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. Clini… Show more
“…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.…”
Section: Discussionmentioning
confidence: 99%
“…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.…”
BackgroundThe spleen plays an important role in systemic antitumor immune response, but whether spleen imaging features have predictive effect for prognosis and immune status was unknown. The aim of this study was to investigate computed tomography (CT)‐based spleen radiomics to predict the prognosis of patients with esophageal squamous cell carcinoma (ESCC) underwent definitive radiotherapy (dRT) and to try to find its association with systemic immunity.MethodsThis retrospective study included 201 ESCC patients who received dRT. Patients were randomly divided into training (n = 142) and validation (n = 59) groups. The pre‐ and delta‐radiomic features were extracted from enhanced CT images. LASSO‐Cox regression was used to select the radiomics signatures most associated with progression‐free survival (PFS) and overall survival (OS). Independent prognostic factors were identified by univariate and multivariate Cox analyses. The ROC curve and C‐index were used to evaluate the predictive performance. Finally, the correlation between spleen radiomics and immune‐related hematological parameters was analyzed by spearman correlation analysis.ResultsIndependent prognostic factors involved TNM stage, treatment regimen, tumor location, pre‐ or delta‐Rad‐score. The AUC of the delta‐radiomics combined model was better than other models in the training and validation groups in predicting PFS (0.829 and 0.875, respectively) and OS (0.857 and 0.835, respectively). Furthermore, some spleen delta‐radiomic features are significantly correlated with delta‐ALC (absolute lymphocyte count) and delta‐NLR (neutrophil‐to‐lymphocyte ratio).ConclusionsSpleen radiomics is expected to be a useful noninvasive tool for predicting the prognosis and evaluating systemic immune status for ESCC patients underwent dRT.
“…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.…”
Section: Discussionmentioning
confidence: 99%
“…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.…”
BackgroundThe spleen plays an important role in systemic antitumor immune response, but whether spleen imaging features have predictive effect for prognosis and immune status was unknown. The aim of this study was to investigate computed tomography (CT)‐based spleen radiomics to predict the prognosis of patients with esophageal squamous cell carcinoma (ESCC) underwent definitive radiotherapy (dRT) and to try to find its association with systemic immunity.MethodsThis retrospective study included 201 ESCC patients who received dRT. Patients were randomly divided into training (n = 142) and validation (n = 59) groups. The pre‐ and delta‐radiomic features were extracted from enhanced CT images. LASSO‐Cox regression was used to select the radiomics signatures most associated with progression‐free survival (PFS) and overall survival (OS). Independent prognostic factors were identified by univariate and multivariate Cox analyses. The ROC curve and C‐index were used to evaluate the predictive performance. Finally, the correlation between spleen radiomics and immune‐related hematological parameters was analyzed by spearman correlation analysis.ResultsIndependent prognostic factors involved TNM stage, treatment regimen, tumor location, pre‐ or delta‐Rad‐score. The AUC of the delta‐radiomics combined model was better than other models in the training and validation groups in predicting PFS (0.829 and 0.875, respectively) and OS (0.857 and 0.835, respectively). Furthermore, some spleen delta‐radiomic features are significantly correlated with delta‐ALC (absolute lymphocyte count) and delta‐NLR (neutrophil‐to‐lymphocyte ratio).ConclusionsSpleen radiomics is expected to be a useful noninvasive tool for predicting the prognosis and evaluating systemic immune status for ESCC patients underwent dRT.
“…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.…”
Background
This research aimed to develop a model for individualized treatment decision-making in inoperable elderly patients with esophageal squamous cell carcinoma (ESCC) using machine learning methods and multi-modal data.
Methods
A total of 169 inoperable elderly ESCC patients aged 65 or older who underwent concurrent chemoradiotherapy (CCRT) or radiotherapy (RT) were included. Multi-task learning models were created using machine learning techniques to analyze multi-modal data, including pre-treatment CT images, clinical information, and blood test results. Nomograms were constructed to predict the objective response rate (ORR) and progression-free survival (PFS) for different treatment strategies. Optimal treatment plans were recommended based on the nomograms. Patients were stratified into high-risk and low-risk groups using the nomograms, and survival analysis was performed using Kaplan-Meier curves.
Results
The identified risk factors influencing ORR were histologic grade (HG), T stage and three radiomic features including original shape elongation, first-order skewness and original shape flatness, while risk factors influencing PFS included BMI, HG and three radiomic features including high gray-level run emphasis, first-order minimum and first-order skewness. These risk factors were incorporated into the nomograms as independent predictive factors. PFS was substantially different between the low-risk group (total score ≤ 110) and the high-risk group (total score > 110) according to Kaplan–Meier curves (P < 0.05).
Conclusions
The developed predictive models for ORR and PFS in inoperable elderly ESCC patients provide valuable insights for predicting treatment efficacy and prognosis. The nomograms enable personalized treatment decision-making and can guide optimal treatment plans for inoperable elderly ESCC patients.
“…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 ].…”
Background: This paper offers an assessment of radiomics tools in the evaluation of intrahepatic cholangiocarcinoma. Methods: The PubMed database was searched for papers published in the English language no earlier than October 2022. Results: We found 236 studies, and 37 satisfied our research criteria. Several studies addressed multidisciplinary topics, especially diagnosis, prognosis, response to therapy, and prediction of staging (TNM) or pathomorphological patterns. In this review, we have covered diagnostic tools developed through machine learning, deep learning, and neural network for the recurrence and prediction of biological characteristics. The majority of the studies were retrospective. Conclusions: It is possible to conclude that many performing models have been developed to make differential diagnosis easier for radiologists to predict recurrence and genomic patterns. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.
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