Differences In Normal Tissue Complication Probability Research Articles

Evaluation of an automated clinical decision system with deep learning dose prediction and NTCP model for prostate cancer proton therapy

Objective. To evaluate the feasibility of using a deep learning dose prediction approach to identify patients who could benefit most from proton therapy based on the normal tissue complication probability (NTCP) model. Approach. Two 3D UNets were established to predict photon and proton doses. A dataset of 95 patients with localized prostate cancer was randomly partitioned into 55, 10, and 30 for training, validation, and testing, respectively. We selected NTCP models for late rectum bleeding and acute urinary urgency of grade 2 or higher to quantify the benefit of proton therapy. Propagated uncertainties of predicted ΔNTCPs resulting from the dose prediction errors were calculated. Patient selection accuracies for a single endpoint and a composite evaluation were assessed under different ΔNTCP thresholds. Main results. Our deep learning-based dose prediction technique can reduce the time spent on plan comparison from approximately 2 days to as little as 5 seconds. The expanded uncertainty of predicted ΔNTCPs for rectum and bladder endpoints propagated from the dose prediction error were 0.0042 and 0.0016, respectively, which is less than one-third of the acceptable tolerance. The averaged selection accuracies for rectum bleeding, urinary urgency, and composite evaluation were 90%, 93.5%, and 93.5%, respectively. Significance. Our study demonstrates that deep learning dose prediction and NTCP evaluation scheme could distinguish the NTCP differences between photon and proton treatment modalities. In addition, the dose prediction uncertainty does not significantly influence the decision accuracy of NTCP-based patient selection for proton therapy. Therefore, automated deep learning dose prediction and NTCP evaluation schemes can potentially be used to screen large patient populations and to avoid unnecessary delays in the start of prostate cancer radiotherapy in the future.

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

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. 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. 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. 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.

Comparative analysis of simultaneous integrated boost and sequential boost radiotherapy in node-positive cervical cancer: dosimetric and radiobiological considerations.

For locally advanced cervical cancer, the standard therapeutic approach involves concomitant chemoradiation therapy, supplemented by a brachytherapy boost. Moreover, an external beam radiotherapy (RT) boost should be considered for treating gross lymph node (LN) volumes. Two boost approaches exist with Volumetric Intensity Modulated Arc Therapy (VMAT): Sequential (SEQ) and Simultaneous Integrated Boost (SIB). This study undertakes a comprehensive dosimetric and radiobiological comparison between these two boost strategies. The study encompassed ten patients who underwent RT for cervical cancer with node-positive disease. Two sets of treatment plans were generated for each patient: SIB-VMAT and SEQ-VMAT. Dosimetric as well as radiobiological parameters including tumour control probability (TCP) and normal tissue complication probability (NTCP) were compared. Both techniques were analyzed for two different levels of LN involvement - only pelvic LNs and pelvic with para-aortic LNs. Statistical analysis was performed using SPSS software version 25.0. SIB-VMAT exhibited superior target coverage, yielding improved doses to the planning target volume (PTV) and gross tumour volume (GTV). Notably, SIB-VMAT plans displayed markedly superior dose conformity. While SEQ-VMAT displayed favorable organ sparing for femoral heads, SIB-VMAT appeared as the more efficient approach for mitigating bladder and bowel doses. TCP was significantly higher with SIB-VMAT, suggesting a higher likelihood of successful tumour control. Conversely, no statistically significant difference in NTCP was observed between the two techniques. This study's findings underscore the advantages of SIB-VMAT over SEQ-VMAT in terms of improved target coverage, dose conformity, and tumour control probability. In particular, SIB-VMAT demonstrated potential benefits for cases involving para-aortic nodes. It is concluded that SIB-VMAT should be the preferred approach in all cases of locally advanced cervical cancer.

Consistency in contouring of organs at risk by artificial intelligence vs oncologists in head and neck cancer patients

Background In the Danish Head and Neck Cancer Group (DAHANCA) 35 trial, patients are selected for proton treatment based on simulated reductions of Normal Tissue Complication Probability (NTCP) for proton compared to photon treatment at the referring departments. After inclusion in the trial, immobilization, scanning, contouring and planning are repeated at the national proton centre. The new contours could result in reduced expected NTCP gain of the proton plan, resulting in a loss of validity in the selection process. The present study evaluates if contour consistency can be improved by having access to AI (Artificial Intelligence) based contours. Materials and Methods The 63 patients in the DAHANCA 35 pilot trial had a CT from the local DAHANCA centre and one from the proton centre. A nationally validated convolutional neural network, based on nnU-Net, was used to contour OARs on both scans for each patient. Using deformable image registration, local AI and oncologist contours were transferred to the proton centre scans for comparison. Consistency was calculated with the Dice Similarity Coefficient (DSC) and Mean Surface Distance (MSD), comparing contours from AI to AI and oncologist to oncologist, respectively. Two NTCP models were applied to calculate NTCP for xerostomia and dysphagia. Results The AI contours showed significantly better consistency than the contours by oncologists. The median and interquartile range of DSC was 0.85 [0.78 − 0.90] and 0.68 [0.51 − 0.80] for AI and oncologist contours, respectively. The median and interquartile range of MSD was 0.9 mm [0.7 − 1.1] mm and 1.9 mm [1.5 − 2.6] mm for AI and oncologist contours, respectively. There was no significant difference in NTCP. Conclusions The study showed that OAR contours made by the AI algorithm were more consistent than those made by oncologists. No significant impact on the NTCP calculations could be discerned.

Impact of delineation errors on the estimated organ at risk dose and of dose errors on the normal tissue complication probability model.

Normal tissue complication probability (NTCP) models are often based on doses retrieved from delineated volumes. For retrospective dose-response studies focusing on organs that have not been delineated historically, automatic segmentation might be considered. However, automatic segmentation risks generating considerable delineation errors and knowledge regarding how these errors impact the estimated organ dose is important. Furthermore, organ-at-risk (OAR) dose uncertainties cannot be eliminated and might affect the resulting NTCP model. Therefore, it is also of interest to study how OAR dose errors impact the NTCP modeling results. To investigate how random delineation errors of the proximal bronchial tree, heart, and esophagus impact the estimated OAR dose, and to investigate how random errors in the doses used for dose-response modeling affect the estimated NTCPs. We investigated the impact of random delineation errors on the estimated OAR dose using the treatment plans of 39 patients treated with conventionally fractionated radiation therapy of non-small-cell lung cancer. Study-specific reference structures were defined by manually contouring the proximal bronchial tree, heart and esophagus. For each patient and organ, 120 reshaped structures were created by introducing random shifts and margins to the entire reference structure. The mean and near-maximum dose to the reference and reshaped structures were compared. In a separate investigation, the impact of random dose errors on the NTCP model was studied performing dose-response modeling with study sets containing treatment outcomes and OAR doses with and without introduced errors. Universal patient populations with defined population risks, dose-response relationships and distributions of OAR doses were used as ground truth. From such a universal population, we randomly sampled data sets consisting of OAR dose and treatment outcome into reference populations. Study sets of different sizes were created by repeatedly introducing errors to the OAR doses of each reference population. The NTCP models generated with dose errors were compared to the reference NTCP model of the corresponding reference population. A total of 14 040 reshaped structures with random delineation errors were created. The delineation errors resulted in systematic mean dose errors of less than 1% of the prescribed dose (PD). Mean dose differences above 15% of PD and near-maximum doses differences above 25% of PD were observed for 211 and 457 reshaped structures, respectively. Introducing random errors to OAR doses used for dose-response modeling resulted in systematic underestimations of the median NTCP. For all investigated scenarios, the median differences in NTCP were within 0.1 percentage points (p.p.) when comparing different study sizes. Introducing random delineation errors to the proximal bronchial tree, heart and esophagus resulted in mean dose and near-maximum dose differences above 15% and 25% of PD, respectively. We did not observe an association between the dose level and the magnitude of the dose errors. For the scenarios investigated in this study, introducing random errors to OAR doses used for dose-response modeling resulted in systematic underestimations of the median NTCP for reference risks higher than the universal population risk. The median NTCP underestimation was similar for different study sizes, all within 0.1 p.p.

The Dutch Initiative for the Model-Based Selection of Patients for Proton Therapy – A Federated Learning IT Infrastructure

<h3>Purpose/Objective(s)</h3> Model-based selection for proton therapy (PT) patients has been implemented since 2018 in the Netherlands. Normal tissue complication probability (NTCP) models translate dose differences in Organs at Risk (OARs) (Δdose) into differences in NTCP (ΔNTCP). With these NTCP models, patients that will benefit most from PT are selected for proton therapy. These models are based on patients treated with modern photon techniques due to a lack of data for proton therapy to base them on, assuming that their performance is similar among patients treated with PT. Our primary objective was to test the accuracy of one of these NTCP models in a proof-of-concept study with the external validation of the grade 3+ dysphagia NTCP model (six months after the treatment). <h3>Materials/Methods</h3> We used data from 320 head and neck (HNC) patients who were treated between 2019-2021 with (chemo)radiotherapy in one of the Dutch PT centers. Semantic Web technologies and the Findable Accessible Interoperable Reusable (FAIR) principles were applied to the variables needed for the computation of the NTCP grade 3+ dysphagia model formula in order to be structured in a machine-readable format for the Personal Health Train (PHT) infrastructure implementation. The PHT enables the secure exchange of statistical algorithms (i.e., "trains") between different hospitals without the exchange of patient data. Implementing the closed testing procedure methodology, the statistical algorithm for the federated NTCP external validation was built in the RStudio software. This algorithm constitutes the statistical "train" that will be distributed among the different PT Dutch centers. <h3>Results</h3> We successfully completed and implemented the statistical analysis-"train" for the external validation procedure, establishing the end-to-end IT-infrastructure needed in our center according to the requirements of the federated learning ProTRAIT (PROton Therapy ReseArch regIsTry) IT-infrastructure. The performance of the original NTCP model for grade 3+ dysphagia (which was adopted as the updated selected model from the closed testing procedure) had reasonable discriminative power in the validation cohort (AUC=0.84, sensitivity=0.69, specificity=0.75). <h3>Conclusion</h3> With this work we externally validated the existing HNC grade 3+ dysphagia NTCP model in our PT center using the Personal Health Train infrastructure (PHT). The next steps will focus on expanding more applications of the different components of the model-based approach such as the model-based clinical evaluation in the PT centers, including NTCP-model development and validation in a privacy-preserving federated manner with data from the other PT centers.

Influence of Beam Angle on Normal Tissue Complication Probability of Knowledge-Based Head and Neck Cancer Proton Planning.

Simple SummaryKnowledge-based treatment planning (KBP) solutions can be used to assist in the planning process by automatically generating patient-specific optimization objectives. A KBP model is typically derived from treatment plans with a similar planning methodology, for example beam angles. An end-user might deviate from this methodology for a variety of reasons. The effect of such deviations on KBP plan quality has not been widely explored. We therefore studied this using a human-interaction free proton planning solution to create comparative plans with the default angles used when building the model, and altered beam angle arrangements. Because normal tissue complication probability (NTCP) can be used to select patients for proton therapy, this was used as the primary outcome metric for plan quality. The results show that the beam angle and number of beams only had a small effect on the plan NTCP. This suggests that the model is robust to the various beam arrangements within the range described in this analysis, although a method that automatically further adapts the KBP planning objectives further decreased the NTCP by 1–3%.Knowledge-based planning solutions have brought significant improvements in treatment planning. However, the performance of a proton-specific knowledge-based planning model in creating knowledge-based plans (KBPs) with beam angles differing from those used to train the model remains unexplored. We used a previously validated RapidPlanPT model and scripting to create nine KBPs, one with default and eight with altered beam angles, for 10 recent oropharynx cancer patients. The altered-angle plans were compared against the default-angle ones in terms of grade 2 dysphagia and xerostomia normal tissue complication probability (NTCP), mean doses of several organs at risk, and dose homogeneity index (HI). As KBP could be suboptimal, a proof of principle automatic iterative optimizer (AIO) was added with the aim of reducing the plan NTCP. There were no statistically significant differences in NTCP or HI between default- and altered-angle KBPs, and the altered-angle plans showed a <1% reduction in NTCP. AIO was able to reduce the sum of grade 2 NTCPs in 66/90 cases with mean a reduction of 3.5 ± 1.8%. While the altered-angle plans saw greater benefit from AIO, both default- and altered-angle plans could be improved, indicating that the KBP model alone was not completely optimal to achieve the lowest NTCP. Overall, the data showed that the model was robust to the various beam arrangements within the range described in this analysis.

Small spot size versus large spot size: Effect on plan quality for lung cancer in pencil beam scanning proton therapy.

PurposeThe purpose of the current study was to evaluate the impact of spot size on the interplay effect, plan robustness, and dose to the organs at risk for lung cancer plans in pencil beam scanning (PBS) proton therapyMethodsThe current retrospective study included 13 lung cancer patients. For each patient, small spot (∼3 mm) plans and large spot (∼8 mm) plans were generated. The Monte Carlo algorithm was used for both robust plan optimization and final dose calculations. Each plan was normalized, such that 99% of the clinical target volume (CTV) received 99% of the prescription dose. Interplay effect was evaluated for treatment delivery starting in two different breathing phases (T0 and T50). Plan robustness was investigated for 12 perturbed scenarios, which combined the isocenter shift and range uncertainty. The nominal and worst‐case scenario (WCS) results were recorded for each treatment plan. Equivalent uniform dose (EUD) and normal tissue complication probability (NTCP) were evaluated for the total lung, heart, and esophagus.ResultsIn comparison to large spot plans, the WCS values of small spot plans at CTV D95%, D96%, D97%, D98%, and D99% were higher with the average differences of 2.2% (range, 0.3%–3.7%), 2.3% (range, 0.5%–4.0%), 2.6% (range, 0.6%–4.4%), 2.7% (range, 0.9%–5.2%), and 2.7% (range, 0.3%–6.0%), respectively. The nominal and WCS mean dose and EUD for the esophagus, heart, and total lung were higher in large spot plans. The difference in NTCP between large spot and small spot plans was up to 1.9% for the total lung, up to 0.3% for the heart, and up to 32.8% for the esophagus. For robustness acceptance criteria of CTV D95% ≥ 98% of the prescription dose, seven small spot plans had all 12 perturbed scenarios meeting the criteria, whereas, for 13 large spot plans, there were ≥2 scenarios failing to meet the criteria. Interplay results showed that, on average, the target coverage in large spot plans was higher by 1.5% and 0.4% in non‐volumetric and volumetric repainting plans, respectively.ConclusionFor robustly optimized PBS lung cancer plans in our study, a small spot machine resulted in a more robust CTV against the setup and range errors when compared to a large spot machine. In the absence of volumetric repainting, large spot PBS lung plans were more robust against the interplay effect. The use of a volumetric repainting technique in both small and large spot PBS lung plans led to comparable interplay target coverage.

Normal tissue complication probability modeling to guide individual treatment planning in pediatric cranial proton and photon radiotherapy.

Proton therapy (PT) is broadly accepted as the gold standard of care for pediatric patients with cranial cancer. The superior dose distribution of PT compared to photon radiotherapy reduces normal tissue complication probability (NTCP) for organs at risk. As NTCPs for pediatric organs are not well understood, clinics generally base radiation response on adult data. However, there is evidence that radiation response strongly depends on the age and even sex of a patient. Furthermore, questions surround the influence of individual intrinsic radiosensitivity (α/β ratio) on pediatric NTCP. While the clinical pediatric NTCP data is scarce, radiobiological modeling and sensitivity analyses can be used to investigate the NTCP trends and its dependence on individual modeling parameters. The purpose of this study was to perform sensitivity analyses of NTCP models to ascertain the dependence of radiosensitivity, sex, and age of a child and predict cranial side-effects following intensity-modulated proton therapy (IMPT) and intensity-modulated radiotherapy (IMRT). Previously, six sex-matched pediatric cranial datasets (5, 9, and 13 years old) were planned in Varian Eclipse treatment planning system (13.7). Up to 108 scanning beam IMPT plans and 108 IMRT plans were retrospectively optimized for a range of simulated target volumes and locations. In this work, dose-volume histograms were extracted and imported into BioSuite Software for radiobiological modeling. Relative-Seriality and Lyman-Kutcher-Burman models were used to calculate NTCP values for toxicity endpoints, where TD50, (based on reported adult clinical data) was varied to simulate sex dependence of NTCP. Plausible parameter ranges, based on published literature for adults, were used in modeling. In addition to sensitivity analyses, a 20% difference in TD50 was used to represent the radiosensitivity between the sexes (with females considered more radiosensitive) for ease of data comparison as a function of parameters such as α/β ratio. IMPT plans resulted in lower NTCP compared to IMRT across all models (p<0.0001). For medulloblastoma treatment, the risk of brainstem necrosis (> 10%) and cochlea tinnitus (>20%) among females could potentially be underestimated considering a lower TD50 value for females. Sensitivity analyses show that the difference in NTCP between sexes was significant (p<0.0001). Similarly, both brainstem necrosis and cochlea tinnitus NTCP varied significantly (p<0.0001) across tested α/β as a function of TD50 values (assumption being that TD50 values are 20% lower in females). If the true α/β of these pediatric tissues is higher than expected (α/β ∼ 3), the risk of tinnitus for IMRT can significantly increase (p<0.0001). Due to the scarcity of pediatric NTCP data available, sensitivity analyses were performed using plausible ranges based on published adult data. In the clinical scenario where, if female pediatric patients were 20% more radiosensitive (lower TD50 value), they could be up to twice as likely to experience side-effects of brainstem necrosis and cochlea tinnitus compared to males, highlighting the need for considering the sex in NTCP models. Based on our sensitivity analyses, age and sex of a pediatric patient could significantly affect the resultant NTCP from cranial radiotherapy, especially at higher α/β values.

Improving GI Toxicity Models Through Deep Learning-Based Segmentation and Biomechanical Model-Based Dose Accumulation

<h3>Purpose/Objective(s)</h3> Current GI toxicity models are limited by the uncertainty in the estimated delivered dose used to develop the models. The purpose of this study is to assess the benefit of harnessing deep learning and biomechanical models to improve the understanding of the dose-toxicity relationship. <h3>Materials/Methods</h3> Retrospective dose accumulation was conducted on 75 patients with primary and metastatic liver cancer treated with external beam radiotherapy guided by daily CT-on-rails (CTOR), based on a novel deformable image registration (DIR) approach that applies biomechanical model-based and intensity-based DIR inside and outside the liver, respectively. The liver was auto-segmented on all images using a clinically validated 2D deep learning model. On a sub-cohort, the combination of stomach and duodenum region of interest (GI ROI) was auto-contoured using a 3D UNet model trained independently on 102 patients. These contours were compared to physician-drawn contours on CTOR, using Dice similarity coefficient (DSC), mean distance to agreement (DTA), Hausdorff distance (HD), and planned dose metrics. Intra-observer variability was evaluated. Doses were converted to equivalent dose in 2Gy fractions (EQD2) for analysis (α/β = 3.5). Differences between planned and delivered dose were calculated, as well as GI ROI daily dose variation. Toxicity probability was calculated based on previously developed accumulated dose normal tissue complication probability (NTCP) models for duodenum and stomach. <h3>Results</h3> Average (SD) DSC, DTA, and HD between the deep learning model and physician-drawn GI ROI were 0.9 (0.1), 0.2 (0.1) cm, and 2.4 (2.2) cm; intra-observer values were 0.9 (0.0), 0.1 (0.0) cm, and 2.3 (0.8) cm, <i>P</i> > 0.1. Average differences in volume (SD), D50 (SD), and D1 (SD) between the AI model and physician-drawn contours were 25.0 (36.7) cc, 0.6 (1.1) Gy, and 0.5 (0.2) Gy; intra-observer values were 7.1 (4.5) cc, 0.2 (0.3) Gy and 0.3 (0.3) Gy, <i>P</i> > 0.1. Average planned and accumulated D1 (EQD2) to the GI ROI were 28.1 Gy and 26.6 Gy, respectively. These distributions were significantly different (<i>P</i> < 0.01), with differences ranging from 0.2 Gy to 2.9 Gy. Daily D1 to the GI ROI varied by up to 47% (average 25%). For 38% of patients, relative differences in NTCP based on planned and accumulated dose were greater than 50%. Compared to planned dose, 63% of the patients had lower NTCP with accumulated dose, and 13% had higher NTCP. <h3>Conclusion</h3> Variability between AI-based and physician-drawn stomach and duodenum segmentation was equivalent to intra-observer variability. Accumulated dose showed statistically significant differences from planned dose for the stomach and duodenum leading to meaningful differences in NTCP. The daily variability in the maximum dose varied by an average of +/- 25%, demonstrating the potential to optimize daily fractionation based on anatomical configuration of the day.

TCP and NTCP Calculations Based on Treatment Doses Instead of Planned Doses for Daily Adaptive Proton Therapy of Lung Cancer

Calculation of tumor control probability (TCP) and normal tissue complication probability (NTCP) is becoming increasingly relevant for optimizing treatment strategies, for example the selection of patients for proton therapy. While radiobiological models are typically built and evaluated on the planned treatment dose, no consensus exists for their evaluation in the context of varying patient anatomy during treatment. For example, different adaptive strategies can lead to different expected treatment outcomes for the same planned dose. This shows that TCP/NTCP needs to be calculated from the cumulative treatment dose resulting from multiple fraction doses and patient geometries. In this study, different methods of applying TCP and NTCP models to treatments with anatomical changes are developed and compared. These methods were evaluated on an example of locally advanced non-small cell lung cancer (NSCLC) patients using two extreme scenarios: no adaption vs online daily adaptive proton therapy (DAPT).Three methods to calculate TCP/NTCP in the context of anatomical changes were compared: (I) based on the accumulated treatment doses. Six different deformable image registration (DIR) algorithms were used to accumulate the dose, then the TCP/NTCP was calculated on all accumulated doses showing the variation due to the DIR. (II) based on the cumulative effective uniform dose (EUD) of each fraction. (III) based on each fraction dose directly, permitting an evaluation of the mean and variation of TCP/NTCP during treatment. To test and compare these methods, proton therapy treatment plans were generated on deep inspiration breath-hold (DIBH) planning CTs for 5 NSCLC patients (66 Gy-RBE, 33 fractions). Non-adapted and DAPT treatment doses were created by recalculating and reoptimizing these plans on nine repeated CTs. TCP/NTCP calculations were performed with all three methods.All methods showed consistent results between no adaption and DAPT. Two patients showed substantial TCP reductions up to 33% without adaption. Three patients, despite anatomical changes and dose differences, did not show a reduced calculated TCP with all methods. For most patients, NTCP differences between DAPT and no adaption were moderate, but one showed improvement in predicted 2-year survival based on heart toxicity with DAPT. Furthermore, the absolute NTCP calculated with method (I) showed variations of up to 7% depending on the DIR algorithm used for dose accumulation.The three proposed methods of calculating clinical outcome based on treatment doses showed consistent result between DAPT and non-adapted treatments. Three patients showed benefits with DAPT, so the calculated NTCP could be reduced or calculated TCP maintained. For two patients the dose differences with adaption did not translate in clinically relevant effects. The proposed outcome calculation methods enable a more realistic quantification of TCP/NTCP of different adaptive strategies.

Optimal Allocation of Proton Therapy Slots in Combined Proton-Photon Radiation Therapy.

Proton therapy is a limited resource that is not available to all patients who may benefit from it. We investigated combined proton-photon treatments, in which some fractions are delivered with protons and the remaining fractions with photons, as an approach to maximize the benefit of limited proton therapy resources at a population level. To quantify differences in normal-tissue complication probability (NTCP) between protons and photons, we considered a cohort of 45 patients with head and neck cancer for whom intensity modulated radiation therapy and intensity modulated proton therapy plans were previously created, in combination with NTCP models for xerostomia and dysphagia considered in the Netherlands for proton patient selection. Assuming limited availability of proton slots, we developed methods to optimally assign proton fractions in combined proton-photon treatments to minimize the average NTCP on a population level. The combined treatments were compared with patient selection strategies in which patients are assigned to single-modality proton or photon treatments. There is a benefit of combined proton-photon treatments compared with patient selection, owing to the nonlinearity of NTCP functions; that is, the initial proton fractions are the most beneficial, whereas additional proton fractions have a decreasing benefit when a flatter part of the NTCP curve is reached. This effect was small for the patient cohort and NTCP models considered, but it may be larger if dose-response relationships are better known. In addition, when proton slots are limited, patient selection methods face a trade-off between leaving slots unused and blocking slots for future patients who may have a larger benefit. Combined proton-photon treatments with flexible proton slot assignment provide a method to make optimal use of all available resources. Combined proton-photon treatments allow for better use of limited proton therapy resources. The benefit over patient selection schemes depends on the NTCP models and the dose differences between protons and photons.

NTCP Modeling of Late Effects for Head and Neck Cancer: A Systematic Review.

NTCP Modeling of Late Effects for Head and Neck Cancer: A Systematic Review.

Development of advanced preselection tools to reduce redundant plan comparisons in model-based selection of head and neck cancer patients for proton therapy

PurposeIn the Netherlands, head and neck cancer (HNC) patients are selected for proton therapy (PT) based on estimated normal tissue complication probability differences (ΔNTCP) between photons and protons, which requires a plan comparison (VMAT vs. IMPT). We aimed to develop tools to improve patient selection for plan comparisons. MethodsThis prospective study consisted of 141 consecutive patients in which a plan comparison was done. IMPT plans of patients not qualifying for PT were classified as ‘redundant’. To prevent redundant IMPT planning, 5 methods that were primarily based on regression models were developed to predict IMPT Dmean to OARs, by using data from VMAT plans and volumetric data from delineated targets and OARs. Then, actual and predicted plan comparison outcomes were compared. The endpoint was being selected for proton therapy. ResultsSeventy out of 141 patients (49.6%) qualified for PT. Using the developed preselection tools, redundant IMPT planning could have been prevented in 49–68% of the remaining 71 patients not qualifying for PT (=specificity) when the sensitivity of all methods was fixed to 100%, i.e., no false negative cases (positive predictive value range: 57–68%, negative predictive value: 100%). ConclusionThe advanced preselection tools, which uses volume and VMAT dose data, prevented labour intensive creation of IMPT plans in up to 68% of non-qualifying patients for PT. No patients qualifying for PT would have been incorrectly denied a plan comparison. This method contributes significantly to a more cost-effective model-based selection of HNC patients for PT.

Identification of patient benefit from proton beam therapy in brain tumour patients based on dosimetric and NTCP analyses

BackgroundThe limited availability of proton beam therapy (PBT) requires individual treatment selection strategies, such as the model-based approach. In this study, we assessed the dosimetric benefit of PBT compared to photon therapy (XRT), analysed the corresponding changes in normal tissue complication probability (NTCP) on a variety of available models, and illustrated model-based patient selection in an in-silico study for patients with brain tumours. MethodsFor 92 patients treated at two PBT centres, volumetric modulated arc therapy treatment plans were retrospectively created for comparison with the clinically applied PBT plans. Several dosimetric parameters for the brain excluding tumour and margins, cerebellum, brain stem, frontal and temporal lobes, hippocampi, cochleae, chiasm, optic nerves, lacrimal glands, lenses, pituitary gland, and skin were compared between both modalities using Wilcoxon signed-rank tests. NTCP differences (ΔNTCP) were calculated for 11 models predicting brain necrosis, delayed recall, temporal lobe injury, hearing loss, tinnitus, blindness, ocular toxicity, cataract, endocrine dysfunction, alopecia, and erythema. A patient was assumed to be selected for PBT if ΔNTCP exceeded a threshold of 10 percentage points for at least one of the side-effects. ResultsPBT substantially reduced the dose in almost all investigated OARs, especially in the low and intermediate dose ranges and for contralateral organs. In general, NTCP predictions were significantly lower for PBT compared to XRT, in particular in ipsilateral organs. Considering ΔNTCP of all models, 80 patients (87.0%) would have been selected for PBT in this in-silico study, mainly due to predictions of a model on delayed recall (51 patients). ConclusionIn this study, substantial dose reductions for PBT were observed, mainly in contralateral organs. However, due to the sigmoidal dose response, NTCP was particularly reduced in ipsilateral organs. This underlines that physical dose-volume parameters alone may not be sufficient to describe the clinical relevance between different treatment techniques and highlights potential benefits of NTCP models. Further NTCP models for different modern treatment techniques are mandatory and existing models have to be externally validated in order to implement the model-based approach in clinical practice for cranial radiotherapy.

Automated Non-Coplanar VMAT for Dose Escalation in Recurrent Head and Neck Cancer Patients.

Simple SummaryThe ability to escalate the radiation dose to head and neck tumors has been shown to offer improved local control, and consequently, survival for recurrent head and neck cancer (rHNC) patients. This study evaluates the HyperArc automated non-coplanar planning technique (originally developed for intracranial treatment) for 20 rHNC patients, and compares this technique to conventional planning methods. HyperArc enables significant tumor dose escalation, with average increases in mean target dose of over 11.5 Gy (26%), while maintaining clinically-equivalent doses to nearby organs. Our results show that the average probability of tumor control is 23% higher for HyperArc than conventional techniques.This study evaluates the potential for tumor dose escalation in recurrent head and neck cancer (rHNC) patients with automated non-coplanar volumetric modulated arc therapy (VMAT) stereotactic body radiation therapy (SBRT) planning (HyperArc). Twenty rHNC patients are planned with conventional VMAT SBRT to 40 Gy while minimizing organ-at-risk (OAR) doses. They are then re-planned with the HyperArc technique to match these minimal OAR doses while escalating the target dose as high as possible. Then, we compare the dosimetry, tumor control probability (TCP), and normal tissue complication probability (NTCP) for the two plan types. Our results show that the HyperArc technique significantly increases the mean planning target volume (PTV) and gross tumor volume (GTV) doses by 10.8 ± 4.4 Gy (25%) and 11.5 ± 5.1 Gy (26%) on average, respectively. There are no clinically significant differences in OAR doses, with maximum dose differences of <2 Gy on average. The average TCP is 23% (± 21%) higher for HyperArc than conventional plans, with no significant differences in NTCP for the brainstem, cord, mandible, or larynx. HyperArc can achieve significant tumor dose escalation while maintaining minimal OAR doses in the head and neck—potentially enabling improved local control for rHNC SBRT patients without increased risk of treatment-related toxicities.

Comparison of a Hybrid IMRT/VMAT technique with non-coplanar VMAT and non-coplanar IMRT for unresectable olfactory neuroblastoma using the RayStation treatment planning system—EUD, NTCP and planning study

The purpose of this study was to compare hybrid intensity-modulated radiotherapy (IMRT) and volumetric-modulated arc therapy (Hybrid IMRT/VMAT), with non-coplanar (nc) IMRT and nc-VMAT treatment plans for unresectable olfactory neuroblastoma (ONB). Hybrid IMRT/VMAT, nc-IMRT and nc-VMAT plans were optimized for 12 patients with modified Kadish C stage ONB. Dose prescription was 65 Gy in 26 fractions. Dose–volume histogram parameters, conformation number (CN), homogeneity index (HI), integral dose and monitor units (MUs) delivered per fraction were assessed. Equivalent uniform dose (EUD) and normal tissue complication probability (NTCP) based on the EUD model (NTCPLogit) and the Lyman–Kutcher–Burman model (NTCPLKB) were also evaluated. We found that the Hybrid IMRT/VMAT plan significantly improved the CN for clinical target volume (CTV) and planning treatment volume (PTV) compared with the nc-VMAT plan. In general, sparing of organs at risk (OARs) is similar with the three techniques, although the Hybrid IMRT/VMAT plan resulted in a significantly reduced Dmax to contralateral (C/L) optic nerve compared with the nc-IMRT plan. The Hybrid IMRT/VMAT plan significantly reduce EUD to the ipsilateral (I/L) and C/L optic nerve in comparison with the nc-IMRT plan and nc-VMAT plan, but the difference in NTCP between the three technique was <1%. We concluded that the Hybrid IMRT/VMAT technique can offer improvement in terms of target conformity and EUD for optic nerves, while achieving equal or better OAR sparing compared with nc-IMRT and nc-VMAT, and can be a viable radiation technique for treating unresectable ONB. However, the clinical benefit of these small differences in dosimetric data, EUD and NTCP of optic nerves may be minimal.

Perspectives on the model-based approach to proton therapy trials: A retrospective study of a lung cancer randomized trial

PurposeThe goal of this study was to assess whether a model-based approach applied retrospectively to a completed randomized controlled trial (RCT) would have significantly altered the selection of patients of the original trial, using the same selection criteria and endpoint for testing the potential clinical benefit of protons compared to photons. Methods and materialsA model-based approach, based on three widely used normal tissue complication probability (NTCP) models for radiation pneumonitis (RP), was applied retrospectively to a completed non-small cell lung cancer RCT (NCT00915005). It was assumed that patients were selected by the model-based approach if their expected ΔNTCP value was above a threshold of 5%. The endpoint chosen matched that of the original trial, the first occurrence of severe (grade ≥3) RP. ResultsOur analysis demonstrates that NTCP differences between proton and photon therapy treatments may be too small to support a model-based trial approach for lung cancer using RP as the normal tissue endpoint. The analyzed lung trial showed that less than 19% (32/165) of patients enrolled in the completed trial would have been enrolled in a model-based trial, prescribing photon therapy to all other patients. The number of patients enrolled was also found to be dependent on the type of NTCP model used for evaluating RP, with the three models enrolling 3%, 13% or 19% of patients. This result does show limitations in NTCP models which would affect the success of a model-based trial approach. No conclusion regarding the development of RP in patients randomized by the model-based approach could statistically be made. ConclusionsUncertainties in the outcome models to predict NTCP are the inherent drawback of a model-based approach to clinical trials. The impact of these uncertainties on enrollment in model-based trials depends on the predicted difference between the two treatment arms and on the set threshold for patient stratification. Our analysis demonstrates that NTCP differences between proton and photon therapy treatments may be too small to support a model-based trial approach for specific treatment sites, such as lung cancer, depending on the chosen normal tissue endpoint.

Differences in lower cranial nerve complications predicted by the NTCP model between RTOG and reduced-volume IMRT planning in radiotherapy for nasopharyngeal carcinoma.

BackgroundThe aim of this study was to assess the differences in lower cranial nerve (LCN) complications predicted by the normal tissue complication probability (NTCP) model between Radiation Therapy Oncology Group (RTOG) and reduced-volume intensity-modulated radiotherapy (IMRT) planning for nasopharyngeal carcinoma (NPC) radiotherapy.MethodsA total of fifty patients with NPC were divided into two groups according to T-stages of T1-2 and T3-4, and the LCNs of each patient were contoured on CT simulation images. The targets were contoured based on the RTOG 0225 clinical trial and a working committee for clinical stage NPC in China in 2010. The NTCP differences in LCNs between the two plans were calculated.ResultsThe LCN volume of the 50 patients was 10.07 cc. The Dmax and Dmean of LCNs in RTOG plans were significantly larger than those in reduced-volume plans (7,453 vs. 7,401 cGy, 6,740 vs. 6,436 cGy, P=0.004, 0.000), and these values were lower in the T1-2 group than in the T3-4 group (7,390 vs. 7,464 cGy, 6,442 vs. 6,733 cGy, P=0.019, 0.000). NTCP in RTOG plans was significantly higher than that in reduced-volume plans (59.98% vs. 51.62%, P=0.000), among which NTCP was significantly lower in the T1-2 group than in the T3-4 group (51.72% vs. 59.88%, P=0.002). There were strong correlations of NTCP with Dmean and irradiation volume for more than 6,600 cGy (R=0.847, P=0.000; R=0.841, P=0.000).Conclusionsthe clinical T-stage, a high Dmean and a large irradiation volume are important factors in predicting LCN complications. Of the two most common IMRT guidance plans in China, the LCN NTCP based on the reduced-volume plan is significantly lower than that based on the RTOG plan.

P1.14-014 Comparative Evaluation of VMAT & 3D-CRT in Locally Advanced NSCLC: Is There Any Radiobiological Advantage?

Our aim was comparative dosimetric evaluation of volumetric modulated arc therapy (VMAT) and 3-dimensional conformal radiotherapy (3D-CRT) in patients with locally advanced non-small cell lung cancer (LA-NSCLC) and to confirm whether this dosimetric benefit translates into a radiobiological advantage with respect to tumor control probability (TCP) and normal-tissue complication probability (NTCP) and finally to generate an algorithm and software to integrate the radiobiological component during plan evaluation and quality assurance. A total of 25 3D-CRT naïve patients of LA-NSCLC, treated with radical radiotherapy were included in this study. We used copies of the Eclipse plan data, organized in separate study treatment courses. For each course, new VMAT plans were generated using the standard provincial dose-volume constraints. The DVH parameters for PTV (planning target volume), lung-GTV (gross tumor volume subtracted from lung), heart, esophagus and spine were reviewed and then the TCP and NTCP values for these regions of interest (ROI), cardio-pulmonary toxicity index i.e. C.P.T.I (∑i NTCPi; i=1-2 representing heart, lung), morbidity index i.e. M.I (∑i NTCPi; i=1-4 representing heart, esophagus, lung and spinal cord) and therapeutic gain (defined as the value of TCP when M.I=0; i.e. TG=TCP(1-M.I)) values were generated. The resulting data sets were compared using the Weltch two sample t-test and p <0.05 was accepted as significant. Parameters were calculated using DVH data exported from the Eclipse and software written in the “R” programming language. The user interface has been developed with the “shiny” “R” web library. Dosimetric parameters included V98% and D99% of PTV, V50Gy of esophagus and mean dose to esophagus, V45Gy of spinal cord, V30Gy and mean dose of heart and V20Gy, V10Gy and V5Gy and mean dose of (lung-GTV). The radiobiological metrics included TCP, NTCP of heart, esophagus, lung and spinal cord, C.P.T.I, M.I and therapeutic gain. Dosimetric evaluation did not show any significant difference in V98% (p=0.169) and D99% (p=0.797) of PTV between VMAT and 3D-CRT, however, there was significant improvement in V50Gy of esophagus (p=0.039), V45Gy of spinal cord (p=0.003) and V30Gy of heart (p=0.038) with VMAT. V10Gy of (lung-GTV) was higher with VMAT (p=0.019). The rest of the dosimetric parameters were not significantly different two modalities. TCP was better with 3D-CRT (p<0.0001) while NTCP of heart (p=0.028), esophagus (p=0.053) and spinal cord (p=0.001) was substantially improved with VMAT. There was no difference in NTCP of lung between two modalities. Overall C.P.T.I (p=0.039), M.I (p=0.019) was remarkably lower and therapeutic gain (p=0.005) was significantly better with VMAT. The study reveals that although TCP is higher with 3D-CRT, majority of the NTCP parameters including C.P.T.I and M.I are substantially improved with VMAT even in 3D-CRT naïve patients. The overall therapeutic gain is therefore notably higher with VMAT which emphasizes its potential to achieve superior oncologic outcome in patients with LA-NSCLC.