Harnessing Machine Learning and Nomogram Models to Aid in Predicting Progression-Free Survival for Gastric Cancer Patients Post-Gastrectomy with Deficient Mismatch Repair(dMMR).

Objective: To assess the effectiveness of a machine learning framework and nomogram in predicting progression-free survival (PFS) post-radical gastrectomy in patients with dMMR.

Method: Machine learning models and nomograms to forecast PFS in patients undergoing radical gastrectomy for nonmetastatic gastric cancer with dMMR. Independent risk factors were identified using Cox regression analysis to develop the nomogram.

Screening high-risk individuals for primary gastric adenocarcinoma: evaluating progression-free survival probability score in the presence and absence of Rictor expression after gastrectomy.

Objective: Developing nomogram-based risk stratification model to determine 3-year and 5-year progression-free survival (PFS) and to identify high-risk patients with gastric adenocarcinoma based on different Rictor statuses.

Methods: 1366 individuals who underwent radical gastric surgery to treat gastric adenocarcinoma at Shanxi Cancer Hospital from May 2002 to December 2020 were analyzed. Cox regression analysis was employed to create the nomograms.

Screening high-risk individuals for primary gastric carcinoma: evaluating overall survival probability score in the presence and absence of lymphatic metastasis post-gastrectomy.

Objective: The aim of this study was to develop and validate prognostic models for predicting overall survival in individuals with gastric carcinoma, specifically focusing on both negative and positive lymphatic metastasis.

Methods: A total of 1650 patients who underwent radical gastric surgery at Shanxi Cancer Hospital between May 2002 and December 2020 were included in the analysis. Multiple Cox Proportional Hazards analysis was performed to identify key variables associated with overall survival in both negative and positive lymphatic metastasis cases.

Relationship between sex differences in drinking, smoking, and exercising and the incidence of malignancies and medical procedures: a cross-sectional study of 21,916 participants in China.

The unresolved issue of the relationship between sex differences in tea, coffee, and beverage consumption and malignancy risk prompted our study in 2022. Logistic proportional hazards models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) in our investigation of the associations between cancer risk and tea, coffee, and beverage consumption. Our findings revealed that frequent consumption of white tea significantly reduced the occurrence of malignant tumours, but this effect was detected only in the fully adjusted model for males (OR: 0.

How to incorporate new agents into precise medicine for cholangiocarcinoma?

Cholangiocarcinoma, a rare and aggressive form of cancer originating from the bile ducts in the liver, poses a significant challenge for treatment. However, the emergence of precision medicine has brought newfound hope for more effective therapies. Several precision medicine approaches have demonstrated promise in the treatment of cholangiocarcinoma.

Pancreatic cancer in young adults - an evolving entity?

The incidence of early-onset pancreatic cancer (EOPC) among young population (<50 years) is rising in the last decade, with gender, medical overtreatment, and genetic factors as the risk factors in EOPC. Nevertheless, the role of genetic factors in the development of EOPC needs further exploration since the studies were carried out with small sample size and ambiguous evidence. Notable, the high incidence of pathogenic germline variant (PGV) appears to be involved in EOPC.

Prognostic nomograms for gastric carcinoma after D2 + total gastrectomy to assist decision-making for postoperative treatment: based on Lasso regression.

Objective: This study aimed to establish novel nomograms that could be used to predict the prognosis of gastric carcinoma patients who underwent D2 + total gastrectomy on overall survival (OS) and progression-free survival (PFS).

Methods: Lasso regression was employed to construct the nomograms. The internal validation process included bootstrapping, which was used to test the accuracy of the predictions.