Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer
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 importan...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
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2022
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| Online Access: | https://hdl.handle.net/10356/163001 |
| _version_ | 1826115901602136064 |
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| author | 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 |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering 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 |
| author_sort | Tan, Hong Qi |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T04:02:41Z |
| format | Journal Article |
| id | ntu-10356/163001 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:02:41Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1630012022-11-15T03:11:05Z Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer 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 School of Mechanical and Aerospace Engineering School of Computer Science and Engineering Engineering::Computer science and engineering Engineering::Mechanical engineering Neoadjuvant Chemotherapy Breast Cancer 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. W.L.N. is supported by the National Medical Research Council Fellowship (NMRC/MOH-000166-00). 2022-11-15T03:11:05Z 2022-11-15T03:11:05Z 2022 Journal Article 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 0167-6806 https://hdl.handle.net/10356/163001 10.1007/s10549-022-06521-7 35262831 2-s2.0-85128488053 1 193 121 138 en Breast Cancer Research and Treatment © 2022 The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Engineering::Mechanical engineering Neoadjuvant Chemotherapy Breast Cancer 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 Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer |
| title | Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer |
| title_full | Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer |
| title_fullStr | Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer |
| title_full_unstemmed | Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer |
| title_short | Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer |
| title_sort | multi center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer |
| topic | Engineering::Computer science and engineering Engineering::Mechanical engineering Neoadjuvant Chemotherapy Breast Cancer |
| url | https://hdl.handle.net/10356/163001 |
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