bluuebunny/crossref_metadata_2025_split huggingface dataset:
Use of a theoretical framework to examine declines in clinical trial enrollment.
Authors: Mackay Christine B. , Hepler Kerry , Daley Christine Makosky , Brooks Joanna Veazey 
Published: May 2019

e18037 Background: Clinical trial enrollment at a group of community oncology sites dropped significantly surrounding acquisition by an academic medical center. While previous research used empirical data to identify enrollment barriers, this study used a theoretical framework to investigate research site personnel behavior associated with enrollment, and to further explore clinical trial enrollment barriers. Methods: Data from focus groups and interviews were analyzed using the integrated TDF and COM-B theoretical framework [1] . [1] Theoretical Domains Framework with Capability, Opportunity, Motivation Behavior Wheel. Results: Fifty-six community personnel [2] participated in focus groups and interviews about enrollment experiences before and after the acquisition. Participants described how changes in the workplace and resources affected their Opportunity to enroll: "It's kind of hard to put somebody on a study when you don't have one available." -cs "You can't grow a research department by cutting staff. What message does it send to the organization as far as the importance of research? …it makes you wonder whether the organization truly backs the research effort." -m Physician participants specifically shared that the lack of time and staff devoted to research hampered enrollment efforts, consequently affecting their Motivation: “Current road block is that doc doesn't have time to go through the patient records, so if [the study coordinator] could determine eligibility before the patient visit, that would be helpful.” -p Participants expressed frustration in not knowing the processes for opening trials, therefore their Capability was affected. “I think [in the past], we knew more about what was out there, where I don't see as much of that here now. I don't know if it's because we don't have a research nurse or we are not getting a list [of trials] anymore or what is going on." -cs [2] Quotes from personnel cs = clinic staff, m = manager, p = provider. Conclusions: Participants perceived many barriers to clinical trial enrollment after the acquisition. To optimally continue providing services as expected, support systems need to be maintained. This study provided evidence of using the TDF for exploring enrollment barriers.
Evaluation of cancer patients’ perspectives on weight management counseling at a comprehensive cancer center.
Authors: Hansra Damien Mikael , Daniels Cynthia , Alvarez Ricardo H. 
Published: May 2019

e18041 Background: Over 2/3 of Americans are Overweight (OW) or obese (OB). Being OW or OB is a risk factor for a variety of cancers & denotes a worse prognosis for some. Evidenced based strategies on management of OW & OB patients (pts) are lacking. Data shows the majority of physicians are OW & OB and confidence in obesity counseling in these individuals is less compared with normal weight (NW) counterparts. Here we report the results of a survey on cancer pt’s opinions weight management counseling. Methods: An HRPP exemption was obtained. Pts asked to complete a 20-question (Q) survey. Clinical info collected: age, race, cancer type, weight class (underweight, NW, OW, OB, unsure but likely overweight). Survey Qs asked about optimal timing, type of weight counseling preferences. Also, Qs regarding importance of their oncologists (ONCO) BMI assessed. Secondary Qs about pt risk factors for obesity, pt willingness, & pt preferred modalities to change dietary & exercise habits also assessed. Statistical analysis: One-way ANOVA used to compare mean responses. P-values reported α = 0.05. Results: 245 cancer pts completed the survey. Demographics: 53% white, 37% black, 8% multiracial, 1% American Indian, 1% Asian. Majority of pts 51-65 years old (55%) and female ( > 75%). 71% (175/245) of pts were OW or OB vs. 29% (70/245) of pts with normal weight. 78% (191/245) of pts had breast cancer vs. 22% (54/245) other cancers. Main results: The best time for an ONCO to address weight reduction with pts is? “Anytime” = 45% (111/245), “First visit” = 27% (65/245), “After primary treatment” = 27% (65/245), “Never” = 1% (2/245) (p < 0.01). The approach pts prefer ONCO use to counsel on weight reduction is? “Educational approach” = 26% (65/245), “Direct approach” = 24% (59/245), “Passive approach” = 8% (20/245), “Passive & educational” = 18% (44/245), “Direct & educational” = 18% (43/245), “Other approaches” = 5% (12/245), “No approach, I don’t want counseling” = 1% (2/245) (p < 0.01). Who would you listen to MOST about weight loss advice? “Normal weight ONCO” = 41% (101/245), “The BMI of my ONCO does not matter” = 53% (131/245), “OB or OW” 5% (13/245) (p < 0.01). Results of secondary Qs included in presentation. Conclusions: Almost all pts would like to discuss weight loss with their oncologist on the first visit or after primary treatment has been completed. Also, most pts would like to receive counseling by their oncologist regardless of their oncologist’s weight. Furthermore, pts seem to prefer education about why weight loss is important be included in their weight loss counseling.
Computer-aided classification of lung nodules on CT scans via 3D CNNs.
Authors: Liu Xiaohua , Luo Ligang , Yang Haiying , Jie Xiaoxiang , Li Peiyu , Ma Ding , Kang Yue , Gao Guangming , Luo Xiangfeng , She Yuli , Li Jinchen , Liu Yuzhe , Xie Weihao , Lu Shijie , Hu Zhongjie , Zhang Tianze 
Published: May 2019

e18047 Background: Computer-aided diagnosis based on deep learning methodologies has demonstrated great potential to assist disease diagnosis with accuracy and efficiency. Specifically, the quantitative and qualitative analysis on lung nodules has proven to be important for the early-detection of lung cancer as well as its treatment in clinical practice. This study developed a 3D CNN model to facilitate the classification of pulmonary nodules. Methods: We collected 3956 lung CT scans (slice thickness≤3mm) with multiple lung nodules from 15 Class-A hospitals in China, 1155 lung CTs from Luna 16 dataset and Data Science Bowl 2017. There were 30 senior radiologists responsible for annotation and each CT scan was annotated by two of them randomly. Another 4 senior associate chief physicians were divided into two groups, each group was responsible for arbitration when conflicts occurred between the annotation doctors. All the annotated CTs were randomly selected and split to construct training, validation and test sets. We pre-processed the CTs and utilized 3D CNNs to classify these nodules as solid, partial-solid, ground glass opacity, calcified, pleural solid and pleural calcified. ROC analysis was used, and the classification capability was assessed by classification accuracy and the AUC score. Results: Table shows the overall results. The proposed model yielded an AUC score of 0.97 for the ground glass opacity and 0.90 for calcified nodules in the training set, while the AUC of them were 0.93 and 0.93 respectively in the validation set. For the test set, we got an AUC score of 0.94 for the ground glass opacity. The average classification time for each nodule was less than 0.005 sec. Conclusions: Our model may assist clinical diagnosis of lung cancer and increase its objectivity and accuracy, and the fast processing speed proves its feasibility to be applied in real clinical practice. In the future, we will enrich the dataset with clinical and genetic information, thus improving our model to boost its performance. [Table: see text]
Mutational analysis of >14,000 solid tumor NGS tests from community oncology practices: The Sarah Cannon molecular landscape.
Authors: McKenzie Andrew , Schlauch Daniel , Sharma Yasha , Picard Shaita , Higgins Connor , Misch Amanda , Xiao Yonghong , Lachs Rebecca , OConnor Niall , Correll Mick , Spigel David R. , Burris Howard A. , Jones Suzanne Fields , Dilks Holli Hutcheson 
Published: May 2019

e18065 Background: Utilization of NGS-based testing for patients in community oncology settings continues to grow. A comprehensive analysis of the molecular landscape of > 14,000 patients with commercial tissue-based and plasma-based NGS (tbNGS and pbNGS) testing from Sarah Cannon’s (SC) network of community oncologists between 2012 and 2018 was performed. These data aid clinical trial design/feasibility and identify rare and actionable mutations in the community oncology setting. Methods: Medical Oncologists in the SC network ordered NGS-based molecular profiling for their patients as standard of care. Data from multiple commercial vendors starting in 2012 were harmonized and analyzed. Results: On average, tbNGS (n = 8938) and pbNGS (n = 5419) tests revealed 14 and 4 mutations/sample, respectively. The top 10 mutated genes from both tbNGS and pbNGS - tests are in the table. The most predominant mutation-types detected were SNVs in both tbNGS and pbNGS tests, and gene amplifications were detected in ~40% of tbNGS and ~22% of pbNGS tests. MET amplifications (range: 2 - 40 copies (pbNGS) and 6 - 82 copies (tbNGS)) and ERBB2 amplifications (range: 2.1 - 90.2 copies (pbNGS) and 4 - 293 copies (tbNGS)) were detected in 1.6% (MET-tbNGS) and 4.6% (MET-pbNGS) and 4.2% (ERBB2-tbNGS) and 2.69% (ERBB2-pbNGS) of patients, respectively, and informed clinical trial enrollment. Of the fusions detected, 28 NTRK rearrangements were detected (NTRK1 – 16, NTRK2 – 5, NTRK3 – 7) by tissue-based testing and 3 (NTRK1) fusions were detected by plasma-based tests. Conclusions: These data describe the mutational landscape of NGS tests ordered within community oncology practices. SC’s data infrastructure links NGS-based testing data and clinical data to support real world evidence research, and highlights the need for healthcare IT systems to marry clinical and genomic data for clinical decision support and clinical research. [Table: see text]
Employing an oncology decision-support system to quantify treatment variation.
Authors: Liang Jun , Dankwa-Mullan Irene , Ren Yan-ping , Chen Alan , Willis Van , Jackson Gretchen , Levitan Nathan , Rhee Kyu , Li Tian-le 
Published: May 2019

e18067 Background: Factors affecting cancer treatment may include evidence for effectiveness, cost, and preference. These influences can lead to treatment variation across institutions and populations. Decision-support systems have been proposed as tools to reduce variation. This study quantified concordance between treatment provided by oncologists in China and therapeutic options presented by a decision-support tool. Methods: We identified and analyzed concordance studies in nine unique institutions located in seven provinces in China, published in 2017-2018 using Watson for Oncology (WFO), a clinical decision-support tool. Published rates of concordance were compared by cancer type and institution. Results: Concordance of all combined cases was 59% (2012/3388). Concordance rates varied by cancer type and institution (Table). Concordance rates were highest for ovarian (96%), rectal (94%) and breast (89%) cancers but lowest in gastric (12%), ovarian (43%) and breast (55%) cancers. Conclusions: Concordance between treatments and therapeutic options from an oncology decision-support tool varied significantly across cancer types and institutions in China, suggesting significant practice variation. Without established guidelines for treatment, clinical decisions may be influenced by preferences and local factors. Future studies are needed to identify reasons for variation and improve adherence to regional evidence-based guidelines. [Table: see text]
Building a data foundation: How MD Anderson and Palantir are partnering to accelerate research and improve patient care.
Authors: Alfaro-Munoz Kristin , Hallatt Gemma , Sookprasong Jamie , De Groot John Frederick , Lari Bryan , Yu Emily , Jin Jeff , Futreal Andrew , Ravi Vinod 
Published: May 2019

e18077 Background: MD Anderson (MDA) strives to maximize learning from the > 44,000 patients seen annually. This has historically been challenging due to the diverse, often unstructured, and siloed nature of patient data. To begin addressing these challenges, MDA constructed an institutional data warehouse and developed Natural Language Processing (NLP) capabilities. This allowed technical users to extract specific cohorts but proved challenging for many non-technical users. In 2018, MDA conducted a proof of concept with Palantir Technologies to integrate disparate data sources into a single cloud-based platform enabling both technical and non-technical users to directly access and analyze data. Methods: Through a highly iterative, collaborative process between clinical/research users, IT experts and Palantir engineers, 150 deidentified datasets were integrated from the existing data warehouse, NLP, molecular reports, public data and manually curated data, representing data from > 1.5 million patients. Data integration was validated and feedback from clinical users gathered. Results: In addition to centralizing data, the platform was found to: (1) Make data accessible to non-technical users by mapping data to real-world concepts, (2) Create reports backed by live data, with clear visibility into inputs, analysis and history, (3) Create templates for common analytical workflows (e.g. KM curve), and (4) Support increased collaboration through transparent, secure access controls. This resulted in dramatic acceleration of cohort identification, data extraction, and outcomes analyses. Conclusions: Integrating siloed data exposed gaps and errors arising from the unstructured nature of medical data and use of NLP. In response, we are piloting a combination of (1) Purposeful structured data capture at the time of care, (2) Use of logic to suggest commonly uncaptured but critical patient events such as progression, and (3) Auditing functionality to allow users to manually fill in gaps and correct data in a transparent manner. Expanding the data foundation unlocks the potential for more advanced research, fueling scientific discovery, while enhancing the quality and safety of patient care.
Proof-of-concept study evaluating a new tool for standardizing radiological assessment of tumor response to treatment in routine clinical practice.
Authors: Mastier Charles , De La Fouchardiere Christelle , Heudel Pierre-Etienne , Fayette Jerome , Sarabi Matthieu , Banaste Nathan , Barma Marion , mercier-Bischoff Elodie , Brahmi Medhi , Cassier Philippe Alexandre , Garin Gwenaelle , Pérol David , Pilleul Frank , Blay Jean-Yves 
Published: May 2019

e18079 Background: Internationally recognised criteria to evaluate tumour response, such as Response Evaluation Criteria In Solid Tumors (RECIST) and iRECIST (for immunotherapeutics) are not widely used in clinical practice. The main reason is that communications amongst health professionals are insufficiently standardised. To this purpose, ON’COHUB (MIRIO in France), a newly developed tool, automatically streamlines interactions between oncologists and radiologists to optimise and facilitate response evaluation according to the standard criteria. This study aims to identify the benefits in terms of user satisfaction and to provide preliminary data for the standardisation of the tool. Methods: Five oncologists, 3 radiologists, 1 clinical research associate and 1 clinical project officer used ON’COHUB to assess patients’ tumour responses according to RECIST, in an anti-cancer center, over five months. An oncologist compared patients’ regular radiology reports (n = 110) and ON’COHUB’s reports (n = 106), using the following report quality indicators: information on the patient’s context, target and non-target lesions , baseline, NADIR, and overall evaluation. Then a questionnaire on the user’s satisfaction was administered. Two-tailed chi-square tests were performed to compare patients’ regular radiology reports and ON’COHUB’s reports. Results: ON’COHUB was reported to significantly improve the following quality indicators compared to regular reports: presence of the treatment (99% vs 42%; p < 0,001), follow-up criteria (98% vs 5%; p < 0,001); target lesion and location (100% vs 75%; p < 0,001), size of target lesions (99% vs 67%; p < 0,001), value and date of the baseline (100% vs 0% and 4%, respectively; p < 0,001 for both), value of the baseline lesion by lesion (99% vs 4%; p < 0,001), value and date of the Nadir (97% vs 0%; p < 0,001), global response according to RECIST (100% vs 17%; p < 0,001), value of target lesions response (80% vs 20%; p < 0,001), referential for RECIST calculation (98% vs 2% p < 0,001).Finally, 9 out of the 10 users declared that they would use ON’COHUB on a daily basis. Conclusions: These preliminary data suggest that ON’COHUB may have a positive impact on the quality of the treatment response evaluation in clinical trials and in routine use. These results will be confirmed with the deployment of the interoperability with IT hospital System.
An evaluation of artificial intelligence-based clinical decision supports use in Brazil.
Authors: Rocha Hermano Alexandre Lima , Dankwa-Mullan Irene , Juacaba Sergio Ferreira , Preininger Anita , Felix Winnie , Thompson Julie V , Bright Tiffani , Jackson Gretchen Purcell , Meneleu Pedro 
Published: May 2019

e18081 Background: The Instituto do Câncer do Ceará (ICC), a 160-bed oncology hospital located in Brazil, serves approximately 23,000 patients monthly. In December of 2017, ICC implemented Watson for Oncology (WFO), an artificial intelligence (AI)-based clinical decision-support (CDS) tool to help enhance personalized cancer care. As of December 2018, 903 cases involving mainly breast, prostate and gastric cancers were entered in WFO. The purpose of this study was to investigate how implementation of WFO and use by oncologists affects clinical decision-making and workflow. Methods: 7 oncologists who employed WfO during and after the patients’ first visit were recruited to complete a survey regarding usability, decision-making and workflow. The group consisted of 1 urologist, 3 gastric surgeons, 1 gynecologist, 1 breast surgeon, 1 head-neck surgeon. Survey questions integrated the CDS Five Rights framework. Results: Most oncologists agreed that WFO is easy to understand and provides complete, relevant and actionable information at an appropriate time (Table). Opinions on the impact on treatment decisions varied. 71.4% expressed positive statements (agree or strongly agree) pertaining to the use of WFO. Conclusions: In this study, oncologists felt WFO met 5 Rights expectations for CDS; 57% felt that WFO exceed expectations. Further research is needed to understand how variation in experience affects decision impact. [Table: see text]
Artificial intelligence (AI) to improve patient outcomes in community oncology practices.
Authors: Frownfelter John , Blau Sibel , Page Ray D. , Showalter John , Miller Kelly , Kish Jonathan , Valley Amy W. , Nabhan Chadi 
Published: May 2019

e18098 Background: Artificial Intelligence(AI) for predictive analytics has been studied extensively in diagnostic imaging and genetic testing. Cognitive analytics adds by suggesting interventions that optimize health outcomes using real-time data and machine learning. Herein, we report the results of a pilot study of the Jvion, Inc. Cognitive Clinical Success Machine (CCSM), an eigen vector-based deep learning AI technology. Methods: The CCSM uses electronic medical record (EMR) and publicly available socioeconomic/behavioral databases to create a n-dimensional space within which patients are mapped along vectors resulting in thousands of relevant clusters of clinically/behaviorally similar patients. These clusters have a mathematical propensity to respond to a clinical intervention which are updated dynamically with new data from the site. The CCSM generates recommendations for the provider to consider as they develop a care plan based on the patients’ cluster. We tested and trained the CCSM technology at 3 US oncology practices for the risk (low, intermediate, high) of 4 specific outcomes: 30 day severe pain, 30 day mortality, 6 month clinical deterioration (ECOG-PS), and 6 month diagnosis of major depressive disorder (MDD). We report the accuracy of the CCSM based on the testing and training data sets. Area under the curve (AUC) was calculated to show goodness of fit of classification models for each outcome. Results: In the training/testing data set there were 371,787 patients from the 3 sites: female = 61.3%; age ≤ 50 = 21.3%, 51-65 = 26.9%, > 65 = 51.9%; white/Caucasian = 43.4%, black/African American = 5.9%, unknown race = 43.4%. Cancer types were unknown/missing for 66.3% of patients and stage for 90.4% of patients. AUC range per vector: 30 day severe/recurrent pain = 0.85-0.90; 30-day mortality = 0.86-0.97; 6-month ECOG-PS decline of 1 point = 0.88-0.92; and 6-month diagnosis of MDD = 0.77-0.90. Conclusions: The high AUC indicates good separation between true positives/negatives (proper model specification for classifying the risk of each outcome) regardless of the degree of missing data for variables including cancer type and stage. Following testing, a 6 month pilot program was implemented (06/2018-11/2018). Final results of the pilot program are pending.
Patient’s insurance status impact on biomarker testing in the United States market.
Authors: Winkelman Daniel F. , Forouzan Amir , Lavin Mike , Tangudu Tanuja , Custer Kristopher , Kachadourian Anise 
Published: May 2019

e18114 Background: Precision medicine in cancer treatment is driven by biomarker testing. If a patient is not biomarker tested, he or she may not receive access to targeted therapies that often offer greater efficacy. The purpose of this research study is to examine the insurance type of patients that are untested for biomarkers in the United States. Methods: BrandImpact is a longitudinal panel of 450 Oncologists who continuously report cancer treatment decisions from the point-of-care. The BrandImpact data set includes 60,465 patient visits reported in 2018. This study followed market research best practices. This research included visits by patients diagnosed with metastatic: NSCLC, melanoma and ovarian cancer who were either tested or untested for the following biomarkers: EGFR, ALK, ROS1, PD-L1, BRAF and BRCA. Results: The baseline measure for this analysis is that 18% of the patient visits reported that were not biomarker tested. The table at the end of document outlines the untested patient visits by insurance type for comparison purposes. Conclusions: The rate of biomarker testing for metastatic cancer patients treated by Oncologists differs by insurance type. Patients with Medicare Only insurance (25%) are untested at a statistically significantly higher rate than the baseline All category. It should be noted that patients without insurance or who were cash-paying had the highest rates of being non-biomarker tested. It is recommended that further research be conducted into the reasons behind the significantly higher rate of non-testing for Medicare Only patients. It seems counter intuitive that this patient population is treated less than optimally due to limited access to targeted therapies. [Table: see text]