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Preoperative CT-Based Radiomics Combined With Nodule Type to Predict the Micropapillary Pattern in Lung Adenocarcinoma of Size 2 cm or Less: A Multicenter Study.
Li, Meirong; Ruan, Yachao; Feng, Zhan; Sun, Fangyu; Wang, Minhong; Zhang, Liang.
Afiliación
  • Li M; Department of Radiology, The First Affiliated Hospital, Zhejiang University, School of Medicine, Hangzhou, China.
  • Ruan Y; Department of Radiology, The First Affiliated Hospital, Zhejiang University, School of Medicine, Hangzhou, China.
  • Feng Z; Department of Radiology, The First Affiliated Hospital, Zhejiang University, School of Medicine, Hangzhou, China.
  • Sun F; Department of Radiology, Xiaoshan Hospital of Traditional Chinese Medicine, Hangzhou, China.
  • Wang M; Department of Radiology, First Affiliated Hospital of Wannan Medical College, Wuhu, China.
  • Zhang L; Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.
Front Oncol ; 11: 788424, 2021.
Article en En | MEDLINE | ID: mdl-34926304
PURPOSE: To construct an optimal radiomics model for preoperative prediction micropapillary pattern (MPP) in adenocarcinoma (ADC) of size ≤ 2 cm, nodule type was used for stratification to construct two radiomics models based on high-resolution computed tomography (HRCT) images. MATERIALS AND METHODS: We retrospectively analyzed patients with pathologically confirmed ADC of size ≤ 2 cm who presented to three hospitals. Patients presenting to the hospital with the greater number of patients were included in the training set (n = 2386) and those presenting to the other two hospitals were included in the external validation set (n = 119). HRCT images were used for delineation of region of interest of tumor and extraction of radiomics features; dimensionality reduction was performed for the features. Nodule type was used to stratify the data and the random forest method was used to construct two models for preoperative prediction MPP in ADC of size ≤ 2 cm. Model 1 included all nodule types and model 2 included only solid nodules. The receiver operating characteristic curve was used to assess the prediction performance of the two models and independent validation was used to assess its generalizability. RESULTS: Both models predicted ADC with MPP preoperatively. The area under the curve (AUC) of prediction performance of models 1 and 2 were 0.91 and 0.78, respectively. The prediction performance of model 2 was lower than that of model 1. The AUCs in the external validation set were 0.81 and 0.72, respectively. The DeLong test showed statistically significant differences between the training and validation sets in model 1 (p = 0.0296) with weak generalizability. There was no statistically significant difference between the training and validation sets in model 2 (p = 0.2865) with some generalizability. CONCLUSION: Nodule type is an important factor that affects the performance of radiomics predictor model for MPP with ADC of size ≤ 2 cm. The radiomics prediction model constructed based on solid nodules alone, can be used to evaluate MPP and may contribute to proper surgical planning in patients with ADC of size ≤ 2 cm.
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Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Clinical_trials / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Oncol Año: 2021 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Clinical_trials / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Oncol Año: 2021 Tipo del documento: Article