Clinically significant prostate cancer detection on MRI: A radiomic shape features study

European Journal of Radiology - Tập 116 - Trang 144-149 - 2019
Renato Cuocolo1, Arnaldo Stanzione1, Andrea Ponsiglione1, Valeria Romeo1, Francesco Verde1, Massimiliano Creta2, Roberto La Rocca2, Nicola Longo2, Leonardo Pace3, Massimo Imbriaco1
1Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples, Italy
2Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples Federico II, Naples, Italy
3Department of Medicine and Surgery, University of Salerno, Salerno, Italy

Tài liệu tham khảo

Siegel, 2019, Cancer statistics, 2019, CA. Cancer J. Clin., 69, 7, 10.3322/caac.21551 Loeb, 2014, Overdiagnosis and overtreatment of prostate cancer, Eur. Urol., 65, 1046, 10.1016/j.eururo.2013.12.062 Bell, 2015, Prevalence of incidental prostate cancer: a systematic review of autopsy studies, Int. J. Cancer, 137, 1749, 10.1002/ijc.29538 Caverly, 2016, Presentation of benefits and harms in US cancer screening and prevention guidelines: systematic review, J. Natl. Cancer Inst., 108, 1, 10.1093/jnci/djv436 Mottet, 2017, EAU-ESTRO-SIOG guidelines on prostate cancer. Part 1: screening, diagnosis, and local treatment with curative intent, Eur. Urol., 71, 618, 10.1016/j.eururo.2016.08.003 Stark, 2009, Gleason score and lethal prostate cancer: does 3 + 4 = 4 + 3?, J. Clin. Oncol., 27, 3459, 10.1200/JCO.2008.20.4669 Ahmed, 2017, Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study, Lancet, 389, 815, 10.1016/S0140-6736(16)32401-1 Barentsz, 2016, Synopsis of the PI-RADS v2 guidelines for multiparametric prostate magnetic resonance imaging and recommendations for use, Eur. Urol., 69, 41, 10.1016/j.eururo.2015.08.038 Schouten, 2017, Why and where do We miss significant prostate cancer with multi-parametric magnetic resonance imaging followed by magnetic resonance-guided and transrectal ultrasound-guided biopsy in biopsy-naïve men?, Eur. Urol., 71, 896, 10.1016/j.eururo.2016.12.006 Lee, 2018, Combined analysis of biparametric MRI and prostate-specific antigen density: role in the prebiopsy diagnosis of gleason score 7 or Greater prostate cancer, Am. J. Roentgenol., 211, W166, 10.2214/AJR.17.19253 Yanai, 2018, Evaluation of prostate‑specific antigen density in the diagnosis of prostate cancer combined with magnetic resonance imaging before biopsy in men aged 70 years and older with elevated PSA, Mol. Clin. Oncol., 656 Cuocolo, 2018, PSA-density does not improve bi-parametric prostate MR detection of prostate cancer in a biopsy naïve patient population, Eur. J. Radiol., 104, 64, 10.1016/j.ejrad.2018.05.004 Donati, 2014, Prostate cancer aggressiveness: assessment with whole-lesion histogram analysis of the apparent diffusion coefficient, Radiology, 271, 143, 10.1148/radiol.13130973 Tamada, 2017, Prostate cancer: diffusion-weighted MR imaging for detection and assessment of aggressiveness—comparison between conventional and kurtosis models, Radiology, 284, 100, 10.1148/radiol.2017162321 Gillies, 2016, Radiomics: images are more than pictures, they are data, Radiology, 278, 563, 10.1148/radiol.2015151169 Khalvati, 2015, Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models, BMC Med. Imaging, 15, 1, 10.1186/s12880-015-0069-9 Ginsburg, 2015, Novel PCA-VIP scheme for ranking MRI protocols and identifying computer-extracted MRI measurements associated with central gland and peripheral zone prostate tumors, J. Magn. Reson. Imaging, 41, 1383, 10.1002/jmri.24676 Litjens, 2016, Computer-extracted features can distinguish noncancerous confounding disease from prostatic adenocarcinoma at multiparametric MR imaging, Radiology, 278, 135, 10.1148/radiol.2015142856 Nketiah, 2017, T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results, Eur. Radiol., 27, 3050, 10.1007/s00330-016-4663-1 Stanzione, 2019, Detection of extraprostatic extension of cancer on biparametric MRI combining texture analysis and machine learning: preliminary results, Acad. Radiol., 1 Lorensen, 1987, Marching cubes: a high resolution 3D surface construction algorithm, ACM SIGGRAPH Comput. Graph., 10.1145/37402.37422 Krishna, 2019, Diagnosis of transition zone prostate cancer using T2-weighted (T2W) MRI: comparison of subjective features and quantitative shape analysis, Eur. Radiol., 29, 1133, 10.1007/s00330-018-5664-z Stamey, 1993, Localized prostate cancer. Relationship of tumor volume to clinical significance for treatment of prostate cancer, Cancer, 71, 933, 10.1002/1097-0142(19930201)71:3+<933::AID-CNCR2820711408>3.0.CO;2-L Yushkevich, 2006, User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability, Neuroimage, 31, 1116, 10.1016/j.neuroimage.2006.01.015 Van Griethuysen, 2017, Computational radiomics system to decode the radiographic phenotype, Cancer Res., 77, e104, 10.1158/0008-5472.CAN-17-0339 Hanley, 1983, A method of comparing the areas under receiver operating characteristic curves derived from the Same cases, Radiology, 148, 839, 10.1148/radiology.148.3.6878708 Choy, 2018, Current applications and future impact of machine learning in radiology, Radiology, 288, 318, 10.1148/radiol.2018171820 Sarkiss, 2019, Machine learning in neuro-oncology: can data analysis from 5346 patients change decision-making paradigms?, World Neurosurg., 124, 287, 10.1016/j.wneu.2019.01.046 Sollini, 2018, Texture analysis and machine learning to characterize suspected thyroid nodules and differentiated thyroid cancer: where do we stand?, Eur. J. Radiol., 99, 1, 10.1016/j.ejrad.2017.12.004 Romeo, 2018, Characterization of adrenal lesions on unenhanced MRI using texture analysis: a machine-learning approach, J. Magn. Reson. Imaging, 48, 198, 10.1002/jmri.25954 Imbriaco, 2018, Does texture analysis of MR images of breast tumors help predict response to treatment?, Radiology, 286, 421, 10.1148/radiol.2017172454 Krishna, 2018, Evaluation of MRI for diagnosis of extraprostatic extension in prostate cancer, J. Magn. Reson. Imaging, 47, 176, 10.1002/jmri.25729 Wibmer, 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, 10.1007/s00330-015-3701-8 Fehr, 2015, Automatic classification of prostate cancer gleason scores from multiparametric magnetic resonance images, Proc. Natl. Acad. Sci., 112, E6265, 10.1073/pnas.1505935112 Kaji, 2018, Diagnostic ability with abbreviated biparametric and full multiparametric prostate MR imaging: Is the use of PI-RADS version 2 appropriate for comparison?, Radiology, 286, 726, 10.1148/radiol.2017172265 Stanzione, 2018, Predicting prognosis with biparametric prostate imaging: one step at a time, Clin. Genitourin. Cancer, 16, e977, 10.1016/j.clgc.2018.04.006 Stanzione, 2018, Biparametric prostate MR imaging protocol: time to revise PI-RADS version 2?, Radiology, 287, 1082, 10.1148/radiol.2018180292 Stanzione, 2016, Biparametric 3T magentic resonance imaging for prostatic cancer detection in a biopsy-naïve patient population: a further improvement of PI-RADS v2?, Eur. J. Radiol., 85, 2269, 10.1016/j.ejrad.2016.10.009 Chatterjee, 2015, changes in epithelium, stroma, and lumen space correlate more strongly with gleason pattern and are stronger predictors of prostate ADC changes than cellularity metrics, Radiology, 277, 751, 10.1148/radiol.2015142414 Hoang Dinh, 2016, Quantitative analysis of prostate multiparametric MR images for detection of aggressive prostate cancer in the peripheral zone: a multiple imager study, Radiology, 280, 117, 10.1148/radiol.2016151406 Peng, 2014, Validation of quantitative analysis of multiparametric prostate MR images for prostate cancer detection and aggressiveness assessment: a Cross-imager study, Radiology, 271, 461, 10.1148/radiol.14131320 Bonekamp, 2018, Radiomic machine learning for characterization of prostate lesions with MRI: comparison to ADC values, Radiology, 289, 128, 10.1148/radiol.2018173064