JCO Clinical Cancer Informatics最新文献

{"title":"Using Bayesian Networks to Predict Urgent Care Visits in Patients Receiving Systemic Therapy for Non-Small Cell Lung Cancer.","authors":"Brian D Gonzalez, Xiaoyin Li, Lisa M Gudenkauf, Jerrin J Pullukkara, Laura B Oswald, Aasha I Hoogland, Trung Le, Issam El Naqa, Andreas N Saltos, Eric B Haura, Yi Luo","doi":"10.1200/CCI-24-00315","DOIUrl":"10.1200/CCI-24-00315","url":null,"abstract":"<p><strong>Purpose: </strong>Patients receiving systemic therapy (ST) for non-small cell lung cancer (NSCLC) experience toxicities that negatively affect patient outcomes. This study aimed to test an approach for prospectively collecting patient-reported outcome (PRO) data, wearable sensor data (WSD), and clinical data, and develop a machine learning (ML) algorithm to predict health care utilization, specifically urgent care (UC) visits.</p><p><strong>Materials and methods: </strong>Patients with NSCLC completed the PROMIS-57 PRO quality-of-life measure and wore a Fitbit to monitor patient-generated health data from ST initiation through day 60. Demographic and clinical data were abstracted from the medical record. ML explainable models on the basis of Bayesian Networks (BNs) were used to develop predictive models for UC visits.</p><p><strong>Results: </strong>Patients in the training data set (N = 58) were age 69 years on average (range, 35-89) and mostly female (57%), White (88%), and non-Hispanic (95%) patients with adenocarcinoma (69%). Initial BN models trained on demographic and clinical data demonstrated moderate predictive accuracy on cross-validation for UC visits before ST (AUC, 0.72 [95% CI, 0.57 to 0.80]) and during ST (AUC, 0.81 [95% CI, 0.63 to 0.89]). Incorporating PRO and WSD during ST yielded enhanced models with significantly improved performance (final AUC, 0.86 [95% CI, 0.76 to 0.95]) via DeLong test (<i>P</i> < .001).</p><p><strong>Conclusion: </strong>Multidimensional data sources, including demographic, clinical, PRO, and WSD, can enhance ML predictive models to elucidate complex, interactive factors influencing health care utilization during the first 60 days of ST. Use of explainable ML to predict and prevent treatment toxicities and health care utilization could improve patient outcomes and enhance the quality of cancer care delivery.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400315"},"PeriodicalIF":2.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12483286/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145056228","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":"Acknowledgment of Reviewers 2025.","authors":"","doi":"10.1200/CCI-25-00243","DOIUrl":"https://doi.org/10.1200/CCI-25-00243","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500243"},"PeriodicalIF":2.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145056160","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 and Validation of an Ipsilateral Breast Tumor Recurrence Risk Estimation Tool Incorporating Real-World Data and Evidence From Meta-Analyses: A Retrospective Multicenter Cohort Study.","authors":"Yasuaki Sagara, Atsushi Yoshida, Yuri Kimura, Makoto Ishitobi, Yuka Ono, Yuko Takahashi, Takahiro Tsukioki, Koji Takada, Yuri Ito, Tomo Osako, Takehiko Sakai","doi":"10.1200/CCI-25-00182","DOIUrl":"10.1200/CCI-25-00182","url":null,"abstract":"<p><strong>Purpose: </strong>Ipsilateral breast tumor recurrence (IBTR) remains a critical concern for patients undergoing breast-conserving surgery (BCS). Reliable risk estimation tools for IBTR risk can support personalized surgical and adjuvant treatment decisions, especially in the era of evolving systemic therapies. We aimed to develop and validate models to estimate IBTR risk.</p><p><strong>Patients and methods: </strong>This multicenter retrospective cohort study included 8,938 women who underwent partial mastectomy for invasive breast cancer between 2008 and 2017. Prediction models were developed using Cox proportional hazards regression and validated via bootstrap resampling. Model performance was assessed using Harrell's C-index, Brier scores, calibration plots, and goodness-of-fit tests.</p><p><strong>Results: </strong>During a median follow-up of 9.0 years (IQR, 6.6-10.9), IBTR occurred in 320 patients (3.6%). The initial model, based on variables from Sanghani et al, achieved a Harrell's C-index of 0.74. Incorporating hormonal receptor status, human epidermal growth factor receptor 2 status, radiotherapy, and targeted therapy as predictors reduced the C-index to 0.65, despite their clinical relevance. Importantly, the inclusion of these factors improved calibration, demonstrating better alignment between predicted and observed IBTR probabilities. Although the hazard ratios (HRs) for radiotherapy aligned with the Early Breast Cancer Trialists' Collaborative Group meta-analyses (MA), those for chemotherapy and endocrine therapy showed slight differences. Therefore, HRs from the MA were used to represent treatment effects in our model.</p><p><strong>Conclusion: </strong>We have developed and internally validated a new risk estimation model for IBTR using Cox regression and bootstrap methods. A Web-based risk estimation tool is now available to facilitate individualized risk assessment and treatment planning.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500182"},"PeriodicalIF":2.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12442782/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145071112","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: Risk Score Model of Aging-Related Genes for Bladder Cancer and Its Application in Clinical Prognosis.","authors":"Kun Lu, Liu Chao, Jin Wang, Xiangyu Wang, Longjun Cai, Jianjun Zhang, Shaoqi Zhang","doi":"10.1200/CCI-25-00251","DOIUrl":"https://doi.org/10.1200/CCI-25-00251","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500251"},"PeriodicalIF":2.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145016664","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":"Risk Score Model of Aging-Related Genes for Bladder Cancer and Its Application in Clinical Prognosis.","authors":"Kun Lu, Liu Chao, Jin Wang, Xiangyu Wang, Longjun Cai, Jianjun Zhang, Shaoqi Zhang","doi":"10.1200/CCI-25-00019","DOIUrl":"10.1200/CCI-25-00019","url":null,"abstract":"<p><strong>Purpose: </strong>Bladder cancer (BLCA) ranks as the tenth most common malignancy worldwide, with rising incidence and mortality rates. Owing to its molecular and clinical heterogeneity, BLCA is associated with high rates of recurrence and metastasis after surgery, contributing to a poor 5-year survival rate. There is a pressing need for highly sensitive and specific molecular biomarkers to enable early identification of high-risk patients, guide clinical management, and improve patient outcomes. This study aimed to develop a prognostic model on the basis of aging-related genes (ARGs) to evaluate survival outcomes and immunotherapy responsiveness in patients with BLCA, and to further explore its relevance to the tumor immune microenvironment and drug sensitivity.</p><p><strong>Materials and methods: </strong>Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus were used to construct a 12-gene ARG-based prognostic signature through LASSO and Cox regression analyses. Patients were stratified into high-risk and low-risk groups according to the median risk score. Kaplan-Meier survival curves, receiver operating characteristic analyses, and nomograms were used to assess the predictive value of the model. Univariate and multivariate Cox regression analyses were conducted to determine its prognostic independence.</p><p><strong>Results: </strong>Twelve ARGs were identified. Patients in the low-risk group exhibited significantly better overall survival (<i>P</i> < .0001). In the TCGA cohort, the model yielded AUC values ranging from 0.772 to 0.794 across 1-5 years. Cox regression confirmed the ARG score as an independent prognostic indicator. External validation using the GSE32894 data set supported its clinical reliability. The ARG signature was also associated with immune cell infiltration and predicted chemosensitivity.</p><p><strong>Conclusion: </strong>The ARG-based risk score independently predicts clinical prognosis in BLCA and correlates with immune microenvironment characteristics, offering potential value in guiding personalized treatment strategies.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500019"},"PeriodicalIF":2.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12360192/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144805312","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":"Review and Commentary on Digital Pathology and Artificial Intelligence in Pathology.","authors":"Sahussapont Joseph Sirintrapun","doi":"10.1200/CCI-25-00017","DOIUrl":"https://doi.org/10.1200/CCI-25-00017","url":null,"abstract":"<p><strong>Purpose: </strong>This Special Article provides a comprehensive review and expert commentary on the prospective clinical implementation of artificial intelligence (AI) in the detection of prostate cancer from digital prostate biopsies, as presented in the original research by Flach et al. It contextualizes the study within broader developments in digital pathology and AI, addressing barriers to adoption and the implications for diagnostic workflows and pathology practice.</p><p><strong>Design: </strong>Drawing on insights from the CONFIDENT-P trial and the author's own experience with digital pathology and AI-assisted workflows, this article critically examines the clinical, regulatory, economic, and operational dimensions of implementing AI in diagnostic pathology. The focus centers on real-world deployment, particularly the integration of Paige Prostate Detect AI (PPD-AI) and its influence on immunohistochemistry (IHC) utilization.</p><p><strong>Results: </strong>The commentary highlights the trial's prospective design as a significant advancement in AI validation. Key findings include a reduction in IHC use, high diagnostic performance of PPD-AI, and improved diagnostic confidence among AI-assisted pathologists. However, variability in IHC practices across institutions, limitations in AI generalizability, and the need for system integration remain major challenges. The article also addresses practical issues such as automation bias, model drift, and lack of interoperability between viewers and laboratory information systems.</p><p><strong>Conclusion: </strong>The adoption of AI in digital pathology is accelerating but requires thoughtful integration into clinical workflows. Although prostate biopsies represent an ideal entry point, broader success will depend on regulatory alignment, workforce training, infrastructure readiness, and data governance. This commentary underscores the importance of clinician-AI synergy and provides practical guidance for laboratories navigating the transition from pilot implementations to scalable clinical use.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500017"},"PeriodicalIF":2.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144978141","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 Machine Learning Model for Aspyre Lung Blood: A New Assay for Rapid Detection of Actionable Variants From Plasma in Patients With Non-Small Cell Lung Cancer.","authors":"Rebecca N Palmer, Sam Abujudeh, Magdalena Stolarek-Januszkiewicz, Ana-Luisa Silva, Justyna M Mordaka, Kristine von Bargen, Alejandra Collazos, Simonetta Andreazza, Nicola D Potts, Chau Ha Ho, Iyelola Turner, Jinsy Jose, Dilyara Nugent, Prarthna Barot, Christina Xyrafaki, Alessandro Tomassini, Ryan T Evans, Katherine E Knudsen, Elizabeth Gillon-Zhang, Julia N Brown, Candace King, Cory Kiser, Mary Beth Rossi, Eleanor R Gray, Robert J Osborne, Barnaby W Balmforth","doi":"10.1200/CCI-25-00050","DOIUrl":"10.1200/CCI-25-00050","url":null,"abstract":"<p><strong>Purpose: </strong>Aspyre Lung is a targeted biomarker panel of 114 genomic variants across 11 guideline-recommended genes with simultaneous DNA and RNA for non-small cell lung cancer (NSCLC). In this study, we developed a machine learning algorithm to interpret fluorescence data outputs from Aspyre Lung, enabling the assay to be applied to both plasma and tissue samples.</p><p><strong>Materials and methods: </strong>Data for model training and testing were generated from over 13,500 DNA and RNA contrived samples, with variants spiked in at a variant allele frequency (VAF) of 0.1%-82% for DNA and 6-5,000 copies for RNA. The training and testing data sets used 67 reagent batches and 23 operators using nine quantitative polymerase chain reaction machines at two sites. Variant calling machine learning models were assessed in terms of median assay-wide 95% limit of detection (LoD95), observed sensitivity, false-positive rate per sample, per-variant LoD95, and per-variant observed sensitivity. The model was optimized by varying the training data subsets, features used, and model hyperparameters. Models were assessed against target specifications.</p><p><strong>Results: </strong>Verification with reference samples established experimental performance characteristics: a LoD95 of 0.19% VAF for SNV/indels, one amplifiable copy for gene fusions, 69 copies for <i>MET</i> exon 14 skipping events, and 100% specificity for all targets.</p><p><strong>Conclusion: </strong>Implementation of the model for liquid biopsy sample analysis enables running of these samples alongside tissue in a single workflow with high sensitivity, specificity, and accuracy. These results demonstrate that the Aspyre Lung assay, powered by a robust machine learning algorithm, offers a reliable and scalable solution for molecular testing in NSCLC, enabling a diverse range of laboratories to confidently perform high-sensitivity, high-specificity testing on both tissue and liquid biopsy samples.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500050"},"PeriodicalIF":2.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366736/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144859911","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":"Evaluating the Minimal Common Oncology Data Elements Suitability in Enhancing Clinical Observational Research.","authors":"May Terry, Janet L Espirito, Lisa Deister, Sutin Chen, Gail Shenk, Wanmei Ou","doi":"10.1200/CCI-25-00065","DOIUrl":"10.1200/CCI-25-00065","url":null,"abstract":"<p><strong>Purpose: </strong>This article explored how suitable the minimal Common Oncology Data Elements (mCODE) standard is for the real-world evidence research of cancer patient characterization, disease characterization, treatment patterns, and treatment outcomes.</p><p><strong>Methods: </strong>We identified research questions for each category, broke them down to clinical information elements, and mapped them to the mCODE model. Gaps were further categorized as model deficiencies, clarifying when the mCODE element availability was explicitly specified as an element, derived through external calculation, or implied as part of its support for Fast Healthcare Interoperability Resources.</p><p><strong>Results: </strong>In our study, 20 research questions were categorized in the following areas: patient characteristics, disease characteristics, treatment patterns, and health outcomes. The mCODE model fully supports patient characterization but shows significant gaps in disease characteristics, treatment patterns, and health outcomes, particularly in areas like treatment regimens and therapy outcomes. Our analysis underscores the need to enhance the mCODE model to better support observational research.</p><p><strong>Conclusion: </strong>We conclude that mCODE is partially suitable for observational research. Although mCODE shows promise for research purposes in patient and disease characterization, it currently lacks data elements needed to fully support identifying treatment patterns and health outcomes essential for comprehensive observational real-world evidence research.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500065"},"PeriodicalIF":2.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144978082","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":"Bayesian Counterfactual Machine Learning Individualizes Radiation Modality Selection to Mitigate Immunosuppression.","authors":"Duo Yu, Michael J Kane, Yiqing Chen, Steven H Lin, Radhe Mohan, Brian P Hobbs","doi":"10.1200/CCI-25-00058","DOIUrl":"10.1200/CCI-25-00058","url":null,"abstract":"<p><strong>Purpose: </strong>Lymphocytes play critical roles in cancer immunity and tumor surveillance. Radiation-induced lymphopenia (RIL) is a common side effect observed in patients with cancer undergoing chemoradiation therapy (CRT), leading to impaired immunity and worse clinical outcomes. Although proton beam therapy (PBT) has been suggested to reduce RIL risk compared with intensity-modulated radiation therapy (IMRT), this study used Bayesian counterfactual machine learning to identify distinct patient profiles and inform personalized radiation modality choice.</p><p><strong>Methods: </strong>A novel Bayesian causal inferential technique is introduced and applied to a matched retrospective cohort of 510 patients with esophageal cancer undergoing CRT to identify patient profiles for which immunosuppression could have been mitigated from radiation modality selection.</p><p><strong>Results: </strong>BMI, age, baseline absolute lymphocyte count (ALC), and planning target volume determined the extent to which reductions in ALCs varied by radiation modality. Five patient profiles were identified. Significant variation in ALC nadir between PBT and IMRT was observed in three of the patient subtypes. Notably, older patients (age >69 years) with normal weight experienced a two-fold reduction in mean ALC nadir when treated with IMRT versus PBT. Mean ALC nadir was reduced significantly for IMRT patients with lower ALC at baseline (<1.6 k/µL) who were overweight or obese when compared with PBT, whereas overweight patients with higher baseline ALC showed clinical equipoise between modalities.</p><p><strong>Conclusion: </strong>Individualized radiation therapy selection can be an important tool to minimize immunosuppression for high-risk patients. The Bayesian counterfactual modeling techniques presented in this article are flexible enough to capture complex, nonlinear patterns while estimating interpretable patient profiles for translation into clinical protocols.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500058"},"PeriodicalIF":2.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12419026/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145024747","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 Model Integrating Computed Tomography Image-Derived Radiomics and Circulating miRNAs to Predict Residual Teratoma in Metastatic Nonseminoma Testicular Cancer.","authors":"Guliz Ozgun, Neda Abdalvand, Gizem Ozcan, Ka Mun Nip, Nastaran Khazamipour, Arman Rahmim, Robert Bell, Corinne MauriceDror, Maryam Soleimani, Kim Chi, Bernhard J Eigl, Craig Nichols, Christian Kollmannsberger, Ren Yuan, Lucia Nappi","doi":"10.1200/CCI-25-00105","DOIUrl":"10.1200/CCI-25-00105","url":null,"abstract":"<p><strong>Purpose: </strong>Chemotherapy is the primary treatment for metastatic nonseminomatous germ cell tumors (mNSGCTs), but patients often encounter postchemotherapy residual disease. Accurate noninvasive methods are needed to predict the histology of these masses, guiding treatment and reserving surgery for those with teratoma. This study aims to enhance predictive accuracy by integrating computed tomography (CT) radiomics features with miRNAs (miR371-375) to distinguish between teratoma and nonteratoma histology in postchemotherapy residual masses.</p><p><strong>Methods: </strong>We retrospectively identified 111 lesions, divided into training and test sets (n = 78 <i>v</i> 33) with equal class distribution. 3D Slicer was used to segment lesions with a short axis of >10 mm from the postchemo-presurgical CT images, and radiomics features were extracted. Presurgery plasma miR371-375 levels were measured by real-time polymerase chain reaction. Four machine learning models evaluated the predictive value of radiomics alone (R-only) and combined with miR371-375 levels, and the best performer was selected. Clinical factors associated with teratoma from univariate analysis were included in multivariate analysis with the best radiomics signature to assess their impact on predicting teratoma histology.</p><p><strong>Results: </strong>The CatBoost (CB) model R + 371 + 375 exhibited the best and most robust overall accuracy for predicting residual teratoma, with the highest AUC values (0.96, 95% CI, 0.88 to 1.0 for training, 0.83, 95% CI, 0.68 to 0.98 for testing) and a well-balanced sensitivity and specificity. Univariate analysis identified presurgery alpha-fetoprotein (<i>P</i> = .01), beta-human chorionic gonadotropin (<i>P</i> = .01), initial teratoma pathology (<i>P</i> = .01), and lymph node metastases (<i>P</i> = .02) as significant predictors for teratoma. Multivariate analysis included these features and the radiomics signature, which was the strongest independent predictor (<i>P</i> < .0001).</p><p><strong>Conclusion: </strong>Combining miR371-375 with CT radiomics features improves the accuracy of predicting teratoma histology of postchemotherapy residual disease in mNSGCTs and, therefore, has the potential to guide treatment decision making.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500105"},"PeriodicalIF":2.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12406995/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144978073","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}