A study to use Artificial Intelligence to predict the outcomes of advanced stage Hodgkin Lymphoma

Not yet recruiting

• Conditions: Hodgkin lymphoma, unspecified,

• Registration Number: CTRI/2024/02/062294
• Lead Sponsor: KOITA CENTRE FOR DIGITAL HEALTH(KCDH)

Brief Summary

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.

Detailed Description

Not available

Study Timeline

• Last Update Posted: 2024-01-24
• Study Start: 2024-10-02

Recruitment & Eligibility

• Status: Not Yet Recruiting
• Sex: All
• Target Recruitment: 200

Inclusion Criteria

• 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.

Exclusion Criteria

Not provided

Study & Design

• Study Type: Observational
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
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	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.	Retrospective Data analysis of PET CT done at the time of diagnosis.

Secondary Outcome Measures

Name	Time	Method
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.

Trial Locations

Locations (1)

Tata Memorial Centre; 🇮🇳 Mumbai, MAHARASHTRA, India

Tata Memorial Centre

🇮🇳Mumbai, MAHARASHTRA, India

Dr Hasmukh Jain

Principal investigator

02224177018

dr.hkjain@gmail.com