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Multi-Center Evaluation of Artificial Intelligent Imaging And Clinical Models For Predicting Neoadjuvant Chemotherapy Response In Breast Cancer

Authors :
Zhang Zewen
Christina Yang Shi Hui
Veronique Tan Kiak Mien
Arjunan Muthu Kumaran
Richard Yeo Ming Chert
Ghislaine Lee Su-Xin
Cai Yiyu
Madhukumar Preetha
Wong Fuh Yong
Tira J. Tan
Ryan Shea Tan Ying Cong
Lester Leong Chee Hao
Raymond Ng Chee Hui
Tan Hong Qi
Tan Su Ming
Elaine Lim Hsuen
Faye Lynette Lim Wei Tching
Wong Ru Xin
Wen Long Nei
Ong Hiok Hian
Joe Yeong
Sim Yirong
Gideon Ooi Su Kai
Publication Year :
2021
Publisher :
Research Square Platform LLC, 2021.

Abstract

Background:Neoadjuvant chemotherapy (NAC) plays an important role in the management of locally advanced breast cancer. It allows for downstaging of tumours, potentially allowing for breast conservation. NAC also allows for in-vivo testing of the tumours’ 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 deep learning 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 augments the predictive ability of pathological complete response when combined with clinical features. The novel space-resolved radiomics and deep learning radiomics approaches outperformed conventional radiomics techniques.

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....b08b9cdf715e9e3155c982bea6890443
Full Text :
https://doi.org/10.21203/rs.3.rs-838461/v1