Risk-predicted dual nomograms consisting of clinical and ultrasound factors for downgrading BI-RADS category 4a breast lesions - A multiple centre study

Niu, Zihan; Tian, Jiawei; Ran, Haitao; Ren, Weidong; Chang, Cai; Yuan, Jianjun; Chen, Kang; Deng, Yansha; Wang, Hui; Luo, Baoming; Guo, Shu; Zhou, Qi; Xue, Ensheng; Zhan, Wei; Zhou, Qing; Li, Jie; Zhou, Ping; Zhang, Chunquan; Chen, Man; Gu, Ying; Xu, Jinfeng; Chen, Wu; Zhang, Yuhong; Wang, Hongqiao; Li, Jianchu; Wang, Hongyan; Jiang, Yuxin

doi:10.7150/jca.51302

Cited by 15 publications

(17 citation statements)

References 26 publications

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“…As shown in Table 2 , nine indicators were included in the model: age over 40 years; conventional US findings, including ill-defined margin, tumor heterogeneity, rich blood flow, and abnormal axillary lymph nodes; CEUS findings, including enhanced area enlargement, contrast agent retention, and irregular shape; SWEmean larger than the cutoff value. Patients with malignant lesions were shown to be older than those with benign lesions ( 17 , 21 ), consistent with our finding. Ill-defined margins can be attributed to the invasion of malignant tumors into the surrounding breast tissue ( 22 – 24 ), confirmed by our findings.…”

Section: Discussionsupporting

confidence: 91%

Multi-parameter ultrasonography-based predictive model for breast cancer diagnosis

Chen

Ma²,

Li³

et al. 2022

Front. Oncol.

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ObjectivesTo develop, validate, and evaluate a predictive model for breast cancer diagnosis using conventional ultrasonography (US), shear wave elastography (SWE), and contrast-enhanced US (CEUS).Materials and methodsThis retrospective study included 674 patients with 674 breast lesions. The data, a main and an independent datasets, were divided into three cohorts. Cohort 1 (80% of the main dataset; n = 448) was analyzed by logistic regression analysis to identify risk factors and establish the predictive model. The area under the receiver operating characteristic curve (AUC) was analyzed in Cohort 2 (20% of the main dataset; n = 119) to validate and in Cohort 3 (the independent dataset; n = 107) to evaluate the predictive model.ResultsMultivariable regression analysis revealed nine independent breast cancer risk factors, including age > 40 years; ill-defined margin, heterogeneity, rich blood flow, and abnormal axillary lymph nodes on US; enhanced area enlargement, contrast agent retention, and irregular shape on CEUS; mean SWE higher than the cutoff value (P < 0.05 for all). The diagnostic performance of the model was good, with AUC values of 0.847, 0.857, and 0.774 for Cohorts 1, 2, and 3, respectively. The model increased the diagnostic specificity (from 31% to 81.3% and 7.3% to 73.1% in cohorts 2 and 3, respectively) without a significant loss in sensitivity (from 100.0% to 90.1% and 100.0% to 81.8% in cohorts 2 and 3, respectively).ConclusionThe multi-parameter US-based model showed good performance in breast cancer diagnosis, improving specificity without a significant loss in sensitivity. Using the model could reduce unnecessary biopsies and guide clinical diagnosis and treatment.

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Section: Discussionsupporting

confidence: 91%

Multi-parameter ultrasonography-based predictive model for breast cancer diagnosis

Chen

Ma²,

Li³

et al. 2022

Front. Oncol.

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“…9 There are only a few predictive nomograms that can be used to differentiate benign lesions from malignant lesions in BI-RADS (US) 4A. 5,9,10,23,24 As these predictive models rely on many detailed US or MG features but a limited number of clinical characteristics, their application in some basic medical institutions may be limited due to the requirement of technology and experience from the radiologists. Furthermore, some nomograms include MRI features, but we all know that MRIs are expensive and not always accessible, which may contribute to the limited use of these nomograms.…”

Section: Discussionmentioning

confidence: 99%

An Improved Nomogram to Reduce False-Positive Biopsy Rates of Breast Imaging Reporting and Data System Ultrasonography Category 4A Lesions

et al. 2022

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Background: The NCCN clinical guidelines recommended core needle biopsy for breast lesions classified as Breast Imaging Reporting and Data System (BI-RADS) 4, while category 4A lesions are only 2-10% likely to be malignant. Thus, a large number of biopsies of BI-RADS 4A lesions were ultimately determined to be benign, and those unnecessary biopsies may incur additional costs and pains. However, it is important to emphasize that the current risk prediction model focuses primarily on the details and complex risk features of US or MG findings, which may be difficult to apply in order to benefit from the model. To stratify and manage BI-RADS 4A lesions effectively and efficiently, a more effective and practical predictive model must be developed. Methods: We retrospectively analyzed 465 patients with BI-RADS ultrasonography (US) category 4A lesions, diagnosed between January 2019 and July 2019 in Tianjin Medical University Cancer Institute and Hospital and National Clinical Research Center for Cancer. Univariate and multivariate logistic regression analyses were conducted to identify risk factors. To stratify and predict the malignancy of BI-RADS 4A lesions, a nomogram combining the risk factors was constructed based on the multivariate logistic regression results. In order to determine the predictive performance of our predictive model, we used the concordance index (C-index), calibration curve, and receiver operating characteristic (ROC), and the decision curve analysis (DCA) to assess the clinical benefits. Results: Based on our analysis, 16.3% (76 out of 465) of patients were pathologically diagnosed with malignant lesions, while 83.6% (389 out of 465) were diagnosed with benign lesions. According to univariate and multivariate logistic regression analysis, age (OR = 3.414, 95%CI:1.849-6.303), nipple discharge (OR = .326, 95%CI:0.157-.835), palpable lesions (OR = 1.907, 95%CI:1.004-3.621), uncircumscribed margin (US) (OR = 1.732, 95%CI:1.033-2.905), calcification (mammography, MG) (OR = 2.384, 95%CI:1.366-4.161), BI-RADS(MG) (OR = 5.345, 95%CI:2.934-9.736) were incorporated into the predictive nomogram (C-index = .773). There was good agreement between the predicted risk and the observed probability of recurrence. Furthermore, we determined that 153 was the best cutoff score for distinguishing between patients in the low- and high-risk groups. Malignant lesions were significantly more prevalent in high-risk patients than in low-risk patients. Conclusion: Based on clinical, US, and MG features, we present a predictive nomogram to reliably predict the malignancy risk of BI-RADS(US) 4A lesions, which may assist clinicians in the selection of patients at low risk of malignancy and reduce the number of false-positive biopsies.

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“…To solve this problem, downgrading of BI‐RADS 4a lesions with elastography and clinical nomograms has been attempted with excellent performance. 25 , 26 Moreover, there are many reports of AUCs of >0.9 for benign and malignant tumor diagnoses by AI‐based image classification. 17 The use of such AI for downgrading BI‐RADS4a lesions should be considered in the future.…”

Section: Discussionmentioning

confidence: 99%

Establishment of a deep‐learning system to diagnose BI‐RADS4a or higher using breast ultrasound for clinical application

et al. 2022

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Although the categorization of ultrasound using the Breast Imaging Reporting and Data System (BI‐RADS) has become widespread worldwide, the problem of inter‐observer variability remains. To maintain uniformity in diagnostic accuracy, we have developed a system in which artificial intelligence (AI) can distinguish whether a static image obtained using a breast ultrasound represents BI‐RADS3 or lower or BI‐RADS4a or higher to determine the medical management that should be performed on a patient whose breast ultrasound shows abnormalities. To establish and validate the AI system, a training dataset consisting of 4028 images containing 5014 lesions and a test dataset consisting of 3166 images containing 3656 lesions were collected and annotated. We selected a setting that maximized the area under the curve (AUC) and minimized the difference in sensitivity and specificity by adjusting the internal parameters of the AI system, achieving an AUC, sensitivity, and specificity of 0.95, 91.2%, and 90.7%, respectively. Furthermore, based on 30 images extracted from the test data, the diagnostic accuracy of 20 clinicians and the AI system was compared, and the AI system was found to be significantly superior to the clinicians (McNemar test, p < 0.001). Although deep‐learning methods to categorize benign and malignant tumors using breast ultrasound have been extensively reported, our work represents the first attempt to establish an AI system to classify BI‐RADS3 or lower and BI‐RADS4a or higher successfully, providing important implications for clinical actions. These results suggest that the AI diagnostic system is sufficient to proceed to the next stage of clinical application.

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Risk-predicted dual nomograms consisting of clinical and ultrasound factors for downgrading BI-RADS category 4a breast lesions - A multiple centre study

Cited by 15 publications

References 26 publications

Multi-parameter ultrasonography-based predictive model for breast cancer diagnosis

Multi-parameter ultrasonography-based predictive model for breast cancer diagnosis

An Improved Nomogram to Reduce False-Positive Biopsy Rates of Breast Imaging Reporting and Data System Ultrasonography Category 4A Lesions

Establishment of a deep‐learning system to diagnose BI‐RADS4a or higher using breast ultrasound for clinical application

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