Building RadiologyNET: Unsupervised annotation of a large-scale multimodal medical database

Background and objective: The usage of machine learning in medical diagnosis and treatment has witnessed significant growth in recent years through the development of computer-aided diagnosis systems that are often relying on annotated medical radiology images. However, the availability of large annotated image datasets remains a major obstacle since the process of annotation is time-consuming and costly. This paper explores how to automatically annotate a database of medical radiology images with regard to their semantic similarity. Material and methods: An automated, unsupervised approach is used to construct a large annotated dataset of medical radiology images originating from Clinical Hospital Centre Rijeka, Croatia, utilising multimodal sources, including images, DICOM metadata, and narrative diagnoses. Several appropriate feature extractors are tested for each of the data sources, and their utility is evaluated using k-means and k-medoids clustering on a representative data subset. Results: The optimal feature extractors are then integrated into a multimodal representation, which is then clustered to create an automated pipeline for labelling a precursor dataset of 1,337,926 medical images into 50 clusters of visually similar images. The quality of the clusters is assessed by examining their homogeneity and mutual information, taking into account the anatomical region and modality representation. Conclusion: The results suggest that fusing the embeddings of all three data sources together works best for the task of unsupervised clustering of large-scale medical data, resulting in the most concise clusters. Hence, this work is the first step towards building a much larger and more fine-grained annotated dataset of medical radiology images

Hypertension Among HIV-infected Patients in Clinical Care, 1996–2013

BACKGROUND: Persons infected with human immunodeficiency virus (HIV) are at higher risk for major cardiovascular disease (CVD) events than uninfected persons. Understanding the epidemiology of major traditional CVD risk determinants, particularly hypertension, in this population is needed. METHODS: The study population included HIV-infected patients participating in the UNC CFAR HIV Clinical Cohort from 1996 to 2013. Annual incidence rates of hypertension were calculated. Multivariable Poisson models were fit to identify factors associated with incident hypertension. RESULTS: 3141 patients contributed 21 956 person-years (PY) of follow-up. Overall, 57% patients were black, 28% were women, and the median age was 35 years. Hypertension age-standardized incidence rates increased from 1.68 cases per 100 PYs in 1996 to 5.35 cases per 100 PYs in 2013 (P < .001). In adjusted analyses, hypertension rates were higher among obese patients (incidence rate ratio [IRR] 1.70, 95% confidence interval [CI], 1.43-2.02), and those with diabetes mellitus (IRR 1.44, 95% CI, 1.14-1.83) and renal insufficiency (IRR 1.36, 95% CI, 1.16-1.61), but lower among patients with a CD4 nadir of ≥500 cells/mm(3) (IRR 0.73, 95% CI, .53-1.01). CONCLUSIONS: The incidence of hypertension increased from 1996 to 2013, alongside increases in traditional hypertension risk determinants. Notably, HIV-related immunosuppression and ongoing viral replication may contribute to an increased hypertension risk. Aggressive CVD risk factor management, early HIV diagnosis, linkage to care, antiretroviral therapy initiation, and durable viral suppression, will be important components of a comprehensive primary CVD prevention strategy in HIV-infected persons

HPV-Reactive T-Cell Receptor Expand in Combination Therapy

https://openworks.mdanderson.org/sumexp22/1001/thumbnail.jp

Selection Bias Due to Loss to Follow Up in Cohort Studies

Selection bias due to loss to follow up represents a threat to the internal validity of estimates derived from cohort studies. Over the last fifteen years, stratification-based techniques as well as methods such as inverse probability-of-censoring weighted estimation have been more prominently discussed and offered as a means to correct for selection bias. However, unlike correcting for confounding bias using inverse weighting, uptake of inverse probability-of-censoring weighted estimation as well as competing methods has been limited in the applied epidemiologic literature. To motivate greater use of inverse probability-of-censoring weighted estimation and competing methods, we use causal diagrams to describe the sources of selection bias in cohort studies employing a time-to-event framework when the quantity of interest is an absolute measure (e.g. absolute risk, survival function) or relative effect measure (e.g., risk difference, risk ratio). We highlight that whether a given estimate obtained from standard methods is potentially subject to selection bias depends on the causal diagram and the measure. We first broadly describe inverse probability-of-censoring weighted estimation and then give a simple example to demonstrate in detail how inverse probability-of-censoring weighted estimation mitigates selection bias and describe challenges to estimation. We then modify complex, real-world data from the University of North Carolina Center for AIDS Research HIV clinical cohort study and estimate the absolute and relative change in the occurrence of death with and without inverse probability-of-censoring weighted correction using the modified University of North Carolina data. We provide SAS code to aid with implementation of inverse probability-of-censoring weighted techniques

Comparison of dynamic monitoring strategies based on CD4 cell counts in virally suppressed, HIV-positive individuals on combination antiretroviral therapy in high-income countries: a prospective, observational study

BACKGROUND: Clinical guidelines vary with respect to the optimal monitoring frequency of HIV-positive individuals. We compared dynamic monitoring strategies based on time-varying CD4 cell counts in virologically suppressed HIV-positive individuals. METHODS: In this observational study, we used data from prospective studies of HIV-positive individuals in Europe (France, Greece, the Netherlands, Spain, Switzerland, and the UK) and North and South America (Brazil, Canada, and the USA) in The HIV-CAUSAL Collaboration and The Centers for AIDS Research Network of Integrated Clinical Systems. We compared three monitoring strategies that differ in the threshold used to measure CD4 cell count and HIV RNA viral load every 3–6 months (when below the threshold) or every 9–12 months (when above the threshold). The strategies were defined by the threshold CD4 counts of 200 cells per μL, 350 cells per μL, and 500 cells per μL. Using inverse probability weighting to adjust for baseline and time-varying confounders, we estimated hazard ratios (HRs) of death and of AIDS-defining illness or death, risk ratios of virological failure, and mean differences in CD4 cell count. FINDINGS: 47 635 individuals initiated an antiretroviral therapy regimen between Jan 1, 2000, and Jan 9, 2015, and met the eligibility criteria for inclusion in our study. During follow-up, CD4 cell count was measured on average every 4·0 months and viral load every 3·8 months. 464 individuals died (107 in threshold 200 strategy, 157 in threshold 350, and 200 in threshold 500) and 1091 had AIDS-defining illnesses or died (267 in threshold 200 strategy, 365 in threshold 350, and 459 in threshold 500). Compared with threshold 500, the mortality HR was 1·05 (95% CI 0·86–1·29) for threshold 200 and 1·02 (0·91·1·14) for threshold 350. Corresponding estimates for death or AIDS-defining illness were 1·08 (0·95–1·22) for threshold 200 and 1·03 (0·96–1·12) for threshold 350. Compared with threshold 500, the 24 month risk ratios of virological failure (viral load more than 200 copies per mL) were 2·01 (1·17–3·43) for threshold 200 and 1·24 (0·89–1·73) for threshold 350, and 24 month mean CD4 cell count differences were 0·4 (−25·5 to 26·3) cells per μL for threshold 200 and −3·5 (−16·0 to 8·9) cells per μL for threshold 350. INTERPRETATION: Decreasing monitoring to annually when CD4 count is higher than 200 cells per μL compared with higher than 500 cells per μL does not worsen the short-term clinical and immunological outcomes of virally suppressed HIV-positive individuals. However, more frequent virological monitoring might be necessary to reduce the risk of virological failure. Further follow-up studies are needed to establish the long-term safety of these strategies. FUNDING National Institutes of Health

Recent cancer incidence trends in an observational clinical cohort of HIV-infected patients in the US, 2000 to 2011

Abstract Background In HIV-infected populations in developed countries, the most recent published cancer incidence trend analyses are only updated through 2008. We assessed changes in the distribution of cancer types and incidence trends among HIV-infected patients in North Carolina up until 2011. Methods We linked the University of North Carolina Center for AIDS Research HIV Clinical Cohort, an observational clinical cohort of 3141 HIV-infected patients, with the North Carolina Cancer registry. Cancer incidence rates were estimated across calendar years from 2000 to 2011. The distribution of cancer types was described. Incidence trends were assessed with linear regression. Results Across 15,022 person-years of follow-up, 202 cancers were identified (incidence rate per 100,000 person-years [IR]: 1345; 95% confidence interval [CI]: 1166, 1544). The majority of cancers were virus-related (61%), including Kaposi sarcoma (N = 32) (IR: 213; 95%CI: 146, 301), non-Hodgkin lymphoma (N = 34) (IR: 226; 95%CI: 157, 316), and anal cancer (N = 16) (IR: 107; 95%CI: 61, 173). Non-Hodgkin lymphoma was observed to decrease from 2000 to 2011 (decline of 15 cases per 100,000 person-years per calendar year, 95%CI: -27, -3). No other changes in incidence or changes in incidence trends were observed for other cancers (all P > 0.20). Conclusions We observed a substantial burden of a variety of cancers in this population in the last decade. Kaposi sarcoma and non-Hodgkin lymphoma were consistently two of the greatest contributors to cancer burden across calendar time. Cancer rates appeared stable across calendar years, except for non-Hodgkin lymphoma, which appeared to decrease throughout the study period