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Thyroid Cancer Prognosis Dataset
Patient Health Records & Digital Health
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About
Clinical data explaining factors relating to the recurrence of well differentiated thyroid cancer. The product contains 13 specific clinicopathologic features relevant for predicting long-term outcomes. The raw data was gathered over a 15-year period, ensuring that every patient included in the collection was followed for a minimum of 10 years to accurately track recurrence status. This resource is highly valuable for building prognostic models in oncology.
Columns
The dataset contains 17 distinct features for analysis:
- Age: The patient's age (ranging from 15 to 82 years).
- Gender: The recorded gender of the individual (81% female, 19% male).
- Smoking: Boolean indicator for current smoking status.
- Hx Smoking: Boolean indicator for having a history of smoking.
- Hx Radiothreapy: Boolean indicator for history of radiotherapy treatment.
- Thyroid Function: Describes the functional status of the thyroid (most commonly Euthyroid at 87%).
- Physical Examination: Findings from physical assessment (commonly Multinodular goiter or Single nodular goiter-right).
- Adenopathy: Status of lymph node disease (72% recorded as No).
- Pathology: The cancer type found (75% are Papillary).
- Focality: Whether the tumour is Uni-Focal (64%) or Multi-Focal (36%).
- Risk: Stratification of patient risk (65% classified as Low).
- T, N, M, Stage: Variables detailing the TNM staging system (e.g., 87% are Stage I).
- Response: The recorded response to initial treatment (54% classified as Excellent).
- Recurred: The binary outcome variable indicating whether the cancer recurred (28% of cases recorded recurrence).
Distribution
The information is delivered as a single data file titled
Thyroid_Diff.csv. The structure includes 17 columns and 383 records. The file size is 43.59 kB. No future updates are anticipated for this specific data collection.Usage
This resource is ideally suited for developing and validating clinical prediction algorithms aimed at forecasting differentiated thyroid cancer recurrence. It can be applied in studies focused on risk stratification, identifying key demographic or clinical drivers of poor outcomes, and generating insights into the long-term prognosis of thyroid malignancies.
Coverage
The clinical observation window for this data spans 15 years for collection, with robust follow-up ensuring that every patient outcome was tracked for a minimum period of 10 years. Demographically, the patient cohort exhibits a mean age of 40.9, and the population is predominantly female (81%). Geographic details are not specified.
License
CC0: Public Domain
Who Can Use It
- Medical Researchers: Utilising machine learning to identify high-risk groups for relapse.
- Public Health Analysts: Studying incidence and correlation factors of long-term cancer recurrence.
- Data Scientists: Practising binary classification problems using real-world medical data.
Dataset Name Suggestions
- Differentiated Thyroid Cancer Recurrence Features
- Long-Term Thyroid Cancer Outcome Predictor
- Clinicopathologic Data for DTC Recurrence
- Thyroid Cancer Prognosis Dataset
Attributes
Original Data Source: Thyroid Cancer Prognosis Dataset
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