performance for MPR following neoadjuvant immunotherapy in LAGC patients across all cohorts. Specifically, within DC, the LightGBM model attained an AUC of 0.828, an overall accuracy of 0.791, a Cohen’s kappa coefficient of 0.552, a sensitivity of 0.742, a specificity of 0.818, a positive predictive value (PPV) of 0.586, a negative predictive value (NPV) of 0.867, a Matthews correlation coefficient (MCC) of 0.473, and a balanced accuracy of 0.780. Comparable performance metrics were validated in both the IVC and the EVC, with AUC values of 0.777 and 0.714, and overall accuracies of 0.729 and 0.654, respectively. These results suggested good fitness and generalization of the Bayesian-LightGBM model. Shapley Additive Explanations (SHAP) analysis identified significant radiomic features contributing to the model’s predictive capability. The SHAP values of the features wavelet.LLH_gldm_SmallDependenceLowGrayLevelEmphasis, wavelet.HHL_glrlm_RunVariance, and wavelet.LLH_glszm_LargeAreaHighGrayLevelEmphasis were ranked among the top three, highlighting their significant contribution to the model’s predictive performance. In contrast to existing radiomic models that exclusively focus on neoadjuvant chemotherapy, our model integrates both neoadjuvant immunotherapy and chemotherapy, thereby offering more precise predictive capabilities.ConclusionThe radiomics-based ML model demonstrated significant efficacy in predicting the pathological response to neoadjuvant immunotherapy in LAGC patients, thereby providing a foundation for personalized treatment strategies.

In the era of precision therapy, early classification of breast cancer (BRCA) molecular subtypes has clinical significance for disease management and prognosis. We explored the accuracy of machine learning (ML) models for early classification of BRCA molecular subtypes through a systematic review of the literature currently available. We retrieved relevant studies published in PubMed, EMBASE, Cochrane, and Web of Science until 15 April 2022. A prediction model risk of bias assessment tool (PROBAST) was applied for the assessment of risk of bias of a genomics-based ML model, and the Radiomics Quality Score (RQS) was simultaneously used to evaluate the quality of this radiomics-based ML model. A random effects model was adopted to analyze the predictive accuracy of genomics-based ML and radiomics-based ML for Luminal A, Luminal B, Basal-like or triple-negative breast cancer (TNBC), and human epidermal growth factor receptor 2 (HER2). The PROSPERO of our study was prospectively registered (CRD42022333611). Of the 38 studies were selected for analysis, 14 ML models were based on gene-transcriptomic, with only 4 external validations; and 43 ML models were based on radiomics, with only 14 external validations. Meta-analysis results showed that c-statistic values of the ML based on radiomics for the identification of BRCA molecular subtypes Luminal A, Luminal B, Basal-like or TNBC, and HER2 were 0.76 [95% confidence interval (CI): 0.60-0.96], 0.78 (95% CI: 0.69-0.87), 0.89 (95% CI: 0.83-0.91), and 0.83 (95% CI: 0.81-0.86), respectively. The c-statistic values of ML based on the gene-transcriptomic analysis cohort for the identification of the previously described BRCA molecular subtypes were 0.96 (95% CI: 0.93-0.99), 0.96 (95% CI: 0.93-0.99), 0.98 (95% CI: 0.95-1.00), and 0.97 (95% CI: 0.96-0.98) respectively. Additionally, the sensitivity of the ML model based on radiomics for each molecular subtype ranged from 0.79 to 0.85, while the sensitivity of the ML model based on gene-transcriptomic was between 0.92 and 0.99. Both radiomics and gene transcriptomics produced ideal effects on BRCA molecular subtype prediction. Compared with radiomics, gene transcriptomics yielded better prediction results, but radiomics was simpler and more convenient from a clinical point of view.