| CTRI Number |
CTRI/2025/07/090044 [Registered on: 02/07/2025] Trial Registered Prospectively |
| Last Modified On: |
02/07/2025 |
| Post Graduate Thesis |
No |
| Type of Trial |
Observational |
|
Type of Study
|
Cross Sectional Study |
| Study Design |
Other |
|
Public Title of Study
|
Classification of liver lesion in MRI using artificial intelligence |
|
Scientific Title of Study
|
Application of the Machine Learning Model in Classification of Hepatic Lesions Based on Time-Signal Intensity Curve on Triphasic Contrast Enhanced MRI and Role of MR Radiomics in Diagnosis of Hepatocellular Carcinoma |
| Trial Acronym |
NIL |
|
Secondary IDs if Any
|
| Secondary ID |
Identifier |
| NIL |
NIL |
|
|
Details of Principal Investigator or overall Trial Coordinator (multi-center study)
|
| Name |
RAJESH NAYAK |
| Designation |
Assistant Professor |
| Affiliation |
Kasturba Medical College Mangalore |
| Address |
Department of Radiodiagnosis and Imaging, KMC Mangalore
Dakshina Kannada
KARNATAKA
575001
India |
| Phone |
7353897394 |
| Fax |
|
| Email |
rajeshnayak.medicalimaging@gmail.com |
|
Details of Contact Person
Scientific Query
|
| Name |
RAJESH NAYAK |
| Designation |
Assistant Professor |
| Affiliation |
Kasturba Medical College Mangalore |
| Address |
Department of Radiodiagnosis and Imaging, KMC Mangalore
KARNATAKA
575001
India |
| Phone |
7353897394 |
| Fax |
|
| Email |
rajeshnayak.medicalimaging@gmail.com |
|
Details of Contact Person
Public Query
|
| Name |
RAJESH NAYAK |
| Designation |
Assistant Professor |
| Affiliation |
Kasturba Medical College Mangalore |
| Address |
Department of Radiodiagnosis and Imaging, KMC Mangalore
KARNATAKA
575001
India |
| Phone |
7353897394 |
| Fax |
|
| Email |
rajeshnayak.medicalimaging@gmail.com |
|
|
Source of Monetary or Material Support
|
| Department of Radiodiagnosis and Imaging,
Kasturba Hospital,
Manipal,
Udupi-576104
Karnataka, India |
|
|
Primary Sponsor
|
| Name |
NA |
| Address |
NA |
| Type of Sponsor |
Other [NA] |
|
|
Details of Secondary Sponsor
|
|
|
Countries of Recruitment
|
India |
|
Sites of Study
|
| No of Sites = 1 |
| Name of Principal
Investigator |
Name of Site |
Site Address |
Phone/Fax/Email |
| Rajesh Nayak |
Kasturba Hospital |
Department of Radiodiagnosis and Imaging, KMC Manipal
Udupi
KARNATAKA |
7353897394
rajeshnayak.medicalimaging@gmail.co |
|
|
Details of Ethics Committee
|
| No of Ethics Committees= 1 |
| Name of Committee |
Approval Status |
| Kasturba Medical College and Kasturba Hospital |
Approved |
|
|
Regulatory Clearance Status from DCGI
|
|
|
Health Condition / Problems Studied
|
| Health Type |
Condition |
| Patients |
(1) ICD-10 Condition: K768||Other specified diseases of liver, |
|
|
Intervention / Comparator Agent
|
| Type |
Name |
Details |
| Comparator Agent |
Liver lesion |
Different types of liver lesions |
|
|
Inclusion Criteria
|
| Age From |
18.00 Year(s) |
| Age To |
80.00 Year(s) |
| Gender |
Both |
| Details |
Study with triphasic contrast MRI of Abdomen showing hepatic lesions.
Age group of 18- 80 years.
|
|
| ExclusionCriteria |
| Details |
Patients with a history of trauma
Patients with a history of surgery/ radiotherapy for hepatic lesions
MRI Abdomen without contrast.
Artefacts present in the area of interest.
|
|
|
Method of Generating Random Sequence
|
Not Applicable |
|
Method of Concealment
|
Not Applicable |
|
Blinding/Masking
|
Not Applicable |
|
Primary Outcome
|
| Outcome |
TimePoints |
The machine learning or deep learning model based on a time signal intensity curve can be used for better classification of hepatic lesions than visual assessment.
• The machine learning based on MR radiomics features can be used to improve the diagnosis of HCC.
|
The machine learning or deep learning model based on a time signal intensity curve can be used for better classification of hepatic lesions than visual assessment.
• The machine learning based on MR radiomics features can be used to improve the diagnosis of HCC.
|
|
|
Secondary Outcome
|
| Outcome |
TimePoints |
| NIL |
NIL |
|
|
Target Sample Size
|
Total Sample Size="120"
Sample Size from India="120"
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)
|
26/07/2025 |
| 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="5"
Days="0" |
|
Recruitment Status of Trial (Global)
|
Not Yet Recruiting |
| 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
|
Hepatic lesions are commonly detected on imaging and
frequently present with diagnostic dilemmas. The diagnosis of liver malignancy
by alpha-fetoprotein (AFP) was 67.8-74.4%. Though biopsy and histopathological
confirmation are required to diagnose the hepatic lesion because of their
invasive nature, these methods are not ideal and need to be or are usually
supported by various imaging modalities
In MRI conventionally, the characterisation of the
hepatic lesions in dynamic post-contrast MRI depends on visual observation of
the enhancement pattern of the lesion, which reflects the physiological
process. A recently developed tracer kinetic model using a time-signal
intensity curve helps in the quantitative analysis of the enhancement pattern
of the lesion in different phases. Tumour Segmentation for time signal intensity curve
The ROI will be drawn manually on hepatic
tumors in pre-contrast T1W, arterial, venous and delay phases of triphasic contrast
phases for hepatic lesions.
Extraction of Features
3D slicer (version 5.1.0) will be used to
identify the mean value of hepatic
lesion and aorta.
Machine Learning Model
For the time-signal intensity curve, a machine
learning or deep learning model will be developed using MATLAB or PYTHON
platform and trained and tested using a retrospective data set.
The obtained curve will be divided into:
i)increase rapidly and decrease rapidly, ii)increase rapidly and decrease
slowly, iii)increase slowly and decrease slowly, iv) increase slowly after no
apparent decline. Maximum slope of increase (MSI) and maximum slope of decrease
(MSD) values will be identified. Validation of Model
Region of interest (ROI) will be drawn
manually in the arterial phase and same ROI will be used in the series of venous
and delay phase images. Using developed model, the time signal intensity curve
will be obtained for hepatic lesions.
The obtained curve will be divided into:
i)increase rapidly and decrease rapidly, ii)increase rapidly and decrease
slowly, iii)increase slowly and decrease slowly, iv) increase slowly after no
apparent decline. Maximum slope of increase (MSI) and maximum slope of decrease
(MSD) values will be identified. Tumour Segmentation for radiomics features in HCC
The ROI will be drawn manually on hepatic
tumors in DWI, pre-contrast T1W, arterial, venous and delayed phase of triphasic
contrast images using a 3D slicer.
Extraction of Features
3D slicer (version 5.1.0) will be used for
the extraction of radiomics features. Any variation in features intensity
normalization will be done.
Machine
Learning Model
Machine learning model will be developed
using MATLAB or PYTHON platform and trained and tested using a retrospective
data set. Radiomic Feature Extraction
The DWI and triphasic contrast images will be
uploaded in a 3D slicer. The ROI will be drawn manually on the hepatic tumor in
DWI, pre-contrast T1W image, arterial, venous, and delay phase of triphasic
contrast phases and radiomics features will be extracted. Any variation in
features intensity normalization will be done.
Validation of Model
Using the developed models, HCC and Non-HCC
will be classified.
|