Correlation of dosimetric factors with the development of symptomatic radiation pneumonitis in stereotactic body radiotherapy

A novel predictor for dosimetry data of lung and the radiation pneumonitis incidence prior to SBRT in lung cancer patients

Normal tissue complication probability (NTCP) models for radiation pneumonitis (RP) in lung cancer patients with stereotactic body radiation therapy (SBRT), which based on dosimetric data from treatment planning, are limited to patients who have already received radiation therapy (RT). This study aims to identify a novel predictive factor for lung dose distribution and RP probability before devising actionable SBRT plans for lung cancer patients. A comprehensive correlation analysis was performed on the clinical and dose parameters of lung cancer patients who underwent SBRT. Linear regression models were utilized to analyze the dosimetric data of lungs. The performance of the regression models was evaluated using mean squared error (MSE) and the coefficient of determination (R2). Correlational analysis revealed that most clinical data exhibited weak correlations with dosimetric data. However, nearly all dosimetric variables showed "strong" or "very strong" correlations with each other, particularly concerning the mean dose of the ipsilateral lung (MI) and the other dosimetric parameters. Further study verified that the lung tumor ratio (LTR) was a significant predictor for MI, which could predict the incidence of RP. As a result, LTR can predict the probability of RP without the need to design an elaborate treatment plan. This study, as the first to offer a comprehensive correlation analysis of dose parameters, explored the specific relationships among them. Significantly, it identified LTR as a novel predictor for both dose parameters and the incidence of RP, without the need to design an elaborate treatment plan.

Improvement of Prediction Performance for Radiation Pneumonitis by Using 3-Dimensional Dosiomic Features

INTRODUCTION

Patients with early non-small-cell lung cancer (NSCLC) have a relatively long survival time after stereotactic body radiation therapy (SBRT). Predicting radiation-induced pneumonia (RP) has important clinical and social implications for improving the quality of life of such patients. This study developed an RP prediction model by using 3-dimensional (3D) dosiomic features. The model can be used to guide radiation therapy to reduce toxicity.

METHODS

Radiomic features were extracted from pre-treatment CT, dose-volume histogram (DVH) parameters and dosiomic features were extracted from the 3D dose distribution of 140 lung cancer patients. Four predictive models: (1) CT; (2) CT + DVH; (3) CT + Rtdose; and (4) Hybrid, CT + DVH + Rtdose, were trained to predict symptomatic RP by extremely randomized trees. Accuracy, sensitivity, specificity, and area under the receiver operator characteristic curve were evaluated.

RESULT

Results showed that the fraction regimen was correlated with symptomatic RP (P < .001). The proposed model achieved promising prediction results. The performance metrics for CT, CT + DVH, CT + Rtdose, and Hybrid were as follows: accuracy: 0.786, 0.821, 0.821, and 0.857; sensitivity: 0.625, 1, 0.875, and 1; specificity: 0.8, 0.565, 0.5, and 0.875; and area under the receiver operator characteristic curve: 0.791, 0.809, 0.907, and 0.920, respectively.

CONCLUSION

Dosiomic features can improve the performance of the predictive model for symptomatic RP compared with that obtained with the CT + DVH model. The model proposed in this study can help radiation oncologists individually predict the incidence rate of RP.

A dynamic nomogram predicting symptomatic pneumonia in patients with lung cancer receiving thoracic radiation

PURPOSE

The most common and potentially fatal side effect of thoracic radiation therapy is radiation pneumonitis (RP). Due to the lack of effective treatments, predicting radiation pneumonitis is crucial. This study aimed to develop a dynamic nomogram to accurately predict symptomatic pneumonitis (RP ≥ 2) following thoracic radiotherapy for lung cancer patients.

METHODS

Data from patients with pathologically diagnosed lung cancer at the Zhongshan People's Hospital Department of Radiotherapy for Thoracic Cancer between January 2017 and June 2022 were retrospectively analyzed. Risk factors for radiation pneumonitis were identified through multivariate logistic regression analysis and utilized to construct a dynamic nomogram. The predictive performance of the nomogram was validated using a bootstrapped concordance index and calibration plots.

RESULTS

Age, smoking index, chemotherapy, and whole lung V5/MLD were identified as significant factors contributing to the accurate prediction of symptomatic pneumonitis. A dynamic nomogram for symptomatic pneumonitis was developed using these risk factors. The area under the curve was 0.89(95% confidence interval 0.83-0.95). The nomogram demonstrated a concordance index of 0.89(95% confidence interval 0.82-0.95) and was well calibrated. Furthermore, the threshold values for high- risk and low- risk were determined to be 154 using the receiver operating curve.

CONCLUSIONS

The developed dynamic nomogram offers an accurate and convenient tool for clinical application in predicting the risk of symptomatic pneumonitis in patients with lung cancer undergoing thoracic radiation.

Integration of dosimetric parameters, clinical factors, and radiomics to predict symptomatic radiation pneumonitis in lung cancer patients undergoing combined immunotherapy and radiotherapy

PURPOSE

This study aimed to combine clinical/dosimetric factors and handcrafted/deep learning radiomic features to establish a predictive model for symptomatic (grade ≥ 2) radiation pneumonitis (RP) in lung cancer patients who received immunotherapy followed by radiotherapy.

MATERIALS AND METHODS

This study retrospectively collected data of 73 lung cancer patients with prior receipt of ICIs who underwent thoracic radiotherapy (TRT). Of these 73 patients, 41 (56.2 %) developed symptomatic grade ≥ 2 RP. RP was defined per multidisciplinary clinician consensus using CTCAE v5.0. Regions of interest (ROIs) (from radiotherapy planning CT images) utilized herein were gross tumor volume (GTV), planning tumor volume (PTV), and PTV-GTV. Clinical/dosimetric (mean lung dose and V5-V30) parameters were collected, and 107 handcrafted radiomic (HCR) features were extracted from each ROI. Deep learning-based radiomic (DLR) features were also extracted based on pre-trained 3D residual network models. HCR models, Fusion HCR model, Fusion HCR + ResNet models, and Fusion HCR + ResNet + Clinical models were built and compared using the receiver operating characteristic (ROC) curve with measurement of the area under the curve (AUC). Five-fold cross-validation was performed to avoid model overfitting.

RESULTS

HCR models across various ROIs and the Fusion HCR model showed good predictive ability with AUCs from 0.740 to 0.808 and 0.740-0.802 in the training and testing cohorts, respectively. The addition of DLR features improved the effectiveness of HCR models (AUCs from 0.826 to 0.898 and 0.821-0.898 in both respective cohorts). The best performing prediction model (HCR + ResNet + Clinical) combined HCR & DLR features with 7 clinical/dosimetric characteristics and achieved an average AUC of 0.936 and 0.946 in both respective cohorts.

CONCLUSIONS

In patients undergoing combined immunotherapy/RT for lung cancer, integrating clinical/dosimetric factors and handcrafted/deep learning radiomic features can offer a high predictive capacity for RP, and merits further prospective validation.

[Stereotactic Body Radiation Therapy for less than 3cm (stage I) and 5cm (stage II) inoperable lung tumors: 10 years experience of Montpellier Cancer Institute]

PURPOSE

Search for predictive factors on survival and local control for less than 3 centimeters (cm) (stage I) and 5cm (stage II) inoperable lung tumors treated by Stereotactic Body Radiation Therapy (SBRT) in a retrospective monocentric study from Montpellier Cancer Institute (ICM) PATIENTS AND METHOD: Every patients treated at ICM for a stage I or II inoperable lung tumors from 2009 to 2019 were analyzed.

RESULTS

One hundred and seventy nine lesions were treated in 176 patients, with a major part (82,7%) in operated due to chronic obstructive pulmonary disease. Median overall survival for all patients was 71,7 months with a 35 months follow-up and the 2 years loco-regional free survival was 94,0 months. Better associated outcomes were stage I (median overall survival 71,7 versus 29,0 months P=0,004 ; HR=2,37 P=0,005), BED≥150Gy (median time-to-progression not reached versus 76,7 months P=0,025), small size of Planning Target Volume (PTV) (HR=0,42 P=0,032 when PTV<15,6 cc). 7,3% of all patients developed radiation pneumonitis.

CONCLUSION

SBRT is associated with an excellent overall survival and a high rate of local control for less than 3cm (stage I) and 5cm (stage II) lung tumors but a low rate of toxicities. For these patients with many comorbidities, BED over 150Gy seems to be associated with a better loco-regional free survival, while cause of death is often other than lung cancer.

Risk analysis of grade ≥ 2 radiation pneumonitis based on radiotherapy timeline in stage III/IV non-small cell lung cancer treated with volumetric modulated arc therapy: a retrospective study

Background

Radiotherapy is an important treatment for patients with stage III/IV non-small cell lung cancer (NSCLC), and due to its high incidence of radiation pneumonitis, it is essential to identify high-risk people as early as possible. The present work investigates the value of the application of different phase data throughout the radiotherapy process in analyzing risk of grade ≥ 2 radiation pneumonitis in stage III/IV NSCLC. Furthermore, the phase data fusion was gradually performed with the radiotherapy timeline to develop a risk assessment model.

Methods

This study retrospectively collected data from 91 stage III/IV NSCLC cases treated with Volumetric modulated arc therapy (VMAT). Patient data were collected according to the radiotherapy timeline for four phases: clinical characteristics, radiomics features, radiation dosimetry parameters, and hematological indexes during treatment. Risk assessment models for single-phase and stepwise fusion phases were established according to logistic regression. In addition, a nomogram of the final fusion phase model and risk classification system was generated. Receiver operating characteristic (ROC), decision curve, and calibration curve analysis were conducted to internally validate the nomogram to analyze its discrimination.

Results

Smoking status, PTV and lung radiomics feature, lung and esophageal dosimetry parameters, and platelets at the third week of radiotherapy were independent risk factors for the four single-phase models. The ROC result analysis of the risk assessment models created by stepwise phase fusion were: (area under curve [AUC]: 0.67,95% confidence interval [CI]: 0.52–0.81), (AUC: 0.82,95%CI: 0.70–0.94), (AUC: 0.90,95%CI: 0.80–1.00), and (AUC:0.90,95%CI: 0.80–1.00), respectively. The nomogram based on the final fusion phase model was validated using calibration curve analysis and decision curve analysis, demonstrating good consistency and clinical utility. The nomogram-based risk classification system could correctly classify cases into three diverse risk groups: low-(ratio:3.6%; 0 < score < 135), intermediate-(ratio:30.7%, 135 < score < 160) and high-risk group (ratio:80.0%, score > 160).

Conclusions

In our study, the risk assessment model makes it easy for physicians to assess the risk of grade ≥ 2 radiation pneumonitis at various phases in the radiotherapy process, and the risk classification system and nomogram identify the patient’s risk level after completion of radiation therapy.

Research Progress on Radiotherapy Combined with Immunotherapy for Associated Pneumonitis During Treatment of Non-Small Cell Lung Cancer

Radiation pneumonitis is a common and serious complication of radiotherapy for thoracic tumours. Although radiotherapy technology is constantly improving, the incidence of radiation pneumonitis is still not low, and severe cases can be life-threatening. Once radiation pneumonitis develops into radiation fibrosis (RF), it will have irreversible consequences, so it is particularly important to prevent the occurrence and development of radiation pneumonitis. Immune checkpoint inhibitors (ICIs) have rapidly altered the treatment landscape for multiple tumour types, providing unprecedented survival in some patients, especially for the treatment of non-small cell lung cancer (NSCLC). However, in addition to its remarkable curative effect, ICls may cause immune-related adverse events. The incidence of checkpoint inhibitor pneumonitis (CIP) is 3% to 5%, and its mortality rate is 10% to 17%. In addition, the incidence of CIP in NSCLC is higher than in other tumour types, reaching 7%–13%. With the increasing use of immune checkpoint inhibitors (ICls) and thoracic radiotherapy in the treatment of patients with NSCLC, ICIs may induce delayed radiation pneumonitis in patients previously treated with radiation therapy, or radiation activation of the systemic immune system increases the toxicity of adverse reactions, which may lead to increased pulmonary toxicity and the incidence of pneumonitis. In this paper, the data about the occurrence of radiation pneumonitis, immune pneumonitis, and combined treatment and the latest related research results will be reviewed.

Pneumonitis after Stereotactic Thoracic Radioimmunotherapy with Checkpoint Inhibitors: Exploration of the Dose-Volume-Effect Correlation

Simple Summary

Stereotactic body radiation therapy (SBRT) is widely applied for treatment of early stage lung cancer and pulmonary metastases. Modern immune checkpoint blockade (ICB) is progressively used in cancer treatment. Pneumonitis is a relevant side effect of both thoracic SBRT and ICB. Currently, it remains unclear whether we can presume the same radiation dose–volume–effect correlations and dose constraints for safe application of SBRT + ICB. We present a dose–volume–effect correlation analysis method using pneumonitis contours and dose–volume histograms (DVH). We showed dosimetric differences for pneumonitis volumes between SBRT + ICB and SBRT alone. We found a large extent of pneumonitis, even bilateral and apart from the radiation field for combined SBRT + ICB. We noticed a shift in pneumonitis DVHs towards lower doses and a trend towards decreased areas under the curve (AUC) for SBRT + ICB. This provides a direction for re-evaluation and potential adaptation of lung dose constraints for combined SBRT and ICB.

Abstract

Thoracic stereotactic body radiation therapy (SBRT) is extensively used in combination with immune checkpoint blockade (ICB). While current evidence suggests that the occurrence of pneumonitis as a side effect of both treatments is not enhanced for the combination, the dose–volume correlation remains unclear. We investigate dose–volume–effect correlations for pneumonitis after combined SBRT + ICB. We analyzed patient clinical characteristics and dosimetric data for 42 data sets for thoracic SBRT with ICB treatment (13) and without (29). Dose volumes were converted into 2 Gy equivalent doses (EQD2), allowing for dosimetric comparison of different fractionation regimes. Pneumonitis volumes were delineated and corresponding DVHs were analyzed. We noticed a shift towards lower doses for combined SBRT + ICB treatment, supported by a trend of smaller areas under the curve (AUC) for SBRT+ ICB (median AUC 1337.37 vs. 5799.10, p = 0.317). We present a DVH-based dose–volume–effect correlation method and observed large pneumonitis volumes, even with bilateral extent in the SBRT + ICB group. We conclude that further studies using this method with enhanced statistical power are needed to clarify whether adjustments of the radiation dose constraints are required to better estimate risks of pneumonitis after the combination of SBRT and ICB.

Synergistic combination of a topologically invariant imaging signature and a biomarker for the accurate prediction of symptomatic radiation pneumonitis before stereotactic ablative radiotherapy for lung cancer: A retrospective analysis

Objectives

We aimed to explore the synergistic combination of a topologically invariant Betti number (BN)-based signature and a biomarker for the accurate prediction of symptomatic (grade ≥2) radiation-induced pneumonitis (RP+) before stereotactic ablative radiotherapy (SABR) for lung cancer.

Methods

A total of 272 SABR cases with early-stage non-small cell lung cancer were chosen for this study. The occurrence of RP+ was predicted using a support vector machine (SVM) model trained with the combined features of the BN-based signature extracted from planning computed tomography (pCT) images and a pretreatment biomarker, serum Krebs von den Lungen-6 (BN+KL-6 model). In all, 242 (20 RP+ and 222 RP–(grade 1)) and 30 cases (8 RP+ and 22 RP–) were used for training and testing the model, respectively. The BN-based features were extracted from BN maps that characterize topologically invariant heterogeneous traits of potential RP+ lung regions on pCT images by applying histogram- and texture-based feature calculations to the maps. The SVM models were built to predict RP+ patients with a BN signature that was constructed based on the least absolute shrinkage and selection operator logistic regression model. The evaluation of the prediction models was performed based on the area under the receiver operating characteristic curves (AUCs) and accuracy in the test. The performance of the BN+KL-6 model was compared to the performance based on the BN, conventional original pCT, and wavelet decomposition (WD) models.

Results

The test AUCs obtained for the BN+KL-6, BN, pCT, and WD models were 0.825, 0.807, 0.642, and 0.545, respectively. The accuracies of the BN+KL-6, BN, pCT, and WD models were found to be 0.724, 0.708, 0.591, and 0.534, respectively.

Conclusion

This study demonstrated the comprehensive performance of the BN+KL-6 model for the prediction of potential RP+ patients before SABR for lung cancer.

Dosimetric Comparisons between Proton Beam Therapy and Modern Photon Radiation Techniques for Stage I Non-Small Cell Lung Cancer According to Tumor Location

Simple Summary

Stereotactic ablative radiotherapy (SABR) is a well-established technique used to treat stage I non-small cell lung cancer (NSCLC). Proton beam therapy (PBT) offers dosimetric advantages over photon SABR techniques by reducing doses to normal organs. Hence, it is believed that PBT is helpful for patients with tumors located centrally in stage I NSCLC. However, the benefits of PBT for other locations, such as peripherally located tumors, have not been well-described. We investigated dosimetric benefits for PBT over modern photon radiation techniques for stage I NSCLC according to tumor locations. A total of 42 patients’ (including tumors that were central (11), peripheral (nine), and close to the chest wall (22)) PBT plans were compared with those of modern photon radiation techniques. In all locations, PBT significantly reduced radiation exposure to the lung and heart. To reduce potential lung and heart toxicities, PBT is ideal, even in the peripherally located stage I NSCLC.

Abstract

Herein, we investigated the dosimetric benefits for proton beam therapy (PBT) over modern photon radiation techniques according to tumor location (central, peripheral, and close to the chest wall) for stage I non-small cell lung cancer (NSCLC) patients. A total of 42 patients with stage I NSCLC were treated with PBT with a total dose of 50–70 Gy in four or 10 fractions considering the risk of treatment-related toxicities. Simulation plans for three-dimensional conformal radiation therapy (3D-CRT), static-field intensity-modulated radiotherapy (IMRT), and volumetric-modulated arc therapy (VMAT) were retrospectively generated using the same treatment volumes as implemented in the PBT plans for these patients. Dosimetric improvements were observed with PBT as compared with all the photon-based radiation techniques with regards to the mean lung dose, lung V5 and V10, mean heart dose, and heart V5 and V10 in all locations. Moreover, lower radiation exposure to the chest wall was observed within PBT for peripherally located and close to the chest wall tumors. All radiotherapy modalities achieved clinically satisfactory treatment plans in the current study. Notably, the usage of PBT resulted in significant dosimetric improvements in the lung and heart over photon-based techniques at all tumor locations, including the periphery, for stage I NSCLC.

Potential Morbidity Reduction for Lung Stereotactic Body Radiation Therapy Using Respiratory Gating

Simple Summary

Lung stereotactic body radiotherapy (SBRT) is the standard of care for early-stage lung cancer and oligometastases. For SBRT, motion has to be considered to avoid misdosage. Respiratory phase gating, meaning to irradiate the target volume only in a predefined gating motion phase window, can be applied to mitigate motion-induced effects. The aim of this study was to exploit the clinical benefit of gating for lung SBRT. For the majority of 14 lung tumor patients and various gating windows, we could prove a reduced dose to normal tissue by gating simulation. A normal tissue complication probability (NTCP) model analysis revealed a major reduction of normal tissue toxicity for moderate gating window sizes. The most beneficial effect of gating was found for those patients with the highest prior toxicity risk. The presented results are useful for personalized risk assessment prior to treatment and may help to select patients and optimal gating windows.

Abstract

We investigated the potential of respiratory gating to mitigate the motion-caused misdosage in lung stereotactic body radiotherapy (SBRT). For fourteen patients with lung tumors, we investigated treatment plans for a gating window (GW) including three breathing phases around the maximum exhalation phase, GW40–60. For a subset of six patients, we also assessed a preceding three-phase GW20–40 and six-phase GW20–70. We analyzed the target volume, lung, esophagus, and heart doses. Using normal tissue complication probability (NTCP) models, we estimated radiation pneumonitis and esophagitis risks. Compared to plans without gating, GW40–60 significantly reduced doses to organs at risk without impairing the tumor doses. On average, the mean lung dose decreased by 0.6 Gy (p < 0.001), treated lung V20Gy by 2.4% (p = 0.003), esophageal dose to 5cc by 2.0 Gy (p = 0.003), and maximum heart dose by 3.2 Gy (p = 0.009). The model-estimated mean risks of 11% for pneumonitis and 12% for esophagitis without gating decreased upon GW40–60 to 7% and 9%, respectively. For the highest-risk patient, gating reduced the pneumonitis risk from 43% to 32%. Gating is most beneficial for patients with high-toxicity risks. Pre-treatment toxicity risk assessment may help optimize patient selection for gating, as well as GW selection for individual patients.

Dosimetric Factors and Radiomics Features Within Different Regions of Interest in Planning CT Images for Improving the Prediction of Radiation Pneumonitis

PURPOSE

This study aimed to establish machine learning models using dosimetric factors and radiomics features within 5 regions of interest (ROIs) in treatment planning computed tomography images to improve the prediction of symptomatic radiation pneumonitis (RP) (grade ≥2).

METHODS AND MATERIALS

This study retrospectively collected data on 79 patients with lung cancer (25 RP ≥2) who underwent chemoradiotherapy between 2015 and 2018. We defined 5 ROIs in planning computed tomography images: gross tumor volume (GTV), planning tumor volume (PTV), PTV-GTV, total lung (TL)-GTV, and TL-PTV. We calculated the mean dose, V5, V10, V20, and V30 within TL-GTV and TL-PTV and the mean dose within the other ROIs. A total of 1924 radiomics features were extracted from all 5 ROIs. We selected the best predictors for classifying 2 groups of patients using a sequential backward elimination support vector machine model. A permutation test was used to assess its statistical significance (P < .05).

RESULTS

The best predictors for symptomatic RP were the combination of 11 radiomics features, 5 dosimetric factors, age, and T stage, achieving an area under the curve (AUC) of 0.94 (95% confidence interval [CI], 0.85-1) (accuracy, 90%; sensitivity, 80% [95% CI, 44%-96%]; specificity, 95% [95% CI, 73%-100%]; P = 8 × 10-4). The clinical characteristics, dosimetric factors, and their combination showed limited predictive power (accuracy, 63.3%, 70%, and 70%; AUC [95% CI]: 0.73 [0.54-0.92], 0.53 [0.31-0.75], and 0.72 [0.51-0.92], respectively). The radiomics features of PTV-GTV and TL-PTV outperformed those of the other ROIs (accuracy, 76.7% and 76.7%; AUC [95% CI]: 0.82 [0.65-0.99] and 0.80 [0.59-1], respectively).

CONCLUSIONS

Combining dosimetric factors and radiomics features within different ROIs can improve the prediction of symptomatic RP. Our results can help physicians adjust the radiation dose distribution of the dose-sensitive lungs and target volumes based on personalized RP estimates.

Multi-institutional dose-segmented dosiomic analysis for predicting radiation pneumonitis after lung stereotactic body radiation therapy

PURPOSE

To predict radiation pneumonitis (RP) grade 2 or worse after lung stereotactic body radiation therapy (SBRT) using dose-based radiomic (dosiomic) features.

METHODS

This multi-institutional study included 247 early-stage nonsmall cell lung cancer patients who underwent SBRT with a prescribed dose of 48-70 Gy at an isocenter between June 2009 and March 2016. Ten dose-volume indices (DVIs) were used, including the mean lung dose, internal target volume size, and percentage of entire lung excluding the internal target volume receiving greater than x Gy (x = 5, 10, 15, 20, 25, 30, 35, and 40). A total of 6,808 dose-segmented dosiomic features, such as shape, first order, and texture features, were extracted from the dose distribution. Patients were randomly partitioned into two groups: model training (70%) and test datasets (30%) over 100 times. Dosiomic features were converted to z-scores (standardized values) with a mean of zero and a standard deviation (SD) of one to put different variables on the same scale. The feature dimension was reduced using the following methods: interfeature correlation based on Spearman's correlation coefficients and feature importance based on a light gradient boosting machine (LightGBM) feature selection function. Three different models were developed using LightGBM as follows: (a) a model with ten DVIs (DVI model), (b) a model with the selected dosiomic features (dosiomic model), and (c) a model with ten DVIs and selected dosiomic features (hybrid model). Suitable hyperparameters were determined by searching the largest average area under the curve (AUC) value in the receiver operating characteristic curve (ROC-AUC) via stratified fivefold cross-validation. Each of the final three models with the closest the ROC-AUC value to the average ROC-AUC value was applied to the test datasets. The classification performance was evaluated by calculating the ROC-AUC, AUC in the precision-recall curve (PR-AUC), accuracy, precision, recall, and f1-score. The entire process was repeated 100 times with randomization, and 100 individual models were developed for each of the three models. Then the mean value and SD for the 100 random iterations were calculated for each performance metric.

RESULTS

Thirty-seven (15.0%) patients developed RP after SBRT. The ROC-AUC and PR-AUC values in the DVI, dosiomic, and hybrid models were 0.660 ± 0.054 and 0.272 ± 0.052, 0.837 ± 0.054 and 0.510 ± 0.115, and 0.846 ± 0.049 and 0.531 ± 0.116, respectively. For each performance metric, the dosiomic and hybrid models outperformed the DVI models (P < 0.05). Texture-based dosiomic feature was confirmed as an effective indicator for predicting RP.

CONCLUSIONS

Our dose-segmented dosiomic approach improved the prediction of the incidence of RP after SBRT.

Stereotactic body radiotherapy for early stage non-small cell lung cancer in patients with subclinical interstitial lung disease

Background

For lung cancer patients with subclinical (untreated and asymptomatic) interstitial lung disease (ILD), there is a lack of relatively safe and effective treatment. Stereotactic body radiation therapy (SBRT) can achieve a high level of tumor control with low toxicity in early-stage non-small cell lung cancer (NSCLC). This study aimed to evaluate the efficacy and toxicity of early stage NSCLC patients with subclinical ILD receiving SBRT.

Methods

A total of 109 early stage NSCLC patients receiving SBRT treatment between December 2011 and August 2016 were reviewed in our institutions; patients with clinical ILD were excluded. The median dose of SBRT was 50 Gy in 5 fractions. The median biologically effective dose (BED; α/β=10) was 100 Gy (range, 72–119 Gy). An experienced radiation oncologist and an experienced radiologist reviewed the presence of subclinical ILD in the CT findings before SBRT. The relationships among the efficacy, radiation-induced lung injury (RILI) and subclinical ILD were explored.

Results

In all, 38 (34.9%) of 109 patients were recognized with subclinical ILD before SBRT, 48 (44.0%) of 109 patients were recognized with grade 2–5 RILI after SBRT, and 18 (47.4%) of 38 patients with subclinical ILD were observed with grade 2–5 RILI. Subclinical ILD was not a significant factor of grade 2–5 RILI (P=0.608); however, 3 patients had extensive RILI, and they all suffered from subclinical ILD. Dosimetric factor of the lungs, such as mean lung dose (MLD) was significantly related with Grade 2–5 RILI in patients with subclinical ILD (P=0.042). The progression-free survival (PFS) rates at 3 years in the subclinical ILD patients and those without ILD were 61.6% and 66.8%, respectively (P=0.266).

Conclusions

Subclinical ILD was not a significant factor for RILI or PFS in early stage NSCLC patients receiving SBRT; however, patients with subclinical ILD receiving SBRT may experience uncommon extensive RILI.