不适合移植的大 B 细胞淋巴瘤二线使用 axicabtagene ciloleucel:ALYCANTE 最终分析
Second-line axicabtagene ciloleucel in large B-cell lymphoma ineligible for transplantation: ALYCANTE final analysis.
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:The Value of Artificial Intelligence-based 18F-FDG PET/CT in Diferential Diagnosis, Efficacy Prediction and Prognosis Prediction of T-NK Cell Lymphoma: a Clinical Study
The Value of Artificial Intelligence-based 18F-FDG PET/CT in Diferential Diagnosis, Efficacy Prediction and Prognosis Prediction of T-NK Cell Lymphoma: a Clinical Study
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⚠ 该试验的登记信息已有 22 个月未更新, 页面上显示的「尚未开始招募」可能已经失效。联系研究中心之前,建议先到 ClinicalTrials.gov 核对登记原文的最新状态与联系方式。
这是一项观察性研究,观察细胞治疗用于淋巴瘤的真实世界诊疗与结局。当前状态:尚未开始招募。计划入组 200 例。试验地点:中国 · 上海(共 1 个中心,其中中国 1 个)。登记号:NCT06747299。
不限性别 · ≥ 18 Years 且 ≤ 75 Years
纳入标准: 1. 病理组织学确诊为T/NK细胞淋巴瘤。 2. 治疗前接受18F-FDG PET/CT检查。 3. 采用现代最佳实践治疗方案。 4. 获得完整的临床病理及随访资料。 排除标准: 1. 既往接受过抗肿瘤治疗。 2. 有其他肿瘤病史。 3. 临床信息或影像资料不完整。 4. 同时患有其他恶性肿瘤。
Inclusion Criteria: 1\. Pathological histology confirmed as T-NK Cell Lymphoma; 2.18F-FDG PET/CT examination before treatment; 3. Using modern best practice treatment options; 4. Complete clinicopathological and follow-up data were obtained. Exclusion Criteria: 1. The patient had previously received antitumor therapy; 2. The patient had a history of other tumors; 3. Incomplete clinical information or imaging data; 4. Concomitant other malignant tumors.
以上为辅助阅读译文。是否适合入组须由主治医生判断,最终以登记平台与研究者确认为准。
主要终点:Evaluation the value of Artificial Intelligence-based 18F-FDG PET/CT of T-NK Cell Lymphoma · The Value of Artificial Intelligence-based 18F-FDG PET/CT in Diferential Diagnosis, Efficacy Prediction and Prognosis Prediction of T-NK Cell Lymphoma · Within 1 week of enrollment and after 3 months treatment
次要终点:Progress free survival;Overall survival
以上邮箱 / 电话是登记库里的申办方联系方式(中国内地手机),通常不直达某家医院。中国中心的联系方式请以医院或登记平台最新公示为准。
本研究基于T/NK细胞淋巴瘤患者的PET/CT影像数据,采用机器学习和深度学习方法提取影像特征,建立T/NK细胞淋巴瘤预测模型,为临床提供更科学、准确的预后预测。
Based on the PET/CT imaging data of patients with T-NK cell lymphoma, machine learning and deep learning methods are used to extract imaging features, establish a T-NK cell lymphoma prediction model, and provide more scientific and accurate prognosis prediction for the clinic.
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