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Preoperative Differentiation of Uterine Sarcoma from Leiomyoma: Comparison of Three Models Based on Different Segmentation Volumes Using Radiomics

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Abstract

Purpose

To investigate the impact of applying three different volume of interests (VOIs) in ADC map-based radiomic analysis and compare their diagnostic performance in the differentiation of uterine sarcoma and atypical leiomyoma.

Procedures

Seventy-eight patients (29 uterine sarcomas, 49 uterine leiomyomas) imaged with pelvic magnetic resonance imaging (MRI) prior to surgery were included in this retrospective study. Manually, segmentations of VOIs covered three different regions on apparent diffusion coefficient (ADC) maps: (1) tumor, (2) tumor and small piece of surrounded tissue, and (3) whole uterus. Texture and non-texture features were extracted from each VOI. The 0.623 + bootstrap method and the area under the receiver-operating characteristic curve (AUC) were used to select the features. Twenty logistic regression models (orders of 1–20) based on different combination of image features were built for each way of image segmentation.

Results

For the first VOI region, model 18 with 18 features yielded the highest AUC of 0.830, sensitivity of 76.0 %, specificity of 73.2 %, and accuracy of 73.9 %. For the second VOI region, model 17 with 17 features yielded the highest AUC of 0.853, sensitivity of 75.5 %, specificity of 75.5 %, and accuracy of 76.8 %. For the third VOI region, model 20 with 20 features yielded the highest AUC of 0.876, sensitivity of 76.3 %, specificity of 84.5 %, and accuracy of 82.4 %.

Conclusions

Radiomic model based on features extracted from VOI that covered the whole uterus had the best diagnostic performance. Adopting VOI contained more image information that was useful in improving diagnostic performance of radiomic model.

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References

  1. Gadducci A, Cosio S, Romanini A, Genazzani AR (2008) The management of patients with uterine sarcoma: a debated clinical challenge. Crit Rev Oncol Hematol 65:129–142

    Article  Google Scholar 

  2. Amant F, Coosemans A, Debiec-Rychter M, Timmerman D, Vergote I (2009) Clinical management of uterine sarcomas. Lancet Oncol 10:1188–1198

    Article  Google Scholar 

  3. Pietzner K, Buttmann-Schweiger N, Sehouli J, Kraywinkel K (2018) Incidence patterns and survival of gynecological sarcoma in Germany: analysis of population-based Cancer registry data on 1066 women. Int J Gynecol Cancer 28:134–138

    Article  Google Scholar 

  4. Owen C, Armstrong AY (2015) Clinical management of leiomyoma. Obstet Gynecol Clin N Am 42:67–85

    Article  Google Scholar 

  5. Kim TH, Kim JW, Kim SY, Kim SH, Cho JY (2018) What MRI features suspect malignant pure mesenchymal uterine tumors rather than uterine leiomyoma with cystic degeneration? J Gynecol Oncol 29:e26

    Article  Google Scholar 

  6. Guo-Fu Z, He Z, Xiao-Mei T, Hao Z (2014) Magnetic resonance and diffusion-weighted imaging in categorization of uterine sarcomas: correlation with pathological findings. Clin Imaging 38:836–844

    Article  Google Scholar 

  7. Namimoto T, Yamashita Y, Awai K, Nakaura T, Yanaga Y, Hirai T, Saito T, Katabuchi H (2009) Combined use of T2-weighted and diffusion-weighted 3-T MR imaging for differentiating uterine sarcomas from benign leiomyomas. Eur Radiol 19:2756–2764

    Article  Google Scholar 

  8. de Leon AD, Kapur P, Pedrosa I (2019) Radiomics in kidney Cancer: MR imaging. Magn Reson Imaging Clin N Am 27:1–13

    Article  Google Scholar 

  9. Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue RTHM, Even AJG, Jochems A, van Wijk Y, Woodruff H, van Soest J, Lustberg T, Roelofs E, van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762

    Article  Google Scholar 

  10. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P, Zegers CML, Gillies R, Boellard R, Dekker A, Aerts HJWL (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446

    Article  Google Scholar 

  11. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    Article  Google Scholar 

  12. Gevaert O, Mitchell LA, Achrol AS, Xu J, Echegaray S, Steinberg GK, Cheshier SH, Napel S, Zaharchuk G, Plevritis SK (2014) Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology 273:168–174

    Article  Google Scholar 

  13. Prasanna P, Patel J, Partovi S, Madabhushi A, Tiwari P (2017) Radiomic features from the peritumoral brain parenchyma on treatment-naive multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: preliminary findings. Eur Radiol 27:4188–4197

    Article  Google Scholar 

  14. Shen C, Liu Z, Guan M, Song J, Lian Y, Wang S, Tang Z, Dong D, Kong L, Wang M, Shi D, Tian J (2017) 2D and 3D CT Radiomics features prognostic performance comparison in non-small cell lung Cancer. Transl Oncol 10:886–894

    Article  Google Scholar 

  15. Li H, Zhu Y, Burnside ES, Drukker K, Hoadley KA, Fan C, Conzen SD, Whitman GJ, Sutton EJ, Net JM, Ganott M, Huang E, Morris EA, Perou CM, Ji Y, Giger ML (2016) MR imaging Radiomics signatures for predicting the risk of breast Cancer recurrence as given by research versions of MammaPrint, Oncotype DX, and PAM50 gene assays. Radiology 281:382–391

    Article  Google Scholar 

  16. Wibmer A, Hricak H, Gondo T, Matsumoto K, Veeraraghavan H, Fehr D, Zheng J, Goldman D, Moskowitz C, Fine SW, Reuter VE, Eastham J, Sala E, Vargas HA (2015) Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores. Eur Radiol 25:2840–2850

    Article  Google Scholar 

  17. Lerski RA, Straughan K, Schad LR, Boyce D, Bluml S, Zuna I (1993) MR image texture analysis--an approach to tissue characterization. Magn Reson Imaging 11:873–887

    Article  CAS  Google Scholar 

  18. Kotrotsou A, Zinn PO, Colen RR (2016) Radiomics in brain tumors: An Emerging Technique for Characterization of Tumor Environment. Magn Reson Imaging 24:719–729

    Article  Google Scholar 

  19. Lakhman Y, Veeraraghavan H, Chaim J, Feier D, Goldman DA, Moskowitz CS, Nougaret S, Sosa RE, Vargas HA, Soslow RA, Abu-Rustum NR, Hricak H, Sala E (2017) Differentiation of uterine Leiomyosarcoma from atypical leiomyoma: diagnostic accuracy of qualitative MR imaging features and feasibility of texture analysis. Eur Radiol 27:2903–2915

    Article  Google Scholar 

  20. Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJWL, Dekker A, Fenstermacher D, Goldgof DB, Hall LO, Lambin P, Balagurunathan Y, Gatenby RA, Gillies RJ (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248

    Article  Google Scholar 

  21. Kotrotsou A, Zinn P, Colen R (2015) Introduction to texture analysis for brain tumors: concept and clinical relevance [abstract]. American Society of Neuroradiology 53rd Annual Meeting

  22. Tanos V, Berry KE (2018) Benign and malignant pathology of the uterus. Best Pract Res Clin Obstet Gynaecol 46:12–30

    Article  CAS  Google Scholar 

  23. Sahdev A, Sohaib SA, Jacobs I, Shepherd JH, Oram DH, Reznek RH (2001) MR imaging of uterine sarcomas. AJR Am J Roentgenol 177:1307–1311

    Article  CAS  Google Scholar 

  24. Bi Q, Xiao Z, Lv F, Liu Y, Zou C, Shen Y (2018) Utility of clinical parameters and multiparametric MRI as predictive factors for differentiating uterine sarcoma from atypical leiomyoma. Acad Radiol 25:993–1002

    Article  Google Scholar 

  25. Vallieres M, Freeman CR, Skamene SR, El Naqa I (2015) A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol 60:5471–5496

    Article  CAS  Google Scholar 

  26. Zhou H, Vallieres M, Bai HX et al (2017) MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro-Oncology 19:862–870

    Article  CAS  Google Scholar 

  27. Dong Y, Feng Q, Yang W, Lu Z, Deng C, Zhang L, Lian Z, Liu J, Luo X, Pei S, Mo X, Huang W, Liang C, Zhang B, Zhang S (2018) Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI. Eur Radiol 28:582–591

    Article  Google Scholar 

  28. Zou KH, Warfield SK, Bharatha A, Tempany CMC, Kaus MR, Haker SJ, Wells WM III, Jolesz FA, Kikinis R (2004) Statistical validation of image segmentation quality based on a spatial overlap index. Acad Radiol 11:178–189

    Article  Google Scholar 

  29. Chen Y, Chen TW, Wu CQ, Lin Q, Hu R, Xie CL, Zuo HD, Wu JL, Mu QW, Fu QS, Yang GQ, Zhang XM (2018) Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis. Eur Radiol. https://doi.org/10.1007/s00330-018-5824-1

  30. Tanadini-Lang S, Bogowicz M, Veit-Haibach P, Huellner M, Pauli C, Shukla V, Guckenberger M, Riesterer O (2018) Exploratory Radiomics in computed tomography perfusion of prostate Cancer. Anticancer Res 38:685–690

    PubMed  Google Scholar 

  31. Nekooeimehr I, Lai-Yuen S, Bao P et al (2016) Automated tracking, segmentation and trajectory classification of pelvic organs on dynamic MRI. Conf Proc IEEE Eng Med Biol Soc 2016:2403–2406

    PubMed  Google Scholar 

  32. Colen R, Hatami M, Kotrotsou A et al (2015) NIMG-11: radiomic subclassification of glioblastoma. Neuro Oncol 17:v155–v155

  33. Malayeri AA, El Khouli RH, Zaheer A et al (2011) Principles and applications of diffusion-weighted imaging in cancer detection, staging, and treatment follow-up. Radiographics 31:1773–1791

    Article  Google Scholar 

  34. Gerges L, Popiolek D, Rosenkrantz AB (2018) Explorative investigation of whole-lesion histogram MRI metrics for differentiating uterine leiomyomas and Leiomyosarcomas. AJR Am J Roentgenol 210:1172–1177

    Article  Google Scholar 

  35. Yip SS, Aerts HJ (2016) Applications and limitations of radiomics. Phys Med Biol 61:R150–R166

    Article  CAS  Google Scholar 

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The authors of this manuscript declare no relationships with any companies, or receive any fund.

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Authors and Affiliations

  1. Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China

    Huihui Xie, Xiaodong Zhang, Shuai Ma, Yi Liu & Xiaoying Wang

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  1. Huihui Xie
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  2. Xiaodong Zhang
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Xie, H., Zhang, X., Ma, S. et al. Preoperative Differentiation of Uterine Sarcoma from Leiomyoma: Comparison of Three Models Based on Different Segmentation Volumes Using Radiomics. Mol Imaging Biol 21, 1157–1164 (2019). https://doi.org/10.1007/s11307-019-01332-7

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