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dc.contributor.authorTan, Hong Qien_US
dc.contributor.authorOng, Hiok Hianen_US
dc.contributor.authorKumaran, Arjunan Muthuen_US
dc.contributor.authorTan, Tira J.en_US
dc.contributor.authorTan, Ryan Ying Congen_US
dc.contributor.authorLee, Ghislaine Su-Xinen_US
dc.contributor.authorLim, Elaine Hsuenen_US
dc.contributor.authorNg, Raymonden_US
dc.contributor.authorYeo, Richard Ming Cherten_US
dc.contributor.authorLim, Faye Lynette Tching Weien_US
dc.contributor.authorZhang, Zewenen_US
dc.contributor.authorYang, Christina Shi Huien_US
dc.contributor.authorWong, Ru Xinen_US
dc.contributor.authorOoi, Gideon Su Kaien_US
dc.contributor.authorChee, Lester Hao Leongen_US
dc.contributor.authorTan, Su Mingen_US
dc.contributor.authorPreetha, Madhukumaren_US
dc.contributor.authorSim, Yirongen_US
dc.contributor.authorTan, Veronique Kiak Mienen_US
dc.contributor.authorYeong, Joeen_US
dc.contributor.authorWong, Fuh Yongen_US
dc.contributor.authorCai, Yiyuen_US
dc.contributor.authorNei, Wen Longen_US
dc.identifier.citationTan, H. Q., Ong, H. H., Kumaran, A. M., Tan, T. J., Tan, R. Y. C., Lee, G. S., Lim, E. H., Ng, R., Yeo, R. M. C., Lim, F. L. T. W., Zhang, Z., Yang, C. S. H., Wong, R. X., Ooi, G. S. K., Chee, L. H. L., Tan, S. M., Preetha, M., Sim, Y., Tan, V. K. M., ...Ai3 (2022). Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer. Breast Cancer Research and Treatment, 193(1), 121-138.
dc.description.abstractBackground: Neoadjuvant chemotherapy (NAC) plays an important role in the management of locally advanced breast cancer. It allows for downstaging of tumors, potentially allowing for breast conservation. NAC also allows for in-vivo testing of the tumors’ response to chemotherapy and provides important prognostic information. There are currently no clearly defined clinical models that incorporate imaging with clinical data to predict response to NAC. Thus, the aim of this work is to develop a predictive AI model based on routine CT imaging and clinical parameters to predict response to NAC. Methods: The CT scans of 324 patients with NAC from multiple centers in Singapore were used in this study. Four different radiomics models were built for predicting pathological complete response (pCR): first two were based on textural features extracted from peri-tumoral and tumoral regions, the third model based on novel space-resolved radiomics which extract feature maps using voxel-based radiomics and the fourth model based on deep learning (DL). Clinical parameters were included to build a final prognostic model. Results: The best performing models were based on space-resolved and DL approaches. Space-resolved radiomics improves the clinical AUCs of pCR prediction from 0.743 (0.650 to 0.831) to 0.775 (0.685 to 0.860) and our DL model improved it from 0.743 (0.650 to 0.831) to 0.772 (0.685 to 0.853). The tumoral radiomics model performs the worst with no improvement of the AUC from the clinical model. The peri-tumoral combined model gives moderate performance with an AUC of 0.765 (0.671 to 0.855). Conclusions: Radiomics features extracted from diagnostic CT augment the predictive ability of pCR when combined with clinical features. The novel space-resolved radiomics and DL radiomics approaches outperformed conventional radiomics techniques.en_US
dc.relation.ispartofBreast Cancer Research and Treatmenten_US
dc.rights© 2022 The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.subjectEngineering::Mechanical engineeringen_US
dc.titleMulti-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast canceren_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.subject.keywordsNeoadjuvant Chemotherapyen_US
dc.subject.keywordsBreast Canceren_US
dc.description.acknowledgementW.L.N. is supported by the National Medical Research Council Fellowship (NMRC/MOH-000166-00).en_US
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