Modeling radiation pneumonitis of pulmonary stereotactic body radiotherapy: The impact of a local dose–effect relationship for lung perfusion loss

A novel predictor for dosimetry data of lung and the radiation pneumonitis incidence prior to SBRT in lung cancer patients

Normal tissue complication probability (NTCP) models for radiation pneumonitis (RP) in lung cancer patients with stereotactic body radiation therapy (SBRT), which based on dosimetric data from treatment planning, are limited to patients who have already received radiation therapy (RT). This study aims to identify a novel predictive factor for lung dose distribution and RP probability before devising actionable SBRT plans for lung cancer patients. A comprehensive correlation analysis was performed on the clinical and dose parameters of lung cancer patients who underwent SBRT. Linear regression models were utilized to analyze the dosimetric data of lungs. The performance of the regression models was evaluated using mean squared error (MSE) and the coefficient of determination (R2). Correlational analysis revealed that most clinical data exhibited weak correlations with dosimetric data. However, nearly all dosimetric variables showed "strong" or "very strong" correlations with each other, particularly concerning the mean dose of the ipsilateral lung (MI) and the other dosimetric parameters. Further study verified that the lung tumor ratio (LTR) was a significant predictor for MI, which could predict the incidence of RP. As a result, LTR can predict the probability of RP without the need to design an elaborate treatment plan. This study, as the first to offer a comprehensive correlation analysis of dose parameters, explored the specific relationships among them. Significantly, it identified LTR as a novel predictor for both dose parameters and the incidence of RP, without the need to design an elaborate treatment plan.

Lung sparing in MR-guided non-adaptive SBRT treatment of peripheral lung tumors

Objective.We aim to: (1) quantify the benefits of lung sparing using non-adaptive magnetic resonance guided stereotactic body radiotherapy (MRgSBRT) with advanced motion management for peripheral lung cancers compared to conventional x-ray guided SBRT (ConvSBRT); (2) establish a practical decision-making guidance metric to assist a clinician in selecting the appropriate treatment modality.Approach.Eleven patients with peripheral lung cancer who underwent breath-hold, gated MRgSBRT on an MR-guided linear accelerator (MR linac) were studied. Four-dimensional computed tomography (4DCT)-based retrospective planning using an internal target volume (ITV) was performed to simulate ConvSBRT, which were evaluated against the original MRgSBRT plans. Metrics analyzed included planning target volume (PTV) coverage, various lung metrics and the generalized equivalent unform dose (gEUD). A dosimetric predictor for achievable lung metrics was derived to assist future patient triage across modalities.Main results.PTV coverage was high (median V100% > 98%) and comparable for both modalities. MRgSBRT had significantly lower lung doses as measured by V20 (median 3.2% versus 4.2%), mean lung dose (median 3.3 Gy versus 3.8 Gy) and gEUD. Breath-hold, gated MRgSBRT resulted in an average reduction of 47% in PTV volume and an average increase of 19% in lung volume. Strong correlation existed between lung metrics and the ratio of PTV to lung volumes (RPTV/Lungs) for both modalities, indicating that RPTV/Lungsmay serve as a good predictor for achievable lung metrics without the need for pre-planning. A threshold value of RPTV/Lungs< 0.035 is suggested to achieve V20 < 10% using ConvSBRT. MRgSBRT should otherwise be considered if the threshold cannot be met.Significance.The benefits of lung sparing using MRgSBRT were quantified for peripheral lung tumors; RPTV/Lungswas found to be an effective predictor for achievable lung metrics across modalities. RPTV/Lungscan assist a clinician in selecting the appropriate modality without the need for labor-intensive pre-planning, which has significant practical benefit for a busy clinic.

Predicting radiation pneumonitis in lung cancer: a EUD-based machine learning approach for volumetric modulated arc therapy patients

Purpose

This study aims to develop an optimal machine learning model that uses lung equivalent uniform dose (lung EUD to predict radiation pneumonitis (RP) occurrence in lung cancer patients treated with volumetric modulated arc therapy (VMAT).

Methods

We analyzed a cohort of 77 patients diagnosed with locally advanced squamous cell lung cancer (LASCLC) receiving concurrent chemoradiotherapy with VMAT. Patients were categorized based on the onset of grade II or higher radiation pneumonitis (RP 2+). Dose volume histogram data, extracted from the treatment planning system, were used to compute the lung EUD values for both groups using a specialized numerical analysis code. We identified the parameter α, representing the most significant relative difference in lung EUD between the two groups. The predictive potential of variables for RP2+, including physical dose metrics, lung EUD, normal tissue complication probability (NTCP) from the Lyman-Kutcher-Burman (LKB) model, and lung EUD-calibrated NTCP for affected and whole lung, underwent both univariate and multivariate analyses. Relevant variables were then employed as inputs for machine learning models: multiple logistic regression (MLR), support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN). Each model's performance was gauged using the area under the curve (AUC), determining the best-performing model.

Results

The optimal α-value for lung EUD was 0.3, maximizing the relative lung EUD difference between the RP 2+ and non-RP 2+ groups. A strong correlation coefficient of 0.929 (P< 0.01) was observed between lung EUD (α = 0.3) and physical dose metrics. When examining predictive capabilities, lung EUD-based NTCP for the affected lung (AUC: 0.862) and whole lung (AUC: 0.815) surpassed LKB-based NTCP for the respective lungs. The decision tree (DT) model using lung EUD-based predictors emerged as the superior model, achieving an AUC of 0.98 in both training and validation datasets.

Discussions

The likelihood of developing RP 2+ has shown a significant correlation with the advancements in RT technology. From traditional 3-D conformal RT, lung cancer treatment methodologies have transitioned to sophisticated techniques like static IMRT. Accurately deriving such a dose-effect relationship through NTCP modeling of RP incidence is statistically challenging due to the increased number of degrees-of-freedom. To the best of our knowledge, many studies have not clarified the rationale behind setting the α-value to 0.99 or 1, despite the closely aligned calculated lung EUD and lung mean dose MLD. Perfect independence among variables is rarely achievable in real-world scenarios. Four prominent machine learning algorithms were used to devise our prediction models. The inclusion of lung EUD-based factors substantially enhanced their predictive performance for RP 2+. Our results advocate for the decision tree model with lung EUD-based predictors as the optimal prediction tool for VMAT-treated lung cancer patients. Which could replace conventional dosimetric parameters, potentially simplifying complex neural network structures in prediction models.

Parameters of the Lyman Model for Calculation of Normal-Tissue Complication Probability: A Systematic Literature Review

PURPOSE

The Lyman model is one of the most used radiobiological models for calculation of normal-tissue complication probability (NTCP). Since its introduction in 1985, many authors have published parameter values for the model based on clinical data of different radiotherapeutic situations. This study attempted to collect the entirety of radiobiological parameter sets published to date and provide an overview of the data basis for different variations of the model. Furthermore, it sought to compare the parameter values and calculated NTCPs for selected endpoints with sufficient data available.

METHODS AND MATERIALS

A systematic literature analysis was performed, searching for publications that provided parameters for the different variations of the Lyman model in the Medline database using PubMed. Parameter sets were grouped into 13 toxicity-related endpoint groups. For 3 selected endpoint groups (≤25% reduction of saliva 12 months after irradiation of the parotid, symptomatic pneumonitis after irradiation of the lung, and bleeding of grade 2 or less after irradiation of the rectum), parameter values were compared and differences in calculated NTCP values were analyzed.

RESULTS

A total of 509 parameter sets from 130 publications were identified. Considerable heterogeneities were detected regarding the number of parameters available for different radio-oncological situations. Furthermore, for the 3 selected endpoints, large differences in published parameter values were found. These translated into great variations of calculated NTCPs, with maximum ranges of 35.2% to 93.4% for the saliva endpoint, of 39.4% to 90.4% for the pneumonitis endpoint, and of 5.4% to 99.3% for the rectal bleeding endpoint.

CONCLUSIONS

The detected heterogeneity of the data as well as the large variations of published radiobiological parameters underline the necessity for careful interpretation when using such parameters for NTCP calculations. Appropriate selection of parameters and validation of values are essential when using the Lyman model.

External validation of pulmonary radiotherapy toxicity models for ultracentral lung tumors

Introduction

Pulmonary toxicity is dose-limiting in stereotactic body radiation therapy (SBRT) for tumors that abut the proximal bronchial tree (PBT), esophagus, or other mediastinal structures. In this work we explored published models of pulmonary toxicity following SBRT for such ultracentral tumors in an independent cohort of patients.

Methods

The PubMed database was searched for pulmonary toxicity models. Identified models were tested in a cohort of patients with ultracentral lung tumors treated between 2008 and 2017 at one large center (N = 88). This cohort included 60 % primary and 40 % metastatic tumors treated to 45 Gy in 5 fractions (fx), 50 Gy in 5 fx, 60 Gy in 8 fx, or 60 Gy in 15 fx prescribed as 100 % dose to PTV.

Results

Seven published NTCP models from two studies were identified. The NTCP models utilized PBT max point dose (Dmax), D0.2 cm³, V65, V100, and V130. Within the independent cohort, the ≥ grade 3 toxicity and grade 5 toxicity rates were 18 % and 7-10 %, respectively, and the Dmax models best described pulmonary toxicity. The Dmax to 0.1 cm³ model was better calibrated and had increased steepness compared to the Dmax model. A re-planning study minimizing PBT 0.1 cm³ to below 122 Gy in EQD2₃ (for a 10 % ≥grade 3 pulmonary toxicity) was demonstrated to be completely feasible in 4/6 patients, and dose to PBT 0.1 cm³ was considerably lowered in all six patients.

Conclusions

Pulmonary toxicity models were identified from two studies and explored within an independent ultracentral lung tumor cohort. A modified Dmax to 0.1 cm³ PBT model displayed the best performance. This model could be utilized as a starting point for rationally constructed airways constraints in ultracentral patients treated with SBRT or hypofractionation.

Mass Transport Model of Radiation Response: Calibration and Application to Chemoradiation for Pancreatic Cancer

PURPOSE

The benefit of radiation therapy for pancreatic ductal adenocarcinoma (PDAC) remains unclear. We hypothesized that a new mechanistic mathematical model of chemotherapy and radiation response could predict clinical outcomes a priori, using a previously described baseline measurement of perfusion from computed tomography scans, normalized area under the enhancement curve (nAUC).

METHODS AND MATERIALS

We simplified an existing mass transport model that predicted cancer cell death by replacing previously unknown variables with averaged direct measurements from randomly selected pathologic sections of untreated PDAC. This allowed using nAUC as the sole model input to approximate tumor perfusion. We then compared the predicted cancer cell death to the actual cell death measured from corresponding resected tumors treated with neoadjuvant chemoradiation in a calibration cohort (n = 80) and prospective cohort (n = 25). After calibration, we applied the model to 2 separate cohorts for pathologic and clinical associations: targeted therapy cohort (n = 101), cetuximab/bevacizumab + radiosensitizing chemotherapy, and standard chemoradiation cohort (n = 81), radiosensitizing chemotherapy to 50.4 Gy in 28 fractions.

RESULTS

We established the relationship between pretreatment computed v nAUC to pathologically verified blood volume fraction of the tumor (r = 0.65; P = .009) and fractional tumor cell death (r = 0.97-0.99; P < .0001) in the calibration and prospective cohorts. On multivariate analyses, accounting for traditional covariates, nAUC independently associated with overall survival in all cohorts (mean hazard ratios, 0.14-0.31). Receiver operator characteristic analyses revealed discrimination of good and bad prognostic groups in the cohorts with area under the curve values of 0.64 to 0.71.

CONCLUSIONS

This work presents a new mathematical modeling approach to predict clinical response from chemotherapy and radiation for PDAC. Our findings indicate that oxygen/drug diffusion strongly influences clinical responses and that nAUC is a potential tool to select patients with PDAC for radiation therapy.

Radiomic and Dosiomic Features for the Prediction of Radiation Pneumonitis Across Esophageal Cancer and Lung Cancer

PURPOSE

The aim was to investigate the advantages of dosiomic and radiomic features over traditional dose-volume histogram (DVH) features for predicting the development of radiation pneumonitis (RP), to validate the generalizability of dosiomic and radiomic features by using features selected from an esophageal cancer dataset and to use these features with a lung cancer dataset.

MATERIALS AND METHODS

A dataset containing 101 patients with esophageal cancer and 93 patients with lung cancer was included in this study. DVH and dosiomic features were extracted from 3D dose distributions. Radiomic features were extracted from pretreatment CT images. Feature selection was performed using only the esophageal cancer dataset. Four predictive models for RP (DVH, dosiomic, radiomic and dosiomic + radiomic models) were compared on the esophageal cancer dataset. We further used a lung cancer dataset for the external validation of the selected dosiomic and radiomic features from the esophageal cancer dataset. The performance of the predictive models was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve (ROCAUC) and the AUC of the precision recall curve (PRAUC) metrics.

RESULT

The ROCAUCs and PRAUCs of the DVH, dosiomic, radiomic and dosiomic + radiomic models on esophageal cancer dataset were 0.67 ± 0.11 and 0.75 ± 0.10, 0.71 ± 0.10 and 0.77 ± 0.09, 0.71 ± 0.11 and 0.79 ± 0.09, and 0.75 ± 0.10 and 0.81 ± 0.09, respectively. The predictive performance of the dosiomic- and radiomic-based models was significantly higher than that of the DVH-based model with respect to esophageal cancer. The ROCAUCs and PRAUCs of the DVH, dosiomic, radiomic and dosiomic + radiomic models on the lung cancer dataset were 0.64 ± 0.18 and 0.37 ± 0.20, 0.67 ± 0.17 and 0.37 ± 0.20, 0.67 ± 0.16 and 0.45 ± 0.23, and 0.68 ± 0.16 and 0.44 ± 0.22, respectively. On the lung cancer dataset, the predictive performance of the radiomic and dosiomic + radiomic models was significantly higher than that of the DVH-based model. However, the PRAUC of the dosiomic-based model showed no significant difference relative to the corresponding RP prediction performance on the lung cancer dataset.

CONCLUSION

The results suggested that dosiomic and CT radiomic features could improve RP prediction in thoracic radiotherapy. Dosiomic and radiomic feature knowledge might be transferrable from esophageal cancer to lung cancer.

Biological dosiomic features for the prediction of radiation pneumonitis in esophageal cancer patients

OBJECTIVE

The purpose of this study was to develop a model using dose volume histogram (DVH) and dosiomic features to predict the risk of radiation pneumonitis (RP) in the treatment of esophageal cancer with radiation therapy and to compare the performance of DVH and dosiomic features after adjustment for the effect of fractionation by correcting the dose to the equivalent dose in 2 Gy (EQD2).

MATERIALS AND METHODS

DVH features and dosiomic features were extracted from the 3D dose distribution of 101 esophageal cancer patients. The features were extracted with and without correction to EQD2. A predictive model was trained to predict RP grade ≥ 1 by logistic regression with L1 norm regularization. The models were then evaluated by the areas under the receiver operating characteristic curves (AUCs).

RESULT

The AUCs of both DVH-based models with and without correction of the dose to EQD2 were 0.66 and 0.66, respectively. Both dosiomic-based models with correction of the dose to EQD2 (AUC = 0.70) and without correction of the dose to EQD2 (AUC = 0.71) showed significant improvement in performance when compared to both DVH-based models. There were no significant differences in the performance of the model by correcting the dose to EQD2.

CONCLUSION

Dosiomic features can improve the performance of the predictive model for RP compared with that obtained with the DVH-based model.

Prediction of Radiation Pneumonitis Using Genome-Scale Flux Analysis of RNA-Seq Derived From Peripheral Blood

Purpose: Radiation pneumonitis (RP) frequently occurs during a treatment course of chest radiotherapy, which significantly reduces the clinical outcome and efficacy of radiotherapy. The ability to easily predict RP before radiotherapy would allow this disease to be avoided.

Methods and Materials: This study recruited 48 lung cancer patients requiring chest radiotherapy. For each participant, RNA sequencing (RNA-Seq) was performed on a peripheral blood sample before radiotherapy. The RNA-Seq data was then integrated into a genome-scale flux analysis to develop an RP scoring system for predicting the probability of occurrence of RP. Meanwhile, the clinical information and radiation dosimetric parameters of this cohort were collected for analysis of any statistical associations between these parameters and RP. A non-parametric rank sum test showed no significant difference between the predicted results from the RP score system and the clinically observed occurrence of RP in this cohort.

Results: The results of the univariant analysis suggested that the tumor stage, exposure dose, and bilateral lung dose of V5 and V20 were significantly associated with the occurrence of RP. The results of the multivariant analysis suggested that the exposure doses of V5 and V20 were independent risk factors associated with RP and a level of RP ≥ 2, respectively. Thus, our results indicate that our RP scoring system could be applied to accurately predict the risk of RP before radiotherapy because the scores were highly consistent with the clinically observed occurrence of RP.

Conclusion: Compared with the standard statistical methods, this genome-scale flux-based scoring system is more accurate, straightforward, and economical, and could therefore be of great significance when making clinical decisions for chest radiotherapy.

The increasing potential of nuclear medicine imaging for the evaluation and reduction of normal tissue toxicity from radiation treatments

Radiation therapy is an effective treatment modality for a variety of cancers. Despite several advances in delivery techniques, its main drawback remains the deposition of dose in normal tissues which can result in toxicity. Common practices of evaluating toxicity, using questionnaires and grading systems, provide little underlying information beyond subjective scores, and this can limit further optimization of treatment strategies. Nuclear medicine imaging techniques can be utilised to directly measure regional baseline function and function loss from internal/external radiation therapy within normal tissues in an in vivo setting with high spatial resolution. This can be correlated with dose delivered by radiotherapy techniques to establish objective dose-effect relationships, and can also be used in the treatment planning step to spare normal tissues more efficiently. Toxicity in radionuclide therapy typically occurs due to undesired off-target uptake in normal tissues. Molecular imaging using diagnostic analogues of therapeutic radionuclides can be used to test various interventional protective strategies that can potentially reduce this normal tissue uptake without compromising tumour uptake. We provide an overview of the existing literature on these applications of nuclear medicine imaging in diverse normal tissue types utilising various tracers, and discuss its future potential.

Monte Carlo evaluation of target dose coverage in lung stereotactic body radiation therapy with flattening filter-free beams

Aim:

Previous studies showed that replacing conventional flattened beams (FF) with flattening filter-free (FFF) beams improves the therapeutic ratio in lung stereotactic body radiation therapy (SBRT), but these findings could have been impacted by dose calculation uncertainties caused by the heterogeneity of the thoracic anatomy and by respiratory motion, which were particularly high for target coverage. In this study, we minimised such uncertainties by calculating doses using high-spatial-resolution Monte Carlo and four-dimensional computed tomography (4DCT) images. We aimed to evaluate more reliably the benefits of using FFF beams for lung SBRT.

Materials and methods:

For a cohort of 15 patients with early-stage lung cancer that we investigated in a previous treatment planning study, we recalculated dose distributions with Monte Carlo using 4DCT images. This included 15 FF and 15 FFF treatment plans.

Results:

Compared to Monte Carlo, the treatment planning system (TPS) over-predicted doses in low-dose regions of the planning target volume (PTV). For most patients, replacing FF beams with FFF beams improved target coverage, tumour control, and uncomplicated tumour control probabilities.

Conclusions:

Monte Carlo tends to reveal deficiencies in target coverage compared to coverage predicted by the TPS. Our data support previously reported benefits of using FFF beams for lung SBRT.

Dose-Volume Predictors of Radiation Pneumonitis After Lung Stereotactic Body Radiation Therapy (SBRT): Implications for Practice and Trial Design

Background and purpose

Recently published HyTEC report summarized lung toxicity data and proposed guidelines of mean lung dose (MLD) <8 Gy and normal lung receiving at least 20 Gy, V_20Gy<10-15% to avoid lung toxicity. Support for preferred use of a particular dosimetric parameter has been limited. We performed a detailed dose-volume analysis of data on radiation pneumonitis (RP) following lung stereotactic body radiation therapy (SBRT) to search for parameters showing the strongest correlation with RP.

Materials and methods

Two patient cohorts (primary and metastatic lung tumor patients) from previously reported studies were analyzed. Total number of patients was 96, and incidence of grade ≥2 RP was 13.5% (13/96). Fitting to the logistic function was performed to investigate correlation between incidence of RP and reported dosimetric and volumetric parameters. Another independent cohort was used to explore correlation between dosimetric parameters.

Results

Among normal lung parameters (MLD and reported V_x), only MLD consistently showed significant correlation with incidence of RP. Gross tumor volume (GTV), internal target volume, planning target volume (PTV), and minimum dose covering 95% of GTV or PTV did not show statistical significance. A significant correlation between reported V_x and MLD was observed in all cohorts.

Conclusions

In considering tumor- and target-specific (e.g., GTV, PTV) and normal lung-specific (e.g., MLD, V_x) metrics, MLD was the only parameter that consistently correlated with incidence of RP across both cohorts. Because SBRT planning constraints allow small normal lung volumes to receive high doses, utility of MLD is not obvious. The parallel structure of lung is one possible explanation, but correlation between dosimetric parameters obscures elucidation of the preferred or mechanistically based parameter to guide radiotherapy planning.

Using FFF beams to improve the therapeutic ratio of lung SBRT

Aim:

The aim of this study was to investigate the extent to which lung stereotactic body radiotherapy (SBRT) treatment plans can be improved by replacing conventional flattening filter (FF) beams with flattening filter-free (FFF) beams.

Materials and methods:

We selected 15 patients who had received SBRT with conventional 6-MV photon beams for early-stage lung cancer. We imported the patients’ treatment plans into the Eclipse 13·6 treatment planning system, in which we configured the AAA dose calculation model using representative beam data for a TrueBeam accelerator operated in 6-MV FFF mode. We then created new treatment plans by replacing the conventional FF beams in the original plans with FFF beams.

Results:

The FFF plans had better target coverage than the original FF plans did. For the planning target volume, FFF plans significantly improved the D98, D95, D90, homogeneity index and uncomplicated tumour control probability. In most cases, the doses to organs at risk were lower in FFF plans. FFF plans significantly reduced the mean lung dose, V10, V20, V30, and normal tissue complication probability for the total lung and improved the dosimetric indices for the ipsilateral lung. For most patients, FFF beams achieved lower maximum doses to the oesophagus, heart and the spinal cord, and a lower chest wall V30.

Conclusions:

Compared with FF beams, FFF beams achieved lower doses to organs at risk, especially the lung, without compromising tumour coverage; in fact, FFF beams improved coverage in most cases. Thus, replacing FF beams with FFF beams can achieve a better therapeutic ratio.

A Minimal PKPD Interaction Model for Evaluating Synergy Effects of Combined NSCLC Therapies

This paper introduces a mathematical compartmental formulation of dose-effect synergy modelling for multiple therapies in non small cell lung cancer (NSCLC): antiangiogenic, immuno- and radiotherapy. The model formulates the dose-effect relationship in a unified context, with tumor proliferating rates and necrotic tissue volume progression as a function of therapy management profiles. The model accounts for inter- and intra-response variability by using surface model response terms. Slow acting peripheral compartments such as fat and muscle for drug distribution are not modelled. This minimal pharmacokinetic-pharmacodynamic (PKPD) model is evaluated with reported data in mice from literature. A systematic analysis is performed by varying only radiotherapy profiles, while antiangiogenesis and immunotherapy are fixed to their initial profiles. Three radiotherapy protocols are selected from literature: (1) a single dose 5 Gy once weekly; (2) a dose of 5 Gy × 3 days followed by a 2 Gy × 3 days after two weeks and (3) a dose of 5 Gy + 2 × 0.075 Gy followed after two weeks by a 2 Gy + 2 × 0.075 Gy dose. A reduction of 28% in tumor end-volume after 30 days was observed in Protocol 2 when compared to Protocol 1. No changes in end-volume were observed between Protocol 2 and Protocol 3, this in agreement with other literature studies. Additional analysis on drug interaction suggested that higher synergy among drugs affects up to three-fold the tumor volume (increased synergy leads to significantly lower growth ratio and lower total tumor volume). Similarly, changes in patient response indicated that increased drug resistance leads to lower reduction rates of tumor volumes, with end-volume increased up to 25–30%. In conclusion, the proposed minimal PKPD model has physiological value and can be used to study therapy management protocols and is an aiding tool in the clinical decision making process. Although developed with data from mice studies, the model is scalable to NSCLC patients.

Current radiotherapy techniques in NSCLC: challenges and potential solutions

Introduction: Radiotherapy is an important therapeutic strategy in the management of non-small cell lung cancer (NSCLC). In recent decades, technological implementations and the introduction of image guided radiotherapy (IGRT) have significantly increased the accuracy and tolerability of radiation therapy.Area covered: In this review, we provide an overview of technological opportunities and future prospects in NSCLC management.Expert opinion: Stereotactic body radiotherapy (SBRT) is now considered the standard approach in patients ineligible for surgery, while in operable cases, it is still under debate. Additionally, in combination with systemic treatment, SBRT is an innovative option for managing oligometastatic patients and features encouraging initial results in clinical outcomes. To date, in inoperable locally advanced NSCLC, the radical dose prescription has not changed (60 Gy in 30 fractions), despite the median overall survival progressively increasing. These results arise from technological improvements in precisely hitting target treatment volumes and organ at risk sparing, which are associated with better treatment qualities. Finally, for the management of NSCLC, proton and carbon ion therapies and the recent development of MR-Linac are new, intriguing technological approaches under investigation.

Radiation-induced lung toxicity predictors: Retrospective analysis of 90 patients treated with stereotactic body radiation therapy for stage I non-small-cell lung carcinoma

BACKGROUND

The main complication after hypofractionated radiotherapy for lung carcinoma is radiation-induced lung toxicity, which can be divided into radiation pneumonitis (acute toxicity, occurring within 6 months) and lung fibrosis (late toxicity, occurring after 6 months). The literature describes several predictive factors related to the patient, to the tumor (volume, central location), to the dosimetry and to biological factors.

MATERIALS AND METHODS

This study is a retrospective analysis of 90 patients treated with stereotactic body irradiation for stage I non-small-cell lung carcinoma between December 2010 and May 2015.

RESULTS

Radiation pneumonitis was observed in 61.5% of the patients who were mainly asymptomatic (34%). Chronic obstructive pulmonary disease was not predictive of radiation pneumonitis, whereas active smoking was protective. Centrally located tumors were not more likely to result in this complication if the radiation schedule utilized adapted fractionation. In our study, no predictive factor was identified. Whereas the mean lung dose was a predictive factor in 3D radiotherapy, the lung volume irradiated at high doses seemed to be involved in the pathogenesis after hypofractionated radiotherapy.

CONCLUSION

The discovery of predictive factors for radiation pneumonitis is difficult due to the rarity of this complication, especially with an 8×7.5Gy schedule. Radiation pneumonitis seems to be correlated with the volume irradiated at high doses, which is in contrast to the known knowledge about the organs in parallel. This finding leads us to raise the hypothesis that vessel damage, organs in series, occurring during hypofractionated radiotherapy could be responsible for this toxicity.