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

TL;DR

A deep-learning tool helps detect missed vertebral fractures in cancer patients, improving diagnosis and treatment options.

Contribution

A deep-learning application is shown to significantly reduce the miss rate of vertebral fractures in cancer patients.

Findings

The deep-learning tool achieved an 87% positive predictive value in detecting vertebral fractures.

83.5% of true positive vertebral fractures were not reported in routine radiology assessments.

Nine out of ten grade 3 fractures suitable for vertebroplasty were missed by radiologists.

Abstract

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.

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 radiologists for validation. Additionally, grade 3 fractures identified by the application were independently assessed by two expert interventional radiologists to determine their eligibility for vertebroplasty.

Of the 1556 cases, 501 were flagged as positive for VCF by the application, with 436 confirmed as true positives by expert review, yielding a positive predictive value (PPV) of 87%. Common causes of false positives included sclerotic vertebral metastases, scoliosis, and vertebrae misidentification. Notably, 83.5% (364/436) of true positive VCFs were absent from radiology reports, indicating a substantial non-report rate in routine practice. Ten grade 3 fractures were overlooked or not reported by radiologists. Among them, 9 were deemed suitable for vertebroplasty by expert interventional radiologists.

This study underscores the potential of DL-based applications to improve the detection of VCFs. The analyzed tool can assist radiologists in detecting more incidental vertebral fractures in adult cancer patients, optimising timely treatment and reducing associated morbidity and economic burden. Moreover, it might enhance patient access to interventional treatments such as vertebroplasty. These findings highlight the transformative role that DL can play in optimising clinical management and outcomes for osteoporosis-related VCFs in cancer patients.

The online version contains supplementary material available at 10.1007/s11547-025-02058-z.

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