Deep learning assists detection of esophageal cancer and precursor lesions in a prospective, randomized controlled study.

Endoscopy is the primary modality for detecting asymptomatic esophageal squamous cell carcinoma (ESCC) and precancerous lesions. Improving detection rate remains challenging. We developed a system based on deep convolutional neural networks (CNNs) for detecting esophageal cancer and precancerous lesions [high-risk esophageal lesions (HrELs)] and validated its efficacy in improving HrEL detection rate in clinical practice (trial registration ChiCTR2100044126 at www.chictr.org.cn). Between April 2021 and March 2022, 3117 patients ≥50 years old were consecutively recruited from Taizhou Hospital, Zhejiang Province, and randomly assigned 1:1 to an experimental group (CNN-assisted endoscopy) or a control group (unassisted endoscopy) based on block randomization. The primary endpoint was the HrEL detection rate. In the intention-to-treat population, the HrEL detection rate [28 of 1556 (1.8%)] was significantly higher in the experimental group than in the control group [14 of 1561 (0.9%), P = 0.029], and the experimental group detection rate was twice that of the control group. Similar findings were observed between the experimental and control groups [28 of 1524 (1.9%) versus 13 of 1534 (0.9%), respectively; P = 0.021]. The system's sensitivity, specificity, and accuracy for detecting HrELs were 89.7, 98.5, and 98.2%, respectively. No adverse events occurred. The proposed system thus improved HrEL detection rate during endoscopy and was safe. Deep learning assistance may enhance early diagnosis and treatment of esophageal cancer and may become a useful tool for esophageal cancer screening.

Developing a Prognostic Model for Primary Biliary Cholangitis Based on a Random Survival Forest Model.

Background: Primary biliary cholangitis (PBC) is a rare autoimmune liver disease with few effective treatments and a poor prognosis, and its incidence is on the rise. There is an urgent need for more targeted treatment strategies to accurately identify high-risk patients. The use of stochastic survival forest models in machine learning is an innovative approach to constructing a prognostic model for PBC that can improve the prognosis by identifying high-risk patients for targeted treatment. Method: Based on the inclusion and exclusion criteria, the clinical data and follow-up data of patients diagnosed with PBC-associated cirrhosis between January 2011 and December 2021 at Taizhou Hospital of Zhejiang Province were retrospectively collected and analyzed. Data analyses and random survival forest model construction were based on the R language. Result: Through a Cox univariate regression analysis of 90 included samples and 46 variables, 17 variables with p-values <0.1 were selected for initial model construction. The out-of-bag (OOB) performance error was 0.2094, and K-fold cross-validation yielded an internal validation C-index of 0.8182. Through model selection, cholinesterase, bile acid, the white blood cell count, total bilirubin, and albumin were chosen for the final predictive model, with a final OOB performance error of 0.2002 and C-index of 0.7805. Using the final model, patients were stratified into high- and low-risk groups, which showed significant differences with a P value <0.0001. The area under the curve was used to evaluate the predictive ability for patients in the first, third, and fifth years, with respective results of 0.9595, 0.8898, and 0.9088. Conclusion: The present study constructed a prognostic model for PBC-associated cirrhosis patients using a random survival forest model, which accurately stratified patients into low- and high-risk groups. Treatment strategies can thus be more targeted, leading to improved outcomes for high-risk patients.

Identification and Validation of a Novel DNA Damage and DNA Repair Related Genes Based Signature for Colon Cancer Prognosis.

Backgrounds: Colorectal cancer (CRC) with high incidence, has the third highest mortality of tumors. DNA damage and repair influence a variety of tumors. However, the role of these genes in colon cancer prognosis has been less systematically investigated. Here, we aim to establish a corresponding prognostic signature providing new therapeutic opportunities for CRC. Method: After related genes were collected from GSEA, univariate Cox regression was performed to evaluate each gene's prognostic relevance through the TCGA-COAD dataset. Stepwise COX regression was used to establish a risk prediction model through the training sets randomly separated from the TCGA cohort and validated in the remaining testing sets and two GEO datasets (GSE17538 and GSE38832). A 12-DNA-damage-and-repair-related gene-based signature able to classify COAD patients into high and low-risk groups was developed. The predictive ability of the risk model or nomogram were evaluated by different bioinformatics- methods. Gene functional enrichment analysis was performed to analyze the co-expressed genes of the risk-based genes. Result: A 12-gene based prognostic signature established within 160 significant survival-related genes from DNA damage and repair related gene sets performed well with an AUC of ROC 0.80 for 5 years in the TCGA-CODA dataset. The signature includes CCNB3, ISY1, CDC25C, SMC1B, MC1R, LSP1P4, RIN2, TPM1, ELL3, POLG, CD36, and NEK4. Kaplan-Meier survival curves showed that the prognosis of the risk status owns more significant differences than T, M, N, and stage prognostic parameters. A nomogram was constructed by LASSO regression analysis with T, M, N, age, and risk as prognostic parameters. ROC curve, C-index, Calibration analysis, and Decision Curve Analysis showed the risk module and nomogram performed best in years 1, 3, and 5. KEGG, GO, and GSEA enrichment analyses suggest the risk involved in a variety of important biological processes and well-known cancer-related pathways. These differences may be the key factors affecting the final prognosis. Conclusion: The established gene signature for CRC prognosis provides a new molecular tool for clinical evaluation of prognosis, individualized diagnosis, and treatment. Therapies based on targeted DNA damage and repair mechanisms may formulate more sensitive and potential chemotherapy regimens, thereby expanding treatment options and potentially improving the clinical outcome of CRC patients.