Multi-modal Fusion Model and Deep Learning for Predicting Treatment Response in NK/T-Cell Lymphoma
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 100
- 主要终点
- Predictive accuracy of first-line treatment response (CR vs non-CR) according to Lugano 2014 criteria
研究概览
简要总结
This is a multicenter prospective study to develop and validate a multimodal, deep learning-based model for predicting treatment response in patients with extranodal natural killer/T-cell lymphoma (NKTCL) receiving first-line asparaginase-based therapy.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •1. Age ≥ 18 years.
- •2. Pathologically confirmed extranodal natural killer/T-cell lymphoma (NKTCL) according to the World Health Organization (WHO) classification.
- •3. Patients who are planned to receive first-line asparaginase-based chemotherapy or chemoradiotherapy.
- •4. Patients who have either contrast-enhanced MRI of the nasopharynx obtained as part of routine clinical care or pretreatment whole-slide images (WSI) of tumor tissue from hematoxylin and eosin (H&E)-stained sections available for analysis.
- •5. Ability to understand the study and provide written informed consent (ICF).
排除标准
- •1. History of other malignant tumors.
- •2. Patients with psychiatric disorders or those unable to provide informed consent.
研究组 & 干预措施
First-line Asparaginase-based Treatment Cohort
Participants in this cohort are patients with extranodal natural killer/T-cell lymphoma (NKTCL) who are planned to receive standard first-line asparaginase-based chemotherapy according to institutional practice. Pretreatment clinical data, contrast-enhanced magnetic resonance imaging (MRI) of the nasopharynx and neck, and digital pathology images from hematoxylin and eosin (H&E)-stained tumor sections will be collected. Patients will be followed for progression-free survival and overall survival according to routine follow-up schedule.
结局指标
主要结局
Predictive accuracy of first-line treatment response (CR vs non-CR) according to Lugano 2014 criteria
时间窗: From baseline to disease response and follow-up assessments, up to 3 years.
The primary outcome is the predictive performance of the multimodal deep learning model for first-line treatment response in patients with extranodal natural killer/T-cell lymphoma (NKTCL). Treatment response is assessed according to the Lugano 2014 criteria. Model performance will be evaluated by receiver operating characteristic (ROC) analysis and quantified using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value by comparing model predictions with observed clinical response.
次要结局
未报告次要终点
研究者
Qingqing Cai
chief phycisian
Sun Yat-sen University