The Association of Diabetes and Obesity With Prostate Cancer Progression: HCaP-NC

BACKGROUND: The role of race in modifying the association among diabetes, obesity, and prostate cancer (CaP) progression is not well studied. We evaluated diabetes and obesity in association with time to CaP progression in White Americans (Whites) and Black Americans (Blacks). METHODS: Our study sample consisted of 363 White and 284 Black research participants from the Health Care Access and CaP Treatment in North Carolina (HCaP-NC) cohort. The association between self-reported diabetes or obesity and CaP progression (mean follow-up time approximately 5 years) was assessed using Cox proportional hazards modeling, with adjustment for potential confounders. Stratum-specific hazard ratio (HR) estimates for Whites and Blacks were evaluated. RESULTS: Self-reported diabetes was not associated with CaP progression in the cohort as a whole (HR: 0.86, 95%CI: 0.54, 1.35), or among racially defined groups (Whites, HR: 1.03, 95%CI: 0.50, 2.13 or Blacks, HR: 0.77, 95%CI: 0.43, 1.39). Obesity was positively associated with CaP progression among Whites, in models including (HR: 1.79, 95%CI: 1.08, 2.97), and excluding (HR: 1.80, 95%CI: 1.09, 2.96) diabetes as a covariate. No association was observed between obesity and CaP progression in Blacks or the cohort as whole. CONCLUSIONS: Self-reported diabetes was not associated with CaP progression In HCaP-NC. Obesity was associated with CaP progression only among White research participants

Automated assessment of COVID-19 reporting and data system and chest CT severity scores in patients suspected of having COVID-19 using artificial intelligence

Background: The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe with alarming speed, morbidity, and mortality. Immediate triage of patients with chest infections suspected to be caused by COVID-19 using chest CT may be of assistance when results from definitive viral testing are delayed.Purpose: To develop and validate an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the COVID-19 Reporting and Data System (CO-RADS) and CT severity scoring systems.Materials and Methods: The CO-RADS AI system consists of three deep-learning algorithms that automatically segment the five pulmonary lobes, assign a CO-RADS score for the suspicion of COVID-19, and assign a CT severity score for the degree of parenchymal involvement per lobe. This study retrospectively included patients who underwent a nonenhanced chest CT examination because of clinical suspicion of COVID-19 at two medical centers. The system was trained, validated, and tested with data from one of the centers. Data from the second center served as an external test set. Diagnostic performance and agreement with scores assigned by eight independent observers were measured using receiver operating characteristic analysis, linearly weighted kappa values, and classification accuracy.Results: A total of 105 patients (mean age, 62 years +/- 16 [standard deviation]; 61 men) and 262 patients (mean age, 64 years +/- 16; 154 men) were evaluated in the internal and external test sets, respectively. The system discriminated between patients with COVID-19 and those without COVID-19, with areas under the receiver operating characteristic curve of 0.95 (95% CI: 0.91, 0.98) and 0.88 (95% CI: 0.84, 0.93), for the internal and external test sets, respectively. Agreement with the eight human observers was moderate to substantial, with mean linearly weighted k values of 0.60 +/- 0.01 for CO-RADS scores and 0.54 +/- 0.01 for CT severity scores.Conclusion: With high diagnostic performance, the CO-RADS AI system correctly identified patients with COVID-19 using chest CT scans and assigned standardized CO-RADS and CT severity scores that demonstrated good agreement with findings from eight independent observers and generalized well to external data. (C) RSNA, 2020Cardiovascular Aspects of Radiolog

Serial Analysis of Circulating Tumor Cells in Metastatic Breast Cancer Receiving First-Line Chemotherapy

BACKGROUND: We examined the prognostic significance of circulating tumor cell (CTC) dynamics during treatment in metastatic breast cancer (MBC) patients receiving first-line chemotherapy. METHODS: Serial CTC data from 469 patients (2202 samples) were used to build a novel latent mixture model to identify groups with similar CTC trajectory (tCTC) patterns during the course of treatment. Cox regression was used to estimate hazard ratios for progression-free survival (PFS) and overall survival (OS) in groups based on baseline CTCs, combined CTC status at baseline to the end of cycle 1, and tCTC. Akaike information criterion was used to select the model that best predicted PFS and OS. RESULTS: Latent mixture modeling revealed 4 distinct tCTC patterns: undetectable CTCs (56.9% ), low (23.7%), intermediate (14.5%), or high (4.9%). Patients with low, intermediate, and high tCTC patterns had statistically significant inferior PFS and OS compared with those with undetectable CTCs (P < .001). Akaike Information Criterion indicated that the tCTC model best predicted PFS and OS compared with baseline CTCs and combined CTC status at baseline to the end of cycle 1 models. Validation studies in an independent cohort of 1856 MBC patients confirmed these findings. Further validation using only a single pretreatment CTC measurement confirmed prognostic performance of the tCTC model. CONCLUSIONS: We identified 4 novel prognostic groups in MBC based on similarities in tCTC patterns during chemotherapy. Prognostic groups included patients with very poor outcome (intermediate + high CTCs, 19.4%) who could benefit from more effective treatment. Our novel prognostic classification approach may be used for fine-tuning of CTC-based risk stratification strategies to guide future prospective clinical trials in MBC

Phylogenetic Positions of the Bothitrematidae and Neocalceostomatidae (Monopisthocotylean Monogeneans) Inferred from 28S rDNA Sequences

A molecular phylogeny was inferred from newly obtained partial (D1 domain) 28S rDNA gene sequences of Bothitrema bothi (Bothitrematidae), Neocalceostoma sp. (Neocalceostomatidae), Bravohollisia sp. (Ancyrocephalidae), and other already available sequences of Ancyrocephalidae, Anoplodiscidae, Pseudodactylogyridae, and Sundanonchidae, with the Diplectanidae as outgroup. Bothitrema, Anoplodiscus, and Sundanonchus formed a very robust clade that was the sister group to a group that included all other species examined. In this latter group, Neocalceostoma and Thaparocleidus were basal to a clade in which the Ancyrocephalidae and Pseudodactylogyridae were sister groups. Molecular results that suggest inclusion of the families Bothitrematidae, Anoplodiscidae, and Sundanonchidae in the same group partially contradict a previous morphological analysis of Boeger and Kritsky in which the first 2 were placed in the Gyrodactylidea and the third in the Dactylogyridea

Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence

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