Using Early Biomarker Change and Treatment Adherence to Predict Risk of Relapse Among Patients With Chronic Myeloid Leukemia Who Are in Remission [0.03%]
利用早期生物标志物变化和治疗依从性预测慢性粒细胞白血病患者缓解期复发风险
J Felipe Montano-Campos,Erin Hahn,Eric Haupt et al.
J Felipe Montano-Campos et al.
Purpose: There is little guidance for decision making in chronic myeloid leukemia (CML) after patients achieve molecular remission. Our study addresses this gap by developing a risk prediction model for molecular relapse ...
Benefits, Problems, and Motivations for Using the Online Patient Portal in Adolescent Oncology: Interviews With Adolescents and Parents [0.03%]
儿童肿瘤学中使用在线患者门户网站的利益、问题和动机：与青少年及其父母的访谈
Bryan A Sisk,Stephanie Chen,Christine Bereitschaft et al.
Bryan A Sisk et al.
Purpose: Communication is central to optimizing adolescent cancer care. Online patient portals are widely available tools that support communication. However, the perspectives of parents and adolescents on parental portal...
Clinical Application of Large Language Models in Generating Pathologic Images [0.03%]
大型语言模型在病理图像生成中的临床应用
Lingxuan Zhu,Yancheng Lai,Na Ta et al.
Lingxuan Zhu et al.
Purpose: This study investigates the potential of DALL·E 3, an artificial intelligence (AI) model, to generate synthetic pathologic images of prostate cancer (PCa) at varying Gleason grades. The aim is to enhance medical...
Assessment of Functional Status of Human Leukocyte Antigen Class I Genes in Cancer Tissues in the Context of Personalized Neoantigen Peptide Vaccine Immunotherapy [0.03%]
针对个性化新抗原肽疫苗免疫治疗的癌症组织的人白细胞抗原Ⅰ类基因的功能状态评估
Vijay G Padul,Nupur Biswas,Mini Gill et al.
Vijay G Padul et al.
Purpose: Accurate human leukocyte antigen (HLA) typing is an essential step for designing peptide vaccines used in the personalized neoantigen peptide vaccine immunotherapy (PNPVT) in patients with cancer. The reasons for...
Machine Learning-Based Prediction of Clinical Outcomes in Patients With Cancer Receiving Systemic Treatment Using Step Count Data Measured With Smartphones [0.03%]
基于智能手机测量的步数数据，使用机器学习预测接受全身治疗的癌症患者的临床结局
Calvin G Brouwer,Branca M Bartelet,Joeri A J Douma et al.
Calvin G Brouwer et al.
Purpose: This study aimed to investigate whether changes in step count, measured using patients' own smartphones, could predict a clinical adverse event in the upcoming week in patients undergoing systemic anticancer trea...
Observational Study
JCO clinical cancer informatics. 2025 Jul:9:e2500023. DOI:10.1200/CCI-25-00023 2025
Linear Federated Learning for Outcome Prediction With Application to Hepatocellular Carcinoma Radiotherapy [0.03%]
线性联合学习在结果预测中的应用——以肝细胞癌放射治疗为例
Keyur D Shah,Harald Paganetti,Pablo Yepes et al.
Keyur D Shah et al.
Purpose: Federated learning (FL) enables multi-institutional predictive modeling without sharing raw patient data, preserving privacy while leveraging diverse data sets. This study evaluates the use of linear FL (LFL) as ...
Deep Learning Model for Natural Language to Assess Effectiveness of Patients With Non-Muscle Invasive Bladder Cancer Receiving Intravesical Bacillus Calmette-Guérin Therapy [0.03%]
基于深度学习的自然语言处理模型评估卡介苗治疗非肌层浸润性膀胱癌效果研究
Makito Miyake,Naohiro Yonemoto,Kanae Togo et al.
Makito Miyake et al.
Purpose: Collecting information on clinical outcomes (recurrence/progression) from complex treatment courses in non-muscle invasive bladder cancer (NMIBC) is challenging and time-consuming. We developed a deep learning na...
Open-Source Hybrid Large Language Model Integrated System for Extraction of Breast Cancer Treatment Pathway From Free-Text Clinical Notes [0.03%]
开源混合大型语言模型集成系统 从临床注释中提取乳腺癌治疗路径
Amara Tariq,Madhu Sikha,Allison W Kurian et al.
Amara Tariq et al.
Purpose: Automated curation of breast cancer treatment data with minimal human involvement could accelerate the collection of statewide and nationwide evidence for patient management and assessing the effectiveness of tre...
Machine-Learning Algorithms and Treatment Response in Advanced Melanoma [0.03%]
机器学习算法与晚期黑色素瘤治疗反应的关系
Hinpetch Daungsupawong,Viroj Wiwanitkit
Hinpetch Daungsupawong
Reply to: Machine-Learning Algorithms and Treatment Response in Advanced Melanoma [0.03%]
对“机器学习算法和晚期黑色素瘤治疗反应”的回复
Richard Brohet,Jan Willem de Groot
Richard Brohet