Evidence map›Paper›PMID 40032790›Full record

ArticleAnnals of biomedical engineering2025

Identification of Novel Biomarkers for Malignant Thyroid Nodules: A Preliminary Study Based on Ultrasound Omics.

Zufei Li, Kaifeng Wang, Junpu Qu, Jian Zhang, Jian Meng, Jing Li, Meilan Zhang, Hai Du

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Article in Annals of biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Zufei LiDepartment of Otorhinolaryngology, Head and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China.
Kaifeng WangFujian Medical University, Fuzhou, China.
Junpu QuDepartment of Ultrasound, Ordos Central Hospital, Ordos, China.
Jian ZhangImaging Department, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou, China.
Jian MengDepartment of Ultrasound, North China University of Science and Technology Affiliated Hospital, Tangshan, China.
Jing LiGraduate School, Baotou Medical College, Baotou, China.
Meilan ZhangGraduate School, Baotou Medical College, Baotou, China.
Hai DuDepartment of Radiology, Ordos Central Hospital, No. 23, Ejin Horo West Street, Dongsheng District, Ordos, 017002, Inner Mongolia, China. nthaity@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectiveThe identification of thyroid nodules primarily relies on the ultrasound physician's assessment of nodule morphology and other visually identifiable features. Ultrasound omics technology can reveal additional features that are not visible to the naked eye, which may assist in the evaluation of malignant thyroid nodules. This study aims to explore novel markers for malignant thyroid nodules using ultrasound omics and machine learning (ML) techniques.

methodsA total of 1056 thyroid nodules with confirmed pathology were included, comprising 469 malignant and 587 benign cases. Traditional ultrasound features, such as "aspect ratio," "shape," "margins," "blood flow signal," and "calcification pattern," were recorded. Regions of interest (ROIs) were drawn for each ultrasound image, and features were extracted using the Python-based pyRadiomics tool. The Least Absolute Shrinkage and Selection Operator (Lasso) algorithm and correlation analysis were applied to select relevant features. Data were divided into training and testing sets at an 80:20 ratio. Various ML algorithms were employed to construct the models, and SHapley Additive exPlanations (SHAP) was used to assess feature importance.

resultsA total of 104 ultrasonic omics features were extracted from each image, and seven ultrasonic omics markers for thyroid malignant nodules were identified. The model developed using the random forest (RF) algorithm performed best on the test set, achieving accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) values of 89.6%, 90.2%, 89.2%, and 89.7%, respectively. However, when the seven ultrasonic omics markers were excluded from the ML features, the model performance decreased to 83.5%, 80.4%, 85.8%, and 83.1%. SHAP analysis indicated that all seven markers were significant features.

conclusionThese novel ultrasonic omics markers may improve the accuracy of thyroid nodule diagnosis, and further research is needed to confirm their clinical utility.

Indexed as

Biomarkers, TumorThyroid NoduleAdultAgedFemaleHumansMachine LearningMaleMiddle AgedUltrasonographyBiomarkers, TumorMachine learningRandom forestThyroid nodulesThyroid ultrasoundUltrasound omics

Identifiers

PMID40032790

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