Adaptive Machine Learning Approach for Importance Evaluation of Multimodal Breast Cancer Radiomic Features.
Breast cancer holds the highest diagnosis rate among female tumors and is the leading cause of death among women. Quantitative analysis of radiological images shows the potential to address several medical challenges, including the early detection and classification of breast tumors. In the P.I.N.K...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 4; pp. 1642 - 1652 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | algorithm 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=179554150&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179554150 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: 179554150 179554150 179554150 10.1007/s10278-024-01064-3 179554150 ppf: 1642 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Adaptive Machine Learning Approach for Importance Evaluation of Multimodal Breast Cancer Radiomic Features. aug: au: Del Corso, Giulio Germanese, Danila Caudai, Claudia Anastasi, Giada Belli, Paolo Formica, Alessia Nicolucci, Alberto Palma, Simone Pascali, Maria Antonietta Pieroni, Stefania Trombadori, Charlotte Colantonio, Sara Franchini, Michela Molinaro, Sabrina affil: Institute of Information Science and Technologies "A. Faedo" (ISTI), National Research Council of Italy (CNR), Pisa, Italy sug: subj: Breast Neoplasms Radiography Breast Neoplasms Classification Radiomics Methods Machine Learning Early Detection of Cancer Human Quantitative Studies Scanners Diagnostic Imaging Comparative Studies Predictive Validity Models, Theoretical Tomography ROC Curve Descriptive Statistics Biopsy Funding Source ab: Breast cancer holds the highest diagnosis rate among female tumors and is the leading cause of death among women. Quantitative analysis of radiological images shows the potential to address several medical challenges, including the early detection and classification of breast tumors. In the P.I.N.K study, 66 women were enrolled. Their paired Automated Breast Volume Scanner (ABVS) and Digital Breast Tomosynthesis (DBT) images, annotated with cancerous lesions, populated the first ABVS+DBT dataset. This enabled not only a radiomic analysis for the malignant vs. benign breast cancer classification, but also the comparison of the two modalities. For this purpose, the models were trained using a leave-one-out nested cross-validation strategy combined with a proper threshold selection approach. This approach provides statistically significant results even with medium-sized data sets. Additionally it provides distributional variables of importance, thus identifying the most informative radiomic features. The analysis proved the predictive capacity of radiomic models even using a reduced number of features. Indeed, from tomography we achieved AUC-ROC 89.9 % using 19 features and 92.1 % using 7 of them; while from ABVS we attained an AUC-ROC of 72.3 % using 22 features and 85.8 % using only 3 features. Although the predictive power of DBT outperforms ABVS, when comparing the predictions at the patient level, only 8.7% of lesions are misclassified by both methods, suggesting a partial complementarity. Notably, promising results (AUC-ROC ABVS-DBT 71.8 % - 74.1 % ) were achieved using non-geometric features, thus opening the way to the integration of virtual biopsy in medical routine. pubtype: Academic Journal doctype: algorithm diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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