ArticlePhysics in medicine and biology2011
Computerized three-class classification of MRI-based prognostic markers for breast cancer.
Article in Physics in medicine and biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
22 citing papers in PubMed.
- Sureness of classification of breast cancers as pure ductal carcinomaJournal of medical imaging (Bellingham, Wash.) · 2025Article
- Characterization of Breast Tumors from MR Images Using Radiomics and Machine Learning Approaches.Journal of personalized medicine · 2023Article
- Predicting Malignancy of Breast Imaging Findings Using Quantitative Analysis of Contrast-Enhanced Mammography (CEM).Diagnostics (Basel, Switzerland) · 2023Article
- Invasive ductal breast cancer molecular subtype prediction by MRI radiomic and clinical features based on machine learning.Frontiers in oncology · 2022Article
- Radiomics for Tumor Characterization in Breast Cancer Patients: A Feasibility Study Comparing Contrast-Enhanced Mammography and Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2020Article
- Article
- Radiogenomics of breast cancer using dynamic contrast enhanced MRI and gene expression profiling.Cancer imaging : the official publication of the International Cancer Imaging Society · 2019Article
- Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm.Journal of digital imaging · 2019Article
- Most-enhancing tumor volume by MRI radiomics predicts recurrence-free survival "early on" in neoadjuvant treatment of breast cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2018Article
- DCE-MRI Pharmacokinetic-Based Phenotyping of Invasive Ductal Carcinoma: A Radiomic Study for Prediction of Histological Outcomes.Contrast media & molecular imaging · 2018Article
- MRI texture analysis in differentiating luminal A and luminal B breast cancer molecular subtypes - a feasibility study.BMC medical imaging · 2017Article
- Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes.European radiology experimental · 2017Article
- Article
- Applying a computer-aided scheme to detect a new radiographic image marker for prediction of chemotherapy outcome.BMC medical imaging · 2016Article
- Article
- Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set.NPJ breast cancer · 2016Article
- Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data.Journal of medical imaging (Bellingham, Wash.) · 2015Article
- Using quantitative image analysis to classify axillary lymph nodes on breast MRI: a new application for the Z 0011 Era.European journal of radiology · 2015Review
- Classification of small lesions on dynamic breast MRI: Integrating dimension reduction and out-of-sample extension into CADx methodology.Artificial intelligence in medicine · 2014Article
- Quantitative ultrasound image analysis of axillary lymph node status in breast cancer patients.International journal of computer assisted radiology and surgery · 2013Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
The purpose of this study is to investigate whether computerized analysis using three-class Bayesian artificial neural network (BANN) feature selection and classification can characterize tumor grades (grade 1, grade 2 and grade 3) of breast lesions for prognostic classification on DCE-MRI. A database of 26 IDC grade 1 lesions, 86 IDC grade 2 lesions and 58 IDC grade 3 lesions was collected. The computer automatically segmented the lesions, and kinetic and morphological lesion features were automatically extracted. The discrimination tasks-grade 1 versus grade 3, grade 2 versus grade 3, and grade 1 versus grade 2 lesions-were investigated. Step-wise feature selection was conducted by three-class BANNs. Classification was performed with three-class BANNs using leave-one-lesion-out cross-validation to yield computer-estimated probabilities of being grade 3 lesion, grade 2 lesion and grade 1 lesion. Two-class ROC analysis was used to evaluate the performances. We achieved AUC values of 0.80 ± 0.05, 0.78 ± 0.05 and 0.62 ± 0.05 for grade 1 versus grade 3, grade 1 versus grade 2, and grade 2 versus grade 3, respectively. This study shows the potential for (1) applying three-class BANN feature selection and classification to CADx and (2) expanding the role of DCE-MRI CADx from diagnostic to prognostic classification in distinguishing tumor grades.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.