Background and purpose: Radiation dose to the cardio-pulmonary system is critical for radiotherapy-induced mortality in non-small cell lung cancer. Our goal was to automatically segment substructures of the cardiopulmonary system for use in outcomes analyses for thoracic cancers. We built and validated a multi-label Deep Learning Segmentation (DLS) model for accurate auto-segmentation of twelve cardio-pulmonary substructures. Materials and methods: The DLS model utilized a convolutional neural network for segmenting substructures from 217 thoracic radiotherapy Computed Tomography (CT) scans. The model was built in the presence of variable image characteristics such as the absence/presence of contrast. We quantitatively evaluated the final model against expert contours for a hold-out dataset of 24 CT scans using Dice Similarity Coefficient (DSC), 95th Percentile of Hausdorff Distance and Dose-volume Histograms (DVH). DLS contours of an additional 25 scans were qualitatively evaluated by a radiation oncologist to determine their clinical acceptability. Results: The DLS model reduced segmentation time per patient from about one hour to 10 s. Quantitatively, the highest accuracy was observed for the Heart (median DSC = (0.96 (0.95-0.97)). The median DSC for the remaining structures was between 0.81 and 0.93. No statistically significant difference was found between DVH metrics of the auto-generated and manual contours (p-value 0.69). The expert judged that, on average, 85% of contours were qualitatively equivalent to state-of-the-art manual contouring. Conclusion: The cardio-pulmonary DLS model performed well both quantitatively and qualitatively for all structures. This model has been incorporated into an open-source tool for the community to use for treatment planning and clinical outcomes analysis.

Purpose To describe a nonlinear finite element analysis method by using magnetic resonance (MR) images for the assessment of the mechanical competence of the hip and to demonstrate the reproducibility of the tool. Materials and Methods This prospective study received institutional review board approval and fully complied with HIPAA regulations for patient data. Written informed consent was obtained from all subjects. A nonlinear finite element analysis method was developed to estimate mechanical parameters that relate to hip fracture resistance by using MR images. Twenty-three women (mean age ± standard deviation, 61.7 years ± 13.8) were recruited from a single osteoporosis center. To thoroughly assess the reproducibility of the finite element method, three separate analyses were performed: a test-retest reproducibility analysis, where each of the first 13 subjects underwent MR imaging on three separate occasions to determine longitudinal variability, and an intra- and interoperator reproducibility analysis, where a single examination was performed in each of the next 10 subjects and four operators independently performed the analysis two times in each of the subjects. Reproducibility of parameters that reflect fracture resistance was assessed by using the intraclass correlation coefficient and the coefficient of variation. Results For test-retest reproducibility analysis and inter- and intraoperator analyses for proximal femur stiffness, yield strain, yield load, ultimate strain, ultimate load, resilience, and toughness in both stance and sideways-fall loading configurations each had an individual median coefficient of variation of less than 10%. Additionally, all measures had an intraclass correlation coefficient higher than 0.99. Conclusion This experiment demonstrates that the finite element analysis model can consistently and reliably provide fracture risk information on correctly segmented bone images. RSNA, 2016 Online supplemental material is available for this article.

Thirty percent of patients with head and neck squamous cell carcinoma (HNSCC) are at least 70 years of age. This number continues to rise as life expectancy continues to increase. Still, older adults with HNSCC remain underrepresented in clinical trials, resulting in ambiguity on optimal management. Older adults are a complex patient population, often requiring increased support due to issues relating to functional and performance status, medical comorbidities, and medication management. Furthermore, in older adults with HNSCC, many of these conditions are independently associated with increased toxicity and worse outcomes. Toxicity in the older adult remains difficult to predict and to understand, and as treatment decisions are based on treatment tolerability, it is essential to understand the toxicities and how to minimize them. Novel predictive scores are being developed specifically for older adults with HNSCC to understand toxicity and to assist in personalized treatment decisions. There are clinical trials presently underway that are investigating shortened radiation courses and novel, less toxic systemic treatments in this population. In the forthcoming sections, we provide a detailed overview of the clinical data, treatment paradigms, and considerations in this population. This review provides a comprehensive overview of existing clinical data and clinical considerations in the older adult head and neck cancer population. Additionally, we provide a detailed overview of pertinent current and ongoing clinical trials, as well as future areas for investigation.

In the past 40 years, the treatment of locally advanced rectal cancer has evolved with the addition of radiotherapy or chemoradiotherapy and providing (neo)adjuvant systemic chemotherapy to major surgery. However, recent trends have focused on improving our ability to risk-stratify patients and tailoring treatment to achieve the best oncologic outcome while limiting the impact on long-term quality of life. Therefore, there has been increasing interest in pursuing a watch-and-wait approach to achieve organ preservation. Several retro- and prospective studies suggest safety of the watch-and-wait approach, though it is still considered controversial due to limited clinical evidence, concerns about tumor regrowth, and subsequent distant progression. To further reduce treatment, MRI risk stratification, together with patient characteristics and patient preferences, can guide personalized treatment and reserve radiation and chemotherapy for a select patient population. Ultimately, improved options for reassessment during neoadjuvant treatment may allow for more adaptive therapy options based on treatment response. This article provides an overview of some major developments in the multimodal treatment of locally advanced rectal cancer. It reviews some relevant, controversial issues of the watch-and-wait approach and opportunities to personally tailor and reduce treatment. It also reviews the overall neoadjuvant treatment, including total neoadjuvant therapy trials, and how to best optimize for a potential complete response. Finally, it provides an algorithm as an example of how such a personalized, tailored, adaptive, and reduced treatment could look like in the future.