Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163001
Title: Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer
Authors: Tan, Hong Qi
Ong, Hiok Hian
Kumaran, Arjunan Muthu
Tan, Tira J.
Tan, Ryan Ying Cong
Lee, Ghislaine Su-Xin
Lim, Elaine Hsuen
Ng, Raymond
Yeo, Richard Ming Chert
Lim, Faye Lynette Tching Wei
Zhang, Zewen
Yang, Christina Shi Hui
Wong, Ru Xin
Ooi, Gideon Su Kai
Chee, Lester Hao Leong
Tan, Su Ming
Preetha, Madhukumar
Sim, Yirong
Tan, Veronique Kiak Mien
Yeong, Joe
Wong, Fuh Yong
Cai, Yiyu
Nei, Wen Long
JBCR
Ai3
Keywords: Engineering::Computer science and engineering
Engineering::Mechanical engineering
Issue Date: 2022
Source: Tan, 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. https://dx.doi.org/10.1007/s10549-022-06521-7
Journal: Breast Cancer Research and Treatment
Abstract: Background: 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.
URI: https://hdl.handle.net/10356/163001
ISSN: 0167-6806
DOI: 10.1007/s10549-022-06521-7
Rights: © 2022 The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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