bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine learning models for accurate pretreatment prediction of chemotherapy associated LV dysfunction in patients with breast cancer and lymphoma receiving chemotherapy (WF-98213 PREVENT and CCCWFU9912 DETECT IV).
Authors: Shyamsunder Suditi , D'Agostino Ralph , O'Connell Nathaniel S. , Ladd Amy , Weaver Kathryn E. , Lesser Glenn Jay , Hundley William Gregory , Hackney Mary Helen , Melin Susan Anitra , Ge Yaorong 
Published: June 2022

1553 Background: Cancer survivors receiving potentially cardiotoxic chemotherapy are at increased risk for developing left ventricular (LV) dysfunction. We implemented machine learning (ML) models to predict future LV dysfunction in patients with breast cancer or lymphoma scheduled to receive potentially cardiotoxic chemotherapy. Methods: We utilized prospectively collected data from NIH studies R01HL118740 (supported by the Wake Forest NCORP Research Base (UG1CA189824)) and R01CA167821. Data included measurements of LV function and demographic factors before, during, and 24 months after initiating potentially cardiotoxic chemotherapy. The two datasets were used both separately and collectively in the development of multiple ML models including penalized linear regression, support vector machine, and random forest (RF). A data preprocessing step properly handled missing information, data imbalance, and encoding. Hyperparameter tuning was performed using cross validation of training data. The final models were assessed with a 20% hold-out test dataset. Cardiotoxicity was defined as a pre- to 24-month post cancer treatment decline in LV ejection fraction (LVEF) of > 10% or to an absolute value of < 50%. Results: 276 patients were included in ML models (7% men, 93% women; age 52±13 years). The RF model based on the combined dataset had the best performance with a prediction accuracy, sensitivity, and specificity of 0.94, 0.81, and 0.98, respectively. The most important variables assessed pre-treatment as measured by the Gini impurity factor were in descending order, LVEF, global LV circumferential strain, LV end-systolic volume, body mass index, LV stroke volume, LV end-diastolic volume, and LV mass. Conclusions: Prior to cancer treatment, supervised ML methods such as RF models predicted declines in LVEF of > 10% and/or to absolute values below 50% would occur 24 months after initiating chemotherapy for breast cancer or lymphoma. With further improvement and validation using larger datasets, these models may play an important role in cardio-oncology care during and following cancer treatment.
Clinical development of new drugs for adults and children with cancer in 2010-2020: Longitudinal study of investigational drugs.
Authors: Arfe Andrea , Narang Claire , DuBois Steven G. , Reaman Gregory H. , Bourgeois Florence 
Published: June 2022

1563 Background: Many investigational drugs start clinical testing to evaluate potential therapeutic benefits for oncology patients, but few eventually receive FDA approval. Moreover, only a small number is evaluated in pediatric populations, potentially contributing to the paucity of new approved drugs for young patients with cancer. Limited information is available on the development pipeline of investigational drugs, including the range of drug types entering clinical trials, trial phases at which development stalls, or rate of regulatory approval. To inform current clinical development efforts, we characterized the development and outcomes for a comprehensive sample of New Molecular Entities (NMEs) that started clinical testing worldwide in 2010-2015. Methods: We performed a longitudinal study using AdisInsight, a commercial database of global pharmaceutical research and development. This is a comprehensive database of drug development activity, which collects and curates data from trial registries, conference proceedings, journal publications, and press releases. Using these data, we identified all NMEs starting their first clinical trial for an oncology indication in 2010-2015. We followed each NME from the start of its first phase I trial to the end of 2020, and identified all associated trials, final development status, and FDA deliberations. We classified trials as pediatric-eligible if patients aged < 18 years were eligible for participation. We used the Drugs@FDA website to identify all FDA actions, including marketing approvals and requests for pediatric trials under pediatric programs (i.e. BPCA requests or PREA requirements). Results: A total of 572 NMEs started initial phase I clinical trials in 2010-2015. Among these, the most studied classes were small molecules (N, %: 316, 55%), antibodies (148, 26%), and antibody-drug conjugates (44, 8%). Overall, the NMEs were studied in 6,141 clinical trials by the end of 2020, with a median of 3 trials per NME. The highest pre-approval development phase reached by an NME was phase I for 325 (57%), phase II for 153 (27%), and phase III for 94 (16%). Only 39 NMEs (7%) were approved by the FDA by the end of 2020. Among approved NMEs, the median time (range) from start of first phase I trial to date of first approval was 6 (3-10) years. Among all NMEs, only 67 (12%) were tested in pediatric-eligible trials by the end of 2020, and 5 (< 1%) were approved for use in selected pediatric populations. Three of these had been subject to BPCA requests, and all had PREA requirements waived. Conclusions: More efficient clinical development strategies are needed to accelerate the production of new cancer therapies, especially for children. Analyses such as this one should be conducted regularly to help identify areas in need of innovation and to assess the potential impact of regulatory initiatives (e.g. the RACE act, effective since August 2020).
Mobile app in oncology: A pilot survey on Latina patients with gynecological cancers and their perception on utilizing a mobile app.
Authors: Ramirez Paulina , Toy Eugene Patrick , Lababidi Suzanne , Greenberg Harvey , Bracamontes Christina , Jones Angela , Mulla Zuber D. , Brown Jennifer L. 
Published: June 2022

1571 Background: Mobile applications have changed the way that users access information and revolutionized healthcare by allowing patients to educate themselves regarding their diagnosis and treatment. Challenges in developing a mobile health application include patient satisfaction and usage over time. Barriers to usage include trust, personalization, and accessibility. Investigating the patient population’s preferences on app content and ease of use is imperative. The use of mobile applications specifically for Latina Gynecologic Oncology patients undergoing treatment has yet to be investigated. Methods: Fifty-six patients were recruited from the Gynecologic Oncology clinic at an urban academic health sciences center located on the Texas-Mexico border. Cross-sectional analyses were performed. Subjects were asked a series of 10-point Likert scale questions including how comfortable they would feel using medical applications on their smartphones. Linear regression models were fit with this scale score as the outcome. Results: The age of the 56 patients ranged from 28 to 77 years with a mean of 53.9 years (SD: 11.1). Spanish was the preferred language of 53.8% of the patients (28/52). Forty-four subjects were available for the regression analyses. Subjects were asked, “Would you feel comfortable using medical applications on your smartphone,” where 1 represented “not at all comfortable” and 10 represented “very comfortable”. The mean comfort scale score was 7.39 (SD: 2.85). Thirty of the 44 subjects (68.2%) replied “Multiple times per day” to the question about how frequently they use mobile apps on their phone. After controlling for the patient’s age in a regression model, patients who used mobile apps multiple times per day had an average comfort scale score that was 1.75 points higher than that of women who did not use mobile apps on their phone multiple times per day (p = 0.03). After adjusting for the frequency of mobile app use, there was a reduction of 0.11 points (p = 0.002) in the comfort scale score for every one year increase in the patient’s age. Conclusions: Our unique pilot study found a positive association between the frequency of current app use and anticipated comfort in using smartphone medical applications. Overall, patients demonstrated a considerable amount of comfort with the prospect of using a mobile app. These findings support the idea of creating a mobile app designed to monitor Latina Gynecologic Oncology patients in efforts to lessen patients’ postoperative burden, improve mortality and morbidity outcomes, and decrease health care system costs.[Table: see text]
PRECISE CURATE.AI: A prospective feasibility trial to dynamically modulate personalized chemotherapy dose with artificial intelligence.
Authors: Blasiak Agata , Truong Anh , Tan Wen Jeit, Lester , Kumar Kirthika Senthil , Tan Shi Bei , Teo Chong Boon , Tan Benjamin Kye Jyn , Tadeo Xavier , Tan Hon Lyn , Chee Cheng Ean , Yong Wei-Peng , Ho Dean , Sundar Raghav 
Published: June 2022

1574 Background: Most treatment guidelines recommend chemotherapy at maximum tolerated doses, which does not always lead to optimal efficacy, but implicitly results in toxicity. To overcome this challenge, we developed CURATE.AI, a small data, AI-derived platform that harnesses only a patient’s own prospectively/longitudinally acquired data to dynamically identify their own optimal and personalized doses. We subsequently harnessed CURATE.AI to dynamically modulate individualized chemotherapy doses for patients in a prospective clinical trial. Methods: We conducted an open-label, multi-center, single-arm, prospective feasibility trial in patients diagnosed with advanced solid tumors and treated with single-agent capecitabine, XELOX or XELIRI (+/- biologics) (NCT04522284). The standard-of-care (SOC) capecitabine dose was 1000 mg/m2, unless adjusted by clinician to account for patient’s comorbidities and organ dysfunction. Using an AI-discovered second-order correlation between patient-specific variation of capecitabine doses and corresponding tumor marker (CEA, CA19-9 or CA-125) readouts for each cycle, CURATE.AI generated individualized patient digital avatars and recommended bespoke dose for the subsequent cycle. The clinicians were permitted to accept CURATE.AI dose recommendations, or reject the recommendations and dose based on clinical judgement. Results: Since August 2020 we recruited ten patients: single-agent capecitabine (n = 1), XELOX (n = 6), and XELIRI (n = 3). As of 20 Jan 2022, one patient remains on the trial. The prescribed dose was on average reduced by 20 % (± 13.8 %) as compared to the projected SOC dose. The nine reported patients completed 3.9 cycles (± 2.2 cycles), with the longest participation lasting 8 cycles. CURATE.AI recommendations were considered in 27 out of 40 total dosing decisions and accepted for prescription in 26 of those decisions. The reasons for not considering CURATE.AI included insufficient time from patient recruitment to the first dose administration and complex medical circumstances at the time of the dosing decisions. Conclusions: CURATE.AI has been successfully incorporated into the clinical workflow of dynamic dose selection in the treatment of solid tumors under a clinical trial. Prospective validation of CURATE.AI led to a reduction of an average prescribed capecitabine dose, which alongside additional preliminary findings may eventually play an important role in improving patient response rates and durations compared to SOC. Results from the PRECISE CURATE.AI trial support the initiation of a randomized clinical trial and potential expansion towards other oncologic indications. Clinical trial information: NCT04522284.
Time to access to novel anticancer drugs in Europe, a case study in seven European countries.
Authors: Post Colinda , Van Laarhoven Hanneke W.M. , Hollak Carla 
Published: June 2022

1586 Background: After European Medicines Agency Marketing Authorization (EMA-MA) different national reimbursement processes may contribute to unequal access throughout the EU. The aim of this study is to investigate the access time to new anticancer medicines in seven high-income Northern European countries and factors influencing the reimbursement process. Methods: We performed a retrospective database study. New anticancer medicines were included with a positive CHMP advice between January 2016 and January 2020, leading to EMA-MA followed by an application for national reimbursement approval in Germany, UK, France, The Netherlands, Belgium, Norway and Switzerland. The relevant Health Technology Assessment (HTA) - and reimbursement websites for each country were used to identify reimbursement dates. Time to access was defined as the time between EMA-MA and date of inclusion in the relevant reimbursement list. In addition we investigated the differences in national approval process and study-, medication- and patient- related factors which might influence the time to reimbursement. Results: We found that for the thirty-six new anticancer medicines, national reimbursement came on average 289 days after EMA-MA, with a variety of -125 in Switzerland (i.e. non EU-member) to 1415 days. Of these EMA-MA new anticancer medicines 42% were reimbursed in all countries. The average number of cancer medicines reimbursed in the examined countries was 27 (75%), with a range of 24 (67%) in Belgium to 36 (100%) in Germany. The shortest average time from EMA-MA to reimbursement were in Germany, 2.33 days, France and Switzerland following with 207 days and 279 days respectively. The median time to reimbursement was 227 days with a range of 3 days in Germany to 553 days in Belgium. In Germany, where there is no pricing and reimbursement approval required when launching a pharmaceutical, 100% of the cancer medicines are reimbursed within five days. After one year, In the UK 56% of the medicines are reimbursed, followed by 53% in the Netherlands, France and Switzerland. Belgium and Norway have a one year reimbursement rate of 14% and 11% respectively. Access to anticancer medicines is dependent on regulatory procedures, HTA and price regulations. Germany has a fast market access for anticancer medicines with a price regulation one year after launching the medicines. In the other countries, price regulation is part of the launching process. Other factors which might accelerate the time to reimbursement we are examining are high clinical benefit score (ESMO-MCBS), (non-)orphan status of the medicines and submission by big pharmaceuticals. Conclusions: This study shows that after EMA-MA, except for Germany, on average it takes a long time for anticancer medicines to be reimbursed in Northern European countries. There is a considerably variety both within and among countries.
Novel use of clinical pathways to identify poor prognosis lung cancer patients: Implementation and outcomes.
Authors: Jackman David Michael , Rice Matthew , Virkler Nicholas , Desravines Belinda , Selesnick Victoria H. , Scalzo Michael , Siegel Jocelyn H. , Tulsky James A. , Jacobson Joseph O. 
Published: June 2022

1589 Background: Cancer patients in the last year of life have different clinical needs and evolving goals of care. Using our oncology decision-support pathways to help clinicians consistently identify such patients in a systematic and prospective fashion, at a discrete moment in the care trajectory, may be an important step towards matching the care of these patients with their stated goals. Methods: Medical oncologists from each disease group at the Dana-Farber Cancer Institute (DFCI) were tasked with identifying clinical settings in each oncology care pathway associated with an expected median survival of < 12 months. This information was embedded into the underlying data model of the pathways platform, allowing us to determine how often clinicians navigated through each poor prognosis node. Results: From 3/1/20 – 6/30/21, there were 264 navigations in 205 unique lung cancer patients receiving standard of care (i.e., not on clinical trial) for a clinical condition associated with poor prognosis. Overall, the median overall survival from the time of a patient’s first navigation through a poor prognosis node during the defined study period was 6.4 months. Table lists outcomes for each specific setting. Patients with squamous or small cell lung cancer being treated in or beyond the third-line setting had notably poor outcomes, with less than a third of these patients surviving 6 months from the time of navigation. Conclusions: A clinical pathways platform can be a key tool in designating clinical scenarios associated with poor prognosis and identifying patients who may be particularly at risk. Pathways analytics provide real-world evidence corroborating the expected poor prognosis based on published studies and can identify specific clinical subsets for whom specific resources are warranted. By embedding this into the pathways data model, we aim to alert physicians to conduct goals of care conversations, offer supportive care resources, and match patients to appropriate treatment options and clinical trials. [Table: see text]
Utilization of telemedicine among patients newly diagnosed with cancer.
Authors: Pan I-Wen , Yen Tina W.F. , Shih Ya-Chen Tina 
Published: June 2022

1595 Background: The use of telemedicine among cancer patients remains limited. Because of the COVID-19 pandemic, CMS expansion of telehealth with 1135 waiver was enacted in March 2020, broadening the opportunities to provide patient care using telemedicine. This study examined the impact of the CMS expansion on the use of telemedicine among cancer patients. Methods: We identified newly diagnosed patients with 5 common cancers (breast, prostate, lung, colorectal, and lymphoma) between 3/2019 and 12/2020 from Optum’s de-identified Clinformatics Data Mart Database. Patients who had 6 months of full enrollment (3 months before and 3 months after the first [index] cancer diagnosis date), had cancer claims on 3 separate dates within 3 months of the index date for the specific cancer diagnosis, and no prior history of cancer were included. We defined telemedicine use as patients who had a telemedicine procedure code within 1 month of their index diagnosis and had the cancer diagnosis on the telemedicine claim. We conducted an interrupted time series analysis to examine the impact of CMS expansion on telemedicine use. A multivariable logistic regression model was used to identify factors associated with telemedicine use during the post-expansion period. Results: Of 96,632 patients included, the average crude rate of telemedicine use was 0.12% before and 14.2% after the expansion in March 2020 (see Table). There was a significant impact of expansion on telemedicine use (21% increase; p<0.001). The peak rate (adjusted) was 28% in April 2020, decreasing and plateauing in July/August 2020, with rates staying in the range of 10-12% between August and December 2020. During the post-expansion period, lymphoma, prostate, and lung cancer patients (adjusted rates: 14.6%, 15.7%, and 15.9%, respectively) were more likely to use telemedicine compared to patients who had breast (12.7%) or colorectal (12.3%) cancer. Patients who were older (adjusted rates: ≥65 years, 13.8%; 50-64, 14.2%; 20-49, 18.6%), Black (12.4% vs 14.4% for White, 15.5% for Hispanic and 16.6% for Asian), resided in East South Central census division (8.4% vs 23.5% in New England) and had Medicare (12.2% vs 20.3% for commercial insurance) were less likely to use telemedicine (all p<.001). Conclusions: After the CMS telehealth expansion, the use of telemedicine among newly diagnosed cancer patients increased significantly. Telemedicine use varied by patient age, geographic location, race/ethnicity, and payer. Further research is needed to understand the pattern of telemedicine use.[Table: see text]
Baseline tumor genomic and gut microbiota association with clinical outcomes in newly diagnosed glioblastoma (GBM) treated with atezolizumab in combination with temozolomide (TMZ) and radiation.
Authors: Weathers Shiao-Pei S. , Zhu Haifeng , Knafl Mark , Damania Ashish , Kamiya-Matsuoka Carlos , Harrison Rebecca A. , Lyons Larry , Yun Cindy , Darbonne Walter C. , Loghin Monica , Penas-Prado Marta , Majd Nazanin , Yung W. K. Alfred , O'Brien Barbara Jane , Wistuba Ignacio Ivan , Futreal Andrew , Wargo Jennifer Ann , Ajami Nadim J. , Woodman Scott Eric , de Groot John Frederick 
Published: June 2022

2006 Background: Checkpoint inhibitor (CPI) therapy has demonstrated overall limited efficacy in the treatment of GBM. Sixty newly diagnosed GBM patients unselected for MGMT status underwent treatment with concurrent atezolizumab with radiation therapy and TMZ followed by adjuvant atezolizumab and TMZ (NCT03174197). Clinical data has been reported previously. Methods: Genomic (WES with somatic mutation and SCNA determination N = total 42 samples, 33 baseline, 9 TP-2), transcriptomic (RNA seq N = total 72 samples, 54 baseline, 18 TP-2), and metagenomic sequencing of fecal samples (N = total 45 samples, 26 pre samples, 13 post RT samples, six 6m samples) analyses were conducted on pre-treatment samples. Findings were correlated with clinical outcome including OS and PFS. Twenty of the 60 patients underwent re-resection for suspected recurrent disease of which nine patients had WES and RNA seq performed successfully on paired pre and post treatment samples. Results: Somatic mutation, copy number and ploidy profiles were consistent with known aberrations in GBM. An unsupervised molecular network-based stratification of pre-treatment tumor mutations resulted in patients being grouped in 3 clusters with survival difference. Patients with GBM harboring an EGFR aberrancy were associated with a relatively worse mOS following treatment compared to patients with tumors enriched with PTEN alterations, while patients with IDH1 mutations had the longest mOS. Gene set enrichment analysis of gene expression in tumors from patients ( < mOS vs ≥mOS) identified genes associated with lymphocyte activation and immune response in patients with longer survival (p < 0.01) Unsupervised hierarchical clustering of bacterial taxa demonstrated two distinct clusters with significant difference by OS. Survival analysis and Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC) revealed distinct taxa associated with OS ( Ruminococcus spp.) and response to treatment ( Eubacterium spp.), respectively. Conclusions: In this small CPI-treated GBM cohort, WES, SCNA and RNA seq identified pre-treatment tumor features that separated patients by survival. The fecal microbiome observations in our GBM cohort warrants further investigation. Clinical trial information: NCT03174197.