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Prediction of lymph node metastasis in patients with papillary thyroid cancer based on radiomics analysis and intraoperative frozen section analysis: A retrospective study.
Lv, Xin; Lu, Jing-Jing; Song, Si-Meng; Hou, Yi-Ru; Hu, Yan-Jun; Yan, Yan; Yu, Tao; Ye, Dong-Man.
Afiliação
  • Lv X; Department of Oncology, Yingkou Central Hospital, Yingkou, People's Republic of China.
  • Lu JJ; Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China.
  • Song SM; Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China.
  • Hou YR; Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China.
  • Hu YJ; Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China.
  • Yan Y; Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China.
  • Yu T; Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China.
  • Ye DM; Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China.
Clin Otolaryngol ; 49(4): 462-474, 2024 Jul.
Article em En | MEDLINE | ID: mdl-38622816
ABSTRACT

INTRODUCTION:

To evaluate the diagnostic efficiency among the clinical model, the radiomics model and the nomogram that combined radiomics features, frozen section (FS) analysis and clinical characteristics for the prediction of lymph node (LN) metastasis in patients with papillary thyroid cancer (PTC).

METHODS:

A total of 208 patients were randomly divided into two groups randomly with a proportion of 73 for the training groups (n = 146) and the validation groups (n = 62). The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for the selection of radiomics features extracted from ultrasound (US) images. Univariate and multivariate logistic analyses were used to select predictors associated with the status of LN. The clinical model, radiomics model and nomogram were subsequently established by logistic regression machine learning. The area under the curve (AUC), sensitivity and specificity were used to evaluate the diagnostic performance of the different models. The Delong test was used to compare the AUC of the three models.

RESULTS:

Multivariate analysis indicated that age, size group, Adler grade, ACR score and the psammoma body group were independent predictors of lymph node metastasis (LNM). The results showed that in both the training and validation groups, the nomogram showed better performance than the clinical model, albeit not statistically significant (p > .05), and significantly outperformed the radiomics model (p < .05). However, the nomogram exhibits a slight improvement in sensitivity that could reduce the incidence of false negatives.

CONCLUSION:

We propose that the nomogram holds substantial promise as an effective tool for predicting LNM in patients with PTC.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias da Glândula Tireoide / Nomogramas / Câncer Papilífero da Tireoide / Secções Congeladas / Metástase Linfática Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias da Glândula Tireoide / Nomogramas / Câncer Papilífero da Tireoide / Secções Congeladas / Metástase Linfática Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2024 Tipo de documento: Article