Multi-resolution super learner for voxel-wise classification of prostate cancer using multi-parametric MRI

References

• C. Agostinelli and L. Greco, Weighted likelihood in Bayesian inference, in Proceedings of the 46th Scientific Meeting of the Italian Statistical Society, Roma, Italy, 2012.
   Google Scholar
• A. Algohary, S. Viswanath, R. Shiradkar, S. Ghose, S. Pahwa, D. Moses, I. Jambor, R. Shnier, M. Böhm, A.M. Haynes, P. Brenner, W. Delprado, J. Thompson, M. Pulbrock, A.S. Purysko, S. Verma, L. Ponsky, P. Stricker, and A. Madabhushi, Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings, J. Magn. Reson. Imaging 48 (2018), pp. 818–828.
   Web of Science ®Google Scholar
• American Cancer Society, Key statistics for prostate cancer. Available at https://www.cancer.org/cancer/prostate-cancer/about/key-statistics.html (2021). Accessed: 2021-02-15.
   Google Scholar
• Y. Artan, M.A. Haider, D.L. Langer, T.H. Van der Kwast, A.J. Evans, Y. Yang, M.N. Wernick, J. Trachtenberg, and I.S. Yetik, Prostate cancer localization with multispectral MRI using cost-sensitive support vector machines and conditional random fields, IEEE. Trans. Image. Process. 19 (2010), pp. 2444–2455.
   PubMed Web of Science ®Google Scholar
• T.Y. Chan, A.W. Partin, P.C. Walsh, and J.I. Epstein, Prognostic significance of gleason score 3 + 4 versus gleason score 4 + 3 tumor at radical prostatectomy, Urology 56 (2000), pp. 823–827.
   PubMed Web of Science ®Google Scholar
• C. De Vente, P. Vos, M. Hosseinzadeh, J. Pluim, and M. Veta, Deep learning regression for prostate cancer detection and grading in bi-parametric MRI, IEEE Trans. Biomed. Eng. 68 (2020), 374–383.
   Web of Science ®Google Scholar
• B. Delahunt, R.J. Miller, J.R. Srigley, A.J. Evans, and H. Samaratunga, Gleason grading: Past, present and future, Histopathology 60 (2012), pp. 75–86.
   PubMed Web of Science ®Google Scholar
• L. Dickinson, H.U. Ahmed, C. Allen, J.O. Barentsz, B. Carey, J.J. Futterer, S.W. Heijmink, P.J. Hoskin, A. Kirkham, A.R. Padhani, R. Persad, P. Puech, S. Punwani, A.S. Sohaib, B. Tombal, A. Villers, J. van der Meulen, and M. Emberton, Magnetic resonance imaging for the detection, localisation, and characterisation of prostate cancer: Recommendations from a European consensus meeting, Eur. Urol. 59 (2011), pp. 477–494.
   PubMed Web of Science ®Google Scholar
• J.I. Epstein, An update of the Gleason grading system, J. Urol. 183 (2010), pp. 433–440.
   PubMed Web of Science ®Google Scholar
• J.I. Epstein, W.C. Allsbrook Jr., M.B. Amin, L.L. Egevad, and ISUP Grading Committee, The 2005 international society of urological pathology (ISUP) consensus conference on Gleason grading of prostatic carcinoma, Am. J. Surg. Pathol. 29 (2005), pp. 1228–1242.
   PubMed Web of Science ®Google Scholar
• D. Fehr, H. Veeraraghavan, A. Wibmer, T. Gondo, K. Matsumoto, H.A. Vargas, E. Sala, H. Hricak, and J.O. Deasy, Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images, Proc. Natl. Acad. Sci. 112 (2015), pp. E6265–E6273.
   PubMed Web of Science ®Google Scholar
• B. Fei, Computer-aided diagnosis of prostate cancer with MRI, Curr. Opin. Biomed. Eng. 3 (2017), pp. 20–27.
   PubMedGoogle Scholar
• K. Garcia-Reyes, N.M. Passoni, M.L. Palmeri, C.R. Kauffman, K.R. Choudhury, T.J. Polascik, and R.T. Gupta, Detection of prostate cancer with multiparametric MRI (mpMRI): Effect of dedicated reader education on accuracy and confidence of index and anterior cancer diagnosis, Abdom. Imaging 40 (2015), pp. 134–142.
   PubMed Web of Science ®Google Scholar
• N. Gholizadeh, J. Simpson, S. Ramadan, J. Denham, P. Lau, S. Siddique, J. Dowling, J. Welsh, S. Chalup, and P.B. Greer, Voxel-based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI, J. Appl. Clin. Med. Phys. 21 (2020), pp. 179–191.
   PubMed Web of Science ®Google Scholar
• T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics, New York, 2009.
   Google Scholar
• J.V. Hegde, R.V. Mulkern, L.P. Panych, F.M. Fennessy, A. Fedorov, S.E. Maier, and C.M. Tempany, Multiparametric MRI of prostate cancer: An update on state-of-the-art techniques and their performance in detecting and localizing prostate cancer, J. Magn. Reson. Imaging 37 (2013),pp. 1035–1054.
   PubMed Web of Science ®Google Scholar
• F. Hu and J.V. Zidek, The weighted likelihood, Canad. J. Statist. 30 (2002), pp. 347–371.
   Web of Science ®Google Scholar
• C. Jensen, J. Carl, L. Boesen, N.C. Langkilde, and L.R. Østergaard, Assessment of prostate cancer prognostic Gleason grade group using zonal-specific features extracted from biparametric MRI using a KNN classifier, J. Appl. Clin. Med. Phys. 20 (2019), pp. 146–153.
   PubMed Web of Science ®Google Scholar
• J. Jin, L. Zhang, E. Leng, G.J. Metzger, and J.S. Koopmeiners, Detection of prostate cancer with multiparametric MRI utilizing the anatomic structure of the prostate, Stat. Med. 37 (2018),pp. 3214–3229.
   PubMed Web of Science ®Google Scholar
• J. Jin, L. Zhang, E. Leng, G.J. Metzger, and J.S. Koopmeiners, Bayesian spatial models for voxel-wise prostate cancer classification using multi-parametric MRI data, preprint (2020). Available at arXiv:2007.00816.
   Google Scholar
• C. Kalavagunta, X. Zhou, S.C. Schmechel, and G.J. Metzger, Registration of in vivo prostate MRI and pseudo-whole mount histology using local affine transformations guided by internal structures (latis), J. Magn. Reson. Imaging 41 (2015), pp. 1104–1114.
   PubMed Web of Science ®Google Scholar
• F. Khalvati, A. Wong, and M.A. Haider, Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models, BMC. Med. Imaging 15 (2015), p. 27.
   PubMed Web of Science ®Google Scholar
• R. Koenker and K.F. Hallock, Quantile regression, J. Econ. Perspect. 15 (2001), pp. 143–156.
   Web of Science ®Google Scholar
• G. Lemaître, R. Martí, J. Freixenet, J.C. Vilanova, P.M. Walker, and F. Meriaudeau, Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review, Comput. Biol. Med. 60 (2015), pp. 8–31.
   PubMed Web of Science ®Google Scholar
• G.P. Liney, A.J. Knowles, D.J. Manton, L.W. Turnbull, S.J. Blackband, and A. Horsman, Comparison of conventional single echo and multi-echo sequences with a fast spin-echo sequence for quantitative T2 mapping: Application to the prostate, J. Magn. Reson. Imaging 6 (1996),pp. 603–607.
   PubMed Web of Science ®Google Scholar
• G. Litjens, O. Debats, J. Barentsz, N. Karssemeijer, and H. Huisman, Computer-aided detection of prostate cancer in MRI, IEEE. Trans. Med. Imaging 33 (2014), pp. 1083–1092.
   PubMed Web of Science ®Google Scholar
• A. Matoso and J.I. Epstein, Defining clinically significant prostate cancer on the basis of pathological findings, Histopathology 74 (2019), pp. 135–145.
   PubMed Web of Science ®Google Scholar
• L. Matulewicz, J.F. Jansen, L. Bokacheva, H.A. Vargas, O. Akin, S.W. Fine, A. Shukla-Dave, J.A. Eastham, H. Hricak, J.A. Koutcher, and K.L. Zakian, Anatomic segmentation improves prostate cancer detection with artificial neural networks analysis of 1H magnetic resonance spectroscopic imaging, J. Magn. Reson. Imaging 40 (2014), pp. 1414–1421.
   PubMed Web of Science ®Google Scholar
• S. Mazzetti, M. De Luca, C. Bracco, A. Vignati, V. Giannini, M. Stasi, F. Russo, E. Armando, S. Agliozzo, and D. Regge, A CAD system based on multi-parametric analysis for cancer prostate detection on DCE-MRI, Med. Imaging 2011 Comput.-Aid. Diag. 7963 (2011), p. 79633Q.
   Google Scholar
• S. Mazzetti, V. Giannini, F. Russo, and D. Regge, Computer-aided diagnosis of prostate cancer using multi-parametric MRI: Comparison between PUN and Tofts models, Phys. Med. Biol. 63 (2018), p. 095004.
   PubMed Web of Science ®Google Scholar
• P. Mehta, M. Antonelli, H.U. Ahmed, M. Emberton, S. Punwani, and S. Ourselin, Computer-aided diagnosis of prostate cancer using multiparametric MRI and clinical features: A patient-level classification framework, Med. Image. Anal. 73 (2021), p. 102153.
   PubMed Web of Science ®Google Scholar
• G.J. Metzger, C. Kalavagunta, B. Spilseth, P.J. Bolan, X. Li, D. Hutter, J.W. Nam, A.D. Johnson, J.C. Henriksen, L. Moench, B. Konety, C.A. Warlick, S.C. Schmechel, and J.S. Koopmeiners, Detection of prostate cancer: Quantitative multiparametric MR imaging models developed using registered correlative histopathology, Radiology 279 (2016), pp. 805–816.
   PubMed Web of Science ®Google Scholar
• E.A. Nadaraya, On estimating regression, Theory Probab. Appl. 9 (1964), pp. 141–142.
   Google Scholar
• E. Niaf, O. Rouvière, F. Mège-Lechevallier, F. Bratan, and C. Lartizien, Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI, Phys. Med. Biol. 57 (2012), p. 3833.
   PubMed Web of Science ®Google Scholar
• S. Parfait, P.M. Walker, G. Créhange, X. Tizon, and J. Miteran, Classification of prostate magnetic resonance spectra using support vector machine, Biomed. Signal Process. Control 7 (2012),pp. 499–508.
   Web of Science ®Google Scholar
• Y. Peng, Y. Jiang, T. Antic, M.L. Giger, S. Eggener, and A. Oto, A study of T2-weighted MR image texture features and diffusion-weighted MR image features for computer-aided diagnosis of prostate cancer, in Proceedings of the SPIE, Vol. 8670, SPIE, Orlando, FL, 2013, p. 86701H.
   Google Scholar
• P. Puech, N. Betrouni, N. Makni, A.S. Dewalle, A. Villers, and L. Lemaitre, Computer-assisted diagnosis of prostate cancer using DCE-MRI data: Design, implementation and preliminary results, Int. J. Comput. Assist. Radiol. Surg. 4 (2009), pp. 1–10.
   PubMed Web of Science ®Google Scholar
• V. Shah, B. Turkbey, H. Mani, Y. Pang, T. Pohida, M.J. Merino, P.A. Pinto, P.L. Choyke, and M. Bernardo, Decision support system for localizing prostate cancer based on multiparametric magnetic resonance imaging, Med. Phys. 39 (2012), pp. 4093–4103.
   PubMed Web of Science ®Google Scholar
• A. Stabile, F. Giganti, A.B. Rosenkrantz, S.S. Taneja, G. Villeirs, I.S. Gill, C. Allen, M. Emberton, C.M. Moore, and V. Kasivisvanathan, Multiparametric MRI for prostate cancer diagnosis: Current status and future directions, Nat. Rev. Urol. 17 (2020), pp. 41–61.
   PubMed Web of Science ®Google Scholar
• A. Tharwat, Linear vs. quadratic discriminant analysis classifier: A tutorial, Int. J. Appl. Patt. Recogn. 3 (2016), pp. 145–180.
   Web of Science ®Google Scholar
• J. Toivonen, I. Montoya Perez, P. Movahedi, H. Merisaari, M. Pesola, P. Taimen, P.J. Boström, J. Pohjankukka, A. Kiviniemi, T. Pahikkala, H.J. Aronen, and I. Jambor, Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization, PLoS ONE 14 (2019), pp. e0217702.
   PubMed Web of Science ®Google Scholar
• R. Trigui, J. Mitéran, P.M. Walker, L. Sellami, and A.B. Hamida, Automatic classification and localization of prostate cancer using multi-parametric MRI/MRS, Biomed. Signal Process. Control 31 (2017), pp. 189–198.
   Web of Science ®Google Scholar
• M.J. Van der Laan, E.C. Polley, and A.E. Hubbard, Super learner, Stat. Appl. Genet. Mol. Biol. 6 (2007).
   Web of Science ®Google Scholar
• S.E. Viswanath, P.V. Chirra, M.C. Yim, N.M. Rofsky, A.S. Purysko, M.A. Rosen, B.N. Bloch, and A. Madabhushi, Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: A multi-site study, BMC. Med. Imaging 19 (2019), pp. 1–12.
   PubMed Web of Science ®Google Scholar
• G.S. Watson, Smooth regression analysis, Sankhyā 26 (1964), pp. 359–372.
   Google Scholar
• Y. Zhu, L. Wang, M. Liu, C. Qian, A. Yousuf, A. Oto, and D. Shen, MRI-based prostate cancer detection with high-level representation and hierarchical classification, Med. Phys. 44 (2017),pp. 1028–1039.
   PubMed Web of Science ®Google Scholar