Prediction of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer Patients Using Harmonized Radiomics of Multcenter 18 F-FDG-PET Image.

Autor: Ju, Hye-Min, Yang, Jingyu, Park, Jung-Mi, Choi, Joon-Ho, Song, Hyejin, Kim, Byung-Il, Shin, Ui-Sup, Moon, Sun Mi, Cho, Sangsik, Woo, Sang-Keun
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Zdroj: Cancers; Dec2023, Vol. 15 Issue 23, p5662, 12p
Abstrakt: Simple Summary: Neoadjuvant chemotherapy is the standard treatment for locally advanced rectal cancer. Preoperative chemoradiotherapy yields clinically significant tumor regression; while some patients exhibit a minimal response, others exhibit a complete pathologic response. We developed deep learning and machine learning models to predict chemoradiotherapy response across external tests using multicenter data. The machine learning model, which used harmonized image features extracted from 18F-FDG PET, showed higher performance and demonstrated reproducibility through external tests compared to the deep learning models using 18F-FDG PET images. Our study highlights the feasibility of predicting the chemoradiotherapy response of individual patients using non-invasive and reliable image feature values. We developed machine and deep learning models to predict chemoradiotherapy in rectal cancer using 18F-FDG PET images and harmonized image features extracted from 18F-FDG PET/CT images. Patients diagnosed with pathologic T-stage III rectal cancer with a tumor size > 2 cm were treated with neoadjuvant chemoradiotherapy. Patients with rectal cancer were divided into an internal dataset (n = 116) and an external dataset obtained from a separate institution (n = 40), which were used in the model. AUC was calculated to select image features associated with radiochemotherapy response. In the external test, the machine-learning signature extracted from 18F-FDG PET image features achieved the highest accuracy and AUC value of 0.875 and 0.896. The harmonized first-order radiomics model had a higher efficiency with accuracy and an AUC of 0.771 than the second-order model in the external test. The deep learning model using the balanced dataset showed an accuracy of 0.867 in the internal test but an accuracy of 0.557 in the external test. Deep-learning models using 18F-FDG PET images must be harmonized to demonstrate reproducibility with external data. Harmonized 18F-FDG PET image features as an element of machine learning could help predict chemoradiotherapy responses in external tests with reproducibility. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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