التفاصيل البيبلوغرافية
| العنوان: |
A contrast-enhanced CT-based radiomic nomogram for the differential diagnosis of intravenous leiomyomatosis and uterine leiomyoma |
| المؤلفون: |
Jiang Shao, Chaonan Wang, Keqiang Shu, Yan Zhou, Ninghai Cheng, Zhichao Lai, Kang Li, Leyin Xu, Junye Chen, Fenghe Du, Xiaoxi Yu, Zhan Zhu, Jiaxian Wang, Yuyao Feng, Yixuan Yang, Xiaolong Liu, Jinghui Yuan, Bao Liu |
| المصدر: |
Frontiers in Oncology, Vol 13 (2023) |
| بيانات النشر: |
Frontiers Media S.A., 2023. |
| سنة النشر: |
2023 |
| المجموعة: |
LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens |
| مصطلحات موضوعية: |
intravenous leiomyomatosis, contrast-enhanced CT, radiomics, preoperative differential, nomogram, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282 |
| الوصف: |
ObjectiveUterine intravenous leiomyomatosis (IVL) is a rare and unique leiomyoma that is difficult to surgery due to its ability to extend into intra- and extra-uterine vasculature. And it is difficult to differentiate from uterine leiomyoma (LM) by conventional CT scanning, which results in a large number of missed diagnoses. This study aimed to evaluate the utility of a contrast-enhanced CT-based radiomic nomogram for preoperative differentiation of IVL and LM.Methods124 patients (37 IVL and 87 LM) were retrospectively enrolled in the study. Radiomic features were extracted from contrast-enhanced CT before surgery. Clinical, radiomic, and combined models were developed using LightGBM (Light Gradient Boosting Machine) algorithm to differentiate IVL and LM. The clinical and radiomic signatures were integrated into a nomogram. The diagnostic performance of the models was evaluated using the area under the curve (AUC) and decision curve analysis (DCA).ResultsClinical factors, such as symptoms, menopausal status, age, and selected imaging features, were found to have significant correlations with the differential diagnosis of IVL and LM. A total of 108 radiomic features were extracted from contrast-enhanced CT images and selected for analysis. 29 radiomics features were selected to establish the Rad-score. A clinical model was developed to discriminate IVL and LM (AUC=0.826). Radiomic models were used to effectively differentiate IVL and LM (AUC=0.980). This radiological nomogram combined the Rad-score with independent clinical factors showed better differentiation efficiency than the clinical model (AUC=0.985, p=0.046).ConclusionThis study provides evidence for the utility of a radiomic nomogram integrating clinical and radiomic signatures for differentiating IVL and LM with improved diagnostic accuracy. The nomogram may be useful in clinical decision-making and provide recommendations for clinical treatment. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2234-943X |
| Relation: |
https://www.frontiersin.org/articles/10.3389/fonc.2023.1239124/full; https://doaj.org/toc/2234-943X |
| DOI: |
10.3389/fonc.2023.1239124 |
| URL الوصول: |
https://doaj.org/article/f8fb027b72cb4befb1f4d3171af6f7b8 |
| رقم الانضمام: |
edsdoj.f8fb027b72cb4befb1f4d3171af6f7b8 |
| قاعدة البيانات: |
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