Generating a multimodal artificial intelligence model to differentiate benign and malignant follicular neoplasms of the thyroid: A proof-of-concept study.

Autor: Lin AC; Department of Surgery, Icahn School of Medicine at Mount Sinai, New York City, NY., Liu Z; BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York City, NY., Lee J; Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York City, NY., Ranvier GF; Department of Surgery, Icahn School of Medicine at Mount Sinai, New York City, NY., Taye A; Department of Surgery, Icahn School of Medicine at Mount Sinai, New York City, NY., Owen R; Department of Surgery, Icahn School of Medicine at Mount Sinai, New York City, NY., Matteson DS; Department of Statistics and Data Science, Cornell University, Ithaca, NY., Lee D; Department of Surgery, Icahn School of Medicine at Mount Sinai, New York City, NY. Electronic address: denise.lee@mountsinai.org.
Jazyk: angličtina
Zdroj: Surgery [Surgery] 2024 Jan; Vol. 175 (1), pp. 121-127. Date of Electronic Publication: 2023 Nov 02.
DOI: 10.1016/j.surg.2023.06.053
Abstrakt: Background: Machine learning has been increasingly used to develop algorithms that can improve medical diagnostics and prognostication and has shown promise in improving the classification of thyroid ultrasound images. This proof-of-concept study aims to develop a multimodal machine-learning model to classify follicular carcinoma from adenoma.
Methods: This is a retrospective study of patients with follicular adenoma or carcinoma at a single institution between 2010 and 2022. Demographics, imaging, and perioperative variables were collected. The region of interest was annotated on ultrasound and used to perform radiomics analysis. Imaging features and clinical variables were then used to create a random forest classifier to predict malignancy. Leave-one-out cross-validation was conducted to evaluate classifier performance using the area under the receiver operating characteristic curve.
Results: Patients with follicular adenomas (n = 7) and carcinomas (n = 11) with complete imaging and perioperative data were included. A total of 910 features were extracted from each image. The t-distributed stochastic neighbor embedding method reduced the dimension to 2 primary represented components. The random forest classifier achieved an area under the receiver operating characteristic curve of 0.76 (clinical only), 0.29 (image only), and 0.79 (multimodal data).
Conclusion: Our multimodal machine learning model demonstrates promising results in classifying follicular carcinoma from adenoma. This approach can potentially be applied in future studies to generate models for preoperative differentiation of follicular thyroid neoplasms.
(Copyright © 2023 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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