bluuebunny/crossref_metadata_2025_split huggingface dataset:
Type of tissue biopsy and outcomes in diffuse large B-cell lymphoma (DLBCL).
Authors: Desai Sanjal , Mwangi Raphael , King Rebecca L. , Maurer Matthew J. , Cerhan James Robert , Feldman Andew L. , Habermann Thomas Matthew , Mou Eric , Farooq Umar , Thompson Carrie A. , Wang Yucai , Witzig Thomas E. , Nowakowski Grzegorz S. 
Published: June 2021

e13569 Background: Progress in understanding the molecular biology of DLBCL has led to development of complex molecular classifications that rely on sequencing methods. These molecular clusters are defined by activation of distinct pathways and may aid in selecting targeted therapy. In the molecular era, tissue requirements in clinical trials have increased to ensure sufficient tissue for molecular testing, potentially resulting in bias for enrollment of patients with larger excisional biopsies (EB). We examined the impact of tissue biopsy method on outcomes of DLBCL in a prospective observational study. Methods: Consecutive adult patients with DLBCL within 9 months from their initial diagnosis were enrolled into the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE). Demographics, clinical variables, type of tissue biopsy (EB vs. core needle biopsy (CNB)) for initial diagnosis and initial treatment were recorded at baseline. Pts were contacted every 6 months from the date of diagnosis for first 3 years and then annually thereafter. Study objectives were event free survival (EFS) and overall survival (OS) by tissue biopsy method. Results: A total of 1061 patients with newly diagnosed DLBCL, from 2002 to 2015, were included. The table lists baseline characteristics of the cohort. 593 (56%) pts underwent EB and 468 (44%) underwent CNB for initial diagnosis. A significantly higher proportion of patients receiving CNB had performance status >=2, advanced stage, high risk disease by international prognostic index (IPI), abnormal lactate dehydrogenase. Median time from diagnosis to treatment initiation (DTI) was significantly shorter in the CNB group (13 days) than EB group (19 days). 2-year EFS (60% vs 75%, HR 0.75 (0.6-0.9), p <0.01) was significantly lower in CNB than EB group. There was a trend towards lower OS (75% vs 80%, HR: 0.8 (0.6-0.95), p =0.049) in CNB than EB group. Conclusions: Patients undergoing CNB are more likely to have higher risk disease, shorter DTI and survival compared to those undergoing EB. A need for large amount of diagnostic tissue in biomarker driven clinical trials may result in disproportional exclusion of high risk DLBCL pts who have inferior outcomes after standard treatment and could potentially benefit from novel agents, resulting in overperformance of the control arm. The need for tissue must be carefully balanced against resulting selection bias.[Table: see text]
Conflicts of interest (COI) among the UpToDate oncology management authors.
Authors: Chengappa Madhuri , Desai Aakash , Go Ronald S. , Poonacha Thejaswi K. 
Published: June 2021

e13573 Background: UpToDate is an evidence based clinical resource designed to provide current clinical information. It is a widely used clinical practice tool providing evidenced based recommendations for diagnosis, management, and therapy. The extent of COI among the UpToDate authors has not been well studied. Our study evaluated the extent of COI of UpToDate authors involved in medical management recommendations for the 10 most common cancers in United States. Methods: We examined the latest November 2020 version of the UpToDate clinical management recommendations for 10 most common cancers. Using disclosure lists, we catalogued COIs for participants in each work group. The categories included: Category I- Grant/Research/Clinical Trial Support; Category II- Consultant/Scientific Advisory Boards/Expert witness; Category III- Speakers Bureau; Category IV- Equity Ownership/Stock Options/Patent holder/Employment/Other Financial Interest; and Category V- Spouse/Domestic Partner/Dependent Potential Conflict. We cataloged the companies/institutions involved in each disclosure. An “episode” describes 1 instance of participation of an individual in 1 company in 1 category for each cancer section. Results: There was a total of 207 authors including section and deputy editors of oncology management section. All authors completed their COI disclosures (100%). 128 (62%) of a total of 207 individuals reported COIs. A total of 1343 episodes were disclosed. We found that each author had an average of 10.49 episodes overall. Authors involved in Category I, II, III, IV & V were 6.3%, 13.5%, 3.2%, 4.7% and 1.6% respectively. 29.36% authors were involved both in Category I and II. A total of 672 company affiliations were associated with COI disclosure. AstraZeneca (6.10%), Merck (4.31%) and Novartis (2.68%) were the companies most frequently reported. The guideline with the maximum episodes (223) was prostate cancer. Conclusions: COIs are prevalent among authors of UpToDate clinical management recommendations. More than ½ of the participants disclosed at least 1 COI, but there appears to be a substantial number of experienced experts without COIs. Further research studies are necessary to determine the impact of these COIs on clinical practice patterns and resource utilization. Distribution of COI and total episodes.[Table: see text]
Advanced tumor progression detection by leveraging EHR based convolutional neural network boosting approaches.
Authors: Wei Guanhao , Zhou Li , Lu Lynn L , Steen Robert G 
Published: June 2021

e13581 Background: Medical research in the areas of immunotherapies and targeted therapies has been drawing heavy attention and investment as the number of approved therapies continue to rise. Advanced findings on cancer treatment based on rich healthcare records help doctors to understand patient’s characteristics. The more accurate one can make a mathematical model predicting the likelihood of disease appearance, progression or treatment initiation, the better a proactive and effective targeting solution can be provided. Methods: Create positive/negative labels for patients who start to receive treatment or not within a time-sensitive window. Based on EHR records as Procedures, Prescription, and Diagnosis (PPD), patient’s feature interrelationship can be determined by a conditional probability matrix. Map PPD data to a well-designed patient-image profiles based a modified genetic algorithm to optimize and measure closeness. A Convolutional Neural Networks (CNN) model is then used to extract characteristics from patients’ feature image and learn the local patterns. Training the CNN model together with other upgraded AIML models is used to enhance overall prediction precision. Results: 144 different aggregated PPD features for patients with Chronic Lymphocytic Leukemia (CLL) were selected from the databases. Overall, the number of positive patients, who received treatment due to disease progression, is around 3% among 60K cases in each cohort, which is an extremely unbalanced dataset that is a challenge task for model training. In practice, we care about the model precision at k, which is the percentage of truly identified patients with k highest prediction scores. Comparing to common machine models like Random Forest, XGBoost and CatBoost, our proposed CNN model boosts the ensemble of baseline model performance in terms of average prediction precision at 1000 from 14.9% to 17.1%, which is about 15% relative percentage increase. Using this approach, many more truly identified patients could potentially receive targeted treatment on time. Patients’ top features and key feature interactions can also be identified as important references. Conclusions: The novel CNN boosting algorithm considers both aggregated PPD feature pairs using the graphical structure, significantly improves prediction model performance, and increases the model interpretability. In summary, the proposed model can help identify more potential patient candidates and determine precise treatment options.
Leveraging machine learning technology to efficiently identify and match patients for precision oncology clinical trials.
Authors: Sachse Laura , Dasari Smriti , Ackermann Marc , Patnaude Emily , OLeary Stephanie , Al-Olimat Hussein , Grigorenko Alexander , Stewart Andrew , Ward AnnaJane , Darmofal Annie , Ballakur Sowmya , Bennett William , Franzen Amy , Blau Sibel , Chandra Abhinav Binod , Nikolinakos Petros , Orsini James Michael , Peguero Julio Antonio , Blackwell Kimberly L. , Cooney Matthew M. 
Published: June 2021

e13588 Background: Pre-screening for clinical trials is becoming more challenging as inclusion/exclusion criteria becomes increasingly complex. Oncology precision medicine provides an exciting opportunity to simplify this process and quickly match patients with trials by leveraging machine learning technology. The Tempus TIME Trial site network matches patients to relevant, open, and recruiting clinical trials, personalized to each patient’s clinical and molecular biology. Methods: Tempus screens patients at sites within the TIME Trial Network to find high-fidelity matches to clinical trials. The patient records include documentation submitted alongside NGS orders as well as electronic medical records (EMR) ingested through EMR Integrations. While Tempus-sequenced patients were automatically matched to trials using a Tempus-built matching application, EMR records were run through a natural language processing (NLP) data abstraction model to identify patients with an actionable gene of interest. Structured data were analyzed to filter to patients that lack a deceased date and have an encounter date within a predefined time period. Tempus abstractors manually validated the resulting unstructured records to ensure each patient was matched to a TIME Trial at a site capable of running the trial. For all high-level patient matches, a Tempus Clinical Navigator manually evaluated other clinical criteria to confirm trial matches and communicated with the site about trial options. Results: Patient matching was accelerated by implementing NLP gene and report detection (which isolated 17% of records) and manual screening. As a result, Tempus facilitated screening of over 190,000 patients efficiently using proprietary NLP technology to match 332 patients to 21 unique interventional clinical trials since program launch. Tempus continues to optimize its NLP models to increase high-fidelity trial matching at scale. Conclusions: The TIME Trial Network is an evolving, dynamic program that efficiently matches patients with clinical trial sites using both EMR and Tempus sequencing data. Here, we show how machine learning technology can be utilized to efficiently identify and recruit patients to clinical trials, thereby personalizing trial enrollment for each patient.[Table: see text]
Postmarketing requirements for drugs approved by the Food and Drug Administration for the treatment of solid tumor cancers, 2010-2019.
Authors: Jeune-Smith Yolaine , Zettler Marjorie E. , Fortier Stephanie , Rupard Skyler , Gajra Ajeet , Feinberg Bruce A. 
Published: June 2021

e13597 Background: In recent years, efforts to improve the efficiency and speed of drug development and approval have driven a surge of Food and Drug Administration (FDA) approvals for cancer drugs. For many cancer therapies, the serious or life-threatening nature of the condition and unmet medical need confers eligibility for expedited programs. Many cancers are also rare diseases, and the increasing use of precision medicine principles to define cancer types further contributes to smaller trial sizes. With limited clinical evidence at the time of approval, cancer drugs may be subject to a greater burden of postmarketing requirements (PMRs). We analyzed PMRs for solid tumor therapies approved by the FDA over the past decade. Methods: The FDA’s novel drug approvals (2010-2019) were reviewed to identify drugs receiving primary approval for solid tumor indications. Approval letters were accessed via the Drugs@FDA database and analyzed for PMRs required under accelerated approval (AA), the Pediatric Research Equity Act (PREA) and the FDA Amendments Act of 2007 Section 505(o) (505(o)). Data are presented using descriptive statistics. Results: A total of 60 drugs received primary approval from the FDA for solid tumor indications between 2010 and 2019 (20 [33.3%] received AA, 33 [55.0%] received orphan designation, and 45 [75.0%] received Fast Track or Breakthrough Therapy designation). The proportion of drugs receiving AA doubled between the period 2010-2014 and 2015-2019 (Table). Of the 60 drugs approved, 52 (86.7%) received a total of 180 PMRs. All 20 drugs approved under AA received PMRs, with a total of 25 PMRs issued under AA. Data from new clinical trials were required for 22 (88.0%) of the 25 PMRs. No PMRs were issued under PREA. Additional safety data required under 505(o) comprised the largest proportion of PMRs; 155 total PMRs (86.1% of all PMRs) were issued for 45 (75.0%) of the drug approvals. Pharmacokinetic or other clinical safety data were required for 96 (61.9%) of the 155 PMRs. Conclusions: More than three-quarters of the cancer drugs approved for the treatment of solid tumors in the past 10 years were issued PMRs, with the majority requiring new safety data. The results of this study indicate that PMRs represent a critical mechanism by which FDA collects safety and efficacy for solid tumor therapies, and underscore the importance of PMR fulfillment. Post-marketing requirements (PMRs) for solid tumor drugs approved 2010-2019.[Table: see text]
Effect of digital technologies on mammographic screening in the Republic of Kazakhstan.
Authors: Saktaganov Murat , Kaidarova Dilyara , Zhylkaidarova Alma , Suleimenova Danara 
Published: June 2021

e13601 Background: Since 2011 breast cancer (BC) has been the most common cancer in the Republic of Kazakhstan (RK) with the incidence of 43 per 100,000, and the third most common cause for cancer deaths with 9.4 deaths per 100,000 in 2019. Nationwide population-based screening program was implemented in the RK in 2008 inviting women aged 50–60 years every 2 years, with the subsequent expansion of the target group age to 40-70 years in 2018. The majority of screening mammography units was screen-film units at a time of program’s initiation. Gradual conversion to digital mammography (DM) has begun in 2014. This paper examines the impact of digital technologies on BC screening results on the example of the largest city in the RK - Almaty. Methods: We analyzed BC screening indicators such as incidence, mortality, proportion of early stage cancers, cancer detection rate. In order to assess the influence of transition to DM we divided the studied period to two stages: stage A (2008-2013)- when screen-film mammography (SFM) was widely used, and stage B (2014-2019)- when DM became prevalent in screening. Results: 598,058 women underwent BC screening from 2008 to 2019 in Almaty, 969 cancers were detected. At a time of program introduction in 2008 all 10 units used for screening were SFM units. The number of SFM units steadily increased from 10 in 2008 to 19 in 2014 and to 30 in 2019. Proportion of DM units concomitantly grew from 0% in 2008 to 47% in 2014 and to 83% in 2019. During the stage A 282 BC cases were detected (cancer detection rate 1 per 100,000 investigations). During the period B 687 BC cases were identified (cancer detection rate 2 per 100,000 investigations). Proportion of early stage cancers (0-I stage) rose from 26.7% in stage A to 41.0% in stage B. Widespread implementation of screening in 2008 led to a steady increase in BC incidence from 51 per 100,000 in 2008 to 63 per 100,000 in 2014. A noticeable decline in BC incidence has been evident since 2015 following 7 years of BC screening with incidence rate as low as 47.2 per 100,000 in 2019. Proportion of early stage BC among all newly diagnosed cases increased from 73.6% in 2008 to 86.6% in 2019. The average proportion of early stage BC was 77.5% for period A compared to 85,9% for stage B. Mortality rate was also affected by screening- it dropped by 38% from 20.6 per 100,000 in 2008 to 12.8 per 100,000 in 2019. Conclusions: BC state in Almaty reflects the general situation with BC in the whole country. Mammographic screening positively affected both BC incidence and mortality in Almaty. Increased utilization of digital technologies in screening resulted in rise in the proportion of early (0-I) stage BC by 21.4% and in the proportion of I-II stage BC by 13%. We suggest that, based on these results, further digitalization of mammographic screening should be strongly advised and digital to screen-film units ratio should be one of the main indicators for BC screening quality audit.
Key features to ensure sustainability of a tele-oncology program at a national cancer center.
Authors: Rariy Chevon , Truesdale Lynn , Greenman Jennifer , Schink Julian C. 
Published: June 2021

e13613 Background: Prior to COVID-19, there were few telehealth services offered in the oncology specialty area. During the pandemic, we at a national cancer center rapidly scaled our oncology telehealth program to meet the needs of our patients. At the peak of the pandemic, telehealth initially served as a risk-mitigation strategy providing continued care to our patients while socially distancing, yet additionally, we have embedded necessary processes in place to create a sustained a telehealth oncology program that encompasses a hybrid model including face to face visits augmented with telehealth visits, where appropriate. Here we describe the key telehealth program features that have enabled a national cancer center to evolve into a hybrid model of oncology care across its five geographically distinct hospitals. Methods: Transitioning into a sustainable hybrid telehealth model of care involves a foundation of clinical leadership and partnerships among multiple departments. The telehealth oncology program leaders collaborate with the operations, technology, finance, clinical care teams, and governance council to implement telehealth growth initiatives and nimbly troubleshoot and ameliorate issues. A concierge service provides telehealth readiness checks to ensure timely resolution of issues. Workflows are followed to standardize processes. Telehealth use-cases ensure patients who need on-site services keep their in-person appointments, allowing telehealth visits for symptom management to enhance patient outcomes. A provider education session includes training on telehealth technology and “webside manner” training to ensure we preserve the personal touch with our patients in each telehealth encounter. Program data is regularly collected and reviewed to track the program’s success and opportunities for improvement. Results: After the initial peak of telehealth visits driven by the COVID pandemic, we continue to see a sustained 10-fold increase in service volume versus Jan/Feb 2020. There were 25,328 total telehealth visits from Mar. 2020-Jan. 2021, 75 clinical trial visits between July-Dec. 2020, and 848 readiness check escalations from Nov. 2020-Dec. 2021. Service lines expanded from 2 to 33, including growing rural health partnerships and a home chemotherapy infusion model. Use-cases expanded to bridge to on-site care, rapid initial visits, preop/postop checks, symptom management, and surveillance. Press Ganey patient satisfaction rates are as high as 92% and 90% of providers reported overall satisfaction with the telehealth consultations. Conclusions: Our key program features have enabled the growth and success of our enterprise tele-oncology program. One of the most promising indicators of success is the positive provider and patient satisfaction rates. Telehealth provides an effective means to provide a bridge to onsite cancer care even for our complex oncology patients.
Telehealth to increase access to transplant survivorship care for allogeneic stem cell transplant recipients regardless of distance to transplant center or neighborhood income.
Authors: Ahmed Nausheen , Fitzmaurice Sarah , Morey Cherie , Torres Tania , Beckman Darla , Ganguly Siddhartha , Bansal Rajat , Rodriguez Liza , Abdelhakim Haitham , Singh Anurag K , Abhyankar Sunil H. , Shune Leyla , Kribs Robert , Devine Clint , Hanses Jennifer , Mushtaq Muhammad Umair , McGuirk Joseph 
Published: June 2021

e13615 Background: Transplant Survivorship Clinic at our institution serves to improve outcomes and overall health of allogeneic transplant survivors. The COVID19 pandemic allowed for growth of telemedicine in our program. We examine the patterns of use of telehealth and hypothesize that the use of telemedicine allowed continued access to care compared to the era prior to availability. We compared our transplant survivorship clinic data from July- December 2020, when telehealth was well established and compared to July-December 2019. Methods: All patients seen by the survivorship team for end of treatment visits, graft versus host disease assessments and survivorship visits annually between July-December 2019 and July-December 2020 were included. Their zip codes were used to get direct distance to survivorship clinic, average drive time, driving distance and average household income as in zip-codes.com database. Results: Total number of office visits in July-Dec 2019 was 163 visits (0% via telehealth) and in July-Dec 2020 was 228 (66.2% via telehealth). All encounters (telehealth and office visits) were lower in July and August 2020 compared to July and August 2019 but higher in months of September -December 2020 compared to 2019. Comparing all encounters during 7/2019-12/2019 to 7/2020-12/2020, there was no statistically significant difference in median age (58yr vs 60 yr), gender (males: 58% vs 59%), race (non-white: 11% vs 8.7%), median years from transplant (4yr vs 3 yrs), median income of patient neighborhood ($63,735 vs $60,465) and average drive time to center from zip code (40 min vs 51min). Comparison of patients who chose telehealth vs. office visit is summarized in table. While there was no statistically significant difference in age when comparing all encounters in 2019 and 2020, those who chose telehealth were younger (55yrs vs 60yrs, p=0.003). Conclusions: There were higher patient encounters in the 2020 period compared to 2019. Most of these 2020 encounters were via were telehealth, demonstrating the role of telehealth in increasing access. Younger patients appeared to choose telehealth, but telehealth served patients up to the age of 77 yrs. Utilization of telehealth appeared to be irrespective of demographics such as gender, neighborhood income and driving distance to the center. Comparison of telehealth vs office visit for July-December 2019 and 2020.[Table: see text]