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.

Radiomics-based hybrid model for predicting radiation pneumonitis: A systematic review and meta-analysis.

PURPOSE: This study reviewed and meta-analyzed evidence on radiomics-based hybrid models for predicting radiation pneumonitis (RP). These models are crucial for improving thoracic radiotherapy plans and mitigating RP, a common complication of thoracic radiotherapy. We examined and compared the RP prediction models developed in these studies with the radiomics features employed in RP models. METHODS: We systematically searched Google Scholar, Embase, PubMed, and MEDLINE for studies published up to April 19, 2024. Sixteen studies met the inclusion criteria. We compared the RP prediction models developed in these studies and the radiomics features employed. RESULTS: Radiomics, as a single-factor evaluation, achieved an area under the receiver operating characteristic curve (AUROC) of 0.73, accuracy of 0.69, sensitivity of 0.64, and specificity of 0.74. Dosiomics achieved an AUROC of 0.70. Clinical and dosimetric factors showed lower performance, with AUROCs of 0.59 and 0.58. Combining clinical and radiomic factors yielded an AUROC of 0.78, while combining dosiomic and radiomics factors produced an AUROC of 0.81. Triple combinations, including clinical, dosimetric, and radiomics factors, achieved an AUROC of 0.81. The study identifies key radiomics features, such as the Gray Level Co-occurrence Matrix (GLCM) and Gray Level Size Zone Matrix (GLSZM), which enhance the predictive accuracy of RP models. CONCLUSIONS: Radiomics-based hybrid models are highly effective in predicting RP. These models, combining traditional predictive factors with radiomic features, particularly GLCM and GLSZM, offer a clinically feasible approach for identifying patients at higher RP risk. This approach enhances clinical outcomes and improves patient quality of life. PROTOCOL REGISTRATION: The protocol of this study was registered on PROSPERO (CRD42023426565).

Multi-omics deep learning for radiation pneumonitis prediction in lung cancer patients underwent volumetric modulated arc therapy.

BACKGROUND AND OBJECTIVE: To evaluate the feasibility and accuracy of radiomics, dosiomics, and deep learning (DL) in predicting Radiation Pneumonitis (RP) in lung cancer patients underwent volumetric modulated arc therapy (VMAT) to improve radiotherapy safety and management. METHODS: Total of 318 and 31 lung cancer patients underwent VMAT from First Affiliated Hospital of Wenzhou Medical University (WMU) and Quzhou Affiliated Hospital of WMU were enrolled for training and external validation, respectively. Models based on radiomics (R), dosiomics (D), and combined radiomics and dosiomics features (R+D) were constructed and validated using three machine learning (ML) methods. DL models trained with CT (DLR), dose distribution (DLD), and combined CT and dose distribution (DL(R+D)) images were constructed. DL features were then extracted from the fully connected layers of the best-performing DL model to combine with features of the ML model with the best performance to construct models of R+DLR, D+DLD, R+D+DL(R+D)) for RP prediction. RESULTS: The R+D model achieved a best area under curve (AUC) of 0.84, 0.73, and 0.73 in the internal validation cohorts with Support Vector Machine (SVM), XGBoost, and Logistic Regression (LR), respectively. The DL(R+D) model achieved a best AUC of 0.89 and 0.86 using ResNet-34 in training and internal validation cohorts, respectively. The R+D+DL(R+D) model achieved a best performance in the external validation cohorts with an AUC, accuracy, sensitivity, and specificity of 0.81(0.62-0.99), 0.81, 0.84, and 0.67, respectively. CONCLUSIONS: The integration of radiomics, dosiomics, and DL features is feasible and accurate for the RP prediction to improve the management of lung cancer patients underwent VMAT.

Radiation pneumonitis prediction with dual-radiomics for esophageal cancer underwent radiotherapy.

BACKGROUND: To integrate radiomics and dosiomics features from multiple regions in the radiation pneumonia (RP grade ≥ 2) prediction for esophageal cancer (EC) patients underwent radiotherapy (RT). METHODS: Total of 143 EC patients in the authors' hospital (training and internal validation: 70%:30%) and 32 EC patients from another hospital (external validation) underwent RT from 2015 to 2022 were retrospectively reviewed and analyzed. Patients were dichotomized as positive (RP+) or negative (RP-) according to CTCAE V5.0. Models with radiomics and dosiomics features extracted from single region of interest (ROI), multiple ROIs and combined models were constructed and evaluated. A nomogram integrating radiomics score (Rad_score), dosiomics score (Dos_score), clinical factors, dose-volume histogram (DVH) factors, and mean lung dose (MLD) was also constructed and validated. RESULTS: Models with Rad_score_Lung&Overlap and Dos_score_Lung&Overlap achieved a better area under curve (AUC) of 0.818 and 0.844 in the external validation in comparison with radiomics and dosiomics models with features extracted from single ROI. Combining four radiomics and dosiomics models using support vector machine (SVM) improved the AUC to 0.854 in the external validation. Nomogram integrating Rad_score, and Dos_score with clinical factors, DVH factors, and MLD further improved the RP prediction AUC to 0.937 and 0.912 in the internal and external validation, respectively. CONCLUSION: CT-based RP prediction model integrating radiomics and dosiomics features from multiple ROIs outperformed those with features from a single ROI with increased reliability for EC patients who underwent RT.

[Normal mouse serum alleviates radiation pneumonitis in mice by inhibiting the focal adhesion signaling pathway].

OBJECTIVE: To evaluate the therapeutic effect of normal mouse serum on radiation pneumonitis in mice and explore the possible mechanism. METHODS: Mouse models of radiation pneumonitis induced by thoracic radiation exposure were given intravenous injections of 100 µL normal mouse serum or normal saline immediately after the exposure followed by injections once every other day for a total of 8 injections. On the 15th day after irradiation, histopathological changes of the lungs of the mice were examined using HE staining, the levels of TNF-α, TGF-ß, IL-1α and IL-6 in the lung tissue and serum were detected using ELISA, and the percentages of lymphocytes in the lung tissue were analyzed with flow cytometry. Highth-roughput sequencing of exosome miRNA was carried out to explore the changes in the signaling pathways. The mRNA expression levels of the immune-related genes were detected by qRT-PCR, and the protein expressions of talin-1, tensin2, FAK, vinculin, α-actinin and paxillin in the focal adhesion signaling pathway were detected with Western blotting. RESULTS: In the mouse models of radiation pneumonitis, injections of normal mouse serum significantly decreased the lung organ coefficient, lowered the levels of TNF-α, TGF-ß, IL-1α and IL-6 in the serum and lung tissues, and ameliorated infiltration of CD45+, CD4+ and Treg lymphocytes in the lung tissue (all P < 0.05). The expression levels of Egfr and Pik3cd genes at both the mRNA and protein levels and the protein expressions of talin-1, tensin2, FAK, vinculin, α?actinin and paxillin were all significantly down-regulated in the mouse models after normal mouse serum treatment. CONCLUSION: Normal mouse serum ameliorates radiation pneumonitis in mice by inhibiting the expressions of key proteins in the Focal adhesion signaling pathway.

Prophylactic Effect of Clarithromycin on Radiation Pneumonitis in IMRT for Lung Cancer.

BACKGROUND/AIM: To evaluate the association between prophylactic administration of clarithromycin (CAM) and the development of radiation pneumonitis (RP) in patients treated with intensity modulated radiation therapy (IMRT) for lung cancer. PATIENTS AND METHODS: A total of 89 patients who underwent definitive or salvage IMRT for lung cancer were retrospectively evaluated. The median total and daily doses were 60 Gy and 2 Gy, respectively. A total of 39 patients (44%) received CAM for a median of three months after the start of IMRT. The relationship between the development of RP and certain clinical factors was analyzed. RESULTS: RP of Grade ≥2 was recognized in 10 (11%) patients; Grade 2 in six patients and Grade 3 in four patients. The incidence of Grade ≥2 RP was 3% (1/39) in patients treated with CAM, which was significantly lower than that of 18% (9/50) in patients without CAM. The median lung V20 and V5 in the 10 patients with RP Grade ≥2 were 24% and 46%, respectively, compared with 18% and 37% in the 79 patients with RP Grade 0-1, and the differences were significant. Durvalumab administration after IMRT was also a significant factor for RP Grade ≥2. CONCLUSION: Prophylactic administration of CAM may reduce Grade ≥2 RP in patients treated with IMRT for lung cancer. Therefore, further clinical trials are warranted.

Establishing a 4D-CT lung function related volumetric dose model to reduce radiation pneumonia.

In order to study how to use pulmonary functional imaging obtained through 4D-CT fusion for radiotherapy planning, and transform traditional dose volume parameters into functional dose volume parameters, a functional dose volume parameter model that may reduce level 2 and above radiation pneumonia was obtained. 41 pulmonary tumor patients who underwent 4D-CT in our department from 2020 to 2023 were included. MIM Software (MIM 7.0.7; MIM Software Inc., Cleveland, OH, USA) was used to register adjacent phase CT images in the 4D-CT series. The three-dimensional displacement vector of CT pixels was obtained when changing from one respiratory state to another respiratory state, and this three-dimensional vector was quantitatively analyzed. Thus, a color schematic diagram reflecting the degree of changes in lung CT pixels during the breathing process, namely the distribution of ventilation function strength, is obtained. Finally, this diagram is fused with the localization CT image. Select areas with Jacobi > 1.2 as high lung function areas and outline them as fLung. Import the patient's DVH image again, fuse the lung ventilation image with the localization CT image, and obtain the volume of fLung different doses (V60, V55, V50, V45, V40, V35, V30, V25, V20, V15, V10, V5). Analyze the functional dose volume parameters related to the risk of level 2 and above radiation pneumonia using R language and create a predictive model. By using stepwise regression and optimal subset method to screen for independent variables V35, V30, V25, V20, V15, and V10, the prediction formula was obtained as follows: Risk = 0.23656-0.13784 * V35 + 0.37445 * V30-0.38317 * V25 + 0.21341 * V20-0.10209 * V15 + 0.03815 * V10. These six independent variables were analyzed using a column chart, and a calibration curve was drawn using the calibrate function. It was found that the Bias corrected line and the Apparent line were very close to the Ideal line, The consistency between the predicted value and the actual value is very good. By using the ROC function to plot the ROC curve and calculating the area under the curve: 0.8475, 95% CI 0.7237-0.9713, it can also be determined that the accuracy of the model is very high. In addition, we also used Lasso method and random forest method to filter out independent variables with different results, but the calibration curve drawn by the calibration function confirmed poor prediction performance. The function dose volume parameters V35, V30, V25, V20, V15, and V10 obtained through 4D-CT are key factors affecting radiation pneumonia. Establishing a predictive model can provide more accurate lung restriction basis for clinical radiotherapy planning.

Quantitative analysis of interstitial lung abnormalities on computed tomography to predict symptomatic radiation pneumonitis after lung stereotactic body radiotherapy.

BACKGROUND AND PURPOSE: Symptomatic radiation pneumonitis (SRP) is a complication of thoracic stereotactic body radiotherapy (SBRT). As visual assessments pose limitations, artificial intelligence-based quantitative computed tomography image analysis software (AIQCT) may help predict SRP risk. We aimed to evaluate high-resolution computed tomography (HRCT) images with AIQCT to develop a predictive model for SRP. MATERIALS AND METHODS: AIQCT automatically labelled HRCT images of patients treated with SBRT for stage I lung cancer according to lung parenchymal pattern. Quantitative data including the volume and mean dose (Dmean) were obtained for reticulation + honeycombing (Ret + HC), consolidation + ground-glass opacities, bronchi (Br), and normal lungs (NL). After associations between AIQCT's quantified metrics and SRP were investigated, we developed a predictive model using recursive partitioning analysis (RPA) for the training cohort and assessed its reproducibility with the testing cohort. RESULTS: Overall, 26 of 207 patients developed SRP. There were significant between-group differences in the Ret + HC, Br-volume, and NL-Dmean in patients with and without SRP. RPA identified the following risk groups: NL-Dmean ≥ 6.6 Gy (high-risk, n = 8), NL-Dmean < 6.6 Gy and Br-volume ≥ 2.5 % (intermediate-risk, n = 13), and NL-Dmean < 6.6 Gy and Br-volume < 2.5 % (low-risk, n = 133). The incidences of SRP in these groups within the training cohort were 62.5, 38.4, and 7.5 %; and in the testing cohort 50.0, 27.3, and 5.0 %, respectively. CONCLUSION: AIQCT identified CT features associated with SRP. A predictive model for SRP was proposed based on AI-detected Br-volume and the NL-Dmean.

Radiation Therapy for Lung Cancer: Imaging Appearances and Pitfalls.

Radiation therapy is part of a multimodality treatment approach to lung cancer. The radiologist must be aware of both the expected and the unexpected imaging findings of the post-radiation therapy patient, including the time course for development of post- radiation therapy pneumonitis and fibrosis. In this review, a brief discussion of radiation therapy techniques and indications is presented, followed by an image-heavy differential diagnostic approach. The review focuses on computed tomography imaging examples to help distinguish normal postradiation pneumonitis and fibrosis from alternative complications, such as infection, local recurrence, or radiation-induced malignancy.

Optimal management of radiation pneumonitis: Findings of an international Delphi consensus study.

PURPOSE: Radiation pneumonitis (RP) is a dose-limiting toxicity for patients undergoing radiotherapy (RT) for lung cancer, however, the optimal practice for diagnosis, management, and follow-up for RP remains unclear. We thus sought to establish expert consensus recommendations through a Delphi Consensus study. METHODS: In Round 1, open questions were distributed to 31 expert clinicians treating thoracic malignancies. In Round 2, participants rated agreement/disagreement with statements derived from Round 1 answers using a 5-point Likert scale. Consensus was defined as ≥ 75 % agreement. Statements that did not achieve consensus were modified and re-tested in Round 3. RESULTS: Response rate was 74 % in Round 1 (n = 23/31; 17 oncologists, 6 pulmonologists); 82 % in Round 2 (n = 19/23; 15 oncologists, 4 pulmonologists); and 100 % in Round 3 (n = 19/19). Thirty-nine of 65 Round 2 statements achieved consensus; a further 10 of 26 statements achieved consensus in Round 3. In Round 2, there was agreement that risk stratification/mitigation includes patient factors; optimal treatment planning; the basis for diagnosis of RP; and that oncologists and pulmonologists should be involved in treatment. For uncomplicated radiation pneumonitis, an equivalent to 60 mg oral prednisone per day, with consideration of gastroprotection, is a typical initial regimen. However, in this study, no consensus was achieved for dosing recommendation. Initial steroid dose should be administered for a duration of 2 weeks, followed by a gradual, weekly taper (equivalent to 10 mg prednisone decrease per week). For severe pneumonitis, IV methylprednisolone is recommended for 3 days prior to initiating oral corticosteroids. Final consensus statements included that the treatment of RP should be multidisciplinary, the uncertainty of whether pneumonitis is drug versus radiation-induced, and the importance risk stratification, especially in the scenario of interstitial lung disease. CONCLUSIONS: This Delphi study achieved consensus recommendations and provides practical guidance on diagnosis and management of RP.

Deep-Learning Model Prediction of Radiation Pneumonitis Using Pretreatment Chest Computed Tomography and Clinical Factors.

Objectives: This study aimed to build a comprehensive deep-learning model for the prediction of radiation pneumonitis using chest computed tomography (CT), clinical, dosimetric, and laboratory data. Introduction: Radiation therapy is an effective tool for treating patients with lung cancer. Despite its effectiveness, the risk of radiation pneumonitis limits its application. Although several studies have demonstrated models to predict radiation pneumonitis, no reliable model has been developed yet. Herein, we developed prediction models using pretreatment chest CT and various clinical data to assess the likelihood of radiation pneumonitis in lung cancer patients. Methods: This retrospective study analyzed 3-dimensional (3D) lung volume data from chest CT scans and 27 features including dosimetric, clinical, and laboratory data from 548 patients who were treated at our institution between 2010 and 2021. We developed a neural network, named MergeNet, which processes lung 3D CT, clinical, dosimetric, and laboratory data. The MergeNet integrates a convolutional neural network with subsequent fully connected layers. A support vector machine (SVM) and light gradient boosting machine (LGBM) model were also implemented for comparison. For comparison, the convolution-only neural network was implemented as well. Three-dimensional Resnet-10 network and 4-fold cross-validation were used. Results: Classification performance was quantified by using the area under the receiver operative characteristic curve (AUC) metrics. MergeNet showed the AUC of 0.689. SVM, LGBM, and convolution-only networks showed AUCs of 0.525, 0.541, and 0.550, respectively. Application of DeLong test to pairs of receiver operating characteristic curves respectively yielded P values of .001 for the MergeNet-SVM pair and 0.001 for the MergeNet-LGBM pair. Conclusion: The MergeNet model, which incorporates chest CT, clinical, dosimetric, and laboratory data, demonstrated superior performance compared to other models. However, since its prediction performance has not yet reached an efficient level for clinical application, further research is required. Contribution: This study showed that MergeNet may be an effective means to predict radiation pneumonitis. Various predictive factors can be used together for the radiation pneumonitis prediction task via the MergeNet.

Assessment of Radiation Induced Pneumonitis and Pericarditis in Patients Undergoing Breast Conservative Treatment Using Hypofractionated Simultaneous Integrated Boost Technique.

INTRODUCTION: To determine the proportion of radiationinduced pneumonitis and pericarditis in patients who have received Hypo-fractionated Radiation along with simultaneous integrated boost technique after breast conservative surgery using a prospective observational study from a tertiary hospital. MATERIALS & METHODS: The incidence of radiationinduced pneumonitis and pericarditis was evaluated in all adult patients with biopsy-proven early-stage unilateral breast cancer who underwent breast-conserving surgery followed by hypo-fractionated radiation with a simultaneous integrated boost technique. Baseline assessments including a six-minute walk test, highresolution computed tomography (HRCT), pulmonary function tests (PFTs), electrocardiography (ECG) and echocardiography (ECHO) were performed. At three months post-radiation treatment, patients underwent follow-up assessments with a six-minute walk test, ECG and ECHO. At six months post-radiation treatment, patients underwent further assessments with a six-minute walk test, ECG, ECHO, PFTs, and HRCT of the thorax. Data analysis was performed using SPSS version 19. RESULTS: Our study investigated the incidence of acute radiation-induced pneumonitis and pericarditis in patients treated with hypofractionated VMAT-SIB technique in 20 eligible early breast cancer patients. The study found that the technique is feasible and achieves encouraging dosimetric parameters, including well achieved ipsilateral lung and heart doses. The reduced treatment time of 3-4 weeks compared to the previous 6-7 weeks with sequential boost was also found to be desirable in resource-constrained settings. The incidence of acute radiation pneumonitis and pericarditis was acceptable and comparable to existing data, with 90% of patients experiencing grade 1 radiation pneumonitis according to CTCAE v5.0. Post-treatment pulmonary function tests showed significant changes, particularly in patients who had received neoadjuvant chemotherapy and nodal irradiation. The six-minute walk test and Borg scale also showed a significant positive correlation with pulmonary function tests. There was no significant pericarditis during the follow-up. The study proposes that the hypofractionated radiotherapy using VMAT-SIB is a suitable alternative to conventional fractionation, with acceptable acute toxicities, but longer follow-up is required to assess the impact on late toxicities. CONCLUSION: Our research has shown that hypofractionated adjuvant radiotherapy with SIB is a safe and feasible treatment for patients with early breast cancer. This treatment method doesn't pose any significant short-term risks to the lungs or heart, and the SIB technique provides better coverage, conformity and sparing of organs at risk. Additionally, patients have reported positive cosmetic outcomes with this treatment. However, to make more accurate conclusions, we need to conduct further studies with larger sample sizes and longer follow-up periods to evaluate the potential longterm side effects of this treatment using VMAT in whole breast radiation.

The incidence of and risk factors for radiation pneumonitis in patients treated with simultaneous bevacizumab and thoracic radiotherapy.

BACKGROUND: First-line chemotherapy combined with bevacizumab is one of the standard treatment modes for patients with advanced non-small cell lung cancer (NSCLC). Thoracic radiotherapy (TRT) can provide significant local control and survival benefits to patients during the treatment of advanced NSCLC. However, the safety of adding TRT has always been controversial, especially because of the occurrence of radiation pneumonia (RP) during bevacizumab treatment. Therefore, in this study, we used an expanded sample size to evaluate the incidence of RP when using bevacizumab in combination with TRT. PATIENTS AND METHODS: Using an institutional query system, all medical records of patients with NSCLC who received TRT during first-line chemotherapy combined with bevacizumab from 2017 to 2020 at Shandong Cancer Hospital and Institute were reviewed. RP was diagnosed via computed tomography and was classified according to the RTOG toxicity scoring system. The risk factors for RP were identified using univariate and multivariate analyses. The Kaplan-Meier method was used to calculate progression-free survival (PFS) and overall survival (OS). RESULTS: Ultimately, 119 patients were included. Thirty-eight (31.9%) patients developed Grade ≥ 2 RP, of whom 27 (68.1%) had Grade 2 RP and 11 (9.2%) had Grade 3 RP. No patients developed Grade 4 or 5 RP. The median time for RP occurrence was 2.7 months (range 1.2-5.4 months). In univariate analysis, male, age, KPS score, V20 > 16.9%, V5 > 33.6%, PTV (planning target volume)-dose > 57.2 Gy, and PTV-volume > 183.85 cm3 were correlated with the occurrence of RP. In multivariate analysis, male, V20 > 16.9%, and PTV-volume > 183.85 cm3 were identified as independent predictors of RP occurrence. The mPFS of all patients was 14.27 (95% CI, 13.1-16.1) months. The one-year and two-year PFS rates were 64.9% and 20.1%, respectively. The mOS of all patients was 37.09 (95% CI, 33.8-42.0) months. The one-year survival rate of all patients was 95%, and the two-year survival rate was 71.4%. CONCLUSIONS: The incidence of Grade ≥ 2 RP in NSCLC patients who received both bevacizumab and TRT was 31.9%. Restricting factors such as V20 and PTV will help reduce the risk of RP in these patients. For patients who receive both bevacizumab and TRT, caution should be exercised when increasing TRT, and treatment strategies should be optimized to reduce the incidence of RP.

Dosimetry and Biochemical Comparison of Early Radiation-Induced Lung Toxicity in Breast Cancer Patients Treated with 3D-CRT and IMRT: the Role of Serum Interleukin-6 and Pulmonary Surfactant Protein-D.

BACKGROUND: Radiation-induced lung disease is a potentially fatal, dose-limiting toxicity commonly seen after radiotherapy of thoracic malignancies, including breast cancer. AIM: To evaluate and compare the early lung toxicity induced by 3D-CRT and IMRT radiotherapy treatment modalities in breast cancer female patients using biochemical, dosimetry and clinical data. SUBJECTS AND METHODS: this study included 15 normal healthy controls, 15 breast cancer patients treated with IMRT, and 15 breast cancer patients treated with 3D-CRT. One blood sample was obtained from the control group and 3 blood samples were withdrawn from cases before RT, after RT and after 3 months of RT. RESULT: IMRT delivered higher radiation dose to the breast tumor and lower doses to the lung as an organ at risk. There was a non-significant increase in the serum levels of IL-6 before IMRT and 3D-CRT compared with its levels in the control group. There were significant increases in serum levels of IL-6 after RT (IMRT and 3DCRT) compared with its levels before RT. There was a non-significant decrease in the serum levels of IL-6 after 3 months of RT (IMRT and 3D-CRT) compared with its serum levels immediately after RT. There was a non-significant increase in the serum levels of SP-D before RT (IMRT and 3D-CRT) compared with its levels in the control group. There were significant-increases in serum levels of SP-D after RT (IMRT and 3D-CRT) compared with its levels before RT. There was a non-significant decrease in the serum levels of SP-D after 3 months of radiotherapy (IMRT and 3D-CRT) compared with its serum levels immediately after RT. CONCLUSION: serum of levels IL-6 and SP-D can be used to diagnose the occurrence of early lung toxicity due to radiotherapy and the rate of recovery from radiation pneumonitis is apparent in case of IMRT than 3D-CRT.

Biomarkers associated with pulmonary exacerbations in a randomized trial of nintedanib for radiation pneumonitis.

BACKGROUND AND PURPOSE: Radiation pneumonitis (RP) is a common side effect of thoracic radiotherapy and often has a long course characterized by acute exacerbations and progression to permanent lung fibrosis. There are no validated biomarkers of prognosis in patients diagnosed with RP. MATERIALS AND METHODS: We analyzed a time course of serum chemokines, cytokines, and other proteins from patients with grade 2+ RP in a randomized clinical trial of a steroid taper plus nintedanib, a multiple tyrosine kinase inhibitor, versus placebo plus a steroid taper for the treatment of RP. Weighted gene correlation network analysis (WGCNA) and univariable zero inflated Poisson models were used to identify groups of correlated analytes and their associations with clinical outcomes. RESULTS: Thirty enrolled patients had biomarker data available, and 17 patients had enough analytes tested for network analysis. WGNCA identified ten analytes, including transforming growth factor beta-1 (TGF-ß1), monocyte chemoattractant protein-1 (MCP-1), and platelet-derived growth factor (PDGF), that in aggregate were correlated with the occurrence of pulmonary exacerbations (p = 0.008), the total number of acute pulmonary exacerbations (p = 0.002), and treatment arm (p = 0.036). By univariable analysis, an increase in rate of change of two components of the RP module were associated with an increased incidence rate of pulmonary exacerbations: interleukin 5 (IL-5, incidence rate ratio (IRR) 1.02, 95% CI 1.01-1.04, p = 0.002), and tumor necrosis factor superfamily 12 (TNFSF12, IRR 1.06, CI 1-1.11, p = 0.036). An increased slope of epidermal growth factor (EGF) was associated with a decreased incidence rate of exacerbations (IRR 0.94, CI 0.89-1, p = 0.036). CONCLUSION: We identified a panel of serum biomarkers that showed association with nintedanib treatment and acute pulmonary exacerbations in patients with RP. A confirmatory study will be needed to validate this panel for use as a prognostic tool in patients with RP.

Associations Between Mean Lung Dose and Prevalence of Radiation Pneumonitis in Elderly Lung Cancer Patients.

BACKGROUND/AIM: Pneumonitis is a serious radiotherapy complication. This study, which is a prerequisite for a prospective trial, aimed to identify the prevalence of pneumonitis and risk factors in elderly patients with lung cancer. PATIENTS AND METHODS: Ninety-eight lung cancer patients aged ≥65 years were included. Seventeen factors were investigated regarding grade ≥2 pneumonitis at 24 weeks following radiotherapy. RESULTS: The prevalence of grade ≥2 pneumonitis at 24 weeks was 27.3%. On univariate analysis, a significant association was observed for mean (ipsilateral) lung dose (MLD; ≤13.0 vs. 13.1-20.0 vs. >20.0 Gy; 0% vs. 24.9% vs. 48.7%). Results were significant also for ≤13.0 vs. >13.0 Gy (0% vs. 37.1%) or ≤20.0 vs. >20.0 Gy (13.4% vs. 48.7%). MLD achieved significance on multivariate analysis. CONCLUSION: Elderly patients receiving MLDs >13.0 Gy, particularly >20.0 Gy, have a high risk of grade ≥2 pneumonitis. These results are important for designing a prospective trial.

Metabolic profiling of murine radiation-induced lung injury with Raman spectroscopy and comparative machine learning.

Radiation-induced lung injury (RILI) is a dose-limiting toxicity for cancer patients receiving thoracic radiotherapy. As such, it is important to characterize metabolic associations with the early and late stages of RILI, namely pneumonitis and pulmonary fibrosis. Recently, Raman spectroscopy has shown utility for the differentiation of pneumonitic and fibrotic tissue states in a mouse model; however, the specific metabolite-disease associations remain relatively unexplored from a Raman perspective. This work harnesses Raman spectroscopy and supervised machine learning to investigate metabolic associations with radiation pneumonitis and pulmonary fibrosis in a mouse model. To this end, Raman spectra were collected from lung tissues of irradiated/non-irradiated C3H/HeJ and C57BL/6J mice and labelled as normal, pneumonitis, or fibrosis, based on histological assessment. Spectra were decomposed into metabolic scores via group and basis restricted non-negative matrix factorization, classified with random forest (GBR-NMF-RF), and metabolites predictive of RILI were identified. To provide comparative context, spectra were decomposed and classified via principal component analysis with random forest (PCA-RF), and full spectra were classified with a convolutional neural network (CNN), as well as logistic regression (LR). Through leave-one-mouse-out cross-validation, we observed that GBR-NMF-RF was comparable to other methods by measure of accuracy and log-loss (p > 0.10 by Mann-Whitney U test), and no methodology was dominant across all classification tasks by measure of area under the receiver operating characteristic curve. Moreover, GBR-NMF-RF results were directly interpretable and identified collagen and specific collagen precursors as top fibrosis predictors, while metabolites with immune and inflammatory functions, such as serine and histidine, were top pneumonitis predictors. Further support for GBR-NMF-RF and the identified metabolite associations with RILI was found as CNN interpretation heatmaps revealed spectral regions consistent with these metabolites.

Validated machine learning tools to distinguish immune checkpoint inhibitor, radiotherapy, COVID-19 and other infective pneumonitis.

BACKGROUND: Pneumonitis is a well-described, potentially disabling, or fatal adverse effect associated with both immune checkpoint inhibitors (ICI) and thoracic radiotherapy. Accurate differentiation between checkpoint inhibitor pneumonitis (CIP) radiation pneumonitis (RP), and infective pneumonitis (IP) is crucial for swift, appropriate, and tailored management to achieve optimal patient outcomes. However, correct diagnosis is often challenging, owing to overlapping clinical presentations and radiological patterns. METHODS: In this multi-centre study of 455 patients, we used machine learning with radiomic features extracted from chest CT imaging to develop and validate five models to distinguish CIP and RP from COVID-19, non-COVID-19 infective pneumonitis, and each other. Model performance was compared to that of two radiologists. RESULTS: Models to distinguish RP from COVID-19, CIP from COVID-19 and CIP from non-COVID-19 IP out-performed radiologists (test set AUCs of 0.92 vs 0.8 and 0.8; 0.68 vs 0.43 and 0.4; 0.71 vs 0.55 and 0.63 respectively). Models to distinguish RP from non-COVID-19 IP and CIP from RP were not superior to radiologists but demonstrated modest performance, with test set AUCs of 0.81 and 0.8 respectively. The CIP vs RP model performed less well on patients with prior exposure to both ICI and radiotherapy (AUC 0.54), though the radiologists also had difficulty distinguishing this test cohort (AUC values 0.6 and 0.6). CONCLUSION: Our results demonstrate the potential utility of such tools as a second or concurrent reader to support oncologists, radiologists, and chest physicians in cases of diagnostic uncertainty. Further research is required for patients with exposure to both ICI and thoracic radiotherapy.

Hydrogen-rich solution alleviates acute radiation pneumonitis by regulating oxidative stress and macrophages polarization.

This study was aimed to investigate the effect of hydrogen-rich solution (HRS) on acute radiation pneumonitis (ARP) in rats. The ARP model was induced by X-ray irradiation. Histopathological changes were assessed using HE and Masson stains. Inflammatory cytokines were detected by ELISA. Immunohistochemistry and flow cytometry were performed to quantify macrophage (CD68) levels and the M2/M1 ratio. Western blot analysis, RT-qPCR, ELISA and flow cytometry were used to evaluate mitochondrial oxidative stress injury indicators. Immunofluorescence double staining was performed to colocalize CD68/LC3B and p-AMPK-α/CD68. The relative expression of proteins associated with autophagy activation and the adenosine 5'-monophosphate-activated protein kinase/mammalian target of rapamycin/Unc-51-like kinase 1 (AMPK/mTOR/ULK1) signaling pathway were detected by western blotting. ARP decreased body weight, increased the lung coefficient, collagen deposition and macrophage infiltration and promoted M1 polarization in rats. After HRS treatment, pathological damage was alleviated, and M1 polarization was inhibited. Furthermore, HRS treatment reversed the ARP-induced high levels of mitochondrial oxidative stress injury and autophagy inhibition. Importantly, the phosphorylation of AMPK-α was inhibited, the phosphorylation of mTOR and ULK1 was activated in ARP rats and this effect was reversed by HRS treatment. HRS inhibited M1 polarization and alleviated oxidative stress to activate autophagy in ARP rats by regulating the AMPK/mTOR/ULK1 signaling pathway.