Bladder cancer treatment response assessment in CT urography by using deep-learning and radiomics

Di Sun; Lubomir Hadjiiski; Heang-Ping Chan; Richard H. Cohan; Elaine M. Caoili; Ajjai Alva; Kenny H. Cha; Ravi K. Samala; Chuan Zhou

doi:10.1117/12.2654659

7 April 2023 Bladder cancer treatment response assessment in CT urography by using deep-learning and radiomics

Di Sun, Lubomir Hadjiiski, Heang-Ping Chan, Richard H. Cohan, Elaine M. Caoili, Ajjai Alva, Kenny H. Cha, Ravi K. Samala, Chuan Zhou

Author Affiliations +

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 1246533 (2023) https://doi.org/10.1117/12.2654659
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

We are developing deep-learning convolutional neural network (DL-CNN) and radiomics models to assist physicians in treatment response assessment in CT urography of bladder cancer to neoadjuvant chemotherapy (NAC). We collected a total of 264 pre- and post-treatment lesion pairs of 227 patients from University of Michigan hospital with IRB approval. The data were split into 3 sets by case: a training set including 35 complete responders (CRs) (T0 stage after treatment) and 113 non-complete responders (NCRs) (< T0 stage after treatment), a validation set including 5 CRs and 5 NCRs, and an independent test set including 19 CRs and 87 NCRs. The training set was used to train the models to classify CRs and NCRs and the selection of optimal models was guided by the validation set. The selected models were deployed on the test set to generate the likelihood score of CR of each pair. The classifying performance was evaluated by the area under ROC curve (AUC). Hybrid ROIs extracted from the lesions in the pre- and post- treatment scan pairs were used as input to the DL-CNN model. The optimal DL-CNN model achieved an AUC of 0.75 ± 0.06 on the test set. For the radiomics model, the random forest classifier was applied to the features extracted from the pre- and post-treatment lesions. The optimal radiomics model achieved an AUC of 0.76 ± 0.05. A combined DL-CNN and radiomics model increased the AUC to 0.77 ± 0.06. The results indicated the feasibility of using the DL-CNN model and radiomics model for assessing treatment response of bladder cancer.

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Di Sun, Lubomir Hadjiiski, Heang-Ping Chan, Richard H. Cohan, Elaine M. Caoili, Ajjai Alva, Kenny H. Cha, Ravi K. Samala, and Chuan Zhou "Bladder cancer treatment response assessment in CT urography by using deep-learning and radiomics", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 1246533 (7 April 2023); https://doi.org/10.1117/12.2654659

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