sing machine learning algorithms to assess response to treatment in rectal cancer
Not Applicable
- Conditions
- Health Condition 1: C20- Malignant neoplasm of rectum
- Registration Number
- CTRI/2021/02/031522
- Lead Sponsor
- Dr Akshay Baheti
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- ot Yet Recruiting
- Sex
- Not specified
- Target Recruitment
- 0
Inclusion Criteria
i. Patients with rectal adenocarcinoma who underwent neoadjuvant treatment followed by surgery
ii. Availability of restaging high-resolution rectal MRI
iii. More than 75% of mucin on tumor on T2WI based on visual assessment
Exclusion Criteria
i. Recurrent rectal cancer
ii. Poor image quality (no high resolution T2WI)
iii. Final pathology not available from the institute
Study & Design
- Study Type
- Observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Texture analysis of rectal tumors will be performed using a machine learning algorithm. These features will be used to build a predictive model to predict response being complete or not complete.Timepoint: The data will be analyzed in June 2022.
- Secondary Outcome Measures
Name Time Method ATimepoint: NA