Adjuvant and post-recurrent treatment patterns in patients with resectable gastric cancer in japan: a retrospective database cohort study.

BACKGROUND: This study examined temporal shifts in adjuvant therapy patterns in Japanese patients with resectable gastric cancer (GC) and treatment patterns of first-line and subsequent therapy among those with recurrent disease. METHODS: This retrospective analysis of hospital-based administrative claims data (April 1, 2008 to March 31, 2022) included adults (aged ≥ 20 years) with GC who started adjuvant therapy on or after October 1, 2008 (adjuvant cohort) and patients in the adjuvant cohort with disease recurrence (recurrent cohort), further defined by the time to recurrence (≤ 180 or > 180 days after adjuvant therapy). RESULTS: In the adjuvant cohort (n = 17,062), the most common regimen during October 2008-May 2016 was tegafur/gimeracil/oteracil potassium (S-1; 95.7%). As new standard adjuvant regimen options were established, adjuvant S-1 use decreased to 65.0% and fluoropyrimidine plus oxaliplatin or docetaxel plus S-1 use increased to 15.0% and 20.0%, respectively, in September 2019-March 2022. In the recurrent cohort with no history of trastuzumab/trastuzumab deruxtecan treatment (n = 1257), the most common first-line regimens were paclitaxel plus ramucirumab (34.0%), capecitabine plus oxaliplatin (CapeOX; 17.0%), and nab-paclitaxel plus ramucirumab (10.1%) in patients with early recurrence, and S-1 plus oxaliplatin (26.3%), S-1 plus cisplatin (15.3%), CapeOX (14.0%), S-1 (13.2%), and paclitaxel plus ramucirumab (10.8%) in those with late recurrence. CONCLUSIONS: This study demonstrated temporal shifts in adjuvant treatment patterns that followed the establishment of novel regimens, and confirmed that post-recurrent treatment patterns were consistent with the Japanese Gastric Cancer Association guideline recommendations.

Spatial Clusters of Cancer Mortality in Brazil: A Machine Learning Modeling Approach.

Objectives: Our aim was to test if machine learning algorithms can predict cancer mortality (CM) at an ecological level and use these results to identify statistically significant spatial clusters of excess cancer mortality (eCM). Methods: Age-standardized CM was extracted from the official databases of Brazil. Predictive features included sociodemographic and health coverage variables. Machine learning algorithms were selected and trained with 70% of the data, and the performance was tested with the remaining 30%. Clusters of eCM were identified using SatScan. Additionally, separate analyses were performed for the 10 most frequent cancer types. Results: The gradient boosting trees algorithm presented the highest coefficient of determination (R 2 = 0.66). For total cancer, all algorithms overlapped in the region of Bagé (27% eCM). For esophageal cancer, all algorithms overlapped in west Rio Grande do Sul (48%-96% eCM). The most significant cluster for stomach cancer was in Macapá (82% eCM). The most important variables were the percentage of the white population and residents with computers. Conclusion: We found consistent and well-defined geographic regions in Brazil with significantly higher than expected cancer mortality.