Radiomic analysis of cohort-specific diagnostic errors in reading dense mammograms using artificial intelligence.

ABSTRACT:

Objectives

This study aims to investigate radiologists' interpretation errors when reading dense screening mammograms using a radiomics-based artificial intelligence approach.

Methods

Thirty-six radiologists from China and Australia read 60 dense mammograms. For each cohort, we identified normal areas that looked suspicious of cancer and the malignant areas containing cancers. Then radiomic features were extracted from these identified areas and random forest models were trained to recognize the areas that were most frequently linked to diagnostic errors within each cohort. The performance of the model and discriminatory power of significant radiomic features were assessed.

Results

We found that in the Chinese cohort, the AUC values for predicting false positives were

SUBMITTER: Tao X 

PROVIDER: S-EPMC11652727 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature