Deep Learning Model for Breast Shear Wave Elastography to Improve Breast Cancer Diagnosis (INSPiRED 006): An International, Multicenter Analysis
Menée à partir de données d'un essai international portant sur 1 294 femmes présentant des masses mammaires suspectes, cette étude évalue la performance, pour détecter avec précision des lésions cancéreuses, d'un algorithme d'apprentissage automatique utilisant des images d'élastographies par ondes de cisaillement
Purpose : Shear wave elastography (SWE) has been investigated as a complement to B-mode ultrasound for breast cancer diagnosis. Although multicenter trials suggest benefits for patients with Breast Imaging Reporting and Data System (BI-RADS) 4(a) breast masses, widespread adoption remains limited because of the absence of validated velocity thresholds. This study aims to develop and validate a deep learning (DL) model using SWE images (artificial intelligence [AI]-SWE) for BI-RADS 3 and 4 breast masses and compare its performance with human experts using B-mode ultrasound.
Methods : We used data from an international, multicenter trial (ClinicalTrials.gov identifier: NCT02638935) evaluating SWE in women with BI-RADS 3 or 4 breast masses across 12 institutions in seven countries. Images from 11 sites were used to develop an EfficientNetB1-based DL model. An external validation was conducted using data from the 12th site. Another validation was performed using the latest SWE software from a separate institutional cohort. Performance metrics included sensitivity, specificity, false-positive reduction, and area under the receiver operator curve (AUROC).
Results : The development set included 924 patients (4,026 images); the external validation sets included 194 patients (562 images) and 176 patients (188 images, latest SWE software). AI-SWE achieved an AUROC of 0.94 (95% CI, 0.91 to 0.96) and 0.93 (95% CI, 0.88 to 0.98) in the two external validation sets. Compared with B-mode ultrasound, AI-SWE significantly reduced false-positive rates by 62.1% (20.4% [30/147] v 53.8% [431/801]; P < .001) and 38.1% (33.3% [14/42] v 53.8% [431/801]; P < .001), with comparable sensitivity (97.9% [46/47] and 97.8% [131/134] v 98.1% [311/317]; P = .912 and P = .810).
Conclusion : AI-SWE demonstrated accuracy comparable with human experts in malignancy detection while significantly reducing false-positive imaging findings (ie, unnecessary biopsies). Future studies should explore its integration into multimodal breast cancer diagnostics.
Journal of Clinical Oncology , résumé, 2025