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Although preoperative biopsy of rectal cancer (RC) is an essential step for confirmation of diagnosis, it currently fails to provide prognostic information to the clinician beyond a rough estimation of tumour grade. In this study we used a risk classification to stratified patient in low-risk and high-risk patients in relation to the disease free survival and the overall survival using histopathological post-operative features. The purpose of this study was to evaluate if low-risk and high-risk RC can be distinguished using a CT-based radiomics model. We retrospectively reviewed the preoperative abdominal contrast-enhanced CT of 40 patients with RC. CT portal-venous phase was used for manual RC segmentation by two radiologists. Radiomics parameters were extracted by using PyRadiomics (3.0) software, which automatically obtained a total of 120 radiomics features. An operator-independent statistical hybrid method was adopted for the selection and reduction of features, while discriminant analysis was used to construct the predictive model. Postoperative histopathological report was used as reference standard. Receiver operating characteristics (ROC) and areas under the ROC curve (AUROC) were calculated to evaluate the diagnostic performance of the most dis-criminating selected parameters. Sensitivity, specificity, and accuracy were calculated. In our study cohort, the original_shape_Maximum3DDiameter and origi-nal_shape_MajorAxisLength demonstrated a good performance in the differentiation between preoperative degree of RC, with an AUROC of 0.680%, sensitivity of 74.02%, specificity of 73.45%, positive predictive value of 81.47%, and accuracy of 73.71%. In conclusion, this preliminary analysis showed statistically significant differences in radiomics features between low-risk and high-risk RC.

Purpose A strong prognostic score that enables a stratification of newly diagnosed Hodgkin Lymphoma (HL) to identify patients at high risk of refractory/relapsed disease is still needed. Our aim was to investigate the potential value of a radiomics analysis pipeline from whole-body MRI (WB-MRI) exams for clinical outcome prediction in patients with Hodgkin Lymphoma (HL). Materials and methods Index lesions from baseline WB-MRIs of 40 patients (22 females; mean age 31.7 ± 11.4 years) with newly diagnosed HL treated by ABVD chemotherapy regimen were manually segmented on T1-weighted, STIR, and DWI images for texture analysis feature extraction. A machine learning approach based on the Extra Trees classifier and incorporating clinical variables, 18F-FDG-PET/CT-derived metabolic tumor volume, and WB-MRI radiomics features was tested using cross-validation to predict refractory/relapsed disease. Results Relapsed disease was observed in 10/40 patients (25%), two of whom died due to progression of disease and graft versus host disease, while eight reached the complete remission. In total, 1403 clinical and radiomics features were extracted, of which 11 clinical variables and 171 radiomics parameters from both original and filtered images were selected. The 3 best performing Extra Trees classifier models obtained an equivalent highest mean accuracy of 0.78 and standard deviation of 0.09, with a mean AUC of 0.82 and standard deviation of 0.08. Conclusions Our preliminary results demonstrate that a combined machine learning and texture analysis model to predict refractory/relapsed HL on WB-MRI exams is feasible and may help in the clinical outcome prediction in HL patients.