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Predicting papillary thyroid cancer recurrence: a machine learning approach

An exploratory study to predict recurrence of papillary thyroid carcinoma using clinico-pathological factors with different machine learning algorithms in a tertiary care hospital - NIL

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2024/05/067432
Enrollment
427
Registered
2024-05-15
Start date
Unknown
Completion date
Unknown
Last updated
2024-05-27

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

Conditions

Health Condition 1: C73- Malignant neoplasm of thyroid gland

Interventions

Intervention1: NIL: NIL Control Intervention1: NIL: NIL

Sponsors

DR Reena Patil
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: Records of the patients diagnosed with papillary thyroid carcinoma

Exclusion criteria

Exclusion criteria: Records of papillary thyroid carcinoma patients without Stimulated Thyroglobulin (Tg) values

Design outcomes

Primary

MeasureTime frame
Train ML model to forecast papillary thyroid cancer recurrence effectively. To pinpoint high-risk variables for papillary thyroid cancer recurrence.Timepoint: Baseline—

Secondary

MeasureTime frame
NILTimepoint: NIL—

Countries

India

Contacts

Public ContactDr Sumeet Suresh Malapure

Manipal College of Health Professions (MCHP)

sabu.km@manipal.edu9845421534

Outcome results

None listed

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