Analyzable Format Research Articles

Global research trends in the treatment of squamous cell carcinoma over the past decade: A bibliometric analysis

ObjectiveThis study aims to identify research trends and hot spots in the treatment of Squamous Cell Carcinoma (SCC) over the past decade using bibliometric analysis. MethodsData were extracted from the Web of Science Core Collection, including Science Citation Index Expanded (SCI-Expanded), Social Sciences Citation Index (SSCI), and Emerging Sources Citation Index (ESCI). The data underwent manual cleaning to remove inaccuracies and irrelevancies, followed by transformation into an analyzable format via the VOSviewer software. This tool facilitated the visualization of co-occurrence networks and keyword maps, highlighting the relationships and the prominence of research themes. ResultsA total of 46448 authors from 7374 institutions across 108 countries contributed to the literature, reflecting a broad international effort. The study documented a consistent increase in SCC-related publications up to 2020, with some variability in subsequent years. Notably, the United States, Germany, China, the United Kingdom, and France were predominant in this research area. The University of Texas MD Anderson Cancer Center and the University of Pittsburgh were leading contributors in terms of publication volume and citation impact. Key journals included ‘Oral Oncology’ and ‘Clinical Cancer Research’, which were central to the dissemination of high-impact research. Our keyword analysis identified three major research clusters focused on molecular mechanisms, clinical treatment strategies, and emerging interests in immunotherapeutic approaches. ConclusionsThe extensive collaboration and the increasing publication trend underscore the growing global commitment to advancing SCC treatment. The high level of engagement from top institutions and the concentration of research in influential journals reflect the field's dynamic evolution towards innovative and effective treatment modalities. This study provides a valuable overview for researchers, guiding future studies towards areas of high impact and emerging trends in SCC treatment. The findings advocate for enhanced focus on personalized medicine and combination therapies, which are poised to improve outcomes for SCC patients.

Machine learning for prediction of ventricular arrhythmia episodes from intracardiac electrograms of automatic implantable cardioverter-defibrillators

BackgroundDespite effectiveness of the implantable cardioverter-defibrillator (ICD) in saving patients with life-threatening ventricular arrhythmias (VAs), the temporal occurrence of VA after ICD implantation is unpredictable. ObjectiveThe study aimed to apply machine learning (ML) to intracardiac electrograms (IEGMs) recorded by ICDs as a unique biomarker for predicting impending VAs. MethodsThe study included 13,516 patients who received Biotronik ICDs and enrolled in the CERTITUDE registry between January 1, 2010, and December 31, 2020. Database extraction included IEGMs from standard quarterly transmissions and VA event episodes. The processed IEGM data were pulled from device transmissions stored in a centralized Home Monitoring Service Center and reformatted into an analyzable format. Long-range (baseline or first scheduled remote recording), mid-range (scheduled remote recording every 90 days), or short-range predictions (IEGM within 5 seconds before the VA onset) were used to determine whether ML-processed IEGMs predicted impending VA events. Convolutional neural network classifiers using ResNet architecture were employed. ResultsOf 13,516 patients (male, 72%; age, 67.5 ± 11.9 years), 301,647 IEGM recordings were collected; 27,845 episodes of sustained ventricular tachycardia or ventricular fibrillation were observed in 4467 patients (33.0%). Neural networks based on convolutional neural networks using ResNet-like architectures on far-field IEGMs yielded an area under the curve of 0.83 with a 95% confidence interval of 0.79–0.87 in the short term, whereas the long-range and mid-range analyses had minimal predictive value for VA events. ConclusionIn this study, applying ML to ICD-acquired IEGMs predicted impending ventricular tachycardia or ventricular fibrillation events seconds before they occurred, whereas midterm to long-term predictions were not successful. This could have important implications for future device therapies.

Cytoreductive surgery plus hyperthermic intraoperative peritoneal chemotherapy for people with peritoneal metastases from colorectal, ovarian or gastric origin: A systematic review of randomized controlled trials.

There is uncertainty in the relative benefits and harms of hyperthermic intraoperative peritoneal chemotherapy (HIPEC) when added to cytoreductive surgery (CRS) +/- systemic chemotherapy or systemic chemotherapy alone in people with peritoneal metastases from colorectal, gastric, or ovarian cancers. We searched randomized controlled trials (RCTs) in the medical literature until April 14, 2022 and applied methods used for high-quality systematic reviews. We included a total of eight RCTs (seven RCTs included in quantitative analysis as one RCT did not provide data in an analyzable format). All comparisons other than ovarian cancer contained only one trial. For gastric cancer, there is high uncertainty about the effect of CRS+HIPEC+systemic chemotherapy. For stage III or greater epithelial ovarian cancer undergoing interval cytoreductive surgery, CRS+HIPEC+systemic chemotherapy probably decreases all-cause mortality compared to CRS+systemic chemotherapy. For colorectal cancer, CRS+HIPEC+systemic chemotherapy probably results in little to no difference in all-cause mortality and may increase the serious adverse events proportions compared to CRS +/- systemic chemotherapy, but probably decreases all-cause mortality compared to fluorouracil-based systemic chemotherapy alone. The role of CRS+HIPEC in gastric peritoneal metastases is uncertain. CRS+HIPEC should be standard of care in women with stage III or greater epithelial ovarian cancer undergoing interval CRS. CRS+systemic chemotherapy should be standard of care for people with colorectal peritoneal metastases, with HIPEC given only as part of a RCT focusing on subgroups and regimes. CRD42019130504.

Using real-world data to predict health outcomes-The prediction design: Application and sample size planning.

The gold standard for investigating the efficacy of a new therapy is a (pragmatic) randomized controlled trial (RCT). This approach is costly, time-consuming, and not always practicable. At the same time, huge quantities of available patient-level control condition data in analyzable format of (former) RCTs or real-world data (RWD) are neglected. Therefore, alternative study designs are desirable. The design presented here consists of setting up a prediction model for determining treatment effects under the control condition for future patients. When a new treatment is intended to be tested against a control treatment, a single-arm trial for the new therapy is conducted. The treatment effect is then evaluated by comparing the outcomes of the single-arm trial against the predicted outcomes under the control condition. While there are obvious advantages of this design compared to classical RCTs (increased efficiency, lower cost, alleviating participants' fear of being on control treatment), there are several sources of bias. Our aim is to investigate whether and how such a design-the prediction design-may be used to provide information on treatment effects by leveraging external data sources. For this purpose, we investigated under what assumptions linear prediction models could be used to predict the counterfactual of patients precisely enough to construct a test and an appropriate sample size formula for evaluating the average treatment effect in the population of a new study. A user-friendly R Shiny application (available at: https://web.imbi.uni-heidelberg.de/PredictionDesignR/) facilitates the application of the proposed methods, while a real-world application example illustrates them.

Spatiotemporal analysis of environmental and physiographic factors related to malaria in Bareilly district, India.

ObjectivesThe aim of this study was to explore the spatiotemporal clustering of reported malaria cases and to study the effects of various environmental and physiographic factors on malaria incidence in Bareilly district, Uttar Pradesh, India.Methods Malaria surveillance data were collected from the state health department and cleaned into an analyzable format. These data were analyzed along with meteorological, physiographic, and 2019 population data, which were obtained from the Indian Meteorological Department, National Aeronautics and Space Administration web portal, the Bhuvan platform of the Indian Space Research Organization, and the 2011 Census of India.ResultsIn total, 46,717 malaria cases were reported in Bareilly district in 2019, of which 25.99% were Plasmodium vivax cases and 74.01% were P. falciparum cases. The reported malaria cases in the district showed clustering, with significant spatial autocorrelation (Moran’s I value=0.63), and space-time clustering (p<0.01). A significant positive correlation was found between monthly malaria incidence and the monthly mean temperature (with a lag of 1−2 months) and rainfall (with a lag of 1 month). A significant negative correlation was detected between the elevation of blocks (i.e., intermediate-level administrative districts) and annual malaria reporting.Conclusion The presence of space-time clustering of malaria cases and its correlation with meteorological and physiographic factors indicate that routine spatial analysis of the surveillance data could help control and manage malaria outbreaks in the district.

Abstract IA09: Data science techniques for understanding your cancer catchment area

Abstract Clinical and population health researchers have the opportunity to create combined datasets for clinical cancer research that are vastly larger and, therefore, offer the potential to be more accurate and useful than those in traditional clinical research environments. The difficulty in creating these datasets is in cleaning and validating the data from disparate sources into an easily analyzable format. In addition, given the emphasis on cancer catchment, linking these data with other publically available data resources, e.g., census, environmental exposures, is critical for formulation the most accurate picture of your cancer patient population. Through the Cleveland Institute for Computational Biology, we have developed two data science techniques that (1) hash and match patients across systems/registries and (2) geocode patients at the individual address level. Identifying potential duplicate records is an essential step for the data cleansing as it allows for a more accurate count of unique patients, a more accurate picture of disease burden, and a better view of clinical outcomes of care over time. This identification of duplicate records is complicated by privacy rules adopted by many countries, such as the Health Insurance Portability and Accountability Act (HIPAA). Individually identifiable health information must be masked from parties that do not have viewing permission. In order to simplify the process of cleaning duplicate medical records and generating a unique patient identifier, thereby retaining HIPAA protections, we have created an easy-to-use software tool called “The De Duplicate and De Identify Research Engine” or “DeDeRE,” which includes 4 services: hashing, matching, reporting, and optimization. The DeDeRE software was developed to provide a solution for integrating and de-duplicating patient records across disparate datasets in order to allow the resulting datasets to be combined into larger sets while still protecting patient privacy. The DeDeRE software was developed in 2018 and used specifically as a test platform for the purpose of determining duplicate patients in multiple U.S. state cancer registries participating in the Surveillance, Epidemiology and End Results (SEER) program. Specifically, by hashing and matching millions of records of multiple state cancer registries, duplicate cases both intrastate and interstate were identified and adjudicated. Cancer health disparities are associated with many factors, including one’s location of residence. Location of residence can be associated with access to healthy food, transportation, access to health services, etc., some of which are known risk factors for specific types of cancer. In Ohio, we have obtained individual-level identified information from the Ohio Cancer Incidence Surveillance System (OCISS) allowing us to geocode all cancer patients in Ohio by individual address. We focus our analysis on the Cleveland Metro area and layer in other available data for our area in order to better understand differences in cancer incidence by location. Both of the data science approaches presented here are being applied in the Cleveland Metro area, allowing us to investigate important questions related to our Cleveland area cancer catchment population. Citation Format: Jill S. Barnholtz-Sloan. Data science techniques for understanding your cancer catchment area [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr IA09.

A state transition framework for patient-level modeling of engagement and retention in HIV care using longitudinal cohort data.

The human immunodeficiency virus (HIV) care cascade is a conceptual model used to outline the benchmarks that reflects effectiveness of HIV care in the whole HIV care continuum. The models can be used to identify barriers contributing to poor outcomes along each benchmark in the cascade such as disengagement from care or death. Recently, the HIV care cascade has been widely applied to monitor progress towards HIV prevention and care goals in an attempt to develop strategies to improve health outcomes along the care continuum. Yet, there are challenges in quantifying successes and gaps in HIV care using the cascade models that are partly due to the lack of analytic approaches. The availability of large cohort data presents an opportunity to develop a coherent statistical framework for analysis of the HIV care cascade. Motivated by data from the Academic Model Providing Access to Healthcare, which has provided HIV care to nearly 200,000 individuals in Western Kenya since 2001, we developed a state transition framework that can characterize patient-level movements through the multiple stages of the HIV care cascade. We describe how to transform large observational data into an analyzable format. We then illustrate the state transition framework via multistate modeling to quantify dynamics in retention aspects of care. The proposed modeling approach identifies the transition probabilities of moving through each stage in the care cascade. In addition, this approach allows regression-based estimation to characterize effects of (time-varying) predictors of within and between state transitions such as retention, disengagement, re-entry into care, transfer-out, and mortality. Copyright © 2017 John Wiley & Sons, Ltd.

Danish Prostate Cancer Registry - methodology and early results from a novel national database.

BackgroundSystematized Nomenclature of Medicine (SNOMED) codes are computer-processable medical terms used to describe histopathological evaluations. SNOMED codes are not readily usable for analysis. We invented an algorithm that converts prostate SNOMED codes into an analyzable format. We present the methodology and early results from a new national Danish prostate database containing clinical data from all males who had evaluation of prostate tissue from 1995 to 2011.Materials and methodsSNOMED codes were retrieved from the Danish Pathology Register. A total of 26,295 combinations of SNOMED codes were identified. A computer algorithm was developed to transcode SNOMED codes into an analyzable format including procedure (eg, biopsy, transurethral resection, etc), diagnosis, and date of diagnosis. For validation, ~55,000 pathological reports were manually reviewed. Prostate-specific antigen, vital status, causes of death, and tumor-node-metastasis classification were integrated from national registries.ResultsOf the 161,525 specimens from 113,801 males identified, 83,379 (51.6%) were sets of prostate biopsies, 56,118 (34.7%) were transurethral/transvesical resections of the prostate (TUR-Ps), and the remaining 22,028 (13.6%) specimens were derived from radical prostatectomies, bladder interventions, etc. A total of 48,078 (42.2%) males had histopathologically verified prostate cancer, and of these, 78.8% and 16.8% were diagnosed on prostate biopsies and TUR-Ps, respectively.Future perspectivesA validated algorithm was successfully developed to convert complex prostate SNOMED codes into clinical useful data. A unique database, including males with both normal and cancerous histopathological data, was created to form the most comprehensive national prostate database to date. Potentially, our algorithm can be used for conversion of other SNOMED data and is available upon request.

AERS spider: an online interactive tool to mine statistical associations in Adverse Event Reporting System

Exploration of the Adverse Event Reporting System (AERS) data by a wide scientific community is limited due to several factors. First, AERS data must be intensively preprocessed to be converted into analyzable format. Second, application of the currently accepted disproportional reporting measures results in false positive signals. We proposed a data mining strategy to improve hypothesis generation with respect to potential associations. By numerous examples, we illustrate that our strategy controls the false positive signals. We implemented a free online tool, AERS spider (www.chemoprofiling.org/AERS). We believe that AERS spider would be a valuable tool for drug safety experts.

Classification Systems for Bacterial Protein-Protein Interaction Document Retrieval

Protein-protein interaction (PPI) networks are essential to understand the fundamental processes governing cell biology. Recently, studying PPI networks becomes possible due to advances in experimental high-throughput genomics and proteomics technologies. Many interactions from such high-throughput studies and most interactions from small-scale studies are reported only in the scientific literature and thus are not accessible in a readily analyzable format. This has led to the birth of manual curation initiatives such as the International Molecular Exchange Consortium (IMEx). The manual curation of PPI knowledge can be accelerated by text mining systems to retrieve PPI-relevant articles (article retrieval) and extract PPI-relevant knowledge (information extraction). In this article, the authors focus on article retrieval and define the task as binary classification where PPI-relevant articles are positives and the others are negatives. In order to build such classifier, an annotated corpus is needed. It is very expensive to obtain an annotated corpus manually but a noisy and imbalanced annotated corpus can be obtained automatically, where a collection of positive documents can be retrieved from existing PPI knowledge bases and a large number of unlabeled documents (most of them are negatives) can be retrieved from PubMed. They compared the performance of several machine learning algorithms by varying the ratio of the number of positives to the number of unlabeled documents and the number of features used.

Opening the black box of cancer surgery quality: WebSMR and the Alberta experience

A web-based synoptic operative report, the WebSMR (Surgical Medical Record), was developed to define and improve the quality of cancer surgery. Surgeons accurately record the essential steps of an operation including important decision-making in an analyzable format. Outcomes can be reviewed with provincial aggregates for quality improvement and maintenance of certification. Future synoptic pathology and follow-up templates will open the "black box" of surgical processes to define quality indicators for the improvement of cancer outcomes.

The Philippine National Ear Institute: Patient and Audiologic Profiles

Background: The Philippine National Ear Institute (PNEI) was created to promote health of hearing and balance among Filipinos. Over the years it, has provided audiologic services to thousands of patients annually and has published relevant hearing and balance research.&#x0D; Objective: To describe the patients served by the PNEI in terms of age, region of origin, occupation, pretest diagnosis, and audiologic results.&#x0D; Methods: &#x0D; Study Design: Cross-sectional study&#x0D; Setting: National tertiary care center&#x0D; Population: All records of patients referred for audiologic testing at PNEI in 2006 were reviewed and encoded into analyzable format.&#x0D; Results: A total of 1,756 patients had audiologic records for review. Median age was 32.5 years, with the age distribution presented according to sex, type of tests done including common reasons for referral, and median threshold levels by frequency. Coverage was national in scope, with most patients coming from the National Capital Region and from Regions III and IVa. Occupation was indicated in 37.8% of the working age group, most of whom were unemployed. The most common pretest diagnosis was chronic otitis media (26.6%), followed by hearing loss of unknown etiology (13.0%) and tinnitus (9.3%). Severity of hearing impairment based on pure tone audiometry was variable, and was presented according to common diagnoses. About 39% of hearing impairment cases were sensorineural, 36% conductive and 25% due to mixed defect. Bilateral Type A ears were found in 45.4% of patients by tympanometry, while 29.3% were bilateral Type B. For otoacoustic emissions, 69.0% were labeled as “refer” in at least one ear.&#x0D; Conclusion: The PNEI is a major national referral center for audiology that holds much promise in developing programs for national surveillance of the hearing status of different sectors in Philippine society.&#x0D; &#x0D; Keywords: Philippine National Ear Institute, Philippines, patient profile, audiology, audiometry, tympanometry, otoacoustic emissions, chronic otitis media