Zheng, Dandan

Abstract The establishment of guidelines and curriculum standards for medical physics residency training is a critical component of setting expectations and competencies for the profession. Since the last publication of these standards, residency training has become integrated into the eligibility criteria for most medical physics certification bodies. The rapid growth of medical physics requires periodic review of the curriculum to remove outdated approaches, update established knowledge, and add emerging technologies. The goal of this document is to provide a reference standard for residency training program directors, training mentors, and residents to guide creation and/or optimization of their program's curriculum. The document is intentionally forward‐looking to capture emerging technological advances including hybrid diagnostic and therapeutic delivery systems and applications of image processing. This document is an update of the current residency training curriculum. Version history of residency training guidelines Publication Title Date 2013 Essentials and Guidelines for Clinical Medical Physics Residency Training Programs—AAPM Report No. 249 2006 Essentials and Guidelines for Hospital‐Based Medical Physics Residency Training Programs—Report No. 90 1992 Essentials and Guidelines for Hospital‐Based Medical Physics Residency Training Programs—Report No. 36 The Work Group on Periodic Review of Medical Physics Residency Training (WGMPRT) would like to acknowledge the authors of past versions of this document as they form the basis for this update. It is the sincere hope of the WGMPRT that this report will continue to be used by the medical physics community to guide training in the profession.

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.