A repository of grade 1 and 2 meningioma MRIs in a public dataset for radiomics reproducibility tests.

PURPOSE: Meningiomas are the most common primary brain tumors in adults with management varying widely based on World Health Organization (WHO) grade. However, there are limited datasets available for researchers to develop and validate radiomic models. The purpose of our manuscript is to report on the first dataset of meningiomas in The Cancer Imaging Archive (TCIA). ACQUISITION AND VALIDATION METHODS: The dataset consists of pre-operative MRIs from 96 patients with meningiomas who underwent resection from 2010-2019 and include axial T1post and T2-FLAIR sequences-55 grade 1 and 41 grade 2. Meningioma grade was confirmed based on the 2016 WHO Bluebook classification guideline by two neuropathologists and one neuropathology fellow. The hyperintense T1post tumor and hyperintense T2-FLAIR regions were manually contoured on both sequences and resampled to an isotropic resolution of 1 × 1 × 1 mm3 . The entire dataset was reviewed by a certified medical physicist. DATA FORMAT AND USAGE NOTES: The data was imported into TCIA for storage and can be accessed at https://doi.org/10.7937/0TKV-1A36. The total size of the dataset is 8.8GB, with 47 519 individual Digital Imaging and Communications in Medicine (DICOM) files consisting of 384 image series, and 192 structures. POTENTIAL APPLICATIONS: Grade 1 and 2 meningiomas have different treatment paradigms and are often treated based on radiologic diagnosis alone. Therefore, predicting grade prior to treatment is essential in clinical decision-making. This dataset will allow researchers to create models to auto-differentiate grade 1 and 2 meningiomas as well as evaluate for other pathologic features including mitotic index, brain invasion, and atypical features. Limitations of this study are the small sample size and inclusion of only two MRI sequences. However, there are no meningioma datasets on TCIA and limited datasets elsewhere although meningiomas are the most common intracranial tumor in adults.

Contrast-Enhanced Liver Magnetic Resonance Image Synthesis Using Gradient Regularized Multi-Modal Multi-Discrimination Sparse Attention Fusion GAN.

PURPOSES: To provide abdominal contrast-enhanced MR image synthesis, we developed an gradient regularized multi-modal multi-discrimination sparse attention fusion generative adversarial network (GRMM-GAN) to avoid repeated contrast injections to patients and facilitate adaptive monitoring. METHODS: With IRB approval, 165 abdominal MR studies from 61 liver cancer patients were retrospectively solicited from our institutional database. Each study included T2, T1 pre-contrast (T1pre), and T1 contrast-enhanced (T1ce) images. The GRMM-GAN synthesis pipeline consists of a sparse attention fusion network, an image gradient regularizer (GR), and a generative adversarial network with multi-discrimination. The studies were randomly divided into 115 for training, 20 for validation, and 30 for testing. The two pre-contrast MR modalities, T2 and T1pre images, were adopted as inputs in the training phase. The T1ce image at the portal venous phase was used as an output. The synthesized T1ce images were compared with the ground truth T1ce images. The evaluation metrics include peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE). A Turing test and experts' contours evaluated the image synthesis quality. RESULTS: The proposed GRMM-GAN model achieved a PSNR of 28.56, an SSIM of 0.869, and an MSE of 83.27. The proposed model showed statistically significant improvements in all metrics tested with p-values < 0.05 over the state-of-the-art model comparisons. The average Turing test score was 52.33%, which is close to random guessing, supporting the model's effectiveness for clinical application. In the tumor-specific region analysis, the average tumor contrast-to-noise ratio (CNR) of the synthesized MR images was not statistically significant from the real MR images. The average DICE from real vs. synthetic images was 0.90 compared to the inter-operator DICE of 0.91. CONCLUSION: We demonstrated the function of a novel multi-modal MR image synthesis neural network GRMM-GAN for T1ce MR synthesis based on pre-contrast T1 and T2 MR images. GRMM-GAN shows promise for avoiding repeated contrast injections during radiation therapy treatment.

A comprehensive review of 30 years of pediatric clinical trial radiotherapy dose constraints.

BACKGROUND: Radiation therapy normal tissue dose constraints are critical when treating pediatric patients. However, there is limited evidence supporting proposed constraints, which has led to variations in constraints over the years. In this study, we identify these variations in dose constraints within pediatric trials both in the United States and in Europe used in the past 30 years. PROCEDURE: All pediatric trials from the Children's Oncology Group website were queried from inception until January 2022 and a sampling of European studies was included. Dose constraints were identified and built into an organ-based interactive web application with filters to display data by organs at risk (OAR), protocol, start date, dose, volume, and fractionation scheme. Dose constraints were evaluated for consistency over time and compared between pediatric US and European trials RESULTS: One hundred five closed trials were included-93 US trials and 12 European trials. Thirty-eight separate OAR were found with high-dose constraint variability. Across all trials, nine organs had greater than 10 different constraints (median 16, range 11-26), including serial organs. When comparing US versus European dose tolerances, the United States constraints were higher for seven OAR, lower for one, and identical for five. No OAR had constraints change systematically over the last 30 years. CONCLUSION: Review of pediatric dose-volume constraints in clinical trials showed substantial variability for all OAR. Continued efforts focused on standardization of OAR dose constraints and risk profiles are essential to increase consistency of protocol outcomes and ultimately to reduce radiation toxicities in the pediatric population.

Knowledge and Understanding of Radiation Therapy Among Patients With Breast Cancer From Culturally Diverse Backgrounds at a Safety-Net Hospital.

INTRODUCTION: Comprehensive understanding of oncologic treatment is essential for shared decision-making. However, comprehension of information in radiation oncology consults is poorly understood, particularly among Spanish-speaking patients at safetynet hospitals. The purpose of this pilot study was to examine post-consultation radiation oncology knowledge and health literacy among breast cancer patients from culturally diverse backgrounds. METHODS: After consultation for curative post-operative breast radiotherapy (cT1-4N1-3M0), the Radiation Oncology Knowledge Assessment Survey (ROKAS) was administered to Spanish- and English-speaking patients ≥ 18 years old, from January 2021 to January 2022 at a safety-net hospital. Radiation knowledge was assessed using the ROKAS which included eight radiation-specific multiple-choice questions and two separate questions regarding short- and long-term side effects. Additional independent variables included validated questionnaires related to health literacy, health numeracy, acculturation, primary language, and sociodemographic factors. Bivariate Pearson correlations and T-test analyses were conducted to examine the relationship between the independent variables and post-consultation radiation knowledge. RESULTS: Fifty ROKAS were obtained from 25 English- and 25 Spanish-speaking breast cancer patients (median age 57 [IQR 49.75-62.25]). When compared to Englishspeaking patients, Spanish-speaking patients had lower health literacy, health numeracy, and acculturation. There was no difference in the multiple-choice ROKAS score between English- and Spanish-speakers, or correlation with the other independent factors. Higher health numeracy correlated with a higher accuracy for identifying short-term side effects. Lower accuracy of identifying long-term side effects was seen in patients with lower education levels, health literacy, health numeracy, and acculturation, with the most missed long-term side effects being arm swelling, skin toxicity, and heart toxicity. CONCLUSIONS: Patients with low health literacy, health numeracy, acculturation, and education levels as well as Spanish-speaking patients were associated with poor understanding of radiotherapy long-term side effects. Determining barriers to radiation knowledge is crucial to improve shared decision-making between patients and providers in a culturally diverse population.

Automatic differentiation of Grade I and II meningiomas on magnetic resonance image using an asymmetric convolutional neural network.

The Grade of meningioma has significant implications for selecting treatment regimens ranging from observation to surgical resection with adjuvant radiation. For most patients, meningiomas are diagnosed radiologically, and Grade is not determined unless a surgical procedure is performed. The goal of this study is to train a novel auto-classification network to determine Grade I and II meningiomas using T1-contrast enhancing (T1-CE) and T2-Fluid attenuated inversion recovery (FLAIR) magnetic resonance (MR) images. Ninety-six consecutive treatment naïve patients with pre-operative T1-CE and T2-FLAIR MR images and subsequent pathologically diagnosed intracranial meningiomas were evaluated. Delineation of meningiomas was completed on both MR images. A novel asymmetric 3D convolutional neural network (CNN) architecture was constructed with two encoding paths based on T1-CE and T2-FLAIR. Each path used the same 3 × 3 × 3 kernel with different filters to weigh the spatial features of each sequence separately. Final model performance was assessed by tenfold cross-validation. Of the 96 patients, 55 (57%) were pathologically classified as Grade I and 41 (43%) as Grade II meningiomas. Optimization of our model led to a filter weighting of 18:2 between the T1-CE and T2-FLAIR MR image paths. 86 (90%) patients were classified correctly, and 10 (10%) were misclassified based on their pre-operative MRs with a model sensitivity of 0.85 and specificity of 0.93. Among the misclassified, 4 were Grade I, and 6 were Grade II. The model is robust to tumor locations and sizes. A novel asymmetric CNN with two differently weighted encoding paths was developed for successful automated meningioma grade classification. Our model outperforms CNN using a single path for single or multimodal MR-based classification.

Automatic segmentation of high-risk clinical target volume for tandem-and-ovoids brachytherapy patients using an asymmetric dual-path convolutional neural network.

PURPOSES: Preimplant diagnostic magnetic resonance imaging is the gold standard for image-guided tandem-and-ovoids (T&O) brachytherapy for cervical cancer. However, high dose rate brachytherapy planning is typically done on postimplant CT-based high-risk clinical target volume (HR-CTVCT ) because the transfer of preimplant Magnetic resonance (MR)-based HR-CTV (HR-CTVMR ) to the postimplant planning CT is difficult due to anatomical changes caused by applicator insertion, vaginal packing, and the filling status of the bladder and rectum. This study aims to train a dual-path convolutional neural network (CNN) for automatic segmentation of HR-CTVCT on postimplant planning CT with guidance from preimplant diagnostic MR. METHODS: Preimplant T2-weighted MR and postimplant CT images for 65 (48 for training, eight for validation, and nine for testing) patients were retrospectively solicited from our institutional database. MR was aligned to the corresponding CT using rigid registration. HR-CTVCT and HR-CTVMR were manually contoured on CT and MR by an experienced radiation oncologist. All images were then resampled to a spatial resolution of 0.5 × 0.5 × 1.25 mm. A dual-path 3D asymmetric CNN architecture with two encoding paths was built to extract CT and MR image features. The MR was masked by HR-CTVMR contour while the entire CT volume was included. The network put an asymmetric weighting of 18:6 for CT: MR. Voxel-based dice similarity coefficient (DSCV ), sensitivity, precision, and 95% Hausdorff distance (95-HD) were used to evaluate model performance. Cross-validation was performed to assess model stability. The study cohort was divided into a small tumor group (<20 cc), medium tumor group (20-40 cc), and large tumor group (>40 cc) based on the HR-CTVCT for model evaluation. Single-path CNN models were trained with the same parameters as those in dual-path models. RESULTS: For this patient cohort, the dual-path CNN model improved each of our objective findings, including DSCV , sensitivity, and precision, with an average improvement of 8%, 7%, and 12%, respectively. The 95-HD was improved by an average of 1.65 mm compared to the single-path model with only CT images as input. In addition, the area under the curve for different networks was 0.86 (dual-path with CT and MR) and 0.80 (single-path with CT), respectively. The dual-path CNN model with asymmetric weighting achieved the best performance with DSCV of 0.65 ± 0.03 (0.61-0.70), 0.79 ± 0.02 (0.74-0.85), and 0.75 ± 0.04 (0.68-0.79) for small, medium, and large group. 95-HD were 7.34 (5.35-10.45) mm, 5.48 (3.21-8.43) mm, and 6.21 (5.34-9.32) mm for the three size groups, respectively. CONCLUSIONS: An asymmetric CNN model with two encoding paths from preimplant MR (masked by HR-CTVMR ) and postimplant CT images was successfully developed for automatic segmentation of HR-CTVCT for T&O brachytherapy patients.

Voxelwise Prediction of Recurrent High-Grade Glioma via Proximity Estimation-Coupled Multidimensional Support Vector Machine.

PURPOSE: To provide early and localized glioblastoma (GBM) recurrence prediction, we introduce a novel postsurgery multiparametric magnetic resonance-based support vector machine (SVM) method coupling with stem cell niche (SCN) proximity estimation. METHODS AND MATERIALS: This study used postsurgery magnetic resonance imaging (MRI) scans from 50 patients with recurrent GBM, obtained approximately 2 months before clinically diagnosed recurrence. The main prediction pipeline consisted of a proximity-based estimator to identify regions with high risk of recurrence (HRRs) and an SVM classifier to provide voxelwise prediction in HRRs. The HRRs were estimated using the weighted sum of inverse distances to 2 possible origins of recurrence-the SCN and the tumor cavity. Subsequently, multiparametric voxels (from T1, T1 contrast-enhanced, fluid-attenuated inversion recovery, T2, and apparent diffusion coefficient) within the HRR were grouped into recurrent (warped from the clinical diagnosis) and nonrecurrent subregions and fed into the proximity estimation-coupled SVM classifier (SVMPE). The cohort was randomly divided into 40% and 60% for training and testing, respectively. The trained SVMPE was then extrapolated to an earlier time point for earlier recurrence prediction. As an exploratory analysis, the SVMPE predictive cluster sizes and the image intensities from the 5 magnetic resonance sequences were compared across time to assess the progressive subclinical traces. RESULTS: On 2-month prerecurrence MRI scans from 30 test cohort patients, the SVMPE classifier achieved a recall of 0.80, a precision of 0.69, an F1-score of 0.73, and a mean boundary distance of 7.49 mm. Exploratory analysis at early time points showed spatially consistent but significantly smaller subclinical clusters and significantly increased T1 contrast-enhanced and apparent diffusion coefficient values over time. CONCLUSIONS: We demonstrated a novel voxelwise early prediction method, SVMPE, for GBM recurrence based on clinical follow-up MR scans. The SVMPE is promising in localizing subclinical traces of recurrence 2 months ahead of clinical diagnosis and may be used to guide more effective personalized early salvage therapy.

Racial disparities and standard treatment in locally advanced rectal cancer: a National Cancer Database study.

Background: Mortality rates in colorectal cancer (CRC) continue to be higher in Black compared to White patients. While standard treatment modalities for locally advanced rectal cancer have been shown to improve outcomes, there are limited studies assessing the receipt of standard treatment in rectal cancer based on race. Therefore, we sought to evaluate the use of standard treatment across racial groups in locally advanced rectal cancer and its effect on survival. Methods: The National Cancer Database (NCDB) was queried for patients ≥18 years old with clinical and pathologic stage II-III rectal adenocarcinoma who received treatment from 2004 to 2014. Standard treatment was defined as complete surgical excision with either neoadjuvant or adjuvant concurrent chemoradiation. Multivariable logistic regressions were used to identify racial differences in receiving standard treatment. Cox proportional hazards were used to estimate the effects of standard vs. nonstandard treatment on survival differences based on race. Results: A total of 70,677 patients with stage II (n=35,079) or stage III (n=35,598) rectal adenocarcinoma met the inclusion criteria. On multivariate analysis, Black [odds ratio (OR): 0.75; 95% confidence interval (CI): 0.71-0.79; P<0.001] and Hispanic White (OR: 0.86; 95% CI: 0.80-0.92; P>0.001) patients were less likely to receive standard treatment compared to non-Hispanic White patients. On multivariable Cox regression, nonstandard treatment was significantly associated with worse survival [hazard ratio (HR): 1.69; 95% CI: 1.65-1.73; P<0.001] compared to standard treatment. Even after adjusting for patient, demographic, and facility characteristics, Black patients had higher mortality rates compared to White patients in the whole population (HR: 1.15; 95% CI: 1.09-1.20; P<0.0001). This survival difference between Black and non-Hispanic White patients persisted in both the standard (HR: 1.10; 95% CI: 1.03-1.19; P=0.008) and nonstandard (HR: 1.17; 95% CI: 1.10-1.25; P<0.0001) treatment subgroups. Decreased survival outcomes in Black patients were more pronounced for those who underwent nonstandard treatment, particularly when treating stage III disease (HR: 1.30; 95% CI: 1.19-1.42; P<0.0001). Conclusions: Nonstandard treatment in stage II and III rectal cancer is associated with worse survival compared to standard treatment regimens. Black patients are more likely to receive nonstandard treatment and have worse survival outcomes compared to White patients.

Surviving Over a Decade With Glioblastoma: A Clinical Course Characterized by Multiple Recurrences, Numerous Salvage Treatments, and Novel Use of Cesium-131 Tiles.

The prognosis for patients diagnosed with recurrent glioblastoma (GBM) remains poor, with no clear standard of care regarding salvage therapy. Common approaches include chemotherapy, re-resection, tumor treating fields, and reirradiation. However, most studies have shown these to have limited benefits. Reirradiation is particularly difficult due to concern for increased risk of toxicity to surrounding normal brain tissue. A novel intracranial brachytherapy system called GammaTile® (GT Medical Technologies, Tempe, Arizona) involves the placement of Cesium-131 radioactive tiles in the tumor cavity following maximal safe resection. This allows for a highly conformal dose distribution with rapid fall-off to minimize overlap with prior radiation fields and for the application of radiation directly to the high-risk tumor bed. This case report highlights a patient with GBM who survived 11.5 years through multiple recurrences and discusses the many salvage treatments he received, including bevacizumab, irinotecan, and stereotactic radiosurgery (SRS). This case exemplifies that aggressive systemic and local therapies can work well in select patients allowing for long-term survival with a good quality of life. Further efforts should be made to identify which patients may benefit from these therapies. The case study additionally reports on the use of GammaTile therapy. Due to prior external beam radiation therapy and SRS to the treatment site, further external beam radiation options were limited, and the patient was offered GammaTile as local therapy. Although it did not provide a survival benefit in this case due to progressive disease outside of the field of treatment, GammaTile serves as a valuable option in providing local therapy to patients who can no longer receive further radiation. It should be used with careful consideration in lesions characterized by aggressive local invasion.

Physical Medicine and Rehabilitation Residency Applicants: A Decade of Change.

Physical medicine and rehabilitation (PM&R) has been an expanding specialty in medicine since its induction into the American Medical Association in 1947. This article quantifies the growth of PM&R by analyzing the number of residency positions and applicants as well as the characteristics of the applicants. Review of the United States (US) National Residency Matching Program (NRMP) data from 2010-2020 shows that the number of PM&R residency positions has grown by 26%, while the number of applicants has grown by 37% with a decreasing ratio of allopathic to osteopathic matched applicants. Matched applicants have had increasing Step 1 and 2 scores, abstracts, presentations, publications, and volunteer experiences. In the last decade there has been a growing interest in PM&R that outpaces residency positions and with increasingly qualified applicants. This analysis can guide residency administration and future applicants on the trends in the PM&R residency selection process.

Residency Match Trends, Racial Disparity, and Matching Amid a Pandemic.

PURPOSE: Radiation oncology has been facing an evolving crisis in recruitment for several years, and the events of 2020 to 2021 will certainly add to that crisis with the urgency of addressing systemic racial injustice amid a global pandemic. The purpose of this study is to examine applicant data to gain insight on residency match trends and evaluate these findings within the backdrop of a novel match year. METHODS AND MATERIALS: National Residency Matching Program (NRMP) data between 2009 and 2020 were assessed for the number of applicants, programs, and positions available, number of ranked applicants needed to fill positions, and successfully matched applicant data. Additionally, Electronic Residency Application Service data were evaluated for race/ethnicity identification among applicants. RESULTS: The number of applicants who ranked radiation oncology as their preferred specialty has declined for 3 consecutive years from 223 in 2017 to 155 in 2020. In 2020 the applicant-to-position ratio was at an all-time low at 0.82, and the unmatched position rate increased to 18.5%. The percentage of Black or African American applicants applying to radiation oncology has also declined to 4.9%, and this population represents 7.2% of all applicants. The number of ranked applicants needed to fill the available radiation oncology PGY2 positions increased from 4.0 in 2010 to 6.0 in 2020. CONCLUSION: Declining interest in radiation oncology among applicants, and an even further decline of black applicants, along with the challenges of interview and travel restrictions during the pandemic provide heightened concern for this year's match. Innovative efforts to expand the reach of radiation oncology to prospective applicants is needed to engage diverse, bright, and committed students for the continued progress of radiation oncology and most importantly, our patients.

Feasibility and Outcomes of Orthotopic Ileal Neobladder Reconstruction Following Pelvic Irradiation.

OBJECTIVES: To evaluate the complications and oncologic outcomes of orthotopic ileal neobladder reconstruction (ONB) following pelvic radiotherapy (RT) compared to patients with no prior pelvic RT. METHODS: Our institutional database was queried for all patients with pT0-4bN0-3M0 urothelial carcinoma of the bladder who underwent radical cystectomy with curative intent and ONB between 1990 and 2018. We then queried this cohort for patients who had prior pelvic RT (>3900 cGy) and compared baseline characteristics, perioperative complications, and recurrence free survival between patients with and without prior pelvic RT. RESULTS: One thousand eight hundred and forty-six patients matched our inclusion criteria; 34 had prior pelvic RT with a median dose of 6280 cGy. Prior RT targeted the bladder, prostate, and cervix in 27, 6, and 1 patient, respectively. Median time from RT to cystectomy was 15.5 months. Patients with prior RT had a longer time from diagnosis to cystectomy, more frequently had ≥5 TURBT and neoadjuvant chemo, and less frequently had multifocal disease. Perioperative complications including operative time, estimated blood loss, days hospitalized, as well as 30- and 90-day complication rates, were not statistically different. There was no statistical difference in recurrence free survival between the 2 groups (P = .48). CONCLUSION: ONB in highly selected patients with prior pelvic RT is feasible with statistically similar perioperative complication rates compared to ONB without prior RT.

Automatic detection and segmentation of multiple brain metastases on magnetic resonance image using asymmetric UNet architecture.

Detection of brain metastases is a paramount task in cancer management due both to the number of high-risk patients and the difficulty of achieving consistent detection. In this study, we aim to improve the accuracy of automated brain metastasis (BM) detection methods using a novel asymmetric UNet (asym-UNet) architecture. An end-to-end asymmetric 3D-UNet architecture, with two down-sampling arms and one up-sampling arm, was constructed to capture the imaging features. The two down-sampling arms were trained using two different kernels (3 × 3 × 3 and 1 × 1 × 3, respectively) with the kernel (1 × 1 × 3) dominating the learning. As a comparison, vanilla single 3D UNets were trained with different kernels and evaluated using the same datasets. Voxel-based Dice similarity coefficient (DSCv), sensitivity (S v), precision (P v), BM-based sensitivity (S BM), and false detection rate (F BM) were used to evaluate model performance. Contrast-enhanced T1 MR images from 195 patients with a total of 1034 BMs were solicited from our institutional stereotactic radiosurgery database. The patient cohort was split into training (160 patients, 809 lesions), validation (20 patients, 136 lesions), and testing (15 patients, 89 lesions) datasets. The lesions in the testing dataset were further divided into two subgroups based on the diameters (small S = 1-10 mm, large L = 11-26 mm). In the testing dataset, there were 72 and 17 BMs in the S and L sub-groups, respectively. Among all trained networks, asym-UNet achieved the highest DSCv of 0.84 and lowest F BM of 0.24. Although vanilla 3D-UNet with a single 1 × 1 × 3 kernel achieved the highest sensitivities for the S group, it resulted in the lowest precision and highest false detection rate. Asym-UNet was shown to balance sensitivity and false detection rate as well as keep the segmentation accuracy high. The novel asym-UNet segmentation network showed overall competitive segmentation performance and more pronounced improvement in hard-to-detect small BMs comparing to the vanilla single 3D UNet.

Merging Orthovoltage X-Ray Minibeams spare the proximal tissues while producing a solid beam at the target.

Conventional radiation therapy of brain tumors often produces cognitive deficits, particularly in children. We investigated the potential efficacy of merging Orthovoltage X-ray Minibeams (OXM). It segments the beam into an array of parallel, thin (~0.3 mm), planar beams, called minibeams, which are known from synchrotron x-ray experiments to spare tissues. Furthermore, the slight divergence of the OXM array make the individual minibeams gradually broaden, thus merging with their neighbors at a given tissue depth to produce a solid beam. In this way the proximal tissues, including the cerebral cortex, can be spared. Here we present experimental results with radiochromic films to characterize the method's dosimetry. Furthermore, we present our Monte Carlo simulation results for physical absorbed dose, and a first-order biologic model to predict tissue tolerance. In particular, a 220-kVp orthovoltage beam provides a 5-fold sharper lateral penumbra than a 6-MV x-ray beam. The method can be implemented in arc-scan, which may include volumetric-modulated arc therapy (VMAT). Finally, OXM's low beam energy makes it ideal for tumor-dose enhancement with contrast agents such as iodine or gold nanoparticles, and its low cost, portability, and small room-shielding requirements make it ideal for use in the low-and-middle-income countries.

Individualized 4-dimensional computed tomography proton treatment for pancreatic tumors.

BACKGROUND: The goal of this study is to determine whether a phase or reconstruction of a 10-phase 4 dimensional computed tomography (4D CT) scan can be used as the primary planning scan for proton treatment of the pancreas, thus eliminating the need for second a slow CT or free breathing CT. METHODS: Ten patients with pancreatic adenocarcinoma were simulated with 4D CT and a proton treatment plan generated based upon one of three primary planning scans, the T0 phase, T50 phase or average reconstruction. These plans were then exported to each of the remaining phases of the 4D CT and the dose to 95% of the target (D95) calculated. Plans were deemed adequate if the D95 remained at 99% of the prescribed dose or greater. RESULTS: For the ten patients in this study anterior abdominal motion was found to range from 2-27 mm (mean 7.50±6.79 mm). For 9 of 10 patients the anterior abdominal motion was ≤8 mm and all three primary planning scans provided adequate target coverage, resulting in minimum D95 coverage per plan of T0_plan 99.7%, T50_plan 99.3% and AVE_plan 99%. However no plan provided adequate target coverage on the single patient with the largest anterior abdominal motion, 27 mm, and cranio-caudal motion, 20 mm, with minimum D95 values of T0_plan 96.3%, T50_plan 68%, and AVE_plan 68%. CONCLUSIONS: The primary plans tested based up on the T0, T50 and average reconstructions provided adequate D95 coverage throughout the respiratory cycle as long as the anterior abdominal motion was ≤8 mm and can be considered for use as the primary proton planning scan.

Tumor Cells Surviving Exposure to Proton or Photon Radiation Share a Common Immunogenic Modulation Signature, Rendering Them More Sensitive to T Cell-Mediated Killing.

PURPOSE: To provide the foundation for combining immunotherapy to induce tumor antigen-specific T cells with proton radiation therapy to exploit the activity of those T cells. METHODS AND MATERIALS: Using cell lines of tumors frequently treated with proton radiation, such as prostate, breast, lung, and chordoma, we examined the effect of proton radiation on the viability and induction of immunogenic modulation in tumor cells by flow cytometric and immunofluorescent analysis of surface phenotype and the functional immune consequences. RESULTS: These studies show for the first time that (1) proton and photon radiation induced comparable up-regulation of surface molecules involved in immune recognition (histocompatibility leukocyte antigen, intercellular adhesion molecule 1, and the tumor-associated antigens carcinoembryonic antigen and mucin 1); (2) proton radiation mediated calreticulin cell-surface expression, increasing sensitivity to cytotoxic T-lymphocyte killing of tumor cells; and (3) cancer stem cells, which are resistant to the direct cytolytic activity of proton radiation, nonetheless up-regulated calreticulin after radiation in a manner similar to non-cancer stem cells. CONCLUSIONS: These findings offer a rationale for the use of proton radiation in combination with immunotherapy, including for patients who have failed radiation therapy alone or have limited treatment options.