Deep learning-driven incidental detection of vertebral fractures in cancer patients: advancing diagnostic precision and clinical management.

ABSTRACT:

Purpose

Vertebral compression fractures (VCFs) are the most prevalent skeletal manifestations of osteoporosis in cancer patients. Yet, they are frequently missed or not reported in routine clinical radiology, adversely impacting patient outcomes and quality of life. This study evaluates the diagnostic performance of a deep-learning (DL)-based application and its potential to reduce the miss rate of incidental VCFs in a high-risk cancer population.

Materials and methods

We retrospectively analysed thoraco-abdomino-pelvic (TAP) CT scans from 1556 patients with stage IV cancer collected consecutively over a 4-month period (September-December 2023) in a tertiary cancer center. A DL-based application flagged cases positive for VCFs, which were subsequently reviewed by two expert

SUBMITTER: Mniai EM 

PROVIDER: S-EPMC12546477 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature