Cancer research funding in South Asia.

BACKGROUND: The rising burden of cancer significantly influences the global economy and healthcare systems. While local and contextual cancer research is crucial, it is often limited by the availability of funds. In South Asia, with 1.7 million new cancer cases and 1.1 million deaths due to cancer in 2020, understanding cancer research funding trends is pivotal. METHODS: We reviewed funded cancer studies conducted between January 1, 2003, and Dec 31, 2022, using ClinicalTrials.gov, International Cancer Research Partnership (ICRP) Database, NIH World RePORT, and WHO International Clinical Trials Registry Platform (ICTRP). We included funded studies related to all cancer types, conducted in South Asian countries, namely Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka. RESULTS: We identified 6561 funded cancer studies from South Asia between 2003 and 2022, increasing from 400 studies in 2003-2007 to 3909 studies in 2018-2022. India had the highest number of funded cancer studies, while Afghanistan, Bhutan, and the Maldives had minimal or no funded cancer research output. Interventional studies (67.3%) were the most common study type funded. The most common cancer sites funded were breast (17.8%), lung (9.9%), oropharyngeal (6.2%), and cervical (5.0%) cancers. On the WHO ICTRP, international funding agencies contributed to a majority of studies (57.5%), except in India where local funding agencies (58.2%) funded more studies. CONCLUSION: This study identified gaps in research funding distribution across cancer types and geographic areas in South Asia. This data can be used to optimize the distribution of cancer research funding in South Asia, fostering equitable advancement in cancer research.

Barriers and Facilitators to Collecting Surgical Outcome Data in Low- and Middle-Income Countries: An International Survey.

Background: Perioperative data are essential to improve the safety of surgical care. However, surgical outcome research (SOR) from low- and middle-income countries (LMICs) is disproportionately sparse. We aimed to assess practices, barriers, facilitators, and perceptions influencing the collection and use of surgical outcome data (SOD) in LMICs. Methods: An internet-based survey was developed and disseminated to stakeholders involved in the care of surgical patients in LMICs. The Performance of Routine Information Systems Management framework was used to explore the frequency and relative importance of organizational, technical, and behavioral barriers. Associations were determined using χ 2 and ANOVA analyses. Results: Final analysis included 229 surgeons, anesthesia providers, nurses, and administrators from 36 separate LMICs. A total of 58.1% of individuals reported that their institution had experience with collection of SOD and 73% of these reported a positive impact on patient care. Mentorship and research training was available in <50% of respondent's institutions; however, those who had these were more likely to publish SOD (P = 0.02). Sixteen barriers met the threshold for significance of which the top 3 were the burden of clinical responsibility, research costs, and accuracy of medical documentation. The most frequently proposed solutions were the availability of an electronic data collection platform (95.3%), dedicated research personnel (93.2%), and access to research training (93.2%). Conclusions: There are several barriers and facilitators to collection of SOD that are common across LMICs. Most of these can be addressed through targeted interventions and are highlighted in this study. We provide a path towards advancing SOR in LMICs.

Barriers to Surgical Outcomes Research in Low- and Middle-Income Countries: A Scoping Review.

INTRODUCTION: Systematic collection and analysis of surgical outcomes data is a cornerstone of surgical quality improvement. Unfortunately, there remains a dearth of surgical outcomes data from low- and middle-income countries (LMICs). To improve surgical outcomes in LMICs, it is essential to have the ability to collect, analyze, and report risk-adjusted postoperative morbidity and mortality data. This study aimed to review the barriers and challenges to developing perioperative registries in LMIC settings. METHODS: We conducted a scoping review of all published literature on barriers to conducting surgical outcomes research in LMICs using PubMed, Embase, Scopus, and GoogleScholar. Keywords included 'surgery', 'outcomes research', 'registries', 'barriers', and synonymous Medical Subject Headings derivatives. Articles found were subsequently reference-mined. All relevant original research and reviews published between 2000 and 2021 were included. The performance of routine information system management framework was used to organize identified barriers into technical, organizational, or behavioral factors. RESULTS: Twelve articles were identified in our search. Ten articles focused specifically on the creation, success, and obstacles faced during the implementation of trauma registries. Technical factors reported by 50% of the articles included limited access to a digital platform for data entry, lack of standardization of forms, and complexity of said forms. 91.7% articles mentioned organizational factors, including the availability of resources, financial constraints, human resources, and lack of consistent electricity. Behavioral factors highlighted by 66.6% of the studies included lack of team commitment, job constraints, and clinical burden, which contributed to poor compliance and dwindling data collection over time. CONCLUSIONS: There is a paucity of published literature on barriers to developing and maintaining perioperative registries in LMICs. There is an immediate need to study and understand barriers and facilitators to the continuous collection of surgical outcomes in LMICs.

Deep learning applications in neuro-oncology.

Deep learning (DL) is a relatively newer subdomain of machine learning (ML) with incredible potential for certain applications in the medical field. Given recent advances in its use in neuro-oncology, its role in diagnosing, prognosticating, and managing the care of cancer patients has been the subject of many research studies. The gamut of studies has shown that the landscape of algorithmic methods is constantly improving with each iteration from its inception. With the increase in the availability of high-quality data, more training sets will allow for higher fidelity models. However, logistical and ethical concerns over a prospective trial comparing prognostic abilities of DL and physicians severely limit the ability of this technology to be widely adopted. One of the medical tenets is judgment, a facet of medical decision making in DL that is often missing because of its inherent nature as a "black box." A natural distrust for newer technology, combined with a lack of autonomy that is normally expected in our current medical practices, is just one of several important limitations in implementation. In our review, we will first define and outline the different types of artificial intelligence (AI) as well as the role of AI in the current advances of clinical medicine. We briefly highlight several of the salient studies using different methods of DL in the realm of neuroradiology and summarize the key findings and challenges faced when using this nascent technology, particularly ethical challenges that could be faced by users of DL.