Combining Radiomics and Autoencoders to Distinguish Benign and Malignant Breast Tumors on US Images

医学 无线电技术 乳房成像 放射科 双雷达 乳腺肿瘤 人工智能 乳腺癌 乳腺摄影术 癌症 内科学 计算机科学
作者
Zuzanna Magnuska,Rijo Roy,Moritz Palmowski,Matthias Kohlen,B. Sophia Winkler,Tatjana Pfeil,Peter Boor,Volkmar Schulz,Katja Krauss,Elmar Stickeler,Fabian Kießling
出处
期刊:Radiology [Radiological Society of North America]
卷期号:312 (3) 被引量:5
标识
DOI:10.1148/radiol.232554
摘要

Background US is clinically established for breast imaging, but its diagnostic performance depends on operator experience. Computer-assisted (real-time) image analysis may help in overcoming this limitation. Purpose To develop precise real-time-capable US-based breast tumor categorization by combining classic radiomics and autoencoder-based features from automatically localized lesions. Materials and Methods A total of 1619 B-mode US images of breast tumors were retrospectively analyzed between April 2018 and January 2024. nnU-Net was trained for lesion segmentation. Features were extracted from tumor segments, bounding boxes, and whole images using either classic radiomics, autoencoder, or both. Feature selection was performed to generate radiomics signatures, which were used to train machine learning algorithms for tumor categorization. Models were evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity and were statistically compared with histopathologically or follow-up-confirmed diagnosis. Results The model was developed on 1191 (mean age, 61 years ± 14 [SD]) female patients and externally validated on 50 (mean age, 55 years ± 15]). The development data set was divided into two parts: testing and training lesion segmentation (419 and 179 examinations) and lesion categorization (503 and 90 examinations). nnU-Net demonstrated precision and reproducibility in lesion segmentation in test set of data set 1 (median Dice score [DS]: 0.90 [IQR, 0.84-0.93];
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