Publications by authors named "J M Simon i Castellvi"
Article Synopsis
- The study investigated the reliability of pathologists in quantifying tumor percentages in whole slide images (WSI) of non-small cell lung cancer (NSCLC) using digital image analysis after training them with QuPath.
- Initial assessments showed poor reliability among pathologists, with a low intraclass correlation coefficient (ICC) of 0.09, which only slightly improved to an ICC of 0.24 in a follow-up trial after receiving feedback.
- The research indicated that errors were mainly due to subjective tasks like annotation, and suggested that future AI technologies could enhance accuracy in digital pathology assessments.
Objective: We aimed to evaluate the performance of endometrial cancer (EC) molecular classification in predicting extrauterine disease after primary surgery alone and in combination with other clinical data available in preoperative setting.
Methods: Retrospective single-center observational study including patients with endometrial adenocarcinoma treated with primary surgery between December 1994 and May 2022. Molecular profiling was performed using immunohistochemistry of p53, MLH1, PMS2, MSH2 and MSH6; and KASP genotyping of the 6 most common mutations of POLE gene.
Men who have sex with men (MSM) with HIV are at high risk for squamous intraepithelial lesion (SIL) and anal cancer. Identifying local immunological mechanisms involved in the development of anal dysplasia could aid treatment and diagnostics. Here, we studied 111 anal biopsies obtained from 101 MSM with HIV, who participated in an anal screening program.
View Article and Find Full Text PDFPurpose: With the growing interest in artificial intelligence (AI) applications in medicine, this study explores ChatGPT's potential to influence surgical technique selection in metabolic and bariatric surgery (MBS), contrasting AI recommendations with established clinical guidelines and expert consensus.
Materials And Methods: Conducting a single-center retrospective analysis, the study involved 161 patients who underwent MBS between January 2022 and December 2023. ChatGPT4 was used to analyze patient data, including demographics, pathological history, and BMI, to recommend the most suitable surgical technique.
Objectives: To investigate the pattern of first recurrence of disease in patients with endometrial cancer according to molecular classification, and to assess the independent role of molecular profiling in each type of failure.
Methods: Retrospective single-center study including patients diagnosed with endometrial cancer stage I-IVB (International Federation of Gynecology and Obstetrics 2009) between December 1994 and May 2022, who underwent primary surgical treatment and had a complete molecular profile. First recurrence was classified as isolated or multiple, and as vaginal, pelvic, peritoneal, nodal, and distant according to its location.