Minimal Common Oncology Data Elements Genomics Pilot Project: Enhancing Oncology Research Through Electronic Health Record Interoperability at Vanderbilt University Medical Center.

PURPOSE: The expanding presence of the electronic health record (EHR) underscores the necessity for improved interoperability. To test the interoperability within the field of oncology research, our team at Vanderbilt University Medical Center (VUMC) enabled our Epic-based EHR to be compatible with the Minimal Common Oncology Data Elements (mCODE), which is a Fast Healthcare Interoperability Resources (FHIR)-based consensus data standard created to facilitate the transmission of EHRs for patients with cancer. METHODS: Our approach used an extract, transform, load tool for converting EHR data from the VUMC Epic Clarity database into mCODE-compatible profiles. We established a sandbox environment on Microsoft Azure for data migration, deployed a FHIR server to handle application programming interface (API) requests, and mapped VUMC data to align with mCODE structures. In addition, we constructed a web application to demonstrate the practical use of mCODE profiles in health care. RESULTS: We developed an end-to-end pipeline that converted EHR data into mCODE-compliant profiles, as well as a web application that visualizes genomic data and provides cancer risk assessments. Despite the complexities of aligning traditional EHR databases with mCODE standards and the limitations of FHIR APIs in supporting advanced statistical methodologies, this project successfully demonstrates the practical integration of mCODE standards into existing health care infrastructures. CONCLUSION: This study provides a proof of concept for the interoperability of mCODE within a major health care institution's EHR system, highlighting both the potential and the current limitations of FHIR APIs in supporting complex data analysis for oncology research.

Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data.

PURPOSE: Although immune checkpoint inhibitors (ICIs) have improved outcomes in certain patients with cancer, they can also cause life-threatening immunotoxicities. Predicting immunotoxicity risks alongside response could provide a personalized risk-benefit profile, inform therapeutic decision making, and improve clinical trial cohort selection. We aimed to build a machine learning (ML) framework using routine electronic health record (EHR) data to predict hepatitis, colitis, pneumonitis, and 1-year overall survival. METHODS: Real-world EHR data of more than 2,200 patients treated with ICI through December 31, 2018, were used to develop predictive models. Using a prediction time point of ICI initiation, a 1-year prediction time window was applied to create binary labels for the four outcomes for each patient. Feature engineering involved aggregating laboratory measurements over appropriate time windows (60-365 days). Patients were randomly partitioned into training (80%) and test (20%) sets. Random forest classifiers were developed using a rigorous model development framework. RESULTS: The patient cohort had a median age of 63 years and was 61.8% male. Patients predominantly had melanoma (37.8%), lung cancer (27.3%), or genitourinary cancer (16.4%). They were treated with PD-1 (60.4%), PD-L1 (9.0%), and CTLA-4 (19.7%) ICIs. Our models demonstrate reasonably strong performance, with AUCs of 0.739, 0.729, 0.755, and 0.752 for the pneumonitis, hepatitis, colitis, and 1-year overall survival models, respectively. Each model relies on an outcome-specific feature set, though some features are shared among models. CONCLUSION: To our knowledge, this is the first ML solution that assesses individual ICI risk-benefit profiles based predominantly on routine structured EHR data. As such, use of our ML solution will not require additional data collection or documentation in the clinic.

Clinician Perspectives Regarding the Impact of Information Technology on Multidisciplinary Tumor Boards: A National Comprehensive Cancer Network Survey.

PURPOSE: Multidisciplinary tumor boards (MTBs) support high-quality cancer care. Little is known about the impact of information technology (IT) tools on the operational and technical aspects of MTBs. The National Comprehensive Cancer Network EHR Oncology Advisory Group formed a workgroup to investigate the impact of IT tools such as EHRs and virtual conferencing on MTBs. METHODS: The workgroup created a cross-sectional survey for oncology clinicians (eg, pathology, medical, surgical, radiation, etc) participating in MTBs at 31 National Comprehensive Cancer Network member institutions. A standard invitation e-mail was shared with each EHR Advisory Group Member with a hyperlink to the survey, and each member distributed the survey to MTB participants at their institution or identified the appropriate person at their institution to do so. The survey was open from February 26, 2022, to April 26, 2022. Descriptive statistics were applied in the analysis of responses, and a qualitative thematic analysis of open-ended responses was completed. RESULTS: Individuals from 27 institutions participated. Almost all respondents (99%, n = 764 of 767) indicated that their MTBs had participants attending virtually. Most indicated increased attendance (69%, n = 514 of 741) after virtualization with the same or improved quality of discussion (75%, n = 557 of 741) compared with in-person MTBs. Several gaps between the current and ideal state emerged regarding EHR integration: 57% (n = 433 of 758) of respondents noted the importance of adding patients for MTB presentation via the EHR, but only 40% (n = 302 of 747) reported being able to do so most of the time. Similarly, 87% (n = 661 of 760) indicated the importance of documenting recommendations in the EHR, but only 53% (n = 394 of 746) reported this occurring routinely. CONCLUSION: Major gaps include the lack of EHR integration for MTBs. Clinical workflows and EHR functionalities could be improved to further optimize EHRs for MTB management and documentation.

Identification and Characterization of Avoidable Hospital Admissions in Patients With Lung Cancer.

BACKGROUND: More than 50% of patients with lung cancer are admitted to the hospital while receiving treatment, which is a burden to patients and the healthcare system. This study characterizes the risk factors and outcomes of patients with lung cancer who were admitted to the hospital. METHODS: A multidisciplinary oncology care team conducted a retrospective medical record review of patients with lung cancer admitted in 2018. Demographics, disease and admission characteristics, and end-of-life care utilization were recorded. Following a multidisciplinary consensus review process, admissions were determined to be either "avoidable" or "unavoidable." Generalized estimating equation logistic regression models assessed risks and outcomes associated with avoidable admissions. RESULTS: In all, 319 admissions for 188 patients with a median age of 66 years (IQR, 59-74 years) were included. Cancer-related symptoms accounted for 65% of hospitalizations. Common causes of unavoidable hospitalizations were unexpected disease progression causing symptoms, chronic obstructive pulmonary disease exacerbation, and infection. Of the 47 hospitalizations identified as avoidable (15%), the median overall survival was 1.6 months compared with 9.7 months (hazard ratio, 2.07; 95% CI, 1.34-3.19; P<.001) for unavoidable hospitalizations. Significant reasons for avoidable admissions included cancer-related pain (P=.02), hypervolemia (P=.03), patient desire to initiate hospice services (P=.01), and errors in medication reconciliation or distribution (P<.001). Errors in medication management caused 26% of the avoidable hospitalizations. Of admissions in patients receiving immunotherapy (n=102) or targeted therapy (n=44), 9% were due to adverse effects of treatment. Patients receiving immunotherapy and targeted therapy were at similar risk of avoidable hospitalizations compared with patients not receiving treatment (P=.3 and P=.1, respectively). CONCLUSIONS: We found that 15% of hospitalizations among patients with lung cancer were potentially avoidable. Uncontrolled symptoms, delayed implementation of end-of-life care, and errors in medication reconciliation were associated with avoidable inpatient admissions. Symptom management tools, palliative care integration, and medication reconciliations may mitigate hospitalization risk.

Implementing Innovation: Informatics-Based Technologies to Improve Care Delivery and Clinical Research.

Improving technology has promised to improved health care delivery and the lives of patients. The realized benefits of technology, however, are delayed or less than anticipated. Three recent technology initiatives are reviewed: the Clinical Trials Rapid Activation Consortium (CTRAC), minimal Common Oncology Data Elements (mCODE), and electronic Patient-Reported Outcomes. Each initiative is at a different stage of maturity but promises to improve the delivery of cancer care. CTRAC is an ambitious initiative funded by the National Cancer Institute (NCI) to develop processes across multiple NCI-supported cancer centers to facilitate the development of centralized electronic health record (EHR) treatment plans. Facilitating interoperability of treatment regimens has the potential to improve sharing between centers and decrease the time to begin clinical trials. The mCODE initiative began in 2019 and is currently Standard for Trial Use version 2. This data standard provides an abstraction layer on top of EHR data and has been implemented across more than 60 organizations. Patient-reported outcomes have been shown to improve patient care in numerous studies. Best practices for how to leverage these in an oncology practice continue to evolve. These three examples show how innovative has diffused into practice and evolved cancer care delivery and highlight a movement toward patient-centered data and interoperability.

Associations of influenza vaccination with severity of immune-related adverse events in patients with advanced thoracic cancers on immune checkpoint inhibitors.

Background: Whether influenza vaccination (FV) is associated with the severity of immune-related adverse events (IRAEs) in patients with advanced thoracic cancer on immune checkpoint inhibitors (ICIs) is not fully understood. Methods: Patients enrolled in this retrospective cohort study were identified from the Vanderbilt BioVU database and their medical records were reviewed. Patients with advanced thoracic cancer who received FV within 3 months prior to or during their ICI treatment period were enrolled in the FV-positive cohort and those who did not were enrolled in the FV-negative cohort. The primary objective was to detect whether FV is associated with decreased IRAE severity. The secondary objectives were to evaluate whether FV is associated with a decreased risk for grade 3-5 IRAEs and better survival times. Multivariable ordinal logistic regression was used for the primary analysis. Results: A total of 142 and 105 patients were enrolled in the FV-positive and FV-negative cohorts, respectively. There was no statistically significant difference in patient demographics or cumulative incidences of IRAEs between the two cohorts. In the primary analysis, FV was inversely associated with the severity of IRAEs (OR 0.63; p=0.046). In the secondary analysis, FV was associated with a decreased risk for grade 3-5 IRAEs (OR 0.42; p=0.005). Multivariable Cox regression showed that FV was not associated with survival times. Conclusions: Our study showed that FV does not increase toxicity for patients with advanced thoracic cancer on ICIs and is associated with a decreased risk for grade 3-5 IRAEs. No statistically significant survival differences were found between patients with and without FV.

Two Uncomplicated Pregnancies on Alectinib in a Woman With Metastatic ALK-Rearranged NSCLC: A Case Report.

Lung cancer incidence is increasing in pregnancy due in part to advanced maternal age. A subset of patients with NSCLC during pregnancy harbor an ALK gene rearrangement. Although ALK inhibitors, such as alectinib, are routinely used to treat ALK-rearranged NSCLC, there are limited safety data regarding use during pregnancy and fetal effects. Here, we report the second case of a patient with metastatic ALK-rearranged lung adenocarcinoma treated with alectinib throughout pregnancy. Notably, the patient had two uncomplicated pregnancies with routine obstetrical and postnatal courses. In this case, alectinib did not seem to affect embryofetal or early childhood development. This does not exclude undetectable or delayed toxic effects, and additional studies are needed to further reveal the safety of alectinib treatment during pregnancy.

Adoption of Patient-Generated Health Data in Oncology: A Report From the NCCN EHR Oncology Advisory Group.

BACKGROUND: Collecting, monitoring, and responding to patient-generated health data (PGHD) are associated with improved quality of life and patient satisfaction, and possibly with improved patient survival in oncology. However, the current state of adoption, types of PGHD collected, and degree of integration into electronic health records (EHRs) is unknown. METHODS: The NCCN EHR Oncology Advisory Group formed a Patient-Reported Outcomes (PRO) Workgroup to perform an assessment and provide recommendations for cancer centers, researchers, and EHR vendors to advance the collection and use of PGHD in oncology. The issues were evaluated via a survey of NCCN Member Institutions. Questions were designed to assess the current state of PGHD collection, including how, what, and where PGHD are collected. Additionally, detailed questions about governance and data integration into EHRs were asked. RESULTS: Of 28 Member Institutions surveyed, 23 responded. The collection and use of PGHD is widespread among NCCN Members Institutions (96%). Most centers (90%) embed at least some PGHD into the EHR, although challenges remain, as evidenced by 88% of respondents reporting the use of instruments not integrated. Forty-seven percent of respondents are leveraging PGHD for process automation and adherence to best evidence. Content type and integration touchpoints vary among the members, as well as governance maturity. CONCLUSIONS: The reported variability regarding PGHD suggests that it may not yet have reached its full potential for oncology care delivery. As the adoption of PGHD in oncology continues to expand, opportunities exist to enhance their utility. Among the recommendations for cancer centers is establishment of a governance process that includes patients. Researchers should consider determining which PGHD instruments confer the highest value. It is recommended that EHR vendors collaborate with cancer centers to develop solutions for the collection, interpretation, visualization, and use of PGHD.

Follow-up Interactive Long-Term Expert Ranking (FILTER): a crowdsourcing platform to adjudicate risk for survivorship care.

OBJECTIVES: To develop an online crowdsourcing platform where oncologists and other survivorship experts can adjudicate risk for complications in follow-up. MATERIALS AND METHODS: This platform, called Follow-up Interactive Long-Term Expert Ranking (FILTER), prompts participants to adjudicate risk between each of a series of pairs of synthetic cases. The Elo ranking algorithm is used to assign relative risk to each synthetic case. RESULTS: The FILTER application is currently live and implemented as a web application deployed on the cloud. DISCUSSION: While guidelines for following cancer survivors exist, refinement of survivorship care based on risk for complications after active treatment could improve both allocation of resources and individual outcomes in long-term follow-up. CONCLUSION: FILTER provides a means for a large number of experts to adjudicate risk for survivorship complications with a low barrier of entry.

My Cancer Genome: Coevolution of Precision Oncology and a Molecular Oncology Knowledgebase.

PURPOSE: The My Cancer Genome (MCG) knowledgebase and resulting website were launched in 2011 with the purpose of guiding clinicians in the application of genomic testing results for treatment of patients with cancer. Both knowledgebase and website were originally developed using a wiki-style approach that relied on manual evidence curation and synthesis of that evidence into cancer-related biomarker, disease, and pathway pages on the website that summarized the literature for a clinical audience. This approach required significant time investment for each page, which limited website scalability as the field advanced. To address this challenge, we designed and used an assertion-based data model that allows the knowledgebase and website to expand with the field of precision oncology. METHODS: Assertions, or computationally accessible cause and effect statements, are both manually curated from primary sources and imported from external databases and stored in a knowledge management system. To generate pages for the MCG website, reusable templates transform assertions into reconfigurable text and visualizations that form the building blocks for automatically updating disease, biomarker, drug, and clinical trial pages. RESULTS: Combining text and graph templates with assertions in our knowledgebase allows generation of web pages that automatically update with our knowledgebase. Automated page generation empowers rapid scaling of the website as assertions with new biomarkers and drugs are added to the knowledgebase. This process has generated more than 9,100 clinical trial pages, 18,100 gene and alteration pages, 900 disease pages, and 2,700 drug pages to date. CONCLUSION: Leveraging both computational and manual curation processes in combination with reusable templates empowers automation and scalability for both the MCG knowledgebase and MCG website.

Framework for Implementing and Tracking a Molecular Tumor Board at a National Cancer Institute-Designated Comprehensive Cancer Center.

BACKGROUND: Over the past few years, tumor next-generation sequencing (NGS) panels have evolved in complexity and have changed from selected gene panels with a handful of genes to larger panels with hundreds of genes, sometimes in combination with paired germline filtering and/or testing. With this move toward increasingly large NGS panels, we have rapidly outgrown the available literature supporting the utility of treatments targeting many reported gene alterations, making it challenging for oncology providers to interpret NGS results and make a therapy recommendation for their patients. METHODS: To support the oncologists at Vanderbilt-Ingram Cancer Center (VICC) in interpreting NGS reports for patient care, we initiated two molecular tumor boards (MTBs)-a VICC-specific institutional board for our patients and a global community MTB open to the larger oncology patient population. Core attendees include oncologists, hematologist, molecular pathologists, cancer geneticists, and cancer genetic counselors. Recommendations generated from MTB were documented in a formal report that was uploaded to our electronic health record system. RESULTS: As of December 2020, we have discussed over 170 patient cases from 77 unique oncology providers from VICC and its affiliate sites, and a total of 58 international patient cases by 25 unique providers from six different countries across the globe. Breast cancer and lung cancer were the most presented diagnoses. CONCLUSION: In this article, we share our learning from the MTB experience and document best practices at our institution. We aim to lay a framework that allows other institutions to recreate MTBs. IMPLICATIONS FOR PRACTICE: With the rapid pace of molecularly driven therapies entering the oncology care spectrum, there is a need to create resources that support timely and accurate interpretation of next-generation sequencing reports to guide treatment decision for patients. Molecular tumor boards (MTB) have been created as a response to this knowledge gap. This report shares implementation strategies and best practices from the Vanderbilt experience of creating an institutional MTB and a virtual global MTB for the larger oncology community. This report describe a reproducible framework that can be adopted to initiate MTBs at other institutions.

Oncologist Perspectives on Telemedicine for Patients With Cancer: A National Comprehensive Cancer Network Survey.

PURPOSE: The use of telemedicine expanded dramatically in March 2020 following the COVID-19 pandemic. We sought to assess oncologist perspectives on telemedicine's present and future roles (both phone and video) for patients with cancer. METHODS: The National Comprehensive Cancer Network (NCCN) Electronic Health Record (EHR) Oncology Advisory Group formed a Workgroup to assess the state of oncology telemedicine and created a 20-question survey. NCCN EHR Oncology Advisory Group members e-mailed the survey to providers (surgical, hematology, gynecologic, medical, and radiation oncology physicians and clinicians) at their home institution. RESULTS: Providers (N = 1,038) from 26 institutions responded in Summer 2020. Telemedicine (phone and video) was compared with in-person visits across clinical scenarios (n = 766). For reviewing benign follow-up data, 88% reported video and 80% reported telephone were the same as or better than office visits. For establishing a personal connection with patients, 24% and 7% indicated video and telephone, respectively, were the same as or better than office visits. Ninety-three percent reported adverse outcomes attributable to telemedicine visits never or rarely occurred, whereas 6% indicated they occasionally occurred (n = 801). Respondents (n = 796) estimated 46% of postpandemic visits could be virtual, but challenges included (1) lack of patient access to technology, (2) inadequate clinical workflows to support telemedicine, and (3) insurance coverage uncertainty postpandemic. CONCLUSION: Telemedicine appears effective across a variety of clinical scenarios. Based on provider assessment, a substantial fraction of visits for patients with cancer could be effectively and safely conducted using telemedicine. These findings should influence regulatory and infrastructural decisions regarding telemedicine postpandemic for patients with cancer.

Women screened for breast cancer are dying from lung cancer: An opportunity to improve lung cancer screening in a mammography population.

OBJECTIVE: Lung cancer is the leading cancer killer in women, resulting in more deaths than breast, cervical and ovarian cancer combined. Screening for lung cancer has been shown to significantly reduce mortality, with some evidence that women may have a greater benefit. This study demonstrates that a population of women being screened for breast cancer may greatly benefit from screening for lung cancer. METHODS: Data from 18,040 women who were screened for breast cancer in 2015 at two imaging facilities that also performed lung screening were reviewed. A natural language-processing algorithm followed by a manual chart review identified women eligible for lung cancer screening by U.S. Preventive Services Task Force (USPSTF) criteria. A chart review of these eligible women was performed to determine subsequent enrollment in a lung screening program (2016-2019), current screening eligibility, cancer diagnoses and cancer-related outcomes. RESULTS: Natural language processing identified 685 women undergoing screening mammography who were also potentially eligible for lung screening based on age and smoking history. Manual chart review confirmed 251 were eligible under USPSTF criteria. By June 2019, 63 (25%) had enrolled in lung screening, of which three were diagnosed with screening-detected lung cancer resulting in zero deaths. Of 188 not screened, seven were diagnosed with lung cancer resulting in five deaths by study end. Four women received a diagnosis of breast cancer with no deaths. CONCLUSION: Women screened for breast cancer are dying from lung cancer. We must capitalize on reducing barriers to improve screening for lung cancer among high-risk women.

Learnings From Precision Clinical Trial Matching for Oncology Patients Who Received NGS Testing.

PURPOSE: Tumor next-generation sequencing reports typically generate trial recommendations for patients based on their diagnosis and genomic profile. However, these require additional refinement and prescreening, which can add to physician burden. We wanted to use human prescreening efforts to efficiently refine these trial options and also elucidate the high-value parameters that have a major impact on efficient trial matching. METHODS: Clinical trial recommendations were generated based on diagnosis and biomarker criteria using an informatics platform and were further refined by manual prescreening. The refined results were then compared with the initial trial recommendations and the reasons for false-positive matches were evaluated. RESULTS: Manual prescreening significantly reduced the number of false positives from the informatics generated trial recommendations, as expected. We found that trial-specific criteria, especially recruiting status for individual trial arms, were a high value parameter and led to the largest number of automated false-positive matches. CONCLUSION: Reflex clinical trial matching approaches that refine trial recommendations based on the clinical details as well as trial-specific criteria have the potential to help alleviate physician burden for selecting the most appropriate trial for their patient. Investing in publicly available resources that capture the recruiting status of a trial at the cohort or arm level would, therefore, allow us to make meaningful contributions to increase the clinical trial enrollments by eliminating false positives.

A retrospective approach to evaluating potential adverse outcomes associated with delay of procedures for cardiovascular and cancer-related diagnoses in the context of COVID-19.

OBJECTIVE: During the COVID-19 pandemic, health systems postponed non-essential medical procedures to accommodate surge of critically-ill patients. The long-term consequences of delaying procedures in response to COVID-19 remains unknown. We developed a high-throughput approach to understand the impact of delaying procedures on patient health outcomes using electronic health record (EHR) data. MATERIALS AND METHODS: We used EHR data from Vanderbilt University Medical Center's (VUMC) Research and Synthetic Derivatives. Elective procedures and non-urgent visits were suspended at VUMC between March 18, 2020 and April 24, 2020. Surgical procedure data from this period were compared to a similar timeframe in 2019. Potential adverse impact of delay in cardiovascular and cancer-related procedures was evaluated using EHR data collected from January 1, 1993 to March 17, 2020. For surgical procedure delay, outcomes included length of hospitalization (days), mortality during hospitalization, and readmission within six months. For screening procedure delay, outcomes included 5-year survival and cancer stage at diagnosis. RESULTS: We identified 416 surgical procedures that were negatively impacted during the COVID-19 pandemic compared to the same timeframe in 2019. Using retrospective data, we found 27 significant associations between procedure delay and adverse patient outcomes. Clinician review indicated that 88.9% of the significant associations were plausible and potentially clinically significant. Analytic pipelines for this study are available online. CONCLUSION: Our approach enables health systems to identify medical procedures affected by the COVID-19 pandemic and evaluate the effect of delay, enabling them to communicate effectively with patients and prioritize rescheduling to minimize adverse patient outcomes.

Improving Cancer Data Interoperability: The Promise of the Minimal Common Oncology Data Elements (mCODE) Initiative.

PURPOSE: Because of expanding interoperability requirements, structured patient data are increasingly available in electronic health records. Many oncology data elements (eg, staging, biomarkers, documentation of adverse events and cancer outcomes) remain challenging. The Minimal Common Oncology Data Elements (mCODE) project is a consensus data standard created to facilitate transmission of data of patients with cancer. METHODS: In 2018, mCODE was developed through a work group convened by ASCO, including oncologists, informaticians, researchers, and experts in terminologies and standards. The mCODE specification is organized by 6 high-level domains: patient, laboratory/vital, disease, genomics, treatment, and outcome. In total, 23 mCODE profiles are composed of 90 data elements. RESULTS: A conceptual model was published for public comment in January 2019 and, after additional refinement, the first public version of the mCODE (version 0.9.1) Fast Healthcare Interoperability Resources (FHIR) implementation guide (IG) was presented at the ASCO Annual Meeting in June 2019. The specification was approved for balloting by Health Level 7 International (HL7) in August 2019. mCODE passed the HL7 ballot in September 2019 with 86.5% approval. The mCODE IG authors worked with HL7 reviewers to resolve all negative comments, leading to a modest expansion in the number of data elements and tighter alignment with FHIR and other HL7 conventions. The mCODE version 1.0 FHIR IG Standard for Trial Use was formally published on March 18, 2020. CONCLUSION: The mCODE project has the potential to offer tremendous benefits to cancer care delivery and research by creating an infrastructure to better share patient data. mCODE is available free from www.mCODEinitiative.org. Pilot implementations are underway, and a robust community of stakeholders has been assembled across the oncology ecosystem.

Seven decades of chemotherapy clinical trials: a pan-cancer social network analysis.

Clinical trials establish the standard of cancer care, yet the evolution and characteristics of the social dynamics between the people conducting this work remain understudied. We performed a social network analysis of authors publishing chemotherapy-based prospective trials from 1946 to 2018 to understand how social influences, including the role of gender, have influenced the growth and development of this network, which has expanded exponentially from fewer than 50 authors in 1946 to 29,197 in 2018. While 99.4% of authors were directly or indirectly connected by 2018, our results indicate a tendency to predominantly connect with others in the same or similar fields, as well as an increasing disparity in author impact and number of connections. Scale-free effects were evident, with small numbers of individuals having disproportionate impact. Women were under-represented and likelier to have lower impact, shorter productive periods (P < 0.001 for both comparisons), less centrality, and a greater proportion of co-authors in their same subspecialty. The past 30 years were characterized by a trend towards increased authorship by women, with new author parity anticipated in 2032. The network of cancer clinical trialists is best characterized as strategic or mixed-motive, with cooperative and competitive elements influencing its appearance. Network effects such as low centrality, which may limit access to high-profile individuals, likely contribute to the observed disparities.

Lung Cancer Detection using Co-learning from Chest CT Images and Clinical Demographics.

Early detection of lung cancer is essential in reducing mortality. Recent studies have demonstrated the clinical utility of low-dose computed tomography (CT) to detect lung cancer among individuals selected based on very limited clinical information. However, this strategy yields high false positive rates, which can lead to unnecessary and potentially harmful procedures. To address such challenges, we established a pipeline that co-learns from detailed clinical demographics and 3D CT images. Toward this end, we leveraged data from the Consortium for Molecular and Cellular Characterization of Screen-Detected Lesions (MCL), which focuses on early detection of lung cancer. A 3D attention-based deep convolutional neural net (DCNN) is proposed to identify lung cancer from the chest CT scan without prior anatomical location of the suspicious nodule. To improve upon the non-invasive discrimination between benign and malignant, we applied a random forest classifier to a dataset integrating clinical information to imaging data. The results show that the AUC obtained from clinical demographics alone was 0.635 while the attention network alone reached an accuracy of 0.687. In contrast when applying our proposed pipeline integrating clinical and imaging variables, we reached an AUC of 0.787 on the testing dataset. The proposed network both efficiently captures anatomical information for classification and also generates attention maps that explain the features that drive performance.