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Using machine learning algorithms to assess response to treatment in rectal cancer

Machine learning in the determination of complete tumor response in patients with significant amount of mucin on MRI after neoadjuvant therapy

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2021/02/031522
Enrollment
100
Registered
2021-02-24
Start date
Unknown
Completion date
Unknown
Last updated
2021-11-24

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Health Condition 1: C20- Malignant neoplasm of rectum

Interventions

Intervention1: Nil: Nil Control Intervention1: Nil: Nil

Sponsors

Dr Akshay Baheti
Lead Sponsor

Eligibility

Inclusion criteria

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

Exclusion criteria: i. Recurrent rectal cancer ii. Poor image quality (no high resolution T2WI) iii. Final pathology not available from the institute

Design outcomes

Primary

MeasureTime frame
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

MeasureTime frame
NATimepoint: NA—

Countries

India, United States of America

Contacts

Public ContactAkshay Baheti

Tata Memorial Center

akshaybaheti@gmail.com9820834236

Outcome results

None listed

Source: CTRI (via WHO ICTRP) · Data processed: Feb 4, 2026