A YOLO-based AI system for classifying calcifications on spot magnification mammograms

BioMedical Engineering OnLine

Research

Ling-Ming Tseng¹  Chin-Yu Chen²  Jian-Ling Chen³  Tun-Wei Hsu⁴  Jane Wang⁵  Lan-Hsin Cheng⁶  Shu-Mei Guo⁶ 

[1] Comprehensive Breast Health Center, Taipei-Veterans General Hospital, No. 201, Sec. 2, Shipai Rd., Beitou Dist., 112, Taipei, Taiwan;Department of Surgery, Taipei Veterans General Hospital, No. 201, Sec. 2, Shipai Rd., Beitou Dist., 112, Taipei, Taiwan;Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Beitou Dist., 112, Taipei, Taiwan;Department of Radiology, Chi-Mei Medical Center, No. 901, Zhonghua Rd. Yongkang Dist., 710, Tainan City, Taiwan;Department of Radiology, Far Eastern Memorial Hospital, No. 21, Sec. 2, Nanya S. Rd., Banciao Dist., 220, New Taipei City, Taiwan;Department of Radiology, Taipei Veterans General Hospital, No. 201, Sec. 2, Shipai Rd., Beitou Dist., 112, Taipei City, Taiwan;Department of Radiology, Taipei Veterans General Hospital, No. 201, Sec. 2, Shipai Rd., Beitou Dist., 112, Taipei City, Taiwan;Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Beitou Dist., 112, Taipei City, Taiwan;Department of Radiology, Taipei Veterans General Hospital, No. 201, Sec. 2, Shipai Rd., Beitou Dist., 112, Taipei City, Taiwan;Department of Radiology, National Taiwan University College of Medicine, No. 1, Jenai Rd., 100, Taipei City, Taiwan;Department of Nurse-Midwifery and Women Health, and School of Nursing, College of Nursing, National Taipei University of Nursing and Health Sciences, No. 365, Mingde Rd., Beitou Dist., 112, Taipei City, Taiwan;Institute of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Rd., 701, Tainan City, Taiwan;

关键词: Breast cancer;    Artificial intelligence;    Mammography;    Calcifications;    Biopsy;   

DOI  :  10.1186/s12938-023-01115-w

 received in 2023-01-29, accepted in 2023-05-13,  发布年份 2023

来源: Springer

【 摘 要 】

ObjectivesUse of an AI system based on deep learning to investigate whether the system can aid in distinguishing malignant from benign calcifications on spot magnification mammograms, thus potentially reducing unnecessary biopsies.MethodsIn this retrospective study, we included public and in-house datasets with annotations for the calcifications on both craniocaudal and mediolateral oblique vies, or both craniocaudal and mediolateral views of each case of mammograms. All the lesions had pathological results for correlation. Our system comprised an algorithm based on You Only Look Once (YOLO) named adaptive multiscale decision fusion module. The algorithm was pre-trained on a public dataset, Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM), then re-trained and tested on the in-house dataset of spot magnification mammograms. The performance of the system was investigated by receiver operating characteristic (ROC) analysis.ResultsWe included 1872 images from 753 calcification cases (414 benign and 339 malignant) from CBIS-DDSM. From the in-house dataset, 636 cases (432 benign and 204 malignant) with 1269 spot magnification mammograms were included, with all lesions being recommended for biopsy by radiologists. The area under the ROC curve for our system on the in-house testing dataset was 0.888 (95% CI 0.868–0.908), with a sensitivity of 88.4% (95% CI 86.9–8.99%), specificity of 80.8% (95% CI 77.6–84%), and an accuracy of 84.6% (95% CI 81.8–87.4%) at the optimal cutoff value. Using the system with two views of spot magnification mammograms, 80.8% benign biopsies could be avoided.ConclusionThe AI system showed good accuracy for classification of calcifications on spot magnification mammograms which were all categorized as suspicious by radiologists, thereby potentially reducing unnecessary biopsies.

【 授权许可】

CC BY   
© The Author(s) 2023

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