bluuebunny/crossref_metadata_2025 huggingface dataset:
Access to breast/ovarian genetic testing: 20 years of a multidisciplinary program.
Authors: Vaz Fatima H , Duarte Teresa , Teixeira Pedro , Sepulveda Rosario , Louro Pedro , Rodrigues Paula , Coelho Irina , Parreira Joana , Rodrigues Fátima , Clara Ana , Luis Ana , Miguel Isalia , Bento Sandra , Dupont Juliette , Machado Patricia , Santos Sidonia , Fragoso Sofia
Published: May 2020

e14015

Background: After institutional and ethical review, a multidisciplinary Breast/Ovarian Cancer Family Risk program started in January 2000. Core team included medical oncologists, molecular biologists, medical geneticists and specialized nurses. Besides access to genetic testing (GT), predictive risk analysis and tailored surveillance plans were provided for women without criteria for GT, and carriers of germline pathogenic/likely pathogenic variants (PV/LPV) were included in prospective follow up. Methods: Descriptive statistics concerning patients (pts), medical visits and genetic tests performed under the program from January 2000-December 2019. Results:A total of 28400 medical appointments (including multidisciplinary meetings with radiology, breast and plastic surgery and gynecology) were performed for 10100 pts: 9281 women (92%) and 819 men (8%), with an average of 46,0 years (45,6 and 51,1 respectively). Most pts were referred from our cancer center or were invited for cascade testing (8044, 79,6%), although we accepted referrals from external health services (2056 pts). GT: 5665 tests performed (4454 for index pts (IP) and 1211 for family relatives); 3170 (71%) IP consented only in BRCA1/2 and 1284 IP (29%) in sequential/panel testing. Detection rate for IP testing was 12,5% [440 BRCA1/2; 118 Other Hereditary Syndromes (OSH)]. OHS IP: CHEK2(32), ATM(14), TP53(11), MUTYH(12), PALB2(10), RAD51D(10), RAD51C(9), BLM(5), BRIP1(4), RAD50 (4), PTEN(2), MSH2(2), MLH1(1) FAM175A(1), BARD1(1). Six- hundred and twenty-six IP with previous inconclusive results consented on panel testing and 62(9,9%) were diagnosed with a non- BRCA1/2 PV/LPV. Until June 2019, 915 BRCA1/2 pts were included in a registry for prospective follow up. Conclusions: our program has allowed for access to Breast/Ovarian GT and risk management in the National Health Service, for the past 20 years. Twenty per cent of all pts are referrals from external health services. The ever increasing challenges to the resources of the program include: new indications for GT, the increase in priority pts for GT due to more therapy options, risk dynamics in previous registered families, evolving molecular methodologies and the management of families with complex genetic variants. The growing number of pts, including men, led to a recent change in the label of the program (Breast/Ovarian/Prostate Cancer Family Risk) and new approaches to promote access of specific populations to GT (like telegenetics) are being tested.
Descriptive evaluation of frequent patient referrals to our cardio-oncology clinic: The Mayo experience.
Authors: Zahid Anza , Peethambaram Prema P. , Thompson Carrie A. , Liu Minetta C. , Ruddy Kathryn Jean , Luis Sushil s. , Anastasius Malcolm s. , Badawy Mohamed s. , Jain Vaibhav s. , Smith Deanne s. , Herrmann Joerg , Villarraga Hector R.
Published: May 2020

e14018

Background: Cancer survival rates are improving. Therefore, management of cardiovascular complications has now become a crucial clinical concern. Cardio-oncology is the sub-specialty that assists in the overall management of cancer patients in a multi-disciplinary manner. Mayo Clinic cardio-oncology practice was initiated to work closely with our oncology colleagues for early detection of cardiovascular complications in response to cancer-therapy. Majority of the patients visit our cardio-oncology clinic once, we thought it is important to study the group of patients that visited frequently due to cardiovascular complications. Aim: To evaluate the most common cardiovascular complication in patients with 2 or more visits to our cardio-oncology clinic. Methods: From 2012-2017, there were > 2500 patients visits to our clinic, with 24 patients having 2 or more visits. Data including patients’ demographics, ethnicity, chemotherapeutic medications, primary cancer type, cardiovascular risk factors, echocardiography and clinical outcomes were collected. Cardiotoxicity was defined as the decrease in left ventricular ejection fraction (LVEF) of > 10% to a value of < 53%. Heart failure was diagnosed based on Framingham’s criteria or by a cardiologist. Results: There were 19 women (80%) and 5 men (20%). Median age at the time of diagnosis was 56 years [19-76]. The most common malignancy was breast cancer (70%), followed by B-cell lymphoma (12%) and acute myeloid leukemia (8%). Thirty percent had > 2 risk factors for cardiovascular disease. 75% of the patients had an LVEF of < 53, of these 67% developed heart failure with 58% preserved and 42% reduced ejection fraction. Those with heart failure had received a mean anthracycline dose of 305 ± 91.8mg/m2. With initiation of ACEI, B-Blockers, and diuretics (GDMT) 79% showed recovery of LVEF to ≥53 during the follow up. Conclusions: In our experience, most patients who were seen at least twice in the cardio-oncology clinic for heart failure had received a dose of > 300mg/m2 anthracycline. With GDMT over 75% of the patients recovered. Care in the cardio-oncology clinic plays a key role in optimizing these clinical outcomes.
Clinical trials navigator: Patient-centered access to clinical trials.
Authors: Hamm Caroline M. , Naccarato Krista , Sundquist Stephen , Kovacevic Suzana , El-Abed Youshaa , Dancey Janet
Published: May 2020

e14024

Background: Despite recommendations from premier institutions such as NCCN that all cancer patients should be entered on clinical trials, only 3 – 5 % of adult cancer patients are enrolled on clinical trials in North America. The reason for this is multi-factorial and includes poor trial design, inappropriate endpoints, inappropriate inclusion/exclusion factors, attitudes about trial participation held by patient and/or treating physician and lack of trial availability. Methods: To address issues of trial availability, in March 2019, we initiated a novel service to help Canadian patients find clinical trial options. The service compares patient demographic and health status information provided against potential opportunities sourced using clinicaltrials.gov and Canadian clinical trials websites. A report presenting outcomes of CTN review is developed for the requesting patient or physician. An interview is provided for the self-referring patient by supporting physicians. Results: To date 96 patients have used this service. Most (94%) were stage IV or refractory/ relapsed. Smaller disease sites represented 23% of our patient population (brain, sarcoma, pancreas). Our turn-around-time from request of services to delivery of report to patient or physician improved over time and is currently 24 hours during the working week. Of those eligible, 25% of patients died before referral, the median time from referral to the CTN to the patient’s death was 109 days (3 – 188 days). Conclusions: Significant interest from both physicians and patients for this service was identified. Strategies are being developed to encourage earlier referrals to clinical trials would improve number of patients entering clinical trials as 25% of our patients. [Table: see text]
Patient-reported outcomes integrated within electronic medical record in patients with head and neck cancer.
Authors: Ossowski Stephanie , Kammerer Amy , Basch Ethan M. , Katzel Jed Abraham
Published: May 2020

e14026

Background: Patient-reported outcome (PRO) tools have been shown to improve outcomes including overall survival for cancer patients. However, routine implementation of PROs in clinical practice within the electronic medical record (EMR) remains limited. We studied the use of a PRO tool for patients with head and neck cancer (HNC) integrated in an EMR. Methods: Between August 2017 and December 2019 patients with newly diagnosed HNC were surveyed at baseline, then every 3 months using the FACT-G7 and FACT H&N (Version 4). A medical assistant (MA) performed a baseline survey around the time of diagnosis and then notified patients electronically every 3 months for two years and then every 6 months until patients completed their 5 years of surveillance. Patients who did not respond to online PRO surveys could complete them via telephone appointments with MAs, or in office pre-rooming. Abnormal findings on PRO surveys were referred to appropriate members of the care team or the treating ENT physicians. Results: 290 patients received baseline surveys. Patients received up to a maximum of 8 subsequent surveys (table). Out of a total of 597 electronic surveys, 585 (97.9%) were completed. The percentage of patient completing each survey ranged from 92-100%. Conclusions: PRO tools can be effectively deployed within the EMR for HNC patients with a high completion rate provided that there is strong engagement from a dedicated member of the care team. This has important implications for designing clinical trials and symptom monitoring in clinical practices that incorporate EMRs.
The impact of integrating an oncology nurse practitioner into an acute care hospital emergency department: An Australian tertiary cancer centre experience.
Authors: Blanchard Gillian , Bonaventura Antonino , Dafters Nicholas , Day Fiona , Gedye Craig , Gildenhuys Johann , Lombard Janine Margaret , Lynam James F. , Mallesara Girish , Nordman Ina , Van Der Westhuizen Andre , Mandaliya Hiren A.
Published: May 2020

e14029

Background: A significant delay in oncology patient journey through emergency department (ED) due to various reasons such as multiple specialists involved, difficulty in accessing timely advice from extremely busy inpatient/outpatient oncology teams, etc. In order to streamline admissions and discharges for oncology patients, a new model of care was created with the introduction of oncology nurse practitioner (ONP) at Calvary Mater Newcastle ED. This model operated during business hours three days a week with an aim to improve continuity of care, to enhance patients’ experience and meet organisations key performance indicators (KPIs). Limited research into the impact of these services on patient care and patient experience, particularly from service users’ perspective. Methods: A retrospective audit (6 months) has been undertaken to determine the impact of this model of care on time to specialty transfer, number of admissions versus discharges and patients representations through ED when ONP was available versus standard care. Results: During the audit period, ONP reviewed 149 patients. Fifty-four (36%) patients were discharged and of those 6 (11%) returned (within 28 days) with same or related issues. There was an average reduction in time to disposition planning for ED oncology patients of approximately 83 (193 vs 110) minutes when ONP was present at ED (one month review). Using a raw bed day costing, this resulted in a significant financial saving. Further cost analysis of this model is underway. Conclusions: The introduction of ONP into an acute care hospital ED has proven to be effective in terms of continuity of patient care, financial savings and assisted ED in meeting KPIs. The perception within the senior emergency management team is that this new model of care has been an unqualified success. ONP has rapidly and effectively joined emergency team and has seamlessly adjusted practice to the new environment. ED remains highly supportive of this new model of care. This model of care is one that could be translated easily to other specialities. Further analysis will be presented at the time of ASCO meeting.
Associations between patient-reported outcomes and physiatry assessments in an integrative model of a survivorship clinic.
Authors: Latif Hira , Graves Kristi D , Kline-Quiroz Cristina , Martone Patrick , Dilawari Asma Ali
Published: May 2020

e14032

Background: The Institute of Medicine and Commission on Cancer recommends delivery of comprehensive survivorship care to all cancer survivors. While models exist for high-quality survivorship care, institutions encounter barriers such as lack of resources and training in cancer survivorship. We introduce a shared-care model with physical medicine and rehab (PM&R) to provide comprehensive care to cancer survivors at MedStar Washington Hospital Center. Methods: We implemented a bimonthly survivorship clinic in September 2018, staffed by a medical oncologist, oncology fellow and a cancer rehabilitation fellow. We assessed patient reported outcomes through PROMIS short forms for physical functioning, companionship, satisfaction with social roles and depression. Physiatry assessments included the 6-minute walk test and the Timed Up and Go test. All patients received a treatment summary and survivorship care plan. Results: We evaluated 30 cancer survivors between Sept 2018 and December 2019; mean age was 55.6 years (SD = 10.6 years). Majority were female (60%) and Black (60%). Most patients were overweight or obese (93%) with a mean body mass index of 30.6 mg/m2 (SD = 4.7). Breast (43%) and hematologic malignancy (33%) were the most common cancer diagnoses. The median time between cancer remission and the clinic visit was 16 months (Range = 1 to 65 months). The average score for Timed Up and Go test was within the reference normal value of < 12 sec (8.22 seconds, n = 23). Average distance for the 6-minute walk test was 465.87 meters (n = 18). Survivor age was negatively correlated with the distance walked for the 6-min walk test (r = -0.51, p = 0.027). Better psychosocial functioning assessed with PROMIS were significantly associated with lower scores on the Timed Up and Go Test: satisfaction with social roles (r = -0.67, p = 0.033) and companionship (r = -0.64, p = 0.046); we identified a trend between Timed Up and Go scores and depression (r = 0.47, p = 0.099). Conclusions: An integrative survivorship clinic to provide multidisciplinary survivorship care is a feasible model and results suggest that patients’ physical functioning is significantly associated with psychosocial outcomes. Our approach highlights the importance of assessing both physical functioning and patient-reported outcomes. Future research can evaluate assessments of these outcomes over time.
Perceptions of cancer care coordination in patient-family caregiver dyads.
Authors: Okado Izumi , Pagano Ian , Hewitt Tracey , Azevedo Kelnia , Guillermo Cherie , Fukaya Erin , Chirieleison Kasimira , Cassel Kevin , Holcombe Randall F.
Published: May 2020

e14036

Background: Family caregivers (FCGs) can play a key role in coordinating care for cancer patients. However, little is known about FCGs’ perspectives of care coordination (CC). In this cross-sectional study, we evaluated perceptions of CC in cancer patient-family caregiver dyads using the Care Coordination Instrument (CCI), a validated self-report measure with excellent psychometric properties. Methods: Patients receiving active treatment for cancer and their primary FCGs ( N = 54 dyads) completed the 29-item CCI (patient) and the CCICG (a parallel FCG version) at private oncology practices or hospital-based facilities from June to Sept. 2019. The CCI and CCICG assess overall perceptions of CC (Total) and across 3 domains: Communication, Navigation, and Operational. The CCICG includes a supplemental questionnaire that assesses the degree of caregiver CC involvement on a 4-point Likert scale. Mixed regression models were used to examine differences between patients and FCGs’ perceptions of CC and to identify predictors of dyadic differences on the CCI scores. Pearson’s correlation was used to evaluate associations between FCGs’ perceptions of CC and the degree of caregiver involvement. Results: CCICG Total scores demonstrated a bimodal distribution, representing FCGs’ perceptions of CC in distinct high and low subgroups; thus, all analyses were conducted by subgroups (highCG, lowCG). Overall, no dyadic differences were found in the highCG group on the CCI scores. However, in the lowCG group, FCGs reported poorer Total ( p < .01), Communication ( p < .01), Navigation ( p < .01), and Operational ( p < .05) scores than patients. Provider setting (private practices) and the absence of an identified patient navigator significantly predicted poorer FCG Total scores compared to patients’ perceptions ( p < .05). FCGs’ Total scores were inversely associated with the degree of caregiver involvement among FCGs in the lowCG group ( p < .05) but not among FCGs in the highCG group. Conclusions: Our findings demonstrate that a subgroup of FCGs has poorer perceptions of CC that differ significantly from patient perceptions and is most apparent for patients receiving treatment in a private practice setting and in the absence of a patient navigator. These FCGs report a high degree of caregiver involvement and may be providing additional CC support that is not recognized by cancer patients. These findings underscore the need for interventions to support FCG CC involvement as a member of the care team in order to improve the quality and value of patient-centered cancer care delivery.
4R Care Delivery Program results: Impact of implementation metrics on patient self-management and 4R usefulness.
Authors: Trosman Julia Rachel , Weldon Christine B. , Makower Della F. , Rapkin Bruce D. , Perez Claudia B. , Kulkarni Swati , Schaeffer Cathleen M. , Hoskins Kent , Gallagher Christopher , Lo Shelly S. , Grace-Louthen Cathy , Throckmorton Alyssa , Krueger Editha , Van Horn Jennifer , Reaves Brieyona Chardee , Ravelo Arliene , Yu Elaine , Schapira Lidia , Benson Al Bowen , Gradishar William John
Published: May 2020

e14039

Background: We previously proposed a 4R care delivery model that facilitates teamwork and patient self-management (pSM) in cancer care (NCI ASCO Teams Project, Trosman JOP 2016). 4R (Right Info / Care / Patient / Time) enables patient and care team to manage complex care with an innovative 4R Care Sequence. We tested 4R at 10 US sites in a stepwise mode 2016-2019. Methods: Step 1 included 1 academic and 2 nonacademic sites; step 2 included 3 academic and 4 nonacademic sites. Patients with stage 0-III breast cancer received 4R Sequences (4R Cohort). We surveyed 4R and historical control cohorts of patients treated at the same sites pre-4R. We assessed the impact of implementation metrics on usefulness of 4R to the 4R cohort and on improvement of pSM in 4R cohort vs historical control cohort. Results: Survey response rates: 63%, 422/670 (4R cohort); 47%, 466/992 (control cohort). Three of the five implementation metrics significantly impacted patient usefulness of 4R (Table). Patients at step 2 sites; sites with a systematic care process; and sites with small practices reported significantly higher 4R usefulness than patients in the comparison subgroup within respective metrics. 4R usefulness was not affected by practice setting or availability of patient navigators. pSM was significantly improved between control and 4R cohorts along all implementation metrics (p < .001), but the magnitude of incremental improvement between comparison subgroups varied across metrics (Table), with the largest increment associated with the program step metric. Conclusions: 4R is useful to patients across settings, with or without patient navigators. Stepwise design is effective in increasing 4R impact overtime. Future 4R Program will investigate an expanded array of implementation metrics and their influence on 4R outcomes. [Table: see text]
Prospective evaluation of patients for stereotactic radiosurgery/radiotherapy (SRS/SRT) by an experienced SRS/SRT radiation oncologist at a hospital-based cancer center lacking SRS/SRT capability.
Authors: Stacy Don , Rucker Christopher , Cottongame Kimberly , Combs Lester , Jones Chad , Cornett Hobie , Ivey Jessica
Published: May 2020

e14043

Background: To prospectively determine the need for SRS/SRT at a hospital-based cancer center lacking SRS/SRT capability. Methods: An experienced SRS/SRT radiation oncologist prospectively evaluated new and established patients for radiation treatment at a hospital-based cancer center from July 30, 2018 through September 20, 2019. All radiation treatment options (per the NCCN Practice Guidelines in Oncology) were explained in detail to each patient. The radiation oncologist’s specific treatment recommendation to each patient was based on the radiation oncologist’s expertise. Cases for which the radiation oncologist recommended SRS/SRT were recorded. Results: From August 06, 2018 through September 25, 2019 an experienced SRS/SRT radiation oncologist evaluated 177 new or established patients for radiation treatment and recommended SRS/SRT for 23 patients (6 lung, 2 adrenal, 9 brain, 3 spine, 1 pituitary, 1 bone, and 1 prostate). Conclusions: To provide comprehensive radiation oncology patient services, SRS/SRT capability is required at a hospital-based cancer center evaluated by an experienced SRS/SRT radiation oncologist in this report, as SRS/SRT was recommended for twenty-three of one hundred seventy-seven (13%) patients evaluated for radiation treatment in a fourteen-month period.
Oncology volume assessment program: Novel approach to determine patient volumes at Vanderbilt-Ingram Cancer Center (VICC) and Vanderbilt University Medical Center (VUMC).
Authors: Ekstrom Leeland , Gibson Michael K. , Berlin Jordan , Schneider Judsen , Stover James
Published: May 2020

e14047

Background: There is currently a lack of easily-accessible data regarding the volume and type of cancer patients seen at VUMC, despite extensive use of an electronic medical record (EMR) with a dedicated tumor registry. This leads to errors in estimating metrics that depend on subject accrual to clinical trials. We aimed to develop a tool to determine the number of head and neck squamous cell carcinoma (HNSCC) patients, stratified by desired subsets, seen yearly at the VUMC in order to achieve a more accurate and efficacious approach to estimating future patient accrual to clinical trials. Methods: Working with Nashville Biosciences, we identified patients with HNSCC using ICD codes in combination with VUMC’s Tumor Registry data (collected for patients diagnosed and treated at VUMC). We estimated the rate of accrual of HNSCC patient blood samples (a proxy for ICD code) based on the number of specimens acquired over the past 3 years. Each patient was counted only once based on their first blood sample acquired during this period, based on a comprehensive list of laboratories and clinical procedures that require a specimen to be drawn. We then evaluated the distribution of patients by primary site, standardized to SEER registry terminology. We also investigated what fraction of patients received treatment with surgery, radiation, and/or chemotherapy (we expect that this includes treatment with biologics) using a combination of ICD9/10 and CPT coding. Results: We identified a total of 1,131 HNSCC patients. SEER summary staging information was available for 603 patients - 432 (72%) had Stage 1-3 disease and 171 (28%) had Stage 4 disease. Primary site data was available for 717 patients - 648 (90%) HNSCC of the oral cavity, 20 (3%) had HNSCC of the oropharynx, 29 (4%) HNSCC of the larynx, 20 (3%) HNSCC of the hypopharynx. Of all patients, we found that 598 had at least 1 code for chemotherapy, 540 for radiotherapy, and 825 for surgery such that the majority (96%) received cancer treatment. We have not evaluated the timing of the treatment relative to diagnosis. Conclusions: By using ICD codes and tumor registry data extracted from VUMC’s EMR, we determined the site distribution and type of treatment for HNSCC over a 3-year period. The disease site distribution did not match prior experience Refinements are ongoing. However, once improved, these methods may be used to estimate future clinical trial accrual, thus reducing start-up time, cost and poor trial site performance, while increasing accrual.
Clinical evaluation and performance of QIAGEN Clinical Insight Interpret (QCI-I) as a reporting tool for a comprehensive cancer panel (TSO 500).
Authors: Sahajpal Nikhil Shri , Mondal Ashis , Ahluwalia Meenakshi , Kota Vamsi , Njau Allan Njoroge , Okechukwu Nwogbo , Kohle Ravindra
Published: May 2020

e14048

Background: The rapid adoption of next-generation sequencing (NGS) methods into clinical diagnostic laboratories have revolutionized the field of genetic testing. To interpret the exhaustive NGS data, bioinformatics has become an inevitable component of any laboratory implementing a clinical NGS test. However, there is an unmet need for accurate, efficient and standardized variant interpretation for actionable insights. In this effort, AMP/ASCO/CAP released a joint consensus recommendation proposing a four-tiered system to categorize somatic alterations based on their clinical significance in cancer diagnosis, prognosis, or therapeutics for standardization of interpretation and reporting of results among laboratories. Thus, the aim of the study was to assess and validate QIAGEN Clinical Insight (QCI) Interpret software for clinical laboratory test interpretation. Methods: 67 malignant cases (myeloid neoplasm and lung) were analyzed using the TSO-500 NGS assay and secondary analysis platform (Illumina, San Diego, CA). The data was uploaded and analyzed using QCI Interpret. Patient case draft reports were produced using a TS0-500 customizable workflow with automated variant filtering, and variant classifications. Results: Data analysis on 67 cases resulted in a total of 251 alterations (SNVs and Indels). A total of 77 and 174 alterations were reported as pathogenic/ likely pathogenic into tier based classification by the QCI Interpret for myeloid and lung malignancies, respectively. For myeloid malignancies, the QCI Interpret was able to detect accurately all the clinically relevant variants and listed them into appropriate tier-based classification compared to the expertly reported alterations. For lung malignancies, 16 alteration were classified in tier 1, 96 alteration in tier 2, 61 alteration in tier 3, and 1 alteration in tier 4. The QCI interpret was able to detect 100% of the alterations being validated, and reported them into tier based classification. Conclusions: Systematic evaluation of an automated classification based on AMP/ASCO/CAP using an up to date knowledge base is critical to accurately classify actionable mutations and detect emerging relevant mutations. The QCI Interpret seems to be a critical bioinformatics tool that adheres to the AMP/ASCO/CAP classification system and incorporates TMB and MSI values from the TSO500 assay, which can be used across clinical laboratories for accurate, efficient and standardized variant interpretation for actionable insights.
Development of an algorithm using natural language processing to identify metastatic breast cancer patients from clinical notes.
Authors: Swaminathan Krishna Kumar , Mendonca Emma , Mukherjee Pranay , Thirumalai Karpagavalli , Newsome Rachel , Narayanan Babu
Published: May 2020

e14056

Background: Determination of the metastatic status of a patient is important for outcomes research and candidacy for clinical trials. Structured data in EMR may not always capture the metastatic status, and it is useful to extract it automatically from physician notes. Contextual understanding of the notes is important to resolve issues such as a) local vs distal metastasis b) statements involving family history of metastasis or physician instructing the patient to look for certain signs of metastasis c) text indicating suspicion of metastasis or absence of metastasis d) indirect utterances, e.g. cancer has spread to the bone. e) corrections to previous findings. Methods: We used a set of 20138 breast cancer patients from Concerto HealthAI real world oncology dataset that includes data from CancerLinQ Discovery to build & validate the set of NLP algorithms. 5300 sentences from 1500 patients were annotated & algorithms manually validated by data abstractors for 500 patients. The algorithms developed were the following: 1) Classification of a sentence into 3 classes: Distal/Local metastasis, Suspicious & Other 2) Classification of a sentence into 2 classes: Distal or Local 3) Classification of a patient into 2 classes: Distal metastasis or not distal metastasis 4) Multi label classification for detecting sites of metastasis. Sentence level algorithms were built using Deep Learning and patient level aggregation of sentence level prediction was done using ML approaches including temporal features. Pretrained ULMFiT model was fine-tuned with Concerto HealthAI’s corpus for sentence classification tasks. Results: At a sentence level, we obtained an accuracy of 0.85 for the distal/local vs suspicious vs irrelevant model and 0.97 for the distal vs not distal metastasis model. Our patient level metrics are shown in the table. The classes used for sites of metastasis are Brain, Bone, Lung, Liver, Distant Lymph nodes & Unknown sites. Subset accuracy (mean fraction of labels which match ) of 0.93 was obtained on the hold out test set at patient level. Conclusions: Metastatic status & site of metastasis can be reliably extracted automatically from clinical notes using deep learning techniques. This information will be valuable for clinical trial matching, outcomes research and other applications. [Table: see text]
A review of gynecological cancers studies of concordance with individual clinicians or multidisciplinary tumor boards for an artificial intelligence-based clinical decision-support system.
Authors: Arriaga Yull Edwin , Hekmat Rezzan , Draulis Karlis , Wang Suwei , Preininger Anita M , Felix Winnie , Dankwa-Mullan Irene , Rhee Kyu , Jackson Gretchen Purcell
Published: May 2020

e14070

Background: Watson for Oncology (WfO) is an artificial intelligence-based clinical decision-support system that offers potential therapeutic options to cancer-treating physicians. We reviewed studies of concordance between therapeutic options offered by WfO and treatment decisions made by individual clinicians (IC) and multidisciplinary tumor boards (MTB) in practice in gynecological cancers. Methods: We searched PubMed and an internal database to identify peer-reviewed WfO concordance studies of gynecological cancers published between 01/01/2015 and 06/30/2019. Concordance was defined as agreement between therapeutic options recommended or offered for consideration by WfO and treatment decisions made by IC or MTB. Mean concordance was calculated as a weighted average based on the number of patients per study. Statistical significance was evaluated by z-test of two proportions. Results: Our search identified 5 retrospective studies with 635 patients with cervical and ovarian cancers in China and Thailand; 4 compared WfO to MTB and 1 to IC. Overall WfO concordance with MTB and IC for both cancers was 77.2% (SD 11.6%). The concordance between MTB and WfO in cervical and ovarian cancers was 80.5% and 86.2%, respectively ( P = .21); IC concordance with WfO in cervical and ovarian cancers was 65.2% and 73.2%, respectively ( P = .18). MTB concordance with WfO for both cancers combined was 81.5%, significantly higher than the 67.9% IC concordance with WfO for both cancers ( P = .01). Conclusions: Studies of cervical and ovarian cancers demonstrated a statistically significantly higher concordance of MTB and WfO than IC and WFO, suggesting a role for WfO in supporting treatment-decision making in gynecological cancers that aligns with decisions made by MTB. Larger prospective studies are needed to evaluate the technical performance, usability, workflow integration, and clinical impact of WfO in gynecological cancers.[Table: see text]
Machine learning models to predict the response to anti-cancer therapy in metastatic melanoma patients.
Authors: Goussault Romain , Frénard Cécile , Maubec Eve , Muller Philippe , Martin Ludovic , Legoupil Delphine , Aubin Francois , De Quatrebarbes Julie , Jouary Thomas , Hervieu Alice , Machet Laurent , Varey Emilie , Lecerf Philippe , Vrignaud Florence , Khammari Amir , Dréno Brigitte ,   The RIC-Mel Network
Published: May 2020

e14071

Background: Machine learning methods are new artificial intelligence tools with promising applications in healthcare. We developed and validated 4 machine learning models to predict the response to immunotherapy and targeted therapy in stage IIIc or IV melanoma patients. Methods: This work was conducted on data from 10 centers participating in the French network for Research and Clinical Investigation on Melanoma (RIC-Mel), launched in 2012. Thus, 935 patients, corresponding to 1978 systemic treatments have been extracted from RIC-Mel database. The following data were considered: age, sex, Breslow, melanoma type, ulceration, spontaneous regression, mitotic index, number of invaded lymph nodes, extracapsular extension, mutational status, melanoma stage, number of metastasis sites, lines of treatments, and time between first melanoma excision and metastatic relapse. Treatment response: complete response, partial response, stable disease, defined as class 1 and progressive disease as class 2. We split this cohort/database into a training set (80%) and test set (20%). The algorithm performances were evaluated on the test set by the percentage of treatments correctly classified in class 1 or 2. Four machine learning algorithms (linear model, random forest, XGBoost and LightGBM) were compared in terms of performance and interpretation for both types of treatments. Results: The accuracies of the best models for immunotherapy (LightGBM) and targeted therapy (random forest) were respectively 66% and 65%. The most significant variables for building the models were respectively: stage (IIIc or IV), response to previous treatments lines, age, number of metastasis sites and time between first melanoma excision and metastatic relapse. Conclusions: We present here the first machine learning models to predict the response to immunotherapy and targeted therapy in stage IIIc or IV melanoma patients. The most predictive variables are coherent with the literature. Future development will include data from 18FDG-PET/CT imaging and other predictive markers recently identified, as circulating DNA to improve the models performance.
Enhanced co-expression extrapolation (COXEN) gene selection method for building anticancer drug response prediction models.
Authors: Zhu Yitan , Brettin Thomas S. , Xia Fangfang , Shukla Maulik , Partin Alexander , Yoo Hyunseung , Stevens Rick L.
Published: May 2020

e14073

Background: Accurate prediction of tumor response to a drug treatment is of paramount importance for precision oncology. The co-expression extrapolation (COXEN) gene selection approach has been successfully used in multiple studies to select genes for predicting the response of tumor cells to a specific drug. Here, we enhance the original COXEN approach to select genes that are predictive of the efficacies of multiple drugs simultaneously for building general drug response prediction model. Methods: We implemented two methods to select predictive genes. The first method ranks the genes according to their prediction power for each individual drug and then takes a union of top predictive genes of all the drugs. The second method uses a linear regression model to evaluate the prediction power of a gene for all drugs while the drugs are one-hot encoded in the regression model. Among the predictive genes, we further select genes by evaluating the preservation of co-expression patterns between cancer cases with drug response data available and cancer cases for which drug response needs to be predicted, because the preservation of co-expression patterns indicates the similarity of genomic regulations between cancer cases. Results: To test the enhanced COXEN method, we used a lightGBM regression model to predict drug response based on the selected genes on two benchmark in vitro drug screening datasets. The table below compares the performance of prediction models built based on 200 genes selected by the enhanced COXEN method to that of models built on 200 genes randomly picked from the LINCS gene set, which includes 976 “landmark” genes well-representing cellular transcriptomic changes identified in the Library of Integrated Network-Based Cellular Signatures (LINCS) project. The enhanced COXEN approach selects genes better than random LINCS genes as demonstrated by the increased average coefficient of determination (R2) for predicting the area under the dose response curve through cross-validation. Pair-wise t-test indicates the improvement is statistically significant (p-value ≤ 0.05) on both datasets. Conclusions: Our result demonstrates the benefit of using an enhanced COXEN approach to select genes for building general drug response prediction model. [Table: see text]
Ephesus: A curation workflow tool for the clinical interpretation of genomic variants.
Authors: Li Jian , Niu Lili , Greene Nicole , Douglas Vivian , Schartner Caitlin , Chu Bernie , Kinshuk Fnu , Mu John , Doupnik Austin , Broka Andi , Rao Priyanka , Mohiyuddin Marghoob , Krishna Shuba
Published: May 2020

e14074

Background: Accurate and comprehensive interpretation of genomic variants has become a bottleneck in clinical sequencing applications due to the accelerated implementation of precision oncology and the rapid growth of relevant biomedical findings. We therefore are motivated to build Ephesus, a framework enabling curation of clinical evidence for biomarkers in somatic cancers. Currently Ephesus is the primary content source for Roche NAVIFY Mutation Profiler (NMP). Methods: Ephesus’ data model ensures adherence to best practice in clinical genomic content curation. Variant classification followed the AMP guidelines for somatic variant interpretation. Approvals and recommendations from regulatory agencies and organizations (FDA, NCCN, etc.) are applied on a regional basis. The data model is mapped to a set of PostgreSQL relational tables. Strategies such as data normalization according to standard ontologies, a submit-review-approval workflow with change logs, and biologically relevant datatype constraints are adopted to enforce data integrity. Ephesus is deployed as a web application, of which the UI allows users to conduct edits, filtering, sorting and bulk operations on entities by attributes. To maximize curation efficiency, a number of inference rules are applied before the content is ingested into NMP. Results: Currently Ephesus is developed to guide data collection, browsing and summarizing related to these primary entities: Biomarkers, Evidence items, Genes, Variants, Variant groups, and Drugs. The content exported on 2020/01/27 contains > 170K biomarker profiles (including combinations), in the context of 13 major cancer types. To evaluate the clinical reporting value of the curated knowledge, more than 60k real clinical cancer patient samples from the AACR GENIE project were queried against Ephesus. We compared the results to the latest content from several major knowledgebases. We see that the performance of Ephesus/NMP excels the others. Conclusions: We have developed Ephesus, a practical content curation framework which provides a highly structured and user-friendly environment for clinically relevant somatic biomarkers. The framework improved the productivity and quality of the manual curation, met clinical interpretation scope and complexity, and assures data integrity and auditability. [Table: see text]
Representing cancer clinical trial criteria and attributes using ontologies: An NLP-assisted approach.
Authors: Lee Kyeryoung , Liu Zhongzhi , Ma Meng , Gilman Chris , Mai Yun , Zhou Xiang , Ghosh Krish , Chen Rong , Schadt Eric , Wang Xiaoyan
Published: May 2020

e14079

Background: Low patient recruitment is one of the main reasons clinical trials fail. Identifying eligible patients for clinical trials using electric health records (EHRs) can help reach accrual targets. Ontology reasoning implemented in Trial2Patient, a scalable system we developed for matching patient to clinical trials, forms the basis for generating patient cohorts in our system. For efficient cohort definition, an attribute ontology for eligibility criteria and entity categorization is a necessary first step. To meet this requirement, we constructed an ontology platform for lung cancer trials. Methods: We classified 128 non-small cell lung cancer and 38 small cell lung cancer trials into different therapy groups. Among the 166 trials we examined, 110 were immuno-oncology therapy-based, 48 were targeted therapy-based, and 8 were chemotherapy or device trials. We analyzed the eligibility criteria for each trial manually to identify entities from all trials as well as indication specific and further therapy group specific entities. To incorporate a semi-automated, natural language process (NLP)-assisted named entity recognition (NER) into the future cohort definition process, we trained NLP and deep learning models for NER and ontology encoding. Attributes generated from 50 processed NSCLC trials were evaluated with our manually curated attributes. The ontology generated from lung cancer was tested in 74 prostate cancer trials for generalizability. Results: The ontology for lung cancer trials, which is generalizable to prostate cancer and other cancer clinical trials, were constructed. Total 507 attributes were extracted and entities were categorized into 8 groups. Evaluation of attributes generated by NLP and deep learning models compared with manually extracted attributes showed high consistency and accuracy. The average precision, recall and F1 values of 15 most commonly appearing entities (disease, histology, targeted therapy, immunotherapy, radiotherapy, neoadjuvant therapy, age, gender, test, vitals, value, drug, gene, mutation, problem) are 0.873, 0.769, and 0.805, respectively. Conclusions: We contribute to a clinical trial ontology platform for lung cancer and prostate cancer trial recruitment. This ontology platform can be expanded to other solid tumors or hematologic malignancies for clinical trial analysis, and can also be applied to generate synthetic control arm cohorts. We believe NLP-assisted NER can be successfully incorporated for the future work of large scale of clinical trial cohort definition.
Survival among breast cancer patients with a history of previous cancer.
Authors: Pruitt Sandi , Zhu Hong , Gerber David E. , Heitjan Daniel , Maddineni Bhumika , Xiong Danyi , Halm Ethan , Murphy Caitlin C.
Published: May 2020

e14080

Background: A growing number of women newly diagnosed with breast cancer have survived a previous cancer. Although little is known about their prognosis, this population is frequently excluded from clinical trials. Among women diagnosed with incident breast cancer, we examined the impact of previous cancer on overall and cancer-specific survival. Methods: This population-based cohort study included patients age ≥66 years and diagnosed with breast cancer between 2005-2015 in linked SEER-Medicare data. Separately by breast cancer stage, we estimated overall survival using Cox regression and cause-specific survival using competing risk regression for women with and without previous cancer, adjusting for numerous covariates and competing risk of death from previous cancer, other causes, or the incident breast cancer. Results: Of 138,576 women diagnosed with incident breast cancer, 10,822 (7.8%) had a previous cancer of another organ site and many of these (n = 5,014, 46.3%) were diagnosed ≤5 years of breast cancer. For all breast cancer stages except IV in which there was no significant survival difference, women with vs. without previous cancer had worse overall survival (see Table). This survival disadvantage was driven by deaths due to the previous cancer and other causes. In contrast, while women with previous cancer generally had favorable breast-cancer specific survival, the impact of previous cancer on this outcome varied over time. Conclusions: Many women newly diagnosed with breast cancer are already cancer survivors. These women have generally worse overall survival, worse survival from other causes, but their disease-specific survival varies depending on their breast cancer stage and over time. Future analyses will explore time-varying effect of previous cancer on breast cancer survival. [Table: see text]
A novel approach to quantitatively synthesize subgroup findings from multiple trials.
Authors: Boyne Devon J , Arora Paul , Brenner Darren
Published: May 2020

e14081

Background: The identification of characteristics that stratify patients according to the likelihood of treatment response is a key goal of precision oncology research. Within randomized trials, effect modification is typically examined through subgroup analyses or the inclusion of an interaction term(s) in a multivariable regression model. To date, systematic reviews of such findings have relied upon qualitative summaries or subgroup-specific meta-analyses. Such approaches are problematic because they do not quantify the magnitude or the degree of uncertainty in the difference in the treatment effects between subgroups. To address this gap, we propose a novel approach that can be used to quantitatively pool subgroup findings from multiple trials. Methods: Our procedure is focused on the estimation of the pooled difference between the subgroup-specific treatment effects within a two-stage meta-analysis. In the first stage, the magnitude and standard error of the difference between the subgroup-specific treatment effects is estimated for each study. When studying relative effect measures, this difference can be estimated on the log-scale, e.g., study-specific difference = log(RRsubgroup A) – log(RRsubgroup B). The standard error of this difference can then be estimated using subgroup-specific 95% CIs. A meta-analysis of the study-specific differences is then conducted. When the differences are estimated on the log-scale, the pooled quantity can be re-expressed as the ratio of the subgroup specific ORs, RRs, or HRs whereby a ratio equal to 1.00 indicates that the subgroup-specific estimates are equal in magnitude. Results: Case-studies and empirical simulations highlighting the application of our approach will be presented. Potential extensions of this methodology will also be discussed. Such extensions include the use of dose-response models to address subgroup categories of a continuous variable and the inclusion of three or more treatments within a network meta-analysis. Conclusions: This method may help to better identify and quantify the degree of heterogeneity of subgroup differences across trials, particularly in settings where the number of patients within each trial is limited.
The role of baseline lesion selection on reader performance in oncologic clinical trials.
Authors: Bansal Anu , Mantri Ninad , McCracken Paul
Published: May 2020

e14082

Background: Blinded independent central radiology review is integral to modern clinical trials in oncology. Based on previously published data based on overall adjudication rates, it is generally accepted that selection of a larger and more complete number of target and non-target lesions at baseline results in greater reader concordance and therefore improved data fidelity. Methods: A retrospective database review was performed across 27 trials spanning 15 clinical indications and 4 response criteria involving a total of 20,942 independent reads across numerous timepoints in 10,471 subjects by 119 readers. Adjudication rates and adjudication selection rates were calculated across and within all trials. Results: Readers selected a mean of 2.3 target lesions and 1.2 non-target lesions in each subject. The mean adjudication rate across all trials was 33%. However, adjudication selection rate should be a more accurate representation of reader performance. Within any one trial, 44% of readers who selected the greatest number of target and non-target lesions at baseline actually had adjudication selection rates below average. Conclusions: Readers who select more target and non-target lesions than their peers at baseline do not demonstrate significantly higher adjudication selection rates. This suggests that the number of lesions chosen at baseline may not be as strong a contributer to reader performance and data quality as previously surmised.