CNN-based tumor progression prediction after thermal ablation with CT imaging

Marjaneh Taghavi; Monique Maas; Femke C. R. Staal; Regina G. H. Beets-Tan; Sean Benson

doi:10.1117/12.2611516

4 April 2022 CNN-based tumor progression prediction after thermal ablation with CT imaging

Marjaneh Taghavi, Monique Maas, Femke C. R. Staal, Regina G. H. Beets-Tan, Sean Benson

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 1203335 (2022) https://doi.org/10.1117/12.2611516
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Local tumor progression (LTP) after ablation treatment in colorectal liver metastases (CRLM) has a detrimental impact on the outcome for patients with advanced colorectal cancer. The ability to predict or even identify LTP at the earliest opportunity is critical to personalise follow-up and subsequent treatment. We present a study of 79 patients (120 lesions) with CRLM who underwent thermal ablation treatment, in which a multi-channel model that identifies patients with LTP from baseline and restaging computed tomography (CT) scans. The study made use of transfer learning strategies in association with a 3-fold cross validation. The area under the receiveroperating characteristic curve was found to be 0.72 (95% confidence interval [CI]: 0.64-0.79), demonstrating that the model was able to generate prognostic features from the CT images.

Citation Download Citation

Marjaneh Taghavi, Monique Maas, Femke C. R. Staal, Regina G. H. Beets-Tan, and Sean Benson "CNN-based tumor progression prediction after thermal ablation with CT imaging", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 1203335 (4 April 2022); https://doi.org/10.1117/12.2611516

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