bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sequential learning for pan-tumor detection of metastatic disease progression.
Authors: Green Foad H. , Rioth Matthew J. , Loving Joshua 
Published: June 2023

e13591 Background: Detection of progression of patients with early stage cancer is a challenge for real world data from electronic health records (EHR). Temporal patterns common to patients’ data can detect patients with metastatic progression. A deep learning approach on longitudinal patient records of cancer lab testing, visit diagnosis codes, and cancer treatments was used to identify metastatic status. Methods: ICD-10-CM diagnosis codes, clinical lab values specific to cancer testing, and antineoplastic treatments were evaluated from longitudinal data of 128,614 patients who did not progress and 5,884 who did develop metastasis, across 47 tumor types in the Syapse Learning Health Network. Patients metastatic at diagnosis were excluded. Data were included beginning at the time of primary cancer diagnosis and ended for all surviving patients at an established administrative cutoff date. In the metastatic group, visits after the progression event were included but censored if they contained diagnosis codes specifically for secondary malignant neoplasms. A binary classification was developed using a multi-headed attention transformer in order to determine metastatic status. Patient history was sequentially aggregated to visit-level embeddings across their longitudinal record. Categorical layers were used for antineoplastics and diagnosis codes. Linear layers were used to embed lab values, as well as for visit intervals from primary cancer date and administrative cutoff date. All embeddings were used as feature inputs for the classifier. Pre-training random sampling of the non-metastatic population was applied to establish equally-weighted labels in a 80:10:10 split for training, validation, and testing sets; to account for the smaller population of patients who progressed to metastatic status. Results: This classification approach achieved 0.86 PR-AUC, 0.79 ROC-AUC, and 0.75 F1. This performance is comparable to published models trained for single tumor cohort prediction. With our censoring constraints, these model results are robust in the absence of routine signals of metastasis in the EHR, such as staging reports and diagnosis coding for distant metastases. Conclusions: This method generalizes, across multiple cancer types, an accurate classification of metastatic progression from patient visit history. This work is immediately useful for real world evidence data analysis complementing patients with metastases already captured in hospital registries. Using sequential deep learning with EHR data classes, this approach may be used to forecast metastatic progression for early intervention.
Leadership knowledge and beliefs regarding diversity, equity, and inclusion at SWOG Cancer Research Network.
Authors: Espinoza Gutarra Manuel Ricardo , Figueroa-Moseley Colmar , Gansauer Lucy , Nagaraj Gayathri , McDermott Jessica Dreger , Bangs Rick , Caban-Holt Allison , Robinson Patricia A. 
Published: June 2023

e13599 Background: Diversity, Equity, and Inclusion (DEI) is recognized as fundamental in the development of generalizable clinical trials. Both ASCO and the FDA have released statements regarding the importance of representativeness in clinical trials. A paucity of research exists regarding the knowledge and beliefs of the people who lead the development of trials within the NCTN. Methods: An electronic survey on knowledge and beliefs regarding DEI was administered to SWOG leadership from 12.14.2021 to 01.13.2022. Questions were divided into 4 categories including: general beliefs regarding DEI, SWOG’s current status regarding DEI efforts, perception of SWOG’s values and readiness in executing DEI. Twenty questions were asked and answers were recorded using a 5-point Likert scale. Results: A total of 53% (N=47/88) respondents completed the survey (Table 1). The majority of SWOG leadership strongly agree that diversity is an asset (68%) and that they have a role to champion DEI (72%). Less than a quarter of SWOG leadership strongly agree that there are sufficient opportunities for committee members to provide DEI input across the clinical trial life cycle (21%). When evaluating SWOGs current role in DEI efforts, 64% of respondents had a neutral or negative response for each of the 4 questions. When evaluating respondents’ perception of SWOG values regarding DEI, 64% of respondents had a positive or very positive response on average for each of the 6 questions. When evaluating SWOG’s readiness to promote DEI, 57% of respondents had a positive or very positive response for each question. Some of the barriers experienced include technological issues with construction and delivery of the survey to protected email addresses as well as lack of dedicated staff at SWOG to assist. Conclusions: This survey provides insight into SWOG leader’s beliefs and knowledge regarding DEI as well as their perception of SWOG’s current efforts and ability to promote DEI in clinical trials. While respondents tended to value DEI positively personally and through SWOG, their perception of SWOG’s role in promoting DEI was neutral or negative. Perception of SWOGs readiness to promote DEI was positive to a lesser degree. These findings emphasize the importance of capturing baseline beliefs to promote DEI efforts with clinical trial leadership. [Table: see text]
Representativeness of patients with lung cancer in an integrated health care delivery system.
Authors: Yang Mike Z. , Liu Raymond , Von Behren Julie , Lin Katherine , Adams Alyce S. , Kushi Lawrence H. , Quesenberry Charles P. , Velotta Jeffrey B. , Young-Wolff Kelly C. , Gomez Scarlett L. , Shariff-Marco Salma , Sakoda Lori C. 
Published: June 2023

e13600 Background: Lung cancer is the leading cause of cancer death in the US, and research is critical to reducing its morbidity and mortality. Minoritized groups (e.g., low income) that face higher lung cancer burden are underrepresented in clinical trials, which are often conducted at NCI-designated cancer centers (NCICCs). Large integrated health systems such as Kaiser Permanente Northern California (KPNC) may be well-positioned to produce research findings that are more representative of patient populations in important clinical and demographic characteristics. In this study, we evaluated the representativeness of lung cancer patients from KPNC compared to those from NCICCs and non-NCICCs in Northern California. Methods: We extracted individual and neighborhood data from the California Cancer Registry, California Neighborhoods Data System, and CalEnviroScreen 4.0 for patients with invasive lung cancer diagnosed from 2015 through 2019 within the KPNC catchment area. We compared these characteristics among patients from KPNC, NCICCs (Stanford, UC Davis, UCSF), and all other non-NCICCs. Results: Of 27,288 patients, 31% are from KPNC, 8% from NCICCs, and 61% from other non-NCICCs (Table). KPNC patients are more similar to other non-NCICC patients and the total lung cancer population than to NCICC patients on most characteristics. KPNC patients are slightly older, more likely to be female, and less likely to be Asian American/Pacific Islander (AAPI), compared to other non-NCICC patients. In contrast, NCICC patients are younger, less likely to be female or non-AAPI minoritized racial and ethnic groups, and present with less stage IV disease and more adenocarcinoma at diagnosis. Both KPNC and NCICC patients are more likely to reside in neighborhoods with the highest socioeconomic status (SES) and lowest pollution burden than other non-NCICC patients. Given large numbers, all differences were statistically significant (p<0.001). Conclusions: These analyses demonstrate that lung cancer patients in KPNC are more broadly representative of the overall lung cancer population than those in NCICCs, and can produce generalizable research findings to improve outcomes for all lung cancer patients. [Table: see text]
FDA analysis of treatment-free survival in frontline advanced immuno-oncology–VEGF TKI renal cell carcinoma trials.
Authors: Chang Elaine , Zhou Jiaxi , Song Chi , Gittleman Haley , Fernandes Laura L. , Weinstock Chana , Atkins Michael B. , Agrawal Sundeep , Sridhara Rajeshwari , Gormley Nicole , Amiri-Kordestani Laleh , Tang Shenghui , Kluetz Paul , Suzman Daniel L. , Pazdur Richard , Rini Brian I. , McDermott David F. , Regan Meredith M. 
Published: June 2023

e13602 Background: Treatment-free survival (TFS) with and without (w/o) toxicity are outcome measures that describe health states where patients are experiencing clinically stable disease while off anti-cancer treatment, a clinically meaningful attribute seen with some immune-oncology (IO) regimens. We sought to characterize TFS with and w/o ongoing toxicity in addition to time on protocol therapy (combination and monotherapy) in trials of frontline IO-VEGF tyrosine kinase inhibitor (TKI) combinations in pts with advanced renal cell carcinoma (aRCC). Methods: We pooled randomized trials submitted to FDA evaluating IO-TKI combination in treatment-naïve aRCC with at least 30 months (mos) of median follow-up. OS was partitioned into health states, including TFS, defined as area between Kaplan-Meier curves for 2 time-to-event endpoints originating at randomization: 1) time to all protocol therapy cessation and 2) time to subsequent systemic therapy initiation or death. Health state areas under the curve were estimated by 30-mo restricted mean times. We pooled data by treatment arm. Results: Three trials (KEYNOTE-426, JAVELIN Renal 100, CheckMate 9ER) met criteria for analysis; in total, 1183 pts received IO-TKI vs. 1184 on control arms receiving sunitinib (SUN). IO-TKI and SUN groups spent 9% (2.7 mos [95% confidence interval (CI): 1.8, 3.5]) and 10% (2.9 mos [95% CI: 2.1, 3.8]) of the 30-mo period alive and treatment-free. Mean TFS with grade ≥3 toxicity were 1 and 0.6 mos in the IO-TKI and SUN groups, respectively. Mean TFS w/o grade ≥3 toxicity were 1.7 and 2.3 mos in IO-TKI and SUN groups, respectively. In IO-TKI group, the 30-mo mean time on combination therapy was 13.7 mos (46% of 30-mo period) and 3.9 mos on monotherapy (13% of 30-mo period). Following cessation of one drug in IO-TKI arm, monotherapy duration of TKI was longer than IO: 2.9 and 1 mos, respectively. Conclusions: In this descriptive, exploratory analysis, TFS and TFS w/o toxicity were similar in IO-TKI group compared to SUN group, in contrast to improved TFS in IO-IO vs. SUN seen in published literature. Among pts on IO-TKI, more time was spent on TKI monotherapy than IO monotherapy. These findings may reflect continuation of TKI until progression per protocol design in all trials and discontinuation of IO after 2 years in 2 trials, and may inform individual pt choice of IO-IO vs. IO-TKI. [Table: see text]
A smart clinical cohort selection system for clinical oncology research.
Authors: Basharat Amna , Farooq Muddassar 
Published: June 2023

e13613 Background: Cohort selection for specialized clinical trials is a cardinal pillar of the evidence-based medicine; however, it is the most difficult, complex, time consuming, and expensive step. Determining the efficacy of a new treatment (or intervention) requires finding eligible patients meeting the inclusion and exclusion criteria. In specialized scenarios, the complex criteria may even require researchers to do time consuming manual reviews and analyses of electronic health records (EHRs) to shortlist qualified patients. The major contribution of this research is a set of novel semantic models to build a cohort for clinical research by enabling semantic search over electronic health records represented as Knowledge graphs (KGs). Methods: We present the design of a novel cohort retrieval system, satisfying inclusion and exclusion criteria of an oncology clinical research study. Knowledge graphs and semantic models: We construct knowledge graphs (KGs) to interconnect different data sources, stored in a data-lake, and develop semantic models that enable semantic search over the processed data. We designed and constructed an oncology knowledge graph that enables semantics driven cohort selection. In addition, we have built a novel cohort retrieval system, satisfying the inclusion and exclusion criteria of an oncology clinical research study, that utilizes a semantics driven dynamic query engine to generate and execute cohort selection queries on heterogeneous EHR data. Results: We obtained real world oncology data of 21,000 oncology patients and then constructed knowledge graphs for five cancer types -- Colon (C18), Lung (C34), Breast (C50), prostate (C61), and Multiple Myeloma (C90). The cohort building scenarios are designed to represent a mix of criterion types and combinations including both inclusion and exclusion criteria, involving conjunctions, disjunctions and numeric ranges. Our cohort builder is evaluated against ten well known key competency scenarios. A team of experts validated the results of our cohort builder obtained against these competency scenarios by directly querying the graph. Our extensive evaluations demonstrate that the cohort builder searches with 100% accuracy the patients that match the criteria specified in all ten competencies. The average time to build the cohort on all graphs for these competencies is less than 10 seconds compared with that of days when patients are manually searched in EHR systems. Conclusions: Our query engine is not tightly coupled with the architecture of our data-lake; rather its architecture is flexible and can be easily integrated with other enterprise data-lakes or EMR systems. In future, we plan to scale the extent of inclusion and exclusion criteria to provide interoperability with existing clinical trial knowledge. We also aim to empirically evaluate the efficiency of cohort selection queries using a knowledge graph with classical database query approaches.
Design, reporting, and disparities of advanced non–small cell lung cancer phase 3 clinical trials in the era of immunotherapy.
Authors: Ayumi Miyawaki Isabele , Mitre Lucas Pari , Ynoe de Moraes Fabio 
Published: June 2023

e13616 Background: Lung cancer remains the main cause of cancer-related mortality globally, with non-small cell (NSCLC) accounting for the majority of cases. The advent of immunotherapy (IO) has expanded treatment options for advanced NSCLC. However, there are no bibliometric studies exploring NSCLC IO-based trials. We aimed to provide a systematic and comprehensive analysis of the design, reporting, population, and disparities of phase 3 trials investigating IO for advanced NSCLC. Methods: MEDLINE search was performed (last decade) for phase 3 trials reporting on patients with advanced (stage IIIA or higher) NSCLC. We assessed relationships between study outcomes, funding transparency, conflict of interest, journal impact factor (IF), region, gender of the first and last authors, population size, and ethnicity. Results: From 2012 to 2022, 2556 articles were screened and 81 were included. Overall survival (OS), followed by progression/disease-free survival (PFS) and overall/objective response (ORR) were the most commonly reported primary endpoints, representing 54%, 44%, and 15% respectively. The majority of the studies ( > 97%) had a funding sources statement and declared COI. Male first and last authorship represented 84% and 74% of studies, respectively. In 62% of trials reporting the patient’s ethnicity, white was the most common (65%). Regarding the source of funding, 74% of trials reported industry-only, 8% academia-only, 11% combined, 4% no funding received and 2.5% were not transparent. 98% of trials were from high-income countries (HIC), being the US (37%) and China (26%) the most reported. The mean number of authors with declared COI was 10.55 [0-25] and the mean total number of authors was 21.3 [5-76]. Publications journals' mean IF was 57.7 [2.1-202.7]. COI declaration was associated with publication in a journal with a higher IF compared to studies with no declared COI (p < 0.05). The journal IF was significantly higher in the US publications compared to China (p < 0.01). Trials assessing OS and PFS as their primary outcome were published with a higher IF in relation to assessing ORR only (p < 0.05). There was a significant association between the first and last authors' gender (p < 0.05), with a higher likelihood of matching genders (OR = 4,5 [1,3-14,4]). There was no association between the proportion of COI among authors, source of funding, and first author’s gender with journal IF. Conclusions: Phase 3 trials exploring IO for advanced NSCLC are majorly done in HIC, report industry funding and COI, and have males as the first and last authors. PFS is the primary outcome of almost 50% of the trials. We identified that among the primary outcomes studied, declaration of COI and author’s geographic affiliation may influence the publication’s IF. Sustained efforts are required to guarantee impartial reporting of clinical trial results and inclusive representation.
The role of micro-CT for intraoperative margin assessment of breast cancer.
Authors: Vasigh Mahtab , Meshkati Yazd Seyed Mostafa , Papazoglou Andreas S. , Liatsos Alexandros , Ranjbar Keivan , Bleicher Richard J. , Williams Austin David , Cruz Pico Christian X. , Pierotti Matthew Lewis , Michaelson James 
Published: June 2023

e13620 Background: Breast-conserving surgery (BCS) followed by radiotherapy requires adequate removal of the malignancy with a negative margin. Accurate pathologic margin status is not available intraoperatively and is found to be positive in up to 20% of cases, necessitating re-excision. Specimen mammography (SM), used to find a biopsy marker, may assist with margin status but is only 55% sensitive for Intraoperative margin assessment (IMA). IMA methods are being increasingly investigated. Because micro-computed tomography (micro-CT) produces three-dimensional volumetric images, we aimed to evaluate the diagnostic sensitivity of micro-CT for IMA. Methods: We performed a systematic review according to the PRISMA guidelines by querying Scopus, MEDLINE, EMBASE, PubMed, and Google Scholar through 12/21/22. (https://osf.io/342h8). Studies evaluating the applications of micro-CT scanning in breast cancer were included for systematic review, and those reporting the diagnostic indices of micro-CT for IMA compared to histopathologic results were incorporated in a study-level meta-analysis. A random effects coefficient binary regression model was used when a high level of heterogeneity was observed. Results: Of the 2920 studies, 18 studies were selected. IMA was reported in 7 studies (388 specimens), tumor size in 3 studies, microcalcifications in 12 studies, and sentinel lymph node assessment in 1 study. Half of the studies were performed in the USA; the participant’s ages ranged from 33 to 82 years, and the tumor size was between 0.8 and 55.8mm. Image procurement time ranged from 4 to 30 minutes. A pooled diagnostic sensitivity of 0.67 (95% CI: 0.48 – 0.82) and specificity of 0.75 (95% CI: 0.63 – 0.84) was revealed, and the pooled accuracy was calculated based on the normal-error estimation as 0.76 (95% CI: 0.69 – 0.83) for IMA by micro-CT compared to the histopathological evaluation. Conclusions: Micro-CT can create 3D images of the internal structure and vasculature in less than 30 minutes and detects up to 67% of positive margins intraoperatively, preventing a future reoperation. Although the results of this review are promising, due to the high level of heterogeneity, further investigations should be pursued as to how and who should report the images and whether micro-CT may have a role in IMA. [Table: see text]
Decentralized elements within oncology trials: Labcorp experience.
Authors: Rami Alicia , Alcaide Maria , de las Heras Begona , Vidal Laura , Pascual Joanna , Lumsden Kurt 
Published: June 2023

e13630 Background: The concept of digital health using Decentralized elements (DE) has rapidly gained acceptance due to the COVID-19 pandemic, moving clinical trials (CTs) to a more patient-centric model (PCM) with fewer site visits. The primary aims of DE are reducing patient burden, increasing enrolment and retention, maintaining quality of life, and decreasing overall trial cost. Methods: Between 2011 and 2022, Labcorp conducted 491 CTs which incorporated DE: telemedicine, electronic clinical outcome assessments (eCOA), electronic informed consent (eConsent) and mobile clinical services (MCS). All CTs were analyzed by therapeutic area, study phase (1-4) and country. Results: The COVID-19 pandemic increased the implementation of DE. However, digital health uptake between therapeutic area differed. Comparing Oncology with Neurology (higher increase in research), whilst eCOA was implemented most in Oncology, the Neurology MCS increase was over 50% since 2020. Overall, Big Pharma implemented ~1.5% more Decentralized elements, vs the biotech sector. The majority were phase 3, followed by phase 2, with MCS increasing its demand in phase 1. By country, USA, Spain, France, Germany and Canada dominated. The majority were hybrid CTs, with few completely virtual. Conclusions: Labcorp experience demonstrates that overall, the interest to implement DE in CTs continues to grow, promoting the transition to a PCM. Nevertheless, solutions like MCS still need to gain more acceptance in Oncology due to the added complexity and logistics and will require training of all stakeholders, as well as understanding the benefits and potential country-specific limitations. [Table: see text]