JCO Clinical Cancer Informatics最新文献

{"title":"Clinical Calculator for Predicting Freedom From Recurrence After Resection of Stage I-III Colon Cancer in Patients With Microsatellite Instability.","authors":"Ayyuce Begum Bektas, Lynn Hakki, Asama Khan, Maria Widmar, Iris H Wei, Emmanouil Pappou, J Joshua Smith, Garrett M Nash, Philip B Paty, Julio Garcia-Aguilar, Andrea Cercek, Zsofia Stadler, Neil H Segal, Jinru Shia, Mithat Gonen, Martin R Weiser","doi":"10.1200/CCI.23.00233","DOIUrl":"10.1200/CCI.23.00233","url":null,"abstract":"<p><strong>Purpose: </strong>Outcome for patients with nonmetastatic, microsatellite instability (MSI) colon cancer is favorable: however, high-risk cohorts exist. This study was aimed at developing and validating a nomogram model to predict freedom from recurrence (FFR) for patients with resected MSI colon cancer.</p><p><strong>Patients and methods: </strong>Data from patients who underwent curative resection of stage I, II, or III MSI colon cancer in 2014-2021 (model training cohort, 384 patients, 33 events; median follow-up, 38.8 months) were retrospectively collected from institutional databases. Variables associated with recurrence in multivariable analysis were selected for inclusion in the clinical calculator. The calculator's predictive accuracy was measured with the concordance index and validated using data from patients who underwent treatment for MSI colon cancer in 2007-2013 (validation cohort, 164 patients, eight events; median follow-up, 84.8 months).</p><p><strong>Results: </strong>T category and number of positive lymph nodes were significantly associated with recurrence in multivariable analysis and were selected for inclusion in the clinical calculator. The calculator's concordance index for FFR in the model training cohort was 0.812 (95% CI, 0.742 to 0.873), compared with 0.759 (95% CI, 0.683 to 0.840) for the staging schema of the eighth edition of the American Joint Committee on Cancer Staging Manual. The concordance index for the validation cohort was 0.744 (95% CI, 0.666 to 0.822), confirming robust predictive accuracy.</p><p><strong>Conclusion: </strong>Although in general patients with nonmetastatic MSI colon cancer had favorable outcome, patients with advanced T category and multiple metastatic lymph nodes had higher risk of recurrence. The clinical calculator identified patients with MSI colon cancer at high risk for recurrence, and this could inform surveillance strategies. In addition, the model could be used in trial design to identify patients suitable for novel adjuvant therapy.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11323037/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141910173","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":"Application of a Data Quality Framework to Ductal Carcinoma In Situ Using Electronic Health Record Data From the <i>All of Us</i> Research Program.","authors":"Lew Berman, Yechiam Ostchega, John Giannini, Lakshmi Priya Anandan, Emily Clark, Matthew Spotnitz, Lina Sulieman, Michael Volynski, Andrea Ramirez","doi":"10.1200/CCI.24.00052","DOIUrl":"https://doi.org/10.1200/CCI.24.00052","url":null,"abstract":"<p><strong>Purpose: </strong>The specific aims of this paper are to (1) develop and operationalize an electronic health record (EHR) data quality framework, (2) apply the dimensions of the framework to the phenotype and treatment pathways of ductal carcinoma in situ (DCIS) using <i>All of Us</i> Research Program data, and (3) propose and apply a checklist to evaluate the application of the framework.</p><p><strong>Methods: </strong>We developed a framework of five data quality dimensions (DQD; completeness, concordance, conformance, plausibility, and temporality). Participants signed a consent and Health Insurance Portability and Accountability Act authorization to share EHR data and responded to demographic questions in the Basics questionnaire. We evaluated the internal characteristics of the data and compared data with external benchmarks with descriptive and inferential statistics. We developed a DQD checklist to evaluate concept selection, internal verification, and external validity for each DQD. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) concept ID codes for DCIS were used to select a cohort of 2,209 females 18 years and older.</p><p><strong>Results: </strong>Using the proposed DQD checklist criteria, (1) concepts were selected and internally verified for conformance; (2) concepts were selected and internally verified for completeness; (3) concepts were selected, internally verified, and externally validated for concordance; (4) concepts were selected, internally verified, and externally validated for plausibility; and (5) concepts were selected, internally verified, and externally validated for temporality.</p><p><strong>Conclusion: </strong>This assessment and evaluation provided insights into data quality for the DCIS phenotype using EHR data from the <i>All of Us</i> Research Program. The review demonstrates that salient clinical measures can be selected, applied, and operationalized within a conceptual framework and evaluated for fitness for use by applying a proposed checklist.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142044174","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":"Response to Kempf et al on Methodological and Practical Aspects of a Distant Metastasis Detection Model.","authors":"Ricardo Ahumada, Jocelyn Dunstan, Inti Paredes, Pablo Báez","doi":"10.1200/CCI-24-00154","DOIUrl":"https://doi.org/10.1200/CCI-24-00154","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142074532","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":"Emergence of Digital Toxicity and the Need for an Integrated, Patient-Centric Approach to the Development, Evaluation, and Use of Digital Health Tools for Oncology.","authors":"Chris Gibbons, Carole Baas, Caroline Chung","doi":"10.1200/CCI.23.00105","DOIUrl":"10.1200/CCI.23.00105","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141898874","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":"Predicting Benefit From FOLFOXIRI Plus Bevacizumab in Patients With Metastatic Colorectal Cancer.","authors":"Marinde J G Bond, Maarten van Smeden, Koen Degeling, Chiara Cremolini, Hans-Joachim Schmoll, Carlotta Antoniotti, Sara Lonardi, Sabina Murgioni, Daniele Rossini, Stefan Ibach, Miriam Koopman, Rutger-Jan Swijnenburg, Cornelis J A Punt, Anne M May, Johannes J M Kwakman","doi":"10.1200/CCI.24.00037","DOIUrl":"https://doi.org/10.1200/CCI.24.00037","url":null,"abstract":"<p><strong>Purpose: </strong>Patient outcomes may differ from randomized trial averages. We aimed to predict benefit from FOLFOXIRI versus infusional fluorouracil, leucovorin, and oxaliplatin/fluorouracil, leucovorin, and irinotecan (FOLFOX/FOLFIRI), both plus bevacizumab, in patients with metastatic colorectal cancer (mCRC).</p><p><strong>Methods: </strong>A Cox model with prespecified clinical, molecular, and laboratory variables was developed in 639 patients from the TRIBE2 trial for predicting 2-year mortality. Data from the CHARTA (n = 232), TRIBE1 (n = 504), and CAIRO5 (liver-only mCRC, n = 287) trials were used for external validation and heterogeneity of treatment effects (HTE) analysis. This involves categorizing patients into risk groups and assessing treatment effects across these groups. Performance was assessed by the C-index and calibration plots. The C-for-benefit was calculated to assess evidence for HTE. The c-for-benefit is specifically designed for HTE analysis. Like the commonly known c-statistic, it summarizes the discrimination of a model. Values over 0.5 indicate evidence for HTE.</p><p><strong>Results: </strong>In TRIBE2, the overoptimism-corrected C-index was 0.66 (95% CI, 0.63 to 0.69). At external validation, the C-index was 0.69 (95% CI, 0.64 to 0.75), 0.68 (95% CI, 0.64 to 0.72), and 0.65 (95% CI, 0.65 to 0.66), in CHARTA, TRIBE1, and CAIRO5, respectively. Calibration plots indicated slight underestimation of mortality. The c-for-benefit indicated evidence for HTE in CHARTA (0.56, 95% CI, 0.48 to 0.65), but not in TRIBE1 (0.49, 95% CI, 0.44 to 0.55) and CAIRO5 (0.40, 95% CI, 0.32 to 0.48).</p><p><strong>Conclusion: </strong>Although 2-year mortality could be reasonably estimated, the HTE analysis showed that clinically available variables did not reliably identify which patients with mCRC benefit from FOLFOXIRI versus FOLFOX/FOLFIRI, both plus bevacizumab, across the three studies.</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":"141635710","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":"Potential Role of Generative Adversarial Networks in Enhancing Brain Tumors.","authors":"Amr Muhammed, Rafaat A Bakheet, Karam Kenawy, Ahmed M A Ahmed, Muhammed Abdelhamid, Walaa Gamal Soliman","doi":"10.1200/CCI.23.00266","DOIUrl":"https://doi.org/10.1200/CCI.23.00266","url":null,"abstract":"<p><strong>Purpose: </strong>Contrast enhancement is necessary for visualizing, diagnosing, and treating brain tumors. Through this study, we aimed to examine the potential role of general adversarial neural networks in generating artificial intelligence-based enhancement of tumors using a lightweight model.</p><p><strong>Patients and methods: </strong>A retrospective study was conducted on magnetic resonance imaging scans of patients diagnosed with brain tumors between 2020 and 2023. A generative adversarial neural network was built to generate images that would mimic the real contrast enhancement of these tumors. The performance of the neural network was evaluated quantitatively by VGG-16, ResNet, binary cross-entropy loss, mean absolute error, mean squared error, and structural similarity index measures. Regarding the qualitative evaluation, nine cases were randomly selected from the test set and were used to build a short satisfaction survey for experienced medical professionals.</p><p><strong>Results: </strong>One hundred twenty-nine patients with 156 scans were identified from the hospital database. The data were randomly split into a training set and validation set (90%) and a test set (10%). The VGG loss function for training, validation, and test sets were 2,049.8, 2,632.6, and 4,276.9, respectively. Additionally, the structural similarity index measured 0.366, 0.356, and 0.3192, respectively. At the time of submitting the article, 23 medical professionals responded to the survey. The median overall satisfaction score was 7 of 10.</p><p><strong>Conclusion: </strong>Our network would open the door for using lightweight models in performing artificial contrast enhancement. Further research is necessary in this field to reach the point of clinical practicality.</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":"141728278","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":"External Validation and Update of the Risk Prediction Model for Denosumab-Induced Hypocalcemia Developed From a Hospital-Based Administrative Database.","authors":"Keisuke Ikegami, Shungo Imai, Osamu Yasumuro, Masami Tsuchiya, Naomi Henmi, Mariko Suzuki, Katsuhisa Hayashi, Chisato Miura, Haruna Abe, Hayato Kizaki, Ryohkan Funakoshi, Yasunori Sato, Satoko Hori","doi":"10.1200/CCI.24.00078","DOIUrl":"10.1200/CCI.24.00078","url":null,"abstract":"<p><strong>Purpose: </strong>Denosumab is used to treat patients with bone metastasis from solid tumors, but sometimes causes severe hypocalcemia, so careful clinical management is important. This study aims to externally validate our previously developed risk prediction model for denosumab-induced hypocalcemia by using data from two facilities with different characteristics in Japan and to develop an updated model with improved performance and generalizability.</p><p><strong>Methods: </strong>In the external validation, retrospective data of Kameda General Hospital (KGH) and Miyagi Cancer Center (MCC) between June 2013 and June 2022 were used and receiver operating characteristic (ROC)-AUC was mainly evaluated. A scoring-based updated model was developed using the same data set from a hospital-based administrative database as previously employed. Selection of variables related to prediction of hypocalcemia was based on the results of external validation.</p><p><strong>Results: </strong>For the external validation, data from 235 KGH patients and 224 MCC patients were collected. ROC-AUC values in the original model were 0.879 and 0.774, respectively. The updated model consisting of clinical laboratory tests (calcium, albumin, and alkaline phosphatase) afforded similar ROC-AUC values in the two facilities (KGH, 0.837; MCC, 0.856).</p><p><strong>Conclusion: </strong>We developed an updated risk prediction model for denosumab-induced hypocalcemia with small interfacility differences. Our results indicate the importance of using data from plural facilities with different characteristics in the external validation of generalized prediction models and may be generally relevant to the clinical application of risk prediction models. Our findings are expected to contribute to improved management of bone metastasis treatment.</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/PMC11371100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141621791","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":"Natural Language Processing Algorithm to Extract Multiple Myeloma Stage From Oncology Notes in the Veterans Affairs Healthcare System.","authors":"Sergey D Goryachev, Cenk Yildirim, Clark DuMontier, Jennifer La, Mayuri Dharne, J Michael Gaziano, Mary T Brophy, Nikhil C Munshi, Jane A Driver, Nhan V Do, Nathanael R Fillmore","doi":"10.1200/CCI.23.00197","DOIUrl":"10.1200/CCI.23.00197","url":null,"abstract":"<p><strong>Purpose: </strong>Stage in multiple myeloma (MM) is an essential measure of disease risk, but its measurement in large databases is often lacking. We aimed to develop and validate a natural language processing (NLP) algorithm to extract oncologists' documentation of stage in the national Veterans Affairs (VA) Healthcare System.</p><p><strong>Methods: </strong>Using nationwide electronic health record (EHR) and cancer registry data from the VA Corporate Data Warehouse, we developed and validated a rule-based NLP algorithm to extract oncologist-determined MM stage. To that end, a clinician annotated MM stage within over 5,000 short snippets of clinical notes, and annotated MM stage at MM treatment initiation for 200 patients. These were allocated into snippet- and patient-level development and validation sets. We developed MM stage extraction and roll-up algorithms within the development sets. After the algorithms were finalized, we validated them using standard measures in held-out validation sets.</p><p><strong>Results: </strong>We developed algorithms for three different MM staging systems that have been in widespread use (Revised International Staging System [R-ISS], International Staging System [ISS], and Durie-Salmon [DS]) and for stage reported without a clearly defined system. Precision and recall were uniformly high for MM stage at the snippet level, ranging from 0.92 to 0.99 for the different MM staging systems. Performance in identifying for MM stage at treatment initiation at the patient level was also excellent, with precision of 0.92, 0.96, 0.90, and 0.86 and recall of 0.99, 0.98, 0.94, and 0.92 for R-ISS, ISS, DS, and unclear stage, respectively.</p><p><strong>Conclusion: </strong>Our MM stage extraction algorithm uses rule-based NLP and data aggregation to accurately measure MM stage documented in oncology notes and pathology reports in VA's national EHR system. It may be adapted to other systems where MM stage is recorded in clinical notes.</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/PMC11371094/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141749645","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":"Assessing Patient Perspectives and the Health Equity of a Digital Cancer Symptom Remote Monitoring and Management System.","authors":"Kathi Mooney, Susan L Beck, Christina Wilson, Lorinda Coombs, Meagan Whisenant, Ann Marie Moraitis, Elizabeth A Sloss, Natalya Alekhina, Jennifer Lloyd, Mary Steinbach, Bridget Nicholson, Eli Iacob, Gary Donaldson","doi":"10.1200/CCI.23.00243","DOIUrl":"10.1200/CCI.23.00243","url":null,"abstract":"<p><strong>Purpose: </strong>People with cancer experience poorly controlled symptoms that persist between treatment visits. Automated digital technology can remotely monitor and facilitate symptom management at home. Essential to digital interventions is patient engagement, user satisfaction, and intervention benefits that are distributed across patient populations so as not to perpetuate inequities. We evaluated Symptom Care at Home (SCH), an automated digital platform, to determine patient engagement, satisfaction, and whether intervention subgroups gained similar symptom reduction benefits.</p><p><strong>Methods: </strong>358 patients with cancer receiving a course of chemotherapy were randomly assigned to SCH or usual care (UC). Both groups reported daily on 11 symptoms and completed the SF36 (Short Form Health Survey) monthly. SCH participants received immediate automated self-care coaching on reported symptoms. As needed, nurse practitioners followed up for poorly controlled symptoms.</p><p><strong>Results: </strong>The average participant was White (83%), female (75%), and urban-dwelling (78.6%). Daily call adherence was 90% of expected days. Participants reported high user satisfaction. SCH participants had lower symptom burden than UC in all subgroups: age, sex, race, income, residence type, diagnosis, and stage (all <i>P</i> < .001 effect size 0.33-0.65), except for stages I and II cancers. Non-White and lower-income SCH participants gained a higher magnitude of symptom reduction than White participants and higher-income participants. Additionally, SCH men gained higher SF36 mental health (MH) benefit. There were no differences on other SF36 indices.</p><p><strong>Conclusion: </strong>Participants were highly satisfied and consistently engaged the SCH platform. SCH men gained large MH improvements, perhaps from increased comfort in sharing concerns through automated interactions. Although all intervention subgroups benefited, non-White participants and those with lower income gained higher symptom reduction benefit, suggesting that systematic care through digital tools can overcome existing disparities in symptom care outcomes.</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":"141753365","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":"Identification of Novel DNA Methylation Prognostic Biomarkers for AML With Normal Cytogenetics.","authors":"Cândida Cardoso, Daniel Pestana, Sreemol Gokuladhas, Ana D Marreiros, Justin M O'Sullivan, Alexandra Binnie, Mónica TFernandes, Pedro Castelo-Branco","doi":"10.1200/CCI.23.00265","DOIUrl":"10.1200/CCI.23.00265","url":null,"abstract":"<p><strong>Purpose: </strong>AML is a hematologic cancer that is clinically heterogeneous, with a wide range of clinical outcomes. DNA methylation changes are a hallmark of AML but are not routinely used as a criterion for risk stratification. The aim of this study was to explore DNA methylation markers that could risk stratify patients with cytogenetically normal AML (CN-AML), currently classified as intermediate-risk.</p><p><strong>Materials and methods: </strong>DNA methylation profiles in whole blood samples from 77 patients with CN-AML in The Cancer Genome Atlas (LAML cohort) were analyzed. Individual 5'-cytosine-phosphate-guanine-3' (CpG) sites were assessed for their ability to predict overall survival. The output was validated using DNA methylation profiles from bone marrow samples of 79 patients with CN-AML in a separate data set from the Gene Expression Omnibus.</p><p><strong>Results: </strong>In the training set, using DNA methylation data derived from the 450K array, we identified 2,549 CpG sites that could potentially distinguish patients with CN-AML with an adverse prognosis (<i>intermediate-poor</i>) from those with a more favorable prognosis (<i>intermediate-favorable</i>) independent of age. Of these, 25 CpGs showed consistent prognostic potential across both the 450K and 27K array platforms. In a separate validation data set, nine of these 25 CpGs exhibited statistically significant differences in 2-year survival. These nine validated CpGs formed the basis for a combined prognostic biomarker panel, which includes an 8-CpG Somatic Panel and the methylation status of cg23947872. This panel displayed strong predictive ability for 2-year survival, 2-year progression-free survival, and complete remission in the validation cohort.</p><p><strong>Conclusion: </strong>This study highlights DNA methylation profiling as a promising approach to enhance risk stratification in patients with CN-AML, potentially offering a pathway to more personalized treatment strategies.</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/PMC11371081/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762536","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}