ArticleUltrasonography (Seoul, Korea)2018
Logistic LASSO regression for the diagnosis of breast cancer using clinical demographic data and the BI-RADS lexicon for ultrasonography.
Article in Ultrasonography (Seoul, Korea), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
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- Dynamic Individualized Prediction of Binary Clinical Outcomes Using Bayesian Generalized Linear Mixed Models With Application in Scleroderma.Statistics in medicine · 2026Article
- Predicting hospital admissions, ICU utilization, and prolonged length of stay among febrile pediatric emergency department patients using incomplete and imbalanced electronic health record (EHR) data strategies.International journal of medical informatics · 2025Article
- Machine learning and metabolomics identify biomarkers associated with the disease extent of ulcerative colitis.Journal of Crohn's & colitis · 2025Article
- An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis.Radiological physics and technology · 2024Review
- Systemic analysis of the prognostic significance and interaction network of miR-26b-3p in cholangiocarcinoma.Applied biochemistry and biotechnology · 2024Article
- Identification of potential cell death-related biomarkers for diagnosis and treatment of osteoporosis.BMC musculoskeletal disorders · 2024Article
- Decisional Conflict Regarding Disease-Modifying Treatment Choices Among Patients with Mid-Stage Relapsing-Remitting Multiple Sclerosis.Patient preference and adherence · 2024Article
- Utilizing an In-silico Approach to Pinpoint Potential Biomarkers for Enhanced Early Detection of Colorectal Cancer.Cancer informatics · 2024Article
- Association between number of medications and hip fractures in Japanese elderly using conditional logistic LASSO regression.Scientific reports · 2023Article
- The use of mammography-based radiomics nomograms for the preoperative prediction of the histological grade of invasive ductal carcinoma.Journal of cancer research and clinical oncology · 2023Article
- Integrated bioinformatics analysis of noncoding RNAs with tumor immune microenvironment in gastric cancer.Scientific reports · 2023Article
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- Developing a resiliency model for survival without major morbidity in preterm infants.Journal of perinatology : official journal of the California Perinatal Association · 2023Article
- A Ribosome-Related Prognostic Signature of Breast Cancer Subtypes Based on Changes in Breast Cancer Patients' Immunological Activity.Medicina (Kaunas, Lithuania) · 2023Article
- Dynamic Nomogram for Predicting Long-Term Survival in Terms of Preoperative and Postoperative Radiotherapy Benefits for Advanced Gastric Cancer.International journal of environmental research and public health · 2023Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
purposeThe aim of this study was to compare the performance of image analysis for predicting breast cancer using two distinct regression models and to evaluate the usefulness of incorporating clinical and demographic data (CDD) into the image analysis in order to improve the diagnosis of breast cancer.
methodsThis study included 139 solid masses from 139 patients who underwent a ultrasonography-guided core biopsy and had available CDD between June 2009 and April 2010. Three breast radiologists retrospectively reviewed 139 breast masses and described each lesion using the Breast Imaging Reporting and Data System (BI-RADS) lexicon. We applied and compared two regression methods-stepwise logistic (SL) regression and logistic least absolute shrinkage and selection operator (LASSO) regression-in which the BI-RADS descriptors and CDD were used as covariates. We investigated the performances of these regression methods and the agreement of radiologists in terms of test misclassification error and the area under the curve (AUC) of the tests.
resultsLogistic LASSO regression was superior (P<0.05) to SL regression, regardless of whether CDD was included in the covariates, in terms of test misclassification errors (0.234 vs. 0.253, without CDD; 0.196 vs. 0.258, with CDD) and AUC (0.785 vs. 0.759, without CDD; 0.873 vs. 0.735, with CDD). However, it was inferior (P<0.05) to the agreement of three radiologists in terms of test misclassification errors (0.234 vs. 0.168, without CDD; 0.196 vs. 0.088, with CDD) and the AUC without CDD (0.785 vs. 0.844, P<0.001), but was comparable to the AUC with CDD (0.873 vs. 0.880, P=0.141).
conclusionLogistic LASSO regression based on BI-RADS descriptors and CDD showed better performance than SL in predicting the presence of breast cancer. The use of CDD as a supplement to the BI-RADS descriptors significantly improved the prediction of breast cancer using logistic LASSO regression.
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