Differentiated Thyroid Cancer Recurrence Dataset

The Differentiated Thyroid Cancer Recurrence Dataset is a comprehensive collection of 13 clinicopathologic features aimed at predicting the recurrence of well-differentiated thyroid cancer. Data was meticulously gathered over a 15-year period, with each patient monitored for a minimum of 10 years. This extensive dataset serves as a valuable resource for researchers and healthcare professionals focusing on predictive analytics and personalized treatment approaches in oncology.

Dataset Details:

• Purpose: Developed as part of research in the intersection of artificial intelligence and medicine, this dataset facilitates the training of machine learning models to predict the likelihood of recurrence in well-differentiated thyroid cancer patients.

• Funding: The creation of this dataset was not funded by any external organization.

• Instances Represent: Each instance corresponds to an individual patient diagnosed with well-differentiated thyroid cancer.

• Data Splits: The dataset does not include predefined data splits; users are encouraged to partition the data as per their research requirements.

• Sensitive Data: The dataset does not contain sensitive information, ensuring compliance with privacy standards.

• Missing Values: The dataset is complete, with no missing values reported.

Key Features:

The dataset encompasses 13 clinicopathologic features, including:

• Age

• Gender

• Tumor Size

• Lymph Node Involvement

• Extrathyroidal Extension

• Vascular Invasion

• Histological Subtype

• Surgical Margin Status

• Radioactive Iodine Therapy

• Follow-Up Duration

• Recurrence Status

These features are critical for assessing the risk of recurrence and tailoring individualized treatment plans.

Usage and Applications:

Researchers and clinicians can utilize this dataset to:

• Develop Predictive Models: Train machine learning algorithms to forecast the likelihood of cancer recurrence, enhancing early detection and intervention strategies.

• Risk Stratification: Identify high-risk patients who may benefit from more aggressive treatment or closer monitoring.

• Personalized Medicine: Inform treatment decisions based on individual patient profiles, leading to improved outcomes.