¹⁸F-FDG PET/CT radiomic analysis and artificial intelligence to predict pathological complete response after neoadjuvant chemotherapy in breast cancer patients.

ABSTRACT:

Purpose

Build machine learning (ML) models able to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on conventional and radiomic signatures extracted from baseline [¹⁸F]FDG PET/CT.

Material and methods

Primary tumor and the most significant lymph node metastasis were manually segmented in baseline [¹⁸F]FDG PET/CT of 52 newly diagnosed BC patients. Clinical parameters, NAC and conventional semiquantitative PET parameters were collected. The standard of reference considered was surgical pCR after NAC (ypT0;ypN0). Eight-hundred-fifty-four radiomic features (RFts) were extracted from both PET and CT datasets, according to IBSI; robust RFTs were selected. The cohort was split in training (

SUBMITTER: Urso L 

PROVIDER: S-EPMC12008070 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature