A series of five population-specific Indian brain templates and atlases spanning ages 6-60 years.

Anatomical brain templates are commonly used as references in neurological MRI studies, for bringing data into a common space for group-level statistics and coordinate reporting. Given the inherent variability in brain morphology across age and geography, it is important to have templates that are as representative as possible for both age and population. A representative-template increases the accuracy of alignment, decreases distortions as well as potential biases in final coordinate reports. In this study, we developed and validated a new set of T1w Indian brain templates (IBT) from a large number of brain scans (total n = 466) acquired across different locations and multiple 3T MRI scanners in India. A new tool in AFNI, make_template_dask.py, was created to efficiently make five age-specific IBTs (ages 6-60 years) as well as maximum probability map (MPM) atlases for each template; for each age-group's template-atlas pair, there is both a "population-average" and a "typical" version. Validation experiments on an independent Indian structural and functional-MRI dataset show the appropriateness of IBTs for spatial normalization of Indian brains. The results indicate significant structural differences when comparing the IBTs and MNI template, with these differences being maximal along the Anterior-Posterior and Inferior-Superior axes, but minimal Left-Right. For each age-group, the MPM brain atlases provide reasonably good representation of the native-space volumes in the IBT space, except in a few regions with high intersubject variability. These findings provide evidence to support the use of age and population-specific templates in human brain mapping studies.

Anxiety about anxiety: a survey of emergency department provider beliefs and practices regarding anxiety-associated low risk chest pain.

BACKGROUND: Approximately 80% of patients presenting to emergency departments (ED) with chest pain do not have any true cardiopulmonary emergency such as acute coronary syndrome (ACS). However, psychological contributors such as anxiety are thought to be present in up to 58%, but often remain undiagnosed leading to chronic chest pain and ED recidivism. METHODS: To evaluate ED provider beliefs and their usual practices regarding the approach and disposition of patients with low risk chest pain associated with anxiety, we constructed a 22-item survey using a modified Delphi technique. The survey was administered to a convenience sample of ED providers attending the 2016 American College of Emergency Physicians Scientific Assembly in Las Vegas. RESULTS: Surveys were completed by 409 emergency medicine providers from 46 states and 7 countries with a wide range of years of experience and primary practice environment (academic versus community centers). Respondents estimated that 30% of patients presenting to the ED with chest pain thought to be low risk for ACS have anxiety or panic as the primary cause but they directly communicate this belief to only 42% of these patients and provide discharge instructions to 48%. Only 39% of respondents reported adequate hospital resources to ensure follow-up. Community-based providers reported more adequate follow-up for these patients than their academic center colleagues (46% vs. 34%; p = 0.015). Most providers (82%) indicated that they wanted to have referral resources available to a specific clinic for further outpatient evaluation. CONCLUSION: Emergency Department providers believe approximately 30% of patients seeking emergency care for chest pain at low risk for ACS have anxiety as a primary problem, yet fewer than half discuss this concern or provide information to help the patient manage anxiety. This highlights an opportunity for patient centered communication.

Correlation analysis of [18F]fluorodeoxyglucose and [18F]fluoroazomycin arabinoside uptake distributions in lung tumours during radiation therapy.

BACKGROUND: PET-guided dose painting (DP) aims to target radioresistant tumour regions in order to improve radiotherapy (RT) outcome. Besides the well-known [18F]fluorodeoxyglucose (FDG), the hypoxia positron emission tomography (PET) tracer [18F]fluoroazomycin arabinoside (FAZA) could provide further useful information to guide the radiation dose prescription. In this study, we compare the spatial distributions of FDG and FAZA PET uptakes in lung tumours. MATERIAL AND METHODS: Fourteen patients with unresectable lung cancer underwent FDG and FAZA 4D-PET/CT on consecutive days at three time-points: prior to RT (pre), and during the second (w2), and the third (w3) weeks of RT. All PET/CT were reconstructed in their time-averaged midposition (MidP). The metabolic tumour volume (MTV: FDG standardised uptake value (SUV) > 50% SUVmax), and the hypoxic volume (HV: FAZA SUV > 1.4) were delineated within the gross tumour volume (GTVCT). FDG and FAZA intratumoral PET uptake distributions were subsequently pairwise compared, using both volume-, and voxel-based analyses. RESULTS: Volume-based analysis showed large overlap between MTV and HV: median overlapping fraction was 0.90, 0.94 and 0.94, at the pre, w2 and w3 time-points, respectively. Voxel-wise analysis between FDG and FAZA intratumoral PET uptake distributions showed high correlation: median Spearman's rank correlation coefficient was 0.76, 0.77 and 0.76, at the pre, w2 and w3 time-points, respectively. Interestingly, tumours with high FAZA uptake tended to show more similarity between FDG and FAZA intratumoral uptake distributions than those with low FAZA uptake. CONCLUSIONS: In unresectable lung carcinomas, FDG and FAZA PET uptake distributions displayed unexpectedly strong similarity, despite the distinct pathways targeted by these tracers. Hypoxia PET with FAZA brought very little added value over FDG from the perspective of DP in this population.

Evolution of [18F]fluorodeoxyglucose and [18F]fluoroazomycin arabinoside PET uptake distributions in lung tumours during radiation therapy.

BACKGROUND: Dose painting (DP) aims to improve radiation therapy (RT) outcome by targeting radioresistant tumour regions identified through functional imaging, e.g., positron emission tomography (PET). Importantly, the expected benefit of DP relies on the ability of PET imaging to identify tumour areas which could be consistently targeted throughout the treatment. In this study, we analysed the spatial stability of two potential DP targets in lung cancer patients undergoing RT: the tumour burden surrogate [18F]fluorodeoxyglucose (FDG) and the hypoxia surrogate [18F]fluoroazomycin arabinoside (FAZA). MATERIALS AND METHODS: Thirteen patients with unresectable lung tumours underwent FDG and FAZA 4D-PET/CT before (pre), and during the second (w2) and third (w3) weeks of RT. All PET/CT were reconstructed in their time-averaged midposition (MidP) for further analysis. The metabolic tumour volume (MTV: FDG standardised uptake value (SUV) > 50% SUVmax) and the hypoxic volume (HV: FAZA SUV >1.4) were delineated within the gross tumour volume (GTVCT). The stability of FDG and FAZA PET uptake distributions during RT was subsequently assessed through volume-overlap analysis and voxel-based correlation analysis. RESULTS: The volume-overlap analysis yielded median overlapping fraction (OF) of 0.86 between MTVpre and MTVw2 and 0.82 between MTVpre and MTVw3. In patients with a detectable HV, median OF was 0.82 between HVpre and HVw2 and 0.90 between HVpre and HVw3. The voxel-based correlation analysis yielded median Spearman's correlation coefficient (rS) of 0.87 between FDGpre and FDGw2 and 0.83 between FDGpre and FDGw3. Median rS was 0.78 between FAZApre and FAZAw2 and 0.79 between FAZApre and FAZAw3. CONCLUSIONS: FDG and FAZA PET uptake distributions were spatially stable during the 3 first weeks of RT in patients with unresectable lung cancer, both based on volume- and voxel-based indicators. This might allow for a consistent targeting of high FDG or FAZA PET uptake regions as part of a DP strategy.

Generation of prescriptions robust against geometric uncertainties in dose painting by numbers.

BACKGROUND: In the context of dose painting by numbers delivered with intensity-modulated radiotherapy, the robustness of dose distributions against geometric uncertainties can be ensured by robust optimization. As robust optimization is seldom available in treatment planning systems (TPS), we propose an alternative method that reaches the same goal by modifying the heterogeneous dose prescription (based on (18)FDG-PET) and guarantees coverage in spite of systematic and random errors with known standard deviations Σ and σ, respectively. MATERIAL AND METHODS: The objective was that 95% of all voxels in the GTVPET received at least 95% of the prescribed dose despite geometric errors. The prescription was modified by a geometric dilation of αΣ for systematic errors and a deconvolution by a Gaussian function of width σ for random errors. For a 90% confidence interval, α = 2.5. Planning was performed on a TomoTherapy system, such that 95% of the voxels received at least 95% of the modified prescription and less than 5% of the voxels received more than 105% of the modified prescription. The applicability of the method was illustrated for two head-and-neck tumors. RESULTS: Systematic and random displacements larger than αΣ and σ degraded coverage. Down to 62.8% of the points received at least 95% of prescribed dose for the largest considered displacements (5 mm systematic translation and 3 mm standard deviation for random errors). When systematic and random displacements were smaller than αΣ and σ, no degradation of target coverage was observed. CONCLUSIONS: The method led to treatment plans with target coverage robust against geometric uncertainties without the need to incorporate these in the optimizer of the TPS. The methodology was illustrated for head-and-neck cancer but can be potentially extended to all treatment sites.

Hypoxia-guided adaptive radiation dose escalation in head and neck carcinoma: a planning study.

OBJECTIVE: To evaluate from a planning point of view the dose distribution of adaptive radiation dose escalation in head and neck squamous cell carcinoma (HNSCC) using (18)F-Fluoroazomycin arabinoside (FAZA) positron emission tomography/computed tomography (PET-CT). MATERIAL/METHODS: Twelve patients with locally advanced HNSCC underwent three FAZA PET-CT before treatment, after 7 fractions and after 17 fractions of a carboplatin-5FU chemo-radiotherapy regimen (70 Gy in 2 Gy per fraction over 7 weeks). The dose constraints were that every hypoxic voxel delineated before and during treatment (newborn hypoxic voxels) should receive a total dose of 86 Gy. A median dose of 2.47 Gy per fraction was prescribed on the hypoxic PTV defined on the pre-treatment FAZA PET-CT; a median dose of 2.57 Gy per fraction was prescribed on the newborn voxels identified on the first per-treatment FAZA PET-CT; a median dose of 2.89 Gy per fraction was prescribed on the newborn voxels identified on the second per-treatment FAZA PET-CT. RESULTS: Ten of 12 patients had hypoxic volumes. Six of 10 patients completed all the FAZA PET-CT during radiotherapy. For the hypoxic PTVs, the average D50% matched the prescribed dose within 2% and the homogeneity indices reached 0.10 and 0.12 for the nodal PTV 86 Gy and the primary PTV 86 Gy, respectively. Compared to a homogeneous 70 Gy mean dose to the PTVs, the dose escalation up to 86 Gy to the hypoxic volumes did not typically modify the dose metrics on the surrounding normal tissues. CONCLUSION: From a planning point of view, FAZA-PET-guided dose adaptive escalation is feasible without substantial dose increase to normal tissues above tolerance limits. Clinical prospective studies, however, need to be performed to validate hypoxia-guided adaptive radiation dose escalation in head and neck carcinoma.

Reprogramming of tumor metabolism by targeting mitochondria improves tumor response to irradiation.

BACKGROUND: The Warburg phenotype identified decades ago describes tumor cells with increased glycolysis and decreased mitochondrial respiration even in the presence of oxygen. This particular metabolism also termed 'aerobic glycolysis' reflects an adaptation of tumor cells to proliferation in a heterogeneous tumor microenvironment. Although metabolic alterations in cancer cells are common features, their impact on the response to radiotherapy is not yet fully elucidated. This study investigated the impact of cellular oxygen consumption inhibition on the tumor response to radiotherapy. MATERIAL AND METHODS: Warburg-phenotype tumor cells with impaired mitochondrial respiration (MD) were produced and compared in respect to their metabolism to the genetically matched parental cells (WT). After characterization of their metabolism we compared the response of MD cells to irradiation in vivo and in vitro to the genetically matched parental cells (WT). RESULTS: We first confirmed that MD cells were exclusively glycolytic while WT cells exhibited mitochondrial respiration. We then used these cells for assessing the response of WT and MD tumors to a single dose of radiation and showed that the in vivo tumor growth delay of the MD group was increased, indicating an increased radiosensitivity compared to WT while the in vitro ability of both cell lines to repair radiation-induced DNA damage was similar. CONCLUSION: Taken together, these results indicate that in addition to intrinsic radiosensitivity parameters the tumor response to radiation will also depend on their metabolic rate of oxygen consumption.

A prospective clinical study of ¹8F-FAZA PET-CT hypoxia imaging in head and neck squamous cell carcinoma before and during radiation therapy.

PURPOSE: Hypoxia in head and neck squamous cell carcinoma (HNSCC) is associated with poor prognosis and outcome. (18) F-Fluoroazomycin arabinoside (FAZA) is a positron emission tomography (PET) tracer developed to enable identification of hypoxic regions within tumor. The aim of this study was to evaluate the use of (18) F-FAZA-PET for assessment of hypoxia before and during radiation therapy. METHODS: Twelve patients with locally advanced HNSCC underwent (18) F-FAZA-PET scans before and at fraction 7 and 17 of concomitant chemo-radiotherapy. A hypoxic voxel was defined as a voxel expressing a standardized uptake value (SUV) equal or above the SUVmean of the posterior contralateral neck muscles plus three standard deviations. The fractional hypoxic volume fraction (FHV) and the spatial move of hypoxic volumes during treatment were analyzed. RESULTS: A hypoxic volume could be identified in ten patients before treatment. FAZA-PET FHV varied from 0 to 54.3% and from 0 to 41.4% in the primary tumor and in the involved node, respectively. Six out of these ten patients completed all the FAZA-PET-computed tomography (CT) during the radiotherapy. In all patients, FHV and SUVmax values decreased. All patient presented a spatial move of hypoxic volume, but only three patients had newborn hypoxic voxels after 17 fractions. CONCLUSION: This study indicated that (18) F-FAZA-PET could be used to identify and quantify tumor hypoxia before and during concomitant radio-chemotherapy in patients with locally advanced HNSCC. In addition to the information on prognostic value, the use of (18) F-FAZA-PET allowed the delineation of hypoxic volumes for dose escalation protocols. However, due to fluctuation of hypoxia during treatment, repeated scan will have to be performed (i.e. adaptive radiotherapy).

Semiautomatic methods for segmentation of the proliferative tumour volume on sequential FLT PET/CT images in head and neck carcinomas and their relation to clinical outcome.

PURPOSE: Radiotherapy of head and neck cancer induces changes in tumour cell proliferation during treatment, which can be depicted by the PET tracer (18)F-fluorothymidine (FLT). In this study, three advanced semiautomatic PET segmentation methods for delineation of the proliferative tumour volume (PV) before and during (chemo)radiotherapy were compared and related to clinical outcome. METHODS: The study group comprised 46 patients with 48 squamous cell carcinomas of the head and neck, treated with accelerated (chemo)radiotherapy, who underwent FLT PET/CT prior to treatment and in the 2nd and 4th week of therapy. Primary gross tumour volumes were visually delineated on CT images (GTV CT). PVs were visually determined on all PET scans (PV VIS). The following semiautomatic segmentation methods were applied to sequential PET scans: background-subtracted relative-threshold level (PV RTL), a gradient-based method using the watershed transform algorithm and hierarchical clustering analysis (PV W&C), and a fuzzy locally adaptive Bayesian algorithm (PV FLAB). RESULTS: Pretreatment PV VIS correlated best with PV FLAB and GTV CT. Correlations with PV RTL and PV W&C were weaker although statistically significant. During treatment, the PV VIS, PV W&C and PV FLAB significant decreased over time with the steepest decline over time for PV FLAB. Among these advanced segmentation methods, PV FLAB was the most robust in segmenting volumes in the third scan (67 % of tumours as compared to 40 % for PV W&C and 27 % for PV RTL). A decrease in PV FLAB above the median between the pretreatment scan and the scan obtained in the 4th week was associated with better disease-free survival (4 years 90 % versus 53 %). CONCLUSION: In patients with head and neck cancer, FLAB proved to be the best performing method for segmentation of the PV on repeat FLT PET/CT scans during (chemo)radiotherapy. This may potentially facilitate radiation dose adaptation to changing PV.

Assessment of tumor motion reproducibility with audio-visual coaching through successive 4D CT sessions.

This study aimed to compare combined audio-visual coaching with audio coaching alone and assess their respective impact on the reproducibility of external breathing motion and, one step further, on the internal lung tumor motion itself, through successive sessions. Thirteen patients with NSCLC were enrolled in this study. The tumor motion was assessed by three to four successive 4D CT sessions, while the breathing signal was measured from magnetic sensors positioned on the epigastric region. For all sessions, the breathing was regularized with either audio coaching alone (AC, n = 5) or combined with a real-time visual feedback (A/VC, n = 8) when tolerated by the patients. Peak-to-peak amplitude, period and signal shape of both breathing and tumor motions were first measured. Then, the correlation between the respiratory signal and internal tumor motion over time was evaluated, as well as the residual tumor motion for a gated strategy. Although breathing and tumor motions were comparable between AC and AV/C groups, A/VC approach achieved better reproducibility through sessions than AC alone (mean tumor motion of 7.2 mm ± 1 vs. 8.6 mm ± 1.8 mm, and mean breathing motion of 14.9 mm ± 1.2 mm vs. 13.3mm ± 3.7 mm, respectively). High internal/external correlation reproducibility was achieved in the superior-inferior tumor motion direction for all patients. For the anterior posterior tumor motion direction, better correlation reproducibility has been observed when visual feedback has been used. For a displacement-based gating approach, A/VC might also be recommended, since it led to smaller residual tumor motion within clinically relevant duty cycles. This study suggests that combining real-time visual feedback with audio coaching might improve the reproducibility of key characteristics of the breathing pattern, and might thus be considered in the implementation of lung tumor radiotherapy.

FDG PET/CT for rectal carcinoma radiotherapy treatment planning: comparison of functional volume delineation algorithms and clinical challenges.

PET/CT imaging could improve delineation of rectal carcinoma gross tumor volume (GTV) and reduce interobserver variability. The objective of this work was to compare various functional volume delineation algorithms. We enrolled 31 consecutive patients with locally advanced rectal carcinoma. The FDG PET/CT and the high dose CT (CTRT) were performed in the radiation treatment position. For each patient, the anatomical GTVRT was delineated based on the CTRT and compared to six different functional/metabolic GTVPET derived from two automatic segmentation approaches (FLAB and a gradient-based method); a relative threshold (45% of the SUVmax) and an absolute threshold (SUV > 2.5), using two different commercially available software (Philips EBW4 and Segami OASIS). The spatial sizes and shapes of all volumes were compared using the conformity index (CI). All the delineated metabolic tumor volumes (MTVs) were significantly different. The MTVs were as follows (mean ± SD): GTVRT (40.6 ± 31.28ml); FLAB (21.36± 16.34 ml); the gradient-based method (18.97± 16.83ml); OASIS 45% (15.89 ± 12.68 ml); Philips 45% (14.52 ± 10.91 ml); OASIS 2.5 (41.6 2 ± 33.26 ml); Philips 2.5 (40 ± 31.27 ml). CI between these various volumes ranged from 0.40 to 0.90. The mean CI between the different MTVs and the GTVCT was < 0.4. Finally, the DICOM transfer of MTVs led to additional volume variations. In conclusion, we observed large and statistically significant variations in tumor volume delineation according to the segmentation algorithms and the software products. The manipulation of PET/CT images and MTVs, such as the DICOM transfer to the Radiation Oncology Department, induced additional volume variations.

The proton arc therapy treatment planning problem is NP-Hard.

Proton arc therapy (PAT) is an advanced radiotherapy technique using charged particles in which the radiation device rotates continuously around the patient while irradiating the tumor. Compared to conventional, fixed-angle beam delivery mode, proton arc therapy has the potential to further improve the quality of cancer treatment by delivering accurate radiation dose to tumors while minimizing damage to surrounding healthy tissues. However, the computational complexity of treatment planning in PAT raises challenges as to its effective implementation. In this paper, we demonstrate that designing a PAT plan through algorithmic methods is a NP-hard problem (in fact, NP-complete), where the problem size is determined by the number of discrete irradiation angles from which the radiation can be delivered. This finding highlights the inherent complexity of PAT treatment planning and emphasizes the need for efficient algorithms and heuristics to address the challenges associated with optimizing the delivery of radiation doses in this context.

Technical note: Beamlet-free optimization for Monte-Carlo-based treatment planning in proton therapy.

BACKGROUND: Dose calculation and optimization algorithms in proton therapy treatment planning often have high computational requirements regarding time and memory. This can hinder the implementation of efficient workflows in clinics and prevent the use of new, elaborate treatment techniques aiming to improve clinical outcomes like robust optimization, arc, and adaptive proton therapy. PURPOSE: A new method, namely, the beamlet-free algorithm, is presented to address the aforementioned issue by combining Monte Carlo dose calculation and optimization into a single algorithm and omitting the calculation of the time-consuming and costly dose influence matrix. METHODS: The beamlet-free algorithm simulates the dose in proton batches of randomly chosen spots and evaluates their relative impact on the objective function at each iteration. Based on the approximated gradient, the spot weights are then updated and used to generate a new spot probability distribution. The beamlet-free method is compared against a conventional, beamlet-based treatment planning algorithm on a brain case and a prostate case. RESULTS: The beamlet-free algorithm maintained a comparable plan quality while largely reducing the dependence of computation time and memory usage on the number of spots. CONCLUSION: The implementation of a beamlet-free treatment planning algorithm for proton therapy is feasible and capable of achieving treatment plans of comparable quality to conventional methods. Its efficient usage of computational resources and low spot dependence makes it a promising method for large plans, robust optimization, and arc proton therapy.

Efficient proton arc optimization and delivery through energy layer pre-selection and post-filtering.

BACKGROUND: Proton arc therapy (PAT) has emerged as a promising approach for improving dose distribution, but also enabling simpler and faster treatment delivery in comparison to conventional proton treatments. However, the delivery speed achievable in proton arc relies on dedicated algorithms, which currently do not generate plans with a clear speed-up and sometimes even result in increased delivery time. PURPOSE: This study aims to address the challenge of minimizing delivery time through a hybrid method combining a fast geometry-based energy layer (EL) pre-selection with a dose-based EL filtering, and comparing its performance to a baseline approach without filtering. METHODS: Three methods of EL filtering were developed: unrestricted, switch-up (SU), and switch-up gap (SU gap) filtering. The unrestricted method filters the lowest weighted EL while the SU gap filtering removes the EL around a new SU to minimize the gantry rotation braking. The SU filtering removes the lowest weighted group of EL that includes a SU. These filters were combined with the RayStation dynamic proton arc optimization framework energy layer selection and spot assignment (ELSA). Four bilateral oropharyngeal and four lung cancer patients' data were used for evaluation. Objective function values, target coverage robustness, organ-at-risk doses and normal tissue complication probability evaluations, as well as comparisons to intensity-modulated proton therapy (IMPT) plans, were used to assess plan quality. RESULTS: The SU gap filtering algorithm performed best in five out of the eight cases, maintaining plan quality within tolerance while reducing beam delivery time, in particular for the oropharyngeal cohort. It achieved up to approximately 22% and 15% reduction in delivery time for oropharyngeal and lung treatment sites, respectively. The unrestricted filtering algorithm followed closely. In contrast, the SU filtering showed limited improvement, suppressing one or two SU without substantial delivery time shortening. Robust target coverage was kept within 1% of variation compared to the PAT baseline plan while organs-at-risk doses slightly decreased or kept about the same for all patients. CONCLUSIONS: This study provides insights to accelerate PAT delivery without compromising plan quality. These advancements could enhance treatment efficiency and patient throughput.

A spot-specific range uncertainty framework for robust optimization of proton therapy treatments.

PURPOSE: An accurate estimation of range uncertainties is essential to exploit the potential of proton therapy. According to Paganetti's study, a value of 2.4% (1.5 standard deviation) is currently recommended for planning robust treatments with Monte Carlo dose engines. This number is based on a dominant contribution from the mean excitation energy of tissues. However, it was recently shown that expressing tissues as a mixture of water and "dry" material in the CT calibration process allowed for a significant reduction of this uncertainty. We thus propose an adapted framework for pencil beam scanning robust optimization. First, we move towards a spot-specific range uncertainty (SSRU) determination. Second, we use the water-based formalism to reduce range uncertainties and, potentially, to spare better the organs at risk. METHODS: The stoichiometric calibration was adapted to provide a molecular decomposition (including water) of each voxel of the CT. The SSRU calculation was implemented in MCsquare, a fast Monte Carlo dose engine dedicated to proton therapy. For each spot, a ray-tracing method was used to propagate molecular I-values uncertainties and obtain the corresponding effective range uncertainty. These were then combined with other sources of range uncertainties, according to Paganetti's study of 2012. The method was then assessed on three head-and-neck patients. Two plans were optimized for each patient: the first one with the classical 2.4% flat range uncertainty (FRU), the second one with the variable range uncertainty. Both plans were then compared in terms of target coverage and OAR mean dose reduction. Robustness evaluations were also performed, using the SSRU for both plans in order to simulate errors as realistically as possible. RESULTS: For patient 1, it was found that the median SSRU was 1.04% (1.5 standard deviation), yielding, therefore, a very large reduction from the 2.4% FRU. All three SSRU plans were found to have a very good robustness level at a 90% confidence interval while sparing OAR better than the classical plan. For instance, in nominal cases, average reductions in the mean dose of 15.7, 8.4, and 13.2% were observed in the left parotid, right parotid, and pharyngeal constrictor muscle, respectively. As expected, the classical plans showed a higher but unnecessary level of robustness. CONCLUSIONS: Promising results of the SSRU framework were observed on three head-and-neck cases, and more patients should now be considered. The method could also benefit to other tumor sites and, in the long run, the variable part of the range uncertainty could be generalized to other sources of uncertainty in order to move towards more and more patient-specific treatments.

Comparison of Ethos template-based planning and AI-based dose prediction: General performance, patient optimality, and limitations.

PURPOSE: Ethos proposes a template-based automatic dose planning (Etb) for online adaptive radiotherapy. This study evaluates the general performance of Etb for prostate cancer, as well as the ability to generate patient-optimal plans, by comparing it with another state-of-the-art automatic planning method, i.e., deep learning dose prediction followed by dose mimicking (DP + DM). MATERIALS: General performances and capability to produce patient-optimal plan were investigated through two studies: Study-S1 generated plans for 45 patients using our initial Ethos clinical goals template (EG_init), and compared them to manually generated plans (MG). For study-S2, 10 patients which showed poor performances at study-S1 were selected. S2 compared the quality of plans generated with four different methods: 1) Ethos initial template (EG_init_selected), 2) Ethos updated template-based on S1 results (EG_upd_selected), 3) DP + DM, and 4) MG plans. RESULTS: EG_init plans showed satisfactory performance for dose level above 50 Gy: reported mean metrics differences (EG_init minus MG) never exceeded 0.6 %. However, lower dose levels showed loosely optimized metrics, mean differences for V30Gy to rectum and V20Gy to anal canal were of 6.6 % and 13.0 %. EG_init_selected showed amplified differences in V30Gy to rectum and V20Gy to anal canal: 8.5 % and 16.9 %, respectively. These dropped to 5.7 % and 11.5 % for EG_upd_selected plans but strongly increased V60Gy to rectum for 2 patients. DP + DM plans achieved differences of 3.4 % and 4.6 % without compromising any V60Gy. CONCLUSION: General performances of Etb were satisfactory. However, optimizing with template of goals might be limiting for some complex cases. Over our test patients, DP + DM outperformed the Etb approach.

Patient selection for proton therapy using Normal Tissue Complication Probability with deep learning dose prediction for oropharyngeal cancer.

BACKGROUND: In cancer care, determining the most beneficial treatment technique is a key decision affecting the patient's survival and quality of life. Patient selection for proton therapy (PT) over conventional radiotherapy (XT) currently entails comparing manually generated treatment plans, which requires time and expertise. PURPOSE: We developed an automatic and fast tool, AI-PROTIPP (Artificial Intelligence Predictive Radiation Oncology Treatment Indication to Photons/Protons), that assesses quantitatively the benefits of each therapeutic option. Our method uses deep learning (DL) models to directly predict the dose distributions for a given patient for both XT and PT. By using models that estimate the Normal Tissue Complication Probability (NTCP), namely the likelihood of side effects to occur for a specific patient, AI-PROTIPP can propose a treatment selection quickly and automatically. METHODS: A database of 60 patients presenting oropharyngeal cancer, obtained from the Cliniques Universitaires Saint Luc in Belgium, was used in this study. For every patient, a PT plan and an XT plan were generated. The dose distributions were used to train the two dose DL prediction models (one for each modality). The model is based on U-Net architecture, a type of convolutional neural network currently considered as the state of the art for dose prediction models. A NTCP protocol used in the Dutch model-based approach, including grades II and III xerostomia and grades II and III dysphagia, was later applied in order to perform automatic treatment selection for each patient. The networks were trained using a nested cross-validation approach with 11-folds. We set aside three patients in an outer set and each fold consists of 47 patients in training, five in validation and five for testing. This method allowed us to assess our method on 55 patients (five patients per test times the number of folds). RESULTS: The treatment selection based on the DL-predicted doses reached an accuracy of 87.4% for the threshold parameters set by the Health Council of the Netherlands. The selected treatment is directly linked with these threshold parameters as they express the minimal gain brought by the PT treatment for a patient to be indicated to PT. To validate the performance of AI-PROTIPP in other conditions, we modulated these thresholds, and the accuracy was above 81% for all the considered cases. The difference in average cumulative NTCP per patient of predicted and clinical dose distributions is very similar (less than 1% difference). CONCLUSIONS: AI-PROTIPP shows that using DL dose prediction in combination with NTCP models to select PT for patients is feasible and can help to save time by avoiding the generation of treatment plans only used for the comparison. Moreover, DL models are transferable, allowing, in the future, experience to be shared with centers that would not have PT planning expertise.

Enhanced UV-B and elevated CO(2) impacts sub-arctic shrub berry abundance, quality and seed germination.

This study investigated the effects of long-term-enhanced UV-B, and combined UV-B with elevated CO(2) on dwarf shrub berry characteristics in a sub-arctic heath community. Germination of Vaccinium myrtillus was enhanced in seeds produced at elevated UV-B, but seed numbers and berry size were unaffected. Elevated UV-B and CO(2) stimulated the abundance of V. myrtillus berries, whilst UV-B alone stimulated the berry abundance of V. vitis-idaea and Empetrum hermaphroditum. Enhanced UV-B reduced concentrations of several polyphenolics in V. myrtillus berries, whilst elevated CO(2) increased quercetin glycosides in V. myrtillus, and syringetin glycosides and anthocyanins in E. hermaphroditum berries. UV-B × CO(2) interactions were found for total anthocyanins, delphinidin-3-hexoside and peonidin-3-pentosidein in V. myrtillus berries but not E. hermaphroditum. Results suggest positive impacts of UV-B on the germination of V. myrtillus and species-specific impacts of UV-B × elevated CO(2) on berry abundance and quality. The findings have relevance and implications for human and animal consumers plus seed dispersal and seedling establishment.

Treatment planning in arc proton therapy: Comparison of several optimization problem statements and their corresponding solvers.

Arc proton therapy (ArcPT) is an emerging modality in cancer treatments. It delivers the proton beams following a sequence of irradiation angles while the gantry is continuously rotating around the patient. Compared to conventional proton treatments (intensity modulated proton therapy, IMPT), the number of beams is significantly increased bringing new degrees of freedom that leads to potentially better cancer care. However, the optimization of such treatment plans becomes more complex and several alternative statements of the problem can be considered and compared in order to solve the ArcPT problem. Three such problem statements, distinct in their mathematical formulation and properties, are investigated and applied to solving the ArcPT optimization problem. They make use of (i) fast iterative shrinkage-thresholding algorithm (FISTA), (ii) local search (LS) and (iii) mixed-integer programming (MIP). The treatment plans obtained with those methods are compared among them, but also with IMPT and an existing state-of-the-art method: Spot-Scanning Proton Arc (SPArc). MIP stands out at low scale problems both in terms of dose quality and time delivery efficiency. FISTA shows high dose quality but experiences difficulty to optimize the energy sequence while LS is mostly the antagonist. This detailed study describes independent approaches to solve the ArcPT problem and depending on the clinical case, one should be cautiously picked rather than the other. This paper gives the first formal definition of the problem at stake, as well as a first reference benchmark. Finally, empirical conclusions are drawn, based on realistic assumptions.

Automated clinical decision support system with deep learning dose prediction and NTCP models to evaluate treatment complications in patients with esophageal cancer.

BACKGROUND AND PURPOSE: This study aims to investigate how accurate our deep learning (DL) dose prediction models for intensity modulated radiotherapy (IMRT) and pencil beam scanning (PBS) treatments, when chained with normal tissue complication probability (NTCP) models, are at identifying esophageal cancer patients who are at high risk of toxicity and should be switched to proton therapy (PT). MATERIALS AND METHODS: Two U-Net were created, for photon (XT) and proton (PT) plans, respectively. To estimate the dose distribution for each patient, they were trained on a database of 40 uniformly planned patients using cross validation and a circulating test set. These models were combined with a NTCP model for postoperative pulmonary complications. The NTCP model used the mean lung dose, age, histology type, and body mass index as predicting variables. The treatment choice is then done by using a ΔNTCP threshold between XT and PT plans. Patients with ΔNTCP ≥ 10% were referred to PT. RESULTS: Our DL models succeed in predicting dose distributions with a mean error on the mean dose to the lungs (MLD) of 1.14 ± 0.93% for XT and 0.66 ± 0.48% for PT. The complete automated workflow (DL chained with NTCP) achieved 100% accuracy in patient referral. The average residual (ΔNTCP ground truth - ΔNTCP predicted) is 1.43 ± 1.49%. CONCLUSION: This study evaluates our DL dose prediction models in a broader patient referral context and demonstrates their ability to support clinical decisions.