Feasibility of UTE-MRI-based radiomics model for prediction of histopathologic subtype of lung adenocarcinoma: in comparison with CT-based radiomics model

被引:2
|
作者
Lee, Suji [1 ]
Lee, Chang Young [2 ]
Kim, Na Young [1 ]
Suh, Yong Joo [1 ]
Lee, Hye-Jeong [1 ]
Yong, Hwan Seok [3 ]
Kim, Hye Ryun [4 ]
Kim, Young Jin [1 ]
机构
[1] Yonsei Univ, Severance Hosp, Res Inst Radiol Sci, Dept Radiol,Coll Med, 50-1 Yonsei Ro, Seoul 03722, South Korea
[2] Yonsei Univ, Coll Med, Severance Hosp, Dept Thorac & Cardiovasc Surg, 50-1 Yonsei Ro, Seoul 03722, South Korea
[3] Korea Univ, Coll Med, Guro Hosp, Dept Radiol, Seoul, South Korea
[4] Yonsei Univ, Coll Med, Severance Hosp, Dept Radiat Oncol, Seoul 03722, South Korea
关键词
Lung neoplasm; Magnetic resonance imaging; Radiomics; Pathology; Progression-free survival; MICROPAPILLARY; PATTERN; CANCER; CLASSIFICATION; ASSOCIATION; COMPONENTS; MUTATIONS; PROGNOSIS; EGFR;
D O I
10.1007/s00330-023-10302-1
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Objectives To assess the feasibility of the UTE-MRI radiomic model in predicting the micropapillary and/or solid (MP/S) patterns of surgically resected lung adenocarcinoma. Materials and methods We prospectively enrolled 74 lesions from 71 patients who underwent UTE-MRI and CT before curative surgery for early lung adenocarcinoma. For conventional radiologic analysis, we analyzed the longest lesion diameter and lesion characteristics at both UTE-MRI and CT. Radiomic features were extracted from the volume of interest of the lesions and Rad-scores were generated using the least absolute shrinkage and selection operator with fivefold cross-validation. Six models were constructed by combining the conventional radiologic model, UTE-MRI Rad-score, and CT Rad-score. The areas under the curves (AUCs) of each model were compared using the DeLong method. Early recurrence after curative surgery was analyzed, and Kaplan-Meier survival analysis was performed. Results Twenty-four lesions were MP/S-positive, and 50 were MP/S-negative. The longitudinal size showed a small systematic difference between UTE-MRI and CT, with fair intermodality agreement of lesion characteristic (kappa = 0.535). The Rad-scores of the UTE-MRI and CT demonstrated AUCs of 0.84 and 0.841, respectively (p = 0.98). Among the six models, mixed conventional, UTE-MRI, and CT Rad-score model showed the highest diagnostic performance (AUC = 0.879). In the survival analysis, the high- and low-risk groups were successfully divided by the Rad-score in UTE-MRI (p = 0.01) and CT (p < 0.01). Conclusion UTE-MRI radiomic model predicting MP/S positivity is feasible compared with the CT radiomic model. Also, it was associated with early recurrence in the survival analysis. Clinical relevance statement A radiomic model utilizing UTE-MRI, which does not present a radiation hazard, was able to successfully predict the histopathologic subtype of lung adenocarcinoma, and it was associated with the patient's recurrence-free survival.Key Points center dot No studies have reported the ultrashort echo time (UTE)-MRI-based radiomic model for lung adenocarcinoma.center dot The UTE-MRI Rad-score showed comparable diagnostic performance with CT Rad-score for predicting micropapillary and/or solid histopathologic pattern.center dot UTE-MRI is feasible not only for conventional radiologic analysis, but also for radiomics analysis.
引用
收藏
页码:3422 / 3430
页数:9
相关论文
共 50 条
  • [21] Dual-energy CT-based radiomics for predicting pathological grading of invasive lung adenocarcinoma
    Zheng, Y.
    Li, H.
    Zhang, K.
    Luo, Q.
    Ding, C.
    Han, X.
    Shi, H.
    CLINICAL RADIOLOGY, 2024, 79 (10)
  • [22] CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma
    Jiang, Changsi
    Luo, Yan
    Yuan, Jialin
    You, Shuyuan
    Chen, Zhiqiang
    Wu, Mingxiang
    Wang, Guangsuo
    Gong, Jingshan
    EUROPEAN RADIOLOGY, 2020, 30 (07) : 4050 - 4057
  • [23] CT-based radiomics: A potential indicator of KRAS mutation in pulmonary adenocarcinoma
    Mahmoud, Menna Allah
    Wu, Sijun
    Su, Ruihua
    Liufu, Yuling
    Wen, Yanhua
    Pan, Xiaohuan
    Guan, Yubao
    TUMORI JOURNAL, 2025, 111 (02): : 147 - 157
  • [24] CT-based radiomics signature for differentiating solitary granulomatous nodules from solid lung adenocarcinoma
    Yang, Xinguan
    He, Jianxing
    Wang, Jiao
    Li, Weiwei
    Liu, Chunbo
    Gao, Dashan
    Guan, Yubao
    LUNG CANCER, 2018, 125 : 109 - 114
  • [25] Ranking the most influential predictors of CT-based radiomics feature values in metastatic lung adenocarcinoma
    Crombe, Amandine
    Lafon, Mathilde
    Nougaret, Stephanie
    Kind, Michele
    Cousin, Sophie
    EUROPEAN JOURNAL OF RADIOLOGY, 2022, 155
  • [26] Radiomics Features of the Spleen as Surrogates for CT-Based Lymphoma Diagnosis and Subtype Differentiation
    Enke, Johanna S.
    Moltz, Jan H.
    D'Anastasi, Melvin
    Kunz, Wolfgang G.
    Schmidt, Christian
    Maurus, Stefan
    Muehlberg, Alexander
    Katzmann, Alexander
    Suehling, Michael
    Hahn, Horst
    Noerenberg, Dominik
    Huber, Thomas
    CANCERS, 2022, 14 (03)
  • [27] A CT-based radiomics model for predicting feeding tube insertion in oropharyngeal cancer
    Chinnery, Tricia
    Lang, Pencilla
    Nichols, Anthony
    Mattonen, Sarah
    MEDICAL IMAGING 2022: COMPUTER-AIDED DIAGNOSIS, 2022, 12033
  • [28] Predicting Microvascular Invasion in Hepatocellular Carcinoma Using CT-based Radiomics Model
    Xia, Tian-Yi
    Zhou, Zheng-Hao
    Meng, Xiang-Pan
    Zha, Jun-Hao
    Yu, Qian
    Wang, Wei-Lang
    Song, Yang
    Wang, Yuan-Cheng
    Tang, Tian-Yu
    Xu, Jun
    Zhang, Tao
    Long, Xue-Ying
    Liang, Yun
    Xiao, Wen-Bo
    Ju, Sheng-Hong
    RADIOLOGY, 2023, 307 (04)
  • [29] A CT-based radiomics model for predicting feeding tube insertion in oropharyngeal cancer
    Chinnery, Tricia
    Lang, Pencilla
    Nichols, Anthony
    Mattonen, Sarah
    MEDICAL PHYSICS, 2021, 48 (08) : 4704 - 4704
  • [30] CT-based radiomics model for predicting perineural invasion status in gastric cancer
    Jiang, Sheng
    Xie, Wentao
    Pan, Wenjun
    Jiang, Zinian
    Xin, Fangjie
    Zhou, Xiaoming
    Xu, Zhenying
    Zhang, Maoshen
    Lu, Yun
    Wang, Dongsheng
    ABDOMINAL RADIOLOGY, 2024, : 1916 - 1926