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Abstract: We present MDP bone scan findings of radiation pneumonitis in a 45-year-old woman with invasive ductal carcinoma of the right breast, classified as stage IIIa, T3N2M0. The patient underwent a modified radical mastectomy, neoadjuvant chemotherapy, and subsequent radiotherapy, receiving the last session 7 months before the bone scan. Whole-body images acquired 3 hours postinjection of 20 mCi (730 MBq) 99m Tc-MDP showed incidentally increased uptake in the right hemithorax, confined to the lung parenchyma of the right lung on SPECT/CT images., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Our purpose was to provide an understanding of current functional lung imaging (FLI) techniques and their potential to improve dosimetry and outcomes for patients with lung cancer receiving radiation therapy (RT). Excerpta Medica dataBASE (EMBASE), PubMed, and Cochrane Library were searched from 1990 until April 2023. Articles were included if they reported on FLI in one of: techniques, incorporation into RT planning for lung cancer, or quantification of RT-related outcomes for patients with lung cancer. Studies involving all RT modalities, including stereotactic body RT and particle therapy, were included. Meta-analyses were conducted to investigate differences in dose-function parameters between anatomic and functional RT planning techniques, as well as to investigate correlations of dose-function parameters with grade 2+ radiation pneumonitis (RP). One hundred seventy-eight studies were included in the narrative synthesis. We report on FLI modalities, dose-response quantification, functional lung (FL) definitions, FL avoidance techniques, and correlations between FL irradiation and toxicity. Meta-analysis results show that FL avoidance planning gives statistically significant absolute reductions of 3.22% to the fraction of well-ventilated lung receiving 20 Gy or more, 3.52% to the fraction of well-perfused lung receiving 20 Gy or more, 1.3 Gy to the mean dose to the well-ventilated lung, and 2.41 Gy to the mean dose to the well-perfused lung. Increases in the threshold value for defining FL are associated with decreases in functional parameters. For intensity modulated RT and volumetric modulated arc therapy, avoidance planning results in a 13% rate of grade 2+ RP, which is reduced compared with results from conventional planning cohorts. A trend of increased predictive ability for grade 2+ RP was seen in models using FL information but was not statistically significant. FLI shows promise as a method to spare FL during thoracic RT, but interventional trials related to FL avoidance planning are sparse. Such trials are critical to understanding the effect of FL avoidance planning on toxicity reduction and patient outcomes., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background and Purpose: Radiation-induced pneumonitis (RP), diagnosed 6-12 weeks after treatment, is a complication of lung tumor radiotherapy. So far, clinical and dosimetric parameters have not been reliable in predicting RP. We propose using non-contrast enhanced magnetic resonance imaging (MRI) based functional parameters acquired over the treatment course for patient stratification for improved follow-up., Materials and Methods: 23 lung tumor patients received MR-guided hypofractionated stereotactic body radiation therapy at a 0.35T MR-Linac. Ventilation- and perfusion-maps were generated from 2D-cine MRI-scans acquired after the first and last treatment fraction (Fx) using non-uniform Fourier decomposition. The relative differences in ventilation and perfusion between last and first Fx in three regions (planning target volume (PTV), lung volume receiving more than 20Gy (V20) excluding PTV, whole tumor-bearing lung excluding PTV) and three dosimetric parameters (mean lung dose, V20, mean dose to the gross tumor volume) were investigated. Univariate receiver operating characteristic curve - area under the curve (ROC-AUC) analysis was performed (endpoint RP grade≥1) using 5000 bootstrapping samples. Differences between RP and non-RP patients were tested for statistical significance with the non-parametric Mann-Whitney U test (α=0.05)., Results: 14/23 patients developed RP of grade≥1 within 3 months. The dosimetric parameters showed no significant differences between RP and non-RP patients. In contrast, the functional parameters, especially the relative ventilation difference in the PTV, achieved a p-value<0.05 and an AUC value of 0.84., Conclusion: MRI-based functional parameters extracted from 2D-cine MRI-scans were found to be predictive of RP development in lung tumor patients., Competing Interests: Declaration of competing interest The Department of Radiation Oncology of the LMU University Hospital, Munich has research agreements with Elekta, Brainlab and C-RAD., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Radiation recall pneumonitis is an inflammatory reaction of previously radiated lung parenchyma triggered by systemic pharmacological agents (such as chemotherapy and immunotherapy) or vaccination. Patients present with non-specific symptoms such as cough, shortness of breath, or hypoxia soon after the initiation of medication or vaccination. Careful assessment of the patient's history, including the thoracic radiation treatment plan and timing of the initiation of the triggering agent, in conjunction with CT findings, contribute to the diagnosis. Once a diagnosis is established, treatment includes cessation of the causative medication and/or initiation of steroid therapy. Differentiating this relatively rare entity from other common post-therapeutic complications in oncology patients, such as recurrent malignancy, infection, or medication-induced pneumonitis, is essential for guiding downstream clinical management., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2024 The Korean Society of Radiology.)

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., Competing Interests: Declaration of competing interest The authors have no relevant financial or non-financial interests to disclose., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Purpose: Radiation-induced lung injury has been shown to alter regional ventilation and perfusion in the lung. However, changes in regional pulmonary gas exchange have not previously been measured., Methods and Materials: Ten patients receiving conventional radiation therapy (RT) for lung cancer underwent pre-RT and 3-month post-RT magnetic resonance imaging (MRI) using an established hyperpolarized 129 Xe gas exchange technique to map lung function. Four patients underwent an additional 8-month post-RT MRI. The MR signal from inhaled xenon was measured in the following 3 pulmonary compartments: the lung airspaces, the alveolar membrane tissue, and the pulmonary capillaries (interacting with red blood cells [RBCs]). Thoracic 1 H MRI scans were acquired, and deformable registration was used to transfer 129 Xe functional maps to the RT planning computed tomography scan. The RT-associated changes in ventilation, membrane uptake, and RBC transfer were computed as a function of regional lung dose (equivalent dose in 2-Gy fractions). Pearson correlations and t tests were used to determine statistical significance, and weighted sum of squares linear regression subsequently characterized the dose dependence of each functional component. The pulmonary function testing metrics of forced vital capacity and diffusing capacity for carbon monoxide were also acquired at each time point., Results: Compared with pre-RT baseline, 3-month post-RT ventilation decreased by an average of -0.24 ± 0.05%/Gy (ρ = -0.88; P < .001), membrane uptake increased by 0.69 ± 0.14%/Gy (ρ = 0.94; P < .001), and RBC transfer decreased by -0.41 ± 0.06%/Gy (ρ = -0.92; P < .001). Membrane uptake maintained a strong positive correlation with regional dose at 8 months post-RT, demonstrating an increase of 0.73 ± 0.11%/Gy (ρ = 0.92; P = .006). Changes in membrane uptake and RBC transfer appeared greater in magnitude (%/Gy) for individuals with low heterogeneity in their baseline lung function. An increase in whole-lung membrane uptake showed moderate correlation with decreases in forced vital capacity (ρ = -0.50; P = .17) and diffusing capacity for carbon monoxide (ρ = -0.44; P = .23), with neither correlation reaching statistical significance., Conclusions: Hyperpolarized 129 Xe MRI measured and quantified regional, RT-associated, dose-dependent changes in pulmonary gas exchange. This tool could enable future work to improve our understanding and management of radiation-induced lung injury., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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 (D mean ) 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-D mean in patients with and without SRP. RPA identified the following risk groups: NL-D mean  ≥ 6.6 Gy (high-risk, n = 8), NL-D mean  < 6.6 Gy and Br-volume ≥ 2.5 % (intermediate-risk, n = 13), and NL-D mean  < 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-D mean ., Competing Interests: Declaration of competing interest Tsuyoshi Oguma has received lecture fees from Kyowa Kirin, AstraZeneca, KYORIN Pharmaceutical, GlaxoSmithKline, Sanofi, and Boehringer Ingelheim. Kiminobu Tanizawa has received a research grant for the Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University from FUJIFILM Corporation and Daiichi Sankyo Company, Limited. He has also received a research grant from the Japan Society for the Promotion of Science KAKENHI. Tomohiro Handa is in the employ of the collaborative research laboratory funded by Teijin Pharma Co., Ltd., and has also received research grants from FUJIFILM Corporation. Toyohiro Hirai has received a grant from FUJIFILM Corporation. Takashi Mizowaki has participated in a commissioned project and a collaborative research project with Varian Medical Systems, a collaborative research project with Hitachi Ltd. He has received scholarship donations from Hitachi Ltd, and speaker fees from Varian Medical Systems and Hitachi Ltd. The other authors have no competing interests to disclose., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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)., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [RL is funded by the Royal Marsden and ICR NIHR BRC, Royal Marsden Cancer charity and SBRI (collaborating with QURE.AI). RL’s institution receives compensation for time spent in a secondment role for the NHS England in lung cancer screening and National Institute of Health and Care Research. He has received research funding from CRUK, Innovate UK (co funded by GE Healthcare, Roche and Optellum), SBRI, RM Partners Cancer Alliance and NIHR (coapplicant with Optellum). He has received honoraria from CRUK and undertakes personal private practice. Dr Merina Ahmed declares research funding from BMS and MSD, honoraria from Astra Zeneca and is on the advisory board for BMS and the data monitoring committee for Astra Zeneca. Dr Fiona McDonald is a speaker for and declares advisory board fees from Astra Zeneca. Matthew Blackledge receives funding from Cancer Research UK, paid to his institution. Simon J Doran receives a salary from Cancer Research UK as the manager of the National Cancer Imaging Translational Accelerator (NCITA) Repository Unit. The Repository Unit provides the repository resource used to store the data for this project, together with the curation expertise and charges the PI of the project a fee for its use. Mitchell Chen is funded by the NIHR (Grant No. CL-2021–21-005) and Academy of Medical Sciences (Grant No. SGL026\1024). Sumeet Hindocha is funded by the UKRI CDT in AI for Healthcare https://ai4health.io (Grant No. P/S023283/1), by Imperial College London and by the Royal Marsden & Institute of Cancer Research NIHR Biomedical Research Centre. Benjamin Hunter is funded by Cancer Research UK (grant reference C309/A31316) and Royal Marsden Partners]., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

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., Competing Interests: Disclosure The authors have nothing to disclose., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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., (© 2024. The Author(s).)

Objective: In this study, the uptake characteristics of [ 18 F]fibroblast activation protein inhibitor (FAPI) molecular imaging probe were investigated in acute radiation pneumonia and lung cancer xenografted mice before and after radiation to assess the future applicability of [ 18 F]FAPI positron emission tomography/computed tomography (PET/CT) imaging in early radiotherapy response., Methods: Initially, the biodistribution of [ 18 F]FAPI tracer in vivo were studied in healthy mice at each time-point. A comparison of [ 18 F]FAPI and [ 18 F]fluorodeoxyglucose (FDG) PET/CT imaging efficacy in normal ICR, LLC tumor-bearing mice was evaluated. A radiation pneumonia model was then investigated using a gamma counter, small animal PET/CT, and autoradiography. The uptake properties of [ 18 F]FAPI in lung cancer and acute radiation pneumonia were investigated using autoradiography and PET/CT imaging in mice., Results: The tumor area was visible in [ 18 F]FAPI imaging and the tracer was swiftly eliminated from normal tissues and organs. There was a significant increase of [ 18 F]FDG absorption in lung tissue after radiotherapy compared to before radiotherapy, but no significant difference of [ 18 F]FAPI uptake under the same condition. Furthermore, both the LLC tumor volume and the expression of FAP-ɑ decreased after thorax irradiation. Correspondingly, there was no notable [ 18 F]FAPI uptake after irradiation, but there was an increase of [ 18 F]FDG uptake in malignancies and lungs., Conclusions: The background uptake of [ 18 F]FAPI is negligible. Moreover, the uptake of [ 18 F]FAPI may not be affected by acute radiation pneumonitis compared to [ 18 F]FDG, which may be used to more accurately evaluate early radiotherapy response of lung cancer with acute radiation pneumonia., (© 2024. The Author(s).)

Radiation pneumonitis (RP) that develops early (i.e., within 3 mo) (RP Early ) after completion of concurrent chemoradiation (cCRT) leads to treatment discontinuation and poorer survival for patients with stage III non-small cell lung cancer. Since no RP Early risk model exists, we explored whether published RP models and pretreatment 18 F-FDG PET/CT-derived features predict RP Early Methods: One hundred sixty patients with stage III non-small cell lung cancer treated with cCRT and consolidative immunotherapy were analyzed for RP Early Three published RP models that included the mean lung dose (MLD) and patient characteristics were examined. Pretreatment 18 F-FDG PET/CT normal-lung SUV featured included the following: 10th percentile of SUV (SUV P10 ), 90th percentile of SUV (SUV P90 ), SUV max , SUV mean , minimum SUV, and SD. Associations between models/features and RP Early were assessed using area under the receiver-operating characteristic curve (AUC), P values, and the Hosmer-Lemeshow test (pHL). The cohort was randomly split, with similar RP Early rates, into a 70%/30% derivation/internal validation subset. Results: Twenty (13%) patients developed RP Early Predictors for RP Early were MLD alone (AUC, 0.72; P = 0.02; pHL, 0.87), SUV P10 , SUV P90, and SUV mean (AUC, 0.70-0.74; P = 0.003-0.006; pHL, 0.67-0.70). The combined MLD and SUV P90 model generalized in the validation subset and was deemed the final RP Early model (RP Early risk = 1/[1+e (- x ) ]; x = -6.08 + [0.17 × MLD] + [1.63 × SUV P90 ]). The final model refitted in the 160 patients indicated improvement over the published MLD-alone model (AUC, 0.77 vs. 0.72; P = 0.0001 vs. 0.02; pHL, 0.65 vs. 0.87). Conclusion: Patients at risk for RP Early can be detected with high certainty by combining the normal lung's MLD and pretreatment 18 F-FDG PET/CT SUV P90 This refined model can be used to identify patients at an elevated risk for premature immunotherapy discontinuation due to RP Early and could allow for interventions to improve treatment outcomes., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

Immune checkpoint inhibitors (ICIs) therapy have revolutionized advanced lung cancer care. Interestingly, the host responses for patients received ICIs therapy are distinguishing from those with cytotoxic drugs, showing potential initial transient worsening of disease burden, pseudoprogression and delayed time to treatment response. Thus, a new imaging criterion to evaluate the response for immunotherapy should be developed. ICIs treatment is associated with unique adverse events, including potential life-threatening immune checkpoint inhibitor-related pneumonitis (ICI-pneumonitis) if treated patients are not managed promptly. Currently, the diagnosis and clinical management of ICI-pneumonitis remain challenging. As the clinical manifestation is often nonspecific, computed tomography (CT) scan and X-ray films play important roles in diagnosis and triage. This article reviews the complications of immunotherapy in lung cancer and illustrates various radiologic patterns of ICI-pneumonitis. Additionally, it is tried to differentiate ICI-pneumonitis from other pulmonary pathologies common to lung cancer such as radiation pneumonitis, bacterial pneumonia and coronavirus disease of 2019 (COVID-19) infection in recent months. Maybe it is challenging to distinguish radiologically but clinical presentation may help., (© 2023 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.)

Objective: To investigate the value of dual-energy dual-source computed tomography (DSCT) in evaluating pulmonary perfusion changes before and after radiotherapy for esophageal cancer, and its clinical use in the early diagnosis of acute radiation pneumonia (ARP)., Methods: We selected 45 patients with pathologically confirmed esophageal cancer who received radiotherapy (total irradiation dose of 60 Gy). Dual-energy DSCT scans were performed before and after radiotherapy and the normalized iodine concentrations (NIC) in the lung fields of the areas irradiated with doses of > 20 Gy, 10-20 Gy, 5-10 Gy, and < 5 Gy were measured. We also checked for the occurrence of ARP in the patients, and the differences in NIC values and NIC reduction rates before and after radiotherapy were calculated and statistically analyzed., Results: A total of 16 of the 45 patients developed ARP. The NIC values in the lung fields of all patients decreased at different degrees after radiotherapy, and the NIC values in the area where ARP developed, decreased significantly. The rate of NIC reduction and incidence rate of ARP increased gradually with the increasing irradiation dose, and the inter-group difference in NIC reduction rate was statistically significant (P < 0.05). Based on the receiver operating characteristic (ROC) curve analysis, the areas under the curves of NIC reduction rate versus ARP occurrence in the V 5-10 Gy , V 10-20 Gy , and V > 20 Gy groups were 0.780, 0.808, and 0.772, respectively. Sensitivity of diagnosis was 81.3%, 75.0%, and 68.8% and the specificity was 65.5%, 82.8%, and 79.3%, when taking 12.50%, 16.50%, and 26.0% as the diagnostic thresholds, respectively. The difference in NIC values in the lung fields of V <5 Gy before and after radiotherapy was not statistically significant (P > 0.05)., Conclusion: The dual-energy DSCT could effectively evaluate pulmonary perfusion changes after radiotherapy for esophageal cancer, and the NIC reduction rate was useful as a reference index to predict ARP and provide further reference for decisions in clinical practice., (© 2024. The Author(s).)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

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., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Abstract: 18 F-FDG uptake in radiation pneumonitis is well documented; however, the same is less so for 18 F-floroestradiol (FES), which specifically binds to the estrogen receptors in vivo. We observed increased FES uptake in the right lung of an estrogen receptor positive breast cancer patient who had undergone right modified radical mastectomy followed by radiation therapy to chest wall. The possibility of FES uptake in radiation pneumonitis must therefore be kept in mind while interpreting FES PET., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Radiation pneumonitis (RP) is a serious side effect of radiotherapy in patients with locally advanced non-small-cell lung cancer (NSCLC). Image cropping reduces training noise and may improve classification accuracy. This study proposes a prediction model for RP grade ≥ 2 using a convolutional neural network (CNN) model with image cropping. The 3D computed tomography (CT) images cropped in the whole-body, normal lung (nLung), and nLung regions overlapping the region over 20 Gy (nLung∩20 Gy) used in treatment planning were used as the input data. The output classifies patients as RP grade < 2 or RP grade ≥ 2. The sensitivity, specificity, accuracy, and area under the curve (AUC) were evaluated using the receiver operating characteristic curve (ROC). The accuracy, specificity, sensitivity, and AUC were 53.9%, 80.0%, 25.5%, and 0.58, respectively, for the whole-body method, and 60.0%, 81.7%, 36.4%, and 0.64, respectively, for the nLung method. For the nLung∩20 Gy method, the accuracy, specificity, sensitivity, and AUC improved to 75.7%, 80.0%, 70.9%, and 0.84, respectively. The CNN model, in which the input image is segmented in the normal lung considering the dose distribution, can help predict an RP grade ≥ 2 for NSCLC patients after definitive radiotherapy., (© 2023. Australasian College of Physical Scientists and Engineers in Medicine.)

Purpose: To develop a deep learning model that combines CT and radiation dose (RD) images to predict the occurrence of radiation pneumonitis (RP) in lung cancer patients who received radical (chemo)radiotherapy., Methods: CT, RD images and clinical parameters were obtained from 314 retrospectively-collected patients (training set) and 35 prospectively-collected patients (test-set-1) who were diagnosed with lung cancer and received radical radiotherapy in the dose range of 50 Gy and 70 Gy. Another 194 (60 Gy group, test-set-2) and 158 (74 Gy group, test-set-3) patients from the clinical trial RTOG 0617 were used for external validation. A ResNet architecture was used to develop a prediction model that combines CT and RD features. Thereafter, the CT and RD weights were adjusted by using 40 patients from test-set-2 or 3 to accommodate cohorts with different clinical settings or dose delivery patterns. Visual interpretation was implemented using a gradient-weighted class activation map (grad-CAM) to observe the area of model attention during the prediction process. To improve the usability, ready-to-use online software was developed., Results: The discriminative ability of a baseline trained model had an AUC of 0.83 for test-set-1, 0.55 for test-set-2, and 0.63 for test-set-3. After adjusting CT and RD weights of the model using a subset of the RTOG-0617 subjects, the discriminatory power of test-set-2 and 3 improved to AUC 0.65 and AUC 0.70, respectively. Grad-CAM showed the regions of interest to the model that contribute to the prediction of RP., Conclusion: A novel deep learning approach combining CT and RD images can effectively and accurately predict the occurrence of RP, and this model can be adjusted easily to fit new cohorts., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Abstract: A 42-year-old woman diagnosed with de-novo stage IV breast cancer underwent 18 F-fluoroestradiol (FES) PET/CT to evaluate the estrogen receptor status of metastatic lesions. The largest pulmonary nodule showed obvious FES uptake, consistent with pulmonary metastases from breast cancer. Interestingly, the images revealed a striking accumulation of FES in ground-glass attenuation in the left lobe of lung, suggestive of radiation pneumonitis., Competing Interests: Conflicts of interest and sources of funding: The authors declare that they have no conflict of interest. This work was sponsored in part by the National Natural Science Foundation of China (grant 82071967), Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (CIFMS) (grant 2021-I2M-1-014, 2021-I2M-1-025, 2021-I2M-1-002, 2021-I2M-1-003), National Key Research and Development Program of China (grant 2016YFC0901500), CAMS fund for Rare Diseases Research (grant 2016ZX310174-4), and National High Level Hospital Clinical Research Funding (grant 2022-PUMCH-A-165, 2022-PUMCH-B-039)., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose: Radiation pneumonitis (RP) is one of the common side effects of radiation therapy in the thoracic region. Radiomics and dosiomics quantify information implicit within medical images and radiation therapy dose distributions. In this study we demonstrate the prognostic potential of radiomics, dosiomics, and clinical features for RP prediction., Methods and Materials: Radiomics, dosiomics, dose-volume histogram (DVH) metrics, and clinical parameters were obtained on 314 retrospectively collected and 35 prospectively enrolled patients diagnosed with lung cancer between 2013 to 2019. A radiomics risk score (R score) and dosiomics risk score (D score), as well as a DVH-score, were calculated based on logistic regression after feature selection. Six models were built using different combinations of R score, D score, DVH score, and clinical parameters to evaluate their added prognostic power. Overoptimism was evaluated by bootstrap resampling from the training set, and the prospectively collected cohort was used as the external test set. Model calibration and decision-curve characteristics of the best-performing models were evaluated. For ease of further evaluation, nomograms were constructed for selected models., Results: A model built by integrating all of the R score, D score, and clinical parameters had the best discriminative ability with areas under the curve of 0.793 (95% confidence interval [CI], 0.735-0.851), 0.774 (95% CI, 0.762-0.786), and 0.855 (95% CI, 0.719-0.990) in the training, bootstrapping, and external test sets, respectively. The calibration curve image showed good agreement between the predicted and actual values, with a slope of 1.21 and intercept of -0.04. The decision curve image showed a positive net benefit for the final model based on the nomogram., Conclusions: Radiomic and dosiomic features have the potential to assist with the prediction of RP, and the combination of radiomics, dosiomics, and clinical parameters led to the best prognostic model in the present study., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Purpose: Radiation pneumonitis (RP) is dose-limiting toxicity for non-small-cell cancer (NSCLC). This study developed an RP prediction model by integrating dose-function features from computed four-dimensional computed tomography (4DCT) ventilation using the least absolute shrinkage and selection operator (LASSO)., Methods: Between 2013 and 2020, 126 NSCLC patients were included in this study who underwent a 4DCT scan to calculate ventilation images. We computed two sets of candidate dose-function features from (1) the percentage volume receiving > 20 Gy or the mean dose on the functioning zones determined with the lower cutoff percentile ventilation value, (2) the functioning zones determined with lower and upper cutoff percentile ventilation value using 4DCT ventilation images. An RP prediction model was developed by LASSO while simultaneously determining the regression coefficient and feature selection through fivefold cross-validation., Results: We found 39.3 % of our patients had a ≥ grade 2 RP. The mean area under the curve (AUC) values for the developed models using clinical, dose-volume, and dose-function features with a lower cutoff were 0.791, and the mean AUC values with lower and upper cutoffs were 0.814. The relative regression coefficient (RRC) on dose-function features with upper and lower cutoffs revealed a relative impact of dose to each functioning zone to RP. RRCs were 0.52 for the mean dose on the functioning zone, with top 20 % of all functioning zone was two times greater than that of 0.19 for these with 60 %-80 % and 0.17 with 40 %-60 % (P < 0.01)., Conclusions: The introduction of dose-function features computed from functioning zones with lower and upper cutoffs in a machine learning framework can improve RP prediction. The RRC given by LASSO using dose-function features allows for the quantification of the RP impact of dose on each functioning zones and having the potential to support treatment planning on functional image-guided radiotherapy., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

Radiation pneumonitis is a rare but possibly fatal side effect of 90 Y radioembolization. It may occur 1-6 mo after therapy, if a significant part of the 90 Y microspheres shunts to the lungs. In current clinical practice, a predicted lung dose greater than 30 Gy is considered a criterion to exclude patients from treatment. However, contrasting findings regarding the occurrence of radiation pneumonitis and lung dose were previously reported in the literature. In this study, the relationship between the lung dose and the eventual occurrence of radiation pneumonitis after 90 Y radioembolization was investigated. Methods: We retrospectively analyzed 317 90 Y liver radioembolization procedures performed during an 8-y period (February 2012 to September 2020). We calculated the predicted lung mean dose (LMD) using 99m Tc-MAA planar scintigraphy (LMD MAA ) acquired during the planning phase and left LMD (LMD Y-90 ) using the 90 Y PET/CT acquired after the treatment. For the lung dose computation, we used the left lung as the representative lung volume, to compensate for scatter from the liver moving in the craniocaudal direction because of breathing and mainly affecting the right lung. Results: In total, 272 patients underwent 90 Y procedures, of which 63% were performed with glass microspheres and 37% with resin microspheres. The median injected activity was 1,974 MBq (range, 242-9,538 MBq). The median LMD MAA was 3.5 Gy (range, 0.2-89.0 Gy). For 14 procedures, LMD MAA was more than 30 Gy. Median LMD Y-90 was 1 Gy (range, 0.0-22.1 Gy). No patients had an LMD Y-90 of more than 30 Gy. Of the 3 patients with an LMD Y-90 of more than 12 Gy, 2 patients (one with an LMD Y-90 of 22.1 Gy and an LMD MAA of 89 Gy; the other with an LMD Y-90 of 17.7 Gy and an LMD MAA of 34.1 Gy) developed radiation pneumonitis and consequently died. The third patient, with an LMD Y-90 of 18.4 Gy (LMD MAA , 29.1 Gy), died 2 mo after treatment, before the imaging evaluation, because of progressive disease. Conclusion: The occurrence of radiation pneumonitis as a consequence of a lung shunt after 90 Y radioembolization is rare (<1%). No radiation pneumonitis developed in patients with a measured LMD Y-90 lower than 12 Gy., (© 2022 by the Society of Nuclear Medicine and Molecular Imaging.)

Background: The combination of immunotherapy and chemoradiotherapy has become the standard therapeutic strategy for patients with unresected locally advance-stage non-small cell lung cancer (NSCLC) and induced treatment-related adverse effects, particularly immune checkpoint inhibitor-related pneumonitis (CIP) and radiation pneumonitis (RP). The aim of this study is to differentiate between CIP and RP by pretreatment CT radiomics and clinical or radiological parameters., Methods: A total of 126 advance-stage NSCLC patients with pneumonitis were enrolled in this retrospective study and divided into the training dataset ( n =88) and the validation dataset ( n = 38). A total of 837 radiomics features were extracted from regions of interest based on the lung parenchyma window of CT images. A radiomics signature was constructed on the basis of the predictive features by the least absolute shrinkage and selection operator. A logistic regression was applied to develop a radiomics nomogram. Receiver operating characteristics curve and area under the curve (AUC) were applied to evaluate the performance of pneumonitis etiology identification., Results: There was no significant difference between the training and the validation datasets for any clinicopathological parameters in this study. The radiomics signature, named Rad-score, consisting of 11 selected radiomics features, has potential ability to differentiate between CIP and RP with the empirical and α-binormal-based AUCs of 0.891 and 0.896. These results were verified in the validation dataset with AUC = 0.901 and 0.874, respectively. The clinical and radiological parameters of bilateral changes ( p < 0.001) and sharp border ( p = 0.001) were associated with the identification of CIP and RP. The nomogram model showed good performance on discrimination in the training dataset (AUC = 0.953 and 0.950) and in the validation dataset (AUC = 0.947 and 0.936)., Conclusions: CT-based radiomics features have potential values for differentiating between patients with CIP and patients with RP. The addition of bilateral changes and sharp border produced superior model performance on classifying, which could be a useful method to improve related clinical decision-making., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Qiu, Xing, Wang, Feng and Wen.)

Purpose: Consolidation immunotherapy after completion of chemoradiotherapy has become the standard of care for unresectable locally advanced non-small cell lung cancer and can induce potentially severe and life-threatening adverse events, including both immune checkpoint inhibitor-related pneumonitis (CIP) and radiation pneumonitis (RP), which are very challenging for radiologists to diagnose. Differentiating between CIP and RP has significant implications for clinical management such as the treatments for pneumonitis and the decision to continue or restart immunotherapy. The purpose of this study is to differentiate between CIP and RP by a CT radiomics approach., Methods: We retrospectively collected the CT images and clinical information of patients with pneumonitis who received immune checkpoint inhibitor (ICI) only (n = 28), radiotherapy (RT) only (n = 31), and ICI+RT (n = 14). Three kinds of radiomic features (intensity histogram, gray-level co-occurrence matrix [GLCM] based, and bag-of-words [BoW] features) were extracted from CT images, which characterize tissue texture at different scales. Classification models, including logistic regression, random forest, and linear SVM, were first developed and tested in patients who received ICI or RT only with 10-fold cross-validation and further tested in patients who received ICI+RT using clinicians' diagnosis as a reference., Results: Using 10-fold cross-validation, the classification models built on the intensity histogram features, GLCM-based features, and BoW features achieved an area under curve (AUC) of 0.765, 0.848, and 0.937, respectively. The best model was then applied to the patients receiving combination treatment, achieving an AUC of 0.896., Conclusions: This study demonstrates the promising potential of radiomic analysis of CT images for differentiating between CIP and RP in lung cancer, which could be a useful tool to attribute the cause of pneumonitis in patients who receive both ICI and RT., (© 2022 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Objectives: We aimed to explore the synergistic combination of a topologically invariant Betti number (BN)-based signature and a biomarker for the accurate prediction of symptomatic (grade ≥2) radiation-induced pneumonitis (RP+) before stereotactic ablative radiotherapy (SABR) for lung cancer., Methods: A total of 272 SABR cases with early-stage non-small cell lung cancer were chosen for this study. The occurrence of RP+ was predicted using a support vector machine (SVM) model trained with the combined features of the BN-based signature extracted from planning computed tomography (pCT) images and a pretreatment biomarker, serum Krebs von den Lungen-6 (BN+KL-6 model). In all, 242 (20 RP+ and 222 RP-(grade 1)) and 30 cases (8 RP+ and 22 RP-) were used for training and testing the model, respectively. The BN-based features were extracted from BN maps that characterize topologically invariant heterogeneous traits of potential RP+ lung regions on pCT images by applying histogram- and texture-based feature calculations to the maps. The SVM models were built to predict RP+ patients with a BN signature that was constructed based on the least absolute shrinkage and selection operator logistic regression model. The evaluation of the prediction models was performed based on the area under the receiver operating characteristic curves (AUCs) and accuracy in the test. The performance of the BN+KL-6 model was compared to the performance based on the BN, conventional original pCT, and wavelet decomposition (WD) models., Results: The test AUCs obtained for the BN+KL-6, BN, pCT, and WD models were 0.825, 0.807, 0.642, and 0.545, respectively. The accuracies of the BN+KL-6, BN, pCT, and WD models were found to be 0.724, 0.708, 0.591, and 0.534, respectively., Conclusion: This study demonstrated the comprehensive performance of the BN+KL-6 model for the prediction of potential RP+ patients before SABR for lung cancer., Competing Interests: The authors have declared that no competing interests exist.