|
|
|
| Value of diffusion kurtosis imaging quantitative parameter histogram analysis in differentiating uterine carcinosarcoma from degenerative hysteromyoma |
| TIAN Shi-feng1, LIU Ai-lian1, NIU Miao1, YANG Wei-ping1, WU Jing-jun1, LIU Jing-hong1, GUO Yan2 |
| 1. Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian Liaoning 116011, China;
2. GE Healthcare, Shanghai 200000, China |
|
|
|
|
Abstract Objective: To evaluate the value of diffusion kurtosis imaging(DKI) quantitative parameter histogram analysis in differentiating uterine carcinosarcoma(UCS) from degenerative hysteromyoma(DH). Methods: The data of 11 patients with UCS and 22 patients with DH confirmed by operation and pathology were retrospectively analyzed. All patients underwent pelvic DKI scanning, quantitative parameters of DKI, including mean kurtosis(MK), mean diffusivity(MD), fractional anisotropy(FA), were obtained after post-processing. Histogram analysis was performed using Omni-Kinetics software, histogram parameters, such as median, average, standard deviation, skewness, kurtosis, 25th percentile, 75th percentile, energy, and entropy were obtained. The independent samples t test(normal distribution) or Mann-Whitney rank sum test(skewed distribution) were used to compare the differences of histogram parameters between UCS and DH. Receiver operator characteristic(ROC) curve was used to evaluate the effectiveness of histogram parameters with statistical differences in differential diagnosis of UCS and DH. Results: There were significant differences in MK(standard deviation, 75th percentile, entropy), MD(median, average, 25th percentile, 75th percentile), FA(median, average, standard deviation, 25th percentile, 75th percentile, energy, entropy) between the two groups(P<0.05). There were no significant differences in MK(median, average, skewness, kurtosis, 25th percentile, energy), MD(standard deviation, skewness, kurtosis, energy, entropy), FA(skewness, kurtosis) between the two groups(P>0.05). The area under ROC of FA(median, 75th percentile) was the largest(0.921). The sensitivity of FA(75th percentile) was the highest(100.0%) and the specificity of MK(standard deviation) was the highest(100.0%). Conclusion: Histogram analysis of DKI quantitative parameters is helpful to distinguish UCS from DH.
|
|
Received: 30 May 2019
|
|
|
|
|
|
| [1]Kurnit KC, Previs RA, Soliman PT, et al. Prognostic factors impacting survival in early stage uterine carcinosarcoma[J]. Gynecol Oncol, 2019, 152(1): 31-37.
[2]Yamada I, Sakamoto J, Kobayashi D, et al. Diffusion kurtosis imaging of endometrial carcinoma: Correlation with histopathological findings[J]. Magn Reson Imaging, 2019, 57: 337-346.
[3]Wang P, Thapa D, Wu G, et al. A study on diffusion and kurtosis features of cervical cancer based on non-Gaussian diffusion weighted model[J]. Magn Reson Imaging, 2018, 47: 60-66.
[4]Baessler B, Luecke C, Lurz J, et al. Cardiac MRI Texture Analysis of T1 and T2 Maps in Patients with Infarctlike Acute Myocarditis[J]. Radiology, 2018, 289(2): 357-365.
[5]Rabban JT, Gilks CB, Malpica A, et al. Issues in the Differential Diagnosis of Uterine Low-grade Endometrioid Carcinoma, Including Mixed Endometrial Carcinomas: Recommendations from the International Society of Gynecological Pathologists[J]. Int J Gynecol Pathol, 2019, 38(Suppl 1): S25-S39.
[6]Hempel JM, Brendle C, Bender B, et al. Diffusion kurtosis imaging histogram parameter metrics predicting survival in integrated molecular subtypes of diffuse glioma: An observational cohort study[J]. Eur J Radiol, 2019, 112: 144-152.
[7]Skogen K, Schulz A, Dormagen JB, et al. Diagnostic performance of texture analysis on MRI in grading cerebralgliomas[J]. Eur J Radiol, 2016, 85(4): 824-829.
[8]Takeuchi M, Matsuzaki K, Harada M. Carcinosarcoma of the uterus: MRI findings including diffusion-weighted imaging and MR spectroscopy[J]. Acta Radiol, 2016, 57(10): 1277-1284.
[9]Sun Q, Huang Y, Wang J, et al. Applying CT texture analysis to determine the prognostic value of subsolid nodules detected during low-dose CT screening[J]. Clin Radiol, 2019, 74(1): 59-66.
[10]Chen T, Li Y, Lu SS, et al. Quantitative evaluation of diffusion-kurtosis imaging for grading endometrial carcinoma: a comparative study with diffusion-weighted imaging[J]. Clin Radiol, 2017, 72(11): 995.e11-995.e20.
[11]Kimura Y, Sato N, Ota M, et al. Brain abnormalities in myalgic encephalomyelitis/chronic fatigue syndrome: Evaluation by diffusional kurtosis imaging and neurite orientation dispersion and density imaging[J]. J Magn Reson Imaging, 2019, 49(3): 818-824.
[12]汪香玉，覃涛，涂宁，等. 扩散峰度成像定量参数直方图分析在诊断前列腺癌及判定其恶性程度中的价值[J]. 中华放射学杂志，2018，52（4）：277-282.
[13]Gerges L, Popiolek D, Rosenkrantz AB. Explorative Investigation of Whole-Lesion Histogram MRI Metrics for Differentiating Uterine Leiomyomas and Leiomyosarcomas[J]. AJR, 2018, 210(5): 1172-1177.
[14]Hectors SJ, Semaan S, Song C, et al. Advanced Diffusion-weighted Imaging Modeling for Prostate Cancer Characterization: Correlation with Quantitative Histopathologic Tumor Tissue Composition-A Hypothesis-generating Study[J]. Radiology, 2018, 286(3): 918-928.
[15]李丽，赵振华，杨建峰，等. 动态增强MRI定量灌注直方图参数对富细胞型子宫肌瘤的诊断价值[J]. 中华放射学杂志，2018，52（11）：852-857. |
|
|
|
