Prognosis and risk of suicide after cancer diagnosis.

INTRODUCTION: Suicide rates are elevated after cancer diagnosis. Existential distress caused by awareness of one's impending death is well-described in patients with cancer. The authors hypothesized that suicide risk is associated with cancer prognosis, and the impact of prognosis on suicide risk is greatest for populations with higher baseline suicide risk. METHODS: The authors identified patients (≥16 years old) with newly diagnosed cancers from 2000 to 2019 in the Surveillance, Epidemiology, and End Results database, representing 27% of US cancers. Multiple primary-standardized mortality ratios (SMR) were used to estimate the relative risk of suicide within 6 months of diagnosis compared to the general US population, adjusted for age, sex, race, and year of follow-up. Suicide rates by 20 most common cancer sites were compared with respective 2-year overall survival rates (i.e., prognosis) using a weighted linear regression model. RESULTS: Among 6,754,704 persons diagnosed with cancer, there were 1610 suicide deaths within 6 months of diagnosis, three times higher than the general population (SMR = 3.1; 95% confidence interval, 3.0-3.3). Suicide risk by cancer site was closely associated with overall prognosis (9.5%/percent survival deficit, R2  = 0.88, p < .0001). The association of prognosis with suicide risk became attenuated over time. For men, the risk of suicide increased by 2.8 suicide deaths per 100,000 person-years (p < .0001) versus 0.3 in women (p < .0001). The risk was also higher for persons ≥60 old and for the White (vs. Black) race. CONCLUSIONS: Poorer prognosis was closely associated with suicide risk early after cancer diagnosis and had a greater effect on populations with higher baseline risks of suicide. This model highlights the need for enhanced psychiatric surveillance and continued research in this patient population.

BOLD asynchrony elucidates tumor burden in IDH-mutated gliomas.

BACKGROUND: Gliomas comprise the most common type of primary brain tumor, are highly invasive, and often fatal. IDH-mutated gliomas are particularly challenging to image and there is currently no clinically accepted method for identifying the extent of tumor burden in these neoplasms. This uncertainty poses a challenge to clinicians who must balance the need to treat the tumor while sparing healthy brain from iatrogenic damage. The purpose of this study was to investigate the feasibility of using resting-state blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) to detect glioma-related asynchrony in vascular dynamics for distinguishing tumor from healthy brain. METHODS: Twenty-four stereotactically localized biopsies were obtained during open surgical resection from ten treatment-naïve patients with IDH-mutated gliomas who received standard-of-care preoperative imaging as well as echo-planar resting-state BOLD fMRI. Signal intensity for BOLD asynchrony and standard-of-care imaging was compared to cell counts of total cellularity (H&E), tumor density (IDH1 & Sox2), cellular proliferation (Ki67), and neuronal density (NeuN), for each corresponding sample. RESULTS: BOLD asynchrony was directly related to total cellularity (H&E, P = 4 × 10-5), tumor density (IDH1, P = 4 × 10-5; Sox2, P = 3 × 10-5), cellular proliferation (Ki67, P = .002), and inversely related to neuronal density (NeuN, P = 1 × 10-4). CONCLUSIONS: Asynchrony in vascular dynamics, as measured by resting-state BOLD fMRI, correlates with tumor burden and provides a radiographic delineation of tumor boundaries in IDH-mutated gliomas.

Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks.

Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery1. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed, pathology workforce4. In the present study, we report a parallel workflow that combines stimulated Raman histology (SRH)5-7, a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNNs, trained on over 2.5 million SRH images, predict brain tumor diagnosis in the operating room in under 150 s, an order of magnitude faster than conventional techniques (for example, 20-30 min)2. In a multicenter, prospective clinical trial (n = 278), we demonstrated that CNN-based diagnosis of SRH images was noninferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% versus 93.9%). Our CNNs learned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors. In addition, we implemented a semantic segmentation method to identify tumor-infiltrated diagnostic regions within SRH images. These results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complementary pathway for tissue diagnosis that is independent of a traditional pathology laboratory.

High Variability in Outcome Reporting Patterns in High-Impact ACL Literature.

BACKGROUND: ACL (anterior cruciate ligament) reconstruction is one of the most commonly performed and studied procedures in modern sports medicine. A multitude of objective and subjective patient outcome measures exists; however, nonstandardized reporting patterns of these metrics may create challenges in objectively analyzing pooled results from different studies. The goal of this study was to document the variability in outcome reporting patterns in high-impact orthopaedic studies of ACL reconstruction. METHODS: All clinical studies pertaining to ACL reconstruction in four high-impact-factor orthopaedic journals over a five-year period were reviewed. Biomechanical, basic science, and imaging studies were excluded, as were studies with fewer than fifty patients, yielding 119 studies for review. Incorporation of various objective and subjective outcomes was noted for each study. RESULTS: Substantial variability in reporting of both objective and subjective measures was noted in the study cohort. Although a majority of studies reported instrumented laxity findings, there was substantial variability in the type and method of laxity reporting. Most other objective outcomes, including range of motion, strength, and complications, were reported in <50% of all studies. Return to pre-injury level of activity was infrequently reported (24% of studies), as were patient satisfaction and pain assessment following surgery (8% and 13%, respectively). Of the patient-reported outcomes, the International Knee Documentation Committee (IKDC), Lysholm, and Tegner scores were most often reported (71%, 63%, and 42%, respectively). CONCLUSIONS: Substantial variability in outcome reporting patterns exists among high-impact studies of ACL reconstruction. Such variability may create challenges in interpreting results and pooling them across different studies.