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Development and Validation of a Radiomics Nomogram Model for Predicting Postoperative Recurrence in Patients With Esophageal Squamous Cell Cancer Who Achieved pCR After Neoadjuvant Chemoradiotherapy Followed by Surgery.
Qiu, Qingtao; Duan, Jinghao; Deng, Hongbin; Han, Zhujun; Gu, Jiabing; Yue, Ning J; Yin, Yong.
Afiliação
  • Qiu Q; Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
  • Duan J; Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
  • Deng H; Department of Medical Imaging Ultrasonography, Second Affiliated Hospital of Nanjing Medical University, Nanjing, China.
  • Han Z; Department of Radiation Oncology, Yantai Yuhuangding Hospital, Yantai, China.
  • Gu J; Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
  • Yue NJ; Department of Radiation Oncology, The Cancer Institute of New Jersey, New Brunswick, NJ, United States.
  • Yin Y; Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Front Oncol ; 10: 1398, 2020.
Article em En | MEDLINE | ID: mdl-32850451
Background and purpose: Although patients with esophageal squamous cell carcinoma (ESCC) can achieve a pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) followed by surgery, one-third of these patients with a pCR may still experience recurrence. The aim of this study is to develop and validate a predictive model to estimate recurrence-free survival (RFS) in those patients who achieved pCR. Materials and methods: Two hundred six patients with ESCC were enrolled and divided into a training cohort (n = 146) and a validation cohort (n = 60). Radiomic features were extracted from contrast-enhanced computed tomography (CT) images of each patient. Feature reduction was then implemented in two steps, including a multiple segmentation test and least absolute shrinkage and selection operator (LASSO) Cox proportional hazards regression method. A radiomics signature was subsequently constructed and evaluated. For better prediction performance, a clinical nomogram based on clinical risk factors and a nomogram incorporating the radiomics signature and clinical risk factors was built. Finally, the prediction models were further validated by calibration and the clinical usefulness was examined in the validation cohort to determine the optimal prediction model. Results: The radiomics signature was constructed using eight radiomic features and displayed a significant correlation with RFS. The nomogram incorporating the radiomics signature with clinical risk factors achieved optimal performance compared with the radiomics signature (P < 0.001) and clinical nomogram (P < 0.001) in both the training cohort [C-index (95% confidence interval [CI]), 0.746 (0.680-0.812) vs. 0.685 (0.620-0.750) vs. 0.614 (0.538-0.690), respectively] and validation cohort [C-index (95% CI), 0.724 (0.696-0.752) vs. 0.671 (0.624-0.718) vs. 0.629 (0.597-0.661), respectively]. The calibration curve and decision curve analysis revealed that the radiomics nomogram outperformed the other two models. Conclusions: A radiomics nomogram model incorporating radiomics features and clinical factors has been developed and has the improved ability to predict the postoperative recurrence risk in patients with ESCC who achieved pCR after nCRT followed by surgery.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Oncol Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Oncol Ano de publicação: 2020 Tipo de documento: Article