An interpretable AI model for recurrence prediction after surgery in gastrointestinal stromal tumour: an observational cohort study.

ABSTRACT:

Background

There are several models that predict the risk of recurrence following resection of localised, primary gastrointestinal stromal tumour (GIST). However, assessment of calibration is not always feasible and when performed, calibration of current GIST models appears to be suboptimal. We aimed to develop a prognostic model to predict the recurrence of GIST after surgery with both good discrimination and calibration by uncovering and harnessing the non-linear relationships among variables that predict recurrence.

Methods

In this observational cohort study, the data of 395 adult patients who underwent complete resection (R0 or R1) of a localised, primary GIST in the pre-imatinib era at Memorial Sloan Kettering Cancer Center (NY, USA) (recruited 1982-2001) and a European

SUBMITTER: Bertsimas D 

PROVIDER: S-EPMC10507206 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature