Autor: |
Lilang Lv, Bowen Xin, Yichao Hao, Ziyi Yang, Junyan Xu, Lisheng Wang, Xiuying Wang, Shaoli Song, Xiaomao Guo |
Jazyk: |
angličtina |
Rok vydání: |
2022 |
Předmět: |
|
Zdroj: |
Journal of Translational Medicine, Vol 20, Iss 1, Pp 1-11 (2022) |
Druh dokumentu: |
article |
ISSN: |
1479-5876 |
DOI: |
10.1186/s12967-022-03262-5 |
Popis: |
Abstract Background To develop and validate a survival model with clinico-biological features and 18F- FDG PET/CT radiomic features via machine learning, and for predicting the prognosis from the primary tumor of colorectal cancer. Methods A total of 196 pathologically confirmed patients with colorectal cancer (stage I to stage IV) were included. Preoperative clinical factors, serum tumor markers, and PET/CT radiomic features were included for the recurrence-free survival analysis. For the modeling and validation, patients were randomly divided into the training (n = 137) and validation (n = 59) set, while the 78 stage III patients [training (n = 55), and validation (n = 23)] was divided for the further experiment. After selecting features by the log-rank test and variable-hunting methods, random survival forest (RSF) models were built on the training set to analyze the prognostic value of selected features. The performance of models was measured by C-index and was tested on the validation set with bootstrapping. Feature importance and the Pearson correlation were also analyzed. Results Radiomics signature (containing four PET/CT features and four clinical factors) achieved the best result for prognostic prediction of 196 patients (C-index 0.780, 95% CI 0.634–0.877). Moreover, four features (including two clinical features and two radiomics features) were selected for prognostic prediction of the 78 stage III patients (C-index was 0.820, 95% CI 0.676–0.900). K–M curves of both models significantly stratified low-risk and high-risk groups (P |
Databáze: |
Directory of Open Access Journals |
Externí odkaz: |
|
Nepřihlášeným uživatelům se plný text nezobrazuje |
K zobrazení výsledku je třeba se přihlásit.
|