bluuebunny/crossref_metadata_2025_split huggingface dataset:
Democratizing cancer innovation by reducing cost and complexity of cancer models.
Authors: Punjani Zeenia , Patel Zeelu H. , Charania Areesha A. , Patel Heli K. , Papachristou Georgios , Contreras Carlo M. , Tsung Allan , Budhwani Karim I. 
Published: June 2022

e13503 Background: Despite advances in high-throughput screening, combinatorial chemistry, databanks, and computational models, drug R&D remains expensive and slow, often over a decade. It is estimated that pre-human costs are > 30% of cost of approved drugs: $1.5 billion of $3.9 billion. Another study estimates that pharma spends $34.4 billion annually on preclinical research. However, 90% of drugs effective in pre-human studies fail in human trials. A reason for this dissonance is the inadequacy of current assays and methods for evaluating efficacy of new treatments during preclinical evaluation. Cell culture dishes fail to recapitulate the in vivo microenvironment, making cells far from physiologically relevant. Animal models, though physiologically relevant, are limited indicators of success in subsequent human trials. Predicting which preclinical formulations will most likely succeed in clinical trials, by matching them against patient biopsy specimens, could reduce cost and time-to-market of new therapeutics. Objective: The aim of this study is to develop an ex vivo cancer model that can predict clinical success of preclinical formulations by screening efficacy on live patient biopsy tissue. Methods: Softlithography with SU-8 photoresist and polydimethylsiloxane (PDMS) was used to fabricate microporous thin membranes with uniform porosity. Electrospinning with collagen and Nylon was used for fabricating nanofibrous membranes with well-interconnected, non-woven, non-uniform porosity. Multicellular biomimetic healthy and tumorous microtissue was cultured with pancreatic and colorectal cell lines. Permeability assays were used to validate barrier and transport functions, scanning electron microscopy was used to validate morphology, and fluorescent and confocal microscopy were used to observe migration, invasion, and metastasis. Results: Permeability assay confirmed transport and barrier functions wherein small molecules (10 kDa) diffused while larger molecules (500 kDa) were prevented from flowing. Migration and invasion were observed when microfluidic circuits connected S2VP10-Luc “tumorous” tissue upstream from “healthy” vascular and muscle tissue. Moreover, subpopulations of invasive cells showed a more aggressive proliferation profile compared to subpopulations of the same cell-line cancer cells that remained at the original “tumorous” site. Conclusions: Microfluidic ex vivo cancer models have the potential to transform cancer treatment from bench – as clinical trial simulation tools to narrow down lead and backup candidates – to bedside – as predictive personalized companion diagnostics to guide treatment strategy – along the drug discovery to market roadmap for novel therapeutic agents.
International consensus guideline for anticancer drug dosing in kidney dysfunction (ADDIKD): A standardized approach to assessing kidney function in cancer patients and its application to anticancer drug dosing.
Authors: Sandhu Geeta , Adattini Josephine , O'Neill Niamh , Armstrong Gordon Evangeline , Kelly Aisling , Shingleton Julia , Liauw Winston S. , Ward Robyn L. ,   Working Group for the International Consensus Guideline for Anticancer Drug Dosing in Kidney Dysfunction
Published: June 2022

e13518 Background: Anticancer drug dosing recommendations in kidney dysfunction are often empirical, based on non-standardised creatinine assays calculated via the Cockcroft-Gault equation, and lack applicability to globally accepted kidney dysfunction classifications. ADDIKD aims to provide a standardised approach to assessing kidney function in cancer patients, and to apply evidence and consensus-based recommendations to anticancer drug dosing in kidney dysfunction. Methods: An expert international multidisciplinary working group was established to develop anticancer drug dosing recommendations. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach was used to critically appraise the quality and strength of evidence and formulate recommendations. The working group participated in workshops to achieve > 80% consensus for a standardised approach to assessing and classifying levels of kidney function, its application to anticancer drug dosing in kidney dysfunction, and dosing recommendations for individual drugs. Upcoming public consultation will refine these recommendations and facilitate acceptance as an international benchmark. Results: Three key recommendations formed the basis of ADDIKD: Application of estimated glomerular filtration rate via the Chronic Kidney Disease-Epidemiology Collaboration equation (eGFRCKD-EPI) to guide the assessment of kidney function, except where directly measured glomerular filtration rate (mGFR) is clinically necessary. Where the anticancer drug dose is dependent on kidney function and mGFR is not clinically necessary, eGFRCKD-EPI is suggested to guide dosing. Application of the Kidney Disease Improving Global Outcomes (KDIGO) Chronic Kidney Disease categories to guide stepwise dose adjustments of anticancer drugs in kidney dysfunction. A review of 2263 published articles and 177 registered product information monographs enabled 127 GRADE assessments by the working party, resulting in evidence and consensus-based dosing recommendations for 59 anticancer drugs. 'Quick reference’ dosing tables incorporated a traffic light system for alerting clinicians to caution around levels of kidney function and specific patient risk factors for consideration. Conclusions: eGFRCKD-EPI is the most accurate and convenient method for assessing kidney function in diverse populations (including cancer patients) and accounts for the standardisation of the creatinine assay. ADDIKD’s expert acceptance of this standardised approach, together with anticancer drug dosing recommendations in kidney dysfunction, provides a practical tool to establish clinically relevant dosing in this patient population.
Relationship between race and anemia in women undergoing surgery for gynecologic cancer.
Authors: Cain Abigail , Galvan-Turner Valerie Bianca 
Published: June 2022

e13529 Background: Anemia is associated with poor outcomes after gynecologic surgery and there is a paucity of data whether race modify its impact on outcome. The goal of this study is to estimate the anemia prevalence in women undergoing surgery for gynecologic cancer and to evaluate the relationship between preoperative anemia and postoperative morbidity and understand whether race and ethnicity modify this relationship. Methods: We performed a retrospective cohort study of patient’s undergoing surgery for a gynecologic malignancy between 2014 and 2019 using data from the National Surgical Quality Improvement Program. Multivariable logistic regression was used to determine adjusted odds for post operative morbidity with anemia. Interaction terms were entered into the model to test for effect modification by race and ethnicity. Results: We found that Black women were twice as likely to suffer from anemia then Caucasian women. In Caucasian women 79% had normal Hgb level, 16% mild anemia, 5% moderate anemia, and 0.5% with severe anemia. However, in Black women only 59% had normal Hgb level, with 30% having mild anemia, 10% moderate anemia, and 1% with severe anemia. We also found that the adjusted odds ratio of postoperative morbidity was higher in Black women with moderate and severe anemia than in Caucasian women. (Table). Logistic regression models to predict the probability of mortality for women with varying stages of anemia. Showing a difference in probability of morbidity between Black women and Caucasian women of undergoing gynecologic oncologic surgery. Conclusions: In this cohort of 56,240 patients who had surgery for a gynecologic malignancy we found that 24% had anemia. when treated may improve postoperative outcomes were more likely to have anemia than Caucasian women in this cohort. Black women had higher rates of postoperative morbidity at all levels of anemia, demonstrating continued disparities in care. Preoperative anemia is a modifiable risk factor that when treated may improve postoperative outcomes.[Table: see text]
Predicting immune checkpoint inhibitor-related pneumonitis using patient medical information.
Authors: Lippenszky Levente , Kiss Zoltán , LeNoue-Newton Michele , Micheel Christine , Wolber Jan , Osterman Travis John , Park Ben Ho 
Published: June 2022

e13566 Background: Immune checkpoint inhibitors (ICI) have improved outcomes in tumor types allowing subgroups of patients to have longer, higher quality lives. However, potential life-threatening immunotoxicities can arise in susceptible patients, including pneumonitis. Identifying patients at high risk of immunotoxicity can help patients understand potential adverse events, improve clinical trial cohort selection, and inform therapy selection in clinical settings. Here, we use electronic health record (EHR) data to build a binary classification model that predicts the probability of developing pneumonitis after the first ICI administration. Methods: We utilized real-world EHR-derived structured and unstructured data from > 2,700 patients from Vanderbilt University Medical Center obtained prior to December 31, 2018. Unstructured data were transformed into structured variables by expert curators, including labels for pneumonitis episodes following ICI initiation. Feature engineering involved aggregating lab measurements over a 60-day time window before the first ICI; other features (conditions, smoking status, etc.) used a 1-year window. To build a small, easily deployable model and assess its performance robustly, we utilized a sequential process. In each step, we decided between two versions of a random forest model, one with the original feature set (M1) and one extended with a candidate feature (M2). We identified candidate features using 90% of the data. We performed nested cross-validation on this partition and compared the inner loop results. If M2 was significantly better, we tested whether it performed better on the 10% partition. If it did, we chose M2 and assessed its performance on the outer loop. This procedure was created as our dataset was rather small and noisy, which is typical for EHR-derived data. Results: All-cause pneumonitis incidence following ICI initiation was 8.4%. Our final model includes only six features: frequency of lung-related ICD-10 codes, frequency of C34 code, frequency of C78 code, smoking status, interaction between smoking and C34/C78 indicators, and median of blood oxygen saturation. This model achieved a mean AUC of 0.66 (SD: 0.07). Our analysis on the outer loop predictions showed that selecting 50% of patients with the lowest predicted probabilities reduced the occurrence of pneumonitis in the cohort to 5%, compared to 8.4%, when we select patients randomly. The model achieved a mean positive predictive value of 0.3 and negative predictive value of 0.96. Conclusions: We utilized a real-world EHR dataset to identify patterns in patient medical history that could predict the development of pneumonitis. We demonstrated that a small number of easily obtainable clinical covariates can result in meaningful predictions. This model illustrates potential future use for identifying the patients with the highest and lowest risks for pneumonitis during treatment.
Sequential EHR-based dynamic graph network for multiple myeloma detection and feature interaction investigation.
Authors: Wei Guanhao , Zhou Li , Lu Lynn L. , Romano Marc 
Published: June 2022

e13591 Background: Electronic Health Records (EHR) provide the possibility of leveraging modern machine learning algorithms for disease detection prediction. Since a patient’s medical journey contains a complex pattern and it is difficult to identify interactive feature impacts, we propose to reconstruct patients’ attributes at different time points into dynamic graphs. Then we can use graph-based learning algorithms to enhance the predictive precision and identify leading feature interactions that reveal key services transitions. Methods: Sequential EHR like diagnosis (Dx), procedure (Px) and prescription (Rx) can be transformed into dynamic temporal graphs with nodes representing attributes, services, and medical status and edges representing patient-object interactions. Also, graph based sequential recommendation can use structured historical behavior trajectories as input, aiming to predict patient’s next behavior. Then, the model can investigate patients’ time-sensitive features interactively identifying a potential disease and then output a probability of getting that disease. Dynamic graphs can also help to track patient’s medical treatment/drug use service transitions. Results: We applied our method on a Newly Diagnosed Multiple Myeloma (NDMM) dataset consisting of 1.5 million candidates’ longitudinal medical claims from 2017 to 2020 with diagnosis rate around 0.6%. In this data set, patients’159 aggregated Dx, Rx and Px features, together with 30 demographic attributes are updated monthly. The model was trained on 4 monthly data cohorts from the 12/2017 to 03/2019 period and its performance was tested on 9 monthly cohorts from year 2020. The 1000 patients scored as having highest risk were evaluated for the predictive precision which was around 6.5% This precision compares most favorably relative to the reported 0.76% lifetime risk of getting multiple myeloma in US. We also captured the top, service transitions in NDMM patients having 30% more frequency than those in overall populations. Conclusions: The novel dynamic graph-based learning approach on a large-scale EHR dataset demonstrates the advancement of the disease detection methodology. While the outcome of this model significantly improves the accuracy of predictive precision, it also helps identify more meaningful key drivers attributing to disease. The incremental new patients identified through this novel model will provide a better basis for the evolving identification solution within Biopharma industry.
The use of ARMCan app to improve the quality of life in breast cancer patients with chemobrain.
Authors: Koehler Abigail N. , Rothman Yehudit , Gomez Enriquez Diego , Kirschner Michelle , Shatz Rhonna , Mahammedi Abdelkader , Rebola Claudia B. , Sengupta Soma 
Published: June 2022

e13619 Background: Breast cancer is the most diagnosed cancer in women with approximately 2 million women diagnosed in 2018. The 10-year survival rate is 78%. Breast cancer patients who undergo chemotherapy treatment can suffer neurocognitive impairment resulting in significant effects on their cognitive functioning. Chemotherapy-related dysfunction is known as “chemobrain”. Chemobrain is the basis of significant neurological morbidities in the breast cancer population. It causes difficulty in people being able to carry out activities of daily living and impacts people’s livelihoods and well-being. Studies have shown that music-based intervention can improve cognitive function. Methods: Since music-based interventions to specifically address chemo-related cognitive deficits have not been explored, we are conducting a pilot feasibility study to beta test the novel Active Receptive Music for Cancer patients application (ARMCan) which allows its user to have an interactive musical experience. Patients are randomized into two arms. In receptive listening, users listen to a genre of their choice, and in active listening, users add sounds over a base trace. At the beginning of the study, patients will be given a FACT-Cog questionnaire and undergo MRI imaging. The study invention will be conducted for six months where patients will use the ARMCan app for 15 minutes daily. They will be followed for one year post-study intervention with FACT-Cog questionnaires given directly after the cessation of the study intervention and at months six and 12 of follow up. An additional MRI will be taken at month 12 of follow up. Results: FACT-Cog and MRI data will be used to analyze the results of active versus receptive music therapy on chemotherapy-related cognition dysfunction. Conclusions: We are currently accruing patients to beta test this interactive application, which may promote executive function recovery in breast cancer patients with chemobrain and advance clinical care in breast cancer survivorship.
National cancer centers addressing climate change: An overview and analysis of organizations' sustainability plans.
Authors: Maniar Ashray , Lichter Katie , Husain Marium , Kishan Raina , Hantel Andrew 
Published: June 2022

e13623 Background: Climate change is one of the largest threats to human health and greenhouse gas (GHG) emissions are a substantial component of climate change. In the U.S., the healthcare industry accounts for an estimated 8.5% of GHG emissions, equating to the loss of 470,000 disability-adjusted life-years annually. Cancer care encapsulates a significant portion of hospital resources and climate change has disproportionate effects on cancer patients. Therefore, it is imperative and urgent that cancer centers actively engage in reducing healthcare related GHG emissions. In this systematic analysis and comprehensive review, we reviewed publicly accessible sustainability plans of National Cancer Institute (NCI)-designated Cancer Centers and their affiliated organizations to understand the involvement Cancer Centers have in their organization's sustainability efforts. Methods: We reviewed 64 NCI designated Cancer Centers, using the first 20 results of a Google search with terms: “Hospital X AND (climate action plan OR climate change OR sustainability plan OR carbon neutral)” and hospital homepages to identify publicly accessible sustainability plans for each organization. We reviewed the plans, performed a Google search/Cancer Center homepage search and messaged sustainability plan developers to identify involvement of the hospital and Cancer Center. We identified which hospitals assigned sustainability personnel. We performed a student’s t-test to assess for a correlation between incidence of lung cancer in a state and presence of a sustainability plan for that state’s Cancer Center. Data was collected by one reviewer, independently validated by a separate reviewer and differences were resolved by a joint data review. Results: Amongst the 65 Cancer Centers and organizations reviewed, 55 (36 university, 12 hospital, 7 joint) had publicly available sustainability plans. Of which, 31% (11/36) of university plans explicitly mentioned an affiliated hospital as part of the sustainability plan. Two Cancer Centers (3%) had independent sustainability plans and 7 (11%) centers mentioned sustainability on their homepage. 77% (23/30) of hospitals with sustainability plans identified sustainability personnel. We did not find an association between the existence of Cancer Center sustainability plan and that state’s lung cancer incidence rates (p = 0.932). Conclusions: In this review and analysis of NCI-designated Cancer Center sustainability plans, we found that while most centers’ affiliated organizations have publicly accessible sustainability plans, the majority (86%) of centers do not independently report on their sustainability efforts. These findings highlight a pressing need to increase engagement between Cancer Centers and their organizations regarding sustainability plans, and a need for greater transparency in publicizing sustainability plans.
Practice consolidation among U.S. medical oncologists over time.
Authors: Milligan Michael , Erfani Parsa , Orav E. John , Brooks Gabriel A. , Lam Miranda 
Published: June 2022

e13627 Background: Physician practices across the U.S. are increasingly merging with hospitals and other practices. The extent of such “practice consolidation” varies by specialty and across geography but has not been well characterized within medical oncology. Methods: Utilizing Medicare data, we identified all medical oncologists (MO) who billed for Medicare services each year between 2015 and January 2022. We associated each physician to one or more unique practices via organizational Taxpayer Identification Numbers (TIN), and classified practices by the number of medical oncologists—including solo (1 MO), small (2-10 MOs), medium (11-24 MOs), and large (25+ MOs) practices. We defined the extent of regional healthcare markets according to Hospital Referral Regions (HRRs), and calculated levels of practice consolidation using the Herfindahl-Hirschman Index (HHI). The HHI is a commonly used measure of consolidation, calculated by summing the square of each practice’s market share. HHI values range from 0 to 1 with higher numbers indicating greater consolidation and less competition among practices. Finally, we generated a multivariable linear regression model to determine which regional market factors were associated with changes in levels of medical oncology practice consolidation over time. Results: Between 2015 and 2022, the number of practices with MOs in the U.S. declined 18.0% from 2,774 to 2,276, while the number of practicing MOs increased 14.5% from 11,727 to 13,433. The median medical oncology practice HHI increased from 0.3204 to 0.3480. Over time the proportion of solo practices fell (48.4% in 2015, 43.9% in 2022) while the proportion of medium and large practices increased (medium: 5.7% to 8.4%; large: 2.7% to 4.5%). By 2022, large practices had grown to employ a significantly larger proportion of all medical oncologists (33.6% to 43.7%, p < 0.001). Between 2015 and 2022, the proportion of practices solely comprised of MOs (as opposed to multispecialty practices) decreased from 40.0% to 28.2%. On multivariable analysis, HRRs with higher levels of hospital consolidation (p < 0.001), greater numbers of hospital beds per 1,000 persons (p = 0.02), and a lesser degree of baseline medical oncology practice consolidation in 2015 (p < 0.001) experienced more consolidation during the study period. Conclusions: Medical oncologists across the U.S. are increasingly working in larger practices. By 2022, more than 40% of all practicing MOs were employed at the largest 5% of practices. Consolidation of medical oncology practices occurred more readily in regions with higher levels of hospital consolidation and a greater supply of hospital beds, suggesting that integration into hospital systems may be a leading source of consolidation. Further study is required to determine the impact of practice consolidation on the cost, quality, and accessibility of cancer care.