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
文献类型：期刊论文
| 作者 | Li, Meirong2; Ruan, Yachao2; Feng, Zhan2; Sun, Fangyu3; Wang, Minhong1; Zhang, Liang4 |
| 刊名 | FRONTIERS IN ONCOLOGY
 |
| 出版日期 | 2021-12-02 |
| 卷号 | 11 |
| 关键词 | lung adenocarcinoma radiomics model micropapillary pattern (MPP) multicenter computed tomography |
| ISSN号 | 2234-943X |
| DOI | 10.3389/fonc.2021.788424 |
| 通讯作者 | Feng, Zhan(gerxyuan@zju.edu.cn) |
| 英文摘要 | PurposeTo 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 MethodsWe 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. ResultsBoth 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. ConclusionNodule 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. |
| WOS关键词 | PROGNOSTIC-SIGNIFICANCE ; IMPACT ; SUBTYPES ; LOBECTOMY ; RESECTION ; SOCIETY |
| WOS研究方向 | Oncology |
| 语种 | 英语 |
| WOS记录号 | WOS:000730565700001 |
| 出版者 | FRONTIERS MEDIA SA |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/126416]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Feng, Zhan |
| 作者单位 | 1.Wannan Med Coll, Affiliated Hosp 1, Dept Radiol, Wuhu, Peoples R China 2.Zhejiang Univ, Affiliated Hosp 1, Sch Med, Dept Radiol, Hangzhou, Peoples R China 3.Xiaoshan Hosp Tradit Chinese Med, Dept Radiol, Hangzhou, Peoples R China 4.Univ Chinese Acad Sci, Zhejiang Canc Hosp, Canc Hosp, Dept Radiol, Hangzhou, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Meirong,Ruan, Yachao,Feng, Zhan,et al. 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[J]. FRONTIERS IN ONCOLOGY,2021,11. |
| APA | Li, Meirong,Ruan, Yachao,Feng, Zhan,Sun, Fangyu,Wang, Minhong,&Zhang, Liang.(2021).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.FRONTIERS IN ONCOLOGY,11. |
| MLA | Li, Meirong,et al."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".FRONTIERS IN ONCOLOGY 11(2021). |
入库方式： OAI收割
来源：合肥物质科学研究院
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