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Objective: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort. Methods: Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015–2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow. Results: The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging. Conclusions: Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status. Clinical relevance statement: The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment. Key Points: • Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances. • The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic. •Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow. [ABSTRACT FROM AUTHOR]

Purpose: Radiomics is an emerging field that utilizes quantitative features extracted from medical images to predict clinically meaningful outcomes. Validating findings is crucial to assess radiomics applicability. We aimed to validate previously published magnetic resonance imaging (MRI) radiomics models to predict oncological outcomes in oral tongue squamous cell carcinoma (OTSCC). Materials and methods: Retrospective multicentric study on OTSCC surgically treated from 2010 to 2019. All patients performed preoperative MRI, including contrast-enhanced T1-weighted (CE-T1), diffusion-weighted sequences and apparent diffusion coefficient map. We evaluated overall survival (OS), locoregional recurrence-free survival (LRRFS), cause-specific mortality (CSM). We elaborated different models based on clinical and radiomic data. C-indexes assessed the prediction accuracy of the models. Results: We collected 112 consecutive independent patients from three Italian Institutions to validate the previously published MRI radiomic models based on 79 different patients. The C-indexes for the hybrid clinical-radiomic models in the validation cohort were lower than those in the training cohort but remained > 0.5 in most cases. CE-T1 sequence provided the best fit to the models: the C-indexes obtained were 0.61, 0.59, 0.64 (pretreatment model) and 0.65, 0.69, 0.70 (posttreatment model) for OS, LRRFS and CSM, respectively. Conclusion: Our clinical-radiomic models retain a potential to predict OS, LRRFS and CSM in heterogeneous cohorts across different centers. These findings encourage further research, aimed at overcoming current limitations, due to the variability of imaging acquisition, processing and tumor volume delineation. [ABSTRACT FROM AUTHOR]

Purpose: To assess the ability of tumor apparent diffusion coefficient (ADC) values obtained from multiparametric magnetic resonance imaging (mpMRI) to predict the risk of 5-year biochemical recurrence (BCR) after radical prostatectomy (RP). Materials and methods: This retrospective analysis included 1207 peripheral and 232 non-peripheral zone prostate cancer (PCa) patients who underwent mpMRI before RP (2012–2015), with the outcome of interest being 5-year BCR. ADC was evaluated as a continuous variable and as categories: low (< 850 µm2/s), intermediate (850–1100 µm2/s), and high (> 1100 µm2/s). Kaplan–Meier curves with log-rank testing of BCR-free survival, multivariable Cox proportional hazard regression models were formed to estimate the risk of BCR. Results: Among the 1439 males with median age 63 (± 7) years, the median follow-up was 59 months, and 306 (25%) patients experienced BCR. Peripheral zone PCa patients with BCR had lower tumor ADC values than those without BCR (874 versus 1025 µm2/s, p < 0.001). Five-year BCR-free survival rates were 52.3%, 74.4%, and 87% for patients in the low, intermediate, and high ADC value categories, respectively (p < 0.0001). Lower ADC was associated with BCR, both as continuously coded variable (HR: 5.35; p < 0.001) and as ADC categories (intermediate versus high ADC-HR: 1.56, p = 0.017; low vs. high ADC-HR; 2.36, p < 0.001). In the non-peripheral zone PCa patients, no association between ADC and BCR was observed. Conclusion: Tumor ADC values and categories were found to be predictive of the 5-year BCR risk after RP in patients with peripheral zone PCa and may serve as a prognostic biomarker. [ABSTRACT FROM AUTHOR]