JCO Clinical Cancer Informatics最新文献

{"title":"Clinical Validation of Mathematically Derived Early Tumor Dynamics for Solid Tumors in Response to Durvalumab.","authors":"Qin Li, Vittorio Cristini, Ashok Gupta, Ikbel Achour, J Carl Barrett, Eugene J Koay","doi":"10.1200/CCI.23.00254","DOIUrl":"10.1200/CCI.23.00254","url":null,"abstract":"<p><strong>Purpose: </strong>Early prediction of response to immunotherapy may help guide patient management by identifying resistance to treatment and allowing adaptation of therapies. This analysis evaluated a mathematical model of response to immunotherapy that provides patient-specific prediction of outcome using the initial change in tumor size/burden from baseline to the first follow-up visit on standard imaging scans.</p><p><strong>Methods: </strong>We applied the model to 600 patients with advanced solid tumors who received durvalumab in Study 1108, a phase I/II trial, and compared outcome prediction performance versus size-based criteria with RECIST version 1.1 best overall response (BOR), baseline circulating tumor (ct)DNA level, and other clinical/pathologic predictors of immunotherapy response.</p><p><strong>Results: </strong>In multiple solid tumors, the mathematical parameter representing net tumor growth rate at the first on-treatment computed tomography (CT) scan assessed around 6 weeks after starting durvalumab (<i>α</i>₁) had a concordance index to predict overall survival (OS) of 0.66-0.77 on multivariate analyses. This measurement of early tumor dynamics significantly improved multivariate OS models that included standard RECIST v1.1 criteria, baseline ctDNA levels, and other clinical/pathologic factors in predicting OS. Furthermore, <i>α</i>₁ was assessed consistently at the first on-treatment CT scan, whereas all traditional RECIST BOR groups were confirmed only after this time.</p><p><strong>Conclusion: </strong>These results support further exploring <i>α</i>₁ as an integral biomarker of response to immunotherapy. This biomarker may be predictive of further benefit and can be assessed before RECIST response groups can be assigned, potentially providing an opportunity to personalize oncologic management.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602106","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":"Feasibility of a Smartphone Application for Education and Symptom Management of Patients With Renal Cell Carcinoma on Combined Tyrosine Kinase and Immune Checkpoint Inhibitors.","authors":"Gabriel Roman Souza, Kea Turner, Keerthi Gullapalli, Mahati Paravathaneni, Filip Ionescu, Adele Semaan, Amayla Budet DeJesus, Gillian Trujillo, Casey Le, Youngchul Kim, Xiaoqi Sun, Sarah Raymond, Amy Schneider, Brandon Manley, Rohit Jain, Scott Gilbert, Heather S L Jim, Philippe E Spiess, Jad Chahoud","doi":"10.1200/CCI.24.00044","DOIUrl":"10.1200/CCI.24.00044","url":null,"abstract":"<p><strong>Purpose: </strong>Patients with advanced renal cell carcinoma (RCC) face significant challenges, stemming both from the complexities of the disease itself and the adverse effects of treatments. This study evaluated the feasibility and acceptability of a mobile health (mHealth) application tailored for education and symptom management of patients with advanced RCC receiving combined immune checkpoint inhibitor and tyrosine kinase inhibitor (ICI-TKI) therapy.</p><p><strong>Methods: </strong>The primary end points were acceptability and feasibility. Acceptability was defined as the proportion of patients approached who consented to participate, setting a benchmark of at least 50% for this metric. Feasibility was gauged by the completion rate of the intervention among the participants; it required at least 50% of participants to fully complete the intervention and at least 70% to finish half of the administered questionnaires. The secondary end points included knowledge assessment and patient-reported outcomes (PROs). PROs were evaluated using validated instruments. To discern the changes between pre- and post-educational module quiz scores, we used the Wilcoxon signed-rank test. Time-course data of PROs were visualized using line plots and then compared using paired t-tests.</p><p><strong>Results: </strong>From November 2022 to July 2023, 20 of 22 (90%) patients approached for the study consented and enrolled. Of the enrolled patients, 60% completed all questionnaires and knowledge assessments at every time point and 75% completed at least half of the surveys and questionnaires. Significant pre/post differences were noted in two of six quizzes in the knowledge assessment. This study population did not experience a significant change in PRO scores after starting therapy.</p><p><strong>Conclusion: </strong>The mHealth application designed for education and symptom management in patients with advanced RCC undergoing combination ICI-TKI has proven to be both acceptable and feasible, meeting previous research benchmarks.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141767984","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":"Deep Learning-Based Dynamic Risk Prediction of Venous Thromboembolism for Patients With Ovarian Cancer in Real-World Settings From Electronic Health Records.","authors":"Dahhay Lee, Seongyoon Kim, Sanghee Lee, Hak Jin Kim, Ji Hyun Kim, Myong Cheol Lim, Hyunsoon Cho","doi":"10.1200/CCI.23.00192","DOIUrl":"https://doi.org/10.1200/CCI.23.00192","url":null,"abstract":"<p><strong>Purpose: </strong>Patients with epithelial ovarian cancer (EOC) have an elevated risk for venous thromboembolism (VTE). To assess the risk of VTE, models were developed by statistical or machine learning algorithms. However, few models have accommodated deep learning (DL) algorithms in realistic clinical settings. We aimed to develop a predictive DL model, exploiting rich information from electronic health records (EHRs), including dynamic clinical features and the presence of competing risks.</p><p><strong>Methods: </strong>We extracted EHRs of 1,268 patients diagnosed with EOC from January 2007 through December 2017 at the National Cancer Center, Korea. DL survival networks using fully connected layers, temporal attention, and recurrent neural networks were adopted and compared with multi-perceptron-based classification models. Prediction accuracy was independently validated in the data set of 423 patients newly diagnosed with EOC from January 2018 to December 2019. Personalized risk plots displaying the individual interval risk were developed.</p><p><strong>Results: </strong>DL-based survival networks achieved a superior area under the receiver operating characteristic curve (AUROC) between 0.95 and 0.98 while the AUROC of classification models was between 0.85 and 0.90. As clinical information benefits the prediction accuracy, the proposed dynamic survival network outperformed other survival networks for the test and validation data set with the highest time-dependent concordance index (0.974, 0.975) and lowest Brier score (0.051, 0.049) at 6 months after a cancer diagnosis. Our visualization showed that the interval risk fluctuating along with the changes in longitudinal clinical features.</p><p><strong>Conclusion: </strong>Adaption of dynamic patient clinical features and accounting for competing risks from EHRs into the DL algorithms demonstrated VTE risk prediction with high accuracy. Our results show that this novel dynamic survival network can provide personalized risk prediction with the potential to assist risk-based clinical intervention to prevent VTE among patients with EOC.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602107","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":"Development of a Web-Based Interactive Tool for Visualizing Breast Cancer Clinical Trial Tolerability Data.","authors":"Michael Luu, Gillian Gresham, Lynn Henry, Sungjin Kim, Andre Rogatko, Greg Yothers, Ron D Hays, Mourad Tighiouart, Patricia A Ganz","doi":"10.1200/CCI.24.00007","DOIUrl":"10.1200/CCI.24.00007","url":null,"abstract":"<p><strong>Purpose: </strong>Longitudinal patient tolerability data collected as part of randomized controlled trials are often summarized in a way that loses information and does not capture the treatment experience. To address this, we developed an interactive web application to empower clinicians and researchers to explore and visualize patient tolerability data.</p><p><strong>Methods: </strong>We used adverse event (AE) data (Common Terminology Criteria for Adverse Events) and patient-reported outcomes (PROs) from the NSABP-B35 phase III clinical trial, which compared anastrozole with tamoxifen for breast cancer-free survival, to demonstrate the tools. An interactive web application was developed using R and the Shiny web application framework that generates Sankey diagrams to visualize AEs and PROs using four tools: AE Explorer, PRO Explorer, Cohort Explorer, and Custom Explorer.</p><p><strong>Results: </strong>To illustrate how users can use the interactive tool, examples for each of the four applications are presented using data from the NSABP-B35 phase III trial and the NSABP-B30 trial for the Custom Explorer. In the AE and PRO explorers, users can select AEs or PROs to visualize within specified time periods and compare across treatments. In the cohort explorer, users can select a subset of patients with a specific symptom, severity, and treatment received to visualize the trajectory over time within a specified time interval. With the custom explorer, users can upload and visualize structured longitudinal toxicity and tolerability data.</p><p><strong>Conclusion: </strong>We have created an interactive web application and tool for clinicians and researchers to explore and visualize clinical trial tolerability data. This adaptable tool can be extended for other clinical trial data visualization and incorporated into future patient-clinician interactions regarding treatment decisions.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11254331/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141629264","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":"National Implementation of an Artificial Intelligence-Based Virtual Dietitian for Patients With Cancer.","authors":"Marissa L Buchan, Keshav Goel, Chelsey K Schneider, Vera Steullet, Susan Bratton, Ethan Basch","doi":"10.1200/CCI.24.00085","DOIUrl":"https://doi.org/10.1200/CCI.24.00085","url":null,"abstract":"<p><strong>Purpose: </strong>Nutritional status is an established driver of cancer outcomes, but there is an insufficient workforce of registered dietitians to meet patient needs for nutritional counseling. Artificial intelligence (AI) and machine learning (ML) afford the opportunity to expand access to guideline-based nutritional support.</p><p><strong>Methods: </strong>An AI-based nutrition assistant called Ina was developed on the basis of a learning data set of >100,000 expert-curated interventions, peer-reviewed literature, and clinical guidelines, and provides a conversational text message-based patient interface to guide dietary habits and answer questions. Ina was implemented nationally in partnership with 25 advocacy organizations. Data on demographics, patient-reported outcomes, and utilization were systematically collected.</p><p><strong>Results: </strong>Between July 2019 and August 2023, 3,310 users from all 50 states registered to use Ina. Users were 73% female; median age was 57 (range, 18-91) years; most common cancer types were genitourinary (22%), breast (21%), gynecologic (19%), GI (14%), and lung (12%). Users were medically complex, with 50% reporting Stage III to IV disease, 37% with metastases, and 50% with 2+ chronic conditions. Nutritional challenges were highly prevalent: 58% had overweight/obese BMIs, 83% reported barriers to good nutrition, and 42% had food allergies/intolerances. Levels of engagement were high: 68% texted questions to Ina; 79% completed surveys; median user retention was 8.8 months; 94% were satisfied with the platform; and 98% found the guidance helpful. In an evaluation of outcomes, 84% used the advice to guide diet; 47% used recommended recipes, 82% felt the program improved quality of life (QoL), and 88% reported improved symptom management.</p><p><strong>Conclusion: </strong>Implementation of an evidence-based AI virtual dietitian is feasible and is reported by patients to be beneficial on diet, QoL, and symptom management. Ongoing evaluations are assessing impact on other outcomes.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141238725","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":"When Will Real-World Data Fulfill Its Promise to Provide Timely Insights in Oncology?","authors":"Marc L Berger, Patricia A Ganz, Kelly H Zou, Sheldon Greenfield","doi":"10.1200/CCI.24.00039","DOIUrl":"https://doi.org/10.1200/CCI.24.00039","url":null,"abstract":"<p><p>Randomized trials provide high-quality, internally consistent data on selected clinical questions, but lack generalizability for the aging population who are most often diagnosed with cancer and have comorbid conditions that may affect the interpretation of treatment benefit. The need for high-quality, relevant, and timely data is greater than ever. Promising solutions lie in the collection and analysis of real-world data (RWD), which can potentially provide timely insights about the patient's course during and after initial treatment and the outcomes of important subgroups such as the elderly, rural populations, children, and patients with greater social health needs. However, to inform practice and policy, real-world evidence must be created from trustworthy and comprehensive sources of RWD; these may include pragmatic clinical trials, registries, prospective observational studies, electronic health records (EHRs), administrative claims, and digital technologies. There are unique challenges in oncology since key parameters (eg, cancer stage, biomarker status, genomic assays, imaging response, side effects, quality of life) are not recorded, siloed in inaccessible documents, or available only as free text or unstructured reports in the EHR. Advances in analytics, such as artificial intelligence, may greatly enhance the ability to obtain more granular information from EHRs and support integrated diagnostics; however, they will need to be validated purpose by purpose. We recommend a commitment to standardizing data across sources and building infrastructures that can produce fit-for-purpose RWD that will provide timely understanding of the effectiveness of individual interventions.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141477942","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":"Minimal Common Oncology Data Elements Genomics Pilot Project: Enhancing Oncology Research Through Electronic Health Record Interoperability at Vanderbilt University Medical Center.","authors":"Yanwei Li, Jiarong Ye, Yuxin Huang, Jiayi Wu, Xiaohan Liu, Shun Ahmed, Travis Osterman","doi":"10.1200/CCI.23.00249","DOIUrl":"10.1200/CCI.23.00249","url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11371088/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141472502","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":"Clarifying Causal Effects of Interest and Underlying Assumptions in Randomized and Nonrandomized Clinical Trials in Oncology Using Directed Acyclic Graphs and Single-World Intervention Graphs.","authors":"Shiro Tanaka, Yuriko Muramatsu, Kosuke Inoue","doi":"10.1200/CCI.23.00262","DOIUrl":"10.1200/CCI.23.00262","url":null,"abstract":"<p><p>Recent clinical trials in oncology have used increasingly complex methodologies, such as causal inference methods for intercurrent events, external control, and covariate adjustment, posing challenges in clarifying the estimand and underlying assumptions. This article proposes expressing causal structures using graphical tools-directed acyclic graphs (DAGs) and single-world intervention graphs (SWIGs)-in the planning phase of a clinical trial. It presents five rules for selecting a sufficient set of adjustment variables on the basis of a diagram representing the clinical trial, along with three case studies of randomized and single-arm trials and a brief tutorial on DAG and SWIG. Through the case studies, DAGs appear effective in clarifying assumptions for identifying causal effects, although SWIGs should complement DAGs due to their limitations in the presence of intercurrent events in oncology research.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11371110/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141447548","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":"Explainable Machine Learning Model to Predict Overall Survival in Patients Treated With Palliative Radiotherapy for Bone Metastases.","authors":"Savino Cilla, Romina Rossi, Ragnhild Habberstad, Pal Klepstad, Monia Dall'Agata, Stein Kaasa, Vanessa Valenti, Costanza M Donati, Marco Maltoni, Alessio G Morganti","doi":"10.1200/CCI.24.00027","DOIUrl":"10.1200/CCI.24.00027","url":null,"abstract":"<p><strong>Purpose: </strong>The estimation of prognosis and life expectancy is critical in the care of patients with advanced cancer. To aid clinical decision making, we build a prognostic strategy combining a machine learning (ML) model with explainable artificial intelligence to predict 1-year survival after palliative radiotherapy (RT) for bone metastasis.</p><p><strong>Materials and methods: </strong>Data collected in the multicentric PRAIS trial were extracted for 574 eligible adults diagnosed with metastatic cancer. The primary end point was the overall survival (OS) at 1 year (1-year OS) after the start of RT. Candidate covariate predictors consisted of 13 clinical and tumor-related pre-RT patient characteristics, seven dosimetric and treatment-related variables, and 45 pre-RT laboratory variables. ML models were developed and internally validated using the Python package. The effectiveness of each model was evaluated in terms of discrimination. A Shapley Additive Explanations (SHAP) explainability analysis to infer the global and local feature importance and to understand the reasons for correct and misclassified predictions was performed.</p><p><strong>Results: </strong>The best-performing model for the classification of 1-year OS was the extreme gradient boosting algorithm, with AUC and F1-score values equal to 0.805 and 0.802, respectively. The SHAP technique revealed that higher chance of 1-year survival is associated with low values of interleukin-8, higher values of hemoglobin and lymphocyte count, and the nonuse of steroids.</p><p><strong>Conclusion: </strong>An explainable ML approach can provide a reliable prediction of 1-year survival after RT in patients with advanced cancer. The implementation of SHAP analysis provides an intelligible explanation of individualized risk prediction, enabling oncologists to identify the best strategy for patient stratification and treatment selection.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141452101","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":"Population-Level Identification of Patients With Lynch Syndrome for Clinical Care, Quality Improvement, and Research.","authors":"Ravi N Sharaf, Natalia Udaltsova, Dan Li, Rish K Pai, Soham Sinha, Zixuan Li, Douglas A Corley","doi":"10.1200/CCI.23.00157","DOIUrl":"https://doi.org/10.1200/CCI.23.00157","url":null,"abstract":"<p><strong>Purpose: </strong>Identification of those at risk of hereditary cancer syndromes using electronic health record (EHR) data sources is important for clinical care, quality improvement, and research. We describe diagnostic processes, previously seldom reported, for a common hereditary cancer syndrome, Lynch syndrome (LS), using EHR data within a community-based, multicenter, demographically diverse health system.</p><p><strong>Methods: </strong>Within a retrospective cohort enrolled between 2015 and 2020 at Kaiser Permanente Northern California, we assessed electronic diagnostic domains for LS including (1) family history of LS-associated cancer; (2) personal history of LS-associated cancer; (3) LS screening via mismatch repair deficiency (MMRD) testing of newly diagnosed malignancy; (4) germline genetic test results; and (5) clinician-entered diagnostic codes for LS. We calculated proportions and overlap for each diagnostic domain descriptively.</p><p><strong>Results: </strong>Among 5.8 million individuals, (1) 28,492 (0.49%) had a family history of LS-associated cancer of whom 3,635 (13%) underwent genetic testing; (2) 100,046 (1.7%) had a personal history of a LS-associated cancer; and (3) 8,711 (0.1%) were diagnosed with colorectal cancer of whom 7,533 (86%) underwent MMRD screening and of the positive screens (486), 130 (27%) underwent germline testing. One thousand seven hundred and fifty-seven (0.03%) were diagnosed with endometrial cancer of whom 1,613 (92%) underwent MMRD screening and of the 195 who screened positive, 55 (28%) underwent genetic testing. (4) 30,790 (0.05%) had LS germline genetic testing with 707 (0.01%) testing positive; and (5) 1,273 (0.02%) had a clinician-entered diagnosis of LS.</p><p><strong>Conclusion: </strong>It is feasible to electronically characterize the diagnostic processes of LS. No single data source comprehensively identifies all LS carriers. There is underutilization of LS genetic testing for those eligible and underdiagnosis of LS. Our work informs similar efforts in other settings for hereditary cancer syndromes.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141262671","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}