Development and validation of a prognostic nomogram for malignant esophageal fistula based on radiomics and clinical factors

Clinical-radiomics nomogram for the risk prediction of esophageal fistula in patients with esophageal squamous cell carcinoma treated with intensity-modulated radiation therapy or volumetric-modulated arc therapy

Background

Esophageal fistula (EF) is a serious adverse event as a result of radiotherapy in patients with esophageal cancer (EC). We aimed to identify the predictive factors and establish a prediction model of EF in patients with esophageal squamous cell carcinoma (ESCC) who underwent intensity-modulated radiation therapy (IMRT) or volumetric-modulated arc therapy (VMAT).

Methods

Patients with ESCC treated with IMRT or VMAT from January 2013 to December 2020 at Xijing Hospital were retrospectively analyzed. Ultimately, 43 patients with EF and 129 patients without EF were included in the analysis and propensity-score matched in a 1:3 ratio. The clinical characteristics and radiomics features were extracted. Univariate and multivariate stepwise logistic regression analyses were used to determine the risk factors associated with EF.

Results

The median follow-up time was 24.0 months (range, 1.3-104.9 months), and the median overall survival (OS) was 13.1 months in patients with EF. A total of 1,158 radiomics features were extracted, and eight radiomics features were selected for inclusion into a model for predicting EF, with an area under the receiver operating characteristic curve (AUC) value of 0.794. Multivariate analysis showed that tumor length, tumor volume, T stage, lymphocyte rate (LR), and grade IV esophagus stenosis were related to EF, and the AUC value of clinical model for predicting EF was 0.849. The clinical-radiomics model had the best performance in predicting EF with an AUC value of 0.896.

Conclusions

The clinical-radiomics nomogram can predict the risk of EF in ESCC patients and is helpful for the individualized treatment of EC.

Multi-omics and Multi-VOIs to predict esophageal fistula in esophageal cancer patients treated with radiotherapy

OBJECTIVE

This study aimed to develop a prediction model for esophageal fistula (EF) in esophageal cancer (EC) patients treated with intensity-modulated radiation therapy (IMRT), by integrating multi-omics features from multiple volumes of interest (VOIs).

METHODS

We retrospectively analyzed pretreatment planning computed tomographic (CT) images, three-dimensional dose distributions, and clinical factors of 287 EC patients. Nine groups of features from different combination of omics [Radiomics (R), Dosiomics (D), and RD (the combination of R and D)], and VOIs [esophagus (ESO), gross tumor volume (GTV), and EG (the combination of ESO and GTV)] were extracted and separately selected by unsupervised (analysis of variance (ANOVA) and Pearson correlation test) and supervised (Student T test) approaches. The final model performance was evaluated using five metrics: average area under the receiver-operator-characteristics curve (AUC), accuracy, precision, recall, and F1 score.

RESULTS

For multi-omics using RD features, the model performance in EG model shows: AUC, 0.817 ± 0.031; 95% CI 0.805, 0.825; p < 0.001, which is better than single VOI (ESO or GTV).

CONCLUSION

Integrating multi-omics features from multi-VOIs enables better prediction of EF in EC patients treated with IMRT. The incorporation of dosiomics features can enhance the model performance of the prediction.

Radiomics analysis based on CT's greater omental caking for predicting pathological grading of pseudomyxoma peritonei

The objective of this study was to predict the preoperative pathological grading and survival period of Pseudomyxoma peritonei (PMP) by establishing models, including a radiomics model with greater omental caking as the imaging observation index, a clinical model including clinical indexes, and a combined model of these two. A total of 88 PMP patients were selected. Clinical data of patients, including age, sex, preoperative serum tumor markers [CEA, CA125, and CA199], survival time, and preoperative computed tomography (CT) images were analyzed. Three models (clinical model, radiomics model and combined model) were used to predict PMP pathological grading. The models’ diagnostic efficiency was compared and analyzed by building the receiver operating characteristic (ROC) curve. Simultaneously, the impact of PMP’s different pathological grades was evaluated. The results showed that the radiomics model based on the CT’s greater omental caking, an area under the ROC curve ([AUC] = 0.878), and the combined model (AUC = 0.899) had diagnostic power for determining PMP pathological grading. The imaging radiomics model based on CT greater omental caking can be used to predict PMP pathological grading, which is important in the treatment selection method and prognosis assessment.

Development and validation of a prognostic nomogram for malignant esophageal fistula based on radiomics and clinical factors

Background

The current study aimed to comprehensively analyze the clinical prognostic factors of malignant esophageal fistula (MEF). Furthermore, this study sought to establish and validate prognostic nomograms incorporating radiomics and clinical factors to predict overall survival and median survival after fistula for patients with MEF.

Methods

The records of 76 patients with MEF were retrospectively analyzed. A stepwise Cox proportional hazards regression model was employed to screen independent prognostic factors and develop clinical nomograms. Radiomic features were extracted from prefistula CT images and post fistula CT images. Least absolute shrinkage and selection operator (LASSO) regression and Cox regression algorithm was used to filter radiomic features and avoid overfitting. Radiomic signature was a linear combination of optimal features and corresponding coefficients. The joint prognostic nomograms was constructed by radiomic signatures and clinical features. All models were validated by Harrell's concordance index (C‐index), caliberation and bootstrap validation.

Results

For overall survival, age, prealbumin, KPS and interval between diagnosis of esophageal cancer and fistula were identified as independent prognostic factors and incorporated into the clinical nomogram. Age, prealbumin, serum albumin, KPS and neutrophil proportion were selected for the clinical nomogram of post fistula survival. The C‐index of overall survival nomogram was 0.719 (95% CI: 0.645–0.793) and that was 0.722 (95% CI: 0.653–0.791) in the post fistula survival nomogram. The radiomic signature developed by radiomic features of prefistula CT showed a significant correlation with both overall survival and post fistula survival. The C‐index of joint nomogarm for overall survival and post fistula survival was 0.831 (95% CI: 0.757–0.905) and 0.77 (95% CI: 0.686–0.854), respectively. The calibration curve showed the joint nomograms outperformed the clinical ones.

Conclusions

The study presents nomograms incorporating independent clinical risk factors and radiomic signature to predict the prognosis of MEF. This prognostic classification system has the potential to guide therapeutic decisions for patients with malignant esophageal fistulas.