Artificial intelligence for assessment of vascular involvement and tumor resectability on CT in patients with pancreatic cancer.

OBJECTIVE: This study aimed to develop and evaluate an automatic model using artificial intelligence (AI) for quantifying vascular involvement and classifying tumor resectability stage in patients with pancreatic ductal adenocarcinoma (PDAC), primarily to support radiologists in referral centers. Resectability of PDAC is determined by the degree of vascular involvement on computed tomography scans (CTs), which is associated with considerable inter-observer variability. METHODS: We developed a semisupervised machine learning segmentation model to segment the PDAC and surrounding vasculature using 613 CTs of 467 patients with pancreatic tumors and 50 control patients. After segmenting the relevant structures, our model quantifies vascular involvement by measuring the degree of the vessel wall that is in contact with the tumor using AI-segmented CTs. Based on these measurements, the model classifies the resectability stage using the Dutch Pancreatic Cancer Group criteria as either resectable, borderline resectable, or locally advanced (LA). RESULTS: We evaluated the performance of the model using a test set containing 60 CTs from 60 patients, consisting of 20 resectable, 20 borderline resectable, and 20 locally advanced cases, by comparing the automated analysis obtained from the model to expert visual vascular involvement assessments. The model concurred with the radiologists on 227/300 (76%) vessels for determining vascular involvement. The model's resectability classification agreed with the radiologists on 17/20 (85%) resectable, 16/20 (80%) for borderline resectable, and 15/20 (75%) for locally advanced cases. CONCLUSIONS: This study demonstrates that an AI model may allow automatic quantification of vascular involvement and classification of resectability for PDAC. RELEVANCE STATEMENT: This AI model enables automated vascular involvement quantification and resectability classification for pancreatic cancer, aiding radiologists in treatment decisions, and potentially improving patient outcomes. KEY POINTS: • High inter-observer variability exists in determining vascular involvement and resectability for PDAC. • Artificial intelligence accurately quantifies vascular involvement and classifies resectability for PDAC. • Artificial intelligence can aid radiologists by automating vascular involvement and resectability assessments.

Amsterdam International Consensus Meeting: tumor response scoring in the pathology assessment of resected pancreatic cancer after neoadjuvant therapy.

Histopathologically scoring the response of pancreatic ductal adenocarcinoma (PDAC) to neoadjuvant treatment can guide the selection of adjuvant therapy and improve prognostic stratification. However, several tumor response scoring (TRS) systems exist, and consensus is lacking as to which system represents best practice. An international consensus meeting on TRS took place in November 2019 in Amsterdam, The Netherlands. Here, we provide an overview of the outcomes and consensus statements that originated from this meeting. Consensus (≥80% agreement) was reached on a total of seven statements: (1) TRS is important because it provides information about the effect of neoadjuvant treatment that is not provided by other histopathology-based descriptors. (2) TRS for resected PDAC following neoadjuvant therapy should assess residual (viable) tumor burden instead of tumor regression. (3) The CAP scoring system is considered the most adequate scoring system to date because it is based on the presence and amount of residual cancer cells instead of tumor regression. (4) The defining criteria of the categories in the CAP scoring system should be improved by replacing subjective terms including "minimal" or "extensive" with objective criteria to evaluate the extent of viable tumor. (5) The improved, consensus-based system should be validated retrospectively and prospectively. (6) Prospective studies should determine the extent of tissue sampling that is required to ensure adequate assessment of the residual cancer burden, taking into account the heterogeneity of tumor response. (7) In future scientific publications, the extent of tissue sampling should be described in detail in the "Materials and methods" section.