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Predicting Malignancy of Thyroid Micronodules: Radiomics Analysis Based on Two Types of Ultrasound Elastography Images.
Zhang, Xian-Ya; Zhang, Di; Han, Lin-Zhi; Pan, Ying-Sha; Wei, Qi; Lv, Wen-Zhi; Dietrich, Christoph F; Wang, Zhi-Yuan; Cui, Xin-Wu.
Afiliação
  • Zhang XY; Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Zhang D; Department of Medical Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Han LZ; Department of Radiology, Xupu Chengnan Hospital, Huaihua, China.
  • Pan YS; Department of Radiology, The First Affiliated Hospital of University of South China, Hengyang, China.
  • Wei Q; Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Lv WZ; Department of Artificial Intelligence, Julei Technology Company, Wuhan, China.
  • Dietrich CF; Department of Internal Medicine, Hirslanden Clinic, Bern, Switzerland.
  • Wang ZY; Department of Medical Ultrasound, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
  • Cui XW; Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Electronic address: cuixinwu@live.cn.
Acad Radiol ; 30(10): 2156-2168, 2023 10.
Article em En | MEDLINE | ID: mdl-37003875
ABSTRACT
RATIONALE AND

OBJECTIVES:

To develop a multimodal ultrasound radiomics nomogram for accurate classification of thyroid micronodules. MATERIALS AND

METHODS:

A retrospective study including 181 thyroid micronodules within 179 patients was conducted. Radiomics features were extracted from strain elastography (SE), shear wave elastography (SWE) and B-mode ultrasound (BMUS) images. Minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithms were used to select malignancy-related features. BMUS, SE, and SWE radiomics scores (Rad-scores) were then constructed. Multivariable logistic regression was conducted using radiomics signatures along with clinical data, and a nomogram was ultimately established. The calibration, discriminative, and clinical usefulness were considered to evaluate its performance. A clinical prediction model was also built using independent clinical risk factors for comparison.

RESULTS:

An aspect ratio ≥ 1, mean elasticity index, BMUS Rad-score, SE Rad-score, and SWE Rad-score were identified as the independent predictors for predicting malignancy of thyroid micronodules by multivariable logistic regression. The radiomics nomogram based on these characteristics showed favorable calibration and discriminative capabilities (AUCs 0.903 and 0.881 for training and validation cohorts, respectively), all outperforming clinical prediction model (AUCs 0.791 and 0.626, respectively). The decision curve analysis also confirmed clinical usefulness of the nomogram. The significant improvement of net reclassification index and integrated discriminatory improvement indicated that multimodal ultrasound radiomics signatures might work as new imaging markers for classifying thyroid micronodules.

CONCLUSION:

The nomogram combining multimodal ultrasound radiomics features and clinical factors has the potential to be used for accurate diagnosis of thyroid micronodules in the clinic.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Técnicas de Imagem por Elasticidade / Neoplasias Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Técnicas de Imagem por Elasticidade / Neoplasias Idioma: En Ano de publicação: 2023 Tipo de documento: Article