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CTRI Number  CTRI/2026/04/109215 [Registered on: 23/04/2026] Trial Registered Prospectively
Last Modified On: 23/04/2026
Post Graduate Thesis  No 
Type of Trial  Observational 
Type of Study   Retrospective Radiomic study 
Study Design  Other 
Public Title of Study   A Study Using Artificial Intelligence to Predict Treatment Success and Survival in Patients with Rectal, Pancreatic, and Liver Cancers 
Scientific Title of Study   Artificial Intelligence and Its clinical Relevance in Gastrointestinal Malignancies: A Comprehensive Study on Rectal, Pancreatic, and Liver Cancer imaging Data. 
Trial Acronym  NIL 
Secondary IDs if Any  
Secondary ID  Identifier 
NIL  NIL 
 
Details of Principal Investigator or overall Trial Coordinator (multi-center study)  
Name  REENA ENGINEER 
Designation  Professor  
Affiliation  TATA MEMORIAL HOSPITAL, MUMBAI 
Address  Department of Radiation Oncology, Room No. 1122, 11th Floor, Homi Bhabha Building, Tata Memorial Hospital, Parel, Mumbai, Maharashtra, India - 400012.

Mumbai
MAHARASHTRA
400012
India 
Phone  9820128662  
Fax    
Email  reena.engineer@gmail.com  
 
Details of Contact Person
Scientific Query
 
Name  REENA ENGINEER 
Designation  Professor  
Affiliation  TATA MEMORIAL HOSPITAL, MUMBAI 
Address  Department of Radiation Oncology, Room No. 1122, 11th Floor, Homi Bhabha Building, Tata Memorial Hospital, Parel, Mumbai, Maharashtra, India - 400012.

Mumbai
MAHARASHTRA
400012
India 
Phone  9820128662  
Fax    
Email  reena.engineer@gmail.com  
 
Details of Contact Person
Public Query
 
Name  REENA ENGINEER 
Designation  Professor  
Affiliation  TATA MEMORIAL HOSPITAL, MUMBAI 
Address  Department of Radiation Oncology, Room No. 1122, 11th Floor, Homi Bhabha Building, Tata Memorial Hospital, Parel, Mumbai, Maharashtra, India - 400012.

Mumbai
MAHARASHTRA
400012
India 
Phone  9820128662  
Fax    
Email  reena.engineer@gmail.com  
 
Source of Monetary or Material Support  
Tata Memorial Centre, Dr. Ernest Borges Marg, Parel, Mumbai, Maharashtra, India - 400012. 
 
Primary Sponsor  
Name  Dr. Reena Engineer 
Address  Tata Memorial Hospital , Mumbai 
Type of Sponsor  Research institution and hospital 
 
Details of Secondary Sponsor  
Name  Address 
nil  nil 
 
Countries of Recruitment     India  
Sites of Study  
No of Sites = 1  
Name of Principal Investigator  Name of Site  Site Address  Phone/Fax/Email 
Dr Reena Engineer  TATA MEMORIAL HOSPITAL, MUMBAI  Department of Radiation Oncology, Room No. 1122, 11th Floor, Homi Bhabha Building, Tata Memorial Hospital, Parel, Mumbai, Maharashtra, India - 400012.
Mumbai
MAHARASHTRA 
9820128662

reena.engineer@gmail.com 
 
Details of Ethics Committee  
No of Ethics Committees= 1  
Name of Committee  Approval Status 
Institutional Ethics Committee - I , Tata Memorial Center  Approved 
 
Regulatory Clearance Status from DCGI  
Status 
Not Applicable 
 
Health Condition / Problems Studied  
Health Type  Condition 
Patients  (1) ICD-10 Condition: C229||Malignant neoplasm of liver, not specified as primary or secondary, (2) ICD-10 Condition: C259||Malignant neoplasm of pancreas, unspecified, (3) ICD-10 Condition: C20||Malignant neoplasm of rectum,  
 
Intervention / Comparator Agent  
Type  Name  Details 
Intervention  Nil  Nil 
Intervention  Nil  Nil 
Intervention  Nil  Nil 
 
Inclusion Criteria  
Age From  18.00 Year(s)
Age To  90.00 Year(s)
Gender  Both 
Details  1. Patients aged 18 years and older.

2. Patients registered at Tata Memorial Hospital as outpatients between 2015 and 2022.

3. Confirmed diagnosis of rectal carcinoma.

4. Confirmed diagnosis of pancreatic carcinoma classified as borderline resectable (BRPC) or locally advanced (LAPC).

5. Confirmed diagnosis of hepatocellular carcinoma (liver cancer).

6. Patients who have undergone neoadjuvant chemoradiotherapy for rectal cancer.

7. Patients who have received stereotactic body radiation therapy (SBRT) for pancreatic cancer.

8. Patients who have received SBRT and systemic therapy for liver cancer.

9. Availability of baseline imaging data (CT or MRI) and digitized pathology slides on the Picture Archiving and Communication System (PACS) in DICOM format 
 
ExclusionCriteria 
Details  1)Non availability of online DICOM images.

2)Non availability of clinical data. 
 
Method of Generating Random Sequence   Not Applicable 
Method of Concealment   Not Applicable 
Blinding/Masking   Not Applicable 
Primary Outcome  
Outcome  TimePoints 
Accuracy of the AI model in predicting "Radiological Response" and treatment success in gastrointestinal cancer patients  At the time of post-treatment or final clinical assessment (typically 6 to 12 weeks after completion of chemoradiotherapy). 
 
Secondary Outcome  
Outcome  TimePoints 
Radiomic feature reproducibility (ICC/CCC)  During the feature extraction phase (at study baseline analysis) 
Overall Survival (OS) defined as the time from the start of treatment to death from any cause  At 1 years & 3 years post-treatment. 
Progression-Free Survival (PFS) defined as the time from the start of treatment to disease progression or death.  At 1 years & 3 years post-treatment. 
 
Target Sample Size   Total Sample Size="750"
Sample Size from India="750" 
Final Enrollment numbers achieved (Total)= "Applicable only for Completed/Terminated trials"
Final Enrollment numbers achieved (India)="Applicable only for Completed/Terminated trials" 
Phase of Trial   N/A 
Date of First Enrollment (India)   16/09/2026 
Date of Study Completion (India) Applicable only for Completed/Terminated trials 
Date of First Enrollment (Global)  Date Missing 
Date of Study Completion (Global) Applicable only for Completed/Terminated trials 
Estimated Duration of Trial   Years="2"
Months="0"
Days="0" 
Recruitment Status of Trial (Global)   Not Applicable 
Recruitment Status of Trial (India)  Not Yet Recruiting 
Publication Details   N/A 
Individual Participant Data (IPD) Sharing Statement

Will individual participant data (IPD) be shared publicly (including data dictionaries)?  

Response - NO
Brief Summary  

This study uses artificial intelligence (AI) to improve the treatment of rectal, pancreatic, and liver cancers. By analyzing medical images like CT scans, MRIs,  PET scans and digitised pathology slides AI can help doctors understand how tumors are responding to treatment and predict future outcomes. This approach aims to make cancer treatment more personalized, allowing doctors to choose the most effective treatment plan for each patient.

Currently, predicting how well a patient will respond to cancer treatments is difficult, as there are no reliable tests for this. This study will create AI tools that can predict which patients will respond well to treatment, which might need a different approach, and how long they are likely to survive or stay cancer-free. The study will analyze medical images from all TMC hospital to develop these tools and improve decision-making in cancer care.

We (TMH) will be providing imaging data of pancreatic , hepatic and rectal malignancies to Dr Satish Viswanath Associate Professor, Emory University, Atlanta,. Emory University will run specialised software for radiomic feature extraction . We will use these radiomic feature for clinical correlation .

The main goal of this study is to use AI to provide more accurate predictions and help doctors make better treatment decisions. By training AI to predict treatment responses, survival chances, and the effectiveness of different therapies, the researchers hope to improve cancer outcomes. This personalized approach could lead to better survival rates, fewer unnecessary treatments, and an overall improvement in the quality of care for cancer patients.

 
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