Abstract
BackgroundTo 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.MethodsA 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.ResultsRadiomics 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 < 0.0001). Pearson correlation analysis demonstrated that selected radiomics features were correlated with tumor metabolic factors, such as SUVmean, SUVmax.ConclusionThis study presents integrated clinico-biological-radiological models that can accurately predict the prognosis in colorectal cancer using the preoperative 18F-FDG PET/CT radiomics in colorectal cancer. It is of potential value in assisting the management and decision making for precision treatment in colorectal cancer.Trial registration The retrospectively registered study was approved by the Ethics Committee of Fudan University Shanghai Cancer Center (No. 1909207-14-1910) and the data were analyzed anonymously.
Highlights
Colorectal cancer (CRC) is one of the most commonly diagnosed cancers all over the world, though its epidemiology is different in various regions [1]
We investigated the prognostic value of 18F-FDG PET/CTbased radiomics features using machine learning for CRC patients of all stages and applied the same method on CRC patients with stage III to analyze the differences
The difference in tumor metabolism after adjuvant therapy was avoided; (4) Patients did not receive any chemotherapy, radiation therapy, or molecular targeted therapy before 18F-FDG Positron emission tomography/computed tomography (PET/computed tomography (CT)) scans yet; (5) Patients were not lost to follow-up
Summary
Colorectal cancer (CRC) is one of the most commonly diagnosed cancers all over the world, though its epidemiology is different in various regions [1] It is one of the leading causes of cancer-related mortality despite the advancement in treatment strategies [2, 3]. Tumor Node Metastasis (TNM) staging classification system plays an important role in colorectal cancer prognostication [4,5,6]. Several studies have attempted to provide clinical assistance in the management strategies of colorectal cancer by utilizing important imaging prognostic features, such as depth of tumor spread, presence of malignant lymph nodes, tumor deposits, extramural vascular invasion, and differentiation of mucinous from nonmucinous tumors [15]. 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
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