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• EXPERIENCE EXCHANGES •
Application of apparent diffusion coefficient histogram analysis in the grading of gastrointestinal stromaltumors
CHEN Tingting  WEI Mingxiang  ZHU Yan  BAI Genji 

Cite this article as: Chen TT, Wei MX, Zhu Y, et al. Application of apparent diffusion coefficient histogram analysis in the grading of gastrointestinal stromaltumors[J]. Chin J Magn Reson Imaging, 2021, 12(2): 79-82. DOI:10.12015/issn.1674-8034.2021.02.018.


[Abstract] Objective To study the application value of tumors volume based apparent diffusion coefficient (ADC) histogram analysis in grading diagnosis of gastrointestinal stromal tumor. Materials andMethods Forty-four patients with gastrointestinal stromal tumors were retrospectively analyzed, including 12 cases of very low-risk and low-risk group, 9 cases of middle-risk group, and 23 cases of high-risk group. Regions of interest (ROI) in the apparent diffusion coefficient maps of three groups on each layer of tumor level were drawn by using 3D Slicer software and were analyzed using the whole tumors gray histogram. ROC curve analysis was used to assess the diagnostic performance of ADC histogram in distinguishing the three groups.Results Through histogram analysis of 11 parameters, two parameters were statistically significant (P＜0.05), including minimum and perc.10%. The remaining 9 parameters had no significant difference between the three groups (P＞0.05). Between the very low-risk and low-risk group and the middle-risk, the sensitivity of minimun was 88.89%, the specificity was 58.33%, the area under the curve was 0.750, and the best cut-off value was 0. Between the middle-risk and high-risk, the sensitivity of perc.10% was 91.3%, the specificity was 44.44%, the area under the curve was 0.638, and the best cut-off value was 1021. Between the very low-risk and low-risk group and the high-risk, the sensitivity of minimun was 91.3%, the specificity was 66.67%, the area under the curve was 0.786, and the best cut-off value was 1021.Conclusions Histograms analysis of ADC maps could provide ancillary diagnosis value in grading diagnosis of gastrointestinal stromal tumors. Minimun and perc.10% had a identification diagnostic efficiency.
[Keywords] apparent diffusion coefficient；magnetic resonance imaging；histogram analysis；ROC curve；gastrointestinal stromal tumor；risk rating

CHEN Tingting1, 2   WEI Mingxiang3   ZHU Yan3   BAI Genji1*  

1 The First People's Hospital of Huaian, the Huaian Clinical College of Xuzhou Medical University, Huaian 223300, China

2 Huaian Huaiyin Hospital, Huaian 223300, China

3 The First People's Hospital of Huaian, the First Huaian Hospital Affiliated to Nanjing Medical University, Huaian 223300, China

Bai GJ, E-mail: hybgj0451@163.com

Conflicts of interest   None.

Received  2020-08-24
Accepted  2021-01-12
DOI: 10.12015/issn.1674-8034.2021.02.018
Cite this article as: Chen TT, Wei MX, Zhu Y, et al. Application of apparent diffusion coefficient histogram analysis in the grading of gastrointestinal stromaltumors[J]. Chin J Magn Reson Imaging, 2021, 12(2): 79-82. DOI:10.12015/issn.1674-8034.2021.02.018.

1
Chen XP, Wang JP. Surgery[M]. 8th edition. Beijing: People's Medical Publishing House, 2013: 366-366.
2
Joensuu H. Risk stratification of patients diagnosed with gastrointestinal stromal tumor[J]. Hum Pathol, 2008, 39(10): 1411-1419.
3
Ma JX, Han LZ, Li XB, et al. Correlation between MRI features and tumor risk grade in gastrointestinal stromal tumor[J]. Chin J Clin Oncol,2019, 46(12): 601-605. DOI: 10.3969/j.issn.1000-8179.2019.12.507.
4
Zhao SL, Ji XB. The value of multi-slice spiral CT and its reconstruction techniques in the diagnosis of gastrointestinal stromal tumor[J]. Chin J CT & MRI, 2018, 16(8): 124-125, 135. DOI: 3969/j.issn.1672-5131.2018.08.038.
5
Takao H, Yamahira K, Doi I, et al. Gastrointestinal stromal tumor ofthe retroperitoneum: CT and MR findings[J]. Eur Radiol, 2004, 14(10): 1926-1929. DOI: 10.1007/s00330-004-2404-3.
6
Tsili AC, Sylakos A, Ntorkou A, et al. Apparent diffusion coefficient values and dynamic contrast enhancement patterns in differentiating seminomas from nonseminomatous testicular neoplasms[J]. Eur J Radiol, 2015, 84(7): 1219-1226.
7
Parikh T, Drew SJ, Lee VS, et al. Focal liver lesion detection and characterization with diffusion-weighted MR imaging: comparison with standard breath-hold T2-weighted imaging[J]. Radiology, 2008, 246(3): 812-822.
8
Taouli B, Vilgrain V, Dumont E, et al. Evaluation of liver diffusion i-sotropy and characterization of focal hepatic lesions with two single-shot echo-planar MR imaging sequences:prospective study in 66 pa-tient[J]. Radiology, 2003, 226(1): 71-78.
9
Thoeny HC, De Keyzer F, Oyen RH, et al. Diffusion-weighted MR imaging of kidneys in healthy volunteers and patients with parenchymal diseases:intial experience[J]. Radiology, 2005, 235(3): 911-917.
10
Paschall AK, Mirmomen SM, Symons R, et al. Differentiating papillary type I RCC from clear cell RCC and oncocytoma: application of whole-lesion volumetric ADC measurement[J]. Abdominal Radiology, 2018(3): 1-7. DOI: 10.1007/s00261-017-1453-4.
11
Li HJ, Liang LL, Li AQ, et al. Value of apparent diffusion coefficient histogram in small field diffusion-weighted imaging in differentiating clear cell from non-clear cell renal cell carcinoma[J]. Chin J Radiol, 2017, 51(9): 665-668.
12
Wang CP, Wang Y, Xiong F, et al. Histogram of apparent diffusion coefficient in the classification of gliomas[J]. J Pract Radiol, 2019, 35(1): 11-14. DOI: 10.3969/j.issn.1002-1671.2019.01.003.
13
Zhang Y, Cheng JL, Zheng RP, et al. Whole-tumor histogram analysis of apparent diffusion coefficient maps in grading diagnosis of ependymoma[J]. Chin J Radiol, 2018, 52(10): 751-755. DOI: 10.3760/cma.j.issn.1005-1201.2018.10.006.
14
Zhang YX, Han FG. Diagnostic value of apparent diffusion coefficient histogram for pathological grading in clear cell renal cell carcinoma[J]. Chin J Cancer Prevention & Treatment, 2019, 11(1): 76-79. DOI: 10.3969/j.issn.1674-5671.2019.01.13.
15
Yu T, Ji LB, Lu ZH, et al. Grading of prostate cancer by ADC histogram analysis[J]. Chin J Med Comput Imaging, 2018, 24(2): 152-157.
16
Liu J, Cheng JL, Zhang Y. Differentiation of stage ⅠB and Ⅱ cervical cancer by apparent diffusion coefficient histogram: A preliminary study[J]. Med Equi Chin, 2020, 35(1): 68-70, 98. DOI: 10.3969/j.issn.1674-1633.2020.01.018.
17
Zhang LJ, Kang LQ, Li GC, et al. Computed tomography-based radiomics model for discriminating the risk stratifcation of gastrointestinal tumors[J]. La Radiologia Medica, 2020, 125(2): 465-473.
18
Wang C, Li HL, Yeerfan J, et al. Building CT radiomics-based models for preoperatively predicting malignant potential and mitotic count of gastrointestinal stromal tumors[J]. Translational Oncology, 2019, 12(9): 1229-1236.
19
Sun ZQ, Hu SD, Li J, et al. Radiomics study for differentiating gastric cancer from gastric stromal tumor based on contrast-enhanced CT images[J]. J X-Ray Sci Technol, 2019, 27(9): 1021-1031.
20
Court LE, Fave X, Mackin D, et al. Computational resources for radiomics[J]. Translation Cancer Res, 2016, 5(4): 340-348.
21
Lu YY, Huang QY, Sun MH, et al. The value of histogram-based apparent diffusion codfficient in distinguishing common pathological subtypes of cervical cancer[J]. Chin J Med Comput Imaging, 2015, 21(3): 255-259. DOI: 10.19627/j.cnki.cn31-1700/th.2015.03.013.

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