The impact of treatment accuracy on proton therapy patient selection for oropharyngeal cancer patients

SISS-MCO: large scale sparsity-induced spot selection for fast and fully-automated robust multi-criteria optimisation of proton plans

Objective.Intensity modulated proton therapy (IMPT) is an emerging treatment modality for cancer. However, treatment planning for IMPT is labour-intensive and time-consuming. We have developed a novel approach for multi-criteria optimisation (MCO) of robust IMPT plans (SISS-MCO) that is fully automated and fast, and we compare it for head and neck, cervix, and prostate tumours to a previously published method for automated robust MCO (IPBR-MCO, van de Water 2013).Approach.In both auto-planning approaches, the applied automated MCO of spot weights was performed with wish-list driven prioritised optimisation (Breedveld 2012). In SISS-MCO, spot weight MCO was applied once for every patient after sparsity-induced spot selection (SISS) for pre-selection of the most relevant spots from a large input set of candidate spots. IPBR-MCO had several iterations of spot re-sampling, each followed by MCO of the weights of the current spots.Main results.Compared to the published IPBR-MCO, the novel SISS-MCO resulted in similar or slightly superior plan quality. Optimisation times were reduced by a factor of 6 i.e. from 287 to 47 min. Numbers of spots and energy layers in the final plans were similar.Significance.The novel SISS-MCO automatically generated high-quality robust IMPT plans. Compared to a published algorithm for automated robust IMPT planning, optimisation times were reduced on average by a factor of 6. Moreover, SISS-MCO is a large scale approach; this enables optimisation of more complex wish-lists, and novel research opportunities in proton therapy.

Evaluating the effect of setup uncertainty reduction and adaptation to geometric changes on normal tissue complication probability using online adaptive head and neck intensity modulated proton therapy

Objective. To evaluate the impact of setup uncertainty reduction (SUR) and adaptation to geometrical changes (AGC) on normal tissue complication probability (NTCP) when using online adaptive head and neck intensity modulated proton therapy (IMPT).Approach.A cohort of ten retrospective head and neck cancer patients with daily scatter corrected cone-beam CT (CBCT) was studied. For each patient, two IMPT treatment plans were created: one with a 3 mm setup uncertainty robustness setting and one with no explicit setup robustness. Both plans were recalculated on the daily CBCT considering three scenarios: the robust plan without adaptation, the non-robust plan without adaptation and the non-robust plan with daily online adaptation. Online-adaptation was simulated using an in-house developed workflow based on GPU-accelerated Monte Carlo dose calculation and partial spot-intensity re-optimization. Dose distributions associated with each scenario were accumulated on the planning CT, where NTCP models for six toxicities were applied. NTCP values from each scenario were intercompared to quantify the reduction in toxicity risk induced by SUR alone, AGC alone and SUR and AGC combined. Finally, a decision tree was implemented to assess the clinical significance of the toxicity reduction associated with each mechanism.Main results. For most patients, clinically meaningful NTCP reductions were only achieved when SUR and AGC were performed together. In these conditions, total reductions in NTCP of up to 30.48 pp were obtained, with noticeable NTCP reductions for aspiration, dysphagia and xerostomia (mean reductions of 8.25, 5.42 and 5.12 pp respectively). While SUR had a generally larger impact than AGC on NTCP reductions, SUR alone did not induce clinically meaningful toxicity reductions in any patient, compared to only one for AGC alone.SignificanceOnline adaptive head and neck proton therapy can only yield clinically significant reductions in the risk of long-term side effects when combining the benefits of SUR and AGC.

Automation of pencil beam scanning proton treatment planning for intracranial tumours

PURPOSE

To evaluate the feasibility of comprehensive automation of an intra-cranial proton treatment planning.

MATERIALS AND METHODS

Class solution (CS) beam configuration selection allows the user to identify predefined beam configuration based on target localization; automatic CS (aCS) will then explore all the possible CS beam geometries. Ten patients, already used for the evaluation of the automatic selection of the beam configuration, have been also employed to training an algorithm based on the computation of a benchmark dose exploit automatic general planning solution (GPS) optimization with a wish list approach for the planning optimization. An independent cohort of ten patients has been then used for the evaluation step between the clinical and the GPS plan in terms of dosimetric quality of plans and the time needed to generate a plan.

RESULTS

The definition of a beam configuration requires on average 22 min (range 9-29 min). The average time for GPS plan generation is 18 min (range 7-26 min). Median dose differences (GPS-Manual) for each OAR constraints are: brainstem -1.60 Gy, left cochlea -1.22 Gy, right cochlea -1.42 Gy, left eye 0.55 Gy, right eye -2.33 Gy, optic chiasm -1.87 Gy, left optic nerve -4.45 Gy, right optic nerve -2.48 Gy and optic tract -0.31 Gy. Dosimetric CS and aCS plan evaluation shows a slightly worsening of the OARs values except for the optic tract and optic chiasm for both CS and aCS, where better results have been observed.

CONCLUSION

This study has shown the feasibility and implementation of the automatic planning system for intracranial tumors. The method developed in this work is ready to be implemented in a clinical workflow.

An online adaptive plan library approach for intensity modulated proton therapy for head and neck cancer

BACKGROUND AND PURPOSE

In intensity modulated proton therapy (IMPT), the impact of setup errors and anatomical changes is commonly mitigated by robust optimization with population-based setup robustness (SR) settings and offline replanning. In this study we propose and evaluate an alternative approach based on daily plan selection from patient-specific pre-treatment established plan libraries (PLs). Clinical implementation of the PL strategy would be rather straightforward compared to daily online re-planning.

MATERIALS AND METHODS

For 15 head-and-neck cancer patients, the planning CT was used to generate a PL with 5 plans, robustly optimized for increasing SR: 0, 1, 2, 3, 5 mm, and 3% range robustness. Repeat CTs (rCTs) and realistic setup and range uncertainty distributions were used for simulation of treatment courses for the PL approach, treatments with fixed SR (fSR₃) and a trigger-based offline adaptive schedule for 3 mm SR (fSR₃OfA). Daily plan selection in the PL approach was based only on recomputed dose to the CTV on the rCT.

RESULTS

Compared to using fSR₃ and fSR₃OfA, the risk of xerostomia grade ≥ II & III and dysphagia ≥ grade III were significantly reduced with the PL. For 6/15 patients the risk of xerostomia and/or dysphagia ≥ grade II could be reduced by > 2% by using PL. For the other patients, adherence to target coverage constraints was often improved. fSR₃OfA resulted in significantly improved coverage compared to PL for selected patients.

CONCLUSION

The proposed PL approach resulted in overall reduced NTCPs compared to fSR₃ and fSR₃OfA at limited cost in target coverage.

Fast, automated knowledge-based treatment planning for selecting patients for proton therapy based on normal tissue complication probabilities

Purpose

Selecting patients who will benefit from proton therapy is laborious and subjective. We demonstrate a novel automated solution for creating high-quality knowledge-based plans (KBPs) using proton and photon beams to identify patients for proton treatment based on their normal tissue complication probabilities (NTCP).

Methods and Materials

Two previously validated RapidPlan PT models for locally advanced head and neck cancer were used in combination with scripting to automatically create proton and photon KBPs for 72 patients with recent oropharynx cancer. NTCPs were calculated for each patient based on the KBPs, and patient selection was simulated according to the current Dutch national protocol.

Results

The photon/proton KBP exhibited good correlation between predicted and achieved organ-at-risk mean doses, with a ≤5 Gy difference in 208/196 out of 215 structures relevant for the head and neck cancer NTCP model. The proton KBPs yielded on average 7.1/6.1/7.6 Gy lower dose to salivary/swallowing structures/oral cavity than the photon KBPs. This reduced average grade 2/3 dysphagia and xerostomia by 7.1/3.3 and 5.5/2.0 percentage points, resulting in 16 of 72 patients (22%) being indicated for proton treatment. The entire automated process took <30 minutes per patient.

Conclusions

Automated support for decision making using KBP is feasible and fast. The planning solution has potential to speed up the planning and patient-selection process significantly without major compromises to the plan quality.

Synthetic CT-aided multiorgan segmentation for CBCT-guided adaptive pancreatic radiotherapy

PURPOSE

The delineation of organs at risk (OARs) is fundamental to cone-beam CT (CBCT)-based adaptive radiotherapy treatment planning, but is time consuming, labor intensive, and subject to interoperator variability. We investigated a deep learning-based rapid multiorgan delineation method for use in CBCT-guided adaptive pancreatic radiotherapy.

METHODS

To improve the accuracy of OAR delineation, two innovative solutions have been proposed in this study. First, instead of directly segmenting organs on CBCT images, a pretrained cycle-consistent generative adversarial network (cycleGAN) was applied to generating synthetic CT images given CBCT images. Second, an advanced deep learning model called mask-scoring regional convolutional neural network (MS R-CNN) was applied on those synthetic CT to detect the positions and shapes of multiple organs simultaneously for final segmentation. The OAR contours delineated by the proposed method were validated and compared with expert-drawn contours for geometric agreement using the Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), mean surface distance (MSD), and residual mean square distance (RMS).

RESULTS

Across eight abdominal OARs including duodenum, large bowel, small bowel, left and right kidneys, liver, spinal cord, and stomach, the geometric comparisons between automated and expert contours are as follows: 0.92 (0.89-0.97) mean DSC, 2.90 mm (1.63-4.19 mm) mean HD95, 0.89 mm (0.61-1.36 mm) mean MSD, and 1.43 mm (0.90-2.10 mm) mean RMS. Compared to the competing methods, our proposed method had significant improvements (p < 0.05) in all the metrics for all the eight organs. Once the model was trained, the contours of eight OARs can be obtained on the order of seconds.

CONCLUSIONS

We demonstrated the feasibility of a synthetic CT-aided deep learning framework for automated delineation of multiple OARs on CBCT. The proposed method could be implemented in the setting of pancreatic adaptive radiotherapy to rapidly contour OARs with high accuracy.

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

BACKGROUND

The 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.

METHODS

For 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.

RESULTS

PBT 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).

CONCLUSION

In 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.

Head-and-neck organs-at-risk auto-delineation using dual pyramid networks for CBCT-guided adaptive radiotherapy

Organ-at-risk (OAR) delineation is a key step for cone-beam CT (CBCT) based adaptive radiotherapy planning that can be a time-consuming, labor-intensive, and subject-to-variability process. We aim to develop a fully automated approach aided by synthetic MRI for rapid and accurate CBCT multi-organ contouring in head-and-neck (HN) cancer patients. MRI has superb soft-tissue contrasts, while CBCT offers bony-structure contrasts. Using the complementary information provided by MRI and CBCT is expected to enable accurate multi-organ segmentation in HN cancer patients. In our proposed method, MR images are firstly synthesized using a pre-trained cycle-consistent generative adversarial network given CBCT. The features of CBCT and synthetic MRI (sMRI) are then extracted using dual pyramid networks for final delineation of organs. CBCT images and their corresponding manual contours were used as pairs to train and test the proposed model. Quantitative metrics including Dice similarity coefficient (DSC), Hausdorff distance 95% (HD95), mean surface distance, and residual mean square distance (RMS) were used to evaluate the proposed method. The proposed method was evaluated on a cohort of 65 HN cancer patients. CBCT images were collected from those patients who received proton therapy. Overall, DSC values of 0.87 ± 0.03, 0.79 ± 0.10/0.79 ± 0.11, 0.89 ± 0.08/0.89 ± 0.07, 0.90 ± 0.08, 0.75 ± 0.06/0.77 ± 0.06, 0.86 ± 0.13, 0.66 ± 0.14, 0.78 ± 0.05/0.77 ± 0.04, 0.96 ± 0.04, 0.89 ± 0.04/0.89 ± 0.04, 0.83 ± 0.02, and 0.84 ± 0.07 for commonly used OARs for treatment planning including brain stem, left/right cochlea, left/right eye, larynx, left/right lens, mandible, optic chiasm, left/right optic nerve, oral cavity, left/right parotid, pharynx, and spinal cord, respectively, were achieved. This study provides a rapid and accurate OAR auto-delineation approach, which can be used for adaptive radiation therapy.

Head and neck IMPT probabilistic dose accumulation: Feasibility of a 2 mm setup uncertainty setting

OBJECTIVE

To establish optimal robust optimization uncertainty settings for clinical head and neck cancer (HNC) patients undergoing 3D image-guided pencil beam scanning (PBS) proton therapy.

METHODS

We analyzed ten consecutive HNC patients treated with 70 and 54.25 Gy_RBE to the primary and prophylactic clinical target volumes (CTV) respectively using intensity-modulated proton therapy (IMPT). Clinical plans were generated using robust optimization with 5 mm/3% setup/range uncertainties (RayStation v6.1). Additional plans were created for 4, 3, 2 and 1 mm setup and 3% range uncertainty and for 3 mm setup and 3%, 2% and 1% range uncertainty. Systematic and random error distributions were determined for setup and range uncertainties based on our quality assurance program. From these, 25 treatment scenarios were sampled for each plan, each consisting of a systematic setup and range error and daily random setup errors. Fraction doses were calculated on the weekly verification CT closest to the date of treatment as this was considered representative of the daily patient anatomy.

RESULTS

Plans with a 2 mm/3% setup/range uncertainty setting adequately covered the primary and prophylactic CTV (V₉₅ ≥ 99% in 98.8% and 90.8% of the treatment scenarios respectively). The average organ-at-risk dose decreased with 1.1 Gy_RBE/mm setup uncertainty reduction and 0.5 Gy_RBE/1% range uncertainty reduction. Normal tissue complication probabilities decreased by 2.0%/mm setup uncertainty reduction and by 0.9%/1% range uncertainty reduction.

CONCLUSION

The results of this study indicate that margin reduction below 3 mm/3% is possible but requires a larger cohort to substantiate clinical introduction.

The Multidisciplinary Team (MDT) Approach and Quality of Care

The core function of a multidisciplinary team (MDT) is to bring together a group of healthcare professionals from different fields in order to determine patients' treatment plan. Most of head and neck cancer (HNC) units are currently led by MDTs that at least include ENT and maxillofacial surgeons, radiation and medical oncologists. HNC often compromise relevant structures of the upper aerodigestive tract involving functions such as speech, swallowing and breathing, among others. The impairment of these functions can significantly impact patients' quality of life and psychosocial status, and highlights the crucial role of specialized nurses, dietitians, psycho-oncologists, social workers, and onco-geriatricians, among others. Hence, these professionals should be integrated in HNC MDTs. In addition, involving translational research teams should also be considered, as it will help reducing the existing gap between basic research and the daily clinical practice. The aim of this comprehensive review is to assess the role of the different supportive disciplines integrated in an MDT and how they help providing a better care to HNC patients during diagnosis, treatment and follow up.

Intensity modulated proton therapy (IMPT) - The future of IMRT for head and neck cancer

Radiation therapy plays an integral role in the management of head and neck cancers (HNCs). While most HNC patients have historically been treated with photon-based radiation techniques such as intensity modulated radiation therapy (IMRT), there is a growing awareness of the potential clinical benefits of proton therapy over IMRT in the definitive, postoperative and reirradiation settings given the unique physical properties of protons. Intensity modulated proton therapy (IMPT), also known as "pencil beam proton therapy," is a sophisticated mode of proton therapy that is analogous to IMRT and an active area of investigation in cancer care. Multifield optimization IMPT allows for high quality plans that can target superficially located HNCs as well as large neck volumes while significantly reducing integral doses. Several dosimetric studies have demonstrated the superiority of IMPT over IMRT to improve dose sparing of nearby organs such as the larynx, salivary glands, and esophagus. Evidence of the clinical translation of these dosimetric advantages has been demonstrated with documented toxicity reductions (such as decreased feeding tube dependency) after IMPT for patients with HNCs. While there are relative challenges to IMPT planning that exist today such as particle range uncertainties and high sensitivity to anatomical changes, ongoing investigations in image-guidance techniques and robust optimization methods are promising. A systematic approach towards utilizing IMPT and additional prospective studies are necessary in order to more accurately estimate the clinical benefit of IMPT over IMRT and passive proton therapy on a case-by-case basis for patients with sub-site specific HNCs.

Introducing Proton Track-End Objectives in Intensity Modulated Proton Therapy Optimization to Reduce Linear Energy Transfer and Relative Biological Effectiveness in Critical Structures

PURPOSE

We propose the use of proton track-end objectives in intensity modulated proton therapy (IMPT) optimization to reduce the linear energy transfer (LET) and the relative biological effectiveness (RBE) in critical structures.

METHODS AND MATERIALS

IMPT plans were generated for 3 intracranial patient cases (1.8 Gy (RBE) in 30 fractions) and 3 head-and-neck patient cases (2 Gy (RBE) in 35 fractions), assuming a constant RBE of 1.1. Two plans were generated for each patient: (1) physical dose objectives only (DOSEopt) and (2) same dose objectives as the DOSEopt plan, with additional proton track-end objectives (TEopt). The track-end objectives penalized protons stopping in the risk volume of choice. Dose evaluations were made using a RBE of 1.1 and the LET-dependent Wedenberg RBE model, together with estimates of normal tissue complication probabilities (NTCPs). In addition, the distributions of proton track-ends and dose-average LET (LET_d) were analyzed.

RESULTS

The TEopt plans reduced the mean LET_d in the critical structures studied by an average of 37% and increased the mean LET_d in the primary clinical target volume (CTV) by an average of 23%. This was achieved through a redistribution of the proton track-ends, concurrently keeping the physical dose distribution virtually unchanged compared to the DOSEopt plans. This resulted in substantial RBE-weighted dose (D_RBE) reductions, allowing the TEopt plans to meet all clinical goals for both RBE models and reduce the NTCPs by 0 to 19 percentage points compared to the DOSEopt plans, assuming the Wedenberg RBE model. The DOSEopt plans met all clinical goals assuming a RBE of 1.1 but failed 10 of 19 normal tissue goals assuming the Wedenberg RBE model.

CONCLUSIONS

Proton track-end objectives allow for LET_d reductions in critical structures without compromising the physical target dose. This approach permits the lowering of D_RBE and NTCP in critical structures, independent of the variable RBE model used, and it could be introduced in clinical practice without changing current protocols based on the constant RBE of 1.1.

Assessing Novel Drugs and Radiation Technology in the Chemoradiation of Oropharyngeal Cancer

Integrating immunotherapy, proton therapy and biological dose escalation into the definitive chemoradiation of oropharyngeal cancer poses several challenges. Reliable and reproducible data must be obtained in a timely fashion. However, despite recent international radiotherapy contouring guidelines, controversy persists as to the applicability of such guidelines to all cases. Similarly, a lack of consensus exists concerning both the definition of the organ at risk for oral mucositis and the most appropriate endpoint to measure for this critical toxicity. Finally, the correlation between early markers of efficacy such as complete response on PET CT following treatment and subsequent survival needs elucidation for biological subsets of oropharyngeal cancer.

"Radiobiology of Proton Therapy": Results of an international expert workshop

The physical properties of proton beams offer the potential to reduce toxicity in tumor-adjacent normal tissues. Toward this end, the number of proton radiotherapy facilities has steeply increased over the last 10-15 years to currently around 70 operational centers worldwide. However, taking full advantage of the opportunities offered by proton radiation for clinical radiotherapy requires a better understanding of the radiobiological effects of protons alone or combined with drugs or immunotherapy on normal tissues and tumors. This report summarizes the main results of the international expert workshop "Radiobiology of Proton Therapy" that was held in November 2016 in Dresden. It addresses the major topics (1) relative biological effectiveness (RBE) in proton beam therapy, (2) interaction of proton radiobiology with radiation physics in current treatment planning, (3) biological effects in proton therapy combined with systemic treatments, and (4) testing biological effects of protons in clinical trials. Finally, important research avenues for improvement of proton radiotherapy based on radiobiological knowledge are identified. The clinical distribution of radiobiological effectiveness of protons alone or in combination with systemic chemo- or immunotherapies as well as patient stratification based on biomarker expressions are key to reach the full potential of proton beam therapy. Dedicated preclinical experiments, innovative clinical trial designs, and large high-quality data repositories will be most important to achieve this goal.