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Background: Tumor-immune interactions shape a developing tumor and its tumor immune microenvironment (TIME) resulting in either well-infiltrated, immunologically inflamed tumor beds, or immune deserts with low levels of infiltration. The pre-treatment immune make-up of the TIME is associated with treatment outcome; immunologically inflamed tumors generally exhibit better responses to radio- and immunotherapy than non-inflamed tumors. However, radiotherapy is known to induce opposing immunological consequences, resulting in both immunostimulatory and inhibitory responses. In fact, it is thought that the radiation-induced tumoricidal immune response is curtailed by subsequent applications of radiation. It is thus conceivable that spatially fractionated radiotherapy (SFRT), administered through GRID blocks (SFRT-GRID) or lattice radiotherapy to create areas of low or high dose exposure, may create protective reservoirs of the tumor immune microenvironment, thereby preserving anti-tumor immune responses that are pivotal for radiation success., Methods: We have developed an agent-based model (ABM) of tumor-immune interactions to investigate the immunological consequences and clinical outcomes after 2 Gy × 35 whole tumor radiation therapy (WTRT) and SFRT-GRID. The ABM is conceptually calibrated such that untreated tumors escape immune surveillance and grow to clinical detection. Individual ABM simulations are initialized from four distinct multiplex immunohistochemistry (mIHC) slides, and immune related parameter rates are generated using Latin Hypercube Sampling., Results: In silico simulations suggest that radiation-induced cancer cell death alone is insufficient to clear a tumor with WTRT. However, explicit consideration of radiation-induced anti-tumor immunity synergizes with radiation cytotoxicity to eradicate tumors. Similarly, SFRT-GRID is successful with radiation-induced anti-tumor immunity, and, for some pre-treatment TIME compositions and modeling parameters, SFRT-GRID might be superior to WTRT in providing tumor control., Conclusion: This study demonstrates the pivotal role of the radiation-induced anti-tumor immunity. Prolonged fractionated treatment schedules may counteract early immune recruitment, which may be protected by SFRT-facilitated immune reservoirs. Different biological responses and treatment outcomes are observed based on pre-treatment TIME composition and model parameters. A rigorous analysis and model calibration for different tumor types and immune infiltration states is required before any conclusions can be drawn for clinical translation., (© 2024. The Author(s).)

Magnetic resonance imaging (MRI) is known for its accurate soft tissue delineation of tumors and normal tissues. This development has significantly impacted the imaging and treatment of cancers. Radiomics is the process of extracting high-dimensional features from medical images. Several studies have shown that these extracted features may be used to build machine-learning models for the prediction of treatment outcomes of cancer patients. Various feature selection techniques and machine models interrogate the relevant radiomics features for predicting cancer treatment outcomes. This study aims to provide an overview of MRI radiomics features used in predicting clinical treatment outcomes with machine learning techniques. The review includes examples from different disease sites. It will also discuss the impact of magnetic field strength, sample size, and other characteristics on outcome prediction performance.

Purpose: A workflow/planning strategy delivering low-dose radiation therapy (LDRT) (1 Gy) to all polymetastatic diseases using conventional planning/delivery (Raystation/Halcyon = "conventional") and the AI-based Ethos online adaptive RT (oART) platform is developed/evaluated., Methods: Using retrospective data for ten polymetastatic non-small cell lung cancer patients (5-52 lesions each) with PET/CTs, gross tumor volumes (GTVs) were delineated using PET standardized-uptake-value (SUV) thresholding. A 1 cm uniform expansion of GTVs to account for setup/contour uncertainty and organ motion-generated planning target volumes (PTVs). Dose optimization/calculation used the diagnostic CT from PET/CT. Dosimetric objectives were: D min,0.03cc ≥ 95% (acceptable variation (Δ) ≥ 90%), V 100% ≥ 95% (Δ ≥ 90%), and D 0.03cc ≤ 120% (Δ ≤ 125%). Additionally, online adaptation was simulated. When available, subsequent diagnostic CT was used to represent on-treatment CBCT. Otherwise, the CT from PET/CT used for initial planning was deformed to simulate clinically representative changes., Results: All initial plans generated, both for Raystation and Ethos, achieved clinical goals within acceptable variation. For all patients, D min,0.03cc ≥ 95%, V 100% ≥ 95%, and D 0.03cc ≤ 120% goals were achieved for 84.8%/99.5%, 97.7%/98.7%, 97.4%/92.3%, in conventional/Ethos plans, respectively. The ratio of 50% isodose volume to PTV volume (R 50% ), maximum dose at 2 cm from PTV (D 2cm ), and the ratio of the 100% isodose volume to PTV volume (conformity index) in Raystation/Ethos plans were 7.9/5.9; 102.3%/88.44%; and 0.99/1.01, respectively. In Ethos, online adapted plans maintained PTV coverage whereas scheduled plans often resulted in geographic misses due to changes in tumor size, patient position, and body habitus. The average total duration of the oART workflow was 26:15 (min:sec) ranging from 6:43 to 57:30. The duration of each oART workflow step as a function of a number of targets showed a low correlation coefficient for influencer generation and editing (R 2  = 0.04 and 0.02, respectively) and high correlation coefficient for target generation, target editing and plan generation (R 2  = 0.68, 0.63 and 0.69, respectively)., Conclusions: This study demonstrates feasibility of conventional planning/treatment with Raystation/Halcyon and highlights efficiency gains when utilizing semi-automated planning/online-adaptive treatment with Ethos for immunostimulatory LDRT conformally delivered to all sites of polymetastatic disease., (© 2024 The Authors. Journal of Applied Clinical Medical Physics is published by Wiley Periodicals, Inc. on behalf of The American Association of Physicists in Medicine.)

Quality of organ at risk (OAR) autosegmentation is often judged by concordance metrics against the human-generated gold standard. However, the ultimate goal is the ability to use unedited autosegmented OARs in treatment planning, while maintaining the plan quality. We tested this approach with head and neck (HN) OARs generated by a prototype deep-learning (DL) model on patients previously treated for oropharyngeal and laryngeal cancer. Forty patients were selected, with all structures delineated by an experienced physician. For each patient, a set of 13 OARs were generated by the DL model. Each patient was re-planned based on original targets and unedited DL-produced OARs. The new dose distributions were then applied back to the manually delineated structures. The target coverage was evaluated with inhomogeneity index (II) and the relative volume of regret. For the OARs, Dice similarity coefficient (DSC) of areas under the DVH curves, individual DVH objectives, and composite continuous plan quality metric (PQM) were compared. The nearly identical primary target coverage for the original and re-generated plans was achieved, with the same II and relative volume of regret values. The average DSC of the areas under the corresponding pairs of DVH curves was 0.97 ± 0.06. The number of critical DVH points which met the clinical objectives with the dose optimized on autosegmented structures but failed when evaluated on the manual ones was 5 of 896 (0.6%). The average OAR PQM score with the re-planned dose distributions was essentially the same when evaluated either on the autosegmented or manual OARs. Thus, rigorous HN treatment planning is possible with OARs segmented by a prototype DL algorithm with minimal, if any, manual editing., (© 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Longitudinal tumour volume data from head-and-neck cancer patients show that tumours of comparable pre-treatment size and stage may respond very differently to the same radiotherapy fractionation protocol. Mathematical models are often proposed to predict treatment outcome in this context, and have the potential to guide clinical decision-making and inform personalised fractionation protocols. Hindering effective use of models in this context is the sparsity of clinical measurements juxtaposed with the model complexity required to produce the full range of possible patient responses. In this work, we present a compartment model of tumour volume and tumour composition, which, despite relative simplicity, is capable of producing a wide range of patient responses. We then develop novel statistical methodology and leverage a cohort of existing clinical data to produce a predictive model of both tumour volume progression and the associated level of uncertainty that evolves throughout a patient's course of treatment. To capture inter-patient variability, all model parameters are patient specific, with a bootstrap particle filter-like Bayesian approach developed to model a set of training data as prior knowledge. We validate our approach against a subset of unseen data, and demonstrate both the predictive ability of our trained model and its limitations., (© 2024. The Author(s).)

Purpose: A planning strategy was developed and the utility of online-adaptation with the Ethos CBCT-guided ring-gantry adaptive radiotherapy (ART) system was evaluated using retrospective data from Head-and-neck (H&N) patients that required clinical offline adaptation during treatment., Methods: Clinical data were used to re-plan 20 H&N patients (10 sequential boost (SEQ) with separate base and boost plans plus 10 simultaneous integrated boost (SIB)). An optimal approach, robust to online adaptation, for Ethos-initial plans using clinical goal prioritization was developed. Anatomically-derived isodose-shaping helper structures, air-density override, goals for controlling hotspot location(s), and plan normalization were investigated. Online adaptation was simulated using clinical offline adaptive simulation-CTs to represent an on-treatment CBCT. Dosimetric comparisons were based on institutional guidelines for Clinical-initial versus Ethos-initial plans and Ethos-scheduled versus Ethos-adapted plans. Timing for five components of the online adaptive workflow was analyzed., Results: The Ethos H&N planning approach generated Ethos-initial SEQ plans with clinically comparable PTV coverage (average PTV High V 100%  = 98.3%, D min,0.03cc  = 97.9% and D 0.03cc  = 105.5%) and OAR sparing. However, Ethos-initial SIB plans were clinically inferior (average PTV High V 100%  = 96.4%, D min,0.03cc  = 93.7%, D 0.03cc  = 110.6%). Fixed-field IMRT was superior to VMAT for 93.3% of plans. Online adaptation succeeded in achieving conformal coverage to the new anatomy in both SEQ and SIB plans that was even superior to that achieved in the initial plans (which was due to the changes in anatomy that simplified the optimization). The average adaptive workflow duration for SIB, SEQ base and SEQ boost was 30:14, 22.56, and 14:03 (min: sec), respectively., Conclusions: With an optimal planning approach, Ethos efficiently auto-generated dosimetrically comparable and clinically acceptable initial SEQ plans for H&N patients. Initial SIB plans were inferior and clinically unacceptable, but adapted SIB plans became clinically acceptable. Online adapted plans optimized dose to new anatomy and maintained target coverage/homogeneity with improved OAR sparing in a time-efficient manner., (© 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.)

Background and Purpose: Diffusion weighted imaging (DWI) allows for the interrogation of tissue cellularity, which is a surrogate for cellular proliferation. Previous attempts to incorporate DWI into the workflow of a 0.35 T MR-linac (MRL) have lacked quantitative accuracy. In this study, accuracy, repeatability, and geometric precision of apparent diffusion coefficient (ADC) maps produced using an echo planar imaging (EPI)-based DWI protocol on the MRL system is illustrated, and in vivo potential for longitudinal patient imaging is demonstrated., Materials and Methods: Accuracy and repeatability were assessed by measuring ADC values in a diffusion phantom at three timepoints and comparing to reference ADC values. System-dependent geometric distortion was quantified by measuring the distance between 93 pairs of phantom features on ADC maps acquired on a 0.35 T MRL and a 3.0 T diagnostic scanner and comparing to spatially precise CT images. Additionally, for five sarcoma patients receiving radiotherapy on the MRL, same-day in vivo ADC maps were acquired on both systems, one of which at multiple timepoints., Results: Phantom ADC quantification was accurate on the 0.35 T MRL with significant discrepancies only seen at high ADC. Average geometric distortions were 0.35 (±0.02) mm and 0.85 (±0.02) mm in the central slice and 0.66 (±0.04) mm and 2.14 (±0.07) mm at 5.4 cm off-center for the MRL and diagnostic system, respectively. In the sarcoma patients, a mean pretreatment ADC of 910x10 -6 (±100x10 -6 ) mm 2 /s was measured on the MRL., Conclusions: The acquisition of accurate, repeatable, and geometrically precise ADC maps is possible at 0.35 T with an EPI approach., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: JW, the first and corresponding author, has nothing to declare. Amongst the coauthors, TA was an employee of ViewRay, Inc at the time this work was performed and owned ViewRay, Inc stocks at that time. EGM, JMF, and SAR have been supported by a grant/contract from ViewRay, Inc. KL and SAR have consulted for ViewRay, Inc., (© 2023 The Author(s).)

FLASH radiation therapy (FLASH-RT), delivered with ultrahigh dose rate (UHDR), may allow patients to be treated with less normal tissue toxicity for a given tumor dose compared with currently used conventional dose rate. Clinical trials are being carried out and are needed to test whether this improved therapeutic ratio can be achieved clinically. During the clinical trials, quality assurance and credentialing of equipment and participating sites, particularly pertaining to UHDR-specific aspects, will be crucial for the validity of the outcomes of such trials. This report represents an initial framework proposed by the NRG Oncology Center for Innovation in Radiation Oncology FLASH working group on quality assurance of potential UHDR clinical trials and reviews current technology gaps to overcome. An important but separate consideration is the appropriate design of trials to most effectively answer clinical and scientific questions about FLASH. This paper begins with an overview of UHDR RT delivery methods. UHDR beam delivery parameters are then covered, with a focus on electron and proton modalities. The definition and control of safe UHDR beam delivery and current and needed dosimetry technologies are reviewed and discussed. System and site credentialing for large, multi-institution trials are reviewed. Quality assurance is then discussed, and new requirements are presented for treatment system standard analysis, patient positioning, and treatment planning. The tables and figures in this paper are meant to serve as reference points as we move toward FLASH-RT clinical trial performance. Some major questions regarding FLASH-RT are discussed, and next steps in this field are proposed. FLASH-RT has potential but is associated with significant risks and complexities. We need to redefine optimization to focus not only on the dose but also on the dose rate in a manner that is robust and understandable and that can be prescribed, validated, and confirmed in real time. Robust patient safety systems and access to treatment data will be critical as FLASH-RT moves into the clinical trials., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Ionizing radiation acoustic imaging (iRAI) allows online monitoring of radiation's interactions with tissues during radiation therapy, providing real-time, adaptive feedback for cancer treatments. We describe an iRAI volumetric imaging system that enables mapping of the three-dimensional (3D) radiation dose distribution in a complex clinical radiotherapy treatment. The method relies on a two-dimensional matrix array transducer and a matching multi-channel preamplifier board. The feasibility of imaging temporal 3D dose accumulation was first validated in a tissue-mimicking phantom. Next, semiquantitative iRAI relative dose measurements were verified in vivo in a rabbit model. Finally, real-time visualization of the 3D radiation dose delivered to a patient with liver metastases was accomplished with a clinical linear accelerator. These studies demonstrate the potential of iRAI to monitor and quantify the 3D radiation dose deposition during treatment, potentially improving radiotherapy treatment efficacy using real-time adaptive treatment., (© 2023. The Author(s).)

Objective . To reduce the magnetic isocenter position variation with gantry rotation on an 0.35 T MRI-guided linac to a practically negligible level. Approach . Central fRequency (CF) offset, eddy current calibration, cross-term calibration, gradient delay, and gradient offsets are tuned for each MR linac installation at every 30° of gantry rotation and stored in a look-up table (LUT). During treatment, the CF is tuned only once in the beginning at an arbitrary gantry angle. After that, imaging paramters are offset based on the stored LUT values for any given gantry angle. Main results . For the same hardware configuration, the implementation of the gantry-angle-specific parameter corrections reduced the total isocenters range of travel in the transverse plane from 1.1 to 0.3 mm and from 0.8 to 0.2 mm in horizontal and vertical directions, respectively. With the longitudinal shift always being negligible (≤0.2 mm), the radius of the sphere encompassing the isocenter locations was reduced from 0.6 to 0.2 mm. Geometric distortion improved as well; in particular, the gantry-angle-averaged maximum longitudinal distortion within a 35 cm diameter sphere was reduced from 1.4 to 0.8 mm. Since the CF is tuned only once during treatment, imaging may resume promptly after the gantry reaches the next target position. Significance . The MRI-guided linear accelerator was conceived primarily as an instrument for precision image-guided therapy. Thus, it is important to keep the treatment and imaging isocentres as close as possible while minimizing the geometric distortion. The described solution reduces the walkout of the imaging isocenter to a fraction of 1 mm, while keeping geometric distortion in a substantial volume below 1 mm. The approach is robust and does not increase the overall procedure time., (© 2023 Institute of Physics and Engineering in Medicine.)

Purpose: To introduce and validate a newly developed deep-learning (DL) auto-segmentation algorithm for head and neck (HN) organs at risk (OARs) and to compare its performance with a published commercial algorithm., Methods: A total of 864 HN cancer cases were available to train and evaluate a prototype algorithm. The algorithm is based on a fully convolutional network with combined U-Net and V-net. A Dice loss plus Cross-Entropy Loss function with Adam optimizer was used in training. For 75 validation cases, OAR sets were generated with three DL-based models (A: the prototype model trained with gold data, B: a commercial software trained with the same data, and C: the same software trained with data from another institution). The auto-segmented structures were evaluated with Dice similarity coefficient (DSC), Hausdorff distance (HD), voxel-penalty metric (VPM) and DSC of area under dose-volume histograms. A subjective qualitative evaluation was performed on 20 random cases., Results: Overall trend was for the prototype algorithm to be the closest to the gold data by all five metrics. The average DSC/VPM/HD for algorithms A, B, and C were 0.81/84.1/1.6 mm, 0.74/62.8/3.2 mm, and 0.66/46.8/3.3 mm, respectively. 93% of model A structures were evaluated to be clinically useful., Conclusion: The superior performance of the prototype was validated, even when trained with the same data. In addition to the challenges of perfecting the algorithms, the auto-segmentation results can differ when the same algorithm is trained at different institutions., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Head and neck cancers present challenges in radiation treatment planning due to the large number of critical structures near the target(s) and highly heterogeneous tissue composition. While Monte Carlo (MC) dose calculations currently offer the most accurate approximation of dose deposition in tissue, the switch to MC presents challenges in preserving the parameters of care. The differences in dose-to-tissue were widely discussed in the literature, but mostly in the context of recalculating the existing plans rather than reoptimizing with the MC dose engine. Also, the target dose homogeneity received less attention. We adhere to strict dose homogeneity objectives in clinical practice. In this study, we started with 21 clinical volumetric-modulated arc therapy (VMAT) plans previously developed in Pinnacle treatment planning system. Those plans were recalculated "as is" with RayStation (RS) MC algorithm and then reoptimized in RS with both collapsed cone (CC) and MC algorithms. MC statistical uncertainty (0.3%) was selected carefully to balance the dose computation time (1-2 min) with the planning target volume (PTV) dose-volume histogram (DVH) shape approaching that of a "noise-free" calculation. When the hot spot in head and neck MC-based treatment planning is defined as dose to 0.03 cc, it is exceedingly difficult to limit it to 105% of the prescription dose, as we were used to with the CC algorithm. The average hot spot after optimization and calculation with RS MC was statistically significantly higher compared to Pinnacle and RS CC algorithms by 1.2 and 1.0 %, respectively. The 95% confidence interval (CI) observed in this study suggests that in most cases a hot spot of ≤107% is achievable. Compared to the 95% CI for the previous clinical plans recalculated with RS MC "as is" (upper limit 108%), in real terms this result is at least as good or better than the historic plans., (© 2022 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.)

Hyperthermia inhibits DNA double-strand break (DSB) repair that utilizes homologous recombination (HR) pathway by a poorly defined mechanism(s); however, the mechanisms for this inhibition remain unclear. Here we report that hyperthermia decreases H4K16 acetylation (H4K16ac), an epigenetic modification essential for genome stability and transcription. Heat-induced reduction in H4K16ac was detected in humans, Drosophila , and yeast, indicating that this is a highly conserved response. The examination of histone deacetylase recruitment to chromatin after heat-shock identified SIRT1 as the major deacetylase subsequently enriched at gene-rich regions. Heat-induced SIRT1 recruitment was antagonized by chromatin remodeler SMARCAD1 depletion and, like hyperthermia, the depletion of the SMARCAD1 or combination of the two impaired DNA end resection and increased replication stress. Altered repair protein recruitment was associated with heat-shock-induced γ-H2AX chromatin changes and DSB repair processing. These results support a novel mechanism whereby hyperthermia impacts chromatin organization owing to H4K16ac deacetylation, negatively affecting the HR-dependent DSB repair., Competing Interests: Authors declare no competing interests., (© 2022 The Authors.)

Purpose: Radiomics entails the extraction of quantitative imaging biomarkers (or radiomics features) hypothesized to provide additional pathophysiological and/or clinical information compared to qualitative visual observation and interpretation. This retrospective study explores the variability of radiomics features extracted from images acquired with the 0.35 T scanner of an integrated MRI-Linac. We hypothesized we would be able to identify features with high repeatability and reproducibility over various imaging conditions using phantom and patient imaging studies. We also compared findings from the literature relevant to our results. Methods: Eleven scans of a Magphan ® RT phantom over 13 months and 11 scans of a ViewRay Daily QA phantom over 11 days constituted the phantom data. Patient datasets included 50 images from ten anonymized stereotactic body radiation therapy (SBRT) pancreatic cancer patients (50 Gy in 5 fractions). A True Fast Imaging with Steady-State Free Precession (TRUFI) pulse sequence was selected, using a voxel resolution of 1.5 mm × 1.5 mm × 1.5 mm and 1.5 mm × 1.5 mm × 3.0 mm for phantom and patient data, respectively. A total of 1087 shape-based, first, second, and higher order features were extracted followed by robustness analysis. Robustness was assessed with the Coefficient of Variation (CoV < 5%). Results: We identified 130 robust features across the datasets. Robust features were found within each category, except for 2 second-order sub-groups, namely, Gray Level Size Zone Matrix (GLSZM) and Neighborhood Gray Tone Difference Matrix (NGTDM). Additionally, several robust features agreed with findings from other stability assessments or predictive performance studies in the literature. Conclusion: We verified the stability of the 0.35 T scanner of an integrated MRI-Linac for longitudinal radiomics phantom studies and identified robust features over various imaging conditions. We conclude that phantom measurements can be used to identify robust radiomics features. More stability assessment research is warranted.

Background: Magnetic Resonance Image guided Stereotactic body radiotherapy (MRgRT) is an emerging technology that is increasingly used in treatment of visceral cancers, such as pancreatic adenocarcinoma (PDAC). Given the variable response rates and short progression times of PDAC, there is an unmet clinical need for a method to assess early RT response that may allow better prescription personalization. We hypothesize that quantitative image feature analysis (radiomics) of the longitudinal MR scans acquired before and during MRgRT may be used to extract information related to early treatment response., Methods: Histogram and texture radiomic features (n = 73) were extracted from the Gross Tumor Volume (GTV) in 0.35T MRgRT scans of 26 locally advanced and borderline resectable PDAC patients treated with 50 Gy RT in 5 fractions. Feature ratios between first (F1) and last (F5) fraction scan were correlated with progression free survival (PFS). Feature stability was assessed through region of interest (ROI) perturbation., Results: Linear normalization of image intensity to median kidney value showed improved reproducibility of feature quantification. Histogram skewness change during treatment showed significant association with PFS (p = 0.005, HR = 2.75), offering a potential predictive biomarker of RT response. Stability analyses revealed a wide distribution of feature sensitivities to ROI delineation and was able to identify features that were robust to variability in contouring., Conclusions: This study presents a proof-of-concept for the use of quantitative image analysis in MRgRT for treatment response prediction and providing an analysis pipeline that can be utilized in future MRgRT radiomic studies., (© 2021. The Author(s).)

Purpose: To model and predict individual patient responses to radiation therapy., Methods and Materials: We modeled tumor dynamics as logistic growth and the effect of radiation as a reduction in the tumor carrying capacity, motivated by the effect of radiation on the tumor microenvironment. The model was assessed on weekly tumor volume data collected for 2 independent cohorts of patients with head and neck cancer from the H. Lee Moffitt Cancer Center (MCC) and the MD Anderson Cancer Center (MDACC) who received 66 to 70 Gy in standard daily fractions or with accelerated fractionation. To predict response to radiation therapy for individual patients, we developed a new forecasting framework that combined the learned tumor growth rate and carrying capacity reduction fraction (δ) distribution with weekly measurements of tumor volume reduction for a given test patient to estimate δ, which was used to predict patient-specific outcomes., Results: The model fit data from MCC with high accuracy with patient-specific δ and a fixed tumor growth rate across all patients. The model fit data from an independent cohort from MDACC with comparable accuracy using the tumor growth rate learned from the MCC cohort, showing transferability of the growth rate. The forecasting framework predicted patient-specific outcomes with 76% sensitivity and 83% specificity for locoregional control and 68% sensitivity and 85% specificity for disease-free survival with the inclusion of 4 on-treatment tumor volume measurements., Conclusions: These results demonstrate that our simple mathematical model can describe a variety of tumor volume dynamics. Furthermore, combining historically observed patient responses with a few patient-specific tumor volume measurements allowed for the accurate prediction of patient outcomes, which may inform treatment adaptation and personalization., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Standard of care radiotherapy (RT) doses have been developed as a one-size-fits all approach designed to maximize tumor control rates across a population. Although this has led to high control rates for head and neck cancer with 66-70 Gy, this is done without considering patient heterogeneity. We present a framework to estimate a personalized RT dose for individual patients, based on pre- and early on-treatment tumor volume dynamics-a dynamics-adapted radiotherapy dose ( D DARD ). We also present the results of an in silico trial of this dose personalization using retrospective data from a combined cohort of n = 39 head and neck cancer patients from the Moffitt and MD Anderson Cancer Centers that received 66-70 Gy RT in 2-2.12 Gy weekday fractions. This trial was repeated constraining D DARD between (54, 82) Gy to test more moderate dose adjustment. D DARD was estimated to range from 8 to 186 Gy, and our in silico trial estimated that 77% of patients treated with standard of care were overdosed by an average dose of 39 Gy, and 23% underdosed by an average dose of 32 Gy. The in silico trial with constrained dose adjustment estimated that locoregional control could be improved by >10%. We demonstrated the feasibility of using early treatment tumor volume dynamics to inform dose personalization and stratification for dose escalation and de-escalation. These results demonstrate the potential to both de-escalate most patients, while still improving population-level control rates.

Purpose: Dose-volume data for injury to carotid artery and other major vessels in stereotactic body radiation therapy (SBRT)/SABR head and neck reirradiation were reviewed, modeled, and summarized., Methods and Materials: A PubMed search of the English-language literature (stereotactic and carotid and radiation) in April 2018 found 238 major vessel maximum point doses in 6 articles that were pooled for logistic modeling. Two subsequent studies with dose-volume major vessel data were modeled separately for comparison. Attempts were made to separate carotid blowout syndrome from other bleeding events (BE) in the analysis, but we acknowledge that all except 1 data set has some element of BE interspersed., Results: Prior radiation therapy (RT) dose was not uniformly reported per patient in the studies included, but a course on the order of conventionally fractionated 70 Gy was considered for the purposes of the analysis (with an approximately ≥6-month estimated interval between prior and subsequent treatment in most cases). Factors likely associated with reduced risk of BE include nonconsecutive daily treatment, lower extent of circumferential tumor involvement around the vessel, and no surgical manipulation before or after SBRT., Conclusions: Initial data pooling for reirradiation involving the carotid artery resulted in 3 preliminary models compared in this Hypofractionated Treatment Effects in the Clinic (HyTEC) report. More recent experiences with alternating fractionation schedules and additional risk-reduction strategies are also presented. Complications data for the most critical structures such as spinal cord and carotid artery are so limited that they cannot be viewed as strong conclusions of probability of risk, but rather, as a general guideline for consideration. There is a great need for better reporting standards as noted in the High Dose per Fraction, Hypofractionated Treatment Effects in the Clinic introductory paper., (Copyright © 2020. Published by Elsevier Inc.)

Purpose: Stereotactic body radiation therapy (SBRT) has emerged as a viable reirradiation strategy for locally recurrent previously-irradiated head and neck cancer. Doses in the literature have varied, which challenges clinical application of SBRT as well as clinical trial design., Material & Methods: A working group was formed through the American Association of Physicists in Medicine to study tumor control probabilities for SBRT in head and neck cancer. We herein present a systematic review of the available literature addressing the dose/volume data for tumor control probability with SBRT in patients with locally recurrent previously-irradiated head and neck cancer. Dose-response models are generated that present tumor control probability as a function of dose., Results: Data from more than 300 cases in 8 publications suggest that there is a dose-response relationship, with superior local control and possibly improved overall survival for doses of 35 to 45 Gy (in 5 fractions) compared with <30 Gy., Conclusion: Stereotactic body radiation therapy doses equivalent to 5-fraction doses of 40 to 50 Gy are suggested for retreatment., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Radiopharmaceutical therapy (RPT) continues to demonstrate tremendous potential in improving the therapeutic gains in radiation therapy by specifically delivering radiation to tumors that can be well assessed in terms of dosimetry and imaging. Dosimetry in external beam radiation therapy is standard practice. This is not the case, however, in RPT. This NRG (acronym formed from the first letter of the 3 original groups: National Surgical Adjuvant Breast and Bowel Project, the Radiation Therapy Oncology Group, and the Gynecologic Oncology Group)-National Cancer Institute Working Group review describes some of the challenges to improving RPT. The main priorities for advancing the field include (1) developing and adopting best practice guidelines for incorporating patient-specific dosimetry for RPT that can be used at both large clinics with substantial resources and more modest clinics that have limited resources, (2) establishing and improving strategies for introducing new radiopharmaceuticals for clinical investigation, (3) developing approaches to address the radiophobia that is associated with the administration of radioactivity for cancer therapy, and (4) solving the financial and logistical issues of expertise and training in the developing field of RPT., (Copyright © 2020. Published by Elsevier Inc.)

Lipophilicity is explored in the biodistribution (BD), pharmacokinetics (PK), radiation dosimetry (RD), and toxicity of an internally administered targeted alpha-particle therapy (TAT) under development for the treatment of metastatic melanoma. The TAT conjugate is comprised of the chelator DOTA (1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetate), conjugated to melanocortin receptor 1 specific peptidic ligand (MC1RL) using a linker moiety and chelation of the 225 Ac radiometal. A set of conjugates were prepared with a range of lipophilicities (log D 7.4 values) by varying the chemical properties of the linker. Reported are the observations that higher log D 7.4 values are associated with decreased kidney uptake, decreased absorbed radiation dose, and decreased kidney toxicity of the TAT, and the inverse is observed for lower log D 7.4 values. Animals administered TATs with lower lipophilicities exhibited acute nephropathy and death, whereas animals administered the highest activity TATs with higher lipophilicities lived for the duration of the 7 month study and exhibited chronic progressive nephropathy. Changes in TAT lipophilicity were not associated with changes in liver uptake, dose, or toxicity. Significant observations include that lipophilicity correlates with kidney BD, the kidney-to-liver BD ratio, and weight loss and that blood urea nitrogen (BUN) levels correlated with kidney uptake. Furthermore, BUN was identified as having higher sensitivity and specificity of detection of kidney pathology, and the liver enzyme alkaline phosphatase (ALKP) had high sensitivity and specificity for detection of liver damage associated with the TAT. These findings suggest that tuning radiopharmaceutical lipophilicity can effectively modulate the level of kidney uptake to reduce morbidity and improve both safety and efficacy., Competing Interests: The authors declare the following competing financial interest(s): D.L.M. and N.K.T. are co-inventors of an awarded patent. D.L.M., T.J.W., M.L.M., H.K., and N.K.T. are co-inventors on a pending patent application. Modulation Therapeutics, Inc., has licensed related intellectual property, and M.L.M. is a co-founder of that company., (© 2021 American Chemical Society.)

Background: The purpose of this study was to characterize pre-treatment non-contrast computed tomography (CT) and 18 F-fluorodeoxyglucose positron emission tomography (PET) based radiomics signatures predictive of pathological response and clinical outcomes in rectal cancer patients treated with neoadjuvant chemoradiotherapy (NACR T)., Materials and Methods: An exploratory analysis was performed using pre-treatment non-contrast CT and PET imaging dataset. The association of tumor regression grade (TRG) and neoadjuvant rectal (NAR) score with pre-treatment CT and PET features was assessed using machine learning algorithms. Three separate predictive models were built for composite features from CT + PET., Results: The patterns of pathological response were TRG 0 (n = 13; 19.7%), 1 (n = 34; 51.5%), 2 (n = 16; 24.2%), and 3 (n = 3; 4.5%). There were 20 (30.3%) patients with low, 22 (33.3%) with intermediate and 24 (36.4%) with high NAR scores. Three separate predictive models were built for composite features from CT + PET and analyzed separately for clinical endpoints. Composite features with α = 0.2 resulted in the best predictive power using logistic regression. For pathological response prediction, the signature resulted in 88.1% accuracy in predicting TRG 0 vs . TRG 1-3; 91% accuracy in predicting TRG 0-1 vs . TRG 2-3. For the surrogate of DFS and OS, it resulted in 67.7% accuracy in predicting low vs . intermediate vs . high NAR scores., Conclusion: The pre-treatment composite radiomics signatures were highly predictive of pathological response in rectal cancer treated with NACR T. A larger cohort is warranted for further validation., Competing Interests: Conflicts of interest None were declared., (© 2021 Greater Poland Cancer Centre.)

Introduction: To develop a radiomic-based model to predict pathological complete response (pCR) and outcome following neoadjuvant chemoradiotherapy (NACRT) in oesophageal cancer., Methods: We analysed 68 patients with oesophageal cancer treated with NACRT followed by esophagectomy, who had staging 18F-fluorodeoxyglucose ( 18 F-FDG) positron emission tomography (PET) and computed tomography (CT) scans performed at our institution. An in-house data-chjmirocterization algorithm was used to extract 3D-radiomic features from the segmented primary disease. Prediction models were constructed and internally validated. Composite feature, F c  = α * F PET  + (1 - α) * F CT , 0 ≤ α ≤ 1, was constructed for each corresponding CT and PET feature. Loco-regional control (LRC), recurrence-free survival (RFS), metastasis-free survival (MFS) and overall survival (OS) were estimated by Kaplan-Meier analysis, and compared using log-rank test., Results: Median follow-up was 59 months. pCR was achieved in 34 (50%) patients. Five-year RFS, LRC, MFS and OS were 67.1%, 88.5%, 75.6% and 57.6%, respectively. Tumour Regression Grade (TRG) 0-1 indicative of complete response or minimal residual disease was significantly associated with improved 5-year LRC [93.7% vs 71.8%; P = 0.020; HR 0.19, 95% CI 0.04-0.85]. Four sepjmirote pCR predictive models were built for CT alone, PET alone, CT+PET and composite. CT, PET and CT+PET models had AUC 0.73 ± 0.08, 0.66 ± 0.08 and 0.77 ± 0.07, respectively. The composite model resulted in an improvement of pCR predicting power with AUC 0.87 ± 0.06. Stratifying patients with a low versus high radiomic score showed clinically relevant improvement in 5-year LRC favouring low-score group (91.1% vs. 80%, 95% CI 0.09-1.77, P = 0.2)., Conclusion: The composite CT/PET radiomics model was highly predictive of pCR following NACRT. Validation in larger data sets is warranted to determine whether the model can predict clinical outcomes., (© 2020 The Royal Australian and New Zealand College of Radiologists.)

The Halcyon™ platform is self-contained, combining a treatment planning (Eclipse) system TPS) with information management and radiation delivery components. The standard TPS beam model is configured and locked down by the vendor. A portal dosimetry-based system for patient-specific QA (PSQA) is also included. While ensuring consistency across the user base, this closed model may not be optimal for every department. We set out to commission independent TPS (RayStation 9B, RaySearch Laboratories) and PSQA (PerFraction, Sun Nuclear Corp.) systems for use with the Halcyon linac. The output factors and PDDs for very small fields (0.5 × 0.5 cm 2 ) were collected to augment the standard Varian dataset. The MLC leaf-end parameters were estimated based on the various static and dynamic tests with simple model fields and honed by minimizing the mean and standard deviation of dose difference between the ion chamber measurements and RayStation Monte Carlo calculations for 15 VMAT and IMRT test plans. Two chamber measurements were taken per plan, in the high (isocenter) and lower dose regions. The ratio of low to high doses ranged from 0.4 to 0.8. All percent dose differences were expressed relative to the local dose. The mean error was 0.0 ± 1.1% (TG119-style confidence limit ± 2%). Gamma analysis with the helical diode array using the standard 3%Global/2mm criteria resulted in the average passing rate of 99.3 ± 0.5% (confidence limit 98.3%-100%). The average local dose error for all detectors across all plans was 0.2% ± 5.3%. The ion chamber results compared favorably with our recalculation with Eclipse and PerFraction, as well as with several published Eclipse reports. Dose distribution gamma analysis comparisons between RayStation and PerFraction with 2%Local/2mm criteria yielded an average passing rate of 98.5% ± 0.8% (confidence limit 96.9%-100%). It is feasible to use the Halcyon accelerator with independent planning and verification systems without sacrificing dosimetric accuracy., (© 2021 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

Purpose: To monitor intrafraction motion during spine stereotactic body radiotherapy(SBRT) treatment delivery with readily available technology, we implemented triggered kV imaging using the on-board imager(OBI) of a modern medical linear accelerator with an advanced imaging package. Methods: Triggered kV imaging for intrafraction motion management was tested with an anthropomorphic phantom and simulated spine SBRT treatments to the thoracic and lumbar spine. The vertebral bodies and spinous processes were contoured as the image guided radiotherapy(IGRT) structures specific to this technique. Upon each triggered kV image acquisition, 2D projections of the IGRT structures were automatically calculated and updated at arbitrary angles for display on the kV images. Various shifts/rotations were introduced in x, y, z, pitch, and yaw. Gantry-angle-based triggering was set to acquire kV images every 45°. A group of physicists/physicians(n = 10) participated in a survey to evaluate clinical efficiency and accuracy of clinical decisions on images containing various phantom shifts. This method was implemented clinically for treatment of 42 patients(94 fractions) with 15 second time-based triggering. Result: Phantom images revealed that IGRT structure accuracy and therefore utility of projected contours during triggered imaging improved with smaller CT slice thickness. Contouring vertebra superior and inferior to the treatment site was necessary to detect clinically relevant phantom rotation. From the survey, detectability was proportional to the shift size in all shift directions and inversely related to the CT slice thickness. Clinical implementation helped evaluate robustness of patient immobilization. Based on visual inspection of projected IGRT contours on planar kV images, appreciable intrafraction motion was detected in eleven fractions(11.7%). Discussion: Feasibility of triggered imaging for spine SBRT intrafraction motion management has been demonstrated in phantom experiments and implementation for patient treatments. This technique allows efficient, non-invasive monitoring of patient position using the OBI and patient anatomy as a direct visual guide.

Introduction: Innovative biomarkers to predict treatment response in rectal cancer would be helpful in optimizing personalized treatment approaches. In this study, we aimed to develop and validate a CT-based radiomic imaging biomarker to predict pathological response., Methods: We used two independent cohorts of rectal cancer patients to develop and validate a CT-based radiomic imaging biomarker predictive of treatment response. A total of 91 rectal cancer cases treated from 2009 to 2018 were assessed for the tumour regression grade (TRG) (0 = pathological complete response, pCR; 1 = moderate response; 2 = partial response; 3 = poor response). Exploratory analysis was performed by combining pre-treatment non-contrast CT images and patterns of TRG. The models built from the training cohort were further assessed using the independent validation cohort., Results: The patterns of pathological response in training and validation groups were TRG 0 (n = 14, 23.3%; n = 6, 19.4%), 1 (n = 31, 51.7%; n = 15, 48.4%), 2 (n = 12, 20.0%; n = 7, 22.6%) and 3 (n = 3, 5.0%; n = 3, 9.7%), respectively. Separate predictive models were built and analysed from CT features for pathological response. For pathological response prediction, the model including 8 radiomic features by random forest method resulted in 83.9% accuracy in predicting TRG 0 vs TRG 1-3 in validation., Conclusion: The pre-treatment CT-based radiomic signatures were developed and validated in two independent cohorts. This imaging biomarker provided a promising way to predict pCR and select patients for non-operative management., (© 2020 The Royal Australian and New Zealand College of Radiologists.)

Image acquisition parameters for computed tomography scans such as slice thickness and field of view may vary depending on tumor size and site. Recent studies have shown that some radiomics features were dependent on voxel size (= pixel size × slice thickness), and with proper normalization, this voxel size dependency could be reduced. Deep features from a convolutional neural network (CNN) have shown great promise in characterizing cancers. However, how do these deep features vary with changes in imaging acquisition parameters? To analyze the variability of deep features, a physical radiomics phantom with 10 different material cartridges was scanned on 8 different scanners. We assessed scans from 3 different cartridges (rubber, dense cork, and normal cork). Deep features from the penultimate layer of the CNN before (pre-rectified linear unit) and after (post-rectified linear unit) applying the rectified linear unit activation function were extracted from a pre-trained CNN using transfer learning. We studied both the interscanner and intrascanner dependency of deep features and also the deep features' dependency over the 3 cartridges. We found some deep features were dependent on pixel size and that, with appropriate normalization, this dependency could be reduced. False discovery rate was applied for multiple comparisons, to mitigate potentially optimistic results. We also used stable deep features for prognostic analysis on 1 non-small cell lung cancer data set., Competing Interests: Conflict of Interest: None reported., (© 2020 The Authors. Published by Grapho Publications, LLC.)

In vivo tumours are highly heterogeneous, often comprising regions of hypoxia and necrosis. Radiotherapy significantly alters the intratumoural composition. Moreover, radiation-induced cell death may occur via a number of different mechanisms that act over different timescales. Dead material may therefore occupy a significant portion of the tumour volume for some time after irradiation and may affect the subsequent tumour dynamics. We present a three phase tumour growth model that accounts for the effects of radiotherapy and use it to investigate how dead material within the tumour may affect the spatio-temporal tumour response dynamics. We use numerical simulation of the model equations to characterise qualitatively different tumour volume dynamics in response to fractionated radiotherapy. We demonstrate examples, and associated parameter values, for which the properties of the dead material significantly alter the observed tumour volume dynamics throughout treatment. These simulations suggest that for some cases it may not be possible to accurately predict radiotherapy response from pre-treatment, gross tumour volume measurements without consideration of the dead material within the tumour.

Targeted alpha-particle therapy (TAT) aims to selectively deliver radionuclides emitting α-particles (cytotoxic payload) to tumors by chelation to monoclonal antibodies, peptides or small molecules that recognize tumor-associated antigens or cell-surface receptors. Because of the high linear energy transfer (LET) and short range of alpha (α) particles in tissue, cancer cells can be significantly damaged while causing minimal toxicity to surrounding healthy cells. Recent clinical studies have demonstrated the remarkable efficacy of TAT in the treatment of metastatic, castration-resistant prostate cancer. In this comprehensive review, we discuss the current consensus regarding the properties of the α-particle-emitting radionuclides that are potentially relevant for use in the clinic; the TAT-mediated mechanisms responsible for cell death; the different classes of targeting moieties and radiometal chelators available for TAT development; current approaches to calculating radiation dosimetry for TATs; and lead optimization via medicinal chemistry to improve the TAT radiopharmaceutical properties. We have also summarized the use of TATs in pre-clinical and clinical studies to date.

A high-resolution diode array has been comprehensively evaluated. It consists of 1013 point diode detectors arranged on the two 7.7 × 7.7 cm 2 printed circuit boards (PCBs). The PCBs are aligned face to face in such a way that the active volumes of all diodes are in the same plane. All individual correction factors required for accurate dosimetry have been validated for conventional and flattening filter free (FFF) 6MV beams. That included diode response equalization, linearity, repetition rate dependence, field size dependence, angular dependence at the central axis and off-axis in the transverse, sagittal, and multiple arbitrary planes. In the end-to-end tests the array and radiochromic film dose distributions for SRS-type multiple-target plans were compared. In the equalization test (180° rotation), the average percent dose error between the normal and rotated positions for all diodes was 0.01% ± 0.1% (range -0.3 to 0.4%) and -0.01% ± 0.2% (range -0.9 to 0.9%) for 6 MV and 6MV FFF beams, respectively. For the axial angular response, corrected dose stayed within 2% from the ion chamber for all gantry angles, until the beam direction approached the detector plane. In azimuthal direction, the device agreed with the scintillator within 1% for both energies. For multiple combinations of couch and gantry angles, the average percent errors were -0.00% ± 0.6% (range: -2.1% to 1.6%) and -0.1% ± 0.5% (range -1.6% to 2.1%) for the 6MV and 6MV FFF beams, respectively. The measured output factors were largely within 2% of the scintillator, except for the 5 mm 6MV beam showing a 3.2% deviation. The 2%/1 mm gamma analysis of composite SRS measurements produced the 97.2 ± 1.3% (range 95.8-98.5%) average passing rate against film. Submillimeter (≤0.5 mm) dose profile alignment with film was demonstrated in all cases., (© 2019 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

Purpose: Radiotherapy prescription dose and dose fractionation protocols vary little between individual patients having the same tumor grade and stage. To personalize radiotherapy a predictive model is needed to simulate radiation response. Previous modeling attempts with multiple variables and parameters have been shown to yield excellent data fits at the cost of non-identifiability and clinically unrealistic results. Materials and methods: We develop a mathematical model based on a proliferation saturation index (PSI) that is a measurement of pre-treatment tumor volume-to-carrying capacity ratio that modulates intrinsic tumor growth and radiation response rates. In an adaptive Bayesian approach, we utilize an increasing number of data points for individual patients to predict patient-specific responses to subsequent radiation doses. Results: Model analysis shows that using PSI as the only patient-specific parameter, model simulations can fit longitudinal clinical data with high accuracy ( R 2 =0.84). By analyzing tumor response to radiation using daily CT scans early in the treatment, response to the remaining treatment fractions can be predicted after two weeks with high accuracy (c-index = 0.89). Conclusion: The PSI model may be suited to forecast treatment response for individual patients and offers actionable decision points for mid-treatment protocol adaptation. The presented work provides an actionable image-derived biomarker prior to and during therapy to personalize and adapt radiotherapy.

Using targeted ligands to deliver alpha-emitting radionuclides directly to tumor cells has become a promising therapeutic strategy. To calculate the radiation dose to patients, activities of parent and daughter radionuclides must be measured. Scintillation detectors can be used to quantify these activities; however, activities found in pre-clinical and clinical studies can exceed their optimal performance range. Therefore, a method of correcting scintillation detector measurements at higher activities was developed using Monte Carlo modeling. Because there are currently no National Institute of Standards and Technology traceable Actinium-225 ( 225 Ac) standards available, a well-type ionization chamber was used to measure 70.3 ± 7.0, 144.3 ± 14.4, 222.0 ± 22.2, 299.7 ± 30.0, 370.0 ± 37.0, and 447.7 ± 44.7 kBq samples of 225 Ac obtained from Oak Ridge National Lab. Samples were then placed in a well-type NaI(Tl) scintillation detector and spectra were obtained. Alpha particle activity for each species was calculated using gamma abundance per alpha decay. MCNP6 Monte Carlo software was used to simulate the 4π-geometry of the NaI(Tl) detector. Using the ionization chamber reading as activity input to the Monte Carlo model, spectra were obtained and compared to NaI(Tl) spectra. Successive simulations of different activities were run until a spectrum minimizing the mean percent difference between the two was identified. This was repeated for each sample activity. Ionization chamber calibration measurements showed increase in error from 3% to 10% as activities decreased, resulting from decreasing detection efficiency. Measurements of 225 Ac using both detector types agreed within 7% of Oak Ridge stated activities. Simulated Monte Carlo spectra of 225 Ac were successfully generated. Activities obtained from these spectra differed with ionization chamber readings up to 156% at 147.7 kBq. Simulated spectra were then adjusted to correct NaI(Tl) measurements to be within 1%. These were compared to ionization chamber readings and a response relationship was determined between the two instruments. Measurements of 225 Ac and daughter activity were conducted using a NaI(Tl) scintillation detector calibrated for energy and efficiency and an ionization chamber calibrated for efficiency using a surrogate calibration reference. Corrections provided by Monte Carlo modeling improve the accuracy of activity quantification for alpha-particle emitting radiopharmaceuticals in pre-clinical and clinical studies.

New effective therapies are greatly needed for metastatic uveal melanoma, which has a very poor prognosis with a median survival of less than 1 y. The melanocortin 1 receptor (MC1R) is expressed in 94% of uveal melanoma metastases, and a MC1R-specific ligand (MC1RL) with high affinity and selectivity for MC1R was previously developed. Methods: The 225 Ac-DOTA-MC1RL conjugate was synthesized in high radiochemical yield and purity and was tested in vitro for biostability and for MC1R-specific cytotoxicity in uveal melanoma cells, and the lanthanum-DOTA-MC1RL analog was tested for binding affinity. Non-tumor-bearing BALB/c mice were tested for maximum tolerated dose and biodistribution. Severe combined immunodeficient mice bearing uveal melanoma tumors or engineered MC1R-positive and -negative tumors were studied for biodistribution and efficacy. Radiation dosimetry was calculated using mouse biodistribution data and blood clearance kinetics from Sprague-Dawley rat data. Results: High biostability, MC1R-specific cytotoxicity, and high binding affinity were observed. Limiting toxicities were not observed at even the highest administered activities. Pharmacokinetics and biodistribution studies revealed rapid blood clearance (<15 min), renal and hepatobillary excretion, MC1R-specific tumor uptake, and minimal retention in other normal tissues. Radiation dosimetry calculations determined pharmacokinetics parameters and absorbed α-emission dosages from 225 Ac and its daughters. Efficacy studies demonstrated significantly prolonged survival and decreased metastasis burden after a single administration of 225 Ac-DOTA-MC1RL in treated mice relative to controls. Conclusion: These results suggest significant potential for the clinical translation of 225 Ac-DOTA-MC1RL as a novel therapy for metastatic uveal melanoma., (© 2019 by the Society of Nuclear Medicine and Molecular Imaging.)

Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology-defined here simply as the use of mathematics in cancer research-complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer.

Purpose: Research in radiation oncology (RO) is imperative to support the discovery of new uses of radiation and improvement of current approaches to radiation delivery and to foster the continued evolution of our field. Therefore, in 2016, the American Society of Radiation Oncology performed an evaluation of research grant funding for RO., Methods and Materials: Members of the Society of Chairs of Academic Radiation Oncology Programs (SCAROP) were asked about funded and unfunded grants that were submitted by their departments between the fiscal years 2014 and 2016. Grants were grouped according to broad categories defined by the 2017 American Society of Radiation Oncology Research Agenda. Additionally, active grants in the National Institutes of Health (NIH) Research Portfolio Online Reporting Tools database were collated using RO faculty names., Results: Overall, there were 816 funded (44%) and 1031 unfunded (56%) SCAROP-reported grants. Total grant funding was over $196 million. The US government funded the plurality (42.2%; 345 of 816) of grants compared with nonprofit and industry funders. Investigators from 10 institutions accounted for >75% of funded grants. Of the funded grants, 43.5% were categorized as "genomic influences and targeted therapies." The proportion of funded to unfunded grants was highest within the category of "tumor microenvironment, normal tissue effects, and reducing toxicity" (53.4% funded). "New clinical trial design and big data" had the smallest share of SCAROP grant applications and the lowest percent funded (38.3% of grants). NIH grants to RO researchers in 2014 to 2016 accounted for $85 million in funding. From the 31 responding SCAROP institutions, there was a 28% average success rate for RO proposals submitted to the NIH during this period., Conclusions: Though RO researchers from responding institutions were relatively successful in obtaining funding, the overall amount awarded remains small. Continued advocacy on behalf of RO is needed, as well as investment to make research careers more attractive areas for emerging faculty., (Copyright © 2018 Elsevier Inc. All rights reserved.)

To assure accurate treatment delivery on any image-guided radiotherapy system, the relative positions and walkout of the imaging and radiation isocenters must be periodically verified and kept within specified tolerances. In this work, we first validated the multiaxis ion chamber array as a tool for finding the radiation isocenter position of a magnetic resonance-guided linear accelerator. The treatment couch with the array on it was shifted in 0.2-mm increments and the reported beam center position was plotted against that shift and fitted to a straight line, in both X and Y directions. From the goodness-of-fit and intercepts of the regression lines, the accuracy and precision were conservatively estimated at 0.2 and 0.1 mm, respectively. This holds true whether the array is irradiated from the front or from the back, which allows efficient collecting the data from the 4 cardinal gantry angles with just 2 array positions. The average isocenter position agreed to within at most 0.4 mm along any cardinal axis with the linac vendor's film-based procedure, and the maximum walkout radii were 0.32 mm and 0.53 mm, respectively. The magnetic resonance imaging isocenter walkout as a function of gantry angle was studied with 2 different phantoms, one employing a single fiducial at the center and another extracting the rigid displacement values from the distortion map fit of 523 fiducials dispersed over a large volume. The results were close between the 2 phantoms and demonstrated variation in the magnetic resonance imaging isocenter location as high as 1.3 mm along a single axis in the transverse plane. Verification of the magnetic resonance imaging isocenter location versus the gantry angle should be a part of quality assurance for magnetic resonance-guided linear accelerators.

Purpose: The voxels in a CT data sets contain density information. Besides its use in dose calculation density has no other application in modern radiotherapy treatment planning. This work introduces the use of density information by integral dose minimization in radiotherapy treatment planning for head-and-neck squamous cell carcinoma (HNSCC)., Materials and Methods: Eighteen HNSCC cases were studied. For each case two intensity modulated radiotherapy (IMRT) plans were created: one based on dose-volume (DV) optimization, and one based on integral dose minimization (Energy hereafter) inverse optimization. The target objective functions in both optimization schemes were specified in terms of minimum, maximum, and uniform doses, while the organs at risk (OAR) objectives were specified in terms of DV- and Energy-objectives respectively. Commonly used dosimetric measures were applied to assess the performance of Energy-based optimization. In addition, generalized equivalent uniform doses (gEUDs) were evaluated. Statistical analyses were performed to estimate the performance of this novel inverse optimization paradigm., Results: Energy-based inverse optimization resulted in lower OAR doses for equivalent target doses and isodose coverage. The statistical tests showed dose reduction to the OARs with Energy-based optimization ranging from ∼2% to ∼15%., Conclusions: Integral dose minimization based inverse optimization for HNSCC promises lower doses to nearby OARs. For comparable therapeutic effect the incorporation of density information into the optimization cost function allows reduction in the normal tissue doses and possibly in the risk and the severity of treatment related toxicities., (Copyright © 2018 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

A commercial semi-empirical volumetric dose verification system (PerFraction [PF], Sun Nuclear Corp.) extracts multi-leaf collimator positions from the electronic portal imaging device movies collected during a pre-treatment run, while the rest of the delivered control point information is harvested from the accelerator log files. This combination is used to reconstruct dose on a patient CT dataset with a fast superposition/convolution algorithm. The method was validated for single-isocenter multi-target SRS VMAT treatments against absolute radiochromic film measurements in a cylindrical phantom. The targets ranged in size from 0.8 to 3.6 cm and in number from 3 to 10 per plan. A total of 17 films rotated at different angles around the cylinder axis were analyzed. Each of 27 total targets was intercepted by at least one film, and 2-4 different films were analyzed per plan. Film dose was always scaled to the ion chamber measurement in a high-dose, low-gradient area deliberately created at the isocenter. The planar dose agreement between PF and film using 3%(Global dose-difference normalization)/1 mm gamma analysis was on average 99.2 ± 1.1%. The point dose difference in the low-gradient area in the middle of every target was below 3%, while PF-reconstructed and film dose centroids for individual targets showed submillimeter agreement when measured on a well aligned accelerator. Volumetrically, all voxels in all plans agreed between PF and the primary treatment planning system at the 3%/1 mm level. With proper understanding of its advantages and shortcomings, the tool can be applied to patient-specific QA in routine radiosurgical clinical practice., (© 2018 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

The AAPM TG 132 Report enumerates important steps for validation of the medical image registration process. While the Report outlines the general goals and criteria for the tests, specific implementation may be obscure to the wider clinical audience. We endeavored to provide a detailed step-by-step description of the quantitative tests' execution, applied as an example to a commercial software package (Mirada Medical, Oxford, UK), while striving for simplicity and utilization of readily available software. We demonstrated how the rigid registration data could be easily extracted from the DICOM registration object and used, following some simple matrix math, to quantify accuracy of rigid translations and rotations. The options for validating deformable image registration (DIR) were enumerated, and it was shown that the most practically viable ones are comparison of propagated internal landmark points on the published datasets, or of segmented contours that can be generated locally. The multimodal rigid registration in our example did not always result in the desired registration error below ½ voxel size, but was considered acceptable with the maximum errors under 1.3 mm and 1°. The DIR target registration errors in the thorax based on internal landmarks were far in excess of the Report recommendations of 2 mm average and 5 mm maximum. On the other hand, evaluation of the DIR major organs' contours propagation demonstrated good agreement for lung and abdomen (Dice Similarity Coefficients, DSC, averaged over all cases and structures of 0.92 ± 0.05 and 0.91 ± 0.06, respectively), and fair agreement for Head and Neck (average DSC = 0.73 ± 0.14). The average for head and neck is reduced by small volume structures such as pharyngeal constrictor muscles. Even these relatively simple tests show that commercial registration algorithms cannot be automatically assumed sufficiently accurate for all applications. Formalized task-specific accuracy quantification should be expected from the vendors., (© 2018 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

Complex interactions occur between tumor and host immune system at each site in the metastatic setting, the outcome of which can determine behavior ranging from dormancy to rapid growth. An additional layer of complexity arises from the understanding that cytotoxic T cells can traffic through the host circulatory system. Coupling mathematical models of local tumor-immune dynamics and systemic T cell trafficking allows us to simulate the evolution of tumor and immune cell populations in anatomically distant sites following local therapy and thus computationally evaluate immune interconnectivity. Results suggest that the presence of a secondary site may either inhibit or promote growth of the primary, depending on the capacity for immune recruitment of each tumor and the resulting systemic redistribution of T cells. Treatment such as surgical resection and radiotherapy can be simulated to estimate both the decrease in tumor volume at the local treatment-targeted site, and the change in overall tumor burden and tumor growth trajectories across all sites. Qualitatively similar responses of distant tumors to local therapy (positive and negative abscopal effects) to those reported in the clinical setting were observed. Such findings may facilitate an improved understanding of general disease kinetics in the metastatic setting: if metastatic sites are interconnected through the immune system, truly local therapy does not exist.

Radiation is commonly used in cancer treatment. Over 50% of all cancer patients will undergo radiotherapy (RT) as part of cancer care. Scientific advances in RT have primarily focused on the physical characteristics of treatment including beam quality and delivery. Only recently have inroads been made into utilizing tumor biology and radiobiology to design more appropriate RT protocols. Tumors are composites of proliferating and growth-arrested cells, and overall response depends on their respective proportions at irradiation. Prokopiou et al. (Radiat Oncol 10:159, 2015) developed the concept of the proliferation saturation index (PSI) to augment the clinical decision process associated with RT. This framework is based on the application of the logistic equation to pre-treatment imaging data in order to estimate a patient-specific tumor carrying capacity, which is then used to recommend a specific RT protocol. It is unclear, however, how dependent clinical recommendations are on the underlying tumor growth law. We discuss a PSI framework with a generalized logistic equation that can capture kinetics of different well-known growth laws including logistic and Gompertzian growth. Estimation of model parameters on the basis of clinical data revealed that the generalized logistic model can describe data equally well for a wide range of the generalized logistic exponent value. Clinical recommendations based on the calculated PSI, however, are strongly dependent on the specific growth law assumed. Our analysis suggests that the PSI framework may best be utilized in clinical practice when the underlying tumor growth law is known, or when sufficiently many tumor growth models suggest similar fractionation protocols.

Current protocols for delivering radiotherapy are based primarily on tumour stage and nodal and metastases status, even though it is well known that tumours and their microenvironments are highly heterogeneous. It is well established that the local oxygen tension plays an important role in radiation-induced cell death, with hypoxic tumour regions responding poorly to irradiation. Therefore, to improve radiation response, it is important to understand more fully the spatiotemporal distribution of oxygen within a growing tumour before and during fractionated radiation. To this end, we have extended a spatially resolved mathematical model of tumour growth, first proposed by Greenspan (Stud Appl Math 51:317-340, 1972), to investigate the effects of oxygen heterogeneity on radiation-induced cell death. In more detail, cell death due to radiation at each location in the tumour, as determined by the well-known linear-quadratic model, is assumed also to depend on the local oxygen concentration. The oxygen concentration is governed by a reaction-diffusion equation that is coupled to an integro-differential equation that determines the size of the assumed spherically symmetric tumour. We combine numerical and analytical techniques to investigate radiation response of tumours with different intratumoral oxygen distribution profiles. Model simulations reveal a rapid transient increase in hypoxia upon regrowth of the tumour spheroid post-irradiation. We investigate the response to different radiation fractionation schedules and identify a tumour-specific relationship between inter-fraction time and dose per fraction to achieve cure. The rich dynamics exhibited by the model suggest that spatial heterogeneity may be important for predicting tumour response to radiotherapy for clinical applications.