Improved Osteoporosis Prediction in Breast Cancer Patients Using a Novel Semi-Foundational Model.
Small cohorts of certain disease states are common especially in medical imaging. Despite the growing culture of data sharing, information safety often precludes open sharing of these datasets for creating generalizable machine learning models. To overcome this barrier and maintain proper health inf...
| Published in: | Journal of Imaging Informatics in Medicine Vol. 38; no. 4; pp. 2063 - 2071 |
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| Main Authors: | , , , , , |
| Format: | diagnostic images research tables/charts Journal Article |
| Published: |
Springer Nature
Aug2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187278974&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187278974 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Aug2025 vid: 38 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187278974 187278974 187278974 10.1007/s10278-024-01337-x 187278974 ppf: 2063 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Improved Osteoporosis Prediction in Breast Cancer Patients Using a Novel Semi-Foundational Model. aug: au: Mayfield, John Tibbetts, Katherine Quesada Rehman, Aziz Levin, Millena Goltz, Dayna Prakash, Neelesh affil: https://ror.org/04tk2gy88 Department of Radiology, USF Health, Tampa, USA sug: subj: Breast Neoplasms Radiography Osteoporosis Risk Factors Prediction Models Machine Learning Natural Language Processing Positron Emission Tomography Computed Tomography Risk Assessment Human Female Adult Middle Age Aged Retrospective Design Record Review Cancer Patients Deep Learning Bone Density Lumbar Vertebrae Analysis of Variance P-Value Artificial Intelligence Diagnosis, Computer Assisted T-Tests Convolutional Neural Networks Bone Diseases, Metabolic Learning Methods Radiography, Thoracic COVID-19 Magnetic Resonance Imaging Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Female ab: Small cohorts of certain disease states are common especially in medical imaging. Despite the growing culture of data sharing, information safety often precludes open sharing of these datasets for creating generalizable machine learning models. To overcome this barrier and maintain proper health information protection, foundational models are rapidly evolving to provide deep learning solutions that have been pretrained on the native feature spaces of the data. Although this has been optimized in Large Language Models (LLMs), there is still a sparsity of foundational models for computer vision tasks. It is in this space that we provide an investigation into pretraining Visual Geometry Group (VGG)-16, Residual Network (ResNet)-50, and Dense Network (DenseNet)-121 on an unrelated dataset of 8500 chest CTs which was subsequently fine-tuned to classify bone mineral density (BMD) in 199 breast cancer patients using the L1 vertebra on CT. These semi-foundational models showed significant improved ternary classification into mild, moderate, and severe demineralization in comparison to ground truth Hounsfield Unit (HU) measurements in trabecular bone with the semi-foundational ResNet50 architecture demonstrating the best relative performance. Specifically, the holdout testing AUC was 0.99 (p-value < 0.05, ANOVA versus no pretraining versus ImageNet transfer learning) and F1-score 0.99 (p-value < 0.05) for the holdout testing set. In this study, the use of a semi-foundational model trained on the native feature space of CT provided improved classification in a completely disparate disease state with different window levels. Future implementation with these models may provide better generalization despite smaller numbers of a disease state to be classified. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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