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.

Correlation between AI-based CT organ features and normal lung dose in adjuvant radiotherapy following breast-conserving surgery: a multicenter prospective study.

BACKGROUND: Radiation pneumonitis (RP) is one of the common side effects after adjuvant radiotherapy in breast cancer. Irradiation dose to normal lung was related to RP. We aimed to propose an organ features based on deep learning (DL) model and to evaluate the correlation between normal lung dose and organ features. METHODS: Patients with pathology-confirmed invasive breast cancer treated with adjuvant radiotherapy following breast-conserving surgery in four centers were included. From 2019 to 2020, a total of 230 patients from four nationwide centers in China were screened, of whom 208 were enrolled for DL modeling, and 22 patients from another three centers formed the external testing cohort. The subset of the internal testing cohort (n = 42) formed the internal correlation testing cohort for correlation analysis. The outline of the ipsilateral breast was marked with a lead wire before the scanning. Then, a DL model based on the High-Resolution Net was developed to detect the lead wire marker in each slice of the CT images automatically, and an in-house model was applied to segment the ipsilateral lung region. The mean and standard deviation of the distance error, the average precision, and average recall were used to measure the performance of the lead wire marker detection model. Based on these DL model results, we proposed an organ feature, and the Pearson correlation coefficient was calculated between the proposed organ feature and ipsilateral lung volume receiving 20 Gray (Gy) or more (V20). RESULTS: For the lead wire marker detection model, the mean and standard deviation of the distance error, AP (5 mm) and AR (5 mm) reached 3.415 ± 4.529, 0.860, 0.883, and 4.189 ± 8.390, 0.848, 0.830 in the internal testing cohort and external testing cohort, respectively. The proposed organ feature calculated from the detected marker correlated with ipsilateral lung V20 (Pearson correlation coefficient, 0.542 with p < 0.001 in the internal correlation testing cohort and 0.554 with p = 0.008 in the external testing cohort). CONCLUSIONS: The proposed artificial Intelligence-based CT organ feature was correlated with normal lung dose in adjuvant radiotherapy following breast-conserving surgery in patients with invasive breast cancer. TRIAL REGISTRATION: NCT05609058 (08/11/2022).

Radiation Dermatitis and Pneumonitis in Patients Irradiated for Breast Cancer.

BACKGROUND/AIM: Adjuvant radiotherapy (RT) for breast cancer can be associated with acute dermatitis (ARD) and pneumonitis (RP). Prevalence and risk factors were characterized. PATIENTS AND METHODS: This study included 489 breast cancer patients receiving adjuvant RT with conventional fractionation (CF) ± sequential or simultaneous integrated boost, or hypo-fractionation ± sequential boost. RT-regimen and 15 characteristics were investigated for grade ≥2 ARD and RP. RESULTS: Prevalence of grade ≥2 ARD and RP was 25.3% and 2.5%, respectively. On univariate analyses, ARD was significantly associated with CF and radiation boost (p<0.0001), age ≤60 years (p=0.008), Ki-67 ≥15% (p=0.012), and systemic treatment (p=0.002). On multivariate analysis, RT-regimen (p<0.0001) and age (p=0.009) were associated with ARD. Chronic inflammatory disease was significantly associated with RP on univariate (p=0.007) and multivariate (p=0.016) analyses. CONCLUSION: Risk factors for grade ≥2 ARD and RP were determined that may help identify patients who require closer monitoring during and after RT.

Analysis of the risk factors of radiation pneumonitis and the predictive ability of dosiomics in non-small-cell lung cancer.

Purpose: This prospective study investigated the incidence of radiation pneumonitis (RP) after immunotherapy followed by radiotherapy in non-small-cell lung cancer, analyzed the risk factors for RP, and explored the predictive performance of dosimetry and dosiomics. Methods & materials: Risk factors for grade ≥2 RP were calculated by using a logistic regression model. Predictive performance was compared on the basis of area under the curve values. Results: Grade ≥2 RP occurred in 16 cases (26.7%). The AUC values of V5 Gy, gray-level dependence matrix-small dependence high gray-level emphasis (GLDM-SDHGLE) and combined features were 0.685, 0.724 and 0.734, respectively. Conclusion: Smoking history, bilateral lung V5 Gy and GLDM-SDHGLE were independent risk factors for RP. Dosiomics can effectively predict RP.

Radiation pneumonia predictive model for radiotherapy in esophageal carcinoma patients.

BACKGROUND: The machine learning models with dose factors and the deep learning models with dose distribution matrix have been used to building lung toxics models for radiotherapy and achieve promising results. However, few studies have integrated clinical features into deep learning models. This study aimed to explore the role of three-dimension dose distribution and clinical features in predicting radiation pneumonitis (RP) in esophageal cancer patients after radiotherapy and designed a new hybrid deep learning network to predict the incidence of RP. METHODS: A total of 105 esophageal cancer patients previously treated with radiotherapy were enrolled in this study. The three-dimension (3D) dose distributions within the lung were extracted from the treatment planning system, converted into 3D matrixes and used as inputs to predict RP with ResNet. In total, 15 clinical factors were normalized and converted into one-dimension (1D) matrixes. A new prediction model (HybridNet) was then built based on a hybrid deep learning network, which combined 3D ResNet18 and 1D convolution layers. Machine learning-based prediction models, which use the traditional dosiomic factors with and without the clinical factors as inputs, were also constructed and their predictive performance compared with that of HybridNet using tenfold cross validation. Accuracy and area under the receiver operator characteristic curve (AUC) were used to evaluate the model effect. DeLong test was used to compare the prediction results of the models. RESULTS: The deep learning-based model achieved superior prediction results compared with machine learning-based models. ResNet performed best in the group that only considered dose factors (accuracy, 0.78 ± 0.05; AUC, 0.82 ± 0.25), whereas HybridNet performed best in the group that considered both dose factors and clinical factors (accuracy, 0.85 ± 0.13; AUC, 0.91 ± 0.09). HybridNet had higher accuracy than that of Resnet (p = 0.009). CONCLUSION: Based on prediction results, the proposed HybridNet model could predict RP in esophageal cancer patients after radiotherapy with significantly higher accuracy, suggesting its potential as a useful tool for clinical decision-making. This study demonstrated that the information in dose distribution is worth further exploration, and combining multiple types of features contributes to predict radiotherapy response.

Ipsilateral lung dose as a correlative measure for radiation pneumonitis in patients treated with definitive concurrent radiochemotherapy.

Objective: Mean lung dose (MLD) and percent of total lung (TL) volume that receive a dose greater than 20 Gy (V20) have been the most validated parameters in the prediction of radiation pneumonitis (RP). However, these parameters present mean values of TL parenchyma and predict the right and the left lung as a unique functional organ unit, not take into account the difference in function and dose density between the lungs. Furthermore, there have been very limited data evaluating ipsilateral lung dosimetric constraints in addition to TL parameters to predict RP in non-small cell lung cancer (NSCLC) patients treated with radiochemotherapy (RCT). Methods: Between 2010 and 2017, clinical-radiological findings of NSCLC patients treated with RCT were evaluated in terms of RP, retrospectively. MLD, V20, and V30 values of ipsilateral lung were assessed from dose-volume histogram and registered. The primary endpoint was to assess the relation between ipsilateral lung dose constraints and RP risk. Results: There were 75 patients. There was ≥Grade 2 RP in 33 cases (%44). In univariate analysis, ipsilateral MLD, ipsilateral V20, ipsilateral V30, and TL V30 were found to be significant. Ipsilateral MLD and PTV were found to be the independent risk factors for RP. Cutoff values for RP risk were determined as 18Gy, 35%, and 28% for ipsilateral MLD, ipsilateral V20, and ipsilateral V30, respectively. Predictive values for ipsilateral MLD and ipsilateral V20 were higher than TL. Conclusions: In NSCLC patients treated with RCT, MLD, V20, and V30 values of ipsilateral lung parameters might increase the predictability of RP risk in addition to TL parameters. Advances in Knowledge: Cutoff values for RP risk were determined as 18Gy, 35%, and 28% for ipsilateral MLD, ipsilateral V20, and ipsilateral V30, respectively. Predictive values for ipsilateral MLD and ipsilateral V20 were higher than TL.

Developing a Prediction Model for Radiation Pneumonitis in Lung Cancer Patients Treated With Stereotactic Body Radiation Therapy Combined With Clinical, Dosimetric Factors, and Laboratory Biomarkers.

BACKGROUND: The study aims to identify the risk factors and develop a model for predicting grade ≥2 radiation pneumonitis (RP) for lung cancer patients treated with stereotactic body radiation therapy (SBRT). MATERIALS AND METHODS: Clinical data, dosimetric data, and laboratory biomarkers from 186 patients treated with lung SBRT were collected. Univariate and multivariate logistic regression were performed to determine the predictive factors for grade ≥2 RP. Three models were developed by using the clinical, dosimetric, and combined factors, respectively. RESULTS: With a median follow-up of 36 months, grade ≥2 RP was recorded in 13.4% of patients. On univariate logistic regression analysis, clinical factors of age and lung volume, dosimetric factors of treatment durations, fractional dose and V10, and laboratory biomarkers of neutrophil, PLT, PLR, and Hb levels were significantly associated with grade ≥2 RP. However, on multivariate analysis, only age, lung volume, fractional dose, V10, and Hb levels were independent factors. AUC values for the clinical, dosimetric, and combined models were 0.730 (95% CI, 0.660-0.793), 0.711 (95% CI, 0.641-0.775) and 0.830 (95% CI, 0.768-0.881), respectively. The combined model provided superior discriminative ability than the clinical and dosimetric models (P < .05). CONCLUSION: Age, lung volume, fractional dose, V10, and Hb levels were demonstrated to be significant factors associated with grade ≥2 RP for lung cancer patients after SBRT. A novel model combining clinical, dosimetric factors, and laboratory biomarkers improved predictive performance compared with the clinical and dosimetric model alone.

Radiation Pneumonitis, Really? A Case of Pulmonary Toxicity from CDK4/6 Inhibitor.

BACKGROUND/AIM: Radiation pneumonitis is a known complication of radiotherapy. It is also a rare complication of CDK4/6 inhibitors, and it can be difficult to differentiate the two. This is a report of a case of pulmonary toxicity from a CDK4/6 inhibitor, which was initially ascribed to radiation pneumonitis. CASE REPORT: A 77-year-old female was diagnosed with pneumonitis after receiving radiation to the thoracic spine. She had also been treated with abemaciclib. Upon review, the patient's lung mean dose was 11.54 Gy with a V20 of 17.02%, and the area of pneumonitis was largely outside of the treatment field. Abemaciclib was ceased. The patient was started on supportive oxygen as well as steroids. She no longer required oxygen and she was discharged from the hospital. Radiation pneumonitis is largely correlated with the volume of lung radiated and dose of radiation to the lung. CDK4/6 inhibitor pulmonary toxicity, while rare, is possible and will likely become more frequent with increasing use of these agents. CONCLUSION: Patients receiving CDK4/6 inhibitors are at an increased risk for pneumonitis. It can be confused with radiation pneumonitis and must be included in the differential diagnosis.

External validation of NTCP-models for radiation pneumonitis in lung cancer patients treated with chemoradiotherapy.

PURPOSE: Normal tissue complication probability (NTCP) models can be used to estimate the risk of radiation pneumonitis (RP). The aim of this study was to externally validate the most frequently used prediction models for RP, i.e., the QUANTEC and APPELT models, in a large cohort of lung cancer patients treated with IMRT or VMAT. [1-2] METHODS AND MATERIALS: This prospective cohort study, included lung cancer patients treated between 2013 and 2018. A closed testing procedure was performed to test the need for model updating. To improve model performance, modification or removal of variables was considered. Performance measures included tests for goodness of fit, discrimination, and calibration. RESULTS: In this cohort of 612 patients, the incidence of RP ≥ grade 2 was 14.5%. For the QUANTEC-model, recalibration was recommended which resulted in a revised intercept and adjusted regression coefficient (from 0.126 to 0.224) of the mean lung dose (MLD),. The APPELT-model needed revision including model updating with modification and elimination of variables. After revision, the New RP-model included the following predictors (and regression coefficients): MLD (B = 0.250), age (B = 0.049, and smoking status (B = 0.902). The discrimination of the updated APPELT-model was higher compared to the recalibrated QUANTEC-model (AUC: 0.79 vs. 0.73). CONCLUSIONS: This study demonstrated that both the QUANTEC- and APPELT-model needed revision. Next to changes of the intercept and regression coefficients, the APPELT model improved further by model updating and performed better than the recalibrated QUANTEC model. This New RP-model is widely applicable containing non-tumour site specific variables, which can easily be collected.

A multiomics approach-based prediction of radiation pneumonia in lung cancer patients: impact on survival outcome.

PURPOSE: To predict the risk of radiation pneumonitis (RP), a multiomics model was built to stratify lung cancer patients. Our study also investigated the impact of RP on survival. METHODS: This study retrospectively collected 100 RP and 99 matched non-RP lung cancer patients treated with radiotherapy from two independent centres. They were divided into training (n = 175) and validation cohorts (n = 24). The radiomics, dosiomics and clinical features were extracted from planning CT and electronic medical records and were analysed by LASSO Cox regression. A multiomics prediction model was developed by the optimal algorithm. Overall survival (OS) between the RP, non-RP, mild RP, and severe RP groups was analysed by the Kaplan‒Meier method. RESULTS: Sixteen radiomics features, two dosiomics features, and one clinical feature were selected to build the best multiomics model. The optimal performance for predicting RP was the area under the receiver operating characteristic curve (AUC) of the testing set (0.94) and validation set (0.92). The RP patients were divided into mild (≤ 2 grade) and severe (> 2 grade) RP groups. The median OS was 31 months for the non-RP group compared with 49 months for the RP group (HR = 0.53, p = 0.0022). Among the RP subgroup, the median OS was 57 months for the mild RP group and 25 months for the severe RP group (HR = 3.72, p < 0.0001). CONCLUSIONS: The multiomics model contributed to improving the accuracy of RP prediction. Compared with the non-RP patients, the RP patients displayed longer OS, especially the mild RP patients.

Metrics of dose to highly ventilated lung are predictive of radiation-induced pneumonitis in lung cancer patients.

PURPOSE: To identify metrics of radiation dose delivered to highly ventilated lung that are predictive of radiation-induced pneumonitis. METHODS AND MATERIALS: A cohort of 90 patients with locally advanced non-small cell lung cancer treated with standard fractionated radiation therapy (RT) (60-66 Gy in 30-33 fractions) were evaluated. Regional lung ventilation was determined from pre-RT 4-dimensional computed tomography (4DCT) using the Jacobian determinant of a B-spline deformable image registration to estimate lung tissue expansion during respiration. Multiple voxel-wise population- and individual-based thresholds for defining high functioning lung were considered. Mean dose and volumes receiving dose ≥ 5-60 Gy were analyzed for both total lung-ITV (MLD,V5-V60) and highly ventilated functional lung-ITV (fMLD,fV5-fV60). The primary endpoint was symptomatic grade 2+ (G2+) pneumonitis. Receiver operator curve (ROC) analyses were used to identify predictors of pneumonitis. RESULTS: G2+ pneumonitis occurred in 22.2% of patients, with no differences between stage, smoking status, COPD, or chemo/immunotherapy use between G<2 and G2+ patients (P≥ 0.18). Highly ventilated lung was defined as voxels exceeding the population-wide median of 18% voxel-level expansion. All total and functional metrics were significantly different between patients with and without pneumonitis (P≤ 0.039). Optimal ROC points predicting pneumonitis from functional lung dose were fMLD ≤ 12.3 Gy, fV5 ≤ 54% and fV20 ≤ 19 %. Patients with fMLD ≤ 12.3 Gy had a 14% risk of developing G2+ pneumonitis whereas risk significantly increased to 35% for those with fMLD > 12.3 Gy (P = 0.035). CONCLUSIONS: Dose to highly ventilated lung is associated with symptomatic pneumonitis and treatment planning strategies should focus on limiting dose to functional regions. These findings provide important metrics to be used in functional lung avoidance RT planning and designing clinical trials.

3D deep convolution neural network for radiation pneumonitis prediction following stereotactic body radiotherapy.

In this study, we investigated 3D convolutional neural networks (CNNs) with input from radiographic and dosimetric datasets of primary lung tumors and surrounding lung volumes to predict the likelihood of radiation pneumonitis (RP). Pre-treatment, 3- and 6-month follow-up computed tomography (CT) and 3D dose datasets from one hundred and ninety-three NSCLC patients treated with stereotactic body radiotherapy (SBRT) were retrospectively collected and analyzed for this study. DenseNet-121 and ResNet-50 models were selected for this study as they are deep neural networks and have been proven to have high accuracy for complex image classification tasks. Both were modified with 3D convolution and max pooling layers to accept 3D datasets. We used a minority class oversampling approach and data augmentation to address the challenges of data imbalance and data scarcity. We built two sets of models for classification of three (No RP, Grade 1 RP, Grade 2 RP) and two (No RP, Yes RP) classes as outputs. The 3D DenseNet-121 models performed better (F1 score [0.81], AUC [0.91] [three class]; F1 score [0.77], AUC [0.84] [two class]) than the 3D ResNet-50 models (F1 score [0.54], AUC [0.72] [three-class]; F1 score [0.68], AUC [0.71] [two-class]) (p = 0.017 for three class predictions). We also attempted to identify salient regions within the input 3D image dataset via integrated gradient (IG) techniques to assess the relevance of the tumor surrounding volume for RP stratification. These techniques appeared to indicate the significance of the tumor and surrounding regions in the prediction of RP. Overall, 3D CNNs performed well to predict clinical RP in our cohort based on the provided image sets and radiotherapy dose information.

[Radiation-induced pneumonitis : pathophysiology, risk factors and work-up in 2022]. / Pneumopathies radiques - Pathophysiologie, classification, facteurs de risque et prise en charge en 2022.

Despite technical improvements concerning lung irradiation modalities, radiation-induced pneumonitis remains a usual complication, notably in the field of lung cancer treatment. This complication may remain asymptomatic but can also lead to respiratory distress. Thus, a low degree of suspicion and a comprehensive work-up is mandatory to evaluate the indication for specific treatment. In this article, we discuss the hypothesized pathophysiologic pathways, risk factors, clinical/radiological presentation and management.

Malgré les améliorations des techniques d'irradiation à l'étage thoracique, la pneumopathie radique (PpR) reste une complication fréquente, en particulier dans le cadre du traitement du cancer pulmonaire. Cette complication, qu'elle soit précoce ou tardive, peut demeurer silencieuse ou causer une détresse respiratoire potentiellement fatale. C'est pourquoi un faible degré de suspicion est nécessaire, de manière à débuter précocement un bilan d'investigation et décider de l'indication à un traitement spécifique. Dans cet article, nous discutons des hypothèses pathophysiologiques qui sous-tendent la PpR, des facteurs de risque de survenue, de la présentation clinique et radiologique, ainsi que de sa prise en charge.

Comprehensive Pneumonitis Profile of Thoracic Radiotherapy Followed by Immune Checkpoint Inhibitor and Risk Factors for Radiation Recall Pneumonitis in Lung Cancer.

Purpose: Whilst survival benefits of thoracic radiotherapy (TRT) followed by immune checkpoint inhibitor (ICI) have been reported in patients with lung cancer, the potential high risk of treatment-related pneumonitis remains a concern. Asians may be more sensitive to lung toxicity than other races. This retrospective study intended to provide a comprehensive pneumonitis profile of TRT followed by ICI and investigate the risk factors from a Chinese cohort of lung cancer. Methods and Materials: From January 2016 to July 2021, 196 patients with lung cancer who received TRT prior to ICI were retrospectively analyzed. Treatment-related pneumonitis, including checkpoint inhibitor pneumonitis (CIP), radiation pneumonitis (RP), and radiation recall pneumonitis (RRP), were recorded and graded through medical records and chest computed tomography. Characteristics predictive of pneumonitis were assessed using logistic regression models, and the receiver operating characteristic analyses were performed to identify optimal cut points for quantitative variables. Results: With a median follow-up of 18 months, a total of 108 patients (55.1%) developed treatment-related pneumonitis during ICI therapy, with an incidence of 25.5% for grade 2 or higher (G2+) and 4.1% for G3+. The overall rates of CIP, RP and RRP were 8.2% (n=16), 46.9% (n=92) and 7.1% (n=14), respectively. With a total mortality rate of 1.5%, vast majority of the patients recovered from pneumonitis or remained stable. No patients died of RRP. Half of the patients with G2+ RP who withheld ICI therapy restarted ICI safely after resolution of RP. The history of chronic pulmonary diseases (P=0.05), mean lung dose (MLD, P=0.038), percent volume of lung receiving ≥5 Gy (V5, P=0.012) and percent volume of lung receiving ≥20 Gy (V20, P=0.030) predicted the occurrence of RRP in univariate analyses. Interval between TRT and ICI less than 3 months was an independent predictor for G2+ treatment-related pneumonitis in a multivariate model (Odds ratio OR=2.787, P=0.004). Conclusions: Treatment-related pneumonitis, especially RRP, is acceptable and manageable in the setting of TRT followed by ICI in this Asian population. Dosimetric parameters MLD, V5 and V20 may improve the predictions of RRP in clinical practice.

Dosimetric predictors of pneumonitis in locally advanced non-small cell lung cancer patients treated with chemoradiation followed by durvalumab.

OBJECTIVES: The incidence and predictors of pneumonitis for patients with unresectable, locally advanced non-small cell lung cancer (NSCLC) in the era of consolidation durvalumab have yet to be fully elucidated. In this large single institution analysis, we report the incidence of and factors associated with grade 2 + pneumonitis in NSCLC patients treated with the PACIFIC regimen. MATERIALS AND METHODS: We identified all patients treated at our institution with definitive CRT followed by durvalumab from 2018 to 2021. Clinical documentation and imaging studies were reviewed to determine grade 2 + pneumonitis events, which required the following: 1) pulmonary symptoms warranting prolonged steroid taper, oxygen dependence, and/or hospital admission and 2) radiographic findings consistent with pneumonitis. RESULTS: One-hundred ninety patients were included. The majority received 60 Gray (Gy) in 30 fractions with concurrent carboplatin and paclitaxel. Median number of durvalumab cycles received was 12 (IQR: 4-22). At a median follow-up of 14.8 months, 50 (26.3%) patients experienced grade 2 + pneumonitis with a 1-year cumulative incidence of 27.8% (95% CI: 21.9-35.4). Seventeen (8.9%) patients experienced grade 3 + pneumonitis and 4 grade 5 (2.1%). Dosimetric predictors of pneumonitis included ipsilateral and total lung volume receiving 5 Gy or greater (V5Gy), V10Gy, V20Gy, V40Gy, and mean dose and contralateral V40Gy. Heart V5Gy, V10Gy, and mean dose were also significant variables. Overall survival estimates at 1 and 3 years were 87.4% (95% CI: 82.4-92.8) and 60.3% (95% CI: 47.9-74.4), respectively. CONCLUSION: We report a risk of pneumonitis higher than that seen on RTOG 0617 and comparable to the PACIFIC study. Multiple lung and heart dosimetric factors were predictive of pneumonitis.

Chemoradiation-induced pneumonitis in patients with unresectable stage III non-small cell lung cancer: A systematic literature review and meta-analysis.

INTRODUCTION: High-grade pneumonitis is a severe and potentially life-threatening adverse event associated with concurrent chemoradiation (cCRT) in patients with non-small cell lung cancer (NSCLC). The aim of this study was to summarize and quantify the incidence of severe (grade 3-5) cCRT-induced pneumonitis in unresectable stage III NSCLC patients. METHODS: A systematic literature review and meta-analysis were performed in accordance with PRISMA guidelines. Published literature was searched for randomized controlled trials (RCTs), observational studies, and non-randomized trials from 2014 to April 2020. The primary outcome of interest was incidence of grade 3-5 pneumonitis. RESULTS: Included were 17 studies for the review and 11 for the meta-analysis (1,788 participants); all studies examined radiation-related pneumonitis (RP). The pooled incidence of cCRT-induced grade 3-5 RP in unresectable stage III NSCLC patients was estimated to be 3.62% [95% confidence interval (CI): 1.65-6.21] in RCTs, 5.98% [95% CI: 2.26-12.91] in observational studies, and 7.85% [95% CI: 4.08-13.10] in observational studies using platinum-based doublet chemotherapies. CONCLUSION: These results suggest the incidence of severe and fatal RP in patients with unresectable stage III NSCLC treated with cCRT ranges from 3.62% to 7.85%, with incidence varying by study design and chemotherapy regimen. Estimates of RP incidence were higher in the real-world setting compared to RCTs. These results can be used to contextualize the baseline risk of cCRT-induced pneumonitis in unresectable stage III NSCLC to better understand the adverse event of pneumonitis associated with novel immunotherapy treatments indicated for concomitant use with this modality.

Lack of dose dependency for radiation pneumonitis after chemoradiotherapy with the use of tomotherapy for lung cancer.

A 71-year-old man with stage IIB (Union for International Cancer Control, 8th edition) non-small cell lung cancer underwent intensity-modulated radiation therapy with a dose of 66 Gy administered in 33 fractions concomitant with carboplatin and paclitaxel therapy. On computed tomography after completion of radiation therapy, ground-glass opacity, which was larger on the contralateral side, was observed, but it was not observed in the high-dose area on the ipsilateral side. Although the adverse event theoretically shows dose dependency, it was finally diagnosed as radiation pneumonitis. The presence of an atypical distribution of radiation pneumonitis should be recognized to improve the diagnosis, and it is suggested that the relative volume of the normal contralateral lung receiving a dose of ≥5 Gy is a possible risk factor for radiation pneumonitis.

Prospective Exploratory Study of the Relationship Between Radiation Pneumonitis and TGF-ß1 in Exhaled Breath Condensate.

BACKGROUND/AIM: We conducted a prospective exploratory study to investigate the relationship between radiation pneumonitis (RP) and transforming growth factor-ß1 (TGF-ß1) in exhaled breath condensate (EBC). PATIENTS AND METHODS: The inclusion criteria were: patients who 1) received thoracic radiotherapy (RT) for lung cancer, 2) were aged ≥20 years, and 3) provided written informed consent. EBC was collected before and 1 month after RT. TGF-ß1 levels in EBC were measured using an enzyme-linked immunosorbent assay. We evaluated RP using the Common Terminology Criteria for Adverse Events v4 and analyzed the relationship between grade (G) 2 RP and TGF-ß1 levels in EBC. RESULTS: Ten patients were enrolled [median age, 75 years (range=60-81 years)], and none of them had interstitial lung disease. Conventional fractionation, accelerated hyperfractionation, hypofractionation, and stereotactic ablative fractionation were used in four, one, two, and three patients, respectively. G1 and G2 RP were observed in five patients each; no G3-G5 RP occurred. The median TGF-ß1 levels in EBC before and 1 month after RT were 79.1 pg/ml (0.1-563.7 pg/ml) and 286.9 pg/ml (33.7-661.3 pg/ml), respectively. Of the seven patients with increased TGF-ß1 levels in EBC 1 month after RT than before RT, five (71%) experienced G2 RP, whereas the remaining three patients with decreased TGF-ß1 levels had G1 RP (p=0.083, one-sided Fisher's exact test). CONCLUSION: Increased TGF-ß1 levels in EBC 1 month after RT might be promising for the detection of G2 RP.

A Prognostic Tool to Estimate the Risk of Pneumonitis in Patients Irradiated for Lung Cancer.

BACKGROUND/AIM: Radiotherapy of lung cancer can lead to pneumonitis. This study aimed to identify risk factors and create a prognostic tool. PATIENTS AND METHODS: Sixteen factors were evaluated in 169 patients irradiated for lung cancer including age, sex, lung function, primary tumor/nodal stage, histology, tumor location, surgery, systemic treatment, radiation volume, total dose, mean dose to ipsilateral lung, history of another malignancy, pack years, chronic inflammatory disease, and cardiovascular disease. RESULTS: Forty-one patients experienced pneumonitis. Significant associations were found for total doses >56 Gy (p=0.023), mean lung doses >20 Gy (p=0.002) or >13 Gy (p<0.001), and chronic inflammatory disease (p=0.034). Considering mean lung dose and chronic inflammatory disease, scores were 2, 3, 4, or 5 points. Pneumonitis rates were 0% (0/35), 24% (14/58), 32% (21/66), and 60% (6/10) (p=0.001), respectively. CONCLUSION: Based on significant risk factors, a prognostic tool was developed that can help estimate the risk of pneumonitis and contribute to personalized follow up of patients.