An MRI-Based Deep Transfer Learning Radiomics Nomogram to Predict Ki-67 Proliferation Index of Meningioma

Chongfeng Duan1, Dapeng Hao1, Jiufa Cui1, Gang Wang1, Wenjian Xu1, Nan Li2, Xuejun Liu1
1Department of Radiology, The Affiliated Hospital of Qingdao University, No. 16, Jiang Su Road, Shinan District, Qingdao City, Shandong Province, China
2Department of Information Management, The Affiliated Hospital of Qingdao University, No. 16, Jiang Su Road, Shinan District, Qingdao City, Shandong Province, China

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Louis DN, Perry A, Wesseling P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol. 2021;23(8):1231-1251. https://doi.org/10.1093/neuonc/noab106

Ostrom QT, Gittleman H, Truitt G, Boscia A, Kruchko C, Barnholtz-Sloan JS. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2011–2015 [published correction appears in Neuro Oncol. 2018 Nov 17;:null]. Neuro Oncol. 2018;20(suppl_4):iv1-iv86. https://doi.org/10.1093/neuonc/noy131

Goldbrunner R, Stavrinou P, Jenkinson MD, et al. EANO guideline on the diagnosis and management of meningiomas. Neuro Oncol. 2021;23(11):1821-1834. https://doi.org/10.1093/neuonc/noab150

Rogers L, Barani I, Chamberlain M, et al. Meningiomas: knowledge base, treatment outcomes, and uncertainties. A RANO review. J Neurosurg. 2015;122(1):4-23. https://doi.org/10.3171/2014.7.JNS131644

Li D, Jiang P, Xu S, et al. Survival impacts of extent of resection and adjuvant radiotherapy for the modern management of high-grade meningiomas. J Neurooncol. 2019;145(1):125-134. https://doi.org/10.1007/s11060-019-03278-w

Black PM, Villavicencio AT, Rhouddou C, Loeffler JS. Aggressive surgery and focal radiation in the management of meningiomas of the skull base: preservation of function with maintenance of local control. Acta Neurochir (Wien). 2001;143(6):555-562. https://doi.org/10.1007/s007010170060

Martin B, Paesmans M, Mascaux C, et al. Ki-67 expression and patients survival in lung cancer: systematic review of the literature with meta-analysis. Br J Cancer. 2004;91(12):2018-2025.

Berlin A, Castro-Mesta JF, Rodriguez-Romo L, et al. Prognostic role of Ki-67 score in localized prostate cancer: a systematic review and meta-analysis. Urol Oncol.2017; 35(8):499-506.

Kim MS, Kim KH, Lee EH, et al. Results of immunohistochemical staining for cell cycle regulators predict the recurrence of atypical meningiomas. J Neurosurg. 2014; 121(5):1189-1200.

Oya S, Kawai K, Nakatomi H, Saito N. Significance of Simpson grading system in modern meningioma surgery: integration of the grade with MIB-1 labeling index as a key to predict the recurrence of WHO grade I meningiomas. J Neurosurg. 2012; 117(1):121-128.

Liu N, Song SY, Jiang JB, Wang TJ, Yan CX. The prognostic role of Ki-67/MIB-1 in meningioma: a systematic review with meta-analysis. Medicine.2020; 99(9):e18644.

Mirian C, Skyrman S, Bartek J Jr, et al. The Ki-67 Proliferation Index as a Marker of Time to Recurrence in Intracranial Meningioma. Neurosurgery. 2020;87(6):1289-1298. https://doi.org/10.1093/neuros/nyaa226

Lu Y, Liu L, Luan S, Xiong J, Geng D, Yin B. The diagnostic value of texture analysis in predicting WHO grades of meningiomas based on ADC maps: an attempt using decision tree and decision forest. Eur Radiol 2019; 29: 1318–28. https://doi.org/10.1007/s00330-018-5632-7

Park YW, Oh J, You SC, et al. Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging. Eur Radiol. 2019;29(8):4068-4076. https://doi.org/10.1007/s00330-018-5830-3

Ke C, Chen H, Lv X, et al. Differentiation Between Benign and Nonbenign Meningiomas by Using Texture Analysis From Multiparametric MRI. J Magn Reson Imaging. 2020;51(6):1810-1820. https://doi.org/10.1002/jmri.26976

Yan PF, Yan L, Hu TT, et al. The Potential Value of Preoperative MRI Texture and Shape Analysis in Grading Meningiomas: A Preliminary Investigation. Transl Oncol. 2017;10(4):570-577. https://doi.org/10.1016/j.tranon.2017.04.006

Duan C, Zhou X, Wang J, et al. A radiomics nomogram for predicting the meningioma grade based on enhanced T1WI images. Br J Radiol. 2022;95(1137):20220141. https://doi.org/10.1259/bjr.20220141

Duan CF, Li N, Li Y, et al. Comparison of different radiomic models based on enhanced T1-weighted images to predict the meningioma grade. Clin Radiol. 2022;77(4):e302-e307. https://doi.org/10.1016/j.crad.2022.01.039

Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016; 278: 563–77. https://doi.org/10.1148/radiol.2015151169

Kumar V, Gu Y, Basu S, et al. Radiomics: the process and the challenges. Magn Reson Imaging. 2012;30(9):1234-1248. https://doi.org/10.1016/j.mri.2012.06.010

Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441-446. https://doi.org/10.1016/j.ejca.2011.11.036

Khanna O, Fathi Kazerooni A, Farrell CJ, et al. Machine Learning Using Multiparametric Magnetic Resonance Imaging Radiomic Feature Analysis to Predict Ki-67 in World Health Organization Grade I Meningiomas. Neurosurgery. 2021;89(5):928-936. https://doi.org/10.1093/neuros/nyab307

Zhao Y, Xu J, Chen B, Cao L, Chen C. Efficient Prediction of Ki-67 Proliferation Index in Meningiomas on MRI: From Traditional Radiological Findings to a Machine Learning Approach. Cancers (Basel). 2022;14(15):3637. Published 2022 Jul 26. https://doi.org/10.3390/cancers14153637

Tustison NJ, Avants BB, Cook PA et al (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29:1310–1320

Depeursinge A, Foncubierta-Rodriguez A, Van De Ville D, Muller H (2014) Three-dimensional solid texture analysis in biomedical imaging: review and opportunities. Med Image Anal 18:176–196

Bozdağ M, Er A, Ekmekçi S. Association of apparent diffusion coefficient with Ki-67 proliferation index, progesterone-receptor status and various histopathological parameters, and its utility in predicting the high grade in meningiomas. Acta Radiol. 2021;62(3):401-413. https://doi.org/10.1177/0284185120922142

Tang Y, Dundamadappa SK, Thangasamy S, et al. Correlation of apparent diffusion coefficient with Ki-67 proliferation index in grading meningioma. AJR Am J Roentgenol. 2014;202(6):1303-1308. https://doi.org/10.2214/AJR.13.11637

Baskan O, Silav G, Bolukbasi FH, Canoz O, Geyik S, Elmaci I. Relation of apparent diffusion coefficient with Ki-67 proliferation index in meningiomas. Br J Radiol. 2016;89(1057):20140842. https://doi.org/10.1259/bjr.20140842

Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749-762. https://doi.org/10.1038/nrclinonc.2017.141

Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach [published correction appears in Nat Commun. 2014;5:4644. Cavalho, Sara [corrected to Carvalho, Sara]]. Nat Commun. 2014;5:4006. Published 2014 Jun 3. https://doi.org/10.1038/ncomms5006

Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60-88. https://doi.org/10.1016/j.media.2017.07.005