Details

Autor(en) / Beteiligte

• Jiang, Meng
• Li, Chang-Li
• Chen, Rui-Xue
• Tang, Shi-Chu
• Lv, Wen-Zhi
• Luo, Xiao-Mao
• Chuan, Zhi-Rui
• Jin, Chao-Ying
• Liao, Jin-Tang
• Cui, Xin-Wu
• Dietrich, Christoph F.

Titel

Management of breast lesions seen on US images: dual-model radiomics including shear-wave elastography may match performance of expert radiologists

Ist Teil von

• European journal of radiology, 2021-08, Vol.141, p.109781-109781, Article 109781

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2021

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •Radiomic analysis helps radiologists to characterize breast lesions with accuracy.•Multicenter retrospective study showed that dual-model ultrasound radiomics nomogram including shear-wave elastography provides good diagnostic confidence.•The performance of radiomic algorithm may be comparable to that of expert radiologists who used ACR BI-RADS guideline. To develop a nomogram incorporating B-mode ultrasound (BMUS) and shear-wave elastography (SWE) radiomics to predict malignant status of breast lesions seen on US non-invasively. Data on 278 consecutive patients from Hospital #1 (training cohort) and 123 cases from Hospital #2 (external validation cohort) referred for breast US with subsequent histopathologic analysis between May 2017 and October 2019 were retrospectively collected. Using their BMUS and SWE images, we built a radiomics nomogram to improve radiology workflow for management of breast lesions. The performance of the algorithm was compared with a consensus of three ACR BI-RADS committee experts and four individual radiologists, all of whom interpreted breast US images in clinical practice. Twelve features from BMUS and three from SWE were selected finally to construct the respective radiomic signature. The nomogram based on the dual-modal US radiomics achieved good diagnostic performance in the training (AUC 0.96; 95% confidence intervals [CI], 0.94–0.98) and the validation set (AUC 0.92; 95% CI, 0.87–0.97). For the 123 test lesions, the algorithm achieved 105 of 123 (85%) accuracy, comparable to the expert consensus (104 of 123 [85%], P =  0.86) and four individual radiologists (93, 99, 95 and 97 of 123, with P value of 0.05, 0.31, 0.10 and 0.18 respectively). Furthermore, the model also performed well in the BI-RADS 4 and 5 categories. Performance of a dual-model US radiomics nomogram based on SWE for breast lesion classification may comparable to that of expert radiologists who used ACR BI-RADS guideline.