Advanced gastric cancer: CT radiomics prediction and early detection of downstaging with neoadjuvant chemotherapy.

ABSTRACT:

Objectives

To develop and evaluate machine learning models using baseline and restaging computed tomography (CT) for predicting and early detecting pathological downstaging (pDS) with neoadjuvant chemotherapy in advanced gastric cancer (AGC).

Methods

We collected 292 AGC patients who received neoadjuvant chemotherapy. They were classified into (a) primary cohort (206 patients with 3-4 cycles chemotherapy) for model development and internal validation, (b) testing cohort I (46 patients with 3-4 cycles chemotherapy) for evaluating models' predictive ability before and after the complete course, and (c) testing cohort II (n = 40) for model evaluation on its performance at early treatment. We extracted 1,231 radiomics features from venous phase CT at baseline and restaging. We s

SUBMITTER: Xu Q 

PROVIDER: S-EPMC8523390 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature