Breast cancer classification through multivariate radiomic time series analysis in DCE-MRI sequences

Prinzi, Francesco; Orlando, Alessia; Gaglio, Salvatore; Vitabile, Salvatore

doi:10.1016/j.eswa.2024.123557

Cited by 6 publications

(1 citation statement)

References 76 publications

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“…However, the former method may provide limited information by utilizing only two of multiple DCE-MR phases, while the latter method disregards the temporal information that is crucial for reflecting the directional flow of contrast agent. A recently published paper implemented several classical time series analysis algorithms in DCE-MRI-derived radiomic feature series and achieved an accuracy of 0.852 in breast cancer diagnosis, demonstrating the significance of serial information as well as the feasibility and efficacy of time series analysis [ 46 ]. In our study, we used radiomic features to comprehensively describe DCE-MR image appearance and adopted Catch22 to systematically analyze the dynamics of radiomic feature series.…”

Section: Discussionmentioning

confidence: 99%

Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity

Zhang,

Teng,

Zhang

et al. 2024

Breast Cancer Res

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Background Early prediction of pathological complete response (pCR) is important for deciding appropriate treatment strategies for patients. In this study, we aimed to quantify the dynamic characteristics of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) and investigate its value to improve pCR prediction as well as its association with tumor heterogeneity in breast cancer patients. Methods The DCE-MRI, clinicopathologic record, and full transcriptomic data of 785 breast cancer patients receiving neoadjuvant chemotherapy were retrospectively included from a public dataset. Dynamic features of DCE-MRI were computed from extracted phase-varying radiomic feature series using 22 CAnonical Time-sereis CHaracteristics. Dynamic model and radiomic model were developed by logistic regression using dynamic features and traditional radiomic features respectively. Various combined models with clinical factors were also developed to find the optimal combination and the significance of each components was evaluated. All the models were evaluated in independent test set in terms of area under receiver operating characteristic curve (AUC). To explore the potential underlying biological mechanisms, radiogenomic analysis was implemented on patient subgroups stratified by dynamic model to identify differentially expressed genes (DEGs) and enriched pathways. Results A 10-feature dynamic model and a 4-feature radiomic model were developed (AUC = 0.688, 95%CI: 0.635–0.741 and AUC = 0.650, 95%CI: 0.595–0.705) and tested (AUC = 0.686, 95%CI: 0.594–0.778 and AUC = 0.626, 95%CI: 0.529–0.722), with the dynamic model showing slightly higher AUC (train p = 0.181, test p = 0.222). The combined model of clinical, radiomic, and dynamic achieved the highest AUC in pCR prediction (train: 0.769, 95%CI: 0.722–0.816 and test: 0.762, 95%CI: 0.679–0.845). Compared with clinical-radiomic combined model (train AUC = 0.716, 95%CI: 0.665–0.767 and test AUC = 0.695, 95%CI: 0.656–0.714), adding the dynamic component brought significant improvement in model performance (train p < 0.001 and test p = 0.005). Radiogenomic analysis identified 297 DEGs, including CXCL9, CCL18, and HLA-DPB1 which are known to be associated with breast cancer prognosis or angiogenesis. Gene set enrichment analysis further revealed enrichment of gene ontology terms and pathways related to immune system. Conclusion Dynamic characteristics of DCE-MRI were quantified and used to develop dynamic model for improving pCR prediction in breast cancer patients. The dynamic model was associated with tumor heterogeniety in prognostic-related gene expression and immune-related pathways.

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Section: Discussionmentioning

confidence: 99%