JCO Clinical Cancer Informatics最新文献

{"title":"Using Real-World Data for Machine-Learning Algorithms to Predict the Treatment Response in Advanced Melanoma: A Pilot Study for Personalizing Cancer Care.","authors":"Richard M Brohet, Elianne C S de Boer, Joram M Mossink, Joni J N van der Eerden, Alexander Oostmeyer, Luuk H W Idzerda, Jan Gerard Maring, Gabriel M R M Paardekooper, Michel Beld, Fiona Lijffijt, Joep Dille, Jan Willem B de Groot","doi":"10.1200/CCI-24-00181","DOIUrl":"10.1200/CCI-24-00181","url":null,"abstract":"<p><strong>Purpose: </strong>The use of real-world data (RWD) in oncology is becoming increasingly important for clinical decision making and tailoring treatment. Despite the significant success of targeted therapy and immunotherapy in advanced melanoma, substantial variability in clinical responses to these treatments emphasizes the need for personalized approaches to therapy.</p><p><strong>Materials and methods: </strong>In this pilot study, 239 patients with melanoma were included to predict the response to both targeted therapies and immunotherapies. We used machine learning (ML) to incorporate RWD and applied explainable artificial intelligence (XAI) to explain the individual predictions.</p><p><strong>Results: </strong>We developed, validated, and compared four ML models to evaluate 2-year survival using RWD. Our research showed encouraging outcomes, achieving an AUC of more than 80% and an estimated accuracy of over 74% across the four ML models. The random forest model exhibited the highest performance in predicting 2-year survival with an AUC of 0.85. Local interpretable model-agnostic explanations was used to explain individual predictions and provide trust and insights into the clinical implications of the ML model.</p><p><strong>Conclusion: </strong>With this proof-of-concept, we integrated RWD into predictive modeling using ML techniques to predict clinical outcomes and explore their potential implications for clinical decision making. The potential of XAI was demonstrated to enhance trust and improve the usability of the model in clinical settings. Further research, including foundation modeling and generative AI, will likely increase the predictive power of prognostic and predictive ML models in advanced melanoma.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400181"},"PeriodicalIF":3.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784496","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":"Implementation of Recovery Tracker: A Postdischarge Electronic Remote Symptom-Monitoring Survey Tool After Major Urologic Oncology Surgeries.","authors":"Richard S Matulewicz, Fady Baky, Samuel Gold, Viranda H Jayalath, Rebecca Yu, Nicole Liso, Amy L Tin, Melissa Assel, Andrew J Vickers, Michael Hannon, Sigrid V Carlsson, Jennifer R Cracchiolo, Alvin C Goh","doi":"10.1200/CCI-24-00328","DOIUrl":"https://doi.org/10.1200/CCI-24-00328","url":null,"abstract":"<p><strong>Purpose: </strong>Remote symptom monitoring shows promise in promoting postdischarge contact between patients and clinicians. Unique strategies may be needed to tailor reach and engagement to specific patient populations. We aimed to assess implementation and effectiveness outcomes of a patient-reported symptom assessment tool (Recovery Tracker [RT]) after major urologic operations.</p><p><strong>Materials and methods: </strong>Patients undergoing one of four procedures (2016-2022) at a metropolitan cancer center-radical prostatectomy, nephrectomy, radical cystectomy, and retroperitoneal lymph node dissection for testicular cancer-were included in the study. Electronic delivery of RT was embedded in routine perioperative patient care. Outcomes were assessed according to the reach, effectiveness, adoption, implementation, and maintenance framework. Descriptive statistics was reported for reach, effectiveness, and adoption; a linear mixed-effects model for implementation; and a general additive model and fixed-effects meta-analysis for maintenance.</p><p><strong>Results: </strong>The cohort comprised 8,934 patients. Reach, defined as patients correctly receiving the survey, was 98% overall, with 81% (95% CI, 80 to 82) of patients completing at least one survey and the majority completing >7. The median time to completion was <2 minutes and improved as patients completed more surveys (<i>P</i> < .001), with slight variation among procedure types. The survey was effective, initiating patient-clinician contact when alert thresholds were triggered, with a marginal increase in the need for clinician office phone calls. Patient engagement with RT was maintained over several years, with a slight improvement after the addition of e-mail reminders (between 3% and 8%).</p><p><strong>Conclusion: </strong>Implementing a daily electronic survey after hospital discharge after major urologic surgeries is feasible and used often by patients.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400328"},"PeriodicalIF":3.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12043049/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144053307","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":"Challenges in Automating Extraction of Real-World Radiographic Images and Adverse Events: Lessons From the ICAREdata Initiative.","authors":"Steven Piantadosi, Nancy Campbell, Selina Chow, Cassandra Elrahi, Michael V Knopp, Vaibhav Kumar, Catherine C Lerro, Donna R Rivera, Paul G Kluetz, Andre Quina, Michelle Casagni, Nareesa Mohammed-Rajput, Amye Tevaarwerk, Suzanne George","doi":"10.1200/CCI-24-00319","DOIUrl":"https://doi.org/10.1200/CCI-24-00319","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400319"},"PeriodicalIF":3.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12011427/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144065287","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":"Risk Stratification for Sentinel Lymph Node Positivity in Older Women With Early-Stage Estrogen Receptor-Positive/Human Epidermal Growth Factor Receptor 2 Neu-Negative Invasive Breast Cancer.","authors":"Ton Wang, Drew Neish, Samantha M Thomas, Astrid Botty van den Bruele, Laura H Rosenberger, Akiko Chiba, Kendra J Modell Parrish, Maggie L DiNome, Lesly A Dossett, Charles D Scales, Leah L Zullig, E Shelley Hwang, Jennifer K Plichta","doi":"10.1200/CCI-24-00186","DOIUrl":"10.1200/CCI-24-00186","url":null,"abstract":"<p><strong>Purpose: </strong>Guidelines recommend omission of sentinel lymph node biopsy (SLNB) for axillary staging in select patients age 70 years and older with early-stage estrogen receptor-positive (ER+), human epidermal growth factor receptor 2 neu-negative (HER2-) invasive breast cancers (BCs). However, many women meeting criteria for SLNB omission continue to receive this procedure. This study aims to stratify patients into risk cohorts for nodal positivity that can be incorporated into deimplementation strategies to reduce low-value SLNB procedures.</p><p><strong>Methods: </strong>A retrospective cohort analysis using the National Cancer Database was performed on patients age 70 years and older with ER+/HER2-, cT1-2, cN0, cM0 BC who underwent breast surgery from 2018 to 2021. Patients who received neoadjuvant therapies were excluded. Recursive partitioning analysis (RPA) was used to develop two models to estimate nodal positivity: (1) a clinical model for preoperative use to decide whether to perform SLNB and (2) a pathologic model for postoperative use to guide adjuvant decisions in cases of SLNB omission.</p><p><strong>Results: </strong>The study included 68,867 patients who received SLNB; 13.4% had a tumor-involved lymph node. RPA on the basis of clinical covariates demonstrated <8% risk of nodal positivity for patients with cT1mi-cT1b and grade 1-2 tumors. RPA on the basis of pathologic covariates found <10% risk of nodal positivity for patients with pT1 tumors without lymphovascular invasion (LVI). Patients with cT2 or pT2 without LVI and nonductal/nonlobular histology had <5% risk of nodal positivity.</p><p><strong>Conclusion: </strong>This study demonstrates a low risk of nodal positivity for patients with cT1 or pT1 tumors. Our RPA-defined subgroups offer a novel approach to predict nodal positivity in patients age 70 years and older with early-stage, ER+/HER2- invasive BC that can be incorporated in deimplementation strategies to reduce low-value axillary surgery.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400186"},"PeriodicalIF":3.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12087273/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732994","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":"Erratum: Real-World Outcomes in Patients With Metastatic Renal Cell Carcinoma Treated With First-Line Nivolumab Plus Ipilimumab in the United States.","authors":"Gurjyot K Doshi, Andrew J Osterland, Ping Shi, Annette Yim, Viviana Del Tejo, Sarah B Guttenplan, Samantha Eiffert, Xin Yin, Lisa Rosenblatt, Paul R Conkling","doi":"10.1200/CCI-25-00026","DOIUrl":"https://doi.org/10.1200/CCI-25-00026","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500026"},"PeriodicalIF":3.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558573","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":"Novel Computational Pipeline Enables Reliable Diagnosis of Inverted Urothelial Papilloma and Distinguishes It From Urothelial Carcinoma.","authors":"Wei Shao, Michael Cheng, Antonio Lopez-Beltran, Adeboye O Osunkoya, Jie Zhang, Liang Cheng, Kun Huang","doi":"10.1200/CCI.24.00059","DOIUrl":"10.1200/CCI.24.00059","url":null,"abstract":"<p><strong>Purpose: </strong>With the aid of ever-increasing computing resources, many deep learning algorithms have been proposed to aid in diagnostic workup for clinicians. However, existing studies usually selected informative patches from whole-slide images for the training of the deep learning model, requiring labor-intensive labeling efforts. This work aimed to improve diagnostic accuracy through the statistic features extracted from hematoxylin and eosin-stained slides.</p><p><strong>Methods: </strong>We designed a computational pipeline for the diagnosis of inverted urothelial papilloma (IUP) of the bladder from its cancer mimics using statistical features automatically extracted from whole-slide images. Whole-slide images from 225 cases of common and uncommon urothelial lesions (64 IUPs; 69 inverted urothelial carcinomas [UCInvs], and 92 low-grade urothelial carcinoma [UCLG]) were analyzed.</p><p><strong>Results: </strong>We identified 68 image features in total that were significantly different between IUP and UCInv and 42 image features significantly different between IUP and UCLG. Our method integrated multiple types of image features and achieved high AUCs (the AUCs) of 0.913 and 0.920 for classifying IUP from UCInv and conventional UC, respectively. Moreover, we constructed an ensemble classifier to test the prediction accuracy of IUP from an external validation cohort, which provided a new workflow to diagnose rare cancer subtypes and test the models with limited validation samples.</p><p><strong>Conclusion: </strong>Our data suggest that the proposed computational pipeline can robustly and accurately capture histopathologic differences between IUP and other UC subtypes. The proposed workflow and related findings have the potential to expand the clinician's armamentarium for accurate diagnosis of urothelial malignancies and other rare tumors.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400059"},"PeriodicalIF":3.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626792","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":"PLSKB: An Interactive Knowledge Base to Support Diagnosis, Treatment, and Screening of Lynch Syndrome on the Basis of Precision Oncology.","authors":"Mahsa Dehghani Soufi, Reza Shirkoohi, Zohreh Sanaat, Anna Torkamannia, Meysam Hashemi, Samaneh Jahandar-Lashaki, Mahsa Yousefpour Marzbali, Yosra Vaez, Reza Ferdousi","doi":"10.1200/CCI-24-00246","DOIUrl":"10.1200/CCI-24-00246","url":null,"abstract":"<p><strong>Purpose: </strong>Understanding the genetic heterogeneity of Lynch syndrome (LS) cancers has led to significant scientific advancements. However, these findings are widely dispersed across various resources, making it difficult for clinicians and researchers to stay informed. Furthermore, the uneven quality of studies and the lack of effective translation of knowledge into clinical practice create challenges in delivering optimal patient care. To address these issues, we developed and launched the Precision Lynch Syndrome Knowledge Base (PLSKB), a specialized, interactive web-based platform that consolidates comprehensive information on LS.</p><p><strong>Methods: </strong>To create the PLSKB, we conducted an extensive literature review and gathered data from reliable sources. Through an extensive literature review and survey of other reliable sources, we have extracted prominent and relevant content with a high level of accuracy, transparency, and detailed provenance. To enhance usability, we implemented an evidence-leveling framework, categorizing studies on the basis of the type of research, reliability, and applicability to clinical care. The platform is designed to be dynamic, with updates performed monthly to incorporate the latest research.</p><p><strong>Results: </strong>The PLSKB integrates a broad spectrum of data related to LS, including biomarkers, cancer types, screening and prevention strategies, diagnostic methods, and therapeutics options. This centralized resource is intended to support clinicians and researchers in making evidence-based decisions throughout surveillance and care processes. Its interactive design and frequent updates ensure that users have access to the most current and relevant findings.</p><p><strong>Conclusion: </strong>The PLSKB bridges the gap between research and clinical practice by offering a reliable, up-to-date repository of evidence-based information. This tool empowers clinicians and researchers to deliver precision care and advance research for LS and related conditions, ultimately improving patient outcomes.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400246"},"PeriodicalIF":3.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143630926","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":"Large Language Models as Decision-Making Tools in Oncology: Comparing Artificial Intelligence Suggestions and Expert Recommendations.","authors":"Loic Ah-Thiane, Pierre-Etienne Heudel, Mario Campone, Marie Robert, Victoire Brillaud-Meflah, Caroline Rousseau, Magali Le Blanc-Onfroy, Florine Tomaszewski, Stéphane Supiot, Tanguy Perennec, Augustin Mervoyer, Jean-Sébastien Frenel","doi":"10.1200/CCI-24-00230","DOIUrl":"10.1200/CCI-24-00230","url":null,"abstract":"<p><strong>Purpose: </strong>To determine the accuracy of large language models (LLMs) in generating appropriate treatment options for patients with early breast cancer on the basis of their medical records.</p><p><strong>Materials and methods: </strong>Retrospective study using anonymized medical records of patients with BC presented during multidisciplinary team meetings (MDTs) between January and April 2024. Three generalist artificial intelligence models (Claude3-Opus, GPT4-Turbo, and LLaMa3-70B) were used to generate treatment suggestions, which were compared with experts' decisions. The primary outcome was the rate of appropriate suggestions from the LLMs, compared with the reference experts' decisions. The secondary outcome was the LLMs' performances (F1 score and specificity) in generating appropriate suggestions for each treatment category.</p><p><strong>Results: </strong>The rates of appropriate suggestions were 86.6% (97/112), 85.7% (96/112), and 75.0% (84/112) for Claude3-Opus, GPT4-Turbo, and LLaMa3-70B, respectively. No significant difference was found between Claude3-Opus and GPT4-Turbo (<i>P</i> = .85), but both tended to perform better than LLaMa3-70B (<i>P</i> = .027 and <i>P</i> = .043, respectively). LLMs showed high accuracy for adjuvant endocrine therapy and targeted therapy indications. However, they tended to overestimate the need for adjuvant radiotherapy and had variable performances in suggesting adjuvant chemotherapy and genomic tests.</p><p><strong>Conclusion: </strong>LLMs, particularly Claude3-Opus and GPT4-Turbo, demonstrated promising accuracy in suggesting appropriate adjuvant treatments for patients with early BC on the basis of their medical records. Although LLMs showed limitations in validating surgery and indicating genomic tests, their performance in other treatment modalities highlights their potential to automate and augment decision making during MDTs. Further studies with fine-tuned LLMs and a prospective design are needed to demonstrate their utility in clinical practice.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400230"},"PeriodicalIF":3.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11949217/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143671672","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":"Prospective Clinical Implementation of Paige Prostate Detect Artificial Intelligence Assistance in the Detection of Prostate Cancer in Prostate Biopsies: CONFIDENT P Trial Implementation of Artificial Intelligence Assistance in Prostate Cancer Detection.","authors":"Rachel N Flach, Carmen van Dooijeweert, Tri Q Nguyen, Mitchell Lynch, Trudy N Jonges, Richard P Meijer, Britt B M Suelmann, Peter-Paul M Willemse, Nikolas Stathonikos, Paul J van Diest","doi":"10.1200/CCI-24-00193","DOIUrl":"10.1200/CCI-24-00193","url":null,"abstract":"<p><strong>Purpose: </strong>Pathologists diagnose prostate cancer (PCa) on hematoxylin and eosin (HE)-stained sections of prostate needle biopsies (PBx). Some laboratories use costly immunohistochemistry (IHC) for all cases to optimize workflow, often exceeding reimbursement for the full specimen. Despite the rise in digital pathology and artificial intelligence (AI) algorithms, clinical implementation studies are scarce. This prospective clinical trial evaluated whether an AI-assisted workflow for detecting PCa in PBx reduces IHC use while maintaining diagnostic safety standards.</p><p><strong>Methods: </strong>Patients suspected of PCa were allocated biweekly to either a control or intervention arm. In the control arm, pathologists assessed whole-slide images (WSI) of PBx using HE and IHC stainings. In the intervention arm, pathologists used the Paige Prostate Detect AI algorithm on HE slides, requesting IHC only as needed. IHC was requested for all morphologically negative slides in the AI arm. The main outcome was the relative risk (RR) of IHC use per detected PCa case at both patient and WSI levels.</p><p><strong>Results: </strong>Overall, 143 of 237 (60.3%) slides of 64 of 82 patients contained PCa (78.0%). AI assistance significantly reduced the risk of IHC use per detected PCa case at both the patient level (RR, 0.55; 95% CI, 0.39 to 0.72) and slide level (RR, 0.41; 95% CI, 0.29 to 0.52). Cost reductions on IHC were €1,700 for the trial, at €50 per IHC stain. AI-assisted pathologists reported higher confidence in their diagnoses (80% <i>v</i> 56% confident or high confidence). The median assessment time per HE slide showed no significant difference between the AI-assisted and control arms (139 seconds <i>v</i> 112 seconds; <i>P</i> = .2).</p><p><strong>Conclusion: </strong>This study demonstrates that AI assistance for PCa detection in PBx significantly reduces IHC costs while maintaining diagnostic safety standards, supporting the business case for AI implementation in PCa detection.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400193"},"PeriodicalIF":3.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558574","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":"Virtual Health Care Encounters for Lung Cancer Screening in a Safety-Net Population: Observations From the COVID-19 Pandemic.","authors":"Mary E Gwin, Urooj Wahid, Sheena Bhalla, Asha Kandathil, Sarah Malone, Vijaya Natchimuthu, Cynthia Watkins, Lauren Vice, Heather Chatriand, Humaira Moten, Cornelia Tan, Kim C Styrvoky, David H Johnson, Andrea R Semlow, Jessica L Lee, Travis Browning, Megan A Mullins, Noel O Santini, George Oliver, Song Zhang, David E Gerber","doi":"10.1200/CCI.24.00086","DOIUrl":"10.1200/CCI.24.00086","url":null,"abstract":"<p><strong>Purpose: </strong>The COVID-19 pandemic disrupted normal mechanisms of health care delivery and facilitated the rapid and widespread implementation of telehealth technology. As a result, the effectiveness of virtual health care visits in diverse populations represents an important consideration. We used lung cancer screening as a prototype to determine whether subsequent adherence differs between virtual and in-person encounters in an urban, safety-net health care system.</p><p><strong>Methods: </strong>We conducted a retrospective analysis of initial low-dose computed tomography (LDCT) ordered for lung cancer screening from March 2020 through February 2023 within Parkland Health, the integrated safety-net provider for Dallas County, TX. We collected data on patient characteristics, visit type, and LDCT completion from the electronic medical record. Associations among these variables were assessed using the chi-square test. We also performed interaction analyses according to visit type.</p><p><strong>Results: </strong>Initial LDCT orders were placed for a total of 1,887 patients, of whom 43% were female, 45% were Black, and 17% were Hispanic. Among these orders, 343 (18%) were placed during virtual health care visits. From March to August 2020, 79 of 163 (48%) LDCT orders were placed during virtual visits; after that time, 264 of 1,724 (15%) LDCT orders were placed during virtual visits. No patient characteristics were significantly associated with visit type (in-person <i>v</i> virtual) or LDCT completion. Rates of LDCT completion were 95% after in-person visits and 97% after virtual visits (<i>P</i> = .13).</p><p><strong>Conclusion: </strong>In a safety-net lung cancer screening population, patients were as likely to complete postvisit initial LDCT when ordered in a virtual encounter as in an in-person encounter.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400086"},"PeriodicalIF":3.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143576051","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}