Han Liu, Chun-Jie Hou, Jing-Lan Tang, An-Ning Liu, Ke-Feng Lu, Ying Liu, Pei Du
{"title":"良/恶性复杂囊性和实性乳腺结节诊断的预测模型。","authors":"Han Liu, Chun-Jie Hou, Jing-Lan Tang, An-Ning Liu, Ke-Feng Lu, Ying Liu, Pei Du","doi":"10.24976/Discov.Med.202335176.23","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To develop an ultrasound predictive model to differentiate between benign and malignant complex cystic and solid nodules (C-SNs).</p><p><strong>Methods: </strong>A total of 211 patients with complex C-SNs rated as American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) category 4 or 5 on the ultrasound reports were included in the study, from June 2018-2021. Multivariate stepwise logistic regression analysis was used to establish a predictive model, based on clinical and ultrasound features. The diagnostic performance of the model was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve.</p><p><strong>Results: </strong>A total of 109 breast nodules, including 74 benign nodules (67.89%) and 35 malignant nodules (32.11%), were detected by surgical pathology or puncture biopsy. Multivariate analysis showed that the blood flow (BF) of complex C-SNs (<i>p</i> = 0.03), cystic fluid transmission (<i>p</i> = 0.02), longitudinal diameter (<i>p</i> < 0.001), and age (<i>p</i> = 0.03) were independent risk factors for malignant complex cystic and solid breast nodules. The ultrasound model equation was Z=-12.14+2.24×X12+1.97×X20+0.40×X7+0.11×X0; M=ez1+ez (<i>M</i> is the malignancy score, <i>e</i> = 2.72). The area under the curve (AUC) was 0.89, which indicated good predictive utility for the model.</p><p><strong>Conclusions: </strong>A prediction model incorporating major risk factors can predict the malignant C-SNs with accuracy.</p>","PeriodicalId":11379,"journal":{"name":"Discovery medicine","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predictive Model for the Diagnosis of Benign/Malignant Complex Cystic and Solid Breast Nodules.\",\"authors\":\"Han Liu, Chun-Jie Hou, Jing-Lan Tang, An-Ning Liu, Ke-Feng Lu, Ying Liu, Pei Du\",\"doi\":\"10.24976/Discov.Med.202335176.23\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To develop an ultrasound predictive model to differentiate between benign and malignant complex cystic and solid nodules (C-SNs).</p><p><strong>Methods: </strong>A total of 211 patients with complex C-SNs rated as American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) category 4 or 5 on the ultrasound reports were included in the study, from June 2018-2021. Multivariate stepwise logistic regression analysis was used to establish a predictive model, based on clinical and ultrasound features. The diagnostic performance of the model was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve.</p><p><strong>Results: </strong>A total of 109 breast nodules, including 74 benign nodules (67.89%) and 35 malignant nodules (32.11%), were detected by surgical pathology or puncture biopsy. Multivariate analysis showed that the blood flow (BF) of complex C-SNs (<i>p</i> = 0.03), cystic fluid transmission (<i>p</i> = 0.02), longitudinal diameter (<i>p</i> < 0.001), and age (<i>p</i> = 0.03) were independent risk factors for malignant complex cystic and solid breast nodules. The ultrasound model equation was Z=-12.14+2.24×X12+1.97×X20+0.40×X7+0.11×X0; M=ez1+ez (<i>M</i> is the malignancy score, <i>e</i> = 2.72). The area under the curve (AUC) was 0.89, which indicated good predictive utility for the model.</p><p><strong>Conclusions: </strong>A prediction model incorporating major risk factors can predict the malignant C-SNs with accuracy.</p>\",\"PeriodicalId\":11379,\"journal\":{\"name\":\"Discovery medicine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2023-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Discovery medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.24976/Discov.Med.202335176.23\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Discovery medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.24976/Discov.Med.202335176.23","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
Predictive Model for the Diagnosis of Benign/Malignant Complex Cystic and Solid Breast Nodules.
Purpose: To develop an ultrasound predictive model to differentiate between benign and malignant complex cystic and solid nodules (C-SNs).
Methods: A total of 211 patients with complex C-SNs rated as American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) category 4 or 5 on the ultrasound reports were included in the study, from June 2018-2021. Multivariate stepwise logistic regression analysis was used to establish a predictive model, based on clinical and ultrasound features. The diagnostic performance of the model was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve.
Results: A total of 109 breast nodules, including 74 benign nodules (67.89%) and 35 malignant nodules (32.11%), were detected by surgical pathology or puncture biopsy. Multivariate analysis showed that the blood flow (BF) of complex C-SNs (p = 0.03), cystic fluid transmission (p = 0.02), longitudinal diameter (p < 0.001), and age (p = 0.03) were independent risk factors for malignant complex cystic and solid breast nodules. The ultrasound model equation was Z=-12.14+2.24×X12+1.97×X20+0.40×X7+0.11×X0; M=ez1+ez (M is the malignancy score, e = 2.72). The area under the curve (AUC) was 0.89, which indicated good predictive utility for the model.
Conclusions: A prediction model incorporating major risk factors can predict the malignant C-SNs with accuracy.
期刊介绍:
Discovery Medicine publishes novel, provocative ideas and research findings that challenge conventional notions about disease mechanisms, diagnosis, treatment, or any of the life sciences subjects. It publishes cutting-edge, reliable, and authoritative information in all branches of life sciences but primarily in the following areas: Novel therapies and diagnostics (approved or experimental); innovative ideas, research technologies, and translational research that will give rise to the next generation of new drugs and therapies; breakthrough understanding of mechanism of disease, biology, and physiology; and commercialization of biomedical discoveries pertaining to the development of new drugs, therapies, medical devices, and research technology.