Spatial signature of dose patterns associated with acute radiation-induced lung damage in lung cancer patients treated with stereotactic body radiation therapy

Voxel-based analysis: Roadmap for clinical translation

Voxel-based analysis (VBA) allows the full, 3-dimensional, dose distribution to be considered in radiotherapy outcome analysis. This provides new insights into anatomical variability of pathophysiology and radiosensitivity by removing the need for a priori definition of organs assumed to drive the dose response associated with patient outcomes. This approach may offer powerful biological insights demonstrating the heterogeneity of the radiobiology across tissues and potential associations of the radiotherapy dose with further factors. As this methodological approach becomes established, consideration needs to be given to translating VBA results to clinical implementation for patient benefit. Here, we present a comprehensive roadmap for VBA clinical translation. Technical validation needs to demonstrate robustness to methodology, where clinical validation must show generalisability to external datasets and link to a plausible pathophysiological hypothesis. Finally, clinical utility requires demonstration of potential benefit for patients in order for successful translation to be feasible. For each step on the roadmap, key considerations are discussed and recommendations provided for best practice.

Voxel-wise analysis: A powerful tool to predict radio-induced toxicity and potentially perform personalised planning in radiotherapy

Dose - volume histograms have been historically used to study the relationship between the planned radiation dose and healthy tissue damage. However, this approach considers neither spatial information nor heterogenous radiosensitivity within organs at risk, depending on the tissue. Recently, voxel-wise analyses have emerged in the literature as powerful tools to fully exploit three-dimensional information from the planned dose distribution. They allow to identify anatomical subregions of one or several organs in which the irradiation dose is associated with a given toxicity. These methods rely on an accurate anatomical alignment, usually obtained by means of a non-rigid registration. Once the different anatomies are spatially normalised, correlations between the three-dimensional dose and a given toxicity can be explored voxel-wise. Parametric or non-parametric statistical tests can be performed on every voxel to identify the voxels in which the dose is significantly different between patients presenting or not toxicity. Several anatomical subregions associated with genitourinary, gastrointestinal, cardiac, pulmonary or haematological toxicity have already been identified in the literature for prostate, head and neck or thorax irradiation. Voxel-wise analysis appears therefore first particularly interesting to increase toxicity prediction capability by identifying specific subregions in the organs at risk whose irradiation is highly predictive of specific toxicity. The second interest is potentially to decrease the radio-induced toxicity by limiting the dose in the predictive subregions, while not decreasing the dose in the target volume. Limitations of the approach have been pointed out.

Dosiomics and radiomics to predict pneumonitis after thoracic stereotactic body radiotherapy and immune checkpoint inhibition

Introduction

Pneumonitis is a relevant side effect after radiotherapy (RT) and immunotherapy with checkpoint inhibitors (ICIs). Since the effect is radiation dose dependent, the risk increases for high fractional doses as applied for stereotactic body radiation therapy (SBRT) and might even be enhanced for the combination of SBRT with ICI therapy. Hence, patient individual pre-treatment prediction of post-treatment pneumonitis (PTP) might be able to support clinical decision making. Dosimetric factors, however, use limited information and, thus, cannot exploit the full potential of pneumonitis prediction.

Methods

We investigated dosiomics and radiomics model based approaches for PTP prediction after thoracic SBRT with and without ICI therapy. To overcome potential influences of different fractionation schemes, we converted physical doses to 2 Gy equivalent doses (EQD2) and compared both results. In total, four single feature models (dosiomics, radiomics, dosimetric, clinical factors) were tested and five combinations of those (dosimetric+clinical factors, dosiomics+radiomics, dosiomics+dosimetric+clinical factors, radiomics+dosimetric+clinical factors, radiomics+dosiomics+dosimetric+clinical factors). After feature extraction, a feature reduction was performed using pearson intercorrelation coefficient and the Boruta algorithm within 1000-fold bootstrapping runs. Four different machine learning models and the combination of those were trained and tested within 100 iterations of 5-fold nested cross validation.

Results

Results were analysed using the area under the receiver operating characteristic curve (AUC). We found the combination of dosiomics and radiomics features to outperform all other models with AUCradiomics+dosiomics, D = 0.79 (95% confidence interval 0.78-0.80) and AUCradiomics+dosiomics, EQD2 = 0.77 (0.76-0.78) for physical dose and EQD2, respectively. ICI therapy did not impact the prediction result (AUC ≤ 0.5). Clinical and dosimetric features for the total lung did not improve the prediction outcome.

Conclusion

Our results suggest that combined dosiomics and radiomics analysis can improve PTP prediction in patients treated with lung SBRT. We conclude that pre-treatment prediction could support clinical decision making on an individual patient basis with or without ICI therapy.

Anatomy-based prediction method for determining ipsilateral lung doses in postoperative breast radiation therapy assisted by diagnostic computed tomography images

Background

This study aimed to investigate whether ipsilateral lung doses (ILDs) could be predicted by anatomical indexes measured using diagnostic computed tomography (CT) prior to the planning stage of breast radiation therapy (RT).

Materials and methods

The thoracic diameters and the length of lines drawn manually were measured on diagnostic CT images. The parameters of interest were the skin maximum lung distance (sMLD), central lung distance (CLD), Haller index (HI), and body mass index (BMI). Lung dose-volume histograms were created with conformal planning, and the lung volumes receiving 5-40 Gy (V5-V40) were calculated. Linear regression models were used to investigate the correlations between the anatomical indexes and dose differences and to estimate the slope and 95% confidence intervals (CIs).

Results

A total of 160 patients who had undergone three-dimensional conformal RT after breast-conserving surgery were included. Univariable analysis revealed that the sMLD (p < 0.001), CLD (p < 0.001), HI (p = 0.002), and BMI (p < 0.001) were significantly correlated with the V20. However, multivariable analysis revealed that only the sMLD (slope: 0.147, p = 0.001, 95% CI: 0.162-0.306) and CLD (0.157, p = 0.005, 0.048-0.266) were strongly correlated with the V20. The p-value for the sMLD was the lowest among the p-values for all indexes, thereby indicating that the sMLD had the best predictive power for ILD.

Conclusions

sMLD and CLD are anatomical markers that can be used to predict ILD in whole breast RT. An sMLD > 20.5 mm or a CLD > 24.3 mm positively correlated with a high ILD.

Radiation-Induced Esophagitis in Non-Small-Cell Lung Cancer Patients: Voxel-Based Analysis and NTCP Modeling

Simple Summary

Radiation-induced esophagitis (RE) is a common dose-limiting complication associated with concurrent chemoradiation therapy for Non-Small-Cell Lung Cancer (NSCLC), and a wide range of esophageal dosimetric parameters have been described as predictive of RE. In this study, we characterize the risk of RE for NSCLC patients enrolled in a prospective trial comparing intensity-modulated RT versus passive scattering proton therapy for locally advanced NSCLC. Dose patterns associated with RE were analyzed by applying voxel-based analysis approaches, and predictive models for RE were finally investigated. Two predictive models for acute RE with good cross-validated predictive performances and discrimination capability were developed (thoracic esophageal model: ROC-AUC = 0.73; whole esophagus model: ROC-AUC = 0.70).

Abstract

The aim of our study is to characterize the risk of radiation-induced esophagitis (RE) in a cohort of Non-Small-Cell Lung Cancer (NSCLC) patients treated with concurrent chemotherapy and photon/proton therapy. For each patient, the RE was graded according to the CTCAE v.3. The esophageal dose-volume histograms (DVHs) were extracted. Voxel-based analyses (VBAs) were performed to assess the spatial patterns of the dose differences between patients with and without RE of grade ≥ 2. Two hierarchical NTCP models were developed by multivariable stepwise logistic regression based on non-dosimetric factors and on the DVH metrics for the whole esophagus and its anatomical subsites identified by the VBA. In the 173 analyzed patients, 76 (44%) developed RE of grade ≥ 2 at a median follow-up time of 31 days. The VBA identified regions of significant association between dose and RE in a region encompassing the thoracic esophagus. We developed two NTCP models, including the RT modality and a dosimetric factor: V_55Gy for the model related to the whole esophagus, and the mean dose for the model designed on the thoracic esophagus. The cross-validated performance showed good predictions for both models (ROC-AUC of 0.70 and 0.73, respectively). The only slight improvement provided by the analysis of the thoracic esophageal subsites might be due to the relevant sparing of cervical and lower thoracic esophagus in the analyzed cohort. Further studies on larger cohorts and a more heterogeneous set of dose distributions are needed to validate these preliminary findings and shed further light on the spatial patterns of RE development.

Lymphocyte dynamics during and after chemo-radiation correlate to dose and outcome in stage III NSCLC patients undergoing maintenance immunotherapy

PURPOSE

We investigated the dynamics of lymphocyte depletion and recovery during and after definitive concurrent chemoradiotherapy (CCRT), dose to which structures is correlated to them, and how they affect the prognosis of stage III non-small cell lung cancer (NSCLC) patients undergoing maintenance immunotherapy.

METHODS AND MATERIALS

In this retrospective study, absolute lymphocyte counts (ALC) of 66 patients were obtained before, during, and after CCRT. Persistent lymphopenia was defined as ALC < 500/μL at 3 months after CCRT. The impact of regional dose on lymphocyte depletion and recovery was investigated using voxel-based analysis (VBA).

RESULTS

Most patients (n = 65) experienced lymphopenia during CCRT: 39 patients (59.0%) had grade (G) 3+ lymphopenia. Fifty-nine patients (89.3%) recovered from treatment-related lymphopenia at 3 months after CCRT, whereas 7 (10.6%) showed persistent lymphopenia. Patient characteristics associated with persistent lymphopenia were older age and ALC before and during treatment. In multivariable Cox regression analysis, recovery from lymphopenia was identified as a significant prognostic factor for Progression Free Survival (HR 0.35, 95% CI 0.13-0.93, p = 0.034) and Overall Survival (HR 0.24, 95% CI 0.08-0.68, p = 0.007). Voxel-based analysis showed strong correlation of dose to the upper mediastinum with lymphopenia at the end of CCRT, but not at 3 months after CCRT.

CONCLUSION

Recovery from lymphopenia is strongly correlated to improved survival of patients undergoing CCRT and adjuvant immunotherapy, and is correlated to lymphocyte counts pre- and post-CCRT. VBA reveals high correlation of dose to large vessels to lymphopenia at the end of CCRT. Therefore, efforts should be made not only for preventing lymphocyte depletion during CCRT but also for helping lymphocyte recovery after CCRT.

On the interplay between dosiomics and genomics in radiation-induced lymphopenia of lung cancer patients

PURPOSE

To investigate the interplay between spatial dose patterns and single nucleotide polymorphisms in the development of radiation-induced lymphopenia (RIL) in 186 non-small-cell lung cancer (NSCLC) patients undergoing chemo-radiotherapy (RT).

METHODS

This study included NSCLC patients enrolled in a randomized trial of protons vs. photons with available absolute lymphocyte counts at baseline and during RT and XRCC1-rs25487 genotyping data. After masking the GTV, planning CT scans and dose maps were spatially normalized to a common anatomical reference. A Voxel-Based Analysis (VBA) was performed to assess voxel-wise relationships of dosiomic and genomic explanatory variables with RIL. The underlying generalized linear model was designed to include both the explanatory variables (3D dose distributions and the XRCC1-rs25487 genotypes) and possible nuisance variables significantly correlated with RIL. The maps of model coefficients as well as their significance maps were generated.

RESULTS

Measures for RIL definition during RT were characterized, including kinetic parameters for lymphocyte loss. The VBA generated three-dimensional maps of correlation between RIL and dose in lymphoid organs as well as organs with abundant blood pools. The identified voxel-wise relationships account for XRCC1-rs25487 polymorphism and demonstrate the variant AA genotype being detrimental to lymphocyte depletion (p = 0.03).

CONCLUSION

The performed analyses blindly highlighted relevant anatomical regions that contributed most to lymphocyte depletion during RT and the interplay of the variant XRCC1-rs25487 AA genotype with the dose delivered to the primary lymphoid organs. These findings may help to guide the development of dosimetric RIL mitigation strategies for the application of effective individualized RT.

Radiation-Induced Dyspnea in Lung Cancer Patients Treated with Stereotactic Body Radiation Therapy

Simple Summary

Dyspnea is a common symptomatic side-effect of thoracic radiation therapy. The aim of this study is to build a predictive model of any-grade radiation-induced dyspnea within six months after stereotactic body radiation therapy in patients treated for non-small cell lung cancer. The occurrence of pre-treatment chronic obstructive pulmonary disease and higher relative lungs volume receiving more than 15 Gy as well as heart volume were shown to be risk factors for dyspnea. The obtained results encourage further studies on the topic, which could validate the present organ-based findings and explore the voxel-based landscape of radiation dose sensitivity in the development of dyspnea.

Abstract

In this study, we investigated the prognostic factors for radiation-induced dyspnea after hypo-fractionated radiation therapy (RT) in 106 patients treated with Stereotactic Body RT for Non-Small-Cell Lung Cancer (NSCLC). The median prescription dose was 50 Gy (range: 40–54 Gy), delivered in a median of four fractions (range: 3–12). Dyspnea within six months after SBRT was scored according to CTCAE v.4.0. Biologically Effective Dose (α/β = 3 Gy) volume histograms for lungs and heart were extracted. Dosimetric parameters along with patient-specific and treatment-related factors were analyzed, multivariable logistic regression method with Leave-One-Out (LOO) internal validation applied. Model performance was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and calibration plot parameters. Fifty-seven patients (53.8%) out of 106 developed dyspnea of any grade after SBRT (25/57 grade ≥ 2 cases). A three-variable predictive model including patient comorbidity (COPD), heart volume and the relative lungs volume receiving more than 15 Gy was selected. The model displays an encouraging performance given by a training ROC-AUC = 0.71 [95%CI 0.61–0.80] and a LOO-ROC-AUC = 0.64 [95%CI 0.53–0.74]. Further modeling efforts are needed for dyspnea prediction in hypo-fractionated treatments in order to identify patients at high risk for developing lung toxicity more accurately.

Dose Calculation Algorithms for External Radiation Therapy: An Overview for Practitioners

Radiation therapy (RT) is a constantly evolving therapeutic technique; improvements are continuously being introduced for both methodological and practical aspects. Among the features that have undergone a huge evolution in recent decades, dose calculation algorithms are still rapidly changing. This process is propelled by the awareness that the agreement between the delivered and calculated doses is of paramount relevance in RT, since it could largely affect clinical outcomes. The aim of this work is to provide an overall picture of the main dose calculation algorithms currently used in RT, summarizing their underlying physical models and mathematical bases, and highlighting their strengths and weaknesses, referring to the most recent studies on algorithm comparisons. This handy guide is meant to provide a clear and concise overview of the topic, which will prove useful in helping clinical medical physicists to perform their responsibilities more effectively and efficiently, increasing patient benefits and improving the overall quality of the management of radiation treatment.

Risk factors for radiation-induced lung injury in patients with advanced non-small cell lung cancer: implication for treatment strategies

BACKGROUND

The radiation-induced lung injury (RILI) in patients with advanced non-small cell lung cancer (NSCLS) is very common in clinical settings; we aimed to evaluate the risk factors of RILI in NSCLS patients, to provide insights into the treatment of NSCLS.

METHODS

NSCLC patients undergoing three-dimensional conformal radiotherapy (3D-CRT) in our hospital from June 1, 2018, to June 30, 2020, were included. The characteristics and treatments of RILI and non-RILI patients were analyzed. Logistic regression analyses were conducted to assess the risk factors of RILI in patients with NSCLC.

RESULTS

A total of 126 NSCLC patients were included; the incidence of RILI in NSCLC patients was 35.71%. There were significant differences in diabetes, smoke, chronic obstructive pulmonary disease (COPD), concurrent chemotherapy, radiotherapy dose, and planning target volume (PTV) between the RILI group and the non-RILI group (all P < 0.05). Logistic regression analyses indicated that diabetes (OR 3.076, 95%CI 1.442~5.304), smoke (OR 2.745, 95%CI 1.288~4.613), COPD (OR 3.949, 95%CI 1.067~5.733), concurrent chemotherapy (OR 2.072, 95%CI 1.121~3.498), radiotherapy dose ≥ 60 Gy (OR 3.841, 95%CI 1.932~5.362), and PTV ≥ 396 (OR 1.247, 95%CI 1.107~1.746) were the independent risk factors of RILI in patients with NSCLC (all P < 0.05).

CONCLUSIONS

RILI is commonly seen in NSCLS patients; early targeted measures are warranted for patients with those risk factors; future studies with larger sample sizes and different areas are needed to further elucidate the influencing factors of RILI in the treatment of NSCLS.

Radiation Pneumonitis in Thoracic Cancer Patients: Multi-Center Voxel-Based Analysis

Simple Summary

The pathophysiology of radiation pneumonitis (RP) after thoracic cancer radiation treatments is still not completely understood although the identification of underlying RP mechanisms may improve the therapeutic window of thoracic cancer patients. The aim of our retrospective study was to explore the dose–response patterns associated with RP by a multi-center voxel-based analysis. In a heterogeneously treated population of 382 thoracic cancer patients, we confirmed the previously described heart–lung interaction in the development of RP. The empowerment of VBA with a novel description of dose map spatial properties based on probabilistic independent component analysis (PICA) and connectograms provided valuable additional and independent information on the radiobiology of RP.

Abstract

This study investigates the dose–response patterns associated with radiation pneumonitis (RP) in patients treated for thoracic malignancies with different radiation modalities. To this end, voxel-based analysis (VBA) empowered by a novel strategy for the characterization of spatial properties of dose maps was applied. Data from 382 lung cancer and mediastinal lymphoma patients from three institutions treated with different radiation therapy (RT) techniques were analyzed. Each planning CT and biologically effective dose map (α/β = 3 Gy) was spatially normalized on a common anatomical reference. The VBA of local dose differences between patients with and without RP was performed and the clusters of voxels with dose differences that significantly correlated with RP at a p-level of 0.05 were generated accordingly. The robustness of VBA inference was evaluated by a novel characterization for spatial properties of dose maps based on probabilistic independent component analysis (PICA) and connectograms. This lays robust foundations to the obtained findings that the lower parts of the lungs and the heart play a prominent role in the development of RP. Connectograms showed that the dataset can support a radiobiological differentiation between the main heart and lung substructures.

Probing thoracic dose patterns associated to pericardial effusion and mortality in patients treated with photons and protons for locally advanced non-small-cell lung cancer

PURPOSE

To investigate thoracic dose-response patterns for pericardial effusion (PCE) and mortality in patients treated for locally advanced Non-Small-Cell Lung Cancer (NSCLC) by Intensity Modulated RT (IMRT) or Passive-Scattering Proton Therapy (PSPT).

METHODS

Among 178 patients, 43.5% developed grade ≥ 2 PCE. Clinical and dosimetric factors associated with PCE or overall survival (OS) were identified via multi-variable Cox proportional hazards modeling. The Voxel-Based Analyses (VBAs) of local dose differences between patients with and without PCE and mortality was performed. The robustness of VBA results was assessed by a novel characterization of spatial properties of dose distributions based on probabilistic independent component analysis (PICA) and connectograms.

RESULTS

Several non-dosimetric variables were selected by the multivariable analysis for the considered outcomes, while the time-dependent PCE onset was uncorrelated with the OS (p = 0.34) at a multi-variable Cox analysis. Despite the significant PSPT dosimetric advantage, the RT technique did not affect the occurrence of PCE or OS. VBAs highlighted largely overlapping clusters significantly associated with PCE endpoints in heart and lungs. No significant dosimetric patterns related to mortality endpoints were found. PICA identified 43 components homogeneously scattered within thorax, while connectograms showed modest correlations between doses in main cardio-pulmonary substructures.

CONCLUSIONS

Spatially resolved analysis highlighted dose patterns related to radiation-induced cardiac toxiciy and the observed organ-based dose-response mismatch in PSPT and IMRT. Indeed, the thoracic regions spared by PSPT poorly overlapped with the areas involved in PCE development, as highlited by VBA. PICA and connectograms proved valuable tools for assessing the robusteness of obtained VBA inferences.

Spatial descriptions of radiotherapy dose: normal tissue complication models and statistical associations

For decades, dose-volume information for segmented anatomy has provided the essential data for correlating radiotherapy dosimetry with treatment-induced complications. Dose-volume information has formed the basis for modelling those associations via normal tissue complication probability (NTCP) models and for driving treatment planning. Limitations to this approach have been identified. Many studies have emerged demonstrating that the incorporation of information describing the spatial nature of the dose distribution, and potentially its correlation with anatomy, can provide more robust associations with toxicity and seed more general NTCP models. Such approaches are culminating in the application of computationally intensive processes such as machine learning and the application of neural networks. The opportunities these approaches have for individualising treatment, predicting toxicity and expanding the solution space for radiation therapy are substantial and have clearly widespread and disruptive potential. Impediments to reaching that potential include issues associated with data collection, model generalisation and validation. This review examines the role of spatial models of complication and summarises relevant published studies. Sources of data for these studies, appropriate statistical methodology frameworks for processing spatial dose information and extracting relevant features are described. Spatial complication modelling is consolidated as a pathway to guiding future developments towards effective, complication-free radiotherapy treatment.

Atlas construction and spatial normalisation to facilitate radiation-induced late effects research in childhood cancer

Reducing radiation-induced side effects is one of the most important challenges in paediatric cancer treatment. Recently, there has been growing interest in using spatial normalisation to enable voxel-based analysis of radiation-induced toxicities in a variety of patient groups. The need to consider three-dimensional distribution of doses, rather than dose-volume histograms, is desirable but not yet explored in paediatric populations. In this paper, we investigate the feasibility of atlas construction and spatial normalisation in paediatric radiotherapy. We used planning computed tomography (CT) scans from twenty paediatric patients historically treated with craniospinal irradiation to generate a template CT that is suitable for spatial normalisation. This childhood cancer population representative template was constructed using groupwise image registration. An independent set of 53 subjects from a variety of childhood malignancies was then used to assess the quality of the propagation of new subjects to this common reference space using deformable image registration (i.e. spatial normalisation). The method was evaluated in terms of overall image similarity metrics, contour similarity and preservation of dose-volume properties. After spatial normalisation, we report a dice similarity coefficient of 0.95 ± 0.05, 0.85 ± 0.04, 0.96 ± 0.01, 0.91 ± 0.03, 0.83 ± 0.06 and 0.65 ± 0.16 for brain and spinal canal, ocular globes, lungs, liver, kidneys and bladder. We then demonstrated the potential advantages of an atlas-based approach to study the risk of second malignant neoplasms after radiotherapy. Our findings indicate satisfactory mapping between a heterogeneous group of patients and the template CT. The poorest performance was for organs in the abdominal and pelvic region, likely due to respiratory and physiological motion and to the highly deformable nature of abdominal organs. More specialised algorithms should be explored in the future to improve mapping in these regions. This study is the first step toward voxel-based analysis in radiation-induced toxicities following paediatric radiotherapy.

Voxel-based analysis in radiation oncology: A methodological cookbook

Recently, 2D or 3D methods for dose distribution analysis have been proposed as evolutions of the Dose Volume Histogram (DVH) approaches. Those methods, collectively referred to as pixel- or voxel-based (VB) methods, evaluate local dose response patterns and go beyond the organ-based philosophy of Normal Tissue Complication Probability (NTCP) modelling. VB methods have been introduced in the context of radiation oncology in the very last years following the virtuous example of neuroimaging experience. In radiation oncology setting, dose mapping is a suitable scheme to compare spatial patterns of local dose distributions between patients who develop toxicity and who do not. In this critical review, we present the methods that include spatial dose distribution information for evaluating different toxicity endpoints after radiation therapy. The review addresses two main topics. First, the critical aspects in dose map building, namely the spatial normalization of the dose distributions from different patients. Then, the issues related to the actual dose map comparison, i.e. the viable options for a robust VB statistical analysis and the potential pitfalls related to the adopted solutions. To elucidate the different theoretical and technical issues, the covered topics are illustrated in relation to practical applications found in the existing literature. We conclude the overview on the VB philosophy in radiation oncology by introducing new phenomenological approaches to NTCP modelling that accounts for inhomogeneous organ radiosensitivity.

A novel framework for spatial normalization of dose distributions in voxel-based analyses of brain irradiation outcomes

PURPOSE

To devise a novel Spatial Normalization framework for Voxel-based analysis (VBA) in brain radiotherapy. VBAs rely on accurate spatial normalization of different patients' planning CTs on a common coordinate system (CCS). The cerebral anatomy, well characterized by MRI, shows instead poor contrast in CT, resulting in potential inaccuracies in VBAs based on CT alone.

METHODS

We analyzed 50 meningioma patients treated with proton-therapy, undergoing planning CT and T1-weighted (T1w) MRI. The spatial normalization pipeline based on MR and CT images consisted in: intra-patient registration of CT to T1w, inter-patient registration of T1w to MNI space chosen as CCS, doses propagation to MNI. The registration quality was compared with that obtained by Statistical Parametric Mapping software (SPM), used as benchmark. To evaluate the accuracy of dose normalization, the dose organ overlap (DOO) score was computed on gray matter, white matter and cerebrospinal fluid before and after normalization. In addition, the trends in the DOOs distribution were investigated by means of cluster analysis.

RESULTS

The registration quality was higher for the proposed method compared to SPM (p < 0.001). The DOO scores showed a significant improvement after normalization (p < 0.001). The cluster analysis highlighted 2 clusters, with one of them including the majority of data and exhibiting acceptable DOOs.

CONCLUSIONS

Our study presents a robust tool for spatial normalization, specifically tailored for brain dose VBAs. Furthermore, the cluster analysis provides a formal criterion for patient exclusion in case of non-acceptable normalization results. The implemented framework lays the groundwork for future reliable VBAs in brain irradiation studies.

Long term results of single high dose Stereotactic Body Radiotherapy in the treatment of primary lung tumors

Stereotactic body radiotherapy (SBRT) is a standard treatment for inoperable early-stage NSCLC, with local control rates comparable to surgical series. Promising results have been achieved utilizing a high single-dose schedule. The aim of our study was to evaluate long-term local control and toxicity in a series of patients treated with SBRT delivered in a single dose of 30 Gy. 44 patients affected by early stage NSCLC were treated with SBRT delivered in a single dose of 30 Gy. Survival and prognostic factors were retrospectively evaluated. Median follow-up was 34 months (range 3-81). Three- and 5-year local progression-free survival (LPFS) were 87.8% and 87.8% respectively (median 30 months; range 6-81 months), 3- and 5-year OS and CSS were 64.9% and 36.9%, 80.9% and 65.5%, respectively. Two (4.6%) cases of grade 3 pneumonitis occurred. At the univariate analysis lesion diameter ≤ 25 mm was predictive of better 5-year LPFS (95.8% versus 56.3%; p = 0.003) and 5-year PFS (69.8% versus 27.8%; p = 0.002). The results of our study indicated a high local control, survival and tolerability after a long-term follow-up with the use of SBRT 30 Gy single dose. Further prospective studies could better define the role of this regimen.