Share：
Share this content in WeChat
X
[Chinese][PDF]4382
• Clinical Article •
The value of ADC texture analysis in differential diagnosis of ovarian epithelial tumors
SUN Bixia  ZHU Dalin  ZHANG Xuxia  QIAN Jifang  LI Yunzhi  ZHANG Yanhui  XU Shengfang 

Cite this article as: SUN B X, ZHU D L, ZHANG X X, et al. The value of ADC texture analysis in differential diagnosis of ovarian epithelial tumors[J]. Chin J Magn Reson Imaging, 2023, 14(2): 83-86, 108. DOI:10.12015/issn.1674-8034.2023.02.014.


[Abstract] Objective To explore the role of whole-lesion histogram analysis of apparent diffusion coefficient (ADC) in differentiating benign, borderline and malignant ovarian epithelial tumors.Materials and Methods A retrospective analysis of 71 patients with ovarian epithelial tumors confirmed by postoperative pathology in Gansu Maternal and Child Health Hospital from January 2019 to September 2022, and patients who underwent routine MRI examination in our hospital before operation, introduced ADC sequence into Fire Voxel software and manually sketched the whole region of interest (ROI) of the lesion, and the software automatically generated histogram texture parameters (entropy, skewness, kurtosis, standard deviation, maximum, minimum, average). Single factor analysis of variance was used to evaluate the difference of texture parameters of ADC map among benign, borderline and malignant ovarian epithelial tumors. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to evaluate the diagnostic efficacy of each parameter in the differential diagnosis of benign, borderline and malignant ovarian epithelial tumors.Results In the texture parameters of ADC map, there were significant differences in entropy, skewness, kurtosis, maximum, minimum and average among benign, borderline and malignant ovarian epithelial tumors (P＜0.001, 0.003,＜0.001, 0.007, 0.005, 0.001). The result of ROC curve shows that the maximum value of AUC is entropy (AUC=0.75).Conclusions The application of ADC texture analysis of whole tumor can improve the value of differential diagnosis of benign, borderline and malignant ovarian epithelial tumors, especially the differential diagnosis of borderline and malignant tumors, and provide clinical diagnosis basis and guide clinical treatment.
[Keywords] ovarian epithelial tumor；magnetic resonance imaging；apparent diffusion coefficient；diffusion weighted imaging；texture analysis；differential diagnosis

SUN Bixia   ZHU Dalin   ZHANG Xuxia   QIAN Jifang*   LI Yunzhi   ZHANG Yanhui   XU Shengfang  

Medical Imaging Center, Gansu Provincial Matemity and Child-care Hospital, Lanzhou 730050, China

*Correspondence to: Qian JF, E-mail: 495248996@qq.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Health Industry Scientific Research Project of Gansu Province (No. GSWSKY2021-059).
Received  2022-06-29
Accepted  2023-01-17
DOI: 10.12015/issn.1674-8034.2023.02.014
Cite this article as: SUN B X, ZHU D L, ZHANG X X, et al. The value of ADC texture analysis in differential diagnosis of ovarian epithelial tumors[J]. Chin J Magn Reson Imaging, 2023, 14(2): 83-86, 108. DOI:10.12015/issn.1674-8034.2023.02.014.

[1]
JAVADI S, GANESHAN D M, QAYYUM A, et al. Ovarian Cancer, the Revised FIGO Staging System, and the Role of Imaging[J]. AJR Am J Roentgenol, 2016, 206(6):1351-1360. DOI: 10.2214/AJR.15.15199.
[2]
CUMMINGS M, NICOLAIS O, SHAHIN M. Surgery in Advanced Ovary Cancer: Primary versus Interval Cytoreduction[J/OL]. Diagnostics(Basel), 2022, 12(4): 988 [2022-10-19]. https://www.mdpi.com/2075-4418/12/4/988. DOI: 10.3390/diagnostics12040988.
[3]
AHOUSSOUGBEMEY MELE A, DANAK S, ELLIS E, et al. Immature Teratoma With Metastatic Gliosis[J/OL]. Cureus, 2022, 14(3): 22748 [2022-10-19]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8970535/. DOI: 0.7759/cureus.22748.
[4]
CIUCCI A, BUTTARELLI M, FAGOTTI A, et al. Preclinical models of epithelial ovarian cancer: practical considerations and challenges for a meaningful application[J/OL]. Cell Mol LifeSc, 2022, 79(7): 364 [2022-10-19]. https://link.springer.com/article/10.1007/s00018-022-04395-y. DOI: 10.1007/s00018-022-04395-y.
[5]
ROETT M A, EVANS P. Ovarian cancer: an overview[J]. Am Fam Physician, 2009, 80(6): 609-616.
[6]
XI S, YANG Y G, SUO J, et al. Research Progress on Gene Editing Based on Nano-Drug Delivery Vectors for Tumor Therapy[J/OL]. Front Bioeng Biotechnol, 2022, 10: 873369 [2022-10-19]. https://www.frontiersin.org/articles/10.3389/fbioe.2022.873369/full. DOI: 10.3389/fbioe.2022.873369.
[7]
WANG N, XIAO F, SHAO H, et al. Clinical Efficacy of Yiqi Yangyin Decoction Combined with Docetaxel on Advanced Ovarian Cancer and the Effect on the Levels of Serum Markers VEGF, HE4, and CA125[J/OL]. J Healthc Eng, 2022, 2022: 8401202 [2022-10-19]. https://www.hindawi.com/journals/jhe/2022/8401202/. DOI: 10.1155/2022/8401202.
[8]
HONG T, HAM J, SONG G, et al. Alpinumisoflavone Disrupts Endoplasmic Reticulum and Mitochondria Leading to Apoptosis in Human Ovarian Cancer[J/OL]. Pharmaceutics, 2022, 14(3): 564 [2022-10-19]. https://www.mdpi.com/1999-4923/14/3/564. DOI: 10.3390/pharmaceutics14030564.
[9]
HU P Z, CHEN X Y, XIONG W, et al. A BRCA1 Splice Site Variant Responsible for Familial Ovarian Cancer in a Han-Chinese Family[J]. Curr Med Sci, 2022, 42(3): 666-672. DOI: 10.1007/s11596-022-2527-2.
[10]
YE R, WENG S, LI Y, et al. Texture Analysis of Three-Dimensional MRI Images May Differentiate Borderline and Malignant Epithelial Ovarian Tumors[J]. Korean J Radiol, 2021, 22(1): 106-117. DOI: 10.3348/kjr.2020.0121.
[11]
WU G H. Clinical application of whole-volume apparent diffusion coefficient histogram parameters of histological grading rectal adenocarcinoma[J]. Chin J Magn Reson Imaging, 2021, 12(4): 72-74, 81. DOI: 10.12015/issn.1674-8034.2021.04.015.
[12]
ZHOU J, WU S G, WANG J, et al. The Effect of Histological Subtypes on Outcomes of Stage Ⅳ Epithelial Ovarian Cancer[J/OL]. Front Oncol, 2018, 8: 577 [2022-10-19]. https://www.frontiersin.org/articles/10.3389/fonc.2018.00577/full. DOI: 10.3389/fonc.2018.00577.
[13]
MIZUNO M, KAJIYAMA H, SHIBATA K, et al. Prognostic value of histological type in stage Ⅳ ovarian carcinoma: a retrospective analysis of 223 patients[J]. Br J Cancer, 2015, 112(8): 1376-1383. DOI: 10.1038/bjc.2015.97.
[14]
GARRIDO M P, FREDES A N, LOBOS-GONZÁLEZ L, et al. Current Treatments and New Possible Complementary Therapies for Epithelial Ovarian Cancer[J/OL]. Biomedicines, 2021, 10(1): 77 [2022-10-19]. https://www.mdpi.com/2227-9059/10/1/77. DOI: 10.3390/biomedicines10010077.
[15]
ZHOU S, LIU Y, YIN W, et al. Clinicopathologic factors associated with short-term survival in advanced epithelial ovarian cancer[J]. Transl Cancer Res, 2019, 8(6): 2396-2404. DOI: 10.21037/tcr.2019.09.53.
[16]
LALWANI N, PRASAD S R, VIKRAM R, et al. Histologic, molecular, and cytogenetic features of ovarian cancers: implications for diagnosis and treatment[J]. Radiographics, 2011, 31(3): 625-646. DOI: 10.1148/rg.313105066.
[17]
LIU X, WANG T, ZHANG G, et al. Two-dimensional and three-dimensional T2 weighted imaging-based radiomic signatures for the preoperative discrimination of ovarian borderline tumors and malignant tumors[J/OL]. J Ovarian Res, 2022, 15(1): 22 [2022-10-19]. https://ovarianresearch.biomedcentral.com/articles/10.1186/s13048-022-00943-z. DOI: 10.1186/s13048-022-00943-z.
[18]
WANG C W, CHANG C C, KHALIL M A, et al. Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer[J/OL]. Sci Data, 2022, 9(1): 25 [2022-10-19]. https://www.nature.com/articles/s41597-022-01127-6. DOI: 10.1038/s41597-022-01127-6.
[19]
COBEC I M, SAS I, MOATAR A E, et al. Ovarian cancer health politics in Romania and Germany: A comparative study[J/OL]. Exp Ther Med, 2021, 22(5): 1217 [2022-10-19]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8422388/. DOI: 10.3892/etm.2021.10651.
[20]
HOANG L N, ZACHARA S, SOMA A, et al. Diagnosis of Ovarian Carcinoma Histotype Based on Limited Sampling: A Prospective Study Comparing Cytology, Frozen Section, and Core Biopsies to Full Pathologic Examination[J]. Int J Gynecol Pathol, 2015, 34(6): 517-527. DOI: 10.1097/PGP.0000000000000199.
[21]
GAVRIELIDES M A, RONNETT B M, VANG R, et al. Selection of Representative Histologic Slides in Interobserver Reproducibility Studies: Insights from Expert Review for Ovarian Carcinoma Subtype Classification[J/OL]. J Pathol Inform, 2021, 12: 15 [2022-10-19]. https://www.sciencedirect.com/science/article/pii/S2153353922001377?via%3Dihub. DOI: 10.4103/jpi.jpi_56_20.
[22]
BAXI V, EDWARDS R, MONTALTO M, et al. Digital pathology and artificial intelligence in translational medicine and clinical practice[J]. Mod Pathol, 2022, 35(1): 23-32. DOI: 10.1038/s41379-021-00919-2.
[23]
ZHAO S H, QIANG J W, ZHANG G F, et al. Diffusion-weighted MR imaging for differentiating borderline from malignant epithelial tumours of the ovary: pathological correlation[J]. Eur Radiol, 2014, 24(9): 2292-2299. DOI: 10.1007/s00330-014-3236-4.
[24]
XIAO F, ZHANG L, YANG S, et al. Quantitative analysis of the MRI features in the differentiation of benign, borderline, and malignant epithelial ovarian tumors[J/OL]. J Ovarian Res, 2022, 15(1): 13 [2022-10-19]. https://ovarianresearch.biomedcentral.com/articles/10.1186/s13048-021-00920-y. DOI: 10.1186/s13048-021-00920-y.
[25]
YU X P, WANG L, YU H Y, et al. MDCT-Based Radiomics Features for the Differentiation of Serous Borderline Ovarian Tumors and Serous Malignant Ovarian Tumors[J]. Cancer Manag Res, 2021, 13: 329-336. DOI: 10.2147/CMAR.S284220.
[26]
HE M, SONG Y, LI H, et al. Histogram Analysis Comparison of Monoexponential, Advanced Diffusion-Weighted Imaging, and Dynamic Contrast-Enhanced MRI for Differentiating Borderline From Malignant Epithelial Ovarian Tumors[J]. J Magn Reson Imaging, 2020, 52(1): 257-268. DOI: 10.1002/jmri.27037.
[27]
LI Y, JIAN J, PICKHARDT P J, et al. MRI-Based Machine Learning for Differentiating Borderline From Malignant Epithelial Ovarian Tumors: A Multicenter Study[J]. J Magn Reson Imaging, 2020, 52(3): 897-904. DOI: 10.1002/jmri.27084.
[28]
JIAN J, LI Y, XIA W, et al. MRI-Based Multiple Instance Convolutional Neural Network for Increased Accuracy in the Differentiation of Borderline and Malignant Epithelial Ovarian Tumors[J]. J Magn Reson Imaging, 2022, 56(1): 173-181. DOI: 10.1002/jmri.28008.
[29]
LI C, WANG H, CHEN Y, et al. Nomograms of Combining MRI Multisequences Radiomics and Clinical Factors for Differentiating High-Grade From Low-Grade Serous Ovarian Carcinoma[J/OL]. Front Oncol, 2022, 12: 816982 [2022-10-19]. https://www.frontiersin.org/articles/10.3389/fonc.2022.816982/full. DOI: 10.3389/fonc.2022.816982.
[30]
GAO L, XIONG M, CHEN X, et al. Multi-Region Radiomic Analysis Based on Multi-Sequence MRI Can Preoperatively Predict Microvascular Invasion in Hepatocellular Carcinoma[J/OL]. Front Oncol, 2022, 12: 818681 [2022-10-19]. https://www.frontiersin.org/articles/10.3389/fonc.2022.818681/full. DOI: 10.3389/fonc.2022.818681.
[31]
JIANG X, JIA H, ZHANG Z, et al. The Feasibility of Combining ADC Value With Texture Analysis of T2WI, DWI and CE-T1WI to Preoperatively Predict the Expression Levels of Ki-67 and p53 of Endometrial Carcinoma[J/OL]. Front Oncol, 2022, 11: 805545 [2022-10-19]. https://www.frontiersin.org/articles/10.3389/fonc.2021.805545/full. DOI: 10.3389/fonc.2021.805545.

PREV Clinical-radiomic analysis of multi-parametric magnetic resonance imaging predicts lymphovascular space invasion and outcomes in cervical cancer
NEXT Whole spine MRI findings in endemic fluorosis
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn