bluuebunny/crossref_metadata_2025_split huggingface dataset:
Incidence of non-breast cancer neoplasms (non-BCN) diagnosed prior and subsequent to breast cancer (BC): Single institution experience.
Authors: Tomasevic Zorica , Tomasevic Zoran , Grujicic Danica , Nikitovic Marina , Milovanovic Zorka 
Published: May 2020

e13689 Background: The risk for developing subsequent non-BCN in BC patients is recognized as growing problem hence more patients are long term survivors. Limited data are available for non-BCN developed prior to BC. Better defining of types and frequency of non-BCN might be important for recognition of cancer predisposing factors. We aim to evaluate incidence, type, and time of development for all non-BCN in BC patients at the Institute for Oncology and Radiology of Serbia (IORS). Methods: During 2019, 2384 BC patients were seen at the IORS for treatment or follow up, all medical records have been evaluated for potential non-BCN diagnosis. Results: 230 (9.7%) patients also had variety of histologically confirmed non-BCN: in 78 (34%) as prior to BC, median 5 years (1-42); as synchronous with BC in 12 (5%) median 4 months (0 < 12 months) and in 140 (61%) as subsequent to BC, median 8 years (≥1-33). Six most frequent non-BCNs are presented in table. Conclusions: Amongst 230 BC patients 34 different non-BCN were identified. All non-BCNs were more frequently developed subsequently to BC, representing 61% of all non-BCN cases, with expected exception of HL hence 12/13 (92%) occurred prior to BC. EC,TC, OC, CRC and LC, represents 54 % of all non-BCN and were diagnosed more frequently as subsequent to BC while TC had similar incidence as prior and subsequent to BC. Subsequent development might be related solely to genetic factors, but at least in some cases, influence of BC treatment (tamoxifen, cyclophosphamide, radiotherapy) cannot be excluded.[Table: see text]
Effect of access to care on outcomes of rural and elderly patients with primary central nervous system lymphoma.
Authors: Howard Scott C. , Napier Nicholas , Cao Xueyuan , Combs Ryan , Watson Mark Layton , Lam Catherine 
Published: May 2020

e14008 Background: Primary central nervous system lymphoma (PCNSL) can often be cured, especially in younger patients, but requires intense chemotherapy with high-dose methotrexate (HDMTX) and rituximab to optimize outcomes. Toxicities can lead to dose reduction or omission that may increase relapse risk, or lead clinicians to select less effective regimens that do not contain HDMTX. Methods: Anonymized, de-identified data of patients from 110 community oncology practices of the Guardian Research Network (GRN, www.GuardianResearch.org ) was analyzed to determine treatments, toxicities, and outcomes of adults with PCNSL. All data from the medical record is available from GRN (diagnoses, demographics, labs, medicines, toxicities, radiology, pathology, procedures, and encounters), so each patient’s journey can be fully characterized. Results: Of 533805 adults with cancer, 49 were treated for PCNSL with HDMTX-containing regimens (n = 35), other chemotherapy regimens (n = 3), or radiation therapy (RT) alone (n = 11). HDMTX patients received HDMTX only in 8 cases, HDMTX plus rituximab in 23 cases, addition of RT in 11 cases, and HDMTX with other chemotherapy but no rituximab in 3 cases. Survival at 5 years was 53% (standard error [SE] 8.6%) for patients treated with HDMTX versus 33% (SE 13%) for those treated with other therapies. Of those treated with HDMTX, survival was 0% for patients who experienced early toxicity that required cessation of HDMTX prior to receiving 3 doses and having response evaluated versus 62% (SE 9.1%) for patients who received 3 or more courses of HDMTX (p < 0.001). In a multivariable Cox proportional hazards model including completion of at least 3 doses of HDMTX, age, race, and sex, only lack of HDMTX toxicity was associated with survival (hazard ratio 0.22, 95% confidence interval 0.07 to 0.70, p = 0.01). Conclusions: Use of HDMTX and prevention of toxicity improves outcomes for PCNSL patients treated in the community.
The pink vans: Bringing cancer screening closer to home.
Authors: Ting Frederic Ivan Leong , Leones Louis Mervyn Banua , Chan Abigail Sy , Ignacio Jorge Garcia 
Published: May 2020

e14021 Background: A global survey reported that among 15 Asian countries, the Philippines has the highest breast cancer mortality rate and the lowest mortality-to-incidence ratio. This may be due to the fact that majority of breast cancers are diagnosed in locally advanced or metastatic stages among low- and middle-income countries. Studies show that in the Philippines more than half (53%) of patients with breast cancer were diagnosed at stages III and IV, while only 2-3% of cases were treated at Stage I. This translates to a dismal 58.6 % 5-year survival rate. Furthermore, socioeconomic factors were identified as one of the major causes for the lack of access to early detection and treatment. These include lower educational level, higher poverty rates, inadequate or no health insurance, lack of transportation, and/or the unavailability of health services in far-flung areas. These findings are currently of urgent national concern since the improvements in breast cancer survival rates brought by advances in treatments are mainly dependent on early detection, screening and timely treatment. Methods: In response to this gap in the delivery of services to promote early cancer detection, the University of the Philippines – Philippine General Hospital (UP-PGH) Cancer Institute established the Mobile Breast Cancer Diagnostic Clinic Program, a first in the country where two fully equipped “pink” vans are sent out to far flung areas and provide breast cancer screening and diagnostic services (eg. Mammogram, breast ultrasound, biopsies of suspicious masses, etc). These vans are manned by medical and surgical oncologists, and various paramedical professionals. Results: In the first 6 months of the program, the “pink” vans were able to visit 3 strategically identified areas in the country. These three tours have served a total of 189 women who all underwent screening mammography. 18 had their suspicious lesions biopsied, and two-thirds of which were found to be malignant. These patients were then referred to our institution for further work-up and management. Conclusions: As the Philippine health department continues to emphasize clinical and self-breast exam as part of its breast cancer control program, efforts like the UP-PGH “Pink Vans” may eventually help supplement the gap in the country’s breast cancer screening endeavors with the goal of improving survival by identifying the disease at its earliest stage.
Primary care provider involvement in thyroid cancer survivorship care.
Authors: Radhakrishnan Archana , Reyes-Gastelum David , Hawley Sarah T. , Hamilton Ann S. , Ward Kevin C. , Wallner Lauren P. , Haymart Megan Rist 
Published: May 2020

e14028 Background: While prior research has examined how primary care providers (PCPs) can help care for breast and colon cancer survivors, little is known about the role of PCPs in thyroid cancer survivorship. We surveyed PCPs to understand their involvement and confidence in providing several aspects of thyroid cancer survivorship care. Methods: Thyroid cancer patients (N = 2632) diagnosed in 2014-2015 and identified from the Georgia and LA SEER registries, were surveyed, and identified their PCPs, who then were also surveyed (N = 162, 56% response rate). PCPs were asked to report their: 1) involvement in thyroid cancer long-term surveillance (not involved: never/rarely vs. involved: sometimes/often/almost always involved); and 2) confidence in handling four aspects of thyroid cancer survivorship care (low: not at all/a little/somewhat vs. high: quite/very confident). We examined PCP factors (individual, practice, attitudes and beliefs) associated with PCP involvement in long-term surveillance using multivariable logistic regression. We then examined associations between PCP involvement in long-term surveillance and PCP confidence in handling the four aspects of thyroid cancer survivorship care using Chi-squared tests. Results: The majority of PCPs (76%) reported involvement in long-term surveillance. Adjusted odds of PCPs reporting involvement was greater among PCPs who somewhat/strongly believed that PCPs have the skills necessary to provide survivorship care (OR 4.33; 95% CI 1.38-15.37), and PCPs who noted clinical guidelines as being the most influential in guiding treatment (OR 4.48; 95% CI 1.67-13.07). PCP reports of high confidence in handling the four aspects of survivorship care were consistently less than 50%: when to refer patient to specialist (40%), role of neck ultrasound (36%), role of random thyroglobulin levels (27%), and when to end long-term surveillance (14%). PCPs who reported involvement in long-term surveillance were more likely to report high confidence in discussing the role of random thyroglobulin levels (33.3% vs. 7.9% for those not involved, p < 0.01) but not the other aspects of survivorship care. Conclusions: Though many PCPs reported they were involved in thyroid cancer long-term surveillance, gaps remain in their confidence in handling several aspects of thyroid cancer survivorship care. Guidelines were noted as important in influencing management; therefore, developing guidelines that specifically delineate PCP roles in thyroid cancer survivorship may present one opportunity to increase PCPs’ confidence about their participation.
National evidence-based oncology navigation metrics: Multisite exploratory study to demonstrate value and sustainability of navigation programs.
Authors: Strusowski Tricia , Johnston Danelle 
Published: May 2020

e14040 Background: As evidence guides practice, it is essential for navigation programs to identify core metrics and standardize data collection to demonstrate program outcomes. Through extensive literature review three main areas of measurements have been defined as patient experience (PE), clinical outcomes (CO), and business performance or return-on-investment (ROI) metrics. To advance the field towards the goal of standardized metrics, a team from Academy of Oncology Nurse & Patient Navigators (AONN+), American Cancer Society (ACS), and The Chartis Group, Chartis Oncology Solutions Practice conducted a first of its kind pilot study to assess the extent to which navigation programs could implement 10 of the 35 AONN+ national evidence-based metrics with the goal of validating these metrics and establishing benchmarks. Methods: Using a mixed methods approach, the study team selected eight sites to collect the metrics over a six-month period. Metrics data were uploaded into the ONC iQ NAV metrics cloud-based business intelligence platform to create participant-specific dashboards. Prior to study launch, sites also submitted three years of historical data, as available. Qualitative data on facilitators and barriers to metrics. Results: The following are the results of the study, (total patients navigated 4682). 10,295 total barriers identified, 2.2 barriers/pt. Time to treatment decreased by 11 days (initial 57 days decreased to 43 days). Readmission rates decreased: 30-day readmission 10.8%-9.8%, 60-day readmission 15.5%-14.3%, 90-day readmission 17.9% - 16.3%. Psychosocial distress screens: 1,987 completed (42% of patients screened). Social support referrals: 0.4/patient. Palliative care referrals: 687 pts. 15%. Satisfaction/Patient Experience survey: discuss goals 82%, encouraged participation in decisions 90%, listened to your needs 82%, listen to emotional concerns 82%, assisted with additional needs 74%. Conclusions: Further research will be needed in navigation metrics.
Patient trial matching using pseudo-siamese network.
Authors: Xiao Cao , Gao Junyi , Glass Lucas , Sun Jimeng 
Published: May 2020

e14050 Background: Clinical trials suffer from insufficient patient (pt) recruitment. The availability of electronic health records (EHR) and trial eligibility criteria (EC) is promising for data driven pt-trial matching. The objective is to find qualified pt given patients' EHR and trial EC in unstructured text EC. Pseudo-Siamese network is a novel subfield within information retrieval and has shown great success in the cross-modal information retrieval problems such as semantic image-text retrieval (e.g., match images with text descriptions). The objective is to find the match between pts and clinical trials using Pseudo-Siamese network based cross-modal retrieval. Our model addresses the following challenges: (1) How to match unstructured EC text with structured EHR where EC often encode more general disease concepts and EHR represent pt conditions using more specific medical codes. (2) How to capture pts' evolving health conditions. (3) How to explicitly handle the difference for inclusion and exclusion criteria. Methods: Our matching model addresses these challenges as follows: (1) we augment the medical codes in pts’ records with their textual descriptions and hierarchical taxonomies, such that concepts can be embedded in finer and more coarse levels for better concept alignment across pt data and ECs. (2) We include an attentive dynamic memory network that extracts the best matching and more recent pt EHR to match with ECs. (3) We introduce a composite loss term to maximize the similarity between pt records and inclusion criteria while minimizes the similarity between pt records and exclusion criteria. Results: We evaluated our model on a pt-trial match dataset on the ECs collected from 590 clinical trials from ClinicalTrials.gov. We also extract 83,371 pt claims data from IQVIA database collected (2002-2018), where each pt is eligible for at least one trial. We compared our model with leading pt-trial matching models. Our model significantly outperforms the best baseline model by 24.3% relatively higher accuracy score. We also tested these models in 34 oncology trials in 25 cancers. Results will be reported. Conclusions: Pseudo-Siamese network successfully solved the cross-modal information retrieval problems. We therefore propose a new pt-trial matching model based on Pseudo-Siamese network model. Experiments on real-world datasets demonstrated that our model significantly outperforms existing works in pt-trial matching for oncology trials.
Automated abstraction of real-world clinical outcome in lung cancer: A natural language processing and artificial intelligence approach from electronic health records.
Authors: Ma Meng , Redfern Arielle , Zhou Xiang , Li Dan , Ru Ying , Lee Kyeryoung , Gilman Christopher , Liu Zongzhi , Jones Scott , Mai Yun , Deitz Matthew , Gong Yunrou , Mullaney Tommy , Prentice Tony , Chen Rong , Schadt Eric , Wang Xiaoyan 
Published: May 2020

e14062 Background: Real world evidence generated from electronic health records (EHRs) is playing an increasing role in health care decisions. It has been recognized as an essential element to assess cancer outcomes in real-world settings. Automatically abstracting outcomes from notes is becoming a fundamental challenge in medical informatics. In this study, we aim to develop a system to automatically abstract outcomes (Progression, Response, Stable Disease) from notes in lung cancer. Methods: A lung cancer cohort (n = 5,003) was obtained from the Mount Sinai Data Warehouse. The progress, pathology and radiology notes of patients were used. We integrated various techniques of Natural Language Processing (NLP) and Artificial Intelligence (AI) and developed a system to automatically abstract outcomes. The corresponding images, biopsies and lines of treatments (LOTs) were abstracted as attributes of outcomes. This system includes four information models: 1. Customized NLP annotator model: preprocessor, section detector, sentence splitter, named entity recognition, relation detector; CRF and LSTM methods were applied to recognize entities and relations. 2. Clinical Outcome container model: biopsy evidence extractor, lines of treatment detector, image evidence extractor, clinical outcome event recognizer, date detector, and temporal reasoning; Domain-specific rules were crafted to automatically infer outcomes. 3. Document Summarizer; 4. Longitudinal Outcome Summarizer. Results: To evaluate the outcomes abstracted, we curated a subset (n = 792) from patient cohort for which LOTs were available. About 61% of the outcomes identified were supported by radiologic images (time window = ±14 days) or biopsy pathology results (time window = ±100 days). In 91% (720/792) of patients, Progression was abstracted within a time window of 90 days prior to first-line treatment. Also, 72% of the Progression events identified were accompanied by a downstream event (e.g., treatment change or death). We randomly selected 250 outcomes for manual curation, and 197 outcomes were assessed to be correct (precision = 79%). Moreover, our automated abstraction system improved human abstractor efficiency to curate outcomes, reducing curation time per patient by 90%. Conclusions: We have demonstrated the feasibility and effectiveness of NLP and AI approaches to abstract outcomes from lung cancer EHR data. It promises to automatically abstract outcomes and other clinical entities from notes across all cancers.
Clinical trial feasibility assessment and start-up tool (CTFAST).
Authors: Tice Amanda , Dima Richard , Saif Wasif M. , Panzo Melissa , Vaiselbuh Sarah R. 
Published: May 2020

e14089 Background: Selecting the right clinical trials for patients remains a challenging job. The better the match between a clinical trial and the target patient population before conducting a study, the more likely the study will successfully reach the target goal of recruitment. We developed a feasibility assessment scoring system based on our vast clinical research experience, managing multiple sites across a large health system, applicable to oncology as well. Methods: Our feasibility team (FT) is responsible for identifying clinical trials and determining if they are an appropriate match for our institution and unique patient population. Once a potential trial is identified, FT performs a feasibility assessment of the research protocol to determine the resources required to conduct the trial. We developed a Clinical Trial Feasibility Assessment & Start-up Tool (CTFAST) to help sites streamline their feasibility assessments and track their trial through the start-up phase. With CTFAST a feasibility score is generated based on the cumulative value assigned to several items such as: sponsor, study type, pharmacokinetic studies, trial phase, etc. A feasibility scale is assigned as follows: > 25 – Accepted; 15-25 – On Hold; < 15- Rejected. Accepted studies are assigned a color coded priority track (fast, intermediate, routine), that allows team members to prioritize their studies accordingly. Once a study is accepted, the study is processed for enrollment. Results: CTFAST has increased productivity and clinical trial revenues by 40%. CTFAST allows for early identification of bottlenecks in workflow, thereby improving outcomes. By appropriately matching of clinical trials to our site, enrollments increased by 50% with an expanded clinical trial portfolio across 9 different departments. Study start-up times have been reduced to a minimum of 21 days and the use of time & effort has been optimized. Conclusions: CTFAST is replicable across all clinical trial sites and provides an expansive and critical feasibility analysis that is not attainable by traditional querying of investigators and questionnaires. It is an excellent work flow improvement tool as it critically analyzes all aspects of a study, prior to enrollment. When conducting an effective feasibility analysis the clinical trial site can optimize clinical trial outcomes.
Assessment of current approaches of starting dose selection and dose escalation for oncology biologics in first-in-patient trials.
Authors: Liao Kai Hsin , Chen Xiaoying , Beaupre Darrin M. , Yin Donghua , Udata Chandrasekhar 
Published: May 2020

e14093 Background: Current approaches for starting dose selection for first-in-patient (FIP) trials of oncology biologics vary according to pharmacological properties and modality of the investigational agent. For biologics with immune agonistic properties, the FIP starting dose is typically based on the minimal anticipated biological effect level (MABEL). For non-immune activating biologics, the starting dose is typically based on allometric scaling of a threshold toxic dose determined in the most appropriate animal species. The aim of this work is to assess the performance of current approaches of FIP starting dose selection and dose escalation for oncology biologics. Methods: FIP clinical trials for oncology biologics licensed by the US FDA from 2010 to 2019 were analyzed to extract information related to starting dose and dose escalation. Source materials for the relevant FIP trials included the Drugs@FDA database, PubMed, and conference proceedings. The data were further analyzed to evaluate parameters related to appropriateness of the starting dose and dose escalation efficiency, which include the number of cohorts to reach the maximum tolerated dose [MTD] (or, when MTD was not identified, the highest human dose [HHD]) and the ratio of MTD/HHD to starting dose. Results: The 26 oncology biologics licensed by the US FDA between 2010 and 2019 included 8 ADCs/immunotoxins, 7 checkpoint inhibitors and 11 other biologics (monoclonal antibody, fusion protein or CD3 bispecific construct). The majority (69%) of these biologics were well tolerated without MTD identified in the FIP trials. All 26 FIP trials with biologics appeared to follow ICH S9 Guidance for starting dose selection, and the dose increment ranged from 1.2- to 10-fold. These FIP trials had a median of 6 cohorts (range: 3-10) to reach MTD (or HHD) from the starting dose. About 50% of these trials involved ≥ 6 cohorts to reach MTD (or HHD). The MTD/HHD to starting dose ratio was > 100 for 4 of the biologics. The median label dose to starting dose ratio was 10 (range: 1-3200), suggesting that a large number of patients in these FIP trials received subtherapeutic dose levels. Conclusions: Current approaches of starting dose selection and dose escalation led to a very low starting dose and many dose escalation cohorts for the FIP trials of a large portion of recently licensed oncology biologics. There exist opportunities to improve current approaches to increase FIP trial efficiency and reduce the number of patients receiving subtherapeutic dose levels in these trials.