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CTRI Number  CTRI/2026/01/100361 [Registered on: 05/01/2026] Trial Registered Prospectively
Last Modified On: 05/01/2026
Post Graduate Thesis  Yes 
Type of Trial  Observational 
Type of Study   Cohort Study 
Study Design  Other 
Public Title of Study   AI-based detection and classification of kidney tumours using CT scans 
Scientific Title of Study   Development and Validation of a Renal Explainable AI Network (RenalXNet) for Detection and Differential Diagnosis of Renal Tumour using CT Radiomic Features 
Trial Acronym  NIL 
Secondary IDs if Any  
Secondary ID  Identifier 
NIL  NIL 
 
Details of Principal Investigator or overall Trial Coordinator (multi-center study)  
Name  Ms Meera Radhakrishnan  
Designation  Assistant Professor 
Affiliation  Manipal College of Health Professions 
Address  Department of Health Information Management MCHP, Manipal

Udupi
KARNATAKA
576104
India 
Phone  8689844096  
Fax    
Email  r.meera@manipal.edu  
 
Details of Contact Person
Scientific Query  
Name  Ms Meera Radhakrishnan  
Designation  Assistant Professor 
Affiliation  Manipal College of Health Professions 
Address  Department of Health Information Management MCHP, Manipal


KARNATAKA
576104
India 
Phone  8689844096  
Fax    
Email  r.meera@manipal.edu  
 
Details of Contact Person
Public Query  
Name  Ms Meera Radhakrishnan  
Designation  Assistant Professor 
Affiliation  Manipal College of Health Professions 
Address  Department of Health Information Management MCHP, Manipal


KARNATAKA
576104
India 
Phone  8689844096  
Fax    
Email  r.meera@manipal.edu  
 
Source of Monetary or Material Support  
NIL 
 
Primary Sponsor  
Name  Ms. Meera Radhakrishnan 
Address  Department of Health Information Management MCHP, Manipal -576104 
Type of Sponsor  Other [Individual] 
 
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 Rajagopal K V  Kasturba Medical College, Manipal  Department of Radiodiagnosis & Imaging, Kasturba Medical College, Manipal - 576104
Udupi
KARNATAKA 		 9448158901

rajagopal.kv@manipal.edu  
 
Details of Ethics Committee  
No of Ethics Committees= 1  
Name of Committee  Approval Status 
Kasturba Medical College and Kasturba Hospital Institutional Ethics Committee  Approved 
 
Regulatory Clearance Status from DCGI  
Status 
Not Applicable 
 
Health Condition / Problems Studied  
Health Type  Condition 
Patients  (1) ICD-10 Condition: C649||Malignant neoplasm of unspecifiedkidney, except renal pelvis,  
 
Intervention / Comparator Agent  
Type  Name  Details 
Intervention  Nil  Nil 
 
Inclusion Criteria  
Age From  20.00 Year(s)
Age To  75.00 Year(s)
Gender  Both 
Details  For Case Group:
Archived CECT abdominal scans with radiologically confirmed renal tumors.
Availability of corresponding histopathological reports confirming renal tumors.
Lesions measuring greater than equal to 5 mm in diameter on axial CT images.

For Control Group:
Archived CECT abdominal scans showing no evidence of renal tumors or pathology.
CT images of good quality with normal renal tissue available for radiomic analysis.
 
 
ExclusionCriteria 
Details  Common for Both Groups:
CT images with significant motion artifacts.
History of renal surgery.
History of chemotherapy or radiation therapy for renal conditions.
 
 
Method of Generating Random Sequence   Not Applicable 
Method of Concealment   Not Applicable 
Blinding/Masking   Not Applicable 
Primary Outcome  
Outcome  TimePoints 
Diagnostic performance of the RenalXNet model for detection and differential diagnosis of renal tumors using CT radiomic features measured by accuracy sensitivity specificity and area under the ROC curve with histopathology as the reference standard  At the time of CT image acquisition and analysis and at the time of availability of histopathology results
 
 
Secondary Outcome  
Outcome  TimePoints 
Identification of significant CT radiomic features associated with benign and malignant renal tumors  During retrospective phase data analysis 
Comparative performance of RenalXNet with traditional machine learning models for renal tumor classification  After model development and testing on retrospective dataset
 
Prospective validation performance of RenalXNet for detection and differential diagnosis of renal tumors  At completion of prospective validation phase 
Subgroup analysis performance of RenalXNet across renal tumor subtypes  During final data analysis
 
 
Target Sample Size   Total Sample Size="390"
Sample Size from India="390" 
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)   01/07/2027 
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="3"
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 aims to develop and validate an explainable artificial intelligence based model RenalXNet for the detection and differential diagnosis of renal tumors using contrast enhanced CT radiomic features. The study is designed as a two phase observational diagnostic accuracy study.

Phase I involves retrospective analysis of CT images and clinical data from 350 patients to extract radiomic features and develop a predictive model for classification of benign and malignant renal tumors. The model performance will be evaluated using standard diagnostic metrics with histopathology as the reference standard.

Phase II involves prospective enrolment of 40 patients undergoing contrast enhanced CT for clinical validation of the developed model. The study also aims to assess model interpretability using explainable AI techniques to improve transparency and clinical applicability.

The hypothesis of the study is that the proposed RenalXNet model can accurately detect and differentiate renal tumors and provide clinically meaningful explanations to support radiological decision making.

 
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