Background and purpose: 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. Materials and methods: 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 featurelearning 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. Results: 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. Conclusions: 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.

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.

Cutaneous wounds are among the most common soft tissue injuries. Wounds involving dermis suffer more from outside influence and higher risk of chronic inflammation. Therefore the appearance and function restoration has become an imperative in tissue engineering research. In this study, cell-aggregates constructed with green fluorescent protein-expressing (GFP+) rat bone marrow mesenchymal stem cells (BMMSCs) were applied to rat acute full-layer cutaneous wound model to confirm its pro-regeneration ability and compare its regenerative efficacy with the currently thriving subcutaneous and intravenous stem cell administration strategy, with a view to sensing the advantages, disadvantages and the mechanism behind. According to results, cell-aggregates cultured in vitro enjoyed higher expression of several pro-healing genes than adherent cultured cells. Animal experiments showed better vascularization along with more regular dermal collagen deposition for cell-aggregate transplanted models. Immunofluorescence staining on inflammatory cells indicated a shorter inflammatory phase for cell-aggregate group, which was backed up by further RT-PCR. The in situ immunofluorescence staining manifested a higher GFP+-cell engraftment for cell-aggregate transplanted models versus cell administered ones. Thus it is safe to say the BMMSCs aggregate could bring superior cutaneous regeneration for full layer cutaneous wound to BMMSCs administration, both intravenous and subcutaneous.