Constructing the Optimal Classification Model for Benign and Malignant Breast Tumors Based on Multifeature Analysis from Multimodal Images.
The purpose of this study was to fuse conventional radiomic and deep features from digital breast tomosynthesis craniocaudal projection (DBT-CC) and ultrasound (US) images to establish a multimodal benign-malignant classification model and evaluate its clinical value. Data were obtained from a total...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 4; pp. 1386 - 1401 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179554129&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179554129 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2024 vid: 37 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 179554129 179554129 179554129 10.1007/s10278-024-01036-7 179554129 ppf: 1386 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Constructing the Optimal Classification Model for Benign and Malignant Breast Tumors Based on Multifeature Analysis from Multimodal Images. aug: au: Tian, Ronghui Lu, Guoxiu Zhao, Nannan Qian, Wei Ma, He Yang, Wei affil: https://ror.org/03awzbc87 College of Medicine and Biological Information Engineering, Northeastern University, No. 195 Chuangxin Road, Hunnan District, 110819, Shenyang, Liaoning Province, China sug: subj: Prediction Models Breast Neoplasms Ultrasonography Breast Neoplasms Radiography Mammography Methods Breast Neoplasms Classification Sensitivity and Specificity Evaluation Radiomics Human Radiographic Image Enhancement Support Vector Machine Decision Trees Descriptive Statistics Confidence Intervals Machine Learning Algorithms Radiologists Ultrasound Technologists Funding Source ab: The purpose of this study was to fuse conventional radiomic and deep features from digital breast tomosynthesis craniocaudal projection (DBT-CC) and ultrasound (US) images to establish a multimodal benign-malignant classification model and evaluate its clinical value. Data were obtained from a total of 487 patients at three centers, each of whom underwent DBT-CC and US examinations. A total of 322 patients from dataset 1 were used to construct the model, while 165 patients from datasets 2 and 3 formed the prospective testing cohort. Two radiologists with 10–20 years of work experience and three sonographers with 12–20 years of work experience semiautomatically segmented the lesions using ITK-SNAP software while considering the surrounding tissue. For the experiments, we extracted conventional radiomic and deep features from tumors from DBT-CCs and US images using PyRadiomics and Inception-v3. Additionally, we extracted conventional radiomic features from four peritumoral layers around the tumors via DBT-CC and US images. Features were fused separately from the intratumoral and peritumoral regions. For the models, we tested the SVM, KNN, decision tree, RF, XGBoost, and LightGBM classifiers. Early fusion and late fusion (ensemble and stacking) strategies were employed for feature fusion. Using the SVM classifier, stacking fusion of deep features and three peritumoral radiomic features from tumors in DBT-CC and US images achieved the optimal performance, with an accuracy and AUC of 0.953 and 0.959 [CI: 0.886–0.996], a sensitivity and specificity of 0.952 [CI: 0.888–0.992] and 0.955 [0.868–0.985], and a precision of 0.976. The experimental results indicate that the fusion model of deep features and peritumoral radiomic features from tumors in DBT-CC and US images shows promise in differentiating benign and malignant breast tumors. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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