Predicting pathological subtypes and stages of thymic epithelial tumors using DWI: value of combining ADC and texture parameters
- 3 Downloads
To explore the value of combining apparent diffusion coefficients (ADC) and texture parameters from diffusion-weighted imaging (DWI) in predicting the pathological subtypes and stages of thymic epithelial tumors (TETs).
Fifty-seven patients with TETs confirmed by pathological analysis were retrospectively enrolled. ADC values and optimal texture feature parameters were compared for differences among low-risk thymoma (LRT), high-risk thymoma (HRT), and thymic carcinoma (TC) by one-way ANOVA, and between early and advanced stages of TETs were tested using the independent samples t test. Receiver operating characteristic (ROC) curve analysis was performed to determine the differentiating efficacy.
The ADC values in LRT and HRT were significantly higher than the values in TC (p = 0.004 and 0.001, respectively), also in early stage, values were significantly higher than ones in advanced stage of TETs (p < 0.001). Among all texture parameters analyzed in order to differentiate LRT from HRT and TC, the V312 achieved higher diagnostic efficacy with an AUC of 0.875, and combination of ADC and V312 achieved the highest diagnostic efficacy with an AUC of 0.933, for differentiating the LRT from HRT and TC. Furthermore, combination of ADC and V1030 achieved a relatively high differentiating ability with an AUC of 0.772, for differentiating early from advanced stages of TETs.
Combination of ADC and DWI texture parameters improved the differentiating ability of TET grades, which could potentially be useful in clinical practice regarding the TET evaluation before treatment.
• DWI texture analysis is useful in differentiating TET subtypes and stages.
• Combination of ADC and DWI texture parameters may improve the differentiating ability of TET grades.
• DWI texture analysis could potentially be useful in clinical practice regarding the TET evaluation before treatment.
KeywordsThymic epithelial tumors Neoplasm staging Diffusion magnetic resonance imaging Texture analysis
Apparent diffusion coefficient
Field of view
Magnetic resonance imaging
Number of excitations
Receiver operating characteristic
Region of interest
Thymic epithelial tumors
Volume of interest
World Health Organization
We would like to thank Dr. Xiao-Cheng Wei in GE Healthcare China for providing technical support regarding the application of Analysis-Kit software and supplementary Material (Texture Parameters Description.PDF).
This study has received funding from the Science and Technology Innovation Development Foundation of Tangdu Hospital (no. 2017LCYJ004).
Compliance with ethical standards
The scientific guarantor of this publication is Guang-bin Cui.
Conflict of interest
The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
Statistics and biometry
Lei Shang kindly provided statistical advice for this manuscript.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained.
Study subjects or cohorts overlap
Some study subjects or cohorts have been previously reported in Li GF, Duan SJ, Yan LF, et al Intravoxel incoherent motion diffusion-weighted MR imaging parameters predict pathological classification in thymic epithelial tumors. Oncotarget 2017;8(27):44579–44592.
• diagnostic or prognostic study
• performed at one institution
- 9.Li GF, Duan SJ, Yan LF et al (2017) Intravoxel incoherent motion diffusion-weighted MR imaging parameters predict pathological classification in thymic epithelial tumors. Oncotarget 8:44579–44592Google Scholar
- 14.Jing Y, Yan WQ, Li GF et al (2018) Usefulness of volume perfusion computed tomography in differentiating histologic subtypes of thymic epithelial tumors. J Comput Assist Tomogr 42:594–600Google Scholar
- 20.Priola AM, Priola SM, Giraudo MT et al (2015) Diffusion-weighted magnetic resonance imaging of thymoma: ability of the apparent diffusion coefficient in predicting the World Health Organization (WHO) classification and the Masaoka-Koga staging system and its prognostic significance on disease-free survival. Eur Radiol 26:2126–2138CrossRefGoogle Scholar
- 22.Choi MH, Lee YJ, Yoon SB, Choi JI, Jung SE, Rha SE (2018) MRI of pancreatic ductal adenocarcinoma: texture analysis of T2-weighted images for predicting long-term outcome. Abdom Radiol (NY). https://doi.org/10.1007/s00261-018-1681-2
- 24.Skogen K, Schulz A, Helseth E, Ganeshan B, Dormagen JB, Server A (2018) Texture analysis on diffusion tensor imaging: discriminating glioblastoma from single brain metastasis. Acta Radiol. https://doi.org/10.1177/0284185118780889:284185118780889
- 25.Fritz B, Muller DA, Sutter R et al (2018) Magnetic resonance imaging-based grading of cartilaginous bone tumors: added value of quantitative texture analysis. Investig Radiol 53:663–672Google Scholar
- 26.Jiang X, Xie F, Liu L, Peng Y, Cai H, Li L (2018) Discrimination of malignant and benign breast masses using automatic segmentation and features extracted from dynamic contrast-enhanced and diffusion-weighted MRI. Oncol Lett 16:1521–1528Google Scholar
- 30.Travis WDBE, Müller-Hermelink HK, Harris CC (2004) World Health Organization classification of tumours. Pathology and genetics of tumours of the lung, thymus and heart. IARC Press, Lyon, pp 152–153Google Scholar
- 40.Priola AM, Priola SM, Gned D et al (2016) Diffusion-weighted quantitative MRI to diagnose benign conditions from malignancies of the anterior mediastinum: improvement of diagnostic accuracy by comparing perfusion-free to perfusion-sensitive measurements of the apparent diffusion coefficient. J Magn Reson Imaging 44:758–769CrossRefGoogle Scholar