Multicenter machine learning model using clinical and radiomic features for prediction of postoperative residual status in meningioma.

Spinal meningiomas: histopathological grading using a benchmark radiomics model with notes on disease control.

A benchmark radiomics model to preoperatively identify the histological grade of spinal meningiomas is constructed, suggesting a need to characterize the interplay between tumor grade and extent of resection as drivers of local disease control in SMs.

A radiomics-based machine learning model for the preoperative differentiation of lung adenocarcinoma subtypes

Background The preoperative differentiation of lung adenocarcinoma subtypes is critical for implementing personalized treatment but is difficult to accomplish with conventional imaging. This study aimed to develop an interpretable multimodal model integrating clinical, peritumoral, radiomic, and deep learning features to improve diagnostic accuracy. Methods A total of 3,038 patients from four hospitals were divided into training (n=1,822), test (n=608), and validation (n=608) sets. Two radiologists manually segmented two-dimensional tumor regions on computed tomography using ITK-SNAP software. After Pearson correlation analysis and least absolute shrinkage and selection operator regression, the radiomic score and deep learning score were generated. Clinical features were selected via univariate analysis, the Boruta algorithm, and recursive feature elimination (RFE). Individual logistic models were built and fused with the optimal combination selected via support vector machine-synthetic minority oversampling technique and extreme gradient boosting. Performance was evaluated in terms of the Obuchowski index, accuracy, F1-score, calibration, and decision curves, while interpretability was assessed via Shapley additive explanations (SHAP) and individual conditional expectation (ICE). Results The fused model achieved Obuchowski indices of 0.85 [95% confidence interval (CI): 0.84–0.87], 0.81 (95% CI: 0.78–0.83), and 0.79 (95% CI: 0.76–0.81) in the training, test, and validation sets, respectively outperforming the single-modality models. The F1-scores for the lepidic, acinar/papillary, and solid/micropapillary subtypes, respectively, were 0.77, 0.61, and 0.63 in the training set; 0.72, 0.57, and 0.59 in the test set; and 0.74, 0.54, and 0.52 in the validation set. Calibration and decision curve analysis confirmed the robustness and clinical utility of the model. SHAP analysis identified ResNet-101 feature as the best predictor, followed by peritumoral radiomic score, and lobulation. ICE plots revealed the linear and monotonic relationships between key features and predicted probabilities across subtypes. Conclusions The radiomics model developed in this study facilitates the accurate and interpretable preoperative classification of lung adenocarcinoma subtypes. Fusion of clinical, peritumoral, and deep learning features enhances diagnostic performance and supports clinical decision-making.

MRI-Based Radiomics and Machine Learning for Predicting Pathological Tumor Invasion and Nodal Status in Rectal Cancer: A Retrospective Study

Introduction: MRI is the gold-standard imaging modality for rectal cancer (RC) local staging, but the ability to determine tumor invasion (pT) and nodal status (pN) remains limited in clinical practice. The main objective of this study is to evaluate the performance of different machine learning (ML) models based on clinical–radiological and radiomic variables for predicting these categories from preoperative MRI. Methods: A retrospective observational study was conducted involving 152 patients with RC (70 without neoadjuvant therapy and 82 with neoadjuvant therapy). Radiomic features were extracted from high-resolution T2 sequences using two independent segmentations: tumor and tumor + mesorectum. Twenty-three ML algorithms were evaluated using cross-validation to predict pT and pN. For each combination of outcome, cohort, data source and segmentation, an optimal model was selected based on the area under the curve (AUC). Results: Models based on clinical and radiological variables showed the most consistent performance, particularly in the overall cohort, with AUCs of 0.767 for pT and 0.764 for pN. The radiomic and combined models achieved a moderate and heterogeneous performance, with maximum AUCs of 0.770 for pT and 0.732 for pN. Conclusions: The clinico-radiological variables analyzed using ML showed a predictive performance similar to that of a radiologist. Radiomics did not show significant improvement in this setting.

Preoperative prediction of positive surgical margins in prostate cancer using multimodal deep learning model: a multicenter study.

Multimodal deep learning for prediction of postoperative recurrence in clear cell renal cell carcinoma: a clinical-radiologic-pathologic approach

Purpose This study intends to develop and validate a multiclass system that efficiently integrates complementary information from patients’ multimodal data (clinical, imaging, and pathological) to identify individuals at a high risk of 5-year postoperative recurrence among patients with clear cell renal cell carcinoma (ccRCC). Methods This retrospective multicenter study enrolled 270 clear cell renal cell carcinoma (ccRCC) patients, allocating 215 (79.6%) for model development/validation and 55 (20.4%) to an independent test cohort. Patients were categorized into recurrence and non-recurrence groups based on whether tumor recurrence occurred within 5 years after surgery. The final included case in this cohort underwent surgery in July 2018. Single-modality models were created using radiomics, deep learning (ResNet34), and deep features-based radiomics. Subsequently, multi-modality models were constructed by combining the predicted probabilities from the single-modality models and transferring them to another classifier. All models was assessed using the area under the receiver operating characteristic curve (AUROC). Results A total of 25 classification models were constructed. Notably, single-modality fusion models generally outperform their radiomics and deep learning-based radiomics (DL) counterparts. Among five single-modality fusion models, the Fused_Non-enhanced model demonstrates best predictive performance, achieving an AUROC value of 0.837 (95% confidence interval [CI]: 0.729-0.946). Similarly, multi-modality radiomics or DL models exhibit superior performance compared to single-modality counterparts. The multi-modality radiomics-DL model demonstrates the highest prediction performance, achieving an AUROC value of 0.967 (95% CI: 0.929-1.0) in the independent testing dataset. Conclusion The multi-modality radiomics-DL model demonstrates high accuracy in predicting the 5-year postoperative recurrence risk of clear cell renal cell carcinoma (ccRCC).

Machine Learning-Driven Radiomics for an Early-Stage Predictive Model of Nodal Upstaging in Thoracic Oncology

Simple Summary This radiomic study evaluated whether radiomic features from preoperative 2-[18F]FDG PET/CT scans can predict occult lymph node metastases in early-stage non-small cell lung cancer (NSCLC). We enrolled patients with cT1N0 NSCLC with or without occult unexpected node metastasis after surgical resection. Radiomic features of the first and second level were evaluated and machine learning protocol was used to elaborate a predictive model. Both PET/CT and clinical protocols were identical for all patients and were performed at the same institution, providing robust, uniform data with consistent reconstruction parameters, thereby minimizing the data harmonization issue. Despite the inherent limitations of radiomics studies, we believe that the use of a homogeneous population, however small, can ensure the data uniformity necessary to identify a predictive model that, once refined, can be integrated into clinical practice to personalize the diagnostic pathway and, potentially, the surgical approach as well.