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Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists’ Diagnosis Performance? An Observer Study

Radiologists’ diagnostic capabilities for breast mass lesions depend on their experience. Junior radiologists may underestimate or overestimate Breast Imaging Reporting and Data System (BI-RADS) categories of mass lesions owing to a lack of diagnostic experience. The computer-aided diagnosis (CAD) m...

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Autores principales: He, Zilong, Li, Yue, Zeng, Weixiong, Xu, Weimin, Liu, Jialing, Ma, Xiangyuan, Wei, Jun, Zeng, Hui, Xu, Zeyuan, Wang, Sina, Wen, Chanjuan, Wu, Jiefang, Feng, Chenya, Ma, Mengwei, Qin, Genggeng, Lu, Yao, Chen, Weiguo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719464/
https://www.ncbi.nlm.nih.gov/pubmed/34976817
http://dx.doi.org/10.3389/fonc.2021.773389
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author He, Zilong
Li, Yue
Zeng, Weixiong
Xu, Weimin
Liu, Jialing
Ma, Xiangyuan
Wei, Jun
Zeng, Hui
Xu, Zeyuan
Wang, Sina
Wen, Chanjuan
Wu, Jiefang
Feng, Chenya
Ma, Mengwei
Qin, Genggeng
Lu, Yao
Chen, Weiguo
author_facet He, Zilong
Li, Yue
Zeng, Weixiong
Xu, Weimin
Liu, Jialing
Ma, Xiangyuan
Wei, Jun
Zeng, Hui
Xu, Zeyuan
Wang, Sina
Wen, Chanjuan
Wu, Jiefang
Feng, Chenya
Ma, Mengwei
Qin, Genggeng
Lu, Yao
Chen, Weiguo
author_sort He, Zilong
collection PubMed
description Radiologists’ diagnostic capabilities for breast mass lesions depend on their experience. Junior radiologists may underestimate or overestimate Breast Imaging Reporting and Data System (BI-RADS) categories of mass lesions owing to a lack of diagnostic experience. The computer-aided diagnosis (CAD) method assists in improving diagnostic performance by providing a breast mass classification reference to radiologists. This study aims to evaluate the impact of a CAD method based on perceptive features learned from quantitative BI-RADS descriptions on breast mass diagnosis performance. We conducted a retrospective multi-reader multi-case (MRMC) study to assess the perceptive feature-based CAD method. A total of 416 digital mammograms of patients with breast masses were obtained from 2014 through 2017, including 231 benign and 185 malignant masses, from which we randomly selected 214 cases (109 benign, 105 malignant) to train the CAD model for perceptive feature extraction and classification. The remaining 202 cases were enrolled as the test set for evaluation, of which 51 patients (29 benign and 22 malignant) participated in the MRMC study. In the MRMC study, we categorized six radiologists into three groups: junior, middle-senior, and senior. They diagnosed 51 patients with and without support from the CAD model. The BI-RADS category, benign or malignant diagnosis, malignancy probability, and diagnosis time during the two evaluation sessions were recorded. In the MRMC evaluation, the average area under the curve (AUC) of the six radiologists with CAD support was slightly higher than that without support (0.896 vs. 0.850, p = 0.0209). Both average sensitivity and specificity increased (p = 0.0253). Under CAD assistance, junior and middle-senior radiologists adjusted the assessment categories of more BI-RADS 4 cases. The diagnosis time with and without CAD support was comparable for five radiologists. The CAD model improved the radiologists’ diagnostic performance for breast masses without prolonging the diagnosis time and assisted in a better BI-RADS assessment, especially for junior radiologists.
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spelling pubmed-87194642022-01-01 Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists’ Diagnosis Performance? An Observer Study He, Zilong Li, Yue Zeng, Weixiong Xu, Weimin Liu, Jialing Ma, Xiangyuan Wei, Jun Zeng, Hui Xu, Zeyuan Wang, Sina Wen, Chanjuan Wu, Jiefang Feng, Chenya Ma, Mengwei Qin, Genggeng Lu, Yao Chen, Weiguo Front Oncol Oncology Radiologists’ diagnostic capabilities for breast mass lesions depend on their experience. Junior radiologists may underestimate or overestimate Breast Imaging Reporting and Data System (BI-RADS) categories of mass lesions owing to a lack of diagnostic experience. The computer-aided diagnosis (CAD) method assists in improving diagnostic performance by providing a breast mass classification reference to radiologists. This study aims to evaluate the impact of a CAD method based on perceptive features learned from quantitative BI-RADS descriptions on breast mass diagnosis performance. We conducted a retrospective multi-reader multi-case (MRMC) study to assess the perceptive feature-based CAD method. A total of 416 digital mammograms of patients with breast masses were obtained from 2014 through 2017, including 231 benign and 185 malignant masses, from which we randomly selected 214 cases (109 benign, 105 malignant) to train the CAD model for perceptive feature extraction and classification. The remaining 202 cases were enrolled as the test set for evaluation, of which 51 patients (29 benign and 22 malignant) participated in the MRMC study. In the MRMC study, we categorized six radiologists into three groups: junior, middle-senior, and senior. They diagnosed 51 patients with and without support from the CAD model. The BI-RADS category, benign or malignant diagnosis, malignancy probability, and diagnosis time during the two evaluation sessions were recorded. In the MRMC evaluation, the average area under the curve (AUC) of the six radiologists with CAD support was slightly higher than that without support (0.896 vs. 0.850, p = 0.0209). Both average sensitivity and specificity increased (p = 0.0253). Under CAD assistance, junior and middle-senior radiologists adjusted the assessment categories of more BI-RADS 4 cases. The diagnosis time with and without CAD support was comparable for five radiologists. The CAD model improved the radiologists’ diagnostic performance for breast masses without prolonging the diagnosis time and assisted in a better BI-RADS assessment, especially for junior radiologists. Frontiers Media S.A. 2021-12-17 /pmc/articles/PMC8719464/ /pubmed/34976817 http://dx.doi.org/10.3389/fonc.2021.773389 Text en Copyright © 2021 He, Li, Zeng, Xu, Liu, Ma, Wei, Zeng, Xu, Wang, Wen, Wu, Feng, Ma, Qin, Lu and Chen https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
He, Zilong
Li, Yue
Zeng, Weixiong
Xu, Weimin
Liu, Jialing
Ma, Xiangyuan
Wei, Jun
Zeng, Hui
Xu, Zeyuan
Wang, Sina
Wen, Chanjuan
Wu, Jiefang
Feng, Chenya
Ma, Mengwei
Qin, Genggeng
Lu, Yao
Chen, Weiguo
Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists’ Diagnosis Performance? An Observer Study
title Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists’ Diagnosis Performance? An Observer Study
title_full Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists’ Diagnosis Performance? An Observer Study
title_fullStr Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists’ Diagnosis Performance? An Observer Study
title_full_unstemmed Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists’ Diagnosis Performance? An Observer Study
title_short Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists’ Diagnosis Performance? An Observer Study
title_sort can a computer-aided mass diagnosis model based on perceptive features learned from quantitative mammography radiology reports improve junior radiologists’ diagnosis performance? an observer study
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719464/
https://www.ncbi.nlm.nih.gov/pubmed/34976817
http://dx.doi.org/10.3389/fonc.2021.773389
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