Potential benefits of volumetric modulated arc therapy to reduce the incidence of ≥ grade 2 radiation pneumonitis in radiotherapy for locally advanced non-small cell lung cancer patients.

BACKGROUND: The use of volumetric modulated arc therapy is gradually widespread for locally advanced non-small cell lung cancer. The purpose of this study was to identify the factors that caused ≥ grade 2 radiation pneumonitis and evaluate the impact of using volumetric modulated arc therapy on the incidence of ≥ grade 2 radiation pneumonitis by comparing three-dimensional conformal radiation therapy. METHODS: We retrospectively evaluated 124 patients who underwent radical radiotherapy for locally advanced non-small cell lung cancer in our institution between 2008 and 2019. The following variables were analysed to detect the factors that affected ≥ grade 2 radiation pneumonitis; age, sex, the presence of interstitial lung disease, pulmonary emphysema, tumour location, stage, PTV/lung volume, lung V20Gy, total dose, concurrent chemoradiotherapy, adjuvant immune checkpoint inhibitor, radiotherapy method. Radiation pneumonitis was evaluated using the common terminology criteria for adverse events (version 5.0). RESULTS: A total of 84 patients underwent three-dimensional conformal radiation therapy (3D-CRT group) and 40 patients underwent volumetric modulated arc therapy (VMAT group). The cumulative incidence of ≥ grade 2 radiation pneumonitis at 12 months was significantly lower in the VMAT group than in the 3D-CRT group (25% vs. 49.1%). The use of volumetric modulated arc therapy was a significant factor for ≥ grade 2 radiation pneumonitis (HR:0.32, 95% CI: 0.15-0.65, P = 0.0017) in addition to lung V20Gy (≥ 24%, HR:5.72 (95% CI: 2.87-11.4), P < 0.0001) and total dose (≥ 70 Gy, HR:2.64 (95% CI: 1.39-5.03), P = 0.0031) even after adjustment by multivariate analysis. CONCLUSIONS: We identified factors associated with ≥ grade 2 radiation pneumonitis in radiotherapy for patients with locally advanced non-small cell lung cancer. Volumetric modulated arc therapy has potential benefits to reduce the risk of ≥ grade 2 radiation pneumonitis.

A nomogram based on pretreatment radiomics and dosiomics features for predicting overall survival associated with esophageal squamous cell cancer.

OBJECTIVES: To propose a nomogram-based survival prediction model for esophageal squamous cell carcinoma (ESCC) treated with definitive chemoradiotherapy using pretreatment computed tomography (CT), positron emission tomography (PET) radiomics and dosiomics features, and common clinical factors. METHODS: Radiomics and dosiomics features were extracted from CT and PET images and dose distribution from 2 institutions. The least absolute shrinkage and selection operator (LASSO) with logistic regression was used to select radiomics and dosiomics features by calculating the radiomics and dosiomics scores (Rad-score and Dos-score), respectively, in the training model. The model was trained in 81 patients and validated in 35 patients at Center 1 using 10-fold cross validation. The model was externally tested in 26 patients at Center 2. The predictive clinical factors, Rad-score, and Dos-score were identified to develop a nomogram model. RESULTS: Using LASSO Cox regression, 13, 11, and 19 CT, PET-based radiomics, and dosiomics features, respectively, were selected. The clinical factors T-stage, N-stage, and clinical stage were selected as significant prognostic factors by univariate Cox regression. In the external validation cohort, the C-index of the combined model of CT-based radiomics, PET-based radiomics, and dosiomics features with clinical factors were 0.74, 0.82, and 0.92, respectively. Significant differences in overall survival (OS) in the combined model of CT-based radiomics, PET-based radiomics, and dosiomics features with clinical factors were observed between the high- and low-risk groups (P = 0.019, 0.038, and 0.014, respectively). CONCLUSION: The dosiomics features have a better predicter for OS than CT- and PET-based radiomics features in ESCC treated with radiotherapy. CLINICAL RELEVANCE STATEMENT: The current study predicted the overall survival for esophageal squamous cell carcinoma patients treated with definitive chemoradiotherapy. The dosiomics features have a better predicter for overall survival than CT- and PET-based radiomics features.

Image masking using convolutional networks improves performance classification of radiation pneumonitis for non-small cell lung cancer.

Radiation pneumonitis (RP) is a serious side effect of radiotherapy in patients with locally advanced non-small-cell lung cancer (NSCLC). Image cropping reduces training noise and may improve classification accuracy. This study proposes a prediction model for RP grade ≥ 2 using a convolutional neural network (CNN) model with image cropping. The 3D computed tomography (CT) images cropped in the whole-body, normal lung (nLung), and nLung regions overlapping the region over 20 Gy (nLung∩20 Gy) used in treatment planning were used as the input data. The output classifies patients as RP grade < 2 or RP grade ≥ 2. The sensitivity, specificity, accuracy, and area under the curve (AUC) were evaluated using the receiver operating characteristic curve (ROC). The accuracy, specificity, sensitivity, and AUC were 53.9%, 80.0%, 25.5%, and 0.58, respectively, for the whole-body method, and 60.0%, 81.7%, 36.4%, and 0.64, respectively, for the nLung method. For the nLung∩20 Gy method, the accuracy, specificity, sensitivity, and AUC improved to 75.7%, 80.0%, 70.9%, and 0.84, respectively. The CNN model, in which the input image is segmented in the normal lung considering the dose distribution, can help predict an RP grade ≥ 2 for NSCLC patients after definitive radiotherapy.

Prognostic Value of Radiomics Analysis of Skeletal Muscle After Radical Irradiation of Esophageal Cancer.

BACKGROUND/AIM: Sarcopenia is an independent survival predictor in several tumor types. Computed tomography (CT) is the standard measurement for body composition assessment. Radiomics analysis of CT images allows for the precise evaluation of skeletal muscles. This study aimed to construct a prognostic survival model for patients with esophageal cancer who underwent radical irradiation using skeletal muscle radiomics. PATIENTS AND METHODS: We retrospectively identified patients with esophageal cancer who underwent radical irradiation at our institution between April 2008 and December 2017. Skeletal muscle radiomics were extracted from an axial pretreatment CT at the third lumbar vertebral level. The prediction model was constructed using machine learning coupled with the least absolute shrinkage and selection operator (LASSO). The predictive nomogram model comprised clinical factors with radiomic features. Three prediction models were created: clinical, radiomics, and combined. RESULTS: Ninety-eight patients with 98 esophageal cancers were enrolled in this study. The median observation period was 57.5 months (range=1-98 months). Thirty-five radiomics features were selected by LASSO analysis, and a prediction model was constructed using training and validation data. The average of the accuracy, specificity, sensitivity, and area under the concentration-time curve for predicting survival in esophageal cancer in the combined model were 75%, 92%, and 0.86, respectively. The C-indices of the clinical, radiomics, and combined models were 0.76, 0.80, and 0.88, respectively. CONCLUSION: A prediction model with skeletal muscle radiomics and clinical data might help determine survival outcomes in patients with esophageal cancer treated with radical radiotherapy.

Prediction of radiation pneumonitis after definitive radiotherapy for locally advanced non-small cell lung cancer using multi-region radiomics analysis.

To predict grade ≥ 2 radiation pneumonitis (RP) in patients with locally advanced non-small cell lung cancer (NSCLC) using multi-region radiomics analysis. Data from 77 patients with NSCLC who underwent definitive radiotherapy between 2008 and 2018 were analyzed. Radiomic feature extraction from the whole lung (whole-lung radiomics analysis) and imaging- and dosimetric-based segmentation (multi-region radiomics analysis) were performed. Patients with RP grade ≥ 2 or < 2 were classified. Predictors were selected with least absolute shrinkage and selection operator logistic regression and the model was built with neural network classifiers. A total of 49,383 radiomics features per patient image were extracted from the radiotherapy planning computed tomography. We identified 4 features and 13 radiomics features in the whole-lung and multi-region radiomics analysis for classification, respectively. The accuracy and area under the curve (AUC) without the synthetic minority over-sampling technique (SMOTE) were 60.8%, and 0.62 for whole-lung and 80.1%, and 0.84 for multi-region radiomics analysis. These were improved 1.7% for whole-lung and 2.1% for multi-region radiomics analysis with the SMOTE. The developed multi-region radiomics analysis can help predict grade ≥ 2 RP. The radiomics features in the median- and high-dose regions, and the local intensity roughness and variation were important factors in predicting grade ≥ 2 RP.