Value of breast MRI omics features and clinical characteristics in Breast Imaging Reporting and Data System (BI-RADS) category 4 breast lesions: an analysis of radiomics-based diagnosis

Cui, Qian; Sun, Liang; Zhang, Yu; Zhao, Zimu; Li, Shuo; Liu, Huan; Ge, Hongwei; Qin, Dongxue; Zhao, Yiping

doi:10.21037/atm-21-5441

Cited by 9 publications

(14 citation statements)

References 31 publications

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“…( 34 ) and Cui et al. ( 33 ) applied MRI-based radiomics models to predict the benignity and malignancy of BI-RADS 4 lesions and yielded a good diagnostic efficiency with the AUC of 0.939 and 0.94, respectively, which were comparable to our results. While in this study, the radiomics were extracted from ultrafast DCE-MRI, which appeared to reduce greatly magnet time.…”

Section: Discussionsupporting

confidence: 89%

Combination of ultrafast dynamic contrast-enhanced MRI-based radiomics and artificial neural network in assessing BI-RADS 4 breast lesions: Potential to avoid unnecessary biopsies

et al. 2023

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ObjectivesTo investigate whether combining radiomics extracted from ultrafast dynamic contrast-enhanced MRI (DCE-MRI) with an artificial neural network enables differentiation of MR BI-RADS 4 breast lesions and thereby avoids false-positive biopsies.MethodsThis retrospective study consecutively included patients with MR BI-RADS 4 lesions. The ultrafast imaging was performed using Differential sub-sampling with cartesian ordering (DISCO) technique and the tenth and fifteenth postcontrast DISCO images (DISCO-10 and DISCO-15) were selected for further analysis. An experienced radiologist used freely available software (FAE) to perform radiomics extraction. After principal component analysis (PCA), a multilayer perceptron artificial neural network (ANN) to distinguish between malignant and benign lesions was developed and tested using a random allocation approach. ROC analysis was performed to evaluate the diagnostic performance.Results173 patients (mean age 43.1 years, range 18–69 years) with 182 lesions (95 benign, 87 malignant) were included. Three types of independent principal components were obtained from the radiomics based on DISCO-10, DISCO-15, and their combination, respectively. In the testing dataset, ANN models showed excellent diagnostic performance with AUC values of 0.915-0.956. Applying the high-sensitivity cutoffs identified in the training dataset demonstrated the potential to reduce the number of unnecessary biopsies by 63.33%-83.33% at the price of one false-negative diagnosis within the testing dataset.ConclusionsThe ultrafast DCE-MRI radiomics-based machine learning model could classify MR BI-RADS category 4 lesions into benign or malignant, highlighting its potential for future application as a new tool for clinical diagnosis.

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Section: Discussionsupporting

confidence: 89%

Combination of ultrafast dynamic contrast-enhanced MRI-based radiomics and artificial neural network in assessing BI-RADS 4 breast lesions: Potential to avoid unnecessary biopsies

et al. 2023

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“…Univariate analysis was performed on the collected clinical and CT image features, and the variables with significant differences were combined with radiomics to establish a multivariate regression model. The above indicators were evaluated and compared with the clinical data and the prediction effect of the radiomics group, and finally, a clinical-radiomics group prediction nomogram was created [ 15 ]. Finally, a decision curve and a calibration curve were drawn to evaluate its predictive performance and calibration.…”

Section: Methodsmentioning

confidence: 99%

[Retracted] Prediction of Changes in Tumor Regression during Radiotherapy for Nasopharyngeal Carcinoma by Using the Computed Tomography‐Based Radiomics

Yang

Mai

et al. 2022

Contrast Media & Molecular Imaging

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This work aimed to explore the application value of computed tomography (CT)-based radiomics in predicting changes in tumor regression during radiotherapy for nasopharyngeal carcinoma. In this work, 144 patients with nasopharyngeal carcinoma who underwent concurrent chemoradiotherapy (CCRT) in our hospital from January 2015 to December 2021 were selected. The patients were divided into a radiosensitive group (79 cases) and an insensitive group (65 cases) according to the tumor volume shrinkage during radiotherapy. The 3D Slicer 4.10.2 software was used to delineate the tumor region of interest (ROI), and a total of 1223 radiomics features were extracted using the radiomics module under the software. After between-group and within-group consistency tests, one-way ANOVA, and LASSO dimensionality reduction, three omics features were finally selected for the establishment of predictive models. At the same time, the age, gender, tumor T stage and N stage, hemoglobin, and albumin of the patients were collected to establish a clinical prediction model. The results showed that compared with logistic regression, decision tree, random forest, and AdaBoost models, the SVM model based on CT radiomics features had the best performance in predicting tumor regression changes during tumor radiotherapy (training group area under the receiver operating characteristic curve (AUC): 0.840 (95% confidence interval (CI): 0.764–0.916); validation group: AUC: 0.810 (95% CI: 0.676–0.944)). Compared with the supported vector machine (SVM) prediction model based on clinical features, the SVM model based on radiomics features had better performance in predicting the change of retraction during tumor radiotherapy (training group: omics feature SVM model AUC: 0.84, clinical feature SVM model: 0.78; validation group: omics feature SVM model AUC: 0.8, clinical feature SVM model: 0.58, P = 0.044). Based on the radiomics characteristics and clinical characteristics of patients, a nomo prediction map was established, and the calibration curve shows good consistency, which can be visualized to assist clinical judgment. In this work, the prediction model composed of CT-based radiomic features combined with clinical features can accurately predict withdrawal changes during tumor radiotherapy, ensuring the accuracy of treatment planning, and minimizing the number of CT scans during radiotherapy.

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“…In this way, it is expected that imaging big data will be used to formulate cancer diagnosis and treatment plans, which not only provides an objective method for assessing tumor heterogeneity, but also adds a new dimension to precision medicine. 8 Radio-omics is mainly used in the imaging of head, neck, and lung diseases. Moreover, some studies believe that the radio-omics model may be helpful in the accurate diagnosis of breast cancer.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Value of Quantitative Features in Multimodal MRI Images to Construct a Radio-Omics Model for Breast Cancer Diagnosis

Zhang,

Lan,

Zhao

2024

BCTT

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Value of breast MRI omics features and clinical characteristics in Breast Imaging Reporting and Data System (BI-RADS) category 4 breast lesions: an analysis of radiomics-based diagnosis

Cited by 9 publications

References 31 publications

Combination of ultrafast dynamic contrast-enhanced MRI-based radiomics and artificial neural network in assessing BI-RADS 4 breast lesions: Potential to avoid unnecessary biopsies

Combination of ultrafast dynamic contrast-enhanced MRI-based radiomics and artificial neural network in assessing BI-RADS 4 breast lesions: Potential to avoid unnecessary biopsies

[Retracted] Prediction of Changes in Tumor Regression during Radiotherapy for Nasopharyngeal Carcinoma by Using the Computed Tomography‐Based Radiomics

Analysis of the Value of Quantitative Features in Multimodal MRI Images to Construct a Radio-Omics Model for Breast Cancer Diagnosis

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