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Updated: Sep 16, 2025

Establishment and Characterization of Patient-Derived Xenograft Models of Anaplastic Thyroid Carcinoma and Head and Neck Squamous Cell Carcinoma

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Predicting the Recurrence of Differentiated Thyroid Cancer Using Whale Optimization-Based XGBoost Algorithm.

Keshika Shrestha¹, H M Jabed Omur Rifat¹, Uzzal Biswas¹

¹Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh.

Diagnostics (Basel, Switzerland)

|July 12, 2025

Summary

This summary is machine-generated.

This study introduces a novel machine learning approach to accurately predict differentiated thyroid cancer (DTC) recurrence. The whale optimization algorithm achieved 99% accuracy, aiding early detection and improving patient outcomes.

Keywords:

WHALE Optimization Algorithm (WOA)XGBoost feature selection hyperparameter optimization recurrence prediction thyroid cancer

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Area of Science:

Oncology
Medical Informatics
Computational Biology

Background:

Differentiated Thyroid Cancer (DTC), including papillary and follicular types, is the most prevalent thyroid malignancy.
Recurrence of DTC can occur even after successful treatment, posing a significant clinical challenge for early detection.
Current healthcare systems face difficulties in timely identification of DTC recurrence, highlighting the need for advanced predictive tools.

Purpose of the Study:

To develop and validate a novel machine learning approach for predicting the recurrence of Differentiated Thyroid Cancer (DTC).
To enhance prediction accuracy by employing hyperparameter optimization techniques.
To address the clinical challenge of early and accurate DTC recurrence detection.

Main Methods:

Utilized the whale optimization algorithm (WOA) and a modified version for hyperparameter optimization of the Extreme Gradient Boosting (XGBoost) model.
Incorporated a piecewise linear chaotic map for population initialization and inertia weight in the modified WOA.
Applied the optimized XGBoost models to a dataset from the UCI Machine Learning Repository, comprising 383 samples and 16 features, to predict DTC recurrence.

Main Results:

The WOA-optimized XGBoost model achieved a prediction accuracy of 99%.
The modified WOA-optimized XGBoost model demonstrated a prediction accuracy of 97%.
Both models showed strong performance in predicting DTC recurrence based on clinical and demographic data.

Conclusions:

The proposed machine learning approach, particularly with WOA optimization, significantly improves the accuracy of DTC recurrence prediction.
The study validates the effectiveness of metaheuristic algorithms for optimizing machine learning models in clinical applications.
This method offers a promising tool for early and accurate identification of DTC recurrence, potentially improving patient management and outcomes.