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  11. Development Of Machine Learning Models To Predict Papillary Carcinoma In Thyroid Nodules: The Role Of Immunological, Radiologic, Cytologic And Radiomic Features.
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Development Of Machine Learning Models To Predict Papillary Carcinoma In Thyroid Nodules: The Role Of Immunological, Radiologic, Cytologic And Radiomic Features.

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Development of machine learning models to predict papillary carcinoma in thyroid nodules: The role of immunological, radiologic, cytologic and radiomic features.

Luca Canali1, Francesca Gaino1, Andrea Costantino2

  • 1Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Milan, Pieve Emanuele 20072, Italy; Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Milan, Rozzano 20089, Italy.

Auris, Nasus, Larynx
|September 21, 2024

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
Artificial intelligenceThyroid cancerThyroid cytologyThyroid nodule

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Machine learning models show promise for identifying papillary thyroid cancer. Incorporating ultrasound data significantly improved diagnostic accuracy, outperforming models reliant solely on clinical or immunological factors.

Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Indeterminate thyroid nodules pose diagnostic challenges, with conventional methods yielding uncertain results for up to 30% of cases.
  • Accurate preoperative diagnosis of papillary thyroid carcinoma is crucial for effective patient management and treatment planning.

Purpose of the Study:

  • To develop and evaluate machine learning (ML) models for the identification of papillary thyroid carcinomas.
  • To assess the impact of incorporating various preoperative variables (clinical-immunological, ultrasonographic, cytological, radiomic) on ML model performance.

Main Methods:

  • A retrospective monocentric study enrolled 186 patients with thyroid nodules undergoing surgery.
  • Six supervised ML models were developed to predict papillary thyroid carcinoma, sequentially integrating different data types.
Thyroid ultrasound
  • Model performance was evaluated using the Area Under the Curve (AUC).

    • Main Results:

    • Papillary thyroid carcinomas were identified in 49.5% of the studied nodules.
    • ML models using only clinical-immunological data showed low performance (AUC: 0.41-0.61).
    • Inclusion of ultrasonographic variables dramatically improved model performance (AUC: 0.95-0.97); cytological and radiomic data did not yield further significant improvements.

    Conclusions:

    • Machine learning algorithms show limited accuracy with clinical-immunological data alone for thyroid nodule diagnosis.
    • Ultrasonographic data significantly enhances the predictive power of ML models for papillary thyroid carcinoma.
    • While cytopathological and radiomic data did not further boost accuracy, ultrasound-integrated ML offers a promising approach for improved diagnostic yield.