bluuebunny/crossref_metadata_2025_split huggingface dataset:
Out-of-pocket cost of screening with breast MRI for women at high risk for breast cancer.
Authors: Pan I-Wen , Oeffinger Kevin C. , Shih Ya-Chen T. 
Published: May 2020

2017 Background: The prevention provision of Affordable Care Act (ACA) mandates private insurance to waive cost sharing for preventive services with grade A or B recommendations from the US Preventive Services Task Force. Although several professional societies have recommended augmenting screening mammography with MRI for women at high risk for breast cancer, the ACA prevention provision does not apply to screening MRI. This study examined the proportion of high-risk women having zero cost sharing associated with breast MRI for screening purposes and estimated out of pocket (OOP) costs as well as sources of variations. Methods: We identified women who underwent MRI and mammography for breast cancer screening from Marketscan database, 2009-2017. We quantified OOP costs as the sum of copayment, coinsurance, and deductible and defined zero cost sharing as having no OOP cost. We calculated the proportion of zero cost sharing for mammography and that for MRI and compared the time trend of each before and after ACA (enacted in 2010). We used multivariable logistic regression to examine factors associated with zero cost sharing for MRI use. We estimated OOP costs of MRI and examined cost variations by geographic regions or whether a woman had high deductible plans. Results: 25,232 women were included in the analysis. For screening mammography, the rate of zero cost sharing increased from 81% in 2009 to 91% in 2011 (post ACA) then 97% in 2017. For MRI, the rate was 41%, 37%, and 25%, respectively. The odds ratio (OR) of zero cost sharing for MRI screening was significantly lower for women with high deductible plans (OR = 0.65, 95% CI: 0.59-0.72) and for those resided in South (vs. Northeast) region (OR = 0.50, 95% CI: 0.46-0.53), after controlling for age, MSA, family breast cancer history, and year. OOP costs of MRI varied by region and insurance plan (Table); the mean OOP cost for women with high deductible plan were more than twice the mean cost for those in other plan types. Conclusions: With the financial protection under the ACA prevention provision applying to only screening mammography, many women at high risk for breast cancer are subject to high OOP costs for MRI screening. Those enrolled in high deductible plans and resided in the South are especially vulnerable financially. [Table: see text]
Disparities in the treatment of brain metastases from breast cancer: Insights from the National Cancer Database.
Authors: Chahine Zena , Faisal Muhammad Salman , Jayakrishnan Thejus Thayyil , Abel Stephen , Wegner Rodney E. 
Published: May 2020

2037 Background: Breast cancer is the most common malignancy in women accounting for over 300,000 cases per year. Unfortunately, brain metastases are found in a sub-group of patients with breast cancer even at presentation. Management of brain metastases typically includes radiotherapy with conventional whole brain radiation therapy (WBRT) or more focused stereotactic radiosurgery (SRS). We queried the National Cancer Database (NCDB) to analyze the incidence of brain metastases at diagnosis in breast cancer patients, as well as trends in radiation use/technique. Methods: The NCDB was queried for patients who were diagnosed with breast cancer between 2004-2015 and had brain metastasis at presentation (N = 4,491). We excluded patients without brain radiation and inadequate follow up. Odds ratios were calculated to identify factors associated with treatment. Multivariable cox regression was used to determine predictors of survival. Results: Using the eligibility criteria above 1,505 patients were identified in the NCDB. The cohort had a median age of 58 years. A small portion were uninsured (7%) population uninsured and 81% of radiation treatments were delivered in metropolitan areas. Two hundred sixty-one (17.3%) patients received SRS while 1,244 (82.7%) received WBRT. Those patients with private insurance, higher income, metro location, and having care delivered at an academic center were more likely to receive SRS. Conversely, the likelihood of receiving WBRT was significantly higher in those with luminal type cancer, African Americans, the uninsured, and those located in urban areas or treated at a community cancer center. On Cox regression, predictors of worse survival were age > 60 with Hazard Ratio (HR) 1.3 (95% CI 1.17-1.49), a comorbidity score > 2 with HR 1.45 (95% CI 1.1-1.9), and extra cranial metastatic disease with HR 1.33 (95% CI 1.15-1.54). Conclusions: This analysis of the NCDB demonstrates socioeconomic and demographic disparities in the treatment of patients with brain metastases from breast cancer. There is a continued need to reduce these disparities and improve access to care for at-risk populations affected by this highly prevalent malignancy.
Development and validation of natural language processing (NLP) algorithm for detection of distant versus local breast cancer recurrence and metastatic site.
Authors: Karimi Yasmin , Blayney Douglas W. , Kurian Allison W. , Rubin Daniel , Banerjee Imon 
Published: May 2020

2043 Background: Electronic health records (EHR) are used for retrospective cancer outcomes analysis. Sites and timing of recurrence are not captured in structured EHR data. Novel computerized methods are necessary to use unstructured longitudinal EHR data for large scale studies. Methods: We previously developed a neural network-based NLP algorithm to identify no recurrence vs. metastatic recurrence cases by analyzing physician notes, pathology and radiology reports in Stanford’s breast cancer database, Oncoshare (Cohort A). To validate this algorithm for local vs. distant recurrence, we identified a distinct Oncoshare cohort (Cohort B). Cases were manually curated for longitudinal development of local or distant recurrence and metastatic sites. A two-sided t-test was used to compare mean probabilities between local and distant recurrence cases. Next, we combined cases in Cohorts A and B to train and validate a novel NLP classifier that identifies metastatic site. The combined cohort was randomly divided into training and validation sets. Sensitivity and specificity were calculated for the NLP algorithm’s ability to detect metastatic sites compared to manual curation. Results: In Cohort B: 350 metastatic cases were identified. Mean probability for local and distant recurrence was 0.43 and 0.79, respectively and differed significantly for patients with local vs. distant recurrence (p<0.01). In Cohorts A and B: 632 metastatic cases were used for determination of sites. Sensitivity and specificity were highest for detection of peritoneal metastasis followed by liver, lung, skin, bone and central nervous system (table). Conclusions: This NLP algorithm is a scalable tool that uses unstructured EHR data to capture breast cancer recurrence, distinguishing local from distant recurrence and identifying metastatic site. This method may facilitate analysis of large datasets and correlation of outcomes with metastatic site. [Table: see text]
Improved prognostication for lung cancer patients from computed tomography imaging using deep learning.
Authors: Torres Felipe , Akbar Shazia , Baldauf-Lenschen Felix , Leighl Natasha B. 
Published: May 2020

2044 Background: Clinical TNM staging derived from computed tomography (CT) imaging is a key prognostic factor for lung cancer patients when making decisions about treatment, monitoring, and clinical trial eligibility. However, heterogeneity among patients, including by molecular subtypes, may result in variability of survival outcomes of patients with the same TNM stage that receive the same treatment. Artificial intelligence may offer additional, individualized prognostic information based on both known and unknown features present in CTs to facilitate more precise clinical decision making. We developed a novel deep learning-based technique to predict 2-year survival from pretreatment CTs of pathologically-confirmed lung cancer patients. Methods: A fully automated, end-to-end model was designed to localize the three-dimensional (3D) space comprising the lungs and heart, and to learn deep prognostic features using a 3D convolutional neural network (3DCNN). The 3DCNN was trained and validated using 1,841 CTs of 1,184 patients from five public datasets made available in The Cancer Imaging Archive. Spearman’s rank correlation (R) and concordance index (C-index) between the model output and survival status of each patient after 2-year follow-up from CT acquisition was assessed, in addition to sensitivity, specificity and accuracy stratified by staging. Results: 3DCNN showed an overall prediction accuracy of 75.0% (R = 0.32, C-index = 0.67, p < 0.0001), with higher performance achieved for stage I patients (Table) . 3DCNN showed better overall correlation with survival for 1,124 patients with available TNM staging, in comparison to TNM staging only (R = 0.19, C-index = 0.63, p < 0.0001); however, a weighted linear combination of both TNM staging and the 3DCNN yielded a superior correlation (R = 0.34, C-index = 0.73, p < 0.0001). Conclusions: Deep learning applied to pretreatment CT images provides personalized prognostic information that complements clinical staging and may help facilitate more precise prognostication of patients diagnosed with lung cancer. [Table: see text]
Semi-automated discovery of real-world patient pathway from U.S. electronic health records: Advanced non-small cell lung cancer (aNSCLC).
Authors: Yang Fei , Zhang Ju , O’Neill Tyler J. , Sharma Vishakha , Prime Matthew Stewart 
Published: May 2020

2048 Background: A good understanding of cancer care continuum presents opportunities to uncover unmet medical needs and improve outcomes and clinical workflow efficiency. However, patient care is poorly understood in real-world clinical practice. This study aimed to discover real-world patient pathways for advanced non-small cell lung cancer (aNSCLC). Methods: This study included patients diagnosed with aNSCLC (stage IIIB and above) at their initial diagnoses between 2011-2018 from the Flatiron Health electronic health records (EHR)-derived deidentified database. Overall survival (OS) was calculated using the Kaplan-Meier method. We also explored the application of process mining analytics (Heuristics Miner & Directly-Follows Graphs) to describe and visualize real-world patient pathways, following patients from initial diagnosis, through any National Comprehensive Cancer Network guideline-recommended companion diagnostics (CDx; including EGFR, ALK, ROS1, KRAS, BRAF, or PD-L1) and treatment patterns, until death or end of the study. Results: A total of 39,156 eligible patients were included. During a median follow-up of 0.78 years (interquartile range [IQR] 1.27), 28,801 (73.6%) patients died (median OS 11.6 months [95% CI 11.4 -11.8]). We established a semi-automated process discovery pipeline that transforms high-dimensional EHR datasets in table format as input into real-world event logs and produces a series of patient pathway graphs as output. The patient pathway graphs showed 19,878 (50.8%) patients had CDx testing within a median 11 days (IQR 18) and 29,241 (74.7%) patients started first-line therapy within a median 1.2 months (IQR 1.2) after the initial diagnosis. When we stratified analysis by years of initial diagnosis (2011-2014 vs 2015-2018), 38.8% (6808 of 17546) vs. 60.5% (13070 of 21610) patients had their first CDx testing within median 12 days (IQR 21) vs. 10 days (IQR 17) respectively. Conclusions: This study suggested an uptake of 56% increase of CDx utilization over the last 8 years in real-world clinical setting and that patient pathways can be analyzed and visualized in a semi-automated fashion.
Concordance study of treatment guidance from an online patient assistance algorithm (OCPAP) and treatment recommendation of a multidisciplinary panel of oncologists in India.
Authors: Jotwani Amit Kumar , Jain Rashie , Goud Rakesh Shankar , Gururao Shashidhar , Poovaiah Thirtha , Rajan Rejil 
Published: May 2020

2049 Background: OCPAP (online cancer patient assistance pathway) is an algorithm-based online platform for patients to help them understand their treatment options. It is based on basic inputs like cancer type, stage, patient’s performance status and treatment received. It is developed by a team of oncologists from onco.com based on standard guidelines. Till date, more than 30,000 users from 18 countries have used OCPAP platform to get directional treatment recommendations. Onco.com also provides onco tumour board (OTB) services to help patients to get a detailed opinion from a multidisciplinary panel of oncologists. We presented initial data on development of OCPAP™ at the ASCO Breakthrough Summit 2019 ( OCPAP Breakthrough Abstract ). Here we present a concordance analysis of treatment recommendations from OCPAP platform against opinion of OTB panel. Methods: We analysed data from 448 eligible cases (those with 15 types of solid cancers) where an OTB opinion was provided and compared it with OCPAP treatment recommendation. We entered data from 448 anonymised OTB case records in to the OCPAP platform and recorded the output in terms of treatment recommendations. The study was blinded by its very design as we took data of cases from the time when the platform was non-existent. Results: We compared and analysed the recommendation provided by OCPAP in terms of surgery, chemotherapy, targeted therapy, radiation, clinical trials, palliative care or best supportive care against the opinion of OTB panel. Overall the concordance rate was found to be 93% for all cases and it was above 90% for all types of cancers included, except for brain tumors where it was 78% mainly due to variation in surgical operability and imaging findings influencing the treatment recommendation. The concordance rate was above 90% for all stages of disease and was highest for metastatic disease where it was 95%. The reasons for discordance were mostly related to availability of more detailed insights about the disease for OTB, like clinical details, performance status, imaging findings, molecular data and oligometastasis. Conclusions: The treatment direction recommended by OCPAP was found to be consistent with the one provided by OTB panel for most solid cancers. This indicates that OCPAP is an effective and simple online tool for patients to understand their treatment options, validate their ongoing treatment and be able to actively participate in their treatment decisions.
An automated EHR-based tool for identification of patients (pts) with metastatic disease to facilitate clinical trial pt ascertainment.
Authors: Kirshner Jeffrey J. , Cohn Kelly , Dunder Steven , Donahue Karri , Richey Madeline , Larson Peter , Sutton Lauren , Siu Evelyn , Donegan Janet , Chen Zexi , Nightingale Caroline , Hamrick James 
Published: May 2020

2051 Background: Efforts to facilitate patient identification for clinical trials in routine practice, such as automating electronic health record (EHR) data reviews, are hindered by the lack of information on metastatic status in structured format. We developed a machine learning tool that infers metastatic status from unstructured EHR data, and we describe its real-world implementation. Methods: This machine learning model scans EHR documents, extracting features from text snippets surrounding key words (ie, ‘Metastatic’ ‘Progression’ ‘Local’). A regularized logistic regression model was trained, and used to classify patients across 5 metastatic status inference categories: highly-likely and likely positive, highly-likely and likely negative, and unknown. The model accuracy was characterized using the Flatiron Health EHR-derived de-identified database of patients with solid tumors, where manually abstracted information served as standard accurate reference. We assessed model accuracy using sensitivity and specificity (patients in the ‘unknown’ category omitted from numerator), negative and positive predictive values (NPV, PPV; patients ‘unknown’ included in denominator), and its performance in a real-world dataset. In a separate validation, we evaluated the accuracy gained upon additional user review of the model outputs after integration of this tool into workflows. Results: This metastatic status inference model was characterized using a sample of 66,532 patients. The model sensitivity and specificity (95%CI) were 82.% (82, 83) and 95% (95, 96), respectively; PPV was 89% (89, 90) and NPV was 94% (94, 94). In the validation sample (N = 200 originated from 5 distinct care sites), and after user review of model outputs, values increased to 97% (85, 100) for sensitivity, 98% (95, 100) for specificity, 92 (78, 98) for PPV and 99% (97, 100) for NPV. The model assigned 163/200 patients to the highly-likely categories, which were deemed not to require further EHR review by users. The prevalence of errors was 4% without user review, and 2% after user review. Conclusions: This machine learning model infers metastatic status from unstructured EHR data with high accuracy. The tool assigns metastatic status with high confidence in more than 75% of cases without requiring additional manual review, allowing more efficient identification of clinical trial candidates and clinical trial matching, thus mitigating a key barrier for clinical trial participation in community clinics.
Novel evidence synthesis system to support living systematic reviews and living guidelines for cancer immunotherapy.
Authors: Riaz Irbaz Bin , Rawal Samarth C , Siddiqi Rabbia , Asghar Noureen , Islam Mahnoor , Gajic Ognjen , Wang Zhen , Montori Victor , Herasevich Vitaly , Go Ronald S. , Baral Chitta , Liu Hongfang , Vandvik Per Olav , Haddad Tufia C. , Bryce Alan Haruo , Rajkumar S. Vincent , Murad M. Hassan 
Published: May 2020

2054 Background: Systematic reviews that summarize the toxicity of Immune checkpoint inhibitors (ICIs) become outdated very soon after publication. Therefore, we reported results of a toxicity meta-analysis at 2019 ASCO meeting and informed the intent to create a living systematic review (LSR). LSRs combine human and machine effort and support rapid evidence synthesis and living clinical practice guidelines. Now, we report our experience maintaining a LSR on toxicity of ICIs. Methods: Steps include quarterly literature searches to identify new clinical trials reporting ICI-associated adverse events (AEs), AI-enabled screening of new citations which meet the inclusion criteria, automated cumulative meta-analysis and an online reporting platform. Standard data formats and protocols were designed for inputting text, tables and graphics. Software was written to interpret these data and output the information in the appropriate format, such as a forest plot and summary tables. Finally, a dynamic interface that enables user inputs and displays the associated output was designed. Results: The LSR is continuously updated incorporating toxicity data from new clinical trials as it becomes available. We have screened 8000 relevant citations and summarized the odds of Grade 3 or higher AEs and AEs of special interest in patient receiving ICIs. The results are updated on quarterly basis and are available online. The results are updated on quarterly basis and will be available on a website at the time of publication. Prototype with dummy data is available at this link . This interface can also be manipulated via user input to organize and sort data tables and forest plots by type of cancer, name or mechanism (PD-1 or PD-L1) of ICI agent, single agent or combination, type of control arm, line of treatment and several other clinically relevant filters. For example, a user can instantaneously generate a meta-analysis summarizing the risk of colitis or pneumonitis in metastatic lung cancer trials with pembrolizmuab. Conclusions: This LSR engine can prospectively synthesize toxicity data from ICI trials in an efficient manner providing accurate and timely information for advanced clinical decision support at point-of-care. Efforts are ongoing to improve efficiency of screening, improve AI-enabled processes for automated screening and data abstraction, and test across multiple clinical questions.
Fragility index of trials supporting approval of anti-cancer drugs in common solid tumors.
Authors: Desnoyers Alexandra , Nadler Michelle , Saleh Ramy , Amir Eitan 
Published: May 2020

2055 Background: The Fragility Index (FI) quantifies the reliability of positive trials by estimating the number of events which would change statistically significant results to non-significant results. Here, we calculate the FI of trials supporting approval of drugs for common solid tumors. Methods: We searched Drugs@FDA to identify randomized trials (RCT) supporting drug approvals by the US Food and Drug Administration between January 2009 and December 2019 in lung, breast, prostate, gastric and colon cancers. We adapted the FI framework (Walsh et al. J Clin Epidemiol 2014) to allow use of time to event data. First, we reconstructed survival tables from reported data using the Parmar Toolkit (Parmar et al. Stat Med 1998) and then calculated the number of events which would result in a non-significant effect for the primary endpoint of each trial. The FI was then compared quantitatively to the number of patients in each trial who withdrew consent or were lost to follow-up. Multivariable linear regression was used to explore association between RCT characteristics and the FI. Results: We identified 69 RCT with a median of 669 patients (range 123-4804) and 358 primary outcome events (range 56-884). The median FI was 26 (range 1-322). The FI was ≤10 in 21 trials (30%) and ≤20 in 31 trials (45%). Among the 69 RCT, the median number of patients who withdrew consent or were lost to follow up was 27 (range, 6-317). The number of patients who withdrew consent or were lost to follow-up was equal or greater than the FI in 42 trials (61%). There was statistically significant inverse association between FI and trial hazard ratio (p0,001) and a positive association with number of patients who were lost to follow-up or withdrew consent (p0,001). There was no association between trial sample size, year of approval or reported p-value and the FI. Conclusions: Statistical significance of trials supporting drug approval in common solid tumors relies often on a small number of events. In most trials the FI was lower than the number of patients lost to follow up or withdrawing consent. Post-approval randomized trials or real-world data analyses should be performed to ensure that effects observed in registration trials are robust.