JCO Clinical Cancer Informatics最新文献

{"title":"Classifying Tumor Reportability Status From Unstructured Electronic Pathology Reports Using Language Models in a Population-Based Cancer Registry Setting.","authors":"Lovedeep Gondara, Jonathan Simkin, Gregory Arbour, Shebnum Devji, Raymond Ng","doi":"10.1200/CCI.24.00110","DOIUrl":"10.1200/CCI.24.00110","url":null,"abstract":"<p><strong>Purpose: </strong>Population-based cancer registries (PBCRs) collect data on all new cancer diagnoses in a defined population. Data are sourced from pathology reports, and the PBCRs rely on manual and rule-based solutions. This study presents a state-of-the-art natural language processing (NLP) pipeline, built by fine-tuning pretrained language models (LMs). The pipeline is deployed at the British Columbia Cancer Registry (BCCR) to detect reportable tumors from a population-based feed of electronic pathology.</p><p><strong>Methods: </strong>We fine-tune two publicly available LMs, GatorTron and BlueBERT, which are pretrained on clinical text. Fine-tuning is done using BCCR's pathology reports. For the final decision making, we combine both models' output using an OR approach. The fine-tuning data set consisted of 40,000 reports from the diagnosis year of 2021, and the test data sets consisted of 10,000 reports from the diagnosis year 2021, 20,000 reports from diagnosis year 2022, and 400 reports from diagnosis year 2023.</p><p><strong>Results: </strong>The retrospective evaluation of our proposed approach showed boosted reportable accuracy, maintaining the true reportable threshold of 98%.</p><p><strong>Conclusion: </strong>Disadvantages of rule-based NLP in cancer surveillance include manual effort in rule design and sensitivity to language change. Deep learning approaches demonstrate superior performance in classification. PBCRs distinguish reportability status of incoming electronic cancer pathology reports. Deep learning methods provide significant advantages over rule-based NLP.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400110"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11593994/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Waiting to Exhale: The Feasibility and Appropriateness of Home Blood Oxygen Monitoring in Oncology Patients Post-Hospital Discharge.","authors":"Si-Yang Liu, Sahil D Doshi, AnnMarie Mazzella Ebstein, Jessie Holland, Ayelet Sapir, Micheal Leung, Jennie Huang, Rosanna Fahy, Rori Salvaggio, Aaron Begue, Gilad Kuperman, Fernanda G C Polubriaginof, Peter D Stetson, Jun J Mao, Katherine Panageas, Bob Li, Bobby Daly","doi":"10.1200/CCI-24-00182","DOIUrl":"10.1200/CCI-24-00182","url":null,"abstract":"<p><strong>Purpose: </strong>Pulse oximetry remote patient monitoring (RPM) post-hospital discharge increased during the COVID-19 pandemic as patients and providers sought to limit in-person encounters and provide more care in the home. However, there is limited evidence on the feasibility and appropriateness of pulse oximetry RPM in patients with cancer after hospital discharge.</p><p><strong>Methods and materials: </strong>This feasibility study enrolled oncology patients discharged after an unexpected admission at the Memorial Sloan Kettering Cancer Center from October 2020 to July 2021. Patients were asked to measure their blood oxygen (O₂) level daily during the hours of 9 am-5 pm during a 10-day monitoring period posthospitalization. An automated system alerted clinicians to blood O₂ levels below 93.0%. We evaluated the feasibility (>50.0% of patients providing at least one measurement from home) and appropriateness (>50.0% of alerts leading to a clinically meaningful patient interaction) of pulse oximetry RPM.</p><p><strong>Results: </strong>Sixty-two patients were enrolled in the study, with 53.2% female patients and a median age of 68 years. The most prevalent malignancy was thoracic (62.9%). The feasibility metric was met, with 45 patients (72.6%, 45 of 62) providing blood O₂ levels at least once during the 10-day monitoring program. The appropriateness threshold was not met; of the 121 alerts, only 39.7% (48 alerts) was linked to a clinically meaningful interaction.</p><p><strong>Conclusion: </strong>This feasibility study showed that while patients with cancer were willing to measure blood O₂ levels at home, most alerts did not result in meaningful clinical interactions. There is a need for improved patient support systems and logistical infrastructure to support appropriate use of RPM at home.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400182"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Electronic Health Record Data Extraction and Curation Using ExtractEHR.","authors":"Tamara P Miller, Kelly D Getz, Edward Krause, Yun Gun Jo, Sandhya Charapala, M Monica Gramatages, Karen Rabin, Michael E Scheurer, Jennifer J Wilkes, Brian T Fisher, Richard Aplenc","doi":"10.1200/CCI.24.00100","DOIUrl":"10.1200/CCI.24.00100","url":null,"abstract":"<p><strong>Purpose: </strong>Although the potential transformative effect of electronic health record (EHR) data on clinical research in adult patient populations has been very extensively discussed, the effect on pediatric oncology research has been limited. Multiple factors contribute to this more limited effect, including the paucity of pediatric cancer cases in commercial EHR-derived cancer data sets and phenotypic case identification challenges in pediatric federated EHR data.</p><p><strong>Methods: </strong>The ExtractEHR software package was initially developed as a tool to improve clinical trial adverse event reporting but has expanded its use cases to include the development of multisite EHR data sets and the support of cancer cohorts. ExtractEHR enables customized, automated data extraction from the EHR that, when implemented across multiple hospitals, can create pediatric cancer EHR data sets to address a very wide range of research questions in pediatric oncology. After ExtractEHR data acquisition, EHR data can be cleaned and graded using CleanEHR and GradeEHR, companion software packages.</p><p><strong>Results: </strong>ExtractEHR has been installed at four leading pediatric institutions: Children's Healthcare of Atlanta, Children's Hospital of Philadelphia, Texas Children's Hospital, and Seattle Children's Hospital.</p><p><strong>Conclusion: </strong>ExtractEHR has supported multiple use cases, including five clinical epidemiology studies, multicenter clinical trials, and cancer cohort assembly. Work is ongoing to develop Fast Health care Interoperability Resources ExtractEHR and implement other sustainability and scalability enhancements.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400100"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11608624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142717764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rethinking Human Abstraction as the Gold Standard.","authors":"Kirk D Wyatt, Brian T Furner, Samuel L Volchenboum","doi":"10.1200/CCI-24-00218","DOIUrl":"10.1200/CCI-24-00218","url":null,"abstract":"<p><p>@PedsDataCommons discusses automated approaches for data extraction from electronic health records.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400218"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142717766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Usage of the National Cancer Institute Cancer Research Data Commons by Researchers: A Scoping Review of the Literature.","authors":"Zhaoyi Chen, Erika Kim, Tanja Davidsen, Jill S Barnholtz-Sloan","doi":"10.1200/CCI.24.00116","DOIUrl":"10.1200/CCI.24.00116","url":null,"abstract":"<p><strong>Purpose: </strong>Over the past decade, significant surges in cancer data of all types have happened. To promote sharing and use of these rich data, the National Cancer Institute's Cancer Research Data Commons (CRDC) was developed as a cloud-based infrastructure that provides a large, comprehensive, and expanding collection of cancer data with tools for analysis. We conducted this scoping review of articles to provide an overview of how CRDC resources are being used by cancer researchers.</p><p><strong>Methods: </strong>A thorough literature search was conducted to identify all relevant publications. We included publications that directly cited CRDC resources to specifically examine the impact and contributions of CRDC by itself. We summarized the distributions and trends of how CRDC components were used by the research community and discussed current research gaps and future opportunities.</p><p><strong>Results: </strong>In terms of CRDC resources used by the research community, encouraging trends in utilization were observed, suggesting that CRDC has become an important building block for fostering a wide range of cancer research. We also noted a few areas where current applications are rather lacking and provided insights on how improvements can be made by CRDC and research community.</p><p><strong>Conclusion: </strong>CRDC, as the foundation of a National Cancer Data Ecosystem, will continue empowering the research community to effectively leverage cancer-related data, uncover novel strategies, and address the needs of patients with cancer, ultimately combatting this disease more effectively.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400116"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11575903/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote Patient Monitoring Using Mobile Health Technology in Cancer Care and Research: Patients' Views and Preferences.","authors":"Dominique G Stuijt, Eva E M van Doeveren, Milan Kos, Marijn Eversdijk, Jacobus J Bosch, Adriaan D Bins, Marieke A R Bak, Martijn G H van Oijen","doi":"10.1200/CCI.24.00092","DOIUrl":"10.1200/CCI.24.00092","url":null,"abstract":"<p><strong>Purpose: </strong>There is an increasing interest in studying the potential of mobile health (mHealth) technologies, such as smartphone apps and wearables, as monitoring tools for patients with cancer during or after their treatment. However, little research is dedicated to exploring the opinions and concerns of patients regarding the adoption of these technologies. This study aimed to gain insight into patients' perspectives and preferences for participating in mHealth-based monitoring in cancer care.</p><p><strong>Methods: </strong>A qualitative study comprising semistructured interviews was conducted in the Netherlands between April and June 2023. Participants were eligible if they were 18 years or older with a current or past diagnosis of cancer. The interview guide was developed on the basis of the technology acceptance model, with main themes being use, communication, trust, privacy, and expectations.</p><p><strong>Results: </strong>Thirteen participants with urologic primary cancer were interviewed. Most patients had already some familiarity with the use of digital monitoring devices or wearables. Main barriers included persistent reminders of the illness, receiving notifications deemed unnecessary or unwanted, and the acknowledgment that mHealth technology does not serve as a substitute for human doctors. Conversely, patients recognized the potential for time-savings through the utilization of mHealth, viewed active monitoring as nonburdensome, considered mHealth a tool for reducing the communication threshold with their doctor, and expressed willingness to adopt such a platform if they perceived personal or societal relevance.</p><p><strong>Conclusion: </strong>This study has elucidated which factors are important for successful development of mHealth for patients with cancer. While both barriers and facilitators play a role, patients' attitudes were positive toward the implementation of remote digital monitoring, showing promising prospects for future research of mHealth in oncology.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400092"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11573098/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Individuals With Hereditary Cancer Risk Through Multiple Data Sources: A Population-Based Method Using the GARDE Platform and The Utah Population Database.","authors":"Guilherme Del Fiol, Michael J Madsen, Richard L Bradshaw, Michael G Newman, Kimberly A Kaphingst, Sean V Tavtigian, Nicola J Camp","doi":"10.1200/CCI-24-00142","DOIUrl":"10.1200/CCI-24-00142","url":null,"abstract":"<p><strong>Purpose: </strong>The GARDE platform uses family history reported in the electronic health record (EHR) to systematically identify eligible patients for genetic testing for hereditary cancer syndromes. The goal of this study was to evaluate the change in effectiveness of GARDE to identify eligible individuals when more comprehensive family history data are provided, thus quantifying the impact of underdocumentation.</p><p><strong>Methods: </strong>A cohort of 133,764 patients at the University of Utah Health was analyzed with GARDE comparing identification rates using EHR data versus EHR plus data from a statewide population database, the Utah Population Database (UPDB).</p><p><strong>Results: </strong>Compared with EHR alone, EHR + UPDB increased the rate of individuals eligible for genetic testing from 4.1% to 9.2%. In the 44,692 individuals with the most comprehensive family history, eligibility more than quadrupled from 4.6% (EHR alone) to 19.3% (EHR + UPDB). The increase was significant across all demographics, but disparities still remained for historically marginalized minorities (9.2%-13.9% in non-White races compared with 19.7% in White races).</p><p><strong>Conclusion: </strong>Augmenting EHR data with family history data from the UPDB substantially improved the detection of individuals eligible for genetic testing of hereditary cancer syndromes in all subgroups. This underscores the importance of improving methods for acquiring family history, in person or in silico. However, these increases did not ameliorate disparities. Continuous disparities are unlikely to be explained by incomplete family history alone and may also be because susceptibility genes, risk variants, and screening guidelines were discovered and developed largely in White races. Addressing disparities will require intentional data collection of family history in historically marginalized minorities and the promotion of genetic and risk assessment studies in more diverse populations to ensure equity and health care.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400142"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11583850/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can Digital Health Improve Therapeutic Compliance in Oncology?","authors":"Pierre Etienne Heudel, Myriam Ait Ichou, Bertrand Favier, Hugo Crochet, Jean-Yves Blay","doi":"10.1200/CCI-24-00205","DOIUrl":"https://doi.org/10.1200/CCI-24-00205","url":null,"abstract":"<p><strong>Purpose: </strong>Therapeutic compliance, or adherence, is critical in oncology because of the complexity and duration of cancer treatment regimens. Nonadherence can lead to suboptimal therapeutic outcomes, increased disease progression, higher mortality rates, and elevated health care costs. Traditional methods to enhance compliance, such as patient education and regular follow-ups, have shown limited success.</p><p><strong>Materials and methods: </strong>This review examines the potential of digital health technologies to improve adherence in oncology. Various studies and trials are analyzed to assess the effectiveness of these technologies in supporting patients with cancer.</p><p><strong>Results: </strong>mHealth applications have been shown to improve medication adherence through features like medication reminders and symptom tracking. Telemedicine facilitates continuous care and reduces the need for travel, significantly improving adherence and patient satisfaction. Patient-reported outcome measures enhance clinical decision making and personalized treatment plans by incorporating patient feedback. Electronic medical records and patient portals improve compliance by providing easy access to medical information and fostering better patient-provider communication. Connected pillboxes aid in consistent medication intake and reduce dispensing errors.</p><p><strong>Conclusion: </strong>Digital health technologies offer significant benefits in oncology by enhancing patient engagement, improving adherence to treatment protocols, and enabling comprehensive cancer care management. However, challenges such as the digital divide, data privacy concerns, and the need for tailored interventions must be addressed. Future research should focus on evaluating the effectiveness of digital interventions and developing personalized digital health tools to maximize therapeutic compliance.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400205"},"PeriodicalIF":3.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142512715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Informatics and Artificial Intelligence-Guided Assessment of the Regulatory and Translational Research Landscape of First-in-Class Oncology Drugs in the United States, 2018-2022.","authors":"Jay G Ronquillo, Brett South, Prakash Naik, Rominder Singh, Magdia De Jesus, Stephen J Watt, Aida Habtezion","doi":"10.1200/CCI.24.00087","DOIUrl":"10.1200/CCI.24.00087","url":null,"abstract":"<p><strong>Purpose: </strong>Cancer drug development remains a critical but challenging process that affects millions of patients and their families. Using biomedical informatics and artificial intelligence (AI) approaches, we assessed the regulatory and translational research landscape defining successful first-in-class drugs for patients with cancer.</p><p><strong>Methods: </strong>This is a retrospective observational study of all novel first-in-class drugs approved by the US Food and Drug Administration (FDA) from 2018 to 2022, stratified by cancer versus noncancer drugs. A biomedical informatics pipeline leveraging interoperability standards and ChatGPT performed integration and analysis of public databases provided by the FDA, National Institutes of Health, and WHO.</p><p><strong>Results: </strong>Between 2018 and 2022, the FDA approved a total of 247 novel drugs, of which 107 (43.3%) were first-in-class drugs involving a new biologic target. Of these first-in-class drugs, 30 (28%) treatments were indicated for patients with cancer, including 19 (63.3%) for solid tumors and the remaining 11 (36.7%) for hematologic cancers. A median of 68 publications of basic, clinical, and other relevant translational science preceded successful FDA approval of first-in-class cancer drugs, with oncology-related treatments involving fewer median years of target-based research than therapies not related to cancer (33 <i>v</i> 43 years; <i>P</i> < .05). Overall, 94.4% of first-in-class drugs had at least 25 years of target-related research papers, while 85.5% of first-in-class drugs had at least 10 years of translational research publications.</p><p><strong>Conclusion: </strong>Novel first-in-class cancer treatments are defined by diverse clinical indications, personalized molecular targets, dependence on expedited regulatory pathways, and translational research metrics reflecting this complex landscape. Biomedical informatics and AI provide scalable, data-driven ways to assess and even address important challenges in the drug development pipeline.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400087"},"PeriodicalIF":3.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142332081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drug's Journey of a Thousand Papers Begins With a Single Step.","authors":"Pasquale F Innominato, Nicholas I Wreglesworth, Alessio Antonini, Zachary S Buchwald","doi":"10.1200/CCI-24-00225","DOIUrl":"https://doi.org/10.1200/CCI-24-00225","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400225"},"PeriodicalIF":3.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142480457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}