跳至主要内容
临床试验/CTRI/2024/02/062294
CTRI/2024/02/062294尚未招募不适用

A novel Artificial Intelligence based prognostic approach using PET-CT images and pathology images, for advanced stage Hodgkin Lymphoma.

KOITA CENTRE FOR DIGITAL HEALTH(KCDH)1 个研究点 分布在 1 个国家目标入组 200 人开始时间: 2024年10月2日最近更新:

试验速览

阶段
不适用
状态
尚未招募
发起方
入组人数
200
试验地点
1
主要终点
Baseline FDG PET-CT results will be used to measure the standardized uptake value of the tumour sites to assess the staging and severity of the disease. At the end of the first stage (Retrospective part), the AI system will learn about the tumour

研究概览

简要总结

The aim of this study is to develop an artificial intelligence algorithm based prognostic model for advanced stage Hodgkin Lymphoma. The study is for patient aged 15 and above and whose PET CT scan reports and paraffin blocks are available at TMH. The treatment of advanced stage Hodgkin’s Lymphoma is often associated with the risk of significant toxicities. The response to the treatment is either known after two cycles of chemotherapy or at the end of the treatment. Since the response is not known until two cycles, the patients are often undertreated or over treated. So, we need better predictive tools at baseline to avoid unnecessary complications. “Artificial intelligence†in simple terms means the ability of computers to learn and solve problems. Hence in this study we aim to develop the artificial intelligence system from the data obtained from the PET CT and radiology images. This AI model would help in assisting the doctors in choosing the treatment regime for patients with advanced stage HL.

研究设计

研究类型
Observational

入排标准

年龄范围
15.00 Year(s) 至 80.00 Year(s)(—)
性别
All

入选标准

  • Patients diagnosed with advanced stage Hodgkins Lymphoma.
  • Patients whose baseline PET-CT reports are available.
  • Patients whose baseline paraffin block is available at TMH.
  • Age should be more than equal to 15 years.

排除标准

  • 未提供

结局指标

主要结局

Baseline FDG PET-CT results will be used to measure the standardized uptake value of the tumour sites to assess the staging and severity of the disease. At the end of the first stage (Retrospective part), the AI system will learn about the tumour

时间窗: Retrospective Data analysis of PET CT done at the time of diagnosis.

and will provide a score in a standardized form such as Deauville PET Criteria for its corresponding PET-CT scan #2. The predicted score and actual score from the retrospective study will be correlated.

时间窗: Retrospective Data analysis of PET CT done at the time of diagnosis.

次要结局

  • 1. correlation of the prognostic model with respect to 2-year Event Free Survival and overall survival.(2. comparing prognostic value of AI models against available gold standard IPS scores.)

研究者

发起方
KOITA CENTRE FOR DIGITAL HEALTH(KCDH)
申办方类型
Research institution

研究点 (1)

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