Recurrence is the main risk for high-grade serous ovarian cancer (HGSOC) and few prognostic biomarkers were reported. In this study, we proposed a novel deep learning (DL) method to extract prognostic biomarkers from preoperative computed tomography (CT) images, aiming at providing a non-invasive recurrence prediction model in HGSOC. We enrolled 245 patients with HGSOC from two hospitals, which included a feature-learning cohort (n = 102), a primary cohort (n = 49) and two independent validation cohorts from two hospitals (n = 49 and n = 45). We trained a novel DL network in 8917 CT images from the feature-learning cohort to extract the prognostic biomarkers (DL feature) of HGSOC. Afterward, a DL-CPH model incorporating the DL feature and Cox proportional hazard (Cox-PH) regression was developed to predict the individual recurrence risk and 3-year recurrence probability of patients. In the two validation cohorts, the concordance-index of the DL-CPH model was 0.713 and 0.694. Kaplan-Meier's analysis clearly identified two patient groups with high and low recurrence risk (p = 0.0038 and 0.0164). The 3-year recurrence prediction was also effective (AUC = 0.772 and 0.825), which was validated by the good calibration and decision curve analysis. Moreover, the DL feature demonstrated stronger prognostic value than clinical characteristics. The DL method extracts effective CT-based prognostic biomarkers for HGSOC, and provides a non-invasive and preoperative model for individualized recurrence prediction in HGSOC. In addition, the DL-CPH model provides a new prognostic analysis method that can utilize CT data without follow-up for prognostic biomarker extraction.

Accurate predictions of distant metastasis (DM) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) are helpful in developing appropriate treatment plans. This study aimed to perform DM prediction through deep learning radiomics. We retrospectively sampled 235 patients receiving nCRT with the minimum 36 months' postoperative follow-up from three hospitals. Through transfer learning, a deep learning radiomic signature (DLRS) based on multiparametric magnetic resonance imaging (MRI) was constructed. A nomogram was established integrating deep MRI information and clinicopathologic factors for better prediction. Harrell's concordance index (C-index) and time-dependent receiver operating characteristic (ROC) were used as performance metrics. Furthermore, the risk of DM in patients with different response to nCRT was evaluated with the nomogram. DLRS performed well in DM prediction, with a C-index of 0·747 and an area under curve (AUC) at three years of 0·894 in the validation cohort. The performance of nomogram was better, with a C-index of 0·775. In addition, the nomogram could stratify patients with different responses to nCRT into high- and low-risk groups of DM (P < 0·05). MRI-based deep learning radiomics had potential in predicting the DM of LARC patients receiving nCRT and could help evaluate the risk of DM in patients who have different responses to nCRT. The funding bodies that contributed to this study are listed in the Acknowledgements section.

Objectives: We used radiomic analysis to establish a radiomic signature based on preoperative contrast enhanced computed tomography (CT) and explore its effectiveness as a novel recurrence risk prognostic marker for advanced high-grade serous ovarian cancer (HGSOC). Methods: This study had a retrospective multicenter (two hospitals in China) design and a radiomic analysis was performed using contrast enhanced CT in advanced HGSOC (FIGO stage III or IV) patients. We used a minimum 18-month follow-up period for all patients (median 38.8 months, range 18.8–81.8 months). All patients were divided into three cohorts according to the timing of their surgery and hospital stay: training cohort (TC) and internal validation cohort (IVC) were from one hospital, and independent external validation cohort (IEVC) was from another hospital. A total of 620 3-D radiomic features were extracted and a Lasso-Cox regression was used for feature dimension reduction and determination of radiomic signature. Finally, we combined the radiomic signature with seven common clinical variables to develop a novel nomogram using a multivariable Cox proportional hazards model. Results: A final 142 advanced HGSOC patients were enrolled. Patients were successfully divided into two groups with statistically significant differences based on radiomic signature, consisting of four radiomic features (log-rank test P = 0.001, <0.001, <0.001 for TC, IVC, and IEVC, respectively). The discrimination accuracies of radiomic signature for predicting recurrence risk within 18 months were 82.4% (95% CI, 77.8–87.0%), 77.3% (95% CI, 74.4–80.2%), and 79.7% (95% CI, 73.8–85.6%) for TC, IVC, and IEVC, respectively. Further, the discrimination accuracies of radiomic signature for predicting recurrence risk within 3 years were 83.4% (95% CI, 77.3–89.6%), 82.0% (95% CI, 78.9–85.1%), and 70.0% (95% CI, 63.6–76.4%) for TC, IVC, and IEVC, respectively. Finally, the accuracy of radiomic nomogram for predicting 18-month and 3-year recurrence risks were 84.1% (95% CI, 80.5–87.7%) and 88.9% (95% CI, 85.8–92.5%), respectively. Conclusions: Radiomic signature and radiomic nomogram may be low-cost, non-invasive means for successfully predicting risk for postoperative advanced HGSOC recurrence before or during the perioperative period. Radiomic signature is a potential prognostic marker that may allow for individualized evaluation of patients with advanced HGSOC.

Purpose: We aimed to analyze ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) images via the radiomic method to develop a model and validate the potential value of features reflecting glioma metabolism for predicting isocitrate dehydrogenase (IDH) genotype and prognosis. Methods: PET images of 127 patients were retrospectively analyzed. A series of quantitative features reflecting the metabolic heterogeneity of the tumors were extracted, and a radiomic signature was generated using the support vector machine method. A combined model that included clinical characteristics and the radiomic signature was then constructed by multivariate logistic regression to predict the IDH genotype status, and the model was evaluated and verified by receiver operating characteristic (ROC) curves and calibration curves. Finally, Kaplan-Meier curves and log-rank tests were used to analyze overall survival (OS) according to the predicted result. Results: The generated radiomic signature was significantly associated with IDH genotype (p < 0.05) and could achieve large areas under the ROC curve of 0.911 and 0.900 on the training and validation cohorts, respectively, with the incorporation of age and type of tumor metabolism. The good agreement of the calibration curves in the validation cohort further validated the efficacy of the constructed model. Moreover, the predicted results showed a significant difference in OS between high- and low-risk groups (p < 0.001). Conclusions: Our results indicate that the ¹⁸F-FDG metabolism-related features could effectively predict the IDH genotype of gliomas and stratify the OS of patients with different prognoses.