Radiomics nomogram analysis of T2-fBLADE-TSE in pulmonary nodules evaluation

Shuyi Yang; Yida Wang; Yuxin Shi; Guang Yang; Qinqin Yan; Jie Shen; Qingle Wang; Haoling Zhang; Shan Yang; Fei Shan; Zhiyong Zhang

doi:10.1016/j.mri.2021.10.010

Radiomics nomogram analysis of T2-fBLADE-TSE in pulmonary nodules evaluation

Magn Reson Imaging. 2022 Jan:85:80-86. doi: 10.1016/j.mri.2021.10.010. Epub 2021 Oct 16.

Authors

Shuyi Yang¹, Yida Wang², Yuxin Shi³, Guang Yang², Qinqin Yan³, Jie Shen³, Qingle Wang⁴, Haoling Zhang⁴, Shan Yang⁴, Fei Shan⁵, Zhiyong Zhang⁶

Affiliations

¹ Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China; Shanghai Institute of Medical Imaging, Shanghai 200032, China; Department of Medical Imaging, Shanghai Medical College, Fudan University, Shanghai 200032, China; Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China.
² Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai 200241, China; Research Center for Artificial Intelligence in Medical Imaging, East China Normal University, Shanghai 200241, China.
³ Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China.
⁴ Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China; Shanghai Institute of Medical Imaging, Shanghai 200032, China; Department of Medical Imaging, Shanghai Medical College, Fudan University, Shanghai 200032, China.
⁵ Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China. Electronic address: shanfei_2901@163.com.
⁶ Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China; Shanghai Institute of Medical Imaging, Shanghai 200032, China; Department of Medical Imaging, Shanghai Medical College, Fudan University, Shanghai 200032, China; Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China; Fudan University, Shanghai 200433, China. Electronic address: zhyzhang@fudan.edu.cn.

PMID: 34666158
DOI: 10.1016/j.mri.2021.10.010

Abstract

Objectives: To develop and validate a radiomics nomogram for differentiating between malignant pulmonary nodules and benign nodules.

Methods: 56 benign and 51 malignant nodules from 96 patients were analyzed using manual segmentation of the T2-fBLADE-TSE, while the nodules signal intensity (SIlesion), lesion muscle ratio (LMR) and nodule size were all measured and recorded. The maximum relevance and minimum redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used to select nonzero coefficients and develop the model in pulmonary nodules diagnosis. The radiomics nomogram was also developed. The clinical prediction value was determined by the decision curve analysis (DCA).

Results: The nodule size, SIlesion and LMR of the benign group were 1.78 ± 0.57 cm, 227.50 ± 81.39 and 2.40 ± 1.27 respectively, in contrast to the 2.00 ± 0.64 cm, 232.87 ± 82.21 and 2.17 ± 0.91, respectively, in the malignant group (P = 0.09, 0.60 and 0.579). A total of 13 radiomics features were retained. The Rad-score of the benign nodules group was lower than that of the malignant nodules group (P < 0.001 & 0.049, training & test set). The AUC of radiomics signature for nodules diagnosis was 0.82 (95% CI, 0.73-0.91) in the training set and 0.71 (95% CI, 0.51-0.90) in the test set. A nomogram, consisting of 13 radiomics features and nodule size, produced good prediction in the training set (AUC, 0.82; 95% CI, 0.73-0.91), which was significantly better than that of T2-based quantitative parameters (AUC, 0.62; 95% CI, 0.50-0.75, P = 0.003). In the test set, the performance of radiomics nomogram (AUC, 0.70; 95% CI, 0.51-0.90) was also better than that of T2-based quantitative parameters (AUC, 0.46; 95% CI, 0.25-0.67) (P = 0.145). The DCA showed that radiomics nomogram and T2-based quantitative parameter had overall net benefits, while the performance of nomogram was better.

Conclusion: We constructed and validated a T2-fBLADE-TSE-based radiomics nomogram that can help to differentiate between malignant pulmonary nodules and benign nodules.

Keywords: Differential diagnosis; MRI; Nomogram; Pulmonary nodule; Radiomics.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Humans
Lung Neoplasms* / diagnostic imaging
Lung Neoplasms* / pathology
Nomograms*
Retrospective Studies
Tomography, X-Ray Computed