O151 A comparison of pathological TNM 7 with TNM 8 staging for pancreatic ductal adenocarcinoma and distal cholangiocarcinoma

Fararouei, Mohammad; Hanks, Matt; Kandiah, Kesavan; Wong, Tom; Adiamah, Alfred; Zaitoun, Abed M.; Lobo, Dileep N.

doi:10.1093/bjs/znad101.151

Cited by 1 publication

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“…This is evident when considering the research conducted by Chen et al (2016) [7], where the prognostic efficacy of the staging characteristics from the 8th edition of the AJCC cancer staging manual [21] was evaluated, finding a C-index of 0.585. Similarly, in a study conducted by Mohammad et al (2023) [22], the observed C-index was found to be 0.633.…”

Section: Survival Modelingmentioning

confidence: 75%

An Automated Prognostic Model for Pancreatic Ductal Adenocarcinoma

Vezakis,

Gourtsoyianni

et al. 2023

Genes

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Pancreatic ductal adenocarcinoma (PDAC) constitutes a leading cause of cancer-related mortality despite advances in detection and treatment methods. While computed tomography (CT) serves as the current gold standard for initial evaluation of PDAC, its prognostic value remains limited, as it relies on diagnostic stage parameters encompassing tumor size, lymph node involvement, and metastasis. Radiomics have recently shown promise in predicting postoperative survival of PDAC patients; however, they rely on manual pancreas and tumor delineation by clinicians. In this study, we collected a dataset of pre-operative CT scans from a cohort of 40 PDAC patients to evaluate a fully automated pipeline for survival prediction. Employing nnU-Net trained on an external dataset, we generated automated pancreas and tumor segmentations. Subsequently, we extracted 854 radiomic features from each segmentation, which we narrowed down to 29 via feature selection. We then combined these features with the Tumor, Node, Metastasis (TNM) system staging parameters, as well as the patient’s age. We trained a random survival forest model to perform an overall survival prediction over time, as well as a random forest classifier for the binary classification of two-year survival, using repeated cross-validation for evaluation. Our results exhibited promise, with a mean C-index of 0.731 for survival modeling and a mean accuracy of 0.76 in two-year survival prediction, providing evidence of the feasibility and potential efficacy of a fully automated pipeline for PDAC prognostication. By eliminating the labor-intensive manual segmentation process, our streamlined pipeline demonstrates an efficient and accurate prognostication process, laying the foundation for future research endeavors.

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