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Comparing deep learning and handcrafted radiomics to predict chemoradiotherapy response for locally advanced cervical cancer using pretreatment MRI.
Jeong, Sungmoon; Yu, Hosang; Park, Shin-Hyung; Woo, Dongwon; Lee, Seoung-Jun; Chong, Gun Oh; Han, Hyung Soo; Kim, Jae-Chul.
Afiliación
  • Jeong S; Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.
  • Yu H; Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, Republic of Korea.
  • Park SH; Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, Republic of Korea.
  • Woo D; Department of Radiation Oncology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea. shinhyungpark@knu.ac.kr.
  • Lee SJ; Department of Radiation Oncology, Kyungpook National University Hospital, Daegu, Republic of Korea. shinhyungpark@knu.ac.kr.
  • Chong GO; Cardiovascular Research Institute, School of Medicine, Kyungpook National University, Daegu, Republic of Korea. shinhyungpark@knu.ac.kr.
  • Han HS; Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, Republic of Korea.
  • Kim JC; Department of Radiation Oncology, Kyungpook National University Hospital, Daegu, Republic of Korea.
Sci Rep ; 14(1): 1180, 2024 01 12.
Article en En | MEDLINE | ID: mdl-38216687
ABSTRACT
Concurrent chemoradiotherapy (CRT) is the standard treatment for locally advanced cervical cancer (LACC), but its responsiveness varies among patients. A reliable tool for predicting CRT responses is necessary for personalized cancer treatment. In this study, we constructed prediction models using handcrafted radiomics (HCR) and deep learning radiomics (DLR) based on pretreatment MRI data to predict CRT response in LACC. Furthermore, we investigated the potential improvement in prediction performance by incorporating clinical factors. A total of 252 LACC patients undergoing curative chemoradiotherapy are included. The patients are randomly divided into two independent groups for the training (167 patients) and test datasets (85 patients). Contrast-enhanced T1- and T2-weighted MR scans are obtained. For HCR analysis, 1890 imaging features are extracted and a support vector machine classifier with a five-fold cross-validation is trained on training dataset to predict CRT response and subsequently validated on test dataset. For DLR analysis, a 3-dimensional convolutional neural network was trained on training dataset and validated on test dataset. In conclusion, both HCR and DLR models could predict CRT responses in patients with LACC. The integration of clinical factors into radiomics prediction models tended to improve performance in HCR analysis. Our findings may contribute to the development of personalized treatment strategies for LACC patients.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias del Cuello Uterino / Aprendizaje Profundo Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias del Cuello Uterino / Aprendizaje Profundo Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido