JCO Clinical Cancer Informatics最新文献

{"title":"Telehealth and Emergency Department Use Among Commercially Insured, Medicaid, and Medicare Patients Receiving Systemic Cancer Therapy in Washington State After COVID-19.","authors":"Scott D Ramsey, Qin Sun, Catherine R Fedorenko, Li Li, Laura E Panattoni, Karma L Kreizenbeck, Veena Shankaran","doi":"10.1200/CCI-24-00217","DOIUrl":"https://doi.org/10.1200/CCI-24-00217","url":null,"abstract":"<p><strong>Purpose: </strong>In oncology, telehealth services were adopted as a means of mitigating the risk of COVID-19 transmission. We hypothesized that Medicaid enrollees would have less access to telehealth than commercially insured or Medicare enrollees during the pandemic, resulting in higher rates of emergency department (ED) visits during systemic cancer treatment.</p><p><strong>Methods: </strong>Linking Washington State SEER records with commercial, Medicaid, and Medicare records, we evaluated adults with new solid tumor malignancies who received initial systemic treatment before the COVID-19 pandemic (January 1, 2017-December 31, 2019) and after the pandemic (March 1, 2020-November 30, 2021). Poisson and logistic regressions were used to evaluate differences in the number of office visits, telehealth visits, and ED visits in the 3 months after starting systemic anticancer treatment between insurance groups before versus after the pandemic.</p><p><strong>Results: </strong>Among 2,936 commercial, 2,039 Medicaid, and 7,333 Medicare enrollees who met inclusion criteria, office-based visits fell substantially for all groups during the COVID-19 period. Medicare enrollees had fewer telehealth visits while Medicaid had more telehealth visits, compared with commercial enrollees. ED visits declined for all patients, but there were no differences between insurance groups.</p><p><strong>Conclusion: </strong>In Washington State, COVID-19 resulted in a substantial decrease in office-based visits, with an accompanying increase in telehealth visits partially offsetting the difference in overall access to care. ED visit rates fell substantially, without differences between insurance groups.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400217"},"PeriodicalIF":3.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121426","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":"Creating a Proxy for Baseline Eastern Cooperative Oncology Group Performance Status in Electronic Health Records for Comparative Effectiveness Research in Advanced Non-Small Cell Lung Cancer.","authors":"Michael Johnson, Peining Tao, Mehmet Burcu, John Kang, Richard Baumgartner, Junshui Ma, Vladimir Svetnik","doi":"10.1200/CCI-24-00185","DOIUrl":"10.1200/CCI-24-00185","url":null,"abstract":"<p><strong>Purpose: </strong>Eastern Cooperative Oncology Group performance status (ECOG PS) is a key confounder in comparative effectiveness research, predicting treatment and survival, but is often incomplete in electronic health records (EHRs). Imputation on the basis of classification metrics alone may introduce differences in survival between patients with known and imputed ECOG PS, complicating comparative effectiveness research. We developed an approach to impute ECOG PS so that those with known and imputed ECOG PS are indistinguishable in their survival, reducing potential biases introduced by the imputation.</p><p><strong>Methods: </strong>We analyzed deidentified data from an EHR-derived database for patients with advanced non-small cell lung cancer (aNSCLC) at their first line of treatment. Our novel imputation method involved (1) sample-splitting patients with known ECOG PS into modeling and thresholding data sets, (2) developing a predictive model of ECOG PS, (3) determining an optimal threshold aligning clinical outcomes, where a choice of outcome metric may depend on the use case, and (4) applying the model and threshold to impute missing ECOG PS. We evaluated the approach using binary classification metrics and alignment of survival metrics between observed and imputed ECOG PS.</p><p><strong>Results: </strong>Of 62,101 patients, 13,297 (21%) had missing ECOG PS at the start of their first treatment. Our method achieved similar or better performance in accuracy (73.3%), sensitivity (42.4%), and specificity (81%) compared with other techniques, with smaller survival metric differences between observed and imputed ECOG PS, with differences of 0.07 in hazard ratio, -0.36 months in median survival for good ECOG PS (<2), and -0.39 months for poor ECOG PS (≥2).</p><p><strong>Conclusion: </strong>Our imputed ECOG PS aligning clinical outcomes enhanced the use of real-world EHR data of patients with aNSCLC for comparative effectiveness research.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400185"},"PeriodicalIF":3.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143781526","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":"Clinician and Patient Perspectives on a Patient-Facing Online Breast Cancer Symptom Visualization Tool.","authors":"Gillian Gresham, Michael Luu, N Lynn Henry, Tyra Nguyen, Katherine Barnhill, Greg Yothers, Sungjin Kim, Andre Rogatko, Deanna J Attai, Mourad Tighiouart, Ron D Hays, Patricia A Ganz","doi":"10.1200/CCI.24.00109","DOIUrl":"10.1200/CCI.24.00109","url":null,"abstract":"<p><strong>Purpose: </strong>Endocrine treatments for patients with hormone-sensitive breast cancer are associated with significant side effects that can negatively affect health-related quality of life and result in treatment discontinuation. The objective of this qualitative study was to obtain feedback from stakeholder clinicians and patients about an online interactive tool that was designed to provide information and visualizations of breast cancer symptoms.</p><p><strong>Methods: </strong>The online Breast Cancer Symptom Explorer tool was developed to allow patients to visualize trajectories for common symptoms associated with tamoxifen and anastrozole using symptom data from the NSABP B35 breast cancer clinical trial. To refine the tool, virtual focus groups were conducted among oncology clinicians and women with a history of breast cancer who had received treatment with an aromatase inhibitor or tamoxifen, seeking feedback on the tool and its potential usefulness. Discussions took place using a secure web-conferencing platform following a semi-structured interview guide. Focus groups were audio-recorded, transcribed, and analyzed using reflexive thematic analysis.</p><p><strong>Results: </strong>Nine focus groups were conducted (n = 21 participants: eight clinicians and 13 patients). Key benefits and barriers to tool use emerged from the discussions. Both patients and oncologists valued the ability to engage with the tool and visualize symptoms over time. They indicated that ideal settings for its use would be at home before treatment initiation. Combinations of graphical representations with text were perceived to be most effective in communicating symptoms. Key barriers identified included concerns about accessibility to the tool and digital literacy, with recommendations to simplify the text and provide health literacy support to enhance its clinical utility in the future.</p><p><strong>Conclusion: </strong>Clinician and patient involvement was critical for refinement of the breast cancer symptom explorer and provided insights into its future use and evaluation of the tool in clinical decision making.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400109"},"PeriodicalIF":3.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784443","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":"Decoding Recurrence in Early-Stage and Locoregionally Advanced Non-Small Cell Lung Cancer: Insights From Electronic Health Records and Natural Language Processing.","authors":"Kyeryoung Lee, Zongzhi Liu, Qing Huang, David Corrigan, Iftekhar Kalsekar, Tomi Jun, Gustavo Stolovitzky, William K Oh, Ravi Rajaram, Xiaoyan Wang","doi":"10.1200/CCI-24-00227","DOIUrl":"https://doi.org/10.1200/CCI-24-00227","url":null,"abstract":"<p><strong>Purpose: </strong>Recurrences after curative resection in early-stage and locoregionally advanced non-small cell lung cancer (NSCLC) are common, necessitating a nuanced understanding of associated risk factors. This study aimed to establish a natural language processing (NLP) system to efficiently curate recurrence data in NSCLC and analyze risk factors longitudinally.</p><p><strong>Patients and methods: </strong>Electronic health records of 6,351 patients with NSCLC with >700,000 notes were obtained from Mount Sinai's data sets. A deep learning-based customized NLP system was developed to identify cohorts experiencing recurrence. Recurrence types and rates over time were stratified by various clinical features. Cohort description analysis, Kaplan-Meier analysis for overall recurrence-free survival (RFS) and distant metastasis-free survival (DMFS), and Cox proportional hazards analysis were performed.</p><p><strong>Results: </strong>Of 1,295 patients with stage I-IIIA NSCLC with surgical resections, 336 patients (25.9%) experienced recurrence, as identified through NLP. The NLP system achieved a precision of 94.3%, a recall of 93%, and an F1 score of 93.5. Among 336 patients, 52.4% had local/regional recurrences, 44% distant metastases, and 3.6% unknown recurrence. RFS rates at years 1-5 were 93%, 81%, 73%, 67%, and 61%, respectively (96%, 89%, 84%, 80%, and 75% for distant metastasis). Stage-specific RFS rates at year 5 were 73% (IA), 62% (IB), 47% (IIA), 46% (IIB), and 20% (IIIA). Stage IB patients had a significantly higher likelihood of recurrence versus stage IA (adjusted hazard ratio [aHR], 1.63; <i>P</i> = .02). The RFS was lower in patients with clinically significant <i>TP53</i> alteration (<i>v</i> <i>TP53</i>-negative or unknown significance), affecting overall RFS (aHR, 1.89; <i>P</i> = .007) and DMFS (aHR, 2.47; <i>P</i> = .009) among stage IA/IB patients.</p><p><strong>Conclusion: </strong>Our scalable NLP system enabled us to generate real-world insights into NSCLC recurrences, paving the way for predictive models for preventing, diagnosing, and treating NSCLC recurrence.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400227"},"PeriodicalIF":3.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12011440/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143994364","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":"Machine Learning Models of Early Longitudinal Toxicity Trajectories Predict Cetuximab Concentration and Metastatic Colorectal Cancer Survival in the Canadian Cancer Trials Group/AGITG CO.17/20 Trials.","authors":"Danielle Lilly Nicholls, Maria C Xu, Luna Zhan, Divya Sharma, Katrina Hueniken, Kaitlyn Chiasson, Mary Wahba, M Catherine Brown, Benjamin Grant, Jeremy Shapiro, Christos S Karapetis, John Simes, Derek Jonker, Dongsheng Tu, Christopher O'Callaghan, Eric Chen, Geoffrey Liu","doi":"10.1200/CCI.24.00114","DOIUrl":"https://doi.org/10.1200/CCI.24.00114","url":null,"abstract":"<p><strong>Purpose: </strong>Cetuximab (CET), targeting the epidermal growth factor receptor, is a systemic treatment option for patients with colorectal cancer. One known predictive factor for CET efficacy is the presence of CET-related rash; other putative toxicity factors include fatigue and nausea. Analysis of early CET-associated toxicities may reveal patient subpopulations that clinically benefit from long-term CET treatment.</p><p><strong>Methods: </strong>We analyzed data from CO.20 (ClinicalTrials.gov identifier: NCT00640471) trial arms, CET + brivanib alaninate (BRIV) (n = 376) and CET + placebo (n = 374), and CO.17 (ClinicalTrials.gov identifier: NCT00079066) trial arms, CET (+best supportive care [BSC]; n = 287) and BSC only (n = 285). Patients were clustered into subpopulations using KmL3D, a machine learning method, to analyze 14 joint longitudinal toxicity trajectories from weeks 0 to 8 of treatment. Landmark survival analyses were performed from 8 weeks after treatment initiation. Regression analyses assessed the relationship between subpopulations and plasma CET concentrations. Three supervised machine learning models were developed to assign patients in the CO.20-CET trial arm into subpopulations, which were then validated using CO.20-CET-BRIV and CO.17-CET trial arm data.</p><p><strong>Results: </strong>Joint longitudinal toxicity clustering revealed dichotomous high- and low-toxicity clusters, with all CET-containing arms showing consistent toxicity trajectories and characteristics. High-toxicity clusters were associated with male predilection, fewer metastatic sites, fewer colon-only primaries, and higher body mass indices. In CO.20 trial samples, higher toxicity clusters were associated with improved overall survival and progression-free survival outcomes (adjusted hazard ratios ranging from 2.21 to 4.36) and higher CET concentrations (<i>P</i> = .003). The random forest predictive model performed the best, with an AUC of 0.981 (0.963-0.999).</p><p><strong>Conclusion: </strong>We used an innovative machine learning approach to analyze longitudinal joint drug toxicities, demonstrating their role in predicting patient outcomes through a putative pharmacokinetic mechanism.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400114"},"PeriodicalIF":3.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144052649","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":"Validation of a Composite Real-World Mortality Variable Among Patients With Hematologic Malignancies Treated in the United States.","authors":"Sharlene Dong, Ankit Kansagra, Gurbakhash Kaur, Anna Barcellos, Andrew J Belli, Laura L Fernandes, Eric Hansen, Jacob Ambrose, Claire Bai, Christina M Zettler, Ming He, Ching-Kun Wang","doi":"10.1200/CCI-24-00233","DOIUrl":"https://doi.org/10.1200/CCI-24-00233","url":null,"abstract":"<p><strong>Purpose: </strong>Accurate survival data are critical for high-quality outcomes research. It has been documented that mortality data capture in the real-world setting may be prone to missingness. Our study sought to evaluate the validity of a composite real-world mortality variable compared with the National Death Index (NDI) as the gold standard.</p><p><strong>Methods: </strong>This was a retrospective, observational research study of patients with hematologic malignancies treated in the United States. Adult patients diagnosed with one of the following cancers between January 1, 2012, and December 31, 2020, were included: AML, chronic lymphocytic leukemia, diffuse large B-cell lymphoma, follicular lymphoma, marginal zone lymphoma, multiple myeloma, and myelodysplastic syndrome. Validation metrics (sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV]) and date concordance (exact, ±7, 15, and 30 days) were assessed.</p><p><strong>Results: </strong>The final study population included N = 21,565 patients across seven cancer types. Validation metrics showed high sensitivity (87.8%), specificity (95.7%), PPV (90.9%), and NPV (94.1%) when comparing the composite real-world mortality variable with the NDI. Exact date concordance was observed in 88.0% of patients, and concordance rates for 7-, 15-, and 30-day intervals were 93.1%, 93.8%, and 94.3%, respectively.</p><p><strong>Conclusion: </strong>Our study found that a composite mortality variable leveraging multiple data sources yields high validity when compared against the gold-standard NDI. Given evidence highlighting the challenges of mortality data documentation in the real-world setting, the use of a composite mortality variable can provide significant benefits in quality of documentation and research results.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400233"},"PeriodicalIF":3.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057465","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":"Patient-Reported Outcomes Program at Scale at a Cancer Center.","authors":"Fernanda C G Polubriaginof, Allison Lipitz-Snyderman, Susan Chimonas, Gilad J Kuperman, Peter D Stetson","doi":"10.1200/CCI-24-00253","DOIUrl":"https://doi.org/10.1200/CCI-24-00253","url":null,"abstract":"<p><strong>Purpose: </strong>Incorporating patient-reported outcomes (PROs) into health care processes can improve engagement with patients; however, adopting PROs at scale is challenging. The aim of this study was to describe the design, development, and adoption at scale of a comprehensive PRO program for standard of care and research at a cancer center.</p><p><strong>Methods: </strong>Requirements for a PRO program were obtained from multiple stakeholders. Components of the program included a governance process to assure a consistent and satisfactory experience for patients completing PRO questionnaires, tools to create and manage questionnaires and related content, methods to send questionnaires to relevant patients at the appropriate time, interactive tools for patients to complete the questionnaires as part of their portal experience, and integration of PRO data into workflows for clinicians. We used descriptive statistics to assess the use of the program from 2016 to 2023.</p><p><strong>Results: </strong>From program launch (on February 1, 2016) until December 31, 2023, 189 unique questionnaires were developed (101 for standard-of-care, 70 for research, and 18 for quality improvement). Of the 432,497 unique patients who were assigned at least one questionnaire, 314,685 (73%) completed at least one. Of 5,948,464 questionnaires sent, 3,098,574 (52%) were completed. The median completion time was 2 minutes.</p><p><strong>Conclusion: </strong>Large-scale adoption of PROs at a cancer center is feasible. Key considerations for success include governance processes, attention to patient experience and clinician workflow, and the ability to manage complex inclusion criteria and timing of delivery of questionnaires. These principles should be disseminated so the full potential of PROs in health care can be realized.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400253"},"PeriodicalIF":3.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12017341/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144049277","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":"Development and Evaluation of an Electronic Health Record-Derived Computable Phenotype to Identify Patients Undergoing Prostate Cancer Screening.","authors":"Patrick Lewicki, Yasmin Benhalim, Joshua Bradin, Kim Dryden, Husain Hakim, Benjamin Heasman, Ana Taylor, Jawad Aqeel, Anuush Vejalla, Marisa Conte, Rachel Richesson, Kristian Stensland","doi":"10.1200/CCI-24-00261","DOIUrl":"https://doi.org/10.1200/CCI-24-00261","url":null,"abstract":"<p><strong>Purpose: </strong>Given challenges with randomized trials, tumor registries, and insurance claims, electronic health record data are an appealing resource for studying prostate-specific antigen (PSA) screening for prostate cancer. Transparent, well-evaluated computable phenotypes that observe a stringent definition of screening (<i>v</i> for-cause diagnosis- or symptom-directed testing) are critical for reproducibility and comparison with prospective cohorts.</p><p><strong>Methods: </strong>A cohort of patients who underwent PSA testing in a primary care setting at a large, tertiary health care system was identified. Gold-standard labels for screening versus not screening were created via a combination of clinical note text review and exclusionary diagnosis codes. Ten computable phenotype definitions were created by urology content experts and then evaluated for sensitivity, specificity, and positive predictive value (PPV) and negative predictive value against gold-standard labels.</p><p><strong>Results: </strong>Three hundred fifty-five patients with gold-standard labels were included in the final study cohort. Varying by how missing text data were classified (not applicable <i>v</i> screening), 149 (50.3%) and 208 (58.6%) patients underwent screening. No single phenotype optimized both sensitivity and PPV, although a composite definition that included either (1) absence of symptoms or (2) presence of an encounter for screening code achieved a very high PPV of 0.99 (95% CI, 0.96 to 1.00) with a reasonable sensitivity of 0.82 (95% CI, 0.75 to 0.88).</p><p><strong>Conclusion: </strong>We identify code-based PSA screening phenotypes with a range of performance characteristics. Prevalence of for-cause diagnosis- and symptom-directed testing are significant and may contaminate cohorts not taking related codes into account.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400261"},"PeriodicalIF":3.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144028106","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":"Preferences of Pediatric Oncology Patients and Caregivers on the Availability of Patient Results in an Online Patient Portal.","authors":"Aryan Patel, Ian Kawpeng, Renee Potashner, Karim Jessa, Adam P Yan","doi":"10.1200/CCI-24-00235","DOIUrl":"https://doi.org/10.1200/CCI-24-00235","url":null,"abstract":"<p><strong>Purpose: </strong>Access to cancer-related data in online patient portals is not uniform. Perspectives of pediatric patients with cancer and caregivers on their desires and experiences accessing cancer-related data via an online patient portal have been poorly described. These perspectives are crucial for informing both hospital-level policies and governmental regulations. This study aims to explore the preferences of pediatric oncology patients and their caregivers regarding the timing of medical test result release into online portals.</p><p><strong>Methods: </strong>A cross-sectional survey was conducted at a tertiary academic pediatric center in Toronto, Canada. English-speaking pediatric patients with cancer age 12 years and older, as well as their caregivers, were invited to participate. A 59-question survey was administered to participants between June and August 2024.</p><p><strong>Results: </strong>A total of 105 participants, including 40 patients and 65 caregivers, completed the survey. Forty-one (53.9%) participants reported that a health care provider had discussed with them the possibility that they might be viewing test results online before their care team had reviewed the result. Immediate release of test results was preferred across most testing domains, with >80% of participants favoring immediate access, even for sensitive oncology-related results. Less than 1% of participants believed that genetic or cancer recurrence results should be withheld until reviewed by an oncology provider. No participants reported increased worry as a result of viewing test results online.</p><p><strong>Conclusion: </strong>This study reveals a strong preference among pediatric oncology patients and their caregivers for immediate access to test results, challenging traditional concerns about the psychological impact of early release. These findings suggest that oncology practices should consider aligning their policies with patient and caregiver preferences.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400235"},"PeriodicalIF":3.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144048545","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":"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}