Machine learning based on automated breast volume scanner (ABVS) radiomics for differential diagnosis of benign and malignant BI‐RADS 4 lesions

BI‐RADS category 4 represents possibly malignant lesions and biopsy is recommended to distinguish benign and malignant. However, studies revealed that up to 67%–78% of BI‐RADS 4 lesions proved to be benign, but received unnecessary biopsies, which may cause unnecessary anxiety and discomfort to pati...

Full description

Saved in:
Bibliographic Details
Published in:International journal of imaging systems and technology 2022-09, Vol.32 (5), p.1577-1587
Main Authors: Wang, Shi‐jie, Liu, Hua‐qing, Yang, Tao, Huang, Ming‐quan, Zheng, Bo‐wen, Wu, Tao, Han, Lan‐qing, Zhang, Yong, Ren, Jie
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
automated breast volume scanner
Automation
Breast
breast neoplasms
Feature extraction
Lesions
Machine learning
Radiomics
Scanners
Training
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:BI‐RADS category 4 represents possibly malignant lesions and biopsy is recommended to distinguish benign and malignant. However, studies revealed that up to 67%–78% of BI‐RADS 4 lesions proved to be benign, but received unnecessary biopsies, which may cause unnecessary anxiety and discomfort to patients and increase the burden on the healthcare system. In this prospective study, machine learning (ML) based on the emerging breast ultrasound technology‐automated breast volume scanner (ABVS) was constructed to distinguish benign and malignant BI‐RADS 4 lesions and compared with different experienced radiologists. A total of 223 pathologically confirmed BI‐RADS 4 lesions were recruited and divided into training and testing cohorts. Radiomics features were extracted from axial, sagittal, and coronal ABVS images for each lesion. Seven feature selection methods and 13 ML algorithms were used to construct different ML pipelines, of which the DNN‐RFE (combination of recursive feature elimination and deep neural networks) had the best performance in both training and testing cohorts. The AUC value of the DNN‐RFE was significantly higher than less experienced radiologist at Delong's test (0.954 vs. 0.776, p = 0.004). Additionally, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the DNN‐RFE were 88.9%, 83.3%, 95.2%, 83.3%, and 95.2%, which also significantly better than less experienced radiologist at McNemar's test (p = 0.043). Therefore, ML based on ABVS radiomics may be a potential method to non‐invasively distinguish benign and malignant BI‐RADS 4 lesions.
ISSN:0899-9457
1098-1098
DOI:10.1002/ima.22724