Successful salvage of a severe COVID-19 patient previously with lung cancer and radiation pneumonitis by mesenchymal stem cells: a case report and literature review

During the COVID-19 pandemic, elderly patients with underlying condition, such as tumors, had poor prognoses after progressing to severe pneumonia and often had poor response to standard treatment. Mesenchymal stem cells (MSCs) may be a promising treatment for patients with severe pneumonia, but MSCs are rarely used for patients with carcinoma. Here, we reported a 67-year-old female patient with lung adenocarcinoma who underwent osimertinib and radiotherapy and suffered from radiation pneumonitis. Unfortunately, she contracted COVID-19 and that rapidly progressed to severe pneumonia. She responded poorly to frontline treatment and was in danger. Subsequently, she received a salvage treatment with four doses of MSCs, and her symptoms surprisingly improved quickly. After a lung CT scan that presented with a significantly improved infection, she was discharged eventually. Her primary disease was stable after 6 months of follow-up, and no tumor recurrence or progression was observed. MSCs may be an effective treatment for hyperactive inflammation due to their ability related to immunomodulation and tissue repair. Our case suggests a potential value of MSCs for severe pneumonia that is unresponsive to conventional therapy after a COVID-19 infection. However, unless the situation is urgent, it needs to be considered with caution for patients with tumors. The safety in tumor patients still needs to be observed.

The relationship of body mass index to setup errors, dosimetric parameters and incidence of radiation pneumonitis in non-small cell lung cancer patients undergoing intensity-modulated radiation therapy: a single-center observational study

BACKGROUND

The relationship among body mass index (BMI), setup error and radiation pneumonitis is not clearly illustrated.

OBJECTIVE

The present study aimed to investigate the role of BMI in non-small cell lung cancer (NSCLC) patients' radiation treatment, focusing on its relationship with setup error of patient positioning, the dosimetric parameters of intensity-modulated radiation therapy (IMRT) and the incidence of radiation pneumonitis.

METHODS

This prospective observational study included 523 cases of NSCLC patients during 2020-2022. Patients were divided into different groups by different BMI. The setup error was obtained by cone beam CT (CBCT) at three positions, lateral (LAT), longitudinal (LNG) and vertical (VRT). IMRT dosimetric parameters of V5, V20, and mean dose were collected.

RESULTS

Patients with BMI ≥28 kg/m2 showed significantly higher absolute values of LAT, LNG and VRT, higher V5, V20, mean dose, as well as higher total incidence of radiation pneumonitis and grade III radiation pneumonitis compared with patients with BMI <24 kg/m2 or 24-28 kg/m2. Spearman's analysis demonstrated that the absolute values of LAT, LNG and VRT were positively correlated with BMI, and positive correlation existed among BMI, dosimetric parameters and setup errors. ROC curves showed that LAT in setup errors and V5 in dosimetric parameters had the best diagnostic value for prediction of radiation pneumonitis. Only BMI, LAT, V5 and V20 were the independent risk factors for radiation pneumonitis.

CONCLUSIONS

Setup error caused by higher BMI might be associated with the dosimetric parameters, as well as the incidence of radiation pneumonitis in NSCLC patients.

Novel model integrating computed tomography-based image markers with genetic markers for discriminating radiation pneumonitis in patients with unresectable stage III non-small cell lung cancer receiving radiotherapy: a retrospective multi-center radiogenomics study

BACKGROUND

Chemoradiotherapy is a critical treatment for patients with locally advanced and unresectable non-small cell lung cancer (NSCLC), and it is essential to identify high-risk patients as early as possible owing to the high incidence of radiation pneumonitis (RP). Increasing attention is being paid to the effects of endogenous factors for RP. This study aimed to investigate the value of computed tomography (CT)-based radiomics combined with genomics in analyzing the risk of grade ≥ 2 RP in unresectable stage III NSCLC.

METHODS

In this retrospective multi-center observational study, 100 patients with unresectable stage III NSCLC who were treated with chemoradiotherapy were analyzed. Radiomics features of the entire lung were extracted from pre-radiotherapy CT images. The least absolute shrinkage and selection operator algorithm was used for optimal feature selection to calculate the Rad-score for predicting grade ≥ 2 RP. Genomic DNA was extracted from formalin-fixed paraffin-embedded pretreatment biopsy tissues. Univariate and multivariate logistic regression analyses were performed to identify predictors of RP for model development. The area under the receiver operating characteristic curve was used to evaluate the predictive capacity of the model. Statistical comparisons of the area under the curve values between different models were performed using the DeLong test. Calibration and decision curves were used to demonstrate discriminatory and clinical benefit ratios, respectively.

RESULTS

The Rad-score was constructed from nine radiomic features to predict grade ≥ 2 RP. Multivariate analysis demonstrated that histology, Rad-score, and XRCC1 (rs25487) allele mutation were independent high-risk factors correlated with RP. The area under the curve of the integrated model combining clinical factors, radiomics, and genomics was significantly higher than that of any single model (0.827 versus 0.594, 0.738, or 0.641). Calibration and decision curve analyses confirmed the satisfactory clinical feasibility and utility of the nomogram.

CONCLUSION

Histology, Rad-score, and XRCC1 (rs25487) allele mutation could predict grade ≥ 2 RP in patients with locally advanced unresectable NSCLC after chemoradiotherapy, and the integrated model combining clinical factors, radiomics, and genomics demonstrated the best predictive efficacy.

Tolerogenic dendritic cells in radiation-induced lung injury

Radiation-induced lung injury is a common complication associated with radiotherapy. It is characterized by early-stage radiation pneumonia and subsequent radiation pulmonary fibrosis. However, there is currently a lack of effective therapeutic strategies for radiation-induced lung injury. Recent studies have shown that tolerogenic dendritic cells interact with regulatory T cells and/or regulatory B cells to stimulate the production of immunosuppressive molecules, control inflammation, and prevent overimmunity. This highlights a potential new therapeutic activity of tolerogenic dendritic cells in managing radiation-induced lung injury. In this review, we aim to provide a comprehensive overview of tolerogenic dendritic cells in the context of radiation-induced lung injury, which will be valuable for researchers in this field.

Cytokine, chemokine alterations and immune cell infiltration in Radiation-induced lung injury: Implications for prevention and management

Radiation therapy is one of the primary treatments for thoracic malignancies, with radiation-induced lung injury (RILI) emerging as its most prevalent complication. RILI encompasses early-stage radiation pneumonitis (RP) and the subsequent development of radiation pulmonary fibrosis (RPF). During radiation treatment, not only are tumor cells targeted, but normal tissue cells, including alveolar epithelial cells and vascular endothelial cells, also sustain damage. Within the lungs, ionizing radiation boosts the intracellular levels of reactive oxygen species across various cell types. This elevation precipitates the release of cytokines and chemokines, coupled with the infiltration of inflammatory cells, culminating in the onset of RP. This pulmonary inflammatory response can persist, spanning a duration from several months to years, ultimately progressing to RPF. This review aims to explore the alterations in cytokine and chemokine release and the influx of immune cells post-ionizing radiation exposure in the lungs, offering insights for the prevention and management of RILI.

Intensity-modulated proton and carbon-ion radiotherapy using a fixed-beam system for locally advanced lung cancer: dosimetric comparison with x-ray radiotherapy and normal tissue complication probability (NTCP) evaluation

Objective. To assess the dosimetric consequences and the normal tissue complication probability (NTCP) for the organs at risk (OARs) in intensity-modulated particle radiotherapy of proton (IMPT) and carbon-ion (IMCT) using a fixed-beam delivery system when compared with intensity-modulated photon radiotherapy (IMRT) for locally advanced small-cell lung cancer.Approach. The plans were all designed under the same total relative biological effectiveness (RBE)-weighted prescription dose, in which the planning target volume (PTV) of the internal gross target volume(IGTV) and the PTV of the clinical target volume was irradiated with 69.3 Gy (RBE) and 63 Gy (RBE), respectively, using a simultaneously integrated boosting (SIB) technique. NTCPs were estimated for heart, lung, esophagus and spinal cord by Lyman-Kutcher-Burman (LKB) and logistic models. Dose escalation was simulated under the desired NTCP values (0.05, 0.10 and 0.50) of the three radiation techniques.Main results. Under the similar target coverage, almost all OARs were significantly better spared (p< 0.05) when using the particle radiotherapy except for D1cc (the dose to 1 cm3of the volume) of the proximal bronchial tree (p> 0.05). At least 57.6% of mean heart dose, 28.8% of mean lung dose and 19.1% of mean esophageal dose were reduced compared with IMRT. The mean NTCP of radiation-induced pneumonitis (RP) in the ipsilateral lung was 0.39 ± 0.33 (0.39 ± 0.31) in IMPT plans and 0.36 ± 0.32 (0.35 ± 0.30) in IMCT plans compared with 0.66 ± 0.30 (0.64 ± 0.28) in IMRT plans by LKB (logistic) models. The target dose could be escalated to 78.3/76.9 Gy (RBE) in IMPT/IMCT plans compared with 61.7 Gy (RBE) in IMRT plans when 0.50 of NTCP in terms of RP in the ipsilateral lung was applied.Significance. This study presents the potential of better control of the side effects and improvement of local control originating from the dosimetric advantage with the application of IMPT and IMCT with the SIB technique for locally advanced lung cancer, even with limited beam directions.

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.

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.

The predictive role of baseline pulmonary function test in lung carcinoma patients for radiation-induced lung toxicity treated with conformal radiation therapy

INTRODUCTION

Radiation-induced lung toxicity (RILT) is a major concern in patients who receive radiation to thorax. The purpose of the study was to evaluate the changes of pulmonary function tests (PFTs) in lung carcinoma patients treated with three-dimensional conformal radiation therapy (3-D CRT) and relation RILT with dosimetric and nondosimetric factors.

METHODS

This was a prospective observational study which included 65 patients of lung carcinoma treated with 3-D CRT. PFTs were assessed before the radiotherapy and at third and sixth month post-radiation therapy. Radiation pneumonitis (RP) was graded according to National Cancer Institute Common Toxicity Criteria (CTCAE) version 4.0.

RESULTS

Majority of patients already had poor lung function before commencing the radiotherapy. There was a modest decrease in pulmonary function after radiation therapy with (3-D CRT). Postradiotherapy, at third month, eight patients (12%) and at the sixth month, 16 patients (25%) were observed with Grade II RP. At the third month, the nondosimetric factors, age >65 years (P = 0.027) and pretreatment Diffusion capacity of the Lung for Carbon monoxide (DLCO) 60% (P = 0.03) were significantly related to grade ≥ II Radiation pneumonitis (RP). Among dosimetric factors, mean lung dose ≥20 Gy (P = 0.001) and volume receving 20Gy ≥35% (P = 0.05) were significantly related to grade ≥ II RP. These factors were also related to grade ≥ II RP at the sixth month with a significant P value.

CONCLUSION

There is a progressive decrease in pulmonary function after (3-D CRT) in lung carcinoma patients. The study suggested that the baseline PFT may be utilized for the identification of high-risk patients for RILT before starting the radiotherapy in lung carcinoma.

Prediction of radiation pneumonitis using the effective α/β of lungs and heart in NSCLC patients treated with proton beam therapy

PURPOSE

Radiation pneumonitis (RP) remains a major complication in non-small cell lung cancer (NSCLC) patients undergoing radiochemotherapy (RCHT). Traditionally, the mean lung dose (MLD) and the volume of the total lung receiving at least 20 Gy (V20Gy) are used to predict RP in patients treated with normo-fractionated photon therapy. However, other models, including the actual dose-distribution in the lungs using the effective α/β model or a combination of radiation doses to the lungs and heart, have been proposed for predicting RP. Moreover, the models established for photons may not hold for patients treated with passively-scattered proton therapy (PSPT). Therefore, we here tested and validated novel predictive parameters for RP in NSCLC patient treated with PSPT.

METHODS

Data on the occurrence of RP, structure files and dose-volume histogram parameters for lungs and heart of 96 NSCLC patients, treated with PSPT and concurrent chemotherapy, was retrospectively retrieved from prospective clinical studies of two international centers. Data was randomly split into a training set (64 patients) and a validation set (32 patients). Statistical analyses were performed using binomial logistic regression.

RESULTS

The biologically effective dose (BED) of the'lungs - GTV' significantly predicted RP ≥ grade 2 in the training-set using both a univariate model (p = 0.019, AUCtrain = 0.72) and a multivariate model in combination with the effective α/β parameter of the heart (pBED = 0.006, [Formula: see text] = 0.043, AUCtrain = 0.74). However, these results did not hold in the validation-set (AUCval = 0.52 andAUCval = 0.50, respectively). Moreover, these models were found to neither outperform a model built with the MLD (p = 0.015, AUCtrain = 0.73, AUCval = 0.51), nor a multivariate model additionally including the V20Gy of the heart (pMLD = 0.039, pV20Gy,heart = 0.58, AUCtrain = 0.74, AUCval = 0.53).

CONCLUSION

Using the effective α/β parameter of the lungs and heart we achieved similar performance to commonly used models built for photon therapy, such as MLD, in predicting RP ≥ grade 2. Therefore, prediction models developed for photon RCHT still hold for patients treated with PSPT.

Quantification of diffuse parenchymal lung disease in non-small cell lung cancer patients with definitive concurrent chemoradiation therapy for predicting radiation pneumonitis

BACKGROUND

We sought to quantify diffuse parenchymal lung disease (DPLD) extent using quantitative computed tomography (CT) analysis and to investigate its association with radiation pneumonitis (RP) development in non-small cell lung cancer (NSCLC) patients receiving definitive concurrent chemoradiation therapy (CCRT).

METHODS

A total of 82 NSCLC patients undergoing definitive CCRT were included in this prospective cohort study. Pretreatment CT scans were analyzed using quantitative CT analysis software. Low-attenuation area (LAA) features based on lung density and texture features reflecting interstitial lung disease (ILD) were extracted from the whole lung. Clinical and dosimetric factors were also evaluated. RP development was assessed using the Common Terminology Criteria for Adverse Events version 5.0. Univariable and multivariable logistic regression analyses were performed to identify independent risk factors for grade ≥3 (≥GR3) RP.

RESULTS

RP was identified in 68 patients (73.9%), with nine patients (10.9%) experiencing ≥GR3 RP. Univariable logistic regression analysis identified excess kurtosis and high-attenuation area (HAA)_volume (cc) as significantly associated with ≥GR3 RP. Multivariable logistic regression analysis showed that the combined use of imaging features and clinical factors (forced expiratory volume in 1 second [FEV1], forced vital capacity [FVC], and CHEMO regimen) demonstrated the best performance (area under the receiver operating characteristic curve = 0.924) in predicting ≥GR3 RP.

CONCLUSION

Quantified imaging features of DPLD obtained from pretreatment CT scans would predict the occurrence of RP in NSCLC patients undergoing definitive CCRT. Combining imaging features with clinical factors could improve the accuracy of the predictive model for severe RP.

Prediction of radiation pneumonia after radiotherapy for esophageal cancer using a unified fractional dosiomics combined model

OBJECTIVE

This study aimed to construct an optimal model to predict radiation pneumonia (RP) after radiotherapy for esophageal cancer using unified fractional dosiomics and to investigate the improvements in the prediction efficiency of each model for RP.

METHODS

The clinical data, DVH, pre-treatment CT, and dose distribution of 182 patients were retrospectively analyzed.The independent risk factors were screened using univariate and multivariate logistic regression. The mutual information (MI),least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE) methods were used to screen the omics features. The AUC values of ROC, calibration curves, and clinical decision curves were calculated to evaluate the efficacy and trends of each model.

RESULTS

The AUC of dosiomics model were 0.783 and 0.760 in the training and test cohorts, higher than 0.585 and 0.579 in the training and test cohorts of the DVH model. The AUC value of the R + D combination was the highest, reaching 0.833. The combined R + D model had a better calibration degree than the other models (mean absolute error = 0.018) and better net benefit in clinical decision-making.

CONCLUSIONS

The radiomics combined dosiomics model was the best combined model to predict RP after radiotherapy for esophageal cancer. The dosiomics model could cover the efficiency of the DVH model and significantly improve the efficiency of the combined model.In the future, we will include other centers for further verification.

ADVANCES IN KNOWLEDGE

For the first time, this study used CT images combined dose distribution to predict the occurrence of radiation pneumonitis after radiotherapy for esophageal cancer.

Salivary metabolites as novel independent predictors of radiation pneumonitis

PURPOSE

Radiotherapy is an integral treatment for non-small cell lung cancer (NSCLC); however, radiation-induced toxicities such as radiation pneumonitis (RP) present a considerable challenge. Herein, we aimed to evaluate the potential of salivary metabolomics as an independent risk factor for predicting RP.

METHODS

This study included 62 consecutive patients with NSCLC who underwent thoracic radiotherapy at Tokyo Medical University between September 2016 and December 2018. The median age of the patients was 75 years (range: 41-89), comprising 47 (75.8%) males and 15 (24.2%) females. Patients with stage I NSCLC received 75 Gy in 30 fractions, whereas those with stage II and III NSCLC received 66 Gy in 33 fractions. Saliva samples were collected before treatment and at 2 weeks, 1 month, 3 months, and 1 year after initiating radiotherapy. Clinical RP was defined as grade 2 according to the Common Toxicity Criteria for Adverse Events. Salivary metabolomics were analyzed using capillary electrophoresis-mass spectrometry. Salivary metabolites were evaluated as potential predictors of RP.

RESULTS

Clinical RP was observed in 11 patients (17.7%); no RP-related deaths were observed. Clinical RP developed at a median of 4 months (range: 2-6 months) after initiating radiotherapy. Three metabolites, butyrate, propionate, and hexanoate, collected before radiotherapy exhibited predictive ability for clinical RP. Multivariate logistic analysis indicated butyrate (P = 0.033) as a predictive factor, along with the previously known factor of lung volume irradiated with > 20 Gy (P = 0.045).

CONCLUSION

Salivary metabolite butyrate was an independent risk factor for clinical RP.

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.

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.

Risk factor analysis of the development of severe radiation pneumonitis in patients with non-small cell lung cancer treated with curative radiotherapy, with focus on underlying pulmonary disease

BACKGROUND

We aim to identify the multifaceted risk factors that can affect the development of severe radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC) treated with curative high-dose radiotherapy with or without concurrent chemotherapy.

METHODS

We retrospectively reviewed the medical records of 175 patients with stage-I-III NSCLC treated with curative thoracic X-ray radiotherapy at the Korea University Guro Hospital between June 2019 and June 2022. Treatment-related complications were evaluated using the Common Terminology Criteria for Adverse Events (version 4.03).

RESULTS

The median follow-up duration was 15 months (range: 3-47 months). Idiopathic pulmonary fibrosis (IPF) as an underlying lung disease (P < 0.001) and clinical stage, regarded as the concurrent use of chemotherapy (P = 0.009), were associated with a high rate of severe RP. In multivariate analyses adjusting confounding variables, the presence of IPF as an underlying disease was significantly associated with severe RP (odds ratio [95% confidence interval] = 48.4 [9.09-347]; P < 0.001). In a subgroup analysis of stage-I-II NSCLC, the incidence of severe RP in the control, chronic obstructive pulmonary disease (COPD), and IPF groups was 3.2%, 4.3%, and 42.9%, respectively (P < 0.001). The incidence of severe RP was 15.2%, 10.7%, and 75.0% in the control, COPD, and IPF groups, respectively (P < 0.001) in the stage-III NSCLC group.

CONCLUSIONS

This study revealed that IPF as an underlying lung disease and the concurrent use of chemotherapy are associated with a high rate of severe RP. In contrast, COPD did not increase the risk of pulmonary toxicity after receiving curative high-dose radiotherapy.

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.

Pneumonitis After Concurrent Chemoradiation and Immune Checkpoint Inhibition in Patients with Locally Advanced Non-small Cell Lung Cancer

AIMS

Pneumonitis is a common and potentially deadly complication of combined chemoradiation and immune checkpoint inhibition (CRT-ICI) in patients with locally advanced non-small cell lung cancer (LA-NSCLC). In this study we sought to identify the risk factors for pneumonitis with CRT-ICI therapy in LA-NSCLC cases and determine its impact on survival.

MATERIALS AND METHODS

We conducted a retrospective chart review of 140 patients with LA-NSCLC who underwent curative-intent CRT-ICI with durvalumab between 2018 and 2021. Pneumonitis was diagnosed by a multidisciplinary team of clinical experts. We used multivariable cause-specific hazard models to identify risk factors associated with grade ≥2 pneumonitis. We constructed multivariable Cox proportional hazard models to investigate the impact of pneumonitis on all-cause mortality.

RESULTS

The median age of the cohort was 67 years; most patients were current or former smokers (86%). The cumulative incidence of grade ≥2 pneumonitis was 23%. Among survivors, 25/28 patients had persistent parenchymal scarring. In multivariable analyses, the mean lung dose (hazard ratio 1.14 per Gy, 95% confidence interval 1.03-1.25) and interstitial lung disease (hazard ratio 3.8, 95% confidence interval 1.3-11.0) increased the risk for pneumonitis. In adjusted models, grade ≥2 pneumonitis (hazard ratio 2.5, 95% confidence interval 1.0-6.2, P = 0.049) and high-grade (≥3) pneumonitis (hazard ratio 8.3, 95% confidence interval 3.0-23.0, P < 0.001) were associated with higher all-cause mortality.

CONCLUSIONS

Risk factors for pneumonitis in LA-NSCLC patients undergoing CRT-ICI include the mean radiation dose to the lung and pre-treatment interstitial lung disease. Although most cases are not fatal, pneumonitis in this setting is associated with markedly increased mortality.

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.

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.

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.

Analysis of clinical and physical dosimetric factors that determine the outcome of severe acute radiation pneumonitis in lung cancer patients

OBJECTIVE

We conducted a retrospective statistical analysis of clinical and physical dosimetric factors of lung cancer patients who had previously undergone lung and/or mediastinal radiotherapy and died of or survived severe acute radiation pneumonitis (SARP). Our study was the first to reveal the heterogeneity in clinical factors, physical dosimetric factors, and SARP onset time that determined the clinical outcomes of lung cancer patients who developed SARP.

MATERIALS AND METHODS

The clinical characteristics, physical dosimetry factors, and SARP onset time of deceased and surviving patients were retrospectively analyzed. SPSS 20.0 was used for data analysis. Student's t-test was used for intergroup comparison, and a Mann-Whitney U test was used for data with skewed distribution. Qualitative data were represented using frequencies (%), and Fisher's exact test or χ2 test was used for intergroup comparison of nonparametric data. Binary logistic analysis was used for univariate and multivariate analyses. Differences with a P < 0.05 were considered statistically significant.

RESULTS

Univariate analysis revealed that the potential predictors of SARP death were as follows: ipsilateral lung V5 and V30, contralateral lung V5, V10, and V30, total lung V5, V10, and V30, mean lung dose, mean heart dose, and maximum spinal cord dose. Multivariate analysis showed that ipsilateral lung V5 and total lung V5 were predictors that determined the final outcome of SARP patients. In addition, we analyzed the time from the completion of radiotherapy to SARP onset, and found significant difference between the two groups.

CONCLUSIONS

There was no decisive correlation between clinical characteristics and SARP outcome (i.e., death or survival) in lung radiotherapy patients. Ipsilateral lung V5 and total lung V5 were independent predictors of death in SARP patients.

Independent association of metabolic tumor response on FDG-PET with pulmonary toxicity following risk-adaptive chemoradiation for unresectable non-small cell lung cancer: Inherent radiosensitivity or immune response?

BACKGROUND

In the context of a phase II trial of risk-adaptive chemoradiation, we evaluated whether tumor metabolic response could serve as a correlate of treatment sensitivity and toxicity.

METHODS

Forty-five patients with AJCCv7 stage IIB-IIIB NSCLC enrolled on the FLARE-RT phase II trial (NCT02773238). [18F]fluorodeoxyglucose (FDG) PET-CT images were acquired prior to treatment and after 24 Gy during week 3. Patients with unfavorable on-treatment tumor response received concomitant boosts to 74 Gy total over 30 fractions rather than standard 60 Gy. Metabolic tumor volume and mean standardized uptake value (SUVmean) were calculated semi-automatically. Risk factors of pulmonary toxicity included concurrent chemotherapy regimen, adjuvant anti-PDL1 immunotherapy, and lung dosimetry. Incidence of CTCAE v4 grade 2+ pneumonitis was analyzed using the Fine-Gray method with competing risks of metastasis or death. Peripheral germline DNA microarray sequencing measured predefined candidate genes from distinct pathways: 96 DNA repair, 53 immunology, 38 oncology, 27 lung biology.

RESULTS

Twenty-four patients received proton therapy, 23 received ICI, 26 received carboplatin-paclitaxel, and 17 pneumonitis events were observed. Pneumonitis risk was significantly higher for patients with COPD (HR 3.78 [1.48, 9.60], p = 0.005), those treated with immunotherapy (HR 2.82 [1.03, 7.71], p = 0.043) but not with carboplatin-paclitaxel (HR 1.98 [0.71, 5.54], p = 0.19). Pneumonitis rates were similar among selected patients receiving 74 Gy radiation vs 60 Gy (p = 0.33), proton therapy vs photon (p = 0.60), or with higher lung dosimetric V20 (p = 0.30). Patients in the upper quartile decrease in SUVmean (>39.7%) were at greater risk for pneumonitis (HR 4.00 [1.54, 10.44], p = 0.005) and remained significant in multivariable analysis (HR 3.34 [1.23, 9.10], p = 0.018). Germline DNA gene alterations in immunology pathways were most frequently associated with pneumonitis.

CONCLUSION

Tumor metabolic response as measured by mean SUV is associated with increased pneumonitis risk in a clinical trial cohort of NSCLC patients independent of treatment factors. This may be partially attributed to patient-specific differences in immunogenicity.

Randomized Phase 2 Placebo-Controlled Trial of Nintedanib for the Treatment of Radiation Pneumonitis

PURPOSE

Radiation pneumonitis (RP) is the most common dose-limiting toxicity for thoracic radiation therapy. Nintedanib is used for the treatment of idiopathic pulmonary fibrosis, which shares pathophysiological pathways with the subacute phase of RP. Our goal was to investigate the efficacy and safety of nintedanib added to a prednisone taper compared with a prednisone taper alone in reducing pulmonary exacerbations in patients with grade 2 or higher (G2+) RP.

METHODS AND MATERIALS

In this phase 2, randomized, double-blinded, placebo-controlled trial, patients with newly diagnosed G2+ RP were randomized 1:1 to nintedanib or placebo in addition to a standard 8-week prednisone taper. The primary endpoint was freedom from pulmonary exacerbations at 1 year. Secondary endpoints included patient-reported outcomes and pulmonary function tests. Kaplan-Meier analysis was used to estimate the probability of freedom from pulmonary exacerbations. The study was closed early due to slow accrual.

RESULTS

Thirty-four patients were enrolled between October 2015 and February 2020. Of 30 evaluable patients, 18 were randomized to the experimental Arm A (nintedanib + prednisone taper) and 12 to the control Arm B (placebo + prednisone taper). Freedom from exacerbation at 1 year was 72% (confidence interval, 54%-96%) in Arm A and 40% (confidence interval, 20%-82%) in Arm B (1-sided, P = .037). In Arm A, there were 16 G2+ adverse events possibly or probably related to treatment compared with 5 in the placebo arm. There were 3 deaths during the study period in Arm A due to cardiac failure, progressive respiratory failure, and pulmonary embolism.

CONCLUSIONS

There was an improvement in pulmonary exacerbations by the addition of nintedanib to a prednisone taper. Further investigation is warranted for the use of nintedanib for the treatment of RP.