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

Cluster-Based Toxicity Estimation of Osteoradionecrosis Via Unsupervised Machine Learning: Moving Beyond Single Dose-Parameter Normal Tissue Complication Probability by Using Whole Dose-Volume Histograms for Cohort Risk Stratification

PURPOSE

Given the limitations of extant models for normal tissue complication probability estimation for osteoradionecrosis (ORN) of the mandible, the purpose of this study was to enrich statistical inference by exploiting structural properties of data and provide a clinically reliable model for ORN risk evaluation through an unsupervised-learning analysis that incorporates the whole radiation dose distribution on the mandible.

METHODS AND MATERIALS

The analysis was conducted on retrospective data of 1259 patients with head and neck cancer treated at The University of Texas MD Anderson Cancer Center between 2005 and 2015. During a minimum 12-month posttherapy follow-up period, 173 patients in this cohort (13.7%) developed ORN (grades I to IV). The (structural) clusters of mandibular dose-volume histograms (DVHs) for these patients were identified using the K-means clustering method. A soft-margin support vector machine was used to determine the cluster borders and partition the dose-volume space. The risk of ORN for each dose-volume region was calculated based on incidence rates and other clinical risk factors.

RESULTS

The K-means clustering method identified 6 clusters among the DVHs. Based on the first 5 clusters, the dose-volume space was partitioned by the soft-margin support vector machine into distinct regions with different risk indices. The sixth cluster entirely overlapped with the others; the region of this cluster was determined by its envelopes. For each region, the ORN incidence rate per preradiation dental extraction status (a statistically significant, nondose related risk factor for ORN) was reported as the corresponding risk index.

CONCLUSIONS

This study presents an unsupervised-learning analysis of a large-scale data set to evaluate the risk of mandibular ORN among patients with head and neck cancer. The results provide a visual risk-assessment tool for ORN (based on the whole DVH and preradiation dental extraction status) as well as a range of constraints for dose optimization under different risk levels.

A marginal structural model for normal tissue complication probability

The goal of radiation therapy for cancer is to deliver prescribed radiation dose to the tumor while minimizing dose to the surrounding healthy tissues. To evaluate treatment plans, the dose distribution to healthy organs is commonly summarized as dose-volume histograms (DVHs). Normal tissue complication probability (NTCP) modeling has centered around making patient-level risk predictions with features extracted from the DVHs, but few have considered adapting a causal framework to evaluate the safety of alternative treatment plans. We propose causal estimands for NTCP based on deterministic and stochastic interventions, as well as propose estimators based on marginal structural models that impose bivariable monotonicity between dose, volume, and toxicity risk. The properties of these estimators are studied through simulations, and their use is illustrated in the context of radiotherapy treatment of anal canal cancer patients.

Normal Tissue Complication Probability Modeling of Severe Radiation-Induced Lymphopenia Using Blood Dose for Patients With Hepatocellular Carcinoma

PURPOSE

This study aimed to develop a normal tissue complication probability (NTCP) model to estimate the risk of severe radiation-induced lymphopenia (SRIL; absolute lymphocyte count [ALC] < 500/μL) by using the blood dose of patients with hepatocellular carcinoma (HCC).

METHODS AND MATERIALS

We retrospectively collected data from 75 patients with HCC who received radiation therapy (RT) between 2015 and 2018. The hematological dose framework calculated blood dose-volume histograms (DVHs) using a predefined blood flow model, organ DVHs, the number of treatment fractions, and beam delivery time. A Lyman-Kutcher-Burman model with a generalized equivalent dose was used to establish the NTCP model, reflecting the whole-blood DVHs. Optimization of the Lyman-Kutcher-Burman parameters was conducted by minimizing a negative log-likelihood function.

RESULTS

There were 6, 4, 18, 33, and 14 patients in the groups with radiation-induced lymphopenia grades 0, 1, 2, 3, and 4, respectively. The median pre- and post-RT ALC values were 1410/μL (range, 520-3710/μL) and 470/μL (range, 60-1760/μL), respectively. There was a correlation between mean blood dose and ALC depletion (Pearson r = -0.664; P < .001). The average mean blood doses in each radiation-induced lymphopenia group were 2.90 Gy (95% CI, 1.96-3.85 Gy) for grade 0 to 1, 5.29 Gy (95% CI, 4.12-6.45 Gy) for grade 2, 8.81 Gy (95% CI, 7.55-10.07 Gy) for grade 3, and 11.69 Gy (95% CI, 9.82-17.57 Gy) for grade 4. When applying the developed NTCP model to predict SRIL, the area under the receiver operating characteristic curve and Brier score values were 0.89 and 0.12, respectively.

CONCLUSIONS

We developed the first NTCP model based on whole-blood DVHs for estimating SRIL after abdominal RT in patients with HCC. Our results showed a strong correlation between blood dose and ALC depletion, suggesting the potential to predict the risk of SRIL occurrence using blood dose.

Dose-toxicity surface histogram-based prediction of radiation dermatitis severity and shape

Objective.This study aimed to develop a new approach to predict radiation dermatitis (RD) by using the skin dose distribution in the actual area of RD occurrence to determine the predictive dose by grade.Approach.Twenty-three patients with head and neck cancer treated with volumetric modulated arc therapy were prospectively and retrospectively enrolled. A framework was developed to segment the RD occurrence area in skin photography by matching the skin surface image obtained using a 3D camera with the skin dose distribution. RD predictive doses were generated using the dose-toxicity surface histogram (DTH) calculated from the skin dose distribution within the segmented RD regions classified by severity. We then evaluated whether the developed DTH-based framework could visually predict RD grades and their occurrence areas and shapes according to severity.Main results.The developed framework successfully generated the DTH for three different RD severities: faint erythema (grade 1), dry desquamation (grade 2), and moist desquamation (grade 3); 48 DTHs were obtained from 23 patients: 23, 22, and 3 DTHs for grades 1, 2, and 3, respectively. The RD predictive doses determined using DTHs were 28.9 Gy, 38.1 Gy, and 54.3 Gy for grades 1, 2, and 3, respectively. The estimated RD occurrence area visualized by the DTH-based RD predictive dose showed acceptable agreement for all grades compared with the actual RD region in the patient. The predicted RD grade was accurate, except in two patients.Significance. The developed DTH-based framework can classify and determine RD predictive doses according to severity and visually predict the occurrence area and shape of different RD severities. The proposed approach can be used to predict the severity and shape of potential RD in patients and thus aid physicians in decision making.

Dosimetric and NTCP analyses for selecting parotid gland cancer patients for proton therapy

PURPOSE/OBJECTIVE

To perform a dosimetric and a normal tissue complication probability (NTCP) comparison between intensity modulated proton therapy and photon volumetric modulated arc therapy in a cohort of patients with parotid gland cancers in a post-operative or radical setting.

MATERIALS AND METHODS

From May 2011 to September 2021, 37 parotid gland cancers patients treated at two institutions were eligible. Inclusion criteria were as follows: patients aged ⩾ 18 years, diagnosis of parotid gland cancers candidate for postoperative radiotherapy or definitive radiotherapy, presence of written informed consent for the use of anonymous data for research purposes. Organs at risk (OARs) were retrospectively contoured. Target coverage goal was defined as D95 > 98%. Six NTCP models were selected. NTCP profiles were calculated for each patient using an internally-developed Python script in RayStation TPS. Average differences in NTCP between photon and proton plans were tested for significance with a two-sided Wilcoxon signed-rank test.

RESULTS

Seventy-four plans were generated. A lower Dmean to the majority of organs at risk (inner ear, cochlea, oral cavity, pharyngeal constrictor muscles, contralateral parotid and submandibular gland) was obtained with intensity modulated proton therapy vs volumetric modulated arc therapy with statistical significance (p < .05). Ten (27%) patients had a difference in NTCP (photon vs proton plans) greater than 10% for hearing loss and tinnitus: among them, seven qualified for both endpoints, two patients for hearing loss only, and one for tinnitus.

CONCLUSIONS

In the current study, nearly one-third of patients resulted eligible for proton therapy and they were the most likely to benefit in terms of prevention of hearing loss and tinnitus.

Predictive Factors for Toxicity After Primary Chemoradiation for Locally Advanced Cervical Cancer: A Systematic Review

PURPOSE

Women with locally advanced cervical cancer (LACC) undergoing primary platinum-based chemoradiotherapy and brachytherapy often experience toxicities. Normal-tissue complication probability (NTCP) models quantify toxicity risk and aid in optimizing radiation therapy to minimize side effects. However, it is unclear which predictors to include in an NTCP model. The aim of this systematic review was to provide an overview of the identified predictors contributing to gastrointestinal (GI), genitourinary (GU), and vaginal toxicities and insufficiency fractures for LACC.

METHODS AND MATERIALS

A systematic search was performed and articles evaluating the relationship between predictors and toxicities in women with LACC treated with primary chemoradiation were included. The Quality In Prognosis Studies tool was used to assess risk of bias, with high-risk studies being excluded from further analysis. Relationships between dose-volume parameters, patient and treatment characteristics, and toxicity endpoints were analyzed.

RESULTS

Seventy-three studies were identified. Twenty-six had a low or moderate risk of bias and were therefore included. Brachytherapy-related dose-volume parameters of the GI tract, including rectum and bowel equivalent dose in 2 Gy fractions (EQD2) D2 cm3, were frequently related to toxicities, unlike GU dose-volume parameters. Furthermore, (recto)vaginal point doses predicted toxicities. Few studies evaluated external beam radiation therapy dose-volume parameters and identified rectum EQD2 V30 Gy, V40 Gy, and V55 Gy, bowel and bladder EQD2 V40 Gy as toxicity predictors. Also, total reference air kerma and vaginal reference length were associated with toxicities. Relationships between patient characteristics and GI toxicity were inconsistent. The extent of vaginal involvement at diagnosis, baseline symptoms, and obesity predicted GU or vaginal toxicities. Only 1 study evaluated insufficiency fractures and demonstrated lower pretreatment bone densities to be associated.

CONCLUSIONS

This review detected multiple candidate predictors of toxicity. Larger studies should consider insufficiency fractures, assess dose levels from external beam radiation therapy, and quantify the relationship between the predictors and treatment-related toxicities in women with LACC to further facilitate NTCP model development for clinical use.

Mechanistic model of radiotherapy-induced lung fibrosis using coupled 3D agent-based and Monte Carlo simulations

BACKGROUND

Mechanistic modelling of normal tissue toxicities is unfolding as an alternative to the phenomenological normal tissue complication probability models. The latter, currently used in the clinics, rely exclusively on limited patient data and neglect spatial dose distribution information. Among the various approaches, agent-based models are appealing as they provide the means to include patient-specific parameters and simulate long-term effects in complex systems. However, Monte Carlo tools remain the state-of-the-art for modelling radiation transport and provide measurements of the delivered dose with unmatched precision.

METHODS

In this work, we develop and characterize a coupled 3D agent-based - Monte Carlo model that mechanistically simulates the onset of the radiation-induced lung fibrosis in an alveolar segment. To the best of our knowledge, this is the first such model.

RESULTS

Our model replicates extracellular matrix patterns, radiation-induced lung fibrosis severity indexes and functional subunits survivals that show qualitative agreement with experimental studies and are consistent with our past results. Moreover, in accordance with experimental results, higher functional subunits survival and lower radiation-induced lung fibrosis severity indexes are achieved when a 5-fractions treatment is simulated. Finally, the model shows increased sensitivity to more uniform protons dose distributions with respect to more heterogeneous ones from photon irradiation.

CONCLUSIONS

This study lays thus the groundwork for further investigating the effects of different radiotherapeutic treatments on the onset of radiation-induced lung fibrosis via mechanistic modelling.

Dosimetric Impact of Voluntary Deep Inspiration Breath Hold (DIBH) in Mediastinal Hodgkin Lymphomas: A Comparative Evaluation of Three Different Intensity Modulated Radiation Therapy (IMRT) Delivery Methods Using Voluntary DIBH and Free Breathing Techniques

Hodgkin lymphomas are radiosensitive and curable tumors that often involve the mediastinum. However, the application of radiation therapy to the mediastinum is associated with late effects including cardiac and pulmonary toxicities and secondary cancers. The adoption of conformal IMRT and deep inspiration breath- hold (DIBH) can reduce the dose to healthy normal tissues (lungs, heart and breast). We compared the dosimetry of organs at risk (OARs) using different IMRT techniques for two breathing conditions, i.e., deep inspiration breath hold (DIBH) and free breathing. Twenty-three patients with early-stage mediastinal Hodgkin lymphomas were accrued in the prospective study. The patients were given treatment plans which utilized full arc volumetric modulated arc therapy (F-VMAT), Butterfly VMAT (B-VMAT), and fixed field IMRT (FF-IMRT) techniques for both DIBH and free breathing methods, respectively. All the plans were optimized to deliver 95% of the prescription dose which was 25.2 Gy to 95% of the PTV volume. The mean dose and standard error of the mean for each OAR, conformity index (CI), and homogeneity index (HI) for the target using the three planning techniques were calculated and compared using Student's t-test for parametric data and Wilcoxon signed-rank test for non-parametric data. The HI and CI of the target was not compromised using the DIBH technique for mediastinal lymphomas. The mean values of CI and HI for both DIBH and FB were comparable. The mean heart doses were reduced by 2.1 Gy, 2.54 Gy, and 2.38 Gy in DIBH compared to FB for the F-VMAT, B-VMAT, and IMRT techniques, respectively. There was a significant reduction in V5Gy, V10Gy, and V15Gy to the heart (p < 0.005) with DIBH. DIBH reduced the mean dose to the total lung by 1.19 Gy, 1.47 Gy, and 1.3 Gy, respectively. Among the 14 female patients, there was a reduction in the mean right breast dose with DIBH compared to FB (4.47 Gy vs. 3.63 Gy, p = 0.004). DIBH results in lower heart, lung, and breast doses than free breathing in mediastinal Hodgkin Lymphoma. Among the different IMRT techniques, FF-IMRT, B-VMAT, and F-VMAT showed similar PTV coverage, with similar conformity and homogeneity indices. However, the time taken for FF-IMRT was much longer than for the F-VMAT and B-VMAT techniques for both breathing methods. B-VMAT and F-VMAT emerged as the optimal planning techniques able to achieve the best target coverage and lower doses to the OARs, with less time required to deliver the prescribed dose.

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.

Application research on reducing radiation-induced lung injury with a trigger operator based on overlap volume histogram (OVH) in breast cancer postoperative radiotherapy

This study aims to develop a trigger operator based on the Overlap Volume Histogram (OVH) and examined its effectiveness in enhancing plan quality to minimize radiation-induced lung injury in postoperative radiotherapy for breast cancer. This trigger operator was applied for plan re-optimization to the previous Volumetric Modulated Arc Therapy (VMAT) plans of 16 left breast conserving surgery cases. These cases were categorized into a Contiguous Group (CG) and a Separated Group (SG) based on the relative position between the target and the Left-Lung (L-Lung). We investigated the changes in Vx, mean dose, and Normal Tissue Complication Probability (NTCP) values of organs-at-risk (OARs) before and after using the trigger operator. The Pairwise Sample T test was employed to evaluate the differences in indices between the two groups before and after optimizations. The trigger operator effectively initiated plan re-optimization. The values of V5, V10, V20, V30, and V40 of the L-Lung, as well as the mean dose of the heart, all decreased after re-optimization. The Pairwise Sample T test results showed statistically significant differences in the V20, V30, and V40 of the L-Lung in the CG (P < 0.01), and in the V5, V10, V20, V30, and V40 of the L-Lung in the SG (P < 0.01). Our findings suggest that the proposed trigger operator can improve plan quality, thereby reducing radiation-induced lung injury in postoperative radiotherapy for breast cancer.

Mathematical modeling of radiotherapy: impact of model selection on estimating minimum radiation dose for tumor control

Introduction

Radiation therapy (RT) is one of the most common anticancer therapies. Yet, current radiation oncology practice does not adapt RT dose for individual patients, despite wide interpatient variability in radiosensitivity and accompanying treatment response. We have previously shown that mechanistic mathematical modeling of tumor volume dynamics can simulate volumetric response to RT for individual patients and estimation personalized RT dose for optimal tumor volume reduction. However, understanding the implications of the choice of the underlying RT response model is critical when calculating personalized RT dose.

Methods

In this study, we evaluate the mathematical implications and biological effects of 2 models of RT response on dose personalization: (1) cytotoxicity to cancer cells that lead to direct tumor volume reduction (DVR) and (2) radiation responses to the tumor microenvironment that lead to tumor carrying capacity reduction (CCR) and subsequent tumor shrinkage. Tumor growth was simulated as logistic growth with pre-treatment dynamics being described in the proliferation saturation index (PSI). The effect of RT was simulated according to each respective model for a standard schedule of fractionated RT with 2 Gy weekday fractions. Parameter sweeps were evaluated for the intrinsic tumor growth rate and the radiosensitivity parameter for both models to observe the qualitative impact of each model parameter. We then calculated the minimum RT dose required for locoregional tumor control (LRC) across all combinations of the full range of radiosensitvity and proliferation saturation values.

Results

Both models estimate that patients with higher radiosensitivity will require a lower RT dose to achieve LRC. However, the two models make opposite estimates on the impact of PSI on the minimum RT dose for LRC: the DVR model estimates that tumors with higher PSI values will require a higher RT dose to achieve LRC, while the CCR model estimates that higher PSI values will require a lower RT dose to achieve LRC.

Discussion

Ultimately, these results show the importance of understanding which model best describes tumor growth and treatment response in a particular setting, before using any such model to make estimates for personalized treatment recommendations.

Prediction of Normal Tissue Complication Probability (NTCP) After Radiation Therapy Using Imaging and Molecular Biomarkers and Multivariate Modelling

The aim of this study was to design a predictive radiobiological model of normal brain tissue in low-grade glioma following radiotherapy based on imaging and molecular biomarkers. Fifteen patients with primary brain tumors prospectively participated in this study and underwent radiation therapy. Magnetic resonance imaging (MRI) was obtained from the patients, including T1- and T2-weighted imaging and diffusion tensor imaging (DTI), and a generalized equivalent dose (gEUD) was calculated. The radiobiological model of the normal tissue complication probability (NTCP) was performed using the variables gEUD; axial diffusivity (AD) and radial diffusivity (RD) of the corpus callosum; and serum protein S100B by univariate and multivariate logistic regression XLIIIrd Sir Peter Freyer Memorial Lecture and Surgical Symposium (2018). Changes in AD, RD, and S100B from baseline up to the 6 months after treatment had an increasing trend and were significant in some time points (P-value < 0.05). The model resulting from RD changes in the 6 months after treatment was significantly more predictable of necrosis than other univariate models. The bivariate model combining RD changes in Gy40 dose-volume and gEUD, as well as the trivariate model obtained using gEUD, RD, and S100B, had a higher predictive value among multivariate models at the sixth month of the treatment. Changes in RD diffusion indices and in serum protein S100B value were used in the early-delayed stage as reliable biomarkers for predicting late-delayed damage (necrosis) caused by radiation in the corpus callosum. Current findings could pave the way for intervention therapies to delay the severity of damage to white matter structures, minimize cognitive impairment, and improve the quality of life of patients with low-grade glioma.

Stereotactic Radiation Therapy for Brain Metastases: Factors Affecting Outcomes and Radiation Necrosis

Stereotactic radiation therapy (SRT) is a proven effective treatment for brain metastases (BM); however, symptomatic radiation necrosis (RN) is a late effect that may impact on patient’s quality of life. The aim of our study was to retrospectively evaluate survival outcomes and characterize the occurrence of RN in a cohort of BM patients treated with ablative SRT at Federico II University Hospital. Clinical and dosimetric factors of 87 patients bearing a total of 220 BMs treated with SRT from 2016 to 2022 were analyzed. Among them, 46 patients with 127 BMs having clinical and MRI follow-up (FUP) ≥ 6 months were selected for RN evaluation. Dosimetric parameters of the uninvolved brain (brain without GTV) were extracted. The crude local control was 91% with neither clinical factors nor prescription dose correlating with local failure (LF). At a median FUP of 9 (1–68) months, the estimated median overall survival (OS), progression-free survival (PFS), and brain progression-free survival (bPFS) were 16, 6, and 9 months, respectively. The estimated OS rates at 1 and 3 years were 59.8% and 18.3%, respectively; bPFS at 1 and 3 years was 29.9% and 13.5%, respectively; PFS at 1 and 3 years was 15.7% and 0%, respectively; and local failure-free survival (LFFS) at 1 and 3 years was 87.2% and 83.8%, respectively. Extracranial disease status was an independent factor related to OS. Fourteen (30%) patients manifested RN. At multivariate analysis, adenocarcinoma histology, left location, and absence of chemotherapy were confirmed as independent risk factors for any-grade RN. Nine (20%) patients developed symptomatic (G2) RN, which improved or stabilized after 1–16 months of steroid therapy. With prompt recognition and, when necessary, medical therapy, RN radiological and clinical amelioration can be obtained.

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.

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.

Inter- and intrafractional 4D dose accumulation for evaluating ΔNTCP robustness in lung cancer

BACKGROUND AND PURPOSE

Model-based selection of proton therapy patients relies on a predefined reduction in normal tissue complication probability (NTCP) with respect to photon therapy. The decision is necessarily made based on the treatment plan, but NTCP can be affected when the delivered treatment deviates from the plan due to delivery inaccuracies. Especially for proton therapy of lung cancer, this can be important because of tissue density changes and, with pencil beam scanning, the interplay effect between the proton beam and breathing motion.

MATERIALS AND METHODS

In this work, we verified whether the expected benefit of proton therapy is retained despite delivery inaccuracies by reconstructing the delivered treatment using log-file based dose reconstruction and inter- and intrafractional accumulation. Additionally, the importance of two uncertain parameters for treatment reconstruction, namely deformable image registration (DIR) algorithm and α/β ratio, was assessed.

RESULTS

The expected benefit or proton therapy was confirmed in 97% of all studied cases, despite regular differences up to 2 percent point (p.p.) NTCP between the delivered and planned treatments. The choice of DIR algorithm affected NTCP up to 1.6 p.p., an order of magnitude higher than the effect of α/β ratio.

CONCLUSION

For the patient population and treatment technique employed, the predicted clinical benefit for patients selected for proton therapy was confirmed for 97.0% percent of all cases, although the NTCP based proton selection was subject to 2 p.p. variations due to delivery inaccuracies.

A New Approach to Quantify and Grade Radiation Dermatitis Using Deep-Learning Segmentation in Skin Photographs

AIMS

Objective evaluation of radiation dermatitis is important for analysing the correlation between the severity of radiation dermatitis and dose distribution in clinical practice and for reliable reporting in clinical trials. We developed a novel radiation dermatitis segmentation system based on convolutional neural networks (CNNs) to consistently evaluate radiation dermatitis.

MATERIALS AND METHODS

The radiation dermatitis segmentation system is designed to segment the radiation dermatitis occurrence area using skin photographs and skin-dose distribution. A CNN architecture with a dilated convolution layer and skip connection was designed to estimate the radiation dermatitis area. Seventy-three skin photographs obtained from patients undergoing radiotherapy were collected for training and testing. The ground truth of radiation dermatitis segmentation is manually delineated from the skin photograph by an experienced radiation oncologist and medical physicist. We converted the skin photographs to RGB (red-green-blue) and CIELAB (lightness (L^∗), red-green (a^∗) and blue-yellow (b^∗)) colour information and trained the network to segment faint and severe radiation dermatitis using three different input combinations: RGB, RGB + CIELAB (RGBLAB) and RGB + CIELAB + skin-dose distribution (RGBLAB_D). The proposed system was evaluated using the Dice similarity coefficient (DSC), sensitivity, specificity and normalised Matthews correlation coefficient (nMCC). A paired t-test was used to compare the results of different segmentation performances.

RESULTS

Optimal data composition was observed in the network trained for radiation dermatitis segmentation using skin photographs and skin-dose distribution. The average DSC, sensitivity, specificity and nMCC values of RGBLAB_D were 0.62, 0.61, 0.91 and 0.77, respectively, in faint radiation dermatitis, and 0.69, 0.78, 0.96 and 0.83, respectively, in severe radiation dermatitis.

CONCLUSION

Our study showed that CNN-based radiation dermatitis segmentation in skin photographs of patients undergoing radiotherapy can describe radiation dermatitis severity and pattern. Our study could aid in objectifying the radiation dermatitis grading and analysing the reliable correlation between dosimetric factors and the morphology of radiation dermatitis.

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.

Changing Role of PET/CT in Cancer Care With a Focus on Radiotherapy

Positron emission tomography (PET) integrated with computed tomography (CT) has brought revolutionary changes in improving cancer care (CC) for patients. These include improved detection of previously unrecognizable disease, ability to identify oligometastatic status enabling more aggressive treatment strategies when the disease burden is lower, its use in better defining treatment targets in radiotherapy (RT), ability to monitor treatment responses early and thus improve the ability for early interventions of non-responding tumors, and as a prognosticating tool as well as outcome predicting tool. PET/CT has enabled the emergence of new concepts such as radiobiotherapy (RBT), radioimmunotherapy, theranostics, and pharmaco-radiotherapy. This is a rapidly evolving field, and this primer is to help summarize the current status and to give an impetus to developing new ideas, clinical trials, and CC outcome improvements.

An Agent-Based Model of Radiation-Induced Lung Fibrosis

Early- and late-phase radiation-induced lung injuries, namely pneumonitis and lung fibrosis (RILF), severely constrain the maximum dose and irradiated volume in thoracic radiotherapy. As the most radiosensitive targets, epithelial cells respond to radiation either by undergoing apoptosis or switching to a senescent phenotype that triggers the immune system and damages surrounding healthy cells. Unresolved inflammation stimulates mesenchymal cells' proliferation and extracellular matrix (ECM) secretion, which irreversibly stiffens the alveolar walls and leads to respiratory failure. Although a thorough understanding is lacking, RILF and idiopathic pulmonary fibrosis share multiple pathways and would mutually benefit from further insights into disease progression. Furthermore, current normal tissue complication probability (NTCP) models rely on clinical experience to set tolerance doses for organs at risk and leave aside mechanistic interpretations of the undergoing processes. To these aims, we implemented a 3D agent-based model (ABM) of an alveolar duct that simulates cell dynamics and substance diffusion following radiation injury. Emphasis was placed on cell repopulation, senescent clearance, and intra/inter-alveolar bystander senescence while tracking ECM deposition. Our ABM successfully replicates early and late fibrotic response patterns reported in the literature along with the ECM sigmoidal dose-response curve. Moreover, surrogate measures of RILF severity via a custom indicator show qualitative agreement with published fibrosis indices. Finally, our ABM provides a fully mechanistic alveolar survival curve highlighting the need to include bystander damage in lung NTCP models.

Roadmap: helium ion therapy

Helium ion beam therapy for the treatment of cancer was one of several developed and studied particle treatments in the 1950s, leading to clinical trials beginning in 1975 at the Lawrence Berkeley National Laboratory. The trial shutdown was followed by decades of research and clinical silence on the topic while proton and carbon ion therapy made debuts at research facilities and academic hospitals worldwide. The lack of progression in understanding the principle facets of helium ion beam therapy in terms of physics, biological and clinical findings persists today, mainly attributable to its highly limited availability. Despite this major setback, there is an increasing focus on evaluating and establishing clinical and research programs using helium ion beams, with both therapy and imaging initiatives to supplement the clinical palette of radiotherapy in the treatment of aggressive disease and sensitive clinical cases. Moreover, due its intermediate physical and radio-biological properties between proton and carbon ion beams, helium ions may provide a streamlined economic steppingstone towards an era of widespread use of different particle species in light and heavy ion therapy. With respect to the clinical proton beams, helium ions exhibit superior physical properties such as reduced lateral scattering and range straggling with higher relative biological effectiveness (RBE) and dose-weighted linear energy transfer (LET_d) ranging from ∼4 keVμm^-1to ∼40 keVμm^-1. In the frame of heavy ion therapy using carbon, oxygen or neon ions, where LET_dincreases beyond 100 keVμm^-1, helium ions exhibit similar physical attributes such as a sharp lateral penumbra, however, with reduced radio-biological uncertainties and without potentially spoiling dose distributions due to excess fragmentation of heavier ion beams, particularly for higher penetration depths. This roadmap presents an overview of the current state-of-the-art and future directions of helium ion therapy: understanding physics and improving modeling, understanding biology and improving modeling, imaging techniques using helium ions and refining and establishing clinical approaches and aims from learned experience with protons. These topics are organized and presented into three main sections, outlining current and future tasks in establishing clinical and research programs using helium ion beams-A. Physics B. Biological and C. Clinical Perspectives.

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.

Mechanisms and Review of Clinical Evidence of Variations in Relative Biological Effectiveness in Proton Therapy

Proton therapy is increasingly being used as a radiation therapy modality. There is uncertainty about the biological effectiveness of protons relative to photon therapies as it depends on several physical and biological parameters. Radiation oncology currently applies a constant and generic value for the relative biological effectiveness (RBE) of 1.1, which was chosen conservatively to ensure tumor coverage. The use of a constant value has been challenged particularly when considering normal tissue constraints. Potential variations in RBE have been assessed in several published reviews but have mostly focused on data from clonogenic cell survival experiments with unclear relevance for clinical proton therapy. The goal of this review is to put in vitro findings in relation to clinical observations. Relevant in vivo pathways determining RBE for tumors and normal tissues are outlined, including not only damage to tumor cells and parenchyma but also vascular damage and immune response. Furthermore, the current clinical evidence of varying RBE is reviewed. The assessment can serve as guidance for treatment planning, personalized dose prescriptions, and outcome analysis.

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.

Local tumor control and treatment related toxicity after plaque brachytherapy for uveal melanoma: A systematic review and a data pooled analysis

Uveal melanoma (UM) represents the most common primary intraocular tumor, and nowadays eye plaque brachytherapy (EPB) is the most frequently used visual acuity preservation treatment option for small to medium sized UMs. The excellent local tumor control (LTC) rate achieved by EPB may be associated with severe complications and adverse events. Several dosimetric and clinical risk factors for the development of EPB-related ocular morbidity can be identified. However, morbidity predictive models specifically developed for EPB are still scarce. PRISMA methodology was used for the present systematic review of articles indexed in PubMed in the last sixteen years on EPB treatment of UM which aims at determining the major factors affecting local tumor control and ocular morbidities. To our knowledge, for the first time in EPB field, local tumor control probability (TCP) and normal tissue complication probability (NTCP) modelling on pooled clinical outcomes were performed. The analyzed literature (103 studies including 21,263 UM patients) pointed out that Ru-106 EPB provided high local control outcomes while minimizing radiation induced complications. The use of treatment planning systems (TPS) was the most influencing factor for EPB outcomes such as metastasis occurrence, enucleation, and disease specific survival, irrespective of radioactive implant type. TCP and NTCP parameters were successfully extracted for 5-year LTC, cataract and optic neuropathy. In future studies, more consistent recordings of ocular morbidities along with accurate estimation of doses through routine use of TPS are needed to expand and improve the robustness of toxicity risk prediction in EPB.

Delineation of organs at risk

The delineation of organs at risk is the basis of radiotherapy oncologists' work. Indeed, the knowledge of this delineation enables to better identify the target volumes and to optimize dose distribution, involving the prognosis of the patients but also their future. The learning of this delineation must continue throughout the clinician's career. Some contour changes have appeared with better imaging, some volumes are now required due to development of knowledge of side effects. In addition, the increasing survival time of patients requires to be more systematic and precise in the delineations, both to avoid complications until now exceptional but also because re-irradiations are becoming more and more frequent. We present the update of the recommendations of the French Society for Radiation Oncology (SFRO) on new findings or adaptations to volumes at risk.

A Systematic Review on Intensity Modulated Radiation Therapy for Mediastinal Hodgkin's Lymphoma

BACKGROUND

Secondary malignant neoplasms (SMNs) and cardiovascular diseases induced by chemotherapy and radiotherapy represent the main cause of excess mortality for early-stage Hodgkin lymphoma patients, especially when the mediastinum is involved. Conformal radiotherapy techniques such as Intensity-Modulated Radiation Therapy (IMRT) could allow a reduction of the dose to the organs-at-risk (OARs) and therefore limit long-term toxicity.

METHODS

We performed a systematic review of the current literature regarding comparisons between IMRT and conventional photon beam radiotherapy, or between different IMRT techniques, for the treatment of mediastinal lymphoma.

RESULTS AND CONCLUSIONS

IMRT allows a substantial reduction of the volumes of OARs exposed to high doses, reducing the risk of long-term toxicity. This benefit is conterbalanced by the increase of volumes receiving low doses, that could potentially increase the risk of SMNs. Treatment planning should be personalized on patient and disease characteristics. Dedicated techniques such as "butterfly" VMAT often provide the best trade-off.

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.

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.

Artificial Intelligence Applications to Improve the Treatment of Locally Advanced Non-Small Cell Lung Cancers

Locally advanced non-small cell lung cancer patients represent around one third of newly diagnosed lung cancer patients. There remains a large unmet need to find treatment strategies that can improve the survival of these patients while minimizing therapeutical side effects. Increasing the availability of patients' data (imaging, electronic health records, patients' reported outcomes, and genomics) will enable the application of AI algorithms to improve therapy selections. In this review, we discuss how artificial intelligence (AI) can be integral to improving clinical decision support systems. To realize this, a roadmap for AI must be defined. We define six milestones involving a broad spectrum of stakeholders, from physicians to patients, that we feel are necessary for an optimal transition of AI into the clinic.

Investigating DWI changes in white matter of meningioma patients treated with proton therapy

PURPOSE

To evaluate changes in diffusion and perfusion-related properties of white matter (WM) induced by proton therapy, which is capable of a greater dose sparing to organs at risk with respect to conventional X-ray radiotherapy, and to eventually expose early manifestations of delayed neuro-toxicities.

METHODS

Apparent diffusion coefficient (ADC) and IVIM parameters (D, D* and f) were estimated from diffusion-weighted MRI (DWI) in 46 patients affected by meningioma and treated with proton therapy. The impact on changes in diffusion and perfusion-related WM properties of dose and time, as well as the influence of demographic and pre-treatment clinical information, were investigated through linear mixed-effects models.

RESULTS

Decreasing trends in ADC and D were found for WM regions hit by medium-high (30-40 Gy(RBE)) and high (>40 Gy(RBE)) doses, which are compatible with diffusion restriction due to radiation-induced cellular injury. Significant influence of dose and time on median ADC changes were observed. Also, D* showed a significant dependency on dose, whereas f consistently showed no dependency on dose and time. Age, gender and surgery extent were also found to affect changes in ADC.

CONCLUSIONS

These results overall agree with those from studies conducted on cohorts of mixed proton and X-ray radiotherapy patients. Future work should focus on relating our findings with clinical information of co-morbidities and thus exploiting such or more advanced imaging data to build normal tissue complication probability models to better integrate clinical and dose information.

Ethical evaluation of artificial intelligence applications in radiotherapy using the Four Topics Approach

Artificial Intelligence is the capability of a machine to imitate intelligent human behavior. An important impact can be expected from Artificial Intelligence throughout the workflow of radiotherapy (such as automated organ segmentation, treatment planning, prediction of outcome and quality assurance). However, ethical concerns regarding the binding agreement between the patient and the physician have followed the introduction of artificial intelligence. Through the recording of personal and social moral values in addition to the usual demographics and the implementation of these as distinctive inputs to matching algorithms, ethical concerns such as consistency, applicability and relevance can be solved. In the meantime, physicians' awareness of the ethical dimension in their decision-making should be challenged, so that they prioritize treating their patients and not diseases, remain vigilant to preserve patient safety, avoid unintended harm and establish institutional policies on these issues.

Radiobiological modeling of radiation-induced acute mucosal toxicity (oral mucositis and pharyngeal mucositis): A single-institutional study of head-and-neck carcinoma

Purpose/Objective(s)

This study aimed to estimate the fitting parameters of sigmoidal dose-response (SDR) curve of radiation-induced acute oral and pharyngeal mucositis in head-and-neck (H and N) cancer patients treated with Intensity Modulated Radiation Therapy (IMRT) for the calculation of normal tissue complication probability (NTCP).

Materials and Methods

Thirty H-and-N cancer patients were enrolled to model the SDR curve for oral and pharyngeal mucositis. The patients were evaluated weekly for acute radiation-induced (ARI) oral and pharyngeal mucositis toxicity, and their scoring was performed as per the common terminology criteria adverse events version 5.0. The radiobiological parameters, namely n, m, TD50, and γ50 were calculated from the fitted SDR curve obtained from the clinical data of H-and-N cancer patients.

Results

ARI toxicity for oral and pharyngeal mucosa in carcinoma of H-and-N cancer patients was calculated for the endpoint oral mucositis and pharyngeal mucositis. The n, m, TD50, and γ50 parameters from the SDR curve of Grade 1 and Grade 2 oral mucositis were found to be [0.10, 0.32, 12.35 ± 3.90 (confidence interval [CI] 95%) and 1.26] and [0.06, 0.33, 20.70 ± 6.95 (CI 95%) and 1.19] respectively. Similarly for pharyngeal mucositis, n, m, TD50, and γ50 parameters for Grade 1 and Grade 2 were found to be [0.07, 0.34, 15.93 ± 5.48 (CI. 95%) and 1.16 ] and [0.04, 0.25, 39.02 ± 9.98(CI. 95%) and 1.56] respectively.

Conclusion

This study presents the fitting parameters for NTCP calculation of Grade 1 and Grade 2 ARI toxicity for the endpoint of oral and pharyngeal mucositis. The provided nomograms of volume versus complication and dose versus complication for different grades of oral mucositis and pharyngeal mucositis help radiation oncologists to decide the limiting dose to reduce the acute toxicities.

Myths, facts and scope of spinal cord tolerance dose revision in Intensity modulated SIB treatment of locally advanced head and neck cancer: A dosimetrical and radiobiological demonstration

PURPOSE

To explore the possibility of revising the spinal cord tolerance dose in Simultaneously Integrated Boost (SIB) intensity modulated treatment plan of locally advanced head and neck (H&N) cancer and assessment of achieved planning gain due to the revision. In SIB regimen, the Organ at Risk (OARs) tolerance dose is equally distributed throughout the treatment. Clinicians have usually considered the spinal cord tolerance to be the same as in conventional technique. However, in SIB fractionation regimen with intensity modulation treatment, the spinal cord may receive a physical dose of 45Gy, with much lesser dose per fraction than 2Gy per fraction. So when the dose of spinal cord is distributed throughout the treatment, the tolerance dose limit of physical dose can be considered higher than the usual conventional dose limits. In this study, an attempt has been made to explore the possibilities of dose escalation and treatment planning benefits while exploiting this "Window of Opportunity (WoO)" of increase in spinal cord and Planning Risk Volume (PRV) spinal cord tolerance dose.

MATERIAL AND METHODS

A total of 12 patients CT data set along with approved structure set of H&N cancer used for treatment planning in. Three independent SIB VMAT plans named as SPC, SPR and SPDE were generated for the 12 patients. First plan (SPC) was generated by considering standard spinal cord tissue constraint of maximum dose of 45Gy and PRV spinal cord maximum dose 50Gy as per QUANTEC summary and second plan (SPR) was generated considering spinal cord tissue constraint of maximum dose 52.50Gy and PRV spinal cord maximum dose 56.35Gy while optimization and dose calculation. The objectives for rest of the Organ at Risk (OAR) were kept same in both the plans during optimization and dose calculation. The SPC plan was copied for creation of third plan (SPDE) in which dose was escalated by increasing dose per fraction for target volumes such that dose to spinal cord reached a maximum dose of 52.50Gy and PRV spinal cord maximum dose of 56.35Gy. In this plan there have been changes to only dose per fraction, however dose optimization and dose calculation have not been performed. Radiobiological parameters TCP and NTCP were also calculated by using indigenously developed software.

RESULTS

Considering the increase of spinal cord tolerance dose as "window of opportunity", a sufficient escalation in physical dose, Biological Effective Dose (BED) and Tumor Control Probability (TCP) was observed for all target volumes with acceptable level of NTCP values.

CONCLUSION

Sufficient dose escalation and increased in TCP for target volumes or effective planning benefits can be achieved by revising the spinal cord tolerance dose in intensity modulated SIB treatment of locally advanced H&N cancers.

Practicalities in running early-phase trials using the time-to-event continual reassessment method (TiTE-CRM) for interventions with long toxicity periods using two radiotherapy oncology trials as examples

BACKGROUND

Awareness of model-based designs for dose-finding studies such as the Continual Reassessment Method (CRM) is now becoming more commonplace amongst clinicians, statisticians and trial management staff. In some settings toxicities can occur a long time after treatment has finished, resulting in extremely long, interrupted, CRM design trials. The Time-to-Event CRM (TiTE-CRM), a modification to the original CRM, accounts for the timing of late-onset toxicities and results in shorter trial duration. In this article, we discuss how to design and deliver a trial using this method, from the grant application stage through to dissemination, using two radiotherapy trials as examples.

METHODS

The TiTE-CRM encapsulates the dose-toxicity relationship with a statistical model. The model incorporates observed toxicities and uses a weight to account for the proportion of completed follow-up of participants without toxicity. This model uses all available data to determine the next participant's dose and subsequently declare the maximum tolerated dose. We focus on two trials designed by the authors to illustrate practical issues when designing, setting up, and running such studies.

RESULTS

In setting up a TiTE-CRM trial, model parameters need to be defined and the time element involved might cause complications, therefore looking at operating characteristics through simulations is essential. At the grant application stage, we suggest resources to fund statisticians' time before funding is awarded and make recommendations for the level of detail to include in funding applications. While running the trial, close contact of all involved staff is required as a dose decision is made each time a participant is recruited. We suggest ways of capturing data in a timely manner and give example code in R for design and delivery of the trial. Finally, we touch upon dissemination issues while the trial is running and upon completion.

CONCLUSION

Model-based designs can be complex. We hope this paper will help clinical trial teams to demystify the conduct of TiTE-CRM trials and be a starting point for using this methodology in practice.

Modelling the risk of radiation induced alopecia in brain tumor patients treated with scanned proton beams

PURPOSE

To develop normal tissue complication probability (NTCP) models for radiation-induced alopecia (RIA) in brain tumor patients treated with proton therapy (PT).

METHODS AND MATERIALS

We analyzed 116 brain tumor adult patients undergoing scanning beam PT (median dose 54 GyRBE; range 36-72) for CTCAE v.4 grade 2 (G2) acute (≤90 days), late (>90 days) and permanent (>12 months) RIA. The relative dose-surface histogram (DSH) of the scalp was extracted and used for Lyman-Kutcher-Burman (LKB) modelling. Moreover, DSH metrics (Sx: the surface receiving ≥ X Gy, D_2%: near maximum dose, D_mean: mean dose) and non-dosimetric variables were included in a multivariable logistic regression NTCP model. Model performances were evaluated by the cross-validated area under the receiver operator curve (ROC-AUC).

RESULTS

Acute, late and permanent G2-RIA was observed in 52%, 35% and 19% of the patients, respectively. The LKB models showed a weak dose-surface effect (0.09 ≤ n ≤ 0.19) with relative steepness 0.29 ≤ m ≤ 0.56, and increasing tolerance dose values when moving from acute and late (22 and 24 GyRBE) to permanent RIA (44 GyRBE). Multivariable modelling selected S_21Gy for acute and S_25Gy, for late G2-RIA as the most predictive DSH factors. Younger age was selected as risk factor for acute G2-RIA while surgery as risk factor for late G2-RIA. D_2% was the only variable selected for permanent G2-RIA. Both LKB and logistic models exhibited high predictive performances (ROC-AUCs range 0.86-0.90).

CONCLUSION

We derived NTCP models to predict G2-RIA after PT, providing a comprehensive modelling framework for acute, late and permanent occurrences that, once externally validated, could be exploited for individualized scalp sparing treatment planning strategies in brain tumor patients.