MRI-Based Radiomics Approach Predicts Tumor Recurrence in ER + /HER2 − Early Breast Cancer Patients.
Oncotype Dx Recurrence Score (RS) has been validated in patients with ER + /HER2 − invasive breast carcinoma to estimate patient risk of recurrence and guide the use of adjuvant chemotherapy. We investigated the role of MRI-based radiomics features extracted from the tumor and the peritumoral tissue...
| Published in: | Journal of Digital Imaging Vol. 36; no. 3; pp. 1071 - 1081 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
| Format: | diagnostic images research tables/charts Journal Article |
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Springer Nature
Jun2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164473107&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164473107 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2023 vid: 36 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 164473107 161474432 164473107 164473107 10.1007/s10278-023-00781-5 164473107 ppf: 1071 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: MRI-Based Radiomics Approach Predicts Tumor Recurrence in ER + /HER2 − Early Breast Cancer Patients. aug: au: Chiacchiaretta, Piero Mastrodicasa, Domenico Chiarelli, Antonio Maria Luberti, Riccardo Croce, Pierpaolo Sguera, Mario Torrione, Concetta Marinelli, Camilla Marchetti, Chiara Domenico, Angelucci Cocco, Giulio Di Credico, Angela Russo, Alessandro D'Eramo, Claudia Corvino, Antonio Colasurdo, Marco Sensi, Stefano L. Muzi, Marzia Caulo, Massimo Delli Pizzi, Andrea affil: Advanced Computing Core, Center of Advanced Studies and Technology (CAST), "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy sug: subj: Breast Neoplasms Radiography Neoplasm Recurrence, Local Radiography Magnetic Resonance Imaging Methods Neoplasm Recurrence, Local Risk Factors Cancer Patients Psychosocial Factors HER-2-neu Oncogene Risk Assessment Prediction Models Human Female Adult Middle Age Regression Multivariate Analysis Machine Learning Algorithms ROC Curve Exploratory Research Conceptual Framework Artificial Intelligence Descriptive Statistics Adult: 19-44 years Middle Aged: 45-64 years Female ab: Oncotype Dx Recurrence Score (RS) has been validated in patients with ER + /HER2 − invasive breast carcinoma to estimate patient risk of recurrence and guide the use of adjuvant chemotherapy. We investigated the role of MRI-based radiomics features extracted from the tumor and the peritumoral tissues to predict the risk of tumor recurrence. A total of 62 patients with biopsy-proved ER + /HER2 − breast cancer who underwent pre-treatment MRI and Oncotype Dx were included. An RS > 25 was considered discriminant between low-intermediate and high risk of tumor recurrence. Two readers segmented each tumor. Radiomics features were extracted from the tumor and the peritumoral tissues. Partial least square (PLS) regression was used as the multivariate machine learning algorithm. PLS β-weights of radiomics features included the 5% features with the largest β-weights in magnitude (top 5%). Leave-one-out nested cross-validation (nCV) was used to achieve hyperparameter optimization and evaluate the generalizable performance of the procedure. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The exploratory analysis for the complete dataset revealed an average absolute correlation among features of 0.51. The nCV framework delivered an AUC of 0.76 (p = 1.1∙10−3). When combining "early" and "peak" DCE images of only T or TST, a tendency toward statistical significance was obtained for TST with an AUC of 0.61 (p = 0.05). The 47 features included in the top 5% were balanced between T and TST (23 and 24, respectively). Moreover, 33/47 (70%) were texture-related, and 25/47 (53%) were derived from high-resolution images (1 mm). A radiomics-based machine learning approach shows the potential to accurately predict the recurrence risk in early ER + /HER2 − breast cancer patients. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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