Citations

AbstractBackgroundDiscrete spot scanning (DSS) is the commonly used method for proton pencil beam scanning (PBS). There is lack of data on the dose‐driven continuous scanning (DDCS).PurposeTo investigate delivery benefits and dosimetric implications of DDCS versus DSS for PBS systems.MethodsThe irradiation duty factor, beam delivery time (BDT), and dose deviation were simulated for eight treatment plans in prostate, head and neck, liver, and lung, with both conventional fractionation and hypofractionation schemes. DDCS results were compared with those of DSS.ResultsThe DDCS irradiation duty factor (range, 11%–41%) was appreciably improved compared to DSS delivery (range, 4%–14%), within which, hypofractionation schemes had greater improvement than conventional fractionation. With decreasing stop ratio constraints, the DDCS BDT reduction was greater, but dose deviation also increased. With stop ratio constraints of 2, 1, 0.5, and 0, DDCS BDT reduction reached to 6%, 10%, 12%, and 15%, respectively, and dose deviation reached to 0.6%, 1.7%, 3.0%, and 5.2% root mean square error in PTV DVH, respectively. The 3%/2‐mm gamma passing rate was greater than 99% with stop ratio constraints of 2 and 1, and greater than 95% with a stop ratio of 0.5. When the stop ratio constraint was removed, five of the eight treatment plans had a 3%/2‐mm gamma passing rate greater than 95%, and the other three plans had a 3%/2‐mm gamma passing rate between 90% and 95%.ConclusionsThe irradiation duty factor was considerably improved with DDCS. Smaller stop ratio constraints led to shorter BDTs, but with the cost of larger dose deviations. Our finding suggested that a stop ratio of 1 constraint seems to yield acceptable DDCS dose deviation.

Abstract Purpose: To determine the rate of rib fracture in a cohort of patients with breast cancer treated with proton therapy and enrolled on a prospective registry. In series investigating photon therapy for breast cancer, the rib fracture rate ranges from 0.3-13% Methods: From a prospective database, we identified patients treated with proton therapy for breast cancer at our institution between January 1, 2012 and December 31, 2020. Clinical and dosimetric data was extracted from the electronic medical record. The cumulative incidence method assessed rib fracture rate; the Fine-Gray test statistic assessed prognostic significance select variables. Results: 225 patients were identified, 223 women and 2 men. Median age at the time of radiation was 57.8 years (range, 25 – 87). 26% of patients were black, 69% white and 5% were other races or race not disclosed. 5% were Hispanic. 74% of patients had left-sided breast cancer, 5% bilateral, and 21% right-sided. DEXA scan was normal in 20%, showed osteopenia in 34%, osteoporosis in 6%, and not performed in 40%. 57% of patients received antiendocrine therapy with an aromatase inhibitor. For 16% of patients, the breast +/- internal mammary nodes (IMN) were treated, while 32% underwent proton therapy to treat the breast and comprehensive regional lymphatics. 1% of patients had chest wall +/- IMN treated, while 51% underwent proton therapy to treat the chest wall and comprehensive regional lymphatics. 41% of patients were treated with passive scatter proton therapy (n=92); 52% of these had mastectomy. 58% of patients received treatment with pencil beam scanning (PBS) proton therapy (n=131); 53% of these had mastectomy. A combination of passive scatter and PBS was used for 2 patients (1%). 85% of patients received a boost. Median follow-up was 3.1 years (range, 0.2 – 9.1). 97% of patients had > 12 months of follow-up. The 3 year cumulative incidence of in-field rib fracture was 3.7% (95%CI: 1.6%-7.1%). In total, 8 patients developed in-field rib fractures, one symptomatic and 7 identified incidentally on surveillance imaging, for a 0.4% rate of symptomatic rib fracture. Median time from completion of radiation to identification of rib fracture was 1.8 years (range, 0.4-7.4 years). Rib fractures occurred within 2.2 years from radiotherapy completion for seven of the eight patients who experienced this side effect. Three patients developed rib fractures outside of the radiation field, for a cumulative incidence of out-of-field rib fracture of 0.9% (95%CI: 0.2-3.0%). No variables were associated with rib fracture on univariate analysis. Of those with in-field rib fractures, 3 had low and 3 had normal bone density while 2 had not undergone testing; of those with out-of-field rib fractures, 2 had osteopenia and 1 did not have testing. Conclusions: Although a proton beam has higher biological dose deposition at end-of-range than the assumed constant RBE=1.1, the 3 year rate of any in-field rib fractures in our series remains low at 3.7%, with a 0.4% rate of symptomatic rib fracture. Contouring ribs as an OAR and establishing a dose constraint warrants further investigation as a means to maintaining a low rate of rib fracture with proton therapy. Citation Format: Julie Bradley, Xiaoying Liang, Raymond Mailhot Vega, Chunbo Liu, Eric Brooks, Teena Burchianti, Emma Viviers, Roi Dagan, Oluwadamilola Oladeru, Christopher Morris, Nancy Mendenhall. Incidence of rib fracture following treatment with proton therapy for breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-10-08.

Background and Objectives: Laparoscopic cholecystectomy (LC) is one of the most performed surgeries worldwide. Procedure difficulty and patient outcomes depend on several factors which are not considered in the current literature, including the learning curve, generating confusing and subjective results. This study aims to create a scoring system to calculate the learning curve of LC based on hepatobiliopancreatic (HPB) experts’ opinions during an educational course. Materials and Methods: A questionnaire was submitted to the panel of experts attending the HPB course at Research Institute against Digestive Cancer-IRCAD (Strasbourg, France) from 27–29 October 2022. Experts scored the proposed variables according to their degree of importance in the learning curve using a Likert scale from 1 (not useful) to 5 (very useful). Variables were included in the composite scoring system only if more than 75% of experts ranked its relevance in the learning curve assessment ≥4. A positive or negative value was assigned to each variable based on its effect on the learning curve. Results: Fifteen experts from six different countries attended the IRCAD HPB course and filled out the questionnaire. Ten variables were finally included in the learning curve scoring system (i.e., patient body weight/BMI, patient previous open surgery, emergency setting, increased inflammatory levels, presence of anatomical bile duct variation(s), and appropriate critical view of safety (CVS) identification), which were all assigned positive values. Minor or major intraoperative injuries to the biliary tract, development of postoperative complications related to biliary injuries, and mortality were assigned negative values. Conclusions: This is the first scoring system on the learning curve of LC based on variables selected through the experts’ opinions. Although the score needs to be validated through future studies, it could be a useful tool to assess its efficacy within educational programs and surgical courses.

Abstract The Mayo Clinic Florida Integrated Oncology Building will be the home of the first spot-scanning only carbon/proton hybrid therapy system by Hitachi, Ltd. It will provide proton beams up to kinetic energies of 230 MeV and carbon beams up to 430 MeV n −1 for clinical deployment. To provide adequate radiation protection, the Geant4 (v10.6) Monte Carlo toolkit was utilized to quantify the ambient dose equivalent at a 10 mm depth ( H *(10)) for photons and neutrons. To perform accurate calculations of the ambient dose equivalent, three-dimensional computer-aided design files of the entire planned facility were imported into Geant4, as well as certain particle system components such as the bending magnets, fast Faraday cup, and gantry. Particle fluence was scored using 60 cm diameter spheres, which were strategically placed throughout areas of interests. Analytical calculations were performed as first-pass design checks. Major shielding slabs were optimized using Geant4 simulations iteratively, with more than 20 alternative designs evaluated within Geant4. The 430 MeV n −1 carbon beams played the most significant role in concrete thickness Requirements. The primary wall thickness for the carbon fixed beam room is 4 meters. The presence of the proton gantry structure in the simulation caused the ambient dose equivalent to increase by around 67% at the maze entrance, but a decrease in the high energy beam transport corridor. All shielding primary and secondary goals for clinical operations were met per state regulation and national guidelines.

Abstract Older adults have the highest incidence of traumatic brain injury globally. Accurate blood-based biomarkers are needed to assist with diagnosis of patients across the spectrum of age and time post-injury. Several reports have suggested lower accuracy for blood-based biomarkers in older adults, and there is a paucity of data beyond day-1 post-injury. Our aims were to investigate age-related differences in diagnostic accuracy and 2-week evolution of four leading candidate blood-based traumatic brain injury biomarkers—plasma glial fibrillary acidic protein, ubiquitin carboxy-terminal hydrolase L1, S100 calcium binding protein B and neuron-specific enolase—among participants in the 18-site prospective cohort study Transforming Research And Clinical Knowledge in Traumatic Brain Injury. Day-1 biomarker data were available for 2602 participants including 2151 patients with traumatic brain injury, 242 orthopedic trauma controls and 209 healthy controls. Participants were stratified into 3 age categories (young: 17–39 years, middle-aged: 40–64 years, older: 65–90 years). We investigated age-stratified biomarker levels and biomarker discriminative abilities across three diagnostic groups: head CT-positive/negative; traumatic brain injury/orthopedic controls; and traumatic brain injury/healthy controls. The difference in day-1 glial fibrillary acidic protein, ubiquitin carboxy-terminal hydrolase L1 and neuron-specific enolase levels across most diagnostic groups was significantly smaller for older versus younger adults, resulting in a narrower range within which a traumatic brain injury diagnosis may be discriminated in older adults. Despite this, day-1 glial fibrillary acidic protein had good to excellent performance across all age-categories for discriminating all three diagnostic groups (area under the curve 0.84–0.96; lower limit of 95% confidence intervals all >0.78). Day-1 S100 calcium-binding protein B and ubiquitin carboxy-terminal hydrolase L1 showed good discrimination of CT-positive versus negative only among adults under age 40 years within 6 hours of injury. Longitudinal blood-based biomarker data were available for 522 hospitalized patients with traumatic brain injury and 24 hospitalized orthopaedic controls. Glial fibrillary acidic protein levels maintained good to excellent discrimination across diagnostic groups until day 3 post-injury irrespective of age, until day 5 post-injury among middle-aged or younger patients and until week 2 post-injury among young patients only. In conclusion, the blood-based glial fibrillary acidic protein assay tested here has good to excellent performance across all age-categories for discriminating key traumatic brain injury diagnostic groups to at least 3 days post-injury in this trauma centre cohort. The addition of a blood-based diagnostic to the evaluation of traumatic brain injury, including geriatric traumatic brain injury, has potential to streamline diagnosis.

People with HIV on combination antiretroviral therapy (ART) have longer life expectancy and are increasingly experiencing age-related comorbidities. Thus, aging with HIV has become a central issue in clinical care and research, which has been particularly challenging with the intersection of the ongoing coronavirus (COVID)-19 pandemic. Since 2009, the International Workshop on HIV and Aging has served as a multidisciplinary platform to share research findings from cross-disciplinary fields along with community advocates to address critical issues in HIV and aging. In this article, we summarize the key oral presentations from the 12th Annual International Workshop on HIV and Aging, held virtually on September 23rd and 24 th , 2021. The topics ranged from basic science research on biological mechanisms of aging to quality of life and delivery of care under the COVID-19 pandemic. This workshop enriched our understanding of HIV and aging under the COVID-19 pandemic, identified challenges and opportunities to combat the impact of COVID-19 on HIV communities, and also provided updated research and future directions of the field to move HIV and aging research forward, with the ultimate goal of successful aging for older people with HIV.

Abstract Objective. To investigate synchrotron-based proton pencil beam scanning (PBS) beam delivery time (BDT) using novel continuous scanning mode. Approach. A BDT calculation model was developed for the Hitachi particle therapy system. The model was validated against the measured BDT of 36 representative clinical proton PBS plans with discrete spot scanning (DSS) in the current Hitachi proton therapy system. BDTs were calculated with the next generation using Mayo Clinic Florida system operating parameters for conventional DSS, and novel dose driven continuous scanning (DDCS). BDTs of DDCS with and without Break Spots were investigated. Main results. For DDCS without Break Spots, the use of Stop Ratio to control the transit dose largely reduced the beam intensity and consequently, severely prolonged the BDT. DDCS with Break Spots was able to maintain a sufficiently high beam intensity while controlling transit dose. In DDCS with Break Spots, tradeoffs were made between beam intensity and number of Break Spots. Therefore, BDT decreased with increased beam intensity but reached a plateau for beam intensity larger than 10 MU s −1 . Averaging over all clinical plans, BDT was reduced by 10% for DDCS with Break Spots compared to DSS. Significance. DDCS with Break Spots reduced BDT. DDCS has the potential to further reduce BDT under the ideal scenario which requests both stable beam intensity extraction and accurately modelling the transit dose. Further investigation is warranted.

As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization.

AbstractPurposeTo evaluate the relative biological effectiveness (RBE)‐weighted dose to the heart and to estimate RBE uncertainties when assuming a constant RBE of 1.1, for breast cancer patients receiving intensity‐modulated proton therapy (IMPT). Further, to study the impact of RBE uncertainties on the risk of an acute coronary event (ACE).Material and methodsWe analyzed 20 patients who received IMPT to either the left breast (n = 10) or left chest wall (n = 10) and regional lymph nodes. The Monte Carlo simulation engine, MCsquare, was used to simulate the dose‐averaged linear energy transfer (LETd) map. The RBE‐weighted dose to the heart and its substructures was calculated using three different RBE models. The risk of ACE was estimated per its linear relationship with mean heart dose (MHD) as established by Darby et al.ResultsThe median MHD increased from 1.33 GyRBE assuming an RBE of 1.1 to 1.64, 1.87, and 1.99 GyRBE when using the RBE‐weighted dose models. The median values (and ranges) of the excess absolute risk of ACE were 0.4% (0.1%–0.8%) when assuming an RBE of 1.1, and 0.6% (0.2%–1.0%), 0.6% (0.2%–1.1%), and 0.7% (0.2%–1.1%) with the RBE‐weighted models. For our patient cohort, the maximum excess absolute risk of ACE increased by 0.3% with the RBE‐weighted doses compared to the constant RBE of 1.1, reaching an excess absolute ACE risk of 1.1%. The interpatient LETd variation was small for the relevant high‐dose regions of the heart.ConclusionAll three RBE models predicted a higher biological dose compared to the clinical standard dose assuming a constant RBE of 1.1. An underestimation of the biological dose results in underestimation of the ACE risk. Analyzing the voxel‐by‐voxel biological dose and the LET map alongside clinical outcomes is warranted in the development of a more accurate normal‐tissue complication probability model.