Predicting 5-year recurrence risk in colorectal cancer: development and validation of a histology-based deep learning approach.

BACKGROUND: Accurate estimation of the long-term risk of recurrence in patients with non-metastatic colorectal cancer (CRC) is crucial for clinical management. Histology-based deep learning is expected to provide more abundant information for risk stratification. METHODS: We developed and validated a weakly supervised deep-learning model for predicting 5-year relapse-free survival (RFS) to stratify patients with different risks based on histological images from three hospitals of 614 cases with non-metastatic CRC. A deep prognostic factor (DL-RRS) was established to stratify patients into high and low-risk group. The areas under the curve (AUCs) were calculated to evaluate the performances of models. RESULTS: Our proposed model achieves the AUCs of 0.833 (95% CI: 0.736-0.905) and 0.715 (95% CI: 0.647-0.776) on validation cohort and external test cohort, respectively. The 5-year RFS rate was 45.7% for high DL-RRS patients, and 82.5% for low DL-RRS patients respectively in the external test cohort (HR: 3.89, 95% CI: 2.51-6.03, P < 0.001). Adjuvant chemotherapy was associated with improved RFS in Stage II patients with high DL-RRS (HR: 0.15, 95% CI: 0.06-0.38, P < 0.001). CONCLUSIONS: DL-RRS has a good predictive performance of 5-year recurrence risk in CRC, and will better serve the clinical decision-making.

Predicting T Cell-Inflamed Gene Expression Profile in Hepatocellular Carcinoma Based on Dynamic Contrast-Enhanced Ultrasound Radiomics.

Purpose: The T cell-inflamed gene expression profile (GEP) quantifies 18 genes' expression indicative of a T-cell immune tumor microenvironment, playing a crucial role in the immunotherapy of hepatocellular carcinoma (HCC). Our study aims to develop a radiomics-based machine learning model using contrast-enhanced ultrasound (CEUS) for predicting T cell-inflamed GEP in HCC. Methods: The primary cohort of HCC patients with preoperative CEUS and RNA sequencing data of tumor tissues at the single center was used to construct the model. A total of 5936 radiomics features were extracted from the regions of interest in representative images of each phase, and the least absolute shrinkage and selection operator and logistic regression were used to construct four models including three phase-specific models and an integrated model. The area under the curve (AUC) was calculated to evaluate the performance of the model. The independent cohort of HCC patients with preoperative CEUS and Immunoscore based on immunohistochemistry and digital pathology was used to validate the correlation between model prediction value and T-cell infiltration. Results: There were 268 patients enrolled in the primary cohort and 46 patients enrolled in the independent cohort. Compared with the other three models, the AP model constructed by 36 arterial phase (AP) features showed good performance with a mean AUC of 0.905 in the 5-fold cross-validation and was easier to apply in the clinical setting. The decision curve and calibration curve confirmed the clinical utility of the model. In the independent cohort, patients with high Immunoscores showed significantly higher GEP prediction values than those with low Immunoscores (t=-2.359, p=0.029). Conclusion: The CEUS-based model is a reliable predictive tool for T cell-inflamed GEP in HCC, and might facilitate individualized immunotherapy decision-making.

Personalized drug screening in patient-derived organoids of biliary tract cancer and its clinical application.

Patients with biliary tract cancer (BTC) show different responses to chemotherapy, and there is no effective way to predict chemotherapeutic response. We have generated 61 BTC patient-derived organoids (PDOs) from 82 tumors (74.4%) that show similar histological and genetic characteristics to the corresponding primary BTC tissues. BTC tumor tissues with enhanced stemness- and proliferation-related gene expression by RNA sequencing can more easily form organoids. As expected, BTC PDOs show different responses to the chemotherapies of gemcitabine, cisplatin, 5-fluoruracil, oxaliplatin, etc. The drug screening results in PDOs are further validated in PDO-based xenografts and confirmed in 92.3% (12/13) of BTC patients with actual clinical response. Moreover, we have identified gene expression signatures of BTC PDOs with different drug responses and established gene expression panels to predict chemotherapy response in BTC patients. In conclusion, BTC PDO is a promising precision medicine tool for anti-cancer therapy in BTC patients.

Numerical study of a DoFP polarimeter based on the self-organized nanograting array.

The self-organized nanograting manufactured by irradiating the transparent materials with the femtosecond laser has aroused wide interests in photonic applications in recent years. Although the mechanism of nanograting formatting has not yet been fully understood, the essential property of the optical birefringence can be precisely acquired by controlling the energy fluence of the femtosecond laser. In this paper, we proposed a novel application of the self-organized nanograting in a division-of-focal-plane polarimeter. Based on the rigid-coupled-wave algorithm, the optical characteristics of the nanograting and the polarimeter were comprehensively analyzed and discussed.