Background The current and the previous editions of the tumor‐node‐metastasis (TNM) system for gastric cancer (GC; TNM8 and TNM7) have a high risk of stage‐migration bias when the node count after gastrectomy is suboptimal. Hence, they are possibly not the optimal staging systems for GC patients. This study aims to compare the TNM with two systems less affected by the stage‐migration bias, namely, the lymph nodes ratio (LNR) and the log odds of positive lymph nodes (LODDS), to assess which one is the best in stratifying the prognosis of GC patients. Methods The sample study included 1221 GC patients. Two 7‐cluster staging systems based on the combination of pT categories and LNR and LODDS categories (TLNR and TLODDS) were compared with the two last editions of TNM, using the Akaike information criteria, the Bayesian information criteria, and the receiver operating characteristic (ROC) curve graphs. Further validation on an independent sample of 251 patients was carried out. Results The univariable and multivariable analyses and the ROC curves detected an advantage of the TLNR and TLODDS systems over the TNM. The TLNR and TLODDS showed the best accuracy both in the subgroup of patients with ≥16 nodes examined. The results were confirmed in the validation analysis. Conclusions TLNR and TLODDS staging systems should be considered a valid implementation of the TNM for the prognostic stratification of GC patients. If these results are confirmed in further studies, the future implementation of the TNM should consider the introduction of the LNR or the LODDS along with the number of metastatic nodes.

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Background: The implementation of multidisciplinary tumor board (MDTB) meetings significantly ameliorated the management of oncological diseases. However, few evidences are currently present on their impact on pancreatic cancer (PC) management. The aim of this study was to evaluate the impact of the MDTB on PC diagnosis, resectability and tumor response to oncological treatment compared with indications before discussion. Patients and methods: All patients with a suspected or proven diagnosis of PC presented at the MDTB from 2017 to 2019 were included in the study. Changes of diagnosis, resectability and tumor response to oncological/radiation treatment between pre-and post-MDTB discussion were analyzed. Results: A total of 438 cases were included in the study: 249 (56.8%) were presented as new diagnoses, 148 (33.8%) for resectability assessment and 41 (9.4%) for tumor response evaluation to oncological treatment. MDTB discussion led to a change in diagnosis in 54/249 cases (21.7%), with a consequent treatment strategy variation in 36 cases (14.5%). Change in resectability was documented in 44/148 cases (29.7%), with the highest discrepancy for borderline lesions. The treatment strategy was thus modified in 27 patients (18.2%). The MDTB brought a modification in the tumor response assessment in 6/41 cases (14.6%), with a consequent protocol modification in four (9.8%) cases. Conclusions: MDTB discussion significantly impacts on PC management, especially in high-volume centers, with consistent variations in terms of diagnosis, resectability and tumor response assessment compared with indications before discussion.

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Artificial intelligence (AI) and computer vision (CV) are beginning to impact medicine. While evidence on the clinical value of AI-based solutions for the screening and staging of colorectal cancer (CRC) is mounting, CV and AI applications to enhance the surgical treatment of CRC are still in their early stage. This manuscript introduces key AI concepts to a surgical audience, illustrates fundamental steps to develop CV for surgical applications, and provides a comprehensive overview on the state-of-the-art of AI applications for the treatment of CRC. Notably, studies show that AI can be trained to automatically recognize surgical phases and actions with high accuracy even in complex colorectal procedures such as transanal total mesorectal excision (TaTME). In addition, AI models were trained to interpret fluorescent signals and recognize correct dissection planes during total mesorectal excision (TME), suggesting CV as a potentially valuable tool for intraoperative decision-making and guidance. Finally, AI could have a role in surgical training, providing automatic surgical skills assessment in the operating room. While promising, these proofs of concept require further development, validation in multi-institutional data, and clinical studies to confirm AI as a valuable tool to enhance CRC treatment.