This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine.

Pancreatic cancer is an aggressive disease that typically presents late with poor outcomes, indicating a pronounced need for early detection. In this study, we applied artificial intelligence methods to clinical data from 6 million patients (24,000 pancreatic cancer cases) in Denmark (Danish National Patient Registry (DNPR)) and from 3 million patients (3,900 cases) in the United States (US Veterans Affairs (US-VA)). We trained machine learning models on the sequence of disease codes in clinical histories and tested prediction of cancer occurrence within incremental time windows (CancerRiskNet). For cancer occurrence within 36 months, the performance of the best DNPR model has area under the receiver operating characteristic (AUROC) curve = 0.88 and decreases to AUROC (3m) = 0.83 when disease events within 3 months before cancer diagnosis are excluded from training, with an estimated relative risk of 59 for 1,000 highest-risk patients older than age 50 years. Cross-application of the Danish model to US-VA data had lower performance (AUROC = 0.71), and retraining was needed to improve performance (AUROC = 0.78, AUROC (3m) = 0.76). These results improve the ability to design realistic surveillance programs for patients at elevated risk, potentially benefiting lifespan and quality of life by early detection of this aggressive cancer.

PURPOSE: Venous thromboembolism (VTE), especially pulmonary embolism (PE) and lower extremity deep vein thrombosis (LE-DVT), is a serious and potentially preventable complication for patients with cancer undergoing systemic therapy.METHODS: Using retrospective data from patients diagnosed with incident cancer from 2011-2020, we derived a parsimonious risk assessment model (RAM) using least absolute shrinkage and selection operator regression from the Harris Health System (HHS, n = 9,769) and externally validated it using the Veterans Affairs (VA) health care system (n = 79,517). Bootstrapped c statistics and calibration curves were used to assess external model discrimination and fit. Dichotomized risk strata using integer scores were created and compared against the Khorana score (KS).RESULTS: Incident VTE and PE/LE-DVT at 6 months occurred in 590 (6.2%) and 437 (4.6%) patients in HHS and 4,027 (5.1%) and 3,331 (4.2%) patients in the VA health care system. Assessed at the time of systemic therapy initiation, the new RAM included components of the KS with the modified cancer subtype, cancer staging, systemic therapy class, history of VTE, history of paralysis/immobility, recent hospitalization, and Asian/Pacific Islander race. The c statistic was 0.71 in HHS and 0.68 in the VA health care system (compared with 0.65 and 0.60, respectively, for KS). Furthermore, the new RAM appropriately reclassified 28% of patients and increased the proportion of VTEs in the high-risk group from 37% to 68% in the validation data set.CONCLUSION: The novel RAM stratified patients with cancer into a high-risk group with 8%-10% cumulative incidence of VTE and 7% PE/LE-DVT at 6 months ( v 3% and 2%, respectively, in the low-risk group). The model had improved performance over the original KS and doubled the number of VTE events in the high-risk stratum. We encourage additional external validation from prospective studies. [Media: see text]

Effective, low-cost therapeutics are needed to prevent and treat COVID-19. Severe COVID-19 disease is linked to excessive inflammation. Disulfiram is an approved oral drug used to treat alcohol use disorder that is a potent anti-inflammatory agent and an inhibitor of the viral proteases. We investigated the potential effects of disulfiram on SARS-CoV-2 infection and disease severity in an observational study using a large database of clinical records from the national US Veterans Affairs healthcare system. A multivariable Cox regression adjusted for demographic information and diagnosis of alcohol use disorder revealed a reduced risk of SARS-CoV-2 infection with disulfiram use at a hazard ratio of 0.66 (34% lower risk, 95% confidence interval 24–43%). There were no COVID-19 related deaths among the 188 SARS-CoV-2 positive patients treated with disulfiram, in contrast to 5–6 statistically expected deaths based on the untreated population (P = 0.03). Our epidemiological results suggest that disulfiram may contribute to the reduced incidence and severity of COVID-19. These results support carefully planned clinical trials to assess the potential therapeutic effects of disulfiram in COVID-19.

PURPOSE: Increasingly broad patient groups are being treated with immune checkpoint inhibitors (ICIs) in clinical practice, but few studies have assessed their usage and outcomes in large, comprehensive real-world cohorts. We identified patients who received ICIs in the Veterans Affairs (VA) health care system and described patient characteristics and survival outcomes across multiple indications. METHODS: We conducted a retrospective analysis using electronic health record data from VA facilities nationwide. Overall survival (OS) from time of ICI initiation for key indications was estimated by Kaplan-Meier. We also stratified OS by frailty status, as defined by a surrogate index developed in VA data. For select indications, we further compared outcomes to historic and concurrent control patients treated with standard-of-care regimens at the VA. RESULTS: We identified 11,888 patients who were treated with ICIs and determined the cancer type and indication for which they were treated. The cohort is enriched for patient groups that are under-represented in pivotal clinical trials (PCTs), including older, non-White, and/or higher disease burdened patients. Generally, OS observed in the VA cohort is lower than that reported in PCTs. After stratifying VA patients by frailty status, OS among nonfrail patients is more similar to OS reported in PCTs for some indications. Compared with internal VA control cohorts, patients treated with ICIs generally exhibited longer OS for all indications considered. CONCLUSION: This study describes ICI outcomes across multiple tumor types in a real-world population at the VA. For most indications, real-world survival outcomes are observed to be lower than those reported in PCTs, but patients receiving ICIs still achieve longer survival relative to patients receiving standard of care.