A Nomogram for Predicting the Risk of Radiotherapy-Related Esophageal Fistula in Esophageal Cancer Patients

Prediction of esophageal fistula in radiotherapy/chemoradiotherapy for patients with advanced esophageal cancer by a clinical-deep learning radiomics model : Prediction of esophageal fistula in radiotherapy/chemoradiotherapy patients

BACKGROUND

Esophageal fistula (EF), a rare and potentially fatal complication, can be better managed with predictive models for personalized treatment plans in esophageal cancers. We aim to develop a clinical-deep learning radiomics model for effectively predicting the occurrence of EF.

METHODS

The study involved esophageal cancer patients undergoing radiotherapy or chemoradiotherapy. Arterial phase enhanced CT images were used to extract handcrafted and deep learning radiomic features. Along with clinical information, a 3-step feature selection method (statistical tests, Least Absolute Shrinkage and Selection Operator, and Recursive Feature Elimination) was used to identify five feature sets in training cohort for constructing random forest EF prediction models. Model performance was compared and validated in both retrospective and prospective test cohorts.

RESULTS

One hundred seventy five patients (122 in training and 53 in test cohort)were retrospectively collected from April 2018 to June 2022. An additional 27 patients were enrolled as a prospective test cohort from June 2022 to December 2023. Post-selection in the training cohort, five feature sets were used for model construction: clinical, handcrafted radiomic, deep learning radiomic, clinical-handcrafted radiomic, and clinical-deep learning radiomic. The clinical-deep learning radiomic model excelled with AUC of 0.89 (95% Confidence Interval: 0.83-0.95) in the training cohort, 0.81 (0.65-0.94) in the test cohort, and 0.85 (0.71-0.97) in the prospective test cohort. Brier-score and calibration curve analyses validated its predictive ability.

CONCLUSIONS

The clinical-deep learning radiomic model can effectively predict EF in patients with advanced esophageal cancer undergoing radiotherapy or chemoradiotherapy.

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.

Risk factors for aortoesophageal fistula in cT4b esophageal squamous cell carcinoma after definitive radiation therapy

Background

Esophageal fistula (EF) is a serious complication in patients with cT4b esophageal squamous cell carcinoma (ESCC) with adjacent organ involvement. Among EFs, aortoesophageal fistula (AEF), forming a fistula with the aorta, could be fatal. This study aimed to identify the risk factors for AEF in patients with cT4b ESCC with obvious or suspected aortic invasion who underwent definitive radiotherapy (DRT).

Methods

Forty-four patients with cT4b ESCC with obvious or suspected invasion to the aorta who underwent DRT were included. Blood tests and computed tomography (CT) findings before and after DRT were compared between the patients with and without AEF to identify the potential risk factors for AEF.

Results

Nine patients (20.5%) developed AEF after DRT. Comparing between patients with and without AEF, pre-DRT white blood cell counts and post-DRT C-reactive protein (CRP) levels were significantly higher in patients with AEF. Furthermore, pre-DRT CT findings were similar between the two groups. However, post-DRT CT findings demonstrated significantly larger picus angle and lower esophageal wall thickness on the aortic side in patients with AEF. Multivariate analysis identified elevated post-DRT CRP levels [<3.3 versus ≥3.3 mg/dL; odds ratio (OR): 30.7; 95% confidence interval (CI): 2.92-323.2; P=0.004] and esophageal wall thinning on post-DRT CT scans (>6 versus ≤6 mm; OR: 13.2; 95% CI: 1.24-140.1; P=0.033) as risk factors for AEF.

Conclusions

We found that post-DRT esophageal wall thinning on the aortic side, as observed on CT scans, and elevated CRP levels were predictive factors for AEF in patients with cT4b ESCC with obvious or suspected invasion to the aorta.