Automated Breast Volume Scanner (ABVS)-Based Radiomic Nomogram: A Potential Tool for Reducing Unnecessary Biopsies of BI-RADS 4 Lesions

Wang, Shijie; Liu, Huaqing; Yang, Tao; Huang, Mingquan; Zheng, Bowen; Wu, Tao; Qiu, Chen; Han, Lanqing; Ren, Jie

doi:10.3390/diagnostics12010172

Cited by 8 publications

(3 citation statements)

References 33 publications

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“…30 Although radiomics has been successfully applied to MRI and CT, its application to US or ABUS is Recently, radiomics has demonstrated the potential in differentiating benign and malignant tumors using ABUS images. 19,31,32 According to the investigation of the radiomics biological mechanisms by Panth et al 33 radiomics was also linked to tumor phenotype and genotype. By measuring the intra-tumoral heterogeneity, radiomics features may consequently be able to assess the status of lymph nodes.…”

Section: Discussionmentioning

confidence: 99%

Optimized Radiomics Nomogram Based on Automated Breast Ultrasound System: A Potential Tool for Preoperative Prediction of Metastatic Lymph Node Burden in Breast Cancer

Li¹,

Song²,

Huang³

et al. 2023

BCTT

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Background Axillary lymph node dissection (ALND) can be safely avoided in women with T1 or T2 primary invasive breast cancer (BC) and one to two metastatic sentinel lymph nodes (SLNs). However, cancellation of ALND based solely on SLN biopsy (SLNB) may lead to adverse outcomes. Therefore, preoperative assessment of LN tumor burden becomes a new focus for ALN status. Objective This study aimed to develop and validate a nomogram incorporating the radiomics score (rad-score) based on automated breast ultrasound system (ABUS) and other clinicopathological features for evaluating the ALN status in patients with early-stage BC preoperatively. Methods Totally 354 and 163 patients constituted the training and validation cohorts. They were divided into ALN low burden (<3 metastatic LNs) and high burden (≥3 metastatic LNs) based on the histopathological diagnosis. The radiomics features of the segmented breast tumor in ABUS images were extracted and selected to generate the rad-score of each patient. These rad-scores, along with the ALN burden predictors identified from the clinicopathologic characteristics, were included in the multivariate analysis to establish a nomogram. It was further evaluated in the training and validation cohorts. Results High ALN burdens accounted for 11.2% and 10.8% in the training and validation cohorts. The rad-score for each patient was developed based on 7 radiomics features extracted from the ABUS images. The radiomics nomogram was built with the rad-score, tumor size, US-reported LN status, and ABUS retraction phenomenon. It achieved better predictive efficacy than the nomogram without the rad-score and exhibited favorable discrimination, calibration and clinical utility in both cohorts. Conclusion We developed an ABUS-based radiomics nomogram for the preoperative prediction of ALN burden in BC patients. It would be utilized for the identification of patients with low ALN burden if further validated, which contributed to appropriate axillary treatment and might avoid unnecessary ALND.

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Section: Discussionmentioning

confidence: 99%

Optimized Radiomics Nomogram Based on Automated Breast Ultrasound System: A Potential Tool for Preoperative Prediction of Metastatic Lymph Node Burden in Breast Cancer

Li¹,

Song²,

Huang³

et al. 2023

BCTT

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“…Based on ABVS images, Wang et al. integrated clinical ultrasound factors and Radscore to develop a nomogram for the diagnosis of BI-RADS category 4 lesions, which achieved an AUC value of 0.925 in the internal validation group and effectively minimized unnecessary biopsies ( 20 ). During this study, we constructed nomogram that also achieved an AUC value of 0.930 for the diagnosis of BI-RADS category 4 lesions in the validation group, surpassing the performance of the clinical model (AUC: 0.839), thereby further validating its good diagnostic efficacy.…”

Section: Discussionmentioning

confidence: 99%

A clinical-radiomics nomogram based on multimodal ultrasound for predicting the malignancy risk in solid hypoechoic breast lesions

Shiyan,

Liqing,

Yueqiong

et al. 2023

Front. Oncol.

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BackgroundIn routine clinical examinations, solid hypoechoic breast lesions are frequently encountered, but accurately distinguishing them poses a challenge. This study proposed a clinical-radiomics nomogram based on multimodal ultrasound that enhances the diagnostic accuracy for solid hypoechoic breast lesions.MethodThis retrospective study analyzed ultrasound strain elastography (SE) and automated breast volume scanner images (ABVS) of 423 solid hypoechoic breast lesions from 423 female patients in our hospital between August 2019 and May 2022. They were assigned to the training (n=296) and validation (n=127) groups in a 7:3 ratio by generating random numbers. Radiomics features were extracted and screened from ABVS and SE images, followed by the calculation of the radiomics score (Radscore) based on these features. Subsequently, a nomogram was constructed through multivariate logistic regression to assess the malignancy risk in breast lesions by combining Radscore with Breast Imaging Reporting and Data System (BI-RADS) scores and clinical risk factors associated with breast malignant lesions. The diagnostic performance, calibration performance, and clinical usefulness of the nomogram were assessed by the area under the curve (AUC) of the receiver operating characteristic curve, the calibration curve, and the decision analysis curve, respectively.ResultsThe diagnostic performance of the nomogram is significantly superior to that of both the clinical diagnostic model (BI-RADS model) and the multimodal radiomics model (SE+ABVS radiomics model) in training (AUC: 0.972 vs 0.930 vs 0.941) and validation group (AUC:0.964 vs 0.916 vs 0.933). In addition, the nomogram also exhibited a favorable goodness-of-fit and could lead to greater net benefits for patients.ConclusionThe nomogram enables a more effective assessment of the malignancy risk of solid hypoechoic breast lesions; therefore, it can serve as a new and efficient diagnostic tool for clinical diagnosis.

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“…However, the large range of malignant risk (2%-95%) results in signi cant uncertainty with regards to diagnosis and treatment. Therefore, over the last ve years, many researchers have begun to use various new technologies such as radiomics, deep learning, contrast-enhanced ultrasound (CEUS), automated breast volume scanners, and UE, to study BI-RADS category 4 lesions, in order to predict the benign and malignant nature of these lesions, and thus provide certain guidance for diagnosis and treatment [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

The diagnostic value of strain elastography for BI-RADS category 4 lesions

Dong,

Zhang,

et al. 2024

Preprint

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Purpose To investigate the diagnostic value of strain elastography for Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions. Methods We recruited a total of 224 patients (259 lesions in all) who were diagnosed with BI-RADS category 4 lesions from July 2021 to December 2022.The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) of conventional ultrasound (US), the elasticity score method, the strain ratio method, a combined group and logistic regression model for the diagnosis of BI-RADS category 4 lesions were calculated. Compare the area under the curve (AUC) values and evaluate the diagnostic value. P < 0.05 was considered statistically significant. Results Of the 259 BI-RADS category 4 lesions,163 were benign and 96 were malignant. The AUC values of five methods: Logistic regression model > combined group > strain ratio method > conventional US > elasticty score method. The combined group was higher than that for the conventional US and elasticity score method. The AUC of the strain ratio method was higher than that of the elasticity score method, and the AUC of the logistic regression model was higher than the strain ratio method; these differences were all significant (P < 0.05). Conclusion Strain elastography has high diagnostic value for BI-RADS category 4 lesions, and the strain ratio method was superior to elasticity score method. The efficacy of conventional US when combined with the elasticity score for the diagnosis of BI-RADS category 4 lesions was better than that of either method alone.

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Automated Breast Volume Scanner (ABVS)-Based Radiomic Nomogram: A Potential Tool for Reducing Unnecessary Biopsies of BI-RADS 4 Lesions

Cited by 8 publications

References 33 publications

Optimized Radiomics Nomogram Based on Automated Breast Ultrasound System: A Potential Tool for Preoperative Prediction of Metastatic Lymph Node Burden in Breast Cancer

Optimized Radiomics Nomogram Based on Automated Breast Ultrasound System: A Potential Tool for Preoperative Prediction of Metastatic Lymph Node Burden in Breast Cancer

A clinical-radiomics nomogram based on multimodal ultrasound for predicting the malignancy risk in solid hypoechoic breast lesions

The diagnostic value of strain elastography for BI-RADS category 4 lesions

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