Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures

Abstract

Purpose To construct machine learning models for predicting progression free survival (PFS) and overall survival (OS) with esophageal squamous cell carcinoma (ESCC) patients. Methods 204 ESCC patients were randomly divided into training cohort (n = 143) and test cohort (n = 61) according to the ratio of 7:3. Two radiomics models were constructed by radiomics features, which were selected by LASSO Cox model to predict PFS and OS, respectively. Clinical features were selected by univariate and multivariate Cox proportional hazards model (p < 0.05). Combined radiomics and clinical model was developed by selected clinical and radiomics features. The receiver operating characteristic curve, Kaplan Meier curve and nomogram were used to display the capability of constructed models. Results There were 944 radiomics features extracted based on volume of interest in CT images. There were six radiomics features and seven clinical features for PFS prediction and three radiomics features and three clinical features for OS prediction; The radiomics models showed general performance in training cohort and test cohort for prediction for prediction PFS (AUC, 0.664, 0.676. C-index, 0.65, 0.64) and OS (AUC, 0.634, 0.646.C-index, 0.64, 0.65). The combined models displayed high performance in training cohort and test cohort for prediction PFS (AUC, 0.856, 0.833. C-index, 0.81, 0.79) and OS (AUC, 0.742, 0.768. C-index, 0.72, 0.71). Conclusion We developed combined radiomics and clinical machine learning models with better performance than radiomics or clinical alone, which were used to accurate predict 3 years PFS and OS of non-surgical ESCC patients. The prediction results could provide a reference for clinical decision.

The role of spleen radiomics model for predicting prognosis in esophageal squamous cell carcinoma patients receiving definitive radiotherapy

“…This study developed splenic CT image‐based survival prediction models for patients with ESCC who had undergone dRT. Several previous studies have demonstrated the utility of radiomics in predicting the survival prognosis of patients with EC 9–12,24–26 . Delta‐radiomics encompasses a vast amount of time‐dependent information, allowing the dynamic assessment of complete image changes throughout the treatment period.…”

“…Radiomics modeling based on magnetic resonance images of primary esophageal lesions to predict DFS and OS was established by Chu et al 24 The C‐index values of the training and validation groups were 0.714 and 0.729, respectively, and the OS prediction model scores for the respective groups were 0.730 and 0.712. Cui et al 25 predicted the PFS and OS of patients undergoing radiotherapy based on esophageal CT images, and the C‐index values were 0.81 and 0.79 for the PFS model and 0.72 and 0.71 for the OS model. Nevertheless, all these studies established their models based on images of the primary esophageal lesion, which more often reflect the biological characteristics of the primary tumor itself.…”

Individualized Treatment Decision Model for Inoperable Elderly Esophageal Squamous Cell Carcinoma based on Multi-Modal Data Fusion

“…In previous studies investigating machine learning methods for exploring prognostic risk factors in esophageal cancer, certain investigations focused on extracting mRNA transcriptomic data from public databases like The Cancer Genome Atlas (TCGA) to assess the predictive capability of models for ORR or PFS 21 . Other studies aimed to identify novel biomarkers that could serve as predictors of treatment outcomes 22 . However, these studies were limited to patients undergoing CCRT for esophageal cancer and did not encompass patients receiving different treatment regimens or speci cally focus on elderly patients.…”

Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

“…The latest fashion in AI application is represented by the role of radiomics in the prediction of response to surgical or medical treatment in cancer patients [ 47 , 48 , 49 , 50 , 51 ]. In this way, radiomics can be used to speculate as to the risk category classification of patients and to predict patient overall survival and risk of complication [ 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ].…”