PACE: A Probabilistic Atlas for Normal Tissue Complication Estimation in Radiation Oncology

Modeling frameworks for radiation induced lymphopenia: A critical review

Radiation-induced lymphopenia (RIL) is a frequent, and often considered unavoidable, side effect of radiation therapy (RT), whether or not chemotherapy is included. However, in the last few years several studies have demonstrated the detrimental effect of RIL on therapeutic outcomes, with conflicting findings concerning possible inferior patient survival. In addition, since immunotherapeutic treatment has become an integral part of cancer therapy, preserving the immune system is recognized as crucial. Given this background, various research groups have reported on different frameworks for modelling RIL, frequently based on different definitions of RIL itself, and discordant results have been reported. Our aim is to critically review the current literature on RIL modelling and summarize the different approaches recently proposed to improve the prediction of RIL after RT and aimed at immunity-sparing RT. A detailed description of these approaches will be outlined and illustrated through their applications as found in the literature from the last five years. Such a critical analysis represents the necessary starting step to develop an effective strategy that ultimately could harmonize the diverse modelling methods.

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.

Technical note: MAMBA-Multi-pAradigM voxel-Based Analysis: A computational cookbot

BACKGROUND

Voxel-Based (VB) analysis embraces a multifaceted ensemble of sophisticated techniques, lying at the boundary between image processing and statistical modeling, that allow for a frequentist inference of pathophysiological properties anchored to an anatomical reference. VB methods has been widely adopted in neuroimaging studies and, more recently, they are gaining momentum in radiation oncology research. However, the price for the power of VB analysis is the complexity of the underlying mathematics and algorithms.

PURPOSE

In this paper, we present the Multi-pAradigM voxel-Based Analysis (MAMBA) toolbox, which is intended for a flexible application of VB analysis in a wide variety of scenarios in medical imaging and radiation oncology.

METHODS

The MAMBA toolbox is implemented in Matlab. It provides open-source functions to compute VB statistical models of the input data, according to a great variety of regression schemes, and to derive VB maps of the observed significance level, performing a non-parametric permutation inference. The toolbox allows for including VB and global outcomes, as well as an arbitrary amount of VB and global Explanatory Variables (EVs). In addition, the Matlab Parallel Computing Toolbox is exploited to take advantage of the perfect parallelizability of most workloads.

RESULTS

The use of MAMBA was demonstrated by means of several realistic examples on a synthetic dataset mimicking a radiation oncology scenario.

CONCLUSION

MAMBA is an open-source toolbox, freely available for academic and non-commercial purposes. It is designed to make state-of-the-art VB analysis accessible to research scientists without the programming resources needed to build from scratch their own software solutions. At the same time, the source code is handed out for more experienced users to complement their own tools, also customizing user-defined models. MAMBA guarantees high generality and flexibility in the design of the statistical models, significantly expanding on the features of available free tools for VB analysis. The presented toolbox aims at increasing the reach of VB studies as well as the sharing of research results.

Clinical Radiobiology for Radiation Oncology

This chapter is focused on radiobiological aspects at the molecular, cellular, and tissue level which are relevant for the clinical use of ionizing radiation (IR) in cancer therapy. For radiation oncology, it is critical to find a balance, i.e., the therapeutic window, between the probability of tumor control and the probability of side effects caused by radiation injury to the healthy tissues and organs. An overview is given about modern precision radiotherapy (RT) techniques, which allow optimal sparing of healthy tissues. Biological factors determining the width of the therapeutic window are explained. The role of the six typical radiobiological phenomena determining the response of both malignant and normal tissues in the clinic, the 6R’s, which are Reoxygenation, Redistribution, Repopulation, Repair, Radiosensitivity, and Reactivation of the immune system, is discussed. Information is provided on tumor characteristics, for example, tumor type, growth kinetics, hypoxia, aberrant molecular signaling pathways, cancer stem cells and their impact on the response to RT. The role of the tumor microenvironment and microbiota is described and the effects of radiation on the immune system including the abscopal effect phenomenon are outlined. A summary is given on tumor diagnosis, response prediction via biomarkers, genetics, and radiomics, and ways to selectively enhance the RT response in tumors. Furthermore, we describe acute and late normal tissue reactions following exposure to radiation: cellular aspects, tissue kinetics, latency periods, permanent or transient injury, and histopathology. Details are also given on the differential effect on tumor and late responding healthy tissues following fractionated and low dose rate irradiation as well as the effect of whole-body exposure.

Potential benefits of using radioactive ion beams for range margin reduction in carbon ion therapy

Sharp dose gradients and high biological effectiveness make ions such as ¹²C an ideal tool to treat deep-seated tumors, however, at the same time, sensitive to errors in the range prediction. Tumor safety margins mitigate these uncertainties, but during the irradiation they lead to unavoidable damage to the surrounding healthy tissue. To fully exploit the Bragg peak benefits, a large effort is put into establishing precise range verification methods. Despite positron emission tomography being widely in use for this purpose in ¹²C therapy, the low count rates, biological washout, and broad activity distribution still limit its precision. Instead, radioactive beams used directly for treatment would yield an improved signal and a closer match with the dose fall-off, potentially enabling precise in vivo beam range monitoring. We have performed a treatment planning study to estimate the possible impact of the reduced range uncertainties, enabled by radioactive ¹¹C ions treatments, on sparing critical organs in tumor proximity. Compared to ¹²C treatments, (i) annihilation maps for ¹¹C ions can reflect sub- millimeter shifts in dose distributions in the patient, (ii) outcomes of treatment planning with ¹¹C significantly improve and (iii) less severe toxicities for serial and parallel critical organs can be expected.

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.

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.

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.

RESUMEN: A flexible class of multi-parameter qMRI protocols

PURPOSE

To introduce a class of fast 3D quantitative MRI (qMRI) schemes (RESUME^N, for N=1,…,4) that allow for a thorough characterization of microstructural properties of brain tissues.

METHODS

An arbitrary multi-echo GRE acquisition optimized for quantitative susceptibility mapping (QSM) is complemented with an appropriate low flip-angle GRE sequence drawn from four possible choices. The acquired signals are processed to analytically derive the longitudinal relaxation (R₁) and free induction decay (R₂^∗) rates, as well as the proton density (PD) and QSM. A comprehensive modeling of the excitation and B₁^- profiles and of the RF-spoiling is included in the acquisition and processing pipeline.

RESULTS

The RESUME^N maps appear homogeneous throughout the field-of-view and exhibit comparable values and high SNR across the considered range of N values.

CONCLUSIONS

The introduced schemes represent a class of robust and flexible strategies to derive a thorough and fast qMRI study, suitable for a whole-brain acquisition with isotropic voxel resolution of 700 μm in less than 15 min.

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.

Relationships between rectal and perirectal doses and rectal bleeding or tenesmus in pooled voxel-based analysis of 3 randomised phase III trials

BACKGROUND AND PURPOSE

This study aimed to identify anatomically-localised regions where planned radiotherapy dose is associated with gastrointestinal toxicities in healthy tissues throughout the pelvic anatomy.

MATERIALS AND METHODS

Planned dose distributions for up to 657 patients of the Trans Tasman Radiation Oncology Group 03.04 RADAR trial were deformably registered onto a single exemplar computed tomography dataset. Voxel-based multiple comparison permutation dose difference testing, Cox regression modelling and LASSO feature selection were used to identify regions where dose-increase was associated with grade ≥2 rectal bleeding (RB) or tenesmus, according to the LENT/SOMA scale. This was externally validated by registering dose distributions from the RT01 (n = 388) and CHHiP (n = 241) trials onto the same exemplar and repeating the tests on each of these data sets, and on all three datasets combined.

RESULTS

Voxel-based Cox regression and permutation dose difference testing revealed regions where increased dose was correlated with gastrointestinal toxicity. Grade ≥2 RB was associated with posteriorly extended lateral beams that manifested high doses (>55 Gy) in a small rectal volume adjacent to the clinical target volume. A correlation was found between grade ≥2 tenesmus and increased low-intermediate dose (∼25 Gy) at the posterior beam region, including the posterior rectum and perirectal fat space (PRFS).

CONCLUSIONS

The serial response of the rectum with respect to RB has been demonstrated in patients with posteriorly extended lateral beams. Similarly, the parallel response of the PRFS with respect to tenesmus has been demonstrated in patients treated with the posterior beam.

Increased Dose to Organs in Urinary Tract Associates With Measures of Genitourinary Toxicity in Pooled Voxel-Based Analysis of 3 Randomized Phase III Trials

Purpose: Dose information from organ sub-regions has been shown to be more predictive of genitourinary toxicity than whole organ dose volume histogram information. This study aimed to identify anatomically-localized regions where 3D dose is associated with genitourinary toxicities in healthy tissues throughout the pelvic anatomy. Methods and Materials: Dose distributions for up to 656 patients of the Trans-Tasman Radiation Oncology Group 03.04 RADAR trial were deformably registered onto a single exemplar CT dataset. Voxel- based multiple comparison permutation dose difference testing, Cox regression modeling and LASSO feature selection were used to identify regions where 3D dose-increase was associated with late grade ≥ 2 genitourinary dysuria, incontinence and frequency, and late grade ≥ 1 haematuria. This was externally validated by registering dose distributions from the RT01 (up to n = 388) and CHHiP (up to n = 247) trials onto the same exemplar and repeating the voxel-based tests on each of these data sets. All three datasets were then combined, and the tests repeated. Results: Voxel-based Cox regression and multiple comparison permutation dose difference testing revealed regions where increased dose was correlated with genitourinary toxicity. Increased dose in the vicinity of the membranous and spongy urethra was associated with dysuria for all datasets. Haematuria was similarly correlated with increased dose at the membranous and spongy urethra, for the RADAR, CHHiP, and combined datasets. Some evidence was found for the association between incontinence and increased dose at the internal and external urethral sphincter for RADAR and the internal sphincter alone for the combined dataset. Incontinence was also strongly correlated with dose from posterior oblique beams. Patients with fields extending inferiorly and posteriorly to the CTV, adjacent to the membranous and spongy urethra, were found to experience increased frequency. Conclusions: Anatomically-localized dose-toxicity relationships were determined for late genitourinary symptoms in the urethra and urinary sphincters. Low-intermediate doses to the extraprostatic urethra were associated with risk of late dysuria and haematuria, while dose to the urinary sphincters was associated with incontinence.

NTCP Models for Severe Radiation Induced Dermatitis After IMRT or Proton Therapy for Thoracic Cancer Patients

Radiation therapy (RT) of thoracic cancers may cause severe radiation dermatitis (RD), which impacts on the quality of a patient's life. Aim of this study was to analyze the incidence of acute RD and develop normal tissue complication probability (NTCP) models for severe RD in thoracic cancer patients treated with Intensity-Modulated RT (IMRT) or Passive Scattering Proton Therapy (PSPT). We analyzed 166 Non-Small-Cell Lung Cancer (NSCLC) patients prospectively treated at a single institution with IMRT (103 patients) or PSPT (63 patients). All patients were treated to a prescribed dose of 60 to 74 Gy in conventional daily fractionation with concurrent chemotherapy. RD was scored according to CTCAE v3 scoring system. For each patient, the epidermis structure (skin) was automatically defined by an in house developed segmentation algorithm. The absolute dose-surface histogram (DSH) of the skin were extracted and normalized using the Body Surface Area (BSA) index as scaling factor. Patient and treatment-related characteristics were analyzed. The Lyman-Kutcher-Burman (LKB) NTCP model recast for DSH and the multivariable logistic model were adopted. Models were internally validated by Leave-One-Out method. Model performance was evaluated by the area under the receiver operator characteristic curve, and calibration plot parameters. Fifteen of 166 (9%) patients developed severe dermatitis (grade 3). RT technique did not impact RD incidence. Total gross tumor volume (GTV) size was the only non dosimetric variable significantly correlated with severe RD (p = 0.027). Multivariable logistic modeling resulted in a single variable model including S_20Gy, the relative skin surface receiving more than 20 Gy (OR = 31.4). The cut off for S_20Gy was 1.1% of the BSA. LKB model parameters were TD₅₀ = 9.5 Gy, m = 0.24, n = 0.62. Both NTCP models showed comparably high prediction and calibration performances. Despite skin toxicity has long been considered a potential limiting factor in the clinical use of PSPT, no significant differences in RD incidence was found between RT modalities. Once externally validated, the availability of NTCP models for prediction of severe RD may advance treatment planning optimization.

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.

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.

Normal tissue complication probability (NTCP) models for modern radiation therapy

Mathematical models of normal tissue complication probability (NTCP) able to robustly predict radiation-induced morbidities (RIM) play an essential role in the identification of a personalized optimal plan, and represent the key to maximizing the benefits of technological advances in radiation therapy (RT). Most modern RT techniques pose, however, new challenges in estimating the risk of RIM. The aim of this report is to schematically review NTCP models in the framework of advanced radiation therapy techniques. Issues relevant to hypofractionated stereotactic body RT and ion beam therapy are critically reviewed. Reirradiation scenarios for new or recurrent malignances and NTCP are also illustrated. A new phenomenological approach to predict RIM is suggested.

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

Thoracic radiation therapy (RT) is often associated with lung side effects, whose etiology is still controversial. Our aim was to explore correlations between local dose in the thoracic anatomy and the radiation-induced lung damage (RILD). To this end, we designed a robust scheme for voxel-based analysis (VBA) to explore dose patterns associated with RILD in non-small-cell lung cancer (NSCLC) patients receiving stereotactic body RT (SBRT). We analyzed 106 NSCLC SBRT patients (median prescription dose: 50 Gy; range: [40-54] Gy) in 4 fractions (range: [3-5]) with clinical and dosimetric records suitable for the analysis. The incidence of acute G1 RILD (RTOG grade  ⩾  1) was 68%. Each planning CT and dose map was spatially normalized to a common anatomical reference using a B-spline inter-patient registration algorithm after masking the gross tumor volume. The tumor-subtracted dose maps were converted into biologically effective dose maps (α/β  =  3 Gy). VBA was performed according to a non-parametric permutation test accounting for multiple comparison, based on a cluster analysis method. The underlying general linear model of RILD was designed to include dose maps and each non-dosimetric variable significantly correlated with RILD. The clusters of voxels with dose differences significantly correlated with RILD at a given p -level (S _p) were generated. The only non-dosimetric variable significantly correlated with RILD was the chronic obstructive pulmonary disease (p   =  0.034). Patients with G1 RILD received significantly (p   ⩽  0.05) higher doses in two voxel clusters S _0.05 in the lower-left lung (14 cm³) and in an area (64 cm³) largely included within the ventricles. The applied VBA represents a powerful tool to probe the dose susceptibility of inhomogeneous organs in clinical radiobiology studies. The identified subregions with dose differences associated with G1 RILD in both the heart and lower lungs endorse a trend of previously reported hypotheses on lung toxicity radiobiology.