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Comparison of radiomics prediction models for lung metastases according to four semiautomatic segmentation methods in soft-tissue sarcomas of the extremities

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Abstract

Our objective was to investigate radiomics signatures and prediction models defined by four segmentation methods in using 2-[18F]fluoro-2-deoxy-d-glucose positron emission tomography (18F-FDG PET) imaging of lung metastases of soft-tissue sarcomas (STSs). For this purpose, three fixed threshold methods using the standardized uptake value (SUV) and gradient-based edge detection (ED) were used for tumor delineation on the PET images of STSs. The Dice coefficients (DCs) of the segmentation methods were compared. The least absolute shrinkage and selection operator (LASSO) regression and Spearman’s rank, and Friedman’s ANOVA test were used for selection and validation of radiomics features. The developed radiomics models were assessed using ROC (receiver operating characteristics) curve and confusion matrices. According to the results, the DC values showed the biggest difference between SUV40% and other segmentation methods (DC: 0.55 and 0.59). Grey-level run-length matrix_run-length nonuniformity (GLRLM_RLNU) was a common radiomics signature extracted by all segmentation methods. The multivariable logistic regression of ED showed the highest area under the ROC (receiver operating characteristic) curve (AUC), sensitivity, specificity, and accuracy (AUC: 0.88, sensitivity: 0.85, specificity: 0.74, accuracy: 0.81). In our research, the ED method was able to derive a significant model of radiomics. GLRLM_RLNU which was selected from all segmented methods as a meaningful feature was considered the obvious radiomics feature associated with the heterogeneity and the aggressiveness. Our results have apparently showed that radiomics signatures have the potential to uncover tumor characteristics.

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References

  1. K.G. Billingsley, M.E. Burt, E. Jara, R.J. Ginsberg, J.M. Woodruff, D.H.Y. Leung et al., Pulmonary metastases from soft tissue sarcoma: analysis of patterns of disease and postmetastasis survival. Ann. Surg. 229, 602 (1999). https://doi.org/10.1097/00000658-199905000-00002

    Article  Google Scholar 

  2. G. Marulli, M. Mammana, G. Comacchio, F. Rea, Survival and prognostic factors following pulmonary metastasectomy for sarcoma. J. Thorac. Dis. 9, S1305 (2017)

    Article  Google Scholar 

  3. M.F. Brennan, Soft tissue sarcoma: advances in understanding and management. Surgeon 3, 216 (2005). https://doi.org/10.1016/S1479-666X(05)80044-7

    Article  Google Scholar 

  4. J.W. Fletcher, B. Djulbegovic, H.P. Soares, B.A. Siegel, V.J. Lowe, G.H. Lyman et al., Recommendations on the use of 18F-FDG PET in oncology. J. Nucl. Med. 49, 480 (2008). https://doi.org/10.2967/jnumed.107.047787

    Article  Google Scholar 

  5. R.T. Hoppe, R.H. Advani, W.Z. Ai, R.F. Ambinder, P. Aoun, P. Armand et al., NCCN guidelines insights: hodgkin lymphoma, version 1.2018. J. Natl. Compr. Can Netw. 16, 245 (2018). https://doi.org/10.6004/jnccn.2018.0013

    Article  Google Scholar 

  6. M. Vallières, C.R. Freeman, S.R. Skamene, I. El Naqa, 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 (2015). https://doi.org/10.1088/0031-9155/60/14/5471

    Article  Google Scholar 

  7. K.C. Genadry, S. Pietrobono, R. Rota, C.M. Linardic, Soft tissue sarcoma cancer stem cells: an overview. Front. Oncol. 8, 475 (2018). https://doi.org/10.3389/fonc.2018.00475

    Article  Google Scholar 

  8. P. Lambin, R.G.P.M. Van Stiphout, M.H.W. Starmans, E. Rios-Velazquez, G. Nalbantov, H.J.W.L. Aerts et al., Predicting outcomes in radiation oncology-multifactorial decision support systems. Nat. Rev. Clin. Oncol. 10, 27 (2013). https://doi.org/10.1038/nrclinonc.2012.196

    Article  Google Scholar 

  9. E. Scalco, G. Rizzo, Texture analysis of medical images for radiotherapy applications ELISA. BJR. 90, 20160642 (2017). https://doi.org/10.1259/bjr.20160642

    Article  Google Scholar 

  10. B. Zhang, F. Ouyang, D. Gu, Y. Dong, L. Zhang, X. Mo et al., Advanced nasopharyngeal carcinoma: pre-treatment prediction of progression based on multi-parametric MRI radiomics. Oncotarget 8, 72457 (2017). https://doi.org/10.18632/oncotarget.19799

    Article  Google Scholar 

  11. B. Foster, U. Bagci, A. Mansoor, Z. Xu, D.J. Mollura, A review on segmentation of positron emission tomography images. Comput Biol Med. 50, 76 (2014). https://doi.org/10.1016/j.compbiomed.2014.04.014

    Article  Google Scholar 

  12. C.A. Owens, C.B. Peterson, C. Tang, E.J. Koay, W. Yu, D.S. Mackin et al., Lung tumor segmentation methods: impact on the uncertainty of radiomics features for non-small cell lung cancer. PLoS One 13, e0205003 (2018). https://doi.org/10.1371/journal.pone.0205003

    Article  Google Scholar 

  13. M. Avanzo, J. Stancanello, I. El Naqa, Beyond imaging: the promise of radiomics. Phys. Medica. 38, 122 (2017). https://doi.org/10.1016/j.ejmp.2017.05.071

    Article  Google Scholar 

  14. S. Ha, H. Choi, J.C. Paeng, G.J. Cheon, Radiomics in oncological PET/CT: a methodological overview. Nucl. Med. Mol. Imaging. 53, 14 (2019). https://doi.org/10.1007/s13139-019-00571-4

    Article  Google Scholar 

  15. B.H. Byun, C.-B. Kong, J. Park, Y. Seo, I. Lim, C.W. Choi et al., Initial metabolic tumor volume measured by 18F-FDG PET/CT can predict the outcome of osteosarcoma of the extremities. J. Nucl. Med. 54, 1725 (2013). https://doi.org/10.2967/jnumed.112.117697

    Article  Google Scholar 

  16. U. Nestle, S. Kremp, A. Schaefer-Schuler, C. Sebastian-Welsch, D. Hellwig, C. Rübe et al., Comparison of different methods for delineation of 18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-Small cell lung cancer. J. Nucl. Med. 46, 1342 (2005)

    Google Scholar 

  17. F. Orlhac, J.J.-A. Maisonobe, C.A. Garcia, B. Vanderlinden, M. Soussan, J.J.-A. Maisonobe et al., Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis. J. Nucl. Med. 55, 414 (2014). https://doi.org/10.2967/jnumed.113.129858

    Article  Google Scholar 

  18. M. Hatt, F. Tixier, L. Pierce, P.E. Kinahan, C. Cheze Le Rest, D. Visvikis et al., Characterization of PET/CT images using texture analysis: The past, the present… any future? Eur. J. Nucl. Med. Mol. Imaging. 44, 151 (2017). https://doi.org/10.1007/s00259-016-3427-0

    Article  Google Scholar 

  19. P.E. Kinahan, J.W. Fletcher, Positron emission tomography-computed tomography standardized uptake values in clinical practice and assessing response to therapy. Semin Ultrasound CT MRI. 31, 496 (2010). https://doi.org/10.1053/j.sult.2010.10.001

    Article  Google Scholar 

  20. J.A. Thie, Understanding the standardized uptake value, its methods, and implications for usage. J. Nucl. Med. 45, 1431 (2004)

    Google Scholar 

  21. M. Werner-Wasik, A.D. Nelson, W. Choi, Y. Arai, P.F. Faulhaber, P. Kang et al., What is the best way to contour lung tumors on PET scans? Multiobserver validation of a gradient-based method using a NSCLC digital PET phantom. Int. J. Radiat. Oncol. Biol. Phys. 82, 1164 (2012). https://doi.org/10.1016/j.ijrobp.2010.12.055

    Article  Google Scholar 

  22. X. Geets, J.A. Lee, A. Bol, M. Lonneux, V. Grégoire, A gradient-based method for segmenting FDG-PET images: methodology and validation. Eur. J. Nucl. Med. Mol. Imaging. 32, 1427 (2007). https://doi.org/10.1007/s00259-006-0363-4

    Article  Google Scholar 

  23. K. Clark, B. Vendt, K. Smith, J. Freymann, J. Kirby, P. Koppel et al., The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging. 26, 1045 (2013). https://doi.org/10.1007/s10278-013-9622-7

    Article  Google Scholar 

  24. Kelly H. Zou, Simon K. Warfield, Aditya Bharatha, et al. Statistical validation of image segmentation quality based on a spatial overlap index. Acad. Radiol. 11, 178 (2004). https://www.academicradiology.org/article/S1076-6332(03)00671-8/

  25. C. Nioche, F. Orlhac, S. Boughdad, S. Reuze, J. Goya-Outi, C. Robert et al., Lifex: A freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 78, 4786 (2018). https://doi.org/10.1158/0008-5472.CAN-18-0125

    Article  Google Scholar 

  26. F. Orlhac, M. Soussan, K. Chouahnia, E. Martinod, I. Buvat, 18F-FDG PET-derived textural indices reflect tissue-specific uptake pattern in non-small cell lung cancer. PLoS One 10, 1 (2015). https://doi.org/10.1371/journal.pone.0145063

    Article  Google Scholar 

  27. R.M. Haralick, K. Shanmugam, I. Dinstein, Textural features for image classification. IEEE Trans. Syst. Man. Cybern. 3, 610 (2007). https://doi.org/10.1109/tsmc.1973.4309314

    Article  Google Scholar 

  28. Xu D, Kurani AS, Furst JD, Raicu DS. Run-length Encoding for Volumetric Texture. In: 4th Iated Int Conf Vis Imaging, IMAGE Process (2004).

  29. G. Thibault, B. Fertil, C. Navarro, S. Pereira, P. Cau, N. Levy et al., Texture indexes and gray level size zone matrix application to cell nuclei classification. Pattern Recognit. Inf. Process. (2009). https://doi.org/10.1142/S0218001413570024

    Article  Google Scholar 

  30. H. Lee, D.S. Lee, H. Kang, B.N. Kim, M.K. Chung, Sparse brain network recovery under compressed sensing. IEEE Trans. Med. Imaging. 30, 1154 (2011). https://doi.org/10.1109/TMI.2011.2140380

    Article  Google Scholar 

  31. W. Zhao, Y. Xu, Z. Yang, Y. Sun, C. Li, L. Jin et al., Development and validation of a radiomics nomogram for identifying invasiveness of pulmonary adenocarcinomas appearing as subcentimeter ground-glass opacity nodules. Eur. J. Radiol. 112, 161 (2019). https://doi.org/10.1016/j.ejrad.2019.01.021

    Article  Google Scholar 

  32. R. Tibshirani, The lasso method for variable selection in the Cox model. Stat. Med. 16, 385 (1997)

    Article  Google Scholar 

  33. M. Kirienko, L. Cozzi, L. Antunovic, L. Lozza, A. Fogliata, E. Voulaz et al., Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery. Eur. J. Nucl. Med. Mol. Imaging. 45, 207 (2018). https://doi.org/10.1007/s00259-017-3837-7

    Article  Google Scholar 

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Funding

This work was supported by a Grant from the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIT) (Nos. 2021R1F1A1050903, 2021M2E7A2079183) and the Korea Institute of Radiological and Medical Sciences (KIRAMS) funded by the Ministry of Science and ICT, Republic of Korea (50536–2021).

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Authors and Affiliations

  1. Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, 81, Irwon-Ro Gangnam-Gu, Seoul, 06351, Republic of Korea

    Heesoon Sheen

  2. Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1, Yonsei-ro, Seodaemun-gu, Seoul, 03788, Korea

    Han-Back Shin

  3. RI Translational Research Team, Division of Applied RI, Korea Institute of Radiological and Medical Sciences, 75 Nowon-ro, Nowon-gu, Seoul, 01812, Republic of Korea

    Jung Young Kim

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HS, H-BS, and JYK contributed equally to this work. Writing—original draft: HS.

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Sheen, H., Shin, HB. & Kim, J.Y. Comparison of radiomics prediction models for lung metastases according to four semiautomatic segmentation methods in soft-tissue sarcomas of the extremities. J. Korean Phys. Soc. 80, 247–256 (2022). https://doi.org/10.1007/s40042-021-00360-3

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