Predicting Chemotherapy Response in Patients With Advanced or Metastatic Pancreatic Cancer Using Machine Learning [0.03%]
使用机器学习预测晚期或转移性胰腺癌患者对化疗的反应
Jamin Koo,Gyucheol Choi,Jaekyung Cheon et al.
Jamin Koo et al.
Purpose: Selecting an optimal first-line chemotherapy regimen for advanced or metastatic pancreatic cancer is challenging because of varying efficacy and toxicity profiles of fluorouracil, leucovorin, irinotecan, and oxal...
Development of a Dynamic Counterfactual Risk Stratification Strategy for Newly Diagnosed Patients With AML Treated With Venetoclax and Azacitidine [0.03%]
用于新诊断的急性髓系白血病患者接受维奈克拉和地西他滨治疗的动态反事实风险分层策略的开发
Nazmul Islam,Justin L Dale,Jamie S Reuben et al.
Nazmul Islam et al.
Purpose: The objective of this study was to develop a flexible risk stratification strategy for AML that is specific for venetoclax plus azacitidine (ven/aza), addresses real-world data (RWD) issues, and is also adaptable...
Reimagining Cancer Care With Generative Artificial Intelligence: The Promise of Large Language Models [0.03%]
借助生成式人工智能重塑癌症治疗：大型语言模型的前景
Ji-Eun Irene Yum,Syed Arsalan Ahmed Naqvi,Ben Zhou et al.
Ji-Eun Irene Yum et al.
The emergence of state-of-the-art large language models (LLMs), which hold the ability to generalize to diverse natural language processing tasks, has led to new opportunities in health care. Oncology is especially well-suited to leverage t...
SmokeBERT: A Bidirectional Encoder Representations From Transformers-Based Model for Quantitative Smoking History Extraction From Clinical Narratives to Improve Lung Cancer Screening [0.03%]
烟雾BERT：一种基于双向编码器表示的临床叙述转型者模型，用于量化吸烟史以改善肺癌筛查
Yiming Xue,Yunzheng Zhu,Luoting Zhuang et al.
Yiming Xue et al.
Purpose: Tobacco use is a major risk factor for diseases such as cancer. Granular quantitative details of smoking (eg, pack years and years since quitting) are essential for assessing disease risk and determining eligibil...
Toward Clinical Readiness: Critical Reflections on PATHOMIQ_PRAD and Artificial Intelligence Histologic Classifiers in Prostate Cancer [0.03%]
迈向临床应用：对PATHOMIQ_PRAD和人工智能前列腺癌组织学分类器的批判性思考
Schawanya Kaewpitoon Rattanapitoon,Thirayu Meererksom,Nav La et al.
Schawanya Kaewpitoon Rattanapitoon et al.
Reply to: Toward Clinical Readiness: Critical Reflections on PATHOMIQ_PRAD and Artificial Intelligence Histologic Classifiers in Prostate Cancer [0.03%]
PATHOMIQ_PRAD和前列腺癌人工智慧組織分類器的臨床應用性評論.reply
Ross Liao,Magdalena Fay,Omar Y Mian
Ross Liao
Meta-Analysis of Bias in Non-Small Cell Lung Cancer External Control Arms That Use Real-World Progression-Free Survival as the End Point [0.03%]
真实世界无进展生存率作为终点的非小细胞肺癌外部对照臂偏倚的元分析
Sanaa Bahmane,Chris Harbron,Devin Incerti et al.
Sanaa Bahmane et al.
Purpose: Results from single-arm clinical trials can be contextualized by comparing against external controls (ECs) derived from real-world data (RWD). However, lack of randomization and differences in variable capture be...
Multimodal Artificial Intelligence Model From Baseline Histopathology Adds Prognostic Information for Distant Recurrence Assessment in Hormone Receptor-Positive/Human Epidermal Growth Factor Receptor 2-Negative Early Breast Cancer [0.03%]
基于基线型病理学的多模态人工智能模型可为激素受体阳性/人表皮生长因子受体阴性早期乳腺癌的远处复发评估提供预后信息
Daniel Kates-Harbeck,Hans Kreipe,Oleg Gluz et al.
Daniel Kates-Harbeck et al.
Purpose: Prognostic assessment in hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) early breast cancer (EBC) remains challenging, given relatively low rates of disease progression....
Informatics Perspectives on the National Cancer Policy Forum Workshop "Enabling 21st Century Applications for Cancer Surveillance Through Enhanced Registries and Beyond" [0.03%]
国家癌症政策论坛工作组会议“通过加强登记制度等手段推动21世纪癌症监测应用”的信息学观点
Peter P Yu,W Scott Campbell,Eric B Durbin et al.
Peter P Yu et al.
The National Cancer Policy Forum workshop Enabling 21st Century Applications for Cancer Surveillance Through Enhanced Registries and Beyond examined the current state of cancer registries and how they might evolve to extend registry mission...
Feasibility of a Smart Label-Enabled Remote Therapeutic Monitoring Intervention to Support Cyclin-Dependent Kinase 4/6 Inhibitor Adherence in Breast Cancer Care [0.03%]
基于智能标签的远程监测干预支持乳腺癌患者cyclin依赖性激酶4/6抑制剂依从性的可行性研究
Ilana Graetz,Sara Arshad,Clara Cai et al.
Ilana Graetz et al.
Purpose: Cyclin-dependent kinase 4 and 6 inhibitors (CDKIs) are effective breast cancer therapies but pose adherence challenges because of cost, side effects, and complexity of medication schedule. We assessed the feasibi...