Machine learning-Based Classification of Papillary Thyroid Carcinoma Versus Multinodular Goiter Using Preoperative Laboratory and Cytology Data

Abstract

Background Thyroid nodules are frequently encountered in clinical practice, with their detection increasing due to advancements in imaging modalities. While most nodules are benign, distinguishing papillary thyroid carcinoma (PTC) from benign entities such as multinodular goiter (MNG) remains a diagnostic challenge. Fine-needle aspiration (FNA) and sonography are standard tools, but their limitations highlight the need for supplementary approaches. This study evaluates the use of machine learning (ML) models to classify PTC versus MNG using routine preoperative clinical, laboratory, and cytological data before performing surgery and Pathology results.

Methods This retrospective multicenter study included 971 patients who underwent total thyroidectomy between 2020 and 2024. The dataset incorporated demographic data, preoperative sonographic findings, hematologic and thyroid function tests, and FNA cytology results. Five supervised ML algorithms—Logistic Regression, Random Forest, XGBoost, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN)—were trained and validated. Model performance was assessed using accuracy, precision, recall, F1-score, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC).

Results The XGBoost model achieved the best performance, with an accuracy of 84.4%, precision of 85.3%, and an AUC-ROC of 0.881. It also demonstrated high sensitivity (0.714) and specificity (0.944). Random Forest also performed well (accuracy: 81.2%, AUC-ROC: 0.919). Logistic Regression, SVM, and KNN underperformed in comparison. Feature importance analysis revealed that the FNA result, nodule size, and TSH were the most influential predictors.

Conclusion Machine learning models, particularly XGBoost and Random Forest, show promise in accurately distinguishing between MNG and PTC using routine clinical data. Their integration into preoperative assessment may enhance diagnostic precision, reduce unnecessary procedures, and support personalized surgical decision-making. Further validation in diverse, multicenter cohorts is warranted to confirm generalizability and clinical utility.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study did not receive any funding

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The Ethics Committee of Iran University of Medical Sciences waived ethical approval for this work due to the use of retrospective and fully anonymized clinical data. (the institutional review board (IRB) Number of 20-169-2024)

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

Data Availability

All data produced in the present study are available upon reasonable request to the authors

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