https://www.wileyhealthlearning.com/acs.aspx The concept of frailty has become increasingly recognized as one of the most important issues in health care and health outcomes and is of particular importance in patients with cancer who are receiving treatment with surgery, chemotherapy, and radiotherapy. Because both cancer itself, as well as the therapies offered, can be significant additional stressors that challenge a patient's physiologic reserve, the incidence of frailty in older patients with cancer is especially high—it is estimated that over one‐half of older patients with cancer have frailty or prefrailty. Defining frailty can be challenging, however. Put simply, frailty is a state of extreme vulnerability to stressors that leads to adverse health outcomes. In reality, frailty is a complex, multidimensional, and cyclical state of diminished physiologic reserve that results in decreased resiliency and adaptive capacity and increased vulnerability to stressors. In addition, over 70 different measures of frailty have been proposed. Still, it has been demonstrated that frail patients are at increased risk of postoperative complications, chemotherapy intolerance, disease progression, and death. Although international standardization of frailty cutoff points are needed, continued efforts by oncology physicians and surgeons to identify frailty and promote multidisciplinary decision making will help to develop more individualized management strategies and optimize care for patients with cancer. CA Cancer J Clin 2017;67:362–377. © 2017 American Cancer Society.

Background: Uncertainty regarding comorbid illness, and ability to tolerate aggressive therapy has led to minimal enrollment of elderly cancer patients into clinical trials and often substandard treatment. Increasingly, comorbid illness scales have proven useful in identifying subgroups of elderly patients who are more likely to tolerate and benefit from aggressive therapy. Unfortunately, the use of such scales has yet to be widely integrated into either clinical practice or clinical trials research.

Purpose Automated synthetic computed tomography (sCT) generation based on magnetic resonance imaging (MRI) images would allow for MRI‐only based treatment planning in radiation therapy, eliminating the need for CT simulation and simplifying the patient treatment workflow. In this work, the authors propose a novel method for generation of sCT based on dense cycle‐consistent generative adversarial networks (cycle GAN), a deep‐learning based model that trains two transformation mappings (MRI to CT and CT to MRI) simultaneously. Methods and materials The cycle GAN‐based model was developed to generate sCT images in a patch‐based framework. Cycle GAN was applied to this problem because it includes an inverse transformation from CT to MRI, which helps constrain the model to learn a one‐to‐one mapping. Dense block‐based networks were used to construct generator of cycle GAN. The network weights and variables were optimized via a gradient difference (GD) loss and a novel distance loss metric between sCT and original CT. Results Leave‐one‐out cross‐validation was performed to validate the proposed model. The mean absolute error (MAE), peak signal‐to‐noise ratio (PSNR), and normalized cross correlation (NCC) indexes were used to quantify the differences between the sCT and original planning CT images. For the proposed method, the mean MAE between sCT and CT were 55.7 Hounsfield units (HU) for 24 brain cancer patients and 50.8 HU for 20 prostate cancer patients. The mean PSNR and NCC were 26.6 dB and 0.963 in the brain cases, and 24.5 dB and 0.929 in the pelvis. Conclusion We developed and validated a novel learning‐based approach to generate CT images from routine MRIs based on dense cycle GAN model to effectively capture the relationship between the CT and MRIs. The proposed method can generate robust, high‐quality sCT in minutes. The proposed method offers strong potential for supporting near real‐time MRI‐only treatment planning in the brain and pelvis.

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Purpose We prospectively evaluated the amino acid analogue positron emission tomography radiotracer anti-3-[18F]FACBC compared to ProstaScint® (111In-capromab pendetide) single photon emission computerized tomography-computerized tomography to detect recurrent prostate carcinoma. Materials and Methods A total of 93 patients met study inclusion criteria who underwent anti-3-[18F]FACBC positron emission tomography-computerized tomography plus 111In-capromab pendetide single photon emission computerized tomography-computerized tomography for suspected recurrent prostate carcinoma within 90 days. Reference standards were applied by a multidisciplinary board. We calculated diagnostic performance for detecting disease. Results In the 91 of 93 patients with sufficient data for a consensus on the presence or absence of prostate/bed disease anti-3-[18F]FACBC had 90.2% sensitivity, 40.0% specificity, 73.6% accuracy, 75.3% positive predictive value and 66.7% negative predictive value compared to 111In-capromab pendetide with 67.2%, 56.7%, 63.7%, 75.9% and 45.9%, respectively. In the 70 of 93 patients with a consensus on the presence or absence of extraprostatic disease anti-3-[18F]FACBC had 55.0% sensitivity, 96.7% specificity, 72.9% accuracy, 95.7% positive predictive value and 61.7% negative predictive value compared to 111In-capromabpendetide with10.0%, 86.7%, 42.9%, 50.0% and 41.9%, respectively. Of 77 index lesions used to prove positivity histological proof was obtained in 74 (96.1%). Anti-3-[18F]FACBC identified 14 more positive prostate bed recurrences (55 vs 41) and 18 more patients with extraprostatic involvement (22 vs 4). Anti-3-[18F]FACBC positron emission tomography-computerized tomography correctly up-staged 18 of 70 cases (25.7%) in which there was a consensus on the presence or absence of extraprostatic involvement. Conclusions Better diagnostic performance was noted for anti-3-[18F]FACBC positron emission tomography-computerized tomography than for 111In-capromab pendetide single photon emission computerized tomography-computerized tomography for prostate carcinoma recurrence. The former method detected significantly more prostatic and extraprostatic disease.