Cross-institutional validation of a polar map-free 3D deep learning model for obstructive coronary artery disease prediction using myocardial perfusion imaging: insights into generalizability and bias.
Purpose: Deep learning (DL) models for predicting obstructive coronary artery disease (CAD) using myocardial perfusion imaging (MPI) have shown potential for enhancing diagnostic accuracy. However, their ability to maintain consistent performance across institutions and demographics remains uncertai...
| Published in: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 52; no. 11; pp. 4213 - 4224 |
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| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Sep2025
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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=187672838&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187672838 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Sep2025 vid: 52 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187672838 184318165 10.1007/s00259-025-07243-w 187672838 ppf: 4213 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Cross-institutional validation of a polar map-free 3D deep learning model for obstructive coronary artery disease prediction using myocardial perfusion imaging: insights into generalizability and bias. aug: au: Shih, Yu-Cheng Ko, Chi-Lun Wang, Shan-Ying Chang, Chen-Yu Lin, Shau-Syuan Huang, Cheng-Wen Cheng, Mei-Fang Chen, Chung-Ming Wu, Yen-Wen affil: https://ror.org/019tq3436 Department of Nuclear Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan sug: ab: Purpose: Deep learning (DL) models for predicting obstructive coronary artery disease (CAD) using myocardial perfusion imaging (MPI) have shown potential for enhancing diagnostic accuracy. However, their ability to maintain consistent performance across institutions and demographics remains uncertain. This study aimed to investigate the generalizability and potential biases of an in-house MPI DL model between two hospital-based cohorts. Methods: We retrospectively included patients from two medical centers in Taiwan who underwent stress/redistribution thallium-201 MPI followed by invasive coronary angiography within 90 days as the reference standard. A polar map-free 3D DL model trained on 928 MPI images from one center to predict obstructive CAD was tested on internal (933 images) and external (3234 images from the other center) validation sets. Diagnostic performance, assessed using area under receiver operating characteristic curves (AUCs), was compared between the internal and external cohorts, demographic groups, and with the performance of stress total perfusion deficit (TPD). Results: The model showed significantly lower performance in the external cohort compared to the internal cohort in both patient-based (AUC: 0.713 vs. 0.813) and vessel-based (AUC: 0.733 vs. 0.782) analyses, but still outperformed stress TPD (all p < 0.001). The performance was lower in patients who underwent treadmill stress MPI in the internal cohort and in patients over 70 years old in the external cohort. Conclusions: This study demonstrated adequate performance but also limitations in the generalizability of the DL-based MPI model, along with biases related to stress type and patient age. Thorough validation is essential before the clinical implementation of DL MPI models. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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