Excluding PTV from lung volume may better predict radiation pneumonitis for intensity modulated radiation therapy in lung cancer patients

Enhancing the prediction of symptomatic radiation pneumonitis for locally advanced non-small-cell lung cancer by combining 3D deep learning-derived imaging features with dose-volume metrics: a two-center study

OBJECTIVE

This study aims to examine the ability of deep learning (DL)-derived imaging features for the prediction of radiation pneumonitis (RP) in locally advanced non-small-cell lung cancer (LA-NSCLC) patients.

MATERIALS AND METHODS

The study cohort consisted of 90 patients from the Fudan University Shanghai Cancer Center and 59 patients from the Affiliated Hospital of Jiangnan University. Occurrences of RP were used as the endpoint event. A total of 512 3D DL-derived features were extracted from two regions of interest (lung-PTV and PTV-GTV) delineated on the pre-radiotherapy planning CT. Feature selection was done using LASSO regression, and the classification models were built using the multilayered perceptron method. Performances of the developed models were evaluated by receiver operating characteristic curve analysis. In addition, the developed models were supplemented with clinical variables and dose-volume metrics of relevance to search for increased predictive value.

RESULTS

The predictive model using DL features derived from lung-PTV outperformed the one based on features extracted from PTV-GTV, with AUCs of 0.921 and 0.892, respectively, in the internal test dataset. Furthermore, incorporating the dose-volume metric V30Gy into the predictive model using features from lung-PTV resulted in an improvement of AUCs from 0.835 to 0.881 for the training data and from 0.690 to 0.746 for the validation data, respectively (DeLong p < 0.05).

CONCLUSION

Imaging features extracted from pre-radiotherapy planning CT using 3D DL networks could predict radiation pneumonitis and may be of clinical value for risk stratification and toxicity management in LA-NSCLC patients.

CLINICAL RELEVANCE STATEMENT

Integrating DL-derived features with dose-volume metrics provides a promising noninvasive method to predict radiation pneumonitis in LA-NSCLC lung cancer radiotherapy, thus improving individualized treatment and patient outcomes.

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.

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.

Dosimetric study of Hounsfield number correction effect in areas influenced by contrast product in lungs case

BACKGROUND

The aim of the study was dosimetric effect quantification of exclusive computed tomography (CT) use with an intravenous (IV) contrast agent (CA ), on dose distribution of 3D-CRT treatment plans for lung cancer. Furthermore, dosimetric advantage investigation of manually contrast-enhanced region overriding, especially the heart.

MATERIALS AND METHODS

Ten patients with lung cancer were considered. For each patient two planning CT sets were initially taken with and without CA. Treatment planning were optimized based on CT scans without CA. All plans were copied and recomputed on scans with CA. In addition, scans with IV contrast were copied and density correction was performed for heart contrast enhanced. Same plans were copied and replaced to undo dose calculation errors that may be caused by CA. Eventually, dosimetric evaluations based on dose volume histograms (DVHs) of planning target volumes (PTV) and organs at-risk were studied and analyzed using the Wilcoxon's signed rank test.

RESULTS

There is no statistically significant difference in dose calculation for the PTV maximum, mean, minimum doses, spinal cord maximum doses and lung volumes that received 20 and 30 Gy, between planes calculated with and without contrast scans (p > 0.05) and also for contrast scan, with manual regions overriding.

CONCLUSIONS

Dose difference caused by the contrast agent is negligible and not significant. Therefore, there is no justification to perform two scans, and using an IV contrast enhanced scan for dose calculation is sufficient.

Construction and Verification of a Radiation Pneumonia Prediction Model Based on Multiple Parameters

Objective:

Patients with lung cancer are at risk of radiation pneumonia (RP) after receiving radiotherapy. We established a prediction model according to the critical indicators extracted from radiation pneumonia patients.

Materials and Methods:

74 radiation pneumonia patients were involved in the training set. Firstly, the clinical data, hematological and radiation dose parameters of the 74 patients were screened by Logistics regression univariate analysis according to the level of radiation pneumonia. Next, Stepwise regression analysis was utilized to construct the regression model. Then, the influence of continuous variables on RP was tested by smoothing function. Finally, the model was externally verified by 30 patients in validation set and visualized by R code.

Results:

In the training set, there was 40 patients suffered≥ level 2 acute radiation pneumonia. Clinical data (diabetes), blood indexes (lymphocyte percentage, basophil percentage, platelet count) and radiation dose (V15 > 40%, V20 > 30%, V35 >18%, V40 > 15%) were related to radiation pneumonia (P < 0.05). Particularly, stepwise regression analysis indicated that the history of diabetes, the basophils percentage, platelet count and V20 could be the best combination used for predicting radiation pneumonia. The column chart was obtained by fitting the regression model with the combined indicator. The receiver operating characteristic (ROC) curve showed that the AUC in the development term was 0.853, the AUC was 0.656 in the validation term. And calibration curves of both groups showed the high stability in efficiently diagnostic. Furthermore, the DCA curve showed that the model had a satisfactory positive net benefit.

Conclusion:

The combination of the basophils percentage, platelet count and V20 is available to build a predictive model of radiation pneumonia for patients with advanced lung cancer.

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

PURPOSE

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

METHODS AND MATERIALS

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

RESULTS

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

CONCLUSIONS

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

The status of medical physics in radiotherapy in China

PURPOSE

To present an overview of the status of medical physics in radiotherapy in China, including facilities and devices, occupation, education, research, etc. MATERIALS AND METHODS: The information about medical physics in clinics was obtained from the 9-th nationwide survey conducted by the China Society for Radiation Oncology in 2019. The data of medical physics in education and research was collected from the publications of the official and professional organizations.

RESULTS

By 2019, there were 1463 hospitals or institutes registered to practice radiotherapy and the number of accelerators per million population was 1.5. There were 4172 medical physicists working in clinics of radiation oncology. The ratio between the numbers of radiation oncologists and medical physicists is 3.51. Approximately, 95% of medical physicists have an undergraduate or graduate degrees in nuclear physics and biomedical engineering. 86% of medical physicists have certificates issued by the Chinese Society of Medical Physics. There has been a fast growth of publications by authors from mainland of China in the top international medical physics and radiotherapy journals since 2018.

CONCLUSIONS

Demand for medical physicists in radiotherapy increased quickly in the past decade. The distribution of radiotherapy facilities in China became more balanced. High quality continuing education and training programs for medical physicists are deficient in most areas. The role of medical physicists in the clinic has not been clearly defined and their contributions have not been fully recognized by the community.

Adaptive intensity-modulated radiotherapy with simultaneous integrated boost for stage III non-small cell lung cancer: Is a routine adaptation beneficial?

PURPOSE

Tumor and anatomical changes during radiotherapy have been observed in stage III non-small cell lung cancer (NSCLC) from many previous studies. We hypothesized that a routinely scheduled adaptive radiotherapy would have clinical important dose benefits to lower the risk of toxicities, without increasing the tumor recurrences.

METHODS

We retrospectively reviewed 92 consecutive patients with inoperable stage III NSCLC between November 2017 and March 2019. All eligible patients should received simultaneously integrated boost (SIB) using intensity-modulated radiation therapy (IMRT). A mid-treatment CT simulation and a new adapted plan were routinely given after the first 20 fractions. The organs at risk (OARs) were delineated per RTOG 1106 atlas. Dose-volume histograms were quantitatively compared between the initial and composite adaptive plans. Logistic regression was applied to analyze the dose-response relationship. Clinical endpoints included acute symptomatic radiation pneumonitis (RP2) and esophagitis (RE2), local and regional tumor control, and progression-free survival (PFS).

RESULTS

Sixty-four eligible patients received adaptive SIB-IMRT were consecutively included. The GTVs reduced by a median of -38.2% after 42 to 44 Gy in 20 fractions of radiotherapy. By adapting to tumor and anatomical changes, dosimetric parameters of OARs decreased significantly. The mean lung dose decreased by an average of -74.8 cGy, and mean esophagus dose was lower by 183.1 cGy. We found grade 2 or higher acute RP in 11 patients (17.2%), and RE2 in 28 patients (43.8%). Commonly used lung and esophagus dose metrics were significantly associated with RP2 and RE2. The adaptation could reduce RP2 probability by 3%, and RE2 risk by 5%. Subgroups with higher OARs dose or larger tumor shrinkage may get more dose and toxicities benefits. The estimated median PFS was 12.5 months from the start of radiotherapy.

CONCLUSIONS

We demonstrated that the routinely adaptive SIB-IMRT strategy could significantly reduce the dose to surrounding normal tissues, potentially lower the associated acute RP and RE, without increasing the risk of tumor recurrences.

Intermediate Dose-Volume Parameters, Not Low-Dose Bath, Is Superior to Predict Radiation Pneumonitis for Lung Cancer Treated With Intensity-Modulated Radiotherapy

Purpose

Although intensity-modulated radiotherapy (IMRT) is now a preferred option for conventionally fractionated RT in lung cancer, the commonly used cutoff values of the dosimetric constraints are still mainly derived from the data using three-dimensional conformal radiotherapy (3D-CRT). We aimed to compare the prediction performance among different dosimetric parameters for acute radiation pneumonitis (RP) in patients with lung cancer received IMRT.

Methods

A total of 236 patients treated with IMRT were retrospectively reviewed in two independent groups of lung cancer from January 2014 to August 2018. The primary endpoint was grade 2 or higher acute RP (RP2). Dose metrics were generated from the bilateral lung volume outside GTV (Vdose_G) and PTV (Vdose_P). The associations of RP2 with clinical variables, dose-volume parameters and mean lung dose (MLD) were analyzed by univariate and multivariate logistic regression. The power of discrimination among each predictor was assessed by employing the bootstrapped area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and the integrated discrimination improvement (IDI).

Results

Thirty-four (14.4%) out of 236 patients developed acute RP2 after the end of IMRT. The clinical parameters were identified as less important predictors for RP2 based on univariate and multivariate analysis. In both studied groups, the significance of association was more convincing in V20_P, V30_P, and MLD_P (smaller Ps) than V5_G and V5_P. The largest bootstrapped AUC was identified for the V30_P. We found a trend of better discriminating performance for the V20_P and V30_P, and MLD_P than the V5_G and V5_P according to the higher values in AUC, IDI, and NRI analysis. To limit RP2 incidence less than 20%, the V30_P cutoff was 14.5%.

Conclusions

This study identified the intermediate dose-volume parameters V20_P and V30_P with better prediction performance for acute RP2 than low-dose metrics V5_G and V5_P. Among all studied predictors, the V30_P had the best discriminating power, and should be considered as a supplement to the traditional dose constraints in lung cancer treated with IMRT.

Radiation recall pneumonitis induced by PD-1/PD-L1 blockades: mechanisms and therapeutic implications

BACKGROUND

The synergistic effect of radiotherapy (RT) in combination with immunotherapy has been shown in several clinical trials and case reports. The overlapping pulmonary toxicity induced by thoracic RT and programmed death 1/programmed death ligand-1 (PD-1/PD-L1) blockades is an important issue of clinical investigation in combination treatment. Thus far, the underlying mechanism of this toxicity remains largely unknown.

MAIN TEXT

In this review, we discuss the unique pattern of radiation recall pneumonitis (RRP) induced by PD-1 blockade. The clinical presentation is different from common radiation pneumonitis (RP) or RRP induced by cytotoxic drugs. The immune checkpoint inhibitors may evoke an inflammatory reaction in patients' previously irradiated fields, with infiltrating lymphocytes and potential involvement of related cytokines. All RRP patients have showed durable response to anti-PD-1/PD-L1. RRP is manageable; however, interruption of checkpoint blockades is necessary and immunosuppressive treatment should be started immediately. Further analyses of the predictive factors, including RT dosimetric parameters, tumor-infiltrating lymphocytes (TILs), and PD-L1 expression, are needed given the wide use of immune checkpoint inhibitors and high mortality from lung toxicity with the combination treatment.

CONCLUSION

Immune checkpoint inhibitors may evoke an RRP in the patients' previously irradiated fields. Interactions between immune checkpoint inhibitors and radiotherapy should be studied further.

Radiation-induced lung toxicity - cellular and molecular mechanisms of pathogenesis, management, and literature review

Lung, breast, and esophageal cancer represent three common malignancies with high incidence and mortality worldwide. The management of these tumors critically relies on radiotherapy as a major part of multi-modality care, and treatment-related toxicities, such as radiation-induced pneumonitis and/or lung fibrosis, are important dose limiting factors with direct impact on patient outcomes and quality of life. In this review, we summarize the current understanding of radiation-induced pneumonitis and pulmonary fibrosis, present predictive factors as well as recent diagnostic and therapeutic advances. Novel candidates for molecularly targeted approaches to prevent and/or treat radiation-induced pneumonitis and pulmonary fibrosis are discussed.