Automated Breast Ultrasound With Remote Reading for Primary Breast Cancer Screening: A Prospective Study Involving 46 Community Health Centers in China

<b>BACKGROUND.</b> China has faced barriers to implementation of a population-based mammographic screening program. Breast ultrasound provides an alternative screening modality to mammography in low-resource settings. <b>OBJECTIVE.</b> The purpose of this study was to evaluate the performance of automated breast ultrasound (ABUS) with remote reading as the primary screening modality for breast cancer. <b>METHODS.</b> This prospective study enrolled asymptomatic women 35-69 years old from 46 community health centers across 18 provinces representing all six regions of China from January 2021 to December 2021. Participants underwent screening ABUS as the sole breast cancer screening modality, with images acquired by a technologist at a community health center. The 3D volumetric data were transferred via cloud-based software to a single remote reading center, where examinations were interpreted independently in batches by two subspecialized breast radiologists using BI-RADS; a third radiologist at the remote reading center resolved discrepancies. Diagnostic reports were returned to the community centers, and patients sought follow-up care at local hospitals. The reference standard incorporated a combination of histopathology and 24-month follow-up. Outcome measures included cancer detection rate, abnormal interpretation rate (AIR), sensitivity, specificity, biopsy rate, and PPV. <b>RESULTS.</b> The final analysis included 5978 enrolled participants (median age, 46 years [IQR, 40-52 years]) who underwent screening ABUS at the community health centers with subsequent remote reading. A total of 24 ABUS-detected cancers and two interval cancers were diagnosed. The cancer detection rate was 4.0 per 1000 women (95% CI: 2.7-5.9), and the AIR was 11.9% (95% CI: 11.1-12.7%). A total of 95.8% (23/24) of ABUS-detected cancers were invasive. The 23 invasive cancers had a median diameter of 10.0 mm, and 73.9% (17/23) were node-negative. Sensitivity was 92.3% (95% CI: 75.9-97.9%), and specificity was 88.4% (95% CI: 87.6-89.2%). The biopsy rate was 1.7% (95% CI: 1.4-2.0%), and the PPV of biopsy was 24.0% (95% CI: 16.7-33.2%). <b>CONCLUSION.</b> ABUS screening with remote reading met benchmark performance for cancer detection in comparison with mammography, with infrequent interval cancers. <b>CLINICAL IMPACT.</b> ABUS with remote reading holds promise in enhancing access to breast cancer screening and early detection in low-resource settings or underserved regions where mammographic screening is not established. <b>TRIAL REGISTRATION.</b> ClinicalTrials.gov NCT04527510.

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Journal
American Journal of Roentgenology
Published
2024-10-23
DOI
https://doi.org/10.2214/ajr.24.31830
Citations
10
Primary Topic
AI in cancer detection
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article
Field-Weighted Citation Impact
3.47
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article

Automated Breast Ultrasound With Remote Reading for Primary Breast Cancer Screening: A Prospective Study Involving 46 Community Health Centers in China

Xiaojie Yuan, Yao Xiao, Huan Lin, Xiaozhi Dang et al.
10 citations
American Journal of Roentgenology
AI in cancer detection
3.47
article

Automated Breast Ultrasound With Remote Reading for Primary Breast Cancer Screening: A Prospective Study Involving 46 Community Health Centers in China

Xiaojie Yuan, Yao Xiao, Huan Lin, Xiaozhi Dang, Hongping Song, Yi Gao, Rui Shu, Yan Ju, Woo Kyung Moon, Yi Ren, Xiang Gu
article en
10 citations

Abstract

<b>BACKGROUND.</b> China has faced barriers to implementation of a population-based mammographic screening program. Breast ultrasound provides an alternative screening modality to mammography in low-resource settings. <b>OBJECTIVE.</b> The purpose of this study was to evaluate the performance of automated breast ultrasound (ABUS) with remote reading as the primary screening modality for breast cancer. <b>METHODS.</b> This prospective study enrolled asymptomatic women 35-69 years old from 46 community health centers across 18 provinces representing all six regions of China from January 2021 to December 2021. Participants underwent screening ABUS as the sole breast cancer screening modality, with images acquired by a technologist at a community health center. The 3D volumetric data were transferred via cloud-based software to a single remote reading center, where examinations were interpreted independently in batches by two subspecialized breast radiologists using BI-RADS; a third radiologist at the remote reading center resolved discrepancies. Diagnostic reports were returned to the community centers, and patients sought follow-up care at local hospitals. The reference standard incorporated a combination of histopathology and 24-month follow-up. Outcome measures included cancer detection rate, abnormal interpretation rate (AIR), sensitivity, specificity, biopsy rate, and PPV. <b>RESULTS.</b> The final analysis included 5978 enrolled participants (median age, 46 years [IQR, 40-52 years]) who underwent screening ABUS at the community health centers with subsequent remote reading. A total of 24 ABUS-detected cancers and two interval cancers were diagnosed. The cancer detection rate was 4.0 per 1000 women (95% CI: 2.7-5.9), and the AIR was 11.9% (95% CI: 11.1-12.7%). A total of 95.8% (23/24) of ABUS-detected cancers were invasive. The 23 invasive cancers had a median diameter of 10.0 mm, and 73.9% (17/23) were node-negative. Sensitivity was 92.3% (95% CI: 75.9-97.9%), and specificity was 88.4% (95% CI: 87.6-89.2%). The biopsy rate was 1.7% (95% CI: 1.4-2.0%), and the PPV of biopsy was 24.0% (95% CI: 16.7-33.2%). <b>CONCLUSION.</b> ABUS screening with remote reading met benchmark performance for cancer detection in comparison with mammography, with infrequent interval cancers. <b>CLINICAL IMPACT.</b> ABUS with remote reading holds promise in enhancing access to breast cancer screening and early detection in low-resource settings or underserved regions where mammographic screening is not established. <b>TRIAL REGISTRATION.</b> ClinicalTrials.gov NCT04527510.

American Journal of RoentgenologyVol. 224(1)
Jiangsu University (CN), Guangzhou University of Chinese Medicine (CN), Shenzhen University (CN), Seoul National University Hospital (KR), Xuzhou Cancer Hospital (CN), Shenzhen University Health Science Center (CN), Xijing Hospital (CN), Air Force Medical University (CN)
Quality Education
Openalex Percentile: Top 6%
AI in cancer detection
3.47
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