A Novel Nomogram Model Based on Cone-Beam CT Radiomics Analysis Technology for Predicting Radiation Pneumonitis in Esophageal Cancer Patients Undergoing Radiotherapy

Abstract

PurposeWe quantitatively analyzed the characteristics of cone-beam computed tomography (CBCT) radiomics in different periods during radiotherapy (RT) and then built a novel nomogram model integrating clinical features and dosimetric parameters for predicting radiation pneumonitis (RP) in patients with esophageal squamous cell carcinoma (ESCC).MethodsAt our institute, a retrospective study was conducted on 96 ESCC patients for whom we had complete clinical feature and dosimetric parameter data. CBCT images of each patient in three different periods of RT were obtained, the images were segmented using both lungs as the region of interest (ROI), and 851 image features were extracted. The least absolute shrinkage selection operator (LASSO) was applied to identify candidate radiomics features, and logistic regression analyses were applied to construct the rad-score. The optimal period for the rad-score, clinical features, and dosimetric parameters were selected to construct the nomogram model and then the receiver operating characteristic (ROC) curve was used to evaluate the prediction capacity of the model. Calibration curves and decision curves were used to demonstrate the discriminatory and clinical benefit ratios, respectively.ResultsThe relative volume of total lung treated with ≥5 Gy (V5), mean lung dose (MLD), and tumor stage were independent predictors of RP and were finally incorporated into the nomogram. When the three time periods were modeled, the first period was better than the others. In the primary cohort, the area under the ROC curve (AUC) was 0.700 (95% confidence interval (CI) 0.568–0.832), and in the independent validation cohort, the AUC was 0.765 (95% CI 0.588–0.941). In the nomogram model that integrates clinical features and dosimetric parameters, the AUC in the primary cohort was 0.836 (95% CI 0.700–0.918), and the AUC in the validation cohort was 0.905 (95% CI 0.799–1.000). The nomogram model exhibits excellent performance. Calibration curves indicate a favorable consistency between the nomogram prediction and the actual outcomes. The decision curve exhibits satisfactory clinical utility.ConclusionThe radiomics model based on early lung CBCT is a potentially valuable tool for predicting RP. V5, MLD, and tumor stage have certain predictive effects for RP. The developed nomogram model has a better prediction ability than any of the other predictors and can be used as a quantitative model to predict RP.

Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma—A Pilot Study

“…Our study established a radiomics-based clinical nomogram with a relatively larger sample size and validated on an external dataset. This was similar to previous findings that nomogram incorporating clinical factors and imaging features were of predictive value for EC patients [ 45 , 46 , 47 ]. Zhang et al [ 45 ] reported that a nomogram based on clinical variables and imaging radiomic features was predictive of lymph node metastases.…”

Hyaluronic Acid Correlates With Bone Metastasis and Predicts Poor Prognosis in Small-Cell Lung Cancer Patients

“…We built a nomogram to identify BM in SCLC to elevate diagnostic efficiency. It provided better discrimination ability than that of nomograms for the other biomarkers of BM (40)(41)(42). The prediction model that we have established is of significant help in minimizing patient risk by predicting SCLC with BM.…”

Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy

“… HF PyRadiomics (V2.0.1) Clinical and classical PET Xie: 2020 [ 58 ] ESCC 2008–2014 CT 120 kV, 180–280 mA, 3 mm CCRT 57 HF IBEX (V1.0β) Clinical factors Hu: 2020 [ 29 ] ESCC 2007–2018 CT 120 kV, 200–400 mA 2.5 mm (inst 1) 5 mm (inst 2) voxel sizes: 1 × 1 × 5 mm3 nCRT 161 (train) 70 (test) HF PyRadiomics (V3.0) No Luo: 2020 [ 41 ] ESCC 2013–2015 CT 120 kV, 120 mAs, 5 mm dCCRT 160 (train) 66 (val.) HF 3DSlicer (V4.10.2) Clinical factors Li: 2020 [ 54 ] ESCC 2012–2019 CT 120 kV/140 kV, 140–300 mA, 5 mm nCRT 121 HF IBEX Clinical factors Zhang: 2020 [ 47 ] EAD 2010–2016 PET/ CT 120 kVp, 20–200 mA surgery alone, neoadjuvant chemotherapy, and nCRT 190 HF Matlab Clinical factors Du: 2020 [ 42 ] …”