Demographic and Viral-Genetic Analyses of COVID-19 Severity in Bahrain Identify Local Risk Factors and a Protective Effect of Polymerase Mutations.

A multitude of demographic, health, and genetic factors are associated with the risk of developing severe COVID-19 following infection by the SARS-CoV-2. There is a need to perform studies across human societies and to investigate the full spectrum of genetic variation of the virus. Using data from 869 COVID-19 patients in Bahrain between March 2020 and March 2021, we analyzed paired viral sequencing and non-genetic host data to understand host and viral determinants of severe COVID-19. We estimated the effects of demographic variables specific to the Bahrain population and found that the impact of health factors are largely consistent with other populations. To extend beyond the common variants of concern in the Spike protein analyzed by previous studies, we used a viral burden approach and detected a protective effect of low-frequency missense viral mutations in the RNA-dependent RNA polymerase (Pol) gene on disease severity. Our results contribute to the survey of severe COVID-19 in diverse populations and highlight the benefits of studying rare viral mutations.

Comparison of radiomic feature aggregation methods for patients with multiple tumors.

Radiomic feature analysis has been shown to be effective at analyzing diagnostic images to model cancer outcomes. It has not yet been established how to best combine radiomic features in cancer patients with multifocal tumors. As the number of patients with multifocal metastatic cancer continues to rise, there is a need for improving personalized patient-level prognosis to better inform treatment. We compared six mathematical methods of combining radiomic features of 3,596 tumors in 831 patients with multiple brain metastases and evaluated the performance of these aggregation methods using three survival models: a standard Cox proportional hazards model, a Cox proportional hazards model with LASSO regression, and a random survival forest. Across all three survival models, the weighted average of the largest three metastases had the highest concordance index (95% confidence interval) of 0.627 (0.595-0.661) for the Cox proportional hazards model, 0.628 (0.591-0.666) for the Cox proportional hazards model with LASSO regression, and 0.652 (0.565-0.727) for the random survival forest model. This finding was consistent when evaluating patients with different numbers of brain metastases and different tumor volumes. Radiomic features can be effectively combined to estimate patient-level outcomes in patients with multifocal brain metastases. Future studies are needed to confirm that the volume-weighted average of the largest three tumors is an effective method for combining radiomic features across other imaging modalities and tumor types.

Comparison of Radiomic Feature Aggregation Methods for Patients with Multiple Tumors.

Background: Radiomic feature analysis has been shown to be effective at modeling cancer outcomes. It has not yet been established how to best combine these radiomic features in patients with multifocal disease. As the number of patients with multifocal metastatic cancer continues to rise, there is a need for improving personalized patient-level prognostication to better inform treatment. Methods: We compared six mathematical methods of combining radiomic features of 3596 tumors in 831 patients with multiple brain metastases and evaluated the performance of these aggregation methods using three survival models: a standard Cox proportional hazards model, a Cox proportional hazards model with LASSO regression, and a random survival forest. Results: Across all three survival models, the weighted average of the largest three metastases had the highest concordance index (95% confidence interval) of 0.627 (0.595-0.661) for the Cox proportional hazards model, 0.628 (0.591-0.666) for the Cox proportional hazards model with LASSO regression, and 0.652 (0.565-0.727) for the random survival forest model. Conclusions: Radiomic features can be effectively combined to establish patient-level outcomes in patients with multifocal brain metastases. Future studies are needed to confirm that the volume-weighted average of the largest three tumors is an effective method for combining radiomic features across other imaging modalities and disease sites.

Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma.

PURPOSE: Extranodal extension (ENE) is a well-established poor prognosticator and an indication for adjuvant treatment escalation in patients with head and neck squamous cell carcinoma (HNSCC). Identification of ENE on pretreatment imaging represents a diagnostic challenge that limits its clinical utility. We previously developed a deep learning algorithm that identifies ENE on pretreatment computed tomography (CT) imaging in patients with HNSCC. We sought to validate our algorithm performance for patients from a diverse set of institutions and compare its diagnostic ability to that of expert diagnosticians. METHODS: We obtained preoperative, contrast-enhanced CT scans and corresponding pathology results from two external data sets of patients with HNSCC: an external institution and The Cancer Genome Atlas (TCGA) HNSCC imaging data. Lymph nodes were segmented and annotated as ENE-positive or ENE-negative on the basis of pathologic confirmation. Deep learning algorithm performance was evaluated and compared directly to two board-certified neuroradiologists. RESULTS: A total of 200 lymph nodes were examined in the external validation data sets. For lymph nodes from the external institution, the algorithm achieved an area under the receiver operating characteristic curve (AUC) of 0.84 (83.1% accuracy), outperforming radiologists' AUCs of 0.70 and 0.71 (P = .02 and P = .01). Similarly, for lymph nodes from the TCGA, the algorithm achieved an AUC of 0.90 (88.6% accuracy), outperforming radiologist AUCs of 0.60 and 0.82 (P < .0001 and P = .16). Radiologist diagnostic accuracy improved when receiving deep learning assistance. CONCLUSION: Deep learning successfully identified ENE on pretreatment imaging across multiple institutions, exceeding the diagnostic ability of radiologists with specialized head and neck experience. Our findings suggest that deep learning has utility in the identification of ENE in patients with HNSCC and has the potential to be integrated into clinical decision making.

Local administration of bone morphogenetic protein-2 and bisphosphonate during non-weight-bearing treatment of ischemic osteonecrosis of the femoral head: an experimental investigation in immature pigs.

BACKGROUND: Non-weight-bearing decreases the femoral head deformity but increases bone resorption without increasing bone formation in an experimental animal model of Legg-Calvé-Perthes disease. We sought to determine if local administration of bone morphogenetic protein (BMP)-2 with or without bisphosphonate can increase the bone formation during the non-weight-bearing treatment in the large animal model of Legg-Calvé-Perthes disease. METHODS: Eighteen piglets were surgically induced with femoral head ischemia. Immediately following the surgery, all animals received an above-the-knee amputation to enforce local non-weight-bearing (NWB). One to two weeks later, six animals received local BMP-2 to the necrotic head (BMP group), six received local BMP-2 and ibandronate (BMP+IB group), and the remaining six received no treatment (NWB group). All animals were killed at eight weeks after the induction of ischemia. Radiographic, microcomputed tomography (micro-CT), and histomorphometric assessments were performed. RESULTS: Radiographic assessment showed that the femoral heads in the NWB, BMP, and BMP+IB groups had a decrease of 20%, 14%, and 10%, respectively, in their mean epiphyseal quotient in comparison with the normal control group. Micro-CT analyses showed significantly higher femoral head bone volume in the BMP+IB group than in the BMP group (p = 0.02) and the NWB group (p < 0.001). BMP+IB and BMP groups had a significantly higher trabecular number (p < 0.01) and lower trabecular separation (p < 0.02) than the NWB group. In addition, the osteoclast number per bone surface was significantly lower in the BMP+IB group compared with the NWB group. Calcein labeling showed significantly higher bone formation in the BMP and BMP+IB groups than in the NWB group (p < 0.05). Heterotopic ossification was found in the capsule of four hips in the BMP+IB group but not in the BMP group. CONCLUSIONS: Administration of BMP-2 with bisphosphonate best decreased bone resorption and increased new bone formation during non-weight-bearing treatment of ischemic osteonecrosis in a pig model, but heterotopic ossification is a concern. CLINICAL RELEVANCE: This preclinical study provides new evidence that BMP-2 with bisphosphonate can effectively prevent the extreme bone loss associated with the non-weight-bearing treatment and increase new bone formation in the femoral head in this animal model of ischemic osteonecrosis.