Health Condition 1: K768- Other specified diseases of liver
Conditions
Interventions
Intervention1: Nil: Nil
Control Intervention1: Liver lesion: Different types of liver lesions
Sponsors
None listed
Eligibility
Inclusion criteria
Inclusion criteria: Study with triphasic contrast MRI of Abdomen showing hepatic lesions. Age group of 18- 80 years.
Exclusion criteria
Exclusion criteria: 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.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| 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. Timepoint: 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
| Measure | Time frame |
|---|---|
| NILTimepoint: NIL | — |
Countries
India
Contacts
Public ContactRAJESH NAYAK
Kasturba Medical College Mangalore
Outcome results
None listed