Habitat escalated adaptive therapy (HEAT): a phase 2 trial utilizing radiomic habitat-directed and genomic-adjusted radiation dose (GARD) optimization for high-grade soft tissue sarcoma.

BACKGROUND: Soft tissue sarcomas (STS), have significant inter- and intra-tumoral heterogeneity, with poor response to standard neoadjuvant radiotherapy (RT). Achieving a favorable pathologic response (FPR ≥ 95%) from RT is associated with improved patient outcome. Genomic adjusted radiation dose (GARD), a radiation-specific metric that quantifies the expected RT treatment effect as a function of tumor dose and genomics, proposed that STS is significantly underdosed. STS have significant radiomic heterogeneity, where radiomic habitats can delineate regions of intra-tumoral hypoxia and radioresistance. We designed a novel clinical trial, Habitat Escalated Adaptive Therapy (HEAT), utilizing radiomic habitats to identify areas of radioresistance within the tumor and targeting them with GARD-optimized doses, to improve FPR in high-grade STS. METHODS: Phase 2 non-randomized single-arm clinical trial includes non-metastatic, resectable high-grade STS patients. Pre-treatment multiparametric MRIs (mpMRI) delineate three distinct intra-tumoral habitats based on apparent diffusion coefficient (ADC) and dynamic contrast enhanced (DCE) sequences. GARD estimates that simultaneous integrated boost (SIB) doses of 70 and 60 Gy in 25 fractions to the highest and intermediate radioresistant habitats, while the remaining volume receives standard 50 Gy, would lead to a > 3 fold FPR increase to 24%. Pre-treatment CT guided biopsies of each habitat along with clip placement will be performed for pathologic evaluation, future genomic studies, and response assessment. An mpMRI taken between weeks two and three of treatment will be used for biological plan adaptation to account for tumor response, in addition to an mpMRI after the completion of radiotherapy in addition to pathologic response, toxicity, radiomic response, disease control, and survival will be evaluated as secondary endpoints. Furthermore, liquid biopsy will be performed with mpMRI for future ancillary studies. DISCUSSION: This is the first clinical trial to test a novel genomic-based RT dose optimization (GARD) and to utilize radiomic habitats to identify and target radioresistance regions, as a strategy to improve the outcome of RT-treated STS patients. Its success could usher in a new phase in radiation oncology, integrating genomic and radiomic insights into clinical practice and trial designs, and may reveal new radiomic and genomic biomarkers, refining personalized treatment strategies for STS. TRIAL REGISTRATION: NCT05301283. TRIAL STATUS: The trial started recruitment on March 17, 2022.

Clinical Outcomes of Prostate SBRT Using Non-adaptive MR-Guided Radiotherapy.

OBJECTIVES: Stereotactic body radiotherapy (SBRT) is widely used for localized prostate cancer and implementation of MR-guided radiotherapy has the advantage of tighter margins and improved sparing of organs at risk. Here we evaluate outcomes and time required to treat using non-adaptive MR-guided SBRT (MRgSBRT) for localized prostate cancer at our institution. METHODS: From 9/2019 to 11/2021 we conducted a retrospective review of 80 consecutive patients who were treated with MRgSBRT to the prostate. Patients included low (LR) (5%), favorable intermediate (FIR) (40%), unfavorable intermediate (UIR) (49%), and high risk (HR) (6%). Short-term androgen deprivation therapy was used in 32% of patients. Target volumes included prostate gland and proximal seminal vesicles with an isotropic 3 mm margin. Treatment was prescribed to 36.25 Gy in 5 fractions every other day with urethral sparing. Hydrogel spacer was used in 18% of patients. Time on the linac was recorded as beam on time (BOT) plus total treatment time (TTT) including gating. Analyzed outcomes included PSA response and patient reported outcomes scored by the American Urological Association (AUA) questionnaire and toxicity per CTCAE v5. General linear regression model was used to analyze factors affecting PSA and AUA in longitudinal follow up, and chi-square test was used to assess factors affecting toxicity. RESULTS: Median follow up was 19.3 months (3.8 - 36.6). Median BOT was 4.6 min (2.6 - 7.2) with a median TTT of 11 min (7.6 - 15.8). Pre-treatment vs post-RT median PSA was 6.36 (2.20 - 19.6) vs 0.85 (0.19 - 3.6), respectively (P < 0.001). PSA decrease differed significantly when patients were stratified by risk category, favoring LR/FIR vs UIF/HR group (P = 0.019). Four (5%) patients experienced a biochemical failure (BCF), with a median time to BCF of 20.4 months (7.9 - 34.5). Median biochemical failure free survival (BCFFS) was not reached, with 2-yr and 4-yr BCFFS of 97.1% and 72.1%, respectively. Patients with LR/FIR disease had 100% 2-yr and 4-yr BCFFS, whereas patients with UIF/HR had 95% and 41% 2-yr and 4-yr BCFFS (P = 0.05). Mean pre-treatment AUA was 7.3 (1 - 25) vs 11.3 (1 - 26) at first follow-up; however, AUA normalized to baseline over time. Urethral Dmax ≥35 Gy trended to lower AUA score at all follow-ups (P = 0.07). Forty-one (51%) patients reported grade 1-2 genitourinary toxicities at the 1 month follow up. Grade 3 toxicity (proctitis) was noted in 1 patient. There was no decrease in any grade rectal toxicity with use of hydrogel spacer (3 vs 6, P = 0.2). No grade ≥4 toxicities was observed. CONCLUSIONS: MRgSBRT has the potential for treatment adaptation but this comes at the cost of increased resource utilization. Our experience with non-adaptive MRgSBRT of the prostate highlights its short treatment times as well as efficacy with good PSA control and low toxicity profile.

Essentially unedited deep-learning-based OARs are suitable for rigorous oropharyngeal and laryngeal cancer treatment planning.

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.

Technical feasibility of novel immunostimulatory low-dose radiation for polymetastatic disease with CBCT-based online adaptive and conventional approaches.

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: Dmin,0.03cc ≥ 95% (acceptable variation (Δ) ≥ 90%), V100% ≥ 95% (Δ ≥ 90%), and D0.03cc ≤ 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, Dmin,0.03cc ≥ 95%, V100% ≥ 95%, and D0.03cc ≤ 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 (R50%), maximum dose at 2 cm from PTV (D2cm), 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 (R2 = 0.04 and 0.02, respectively) and high correlation coefficient for target generation, target editing and plan generation (R2 = 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.

Maintaining dosimetric quality when switching to a Monte Carlo dose engine for head and neck volumetric-modulated arc therapy planning.

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.

On the MLC leaves alignment in the direction orthogonal to movement.

The main focus of the recommended spatial accuracy tests for the multi-leaf collimators (MLC) is calibration of the leaf position along the movement direction and overall alignment to the radiation isocenter. No explicit attention was typically paid to the alignment of the leaves from the opposing banks in the direction orthogonal to movement. This paper is a case study demonstrating that verification of such alignment at the time of acceptance testing is prudent. The original standard MLC (SMLC) on an MRIdian MRI-guided linac (ViewRay Inc., Mountain View, CA, USA) was upgraded to a high-speed MLC (HSMLC), which is supposed to be mechanically identical to the SMLC except for the higher drive screw pitch. The results of the end-to-end IMRT tests demonstrated unacceptable dosimetric results exemplified by an average and maximum ion chamber (IC) point dose error in the high-dose low-gradient region of 2.5 ± 1.4% and 4.6%, respectively. Before the upgrade, those values were 0.3 ± 0.7% and 0.9%, respectively. An exhaustive analysis of possible failure modes eventually zeroed in on the average misalignment of about 1 mm in the Y (along the couch) direction between the right and left upper MLC banks. The MLC was replaced, reducing the Y-direction misalignment to 0.4 mm. As a result, the average and maximum IC dose-errors became acceptable 1.0 ± 0.7% and 1.6%, respectively. Simple film and/or chamber array tests during acceptance testing can easily detect Y-direction misalignments between opposing leaves banks measuring a fraction of a mm at isocenter. Left undetected, such misalignment can cause nontrivial dosimetric consequences.

Gonadal-sparing total body irradiation with the use of helical tomotherapy for nonmalignant indications.

BACKGROUND: The aim was to demonstrate the feasibility and technique of gonadal sparing total body irradiation (TBI) with helical tomotherapy. Total body irradiation is a common part of the conditioning regimen prior to allogeneic stem cell transplantation. Shielding or dose-reduction to the gonads is often desired to preserve fertility, particularly in young patients undergoing transplant for non-malignant indications. Helical tomotherapy (HT) has been shown to be superior to traditional TBI delivery for organ at risk (OA R) doses and dose homogeneity. MATERIALS AND METHODS: We present two representative cases (one male and one female) to illustrate the feasibility of this technique, each of whom received 3Gy in a single fraction prior to allogeneic stem cell transplant for benign indications. The planning target volume (PTV) included the whole body with a subtraction of OA Rs including the lungs, heart, and brain (each contracted by 1cm) as well as the gonads (testicles expanded by 5 cm and ovaries expanded by 0.5 cm). RESULTS: For the male patient we achieved a homogeneity index of 1.35 with a maximum and median planned dose to the testes of 0.53 Gy and 0.35 Gy, respectively. In-vivo dosimetry demonstrated an actual received dose of 0.48 Gy. For the female patient we achieved a homogeneity index of 1.13 with a maximum and median planned dose to the ovaries of 1.66 Gy and 0.86 Gy, respectively. CONCLUSION: Gonadal sparing TBI is feasible and deliverable using HT in patients with non-malignant diseases requiring TBI as part of a pre-stem cell transplant conditioning regimen.

Pretreatment CT and PET radiomics predicting rectal cancer patients in response to neoadjuvant chemoradiotherapy.

BACKGROUND: The purpose of this study was to characterize pre-treatment non-contrast computed tomography (CT) and 18F-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.

Magnetic Resonance Guided Radiotherapy for Rectal Cancer: Expanding Opportunities for Non-Operative Management.

Colorectal cancer is the third most common cancer in men and the second most common in women worldwide, and the incidence is increasing among younger patients. 30% of these malignancies arise in the rectum. Patients with rectal cancer have historically been managed with preoperative radiation, followed by radical surgery, and adjuvant chemotherapy, with permanent colostomies in up to 20% of patients. Beginning in the early 2000s, non-operative management (NOM) of rectal cancer emerged as a viable alternative to radical surgery in select patients. Efforts have been ongoing to optimize neoadjuvant therapy for rectal cancer, thereby increasing the number of patients potentially eligible to forgo radical surgery. Magnetic resonance guided radiotherapy (MRgRT) has recently emerged as a treatment modality capable of intensifying preoperative radiation therapy for rectal cancer patients. This technology may also predict which patients will achieve a complete response to preoperative therapy, thereby allowing for more appropriate selection of patients for NOM. The present work seeks to illustrate the potential role MRgRT could play in personalizing rectal cancer treatment thus expanding the role of NOM in rectal cancer.

Radiotherapy for Hepatocellular Carcinoma in Russia: a Survey-Based Analysis of Current Practice and the Impact of an Educational Workshop on Clinical Expertise.

Radiation therapy (RT) is an effective treatment modality for hepatocellular carcinoma (HCC), but globally, it is underutilized. In Russia, practice patterns with regard to liver-directed radiation are unknown. Under the auspices of Russian Society of Clinical Oncology (RUSSCO), our team conducted an IRB-approved contouring workshop for Russian radiation oncologists. Pre- and post-workshop surveys were analyzed to determine baseline clinical experience and patterns of care for liver-directed RT among Russian providers. The effect of the contouring workshop on participants' knowledge was tested using mixed effects model. Forty pre-workshop and 24 post-workshop questionnaires were analyzable with a 100% response rate. Sixty percent of respondents had never evaluated a patient with HCC and only 8% (3 out of 40) reported treating an HCC patient with liver-directed RT. Nonetheless, 73% of respondents were comfortable offering liver-directed RT prior to the workshop. After the workshop, 85% of respondents felt comfortable treating a patient with HCC with liver-directed RT and 50% were comfortable recommending stereotactic body radiation therapy (SBRT). Measures of knowledge pertaining to evaluation of HCC patients and selection for appropriate liver-directed therapies were dramatically improved after the workshop. Liver-directed RT is not commonly used in Russia in the management of patients with HCC, and few centers are equipped for motion management. Our contouring workshop resulted in dramatically improved understanding of the evaluation and management of HCC patients. We recommend starting with a more protracted fractionated RT and building experience through attendance of additional educational activities, participation in multidisciplinary liver tumor boards, and prospective analysis of treatment toxicity and outcomes.

Predicting Patient-Specific Radiotherapy Protocols Based on Mathematical Model Choice for Proliferation Saturation Index.

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.

Practical quantification of image registration accuracy following the AAPM TG-132 report framework.

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.

Rectal dose to prostate cancer patients treated with proton therapy with or without rectal spacer.

The purpose of this study was to evaluate whether a spacer inserted in the prerectal space could reduce modeled rectal dose and toxicity rates for patients with prostate cancer treated in silico with pencil beam scanning (PBS) proton therapy. A total of 20 patients were included in this study who received photon therapy (12 with rectal spacer (DuraSeal™ gel) and 8 without). Two PBS treatment plans were retrospectively created for each patient using the following beam arrangements: (1) lateral-opposed (LAT) fields and (2) left and right anterior oblique (LAO/RAO) fields. Dose volume histograms (DVH) were generated for the prostate, rectum, bladder, and right and left femoral heads. The normal tissue complication probability (NTCP) for ≥grade 2 rectal toxicity was calculated using the Lyman-Kutcher-Burman model and compared between patients with and without the rectal spacer. A significantly lower mean rectal DVH was achieved in patients with rectal spacer compared to those without. For LAT plans, the mean rectal V70 with and without rectal spacer was 4.19 and 13.5%, respectively. For LAO/RAO plans, the mean rectal V70 with and without rectal spacer was 5.07 and 13.5%, respectively. No significant differences were found in any rectal dosimetric parameters between the LAT and the LAO/RAO plans generated with the rectal spacers. We found that ≥ 9 mm space resulted in a significant decrease in NTCP modeled for ≥grade 2 rectal toxicity. Rectal spacers can significantly decrease modeled rectal dose and predicted ≥grade 2 rectal toxicity in prostate cancer patients treated in silico with PBS. A minimum of 9 mm separation between the prostate and anterior rectal wall yields the largest benefit.

Superficial and peripheral dose in compensator-based FFF beam IMRT.

Flattening filter-free (FFF) beams produce higher dose rates. Combined with compensator-based intensity modulated radiotherapy (IMRT) techniques, the dose delivery for each beam can be much shorter compared to the flattened beam MLC-based or flattened beam compensator-based IMRT. This 'snap shot' IMRT delivery is beneficial to patients for tumor motion management. Due to softer energy, superficial doses in FFF beam treatment are usually higher than those from flattened beams. Due to no flattening filter, thus less photon scattering, peripheral doses are usually lower in FFF beam treatment. However, in compensator-based IMRT using FFF beams, the compensator is in the beam pathway. Does it introduce beam hardening effects and scattering such that the superficial dose is lower and peripheral dose is higher compared to FFF beam MLC-based IMRT? This study applied Monte Carlo techniques to investigate the superficial and peripheral doses in compensator-based IMRT using FFF beams and compared it to the MLC-based IMRT using FFF beams and flattened beams. Besides varying thicknesses of brass slabs to simulate varying thicknesses of compensators, a simple cone-shaped compensator was simulated to mimic a clinical application. The dose distribution in water phantom by the cone-shaped compensator was then simulated by multiple MLC-defined FFF and flattened beams with varying apertures. After normalization to the maximum dose, Dmax, the superficial and peripheral doses were compared between the FFF beam compensator-based IMRT and FFF/flattened beam MLC-based IMRT. The superficial dose at the central 0.5 mm depth was about 1% (of Dmax) lower in the compensator-based 6 MV FFF (6FFF) IMRT compared to the MLC-based 6FFF IMRT, and about 8% higher than the flattened 6 MV MLC-based IMRT dose. At 8 cm off-axis at depth of central maximum dose, dmax, the peripheral dose between the 6FFF and flattened 6 MV MLC demonstrated similar doses, while the compensator dose was about 1% (of Dmax) higher. Compensators reduce the superficial doses slightly compared to open FFF beams, but increases the peripheral doses due to scatter in the compensator.

Initial evaluation of automated treatment planning software.

Even with advanced inverse-planning techniques, radiation treatment plan opti-mization remains a very time-consuming task with great output variability, which prompted the development of more automated approaches. One commercially available technique mimics the actions of experienced human operators to pro-gressively guide the traditional optimization process with automatically created regions of interest and associated dose-volume objectives. We report on the initial evaluation of this algorithm on 10 challenging cases of locoreginally advanced head and neck cancer. All patients were treated with VMAT to 70 Gy to the gross disease and 56 Gy to the elective bilateral nodes. The results of post-treatment autoplanning (AP) were compared to the original human-driven plans (HDP). We used an objective scoring system based on defining a collection of specific dosimetric metrics and corresponding numeric score functions for each. Five AP techniques with different input dose goals were applied to all patients. The best of them averaged the composite score 8% lower than the HDP, across the patient population. The difference in median values was statistically significant at the 95% confidence level (Wilcoxon paired signed-rank test p = 0.027). This result reflects the premium the institution places on dose homogeneity, which was consistently higher with the HDPs. The OAR sparing was consistently better with the APs, the differences reaching statistical significance for the mean doses to the parotid glands (p < 0.001) and the inferior pharyngeal constrictor (p = 0.016), as well as for the maximum doses to the spinal cord (p = 0.018) and brainstem (p = 0.040). If one is prepared to accept less stringent dose homogeneity criteria from the RTOG 1016 protocol, nine APs would comply with the protocol, while providing lower OAR doses than the HDPs. Overall, AP is a promising clinical tool, but it could benefit from a better process for shifting the balance between the target dose coverage/homogeneity and OAR sparing.

Evaluation of the ΔV 4D CT ventilation calculation method using in vivo xenon CT ventilation data and comparison to other methods.

Ventilation distribution calculation using 4D CT has shown promising potential in several clinical applications. This study evaluated the direct geometric ventilation calculation method, namely the ΔV method, with xenon-enhanced CT (XeCT) ventilation data from four sheep, and compared it with two other published meth-ods, the Jacobian and the Hounsfield unit (HU) methods. Spearman correlation coefficient (SCC) and Dice similarity coefficient (DSC) were used for the evaluation and comparison. The average SCC with one standard deviation was 0.44 ± 0.13 with a range between 0.29 and 0.61 between the XeCT and ΔV ventilation distributions. The average DSC value for lower 30% ventilation volumes between the XeCT and ΔV ventilation distributions was 0.55 ± 0.07 with a range between 0.48 and 0.63. Ventilation difference introduced by deformable image registration errors improved with smoothing. In conclusion, ventilation distributions generated using ΔV-4D CT and deformable image registration are in reasonably agreement with the in vivo XeCT measured ventilation distribution.

Novel Postoperative Hypofractionated Accelerated Radiation Dose-Painting Approach for Soft Tissue Sarcoma.

Purpose: Hypofractionated radiation therapy (RT) offers benefits in the treatment of soft tissue sarcomas (STS), including exploitation of the lower α/ß, patient convenience, and cost. This study evaluates the acute toxicity of a hypofractionated accelerated RT dose-painting (HARD) approach for postoperative treatment of STS. Methods and Materials: This is a retrospective review of 53 consecutive patients with STS who underwent resection followed by postoperative RT. Standard postoperative RT dosing for R0/R1/gross disease with sequential boost (50 Gy + 14/16/20 Gy in 32-35 fractions) were replaced with dose-painting, which adapts dose based on risk of disease burden, to 50.4 and 63, 64.4, 70 Gy in 28 fractions, respectively. The first 10 patients were replanned with a sequential boost RT approach and dosimetric indices were compared. Time-to-event outcomes, including local control, regional control, distant control, and overall survival, were estimated with Kaplan-Meier analysis. Results: Median follow-up was 25.2 months. Most patients had high-grade (59%) STS of the extremity (63%) who underwent resection with either R1 (40%) or close (36%) margins. Four patients experienced grade 3 acute dermatitis which resolved by the 3-month follow-up visit. The 2-year local control, regional control, distant control, and overall survival were 100%, 92%, 68%, and 86%, respectively. Compared with the sequential boost plan, HARD had a significantly lower field size (total V50 Gy; P = .002), bone V50 (P = .031), and maximum skin dose (P = .008). Overall treatment time was decreased by 4 to 7 fractions, which translated to a decrease in estimated average treatment cost of $3056 (range, $2651-$4335; P < .001). Conclusions: In addition to benefits in cost, convenience, and improved biologic effect in STS, HARD regimen offers a safe treatment approach with dosimetric advantages compared with conventional sequential boost, which may translate to improved long-term toxicity.

Magnetic Resonance-Guided Cancer Therapy Radiomics and Machine Learning Models for Response Prediction.

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.

NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions.

Deep learning neural networks (DLNN) in Artificial intelligence (AI) have been extensively explored for automatic segmentation in radiotherapy (RT). In contrast to traditional model-based methods, data-driven AI-based models for auto-segmentation have shown high accuracy in early studies in research settings and controlled environment (single institution). Vendor-provided commercial AI models are made available as part of the integrated treatment planning system (TPS) or as a stand-alone tool that provides streamlined workflow interacting with the main TPS. These commercial tools have drawn clinics' attention thanks to their significant benefit in reducing the workload from manual contouring and shortening the duration of treatment planning. However, challenges occur when applying these commercial AI-based segmentation models to diverse clinical scenarios, particularly in uncontrolled environments. Contouring nomenclature and guideline standardization has been the main task undertaken by the NRG Oncology. AI auto-segmentation holds the potential clinical trial participants to reduce interobserver variations, nomenclature non-compliance, and contouring guideline deviations. Meanwhile, trial reviewers could use AI tools to verify contour accuracy and compliance of those submitted datasets. In recognizing the growing clinical utilization and potential of these commercial AI auto-segmentation tools, NRG Oncology has formed a working group to evaluate the clinical utilization and potential of commercial AI auto-segmentation tools. The group will assess in-house and commercially available AI models, evaluation metrics, clinical challenges, and limitations, as well as future developments in addressing these challenges. General recommendations are made in terms of the implementation of these commercial AI models, as well as precautions in recognizing the challenges and limitations.

Novel Virtual Reality App for Training Patients on MRI-guided Radiation Therapy.

Purpose: Patients receiving respiratory gated magnetic resonance imaging-guided radiation therapy (MRIgRT) for abdominal targets must hold their breath for ≥25 seconds at a time. Virtual reality (VR) has shown promise for improving patient education and experience for diagnostic MRI scan acquisition. We aimed to develop and pilot-test the first VR app to educate, train, and reduce anxiety and discomfort in patients preparing to receive MRIgRT. Methods and Materials: A multidisciplinary team iteratively developed a new VR app with patient input. The app begins with minigames to help orient patients to using the VR device and to train patients on breath-holding. Next, app users are introduced to the MRI linear accelerator vault and practice breath-holding during MRIgRT. In this quality improvement project, clinic personnel and MRIgRT-eligible patients with pancreatic cancer tested the VR app for feasibility, acceptability, and potential efficacy for training patients on using breath-holding during MRIgRT. Results: The new VR app experience was tested by 19 patients and 67 clinic personnel. The experience was completed on average in 18.6 minutes (SD = 5.4) by patients and in 14.9 (SD = 3.5) minutes by clinic personnel. Patients reported the app was "extremely helpful" (58%) or "very helpful" (32%) for learning breath-holding used in MRIgRT and "extremely helpful" (28%) or "very helpful (50%) for reducing anxiety. Patients and clinic personnel also provided qualitative feedback on improving future versions of the VR app. Conclusion: The VR app was feasible and acceptable for training patients on breath-holding for MRIgRT. Patients eligible for MRIgRT for pancreatic cancer and clinic personnel reported on future improvements to the app to enhance its usability and efficacy.