Differences in Lymph Node Metastases Patterns Among Non-pancreatic Periampullary Cancers and Histologic Subtypes: An International Multicenter Retrospective Cohort Study and Systematic Review.

BACKGROUND: Standard lymphadenectomy for pancreatoduodenectomy is defined for pancreatic ductal adenocarcinoma and adopted for patients with non-pancreatic periampullary cancer (NPPC), ampullary adenocarcinoma (AAC), distal cholangiocarcinoma (dCCA), or duodenal adenocarcinoma (DAC). This study aimed to compare the patterns of lymph node metastases among the different NPPCs in a large series and in a systematic review to guide the discussion on surgical lymphadenectomy and pathology assessment. METHODS: This retrospective cohort study included patients after pancreatoduodenectomy for NPPC with at least one lymph node metastasis (2010-2021) from 24 centers in nine countries. The primary outcome was identification of lymph node stations affected in case of a lymph node metastasis per NPPC. A separate systematic review included studies on lymph node metastases patterns of AAC, dCCA, and DAC. RESULTS: The study included 2367 patients, of whom 1535 had AAC, 616 had dCCA, and 216 had DAC. More patients with pancreatobiliary type AAC had one or more lymph node metastasis (67.2% vs 44.8%; P < 0.001) compared with intestinal-type, but no differences in metastasis pattern were observed. Stations 13 and 17 were most frequently involved (95%, 94%, and 90%). Whereas dCCA metastasized more frequently to station 12 (13.0% vs 6.4% and 7.0%, P = 0.005), DAC metastasized more frequently to stations 6 (5.0% vs 0% and 2.7%; P < 0.001) and 14 (17.0% vs 8.4% and 11.7%, P = 0.015). CONCLUSION: This study is the first to comprehensively demonstrate the differences and similarities in lymph node metastases spread among NPPCs, to identify the existing research gaps, and to underscore the importance of standardized lymphadenectomy and pathologic assessment for AAC, dCCA, and DAC.

Surgical optomics: hyperspectral imaging and deep learning towards precision intraoperative automatic tissue recognition-results from the EX-MACHYNA trial.

BACKGROUND: Hyperspectral imaging (HSI), combined with machine learning, can help to identify characteristic tissue signatures enabling automatic tissue recognition during surgery. This study aims to develop the first HSI-based automatic abdominal tissue recognition with human data in a prospective bi-center setting. METHODS: Data were collected from patients undergoing elective open abdominal surgery at two international tertiary referral hospitals from September 2020 to June 2021. HS images were captured at various time points throughout the surgical procedure. Resulting RGB images were annotated with 13 distinct organ labels. Convolutional Neural Networks (CNNs) were employed for the analysis, with both external and internal validation settings utilized. RESULTS: A total of 169 patients were included, 73 (43.2%) from Strasbourg and 96 (56.8%) from Verona. The internal validation within centers combined patients from both centers into a single cohort, randomly allocated to the training (127 patients, 75.1%, 585 images) and test sets (42 patients, 24.9%, 181 images). This validation setting showed the best performance. The highest true positive rate was achieved for the skin (100%) and the liver (97%). Misclassifications included tissues with a similar embryological origin (omentum and mesentery: 32%) or with overlaying boundaries (liver and hepatic ligament: 22%). The median DICE score for ten tissue classes exceeded 80%. CONCLUSION: To improve automatic surgical scene segmentation and to drive clinical translation, multicenter accurate HSI datasets are essential, but further work is needed to quantify the clinical value of HSI. HSI might be included in a new omics science, namely surgical optomics, which uses light to extract quantifiable tissue features during surgery.