Artificial intelligence algorithms aimed at characterizing or detecting prostate cancer on MRI: How accurate are they when tested on independent cohorts? – A systematic review

Diagnostic and interventional imaging - Tập 104 - Trang 221-234 - 2023
Olivier Rouvière1,2,3, Tristan Jaouen3, Pierre Baseilhac1, Mohammed Lamine Benomar3,4, Raphael Escande1, Sébastien Crouzet2,3,5, Rémi Souchon3
1Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon 69003, France
2Université Lyon 1, Faculté de médecine Lyon Est, Lyon 69003, France
3LabTAU, INSERM, U1032, Lyon 69003, France
4University of Ain Temouchent, Faculty of Science and Technology, Algeria
5Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Urology, Lyon 69003, France

Tài liệu tham khảo

Drost, 2019, Prostate MRI, with or without MRI-targeted biopsy, and systematic biopsy for detecting prostate cancer, Cochrane Database Syst Rev, 4 Mottet, 2021, EAU-EANM-ESTRO-ESUR-SIOG guidelines on prostate cancer-2020 update. Part 1: Screening, diagnosis, and local treatment with curative intent, Eur Urol, 79, 243, 10.1016/j.eururo.2020.09.042 Zhang-Yin, 2022, Diagnosis of early biochemical recurrence after radical prostatectomy or radiation therapy in patients with prostate cancer: State of the art, Diagn Interv Imaging, 103, 191, 10.1016/j.diii.2022.02.005 Richenberg, 2019, The primacy of multiparametric MRI in men with suspected prostate cancer, Eur Radiol, 29, 6940, 10.1007/s00330-019-06166-z Bluemke, 2020, Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers-from the Radiology editorial board, Radiology, 294, 487, 10.1148/radiol.2019192515 Nakaura, 2020, A primer for understanding radiology articles about machine learning and deep learning, Diagn Interv Imaging, 101, 765, 10.1016/j.diii.2020.10.001 Schwier, 2019, Repeatability of multiparametric prostate MRI radiomics features, Sci Rep, 9, 9441, 10.1038/s41598-019-45766-z Penzkofer, 2021, ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging, Eur Radiol, 31, 9567, 10.1007/s00330-021-08021-6 Merisaari, 2020, Repeatability of radiomics and machine learning for DWI: short-term repeatability study of 112 patients with prostate cancer, Magn Reson Med, 83, 2293, 10.1002/mrm.28058 Chirra, 2019, Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI, J Med Imaging, 6, 10.1117/1.JMI.6.2.024502 Lemaitre, 2015, Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: a review, Comput Biol Med, 60, 8, 10.1016/j.compbiomed.2015.02.009 Twilt, 2021, Artificial Intelligence based algorithms for prostate cancer classification and detection on magnetic resonance imaging: a Narrative Review, Diagnostics, 11 Syer, 2021, Artificial intelligence compared to radiologists for the initial diagnosis of prostate cancer on magnetic resonance imaging: a systematic review and recommendations for future studies, Cancers (Basel), 13, 3318, 10.3390/cancers13133318 Sushentsev, 2022, Comparative performance of fully-automated and semi-automated artificial intelligence methods for the detection of clinically significant prostate cancer on MRI: a systematic review, Insights Imaging, 13, 59, 10.1186/s13244-022-01199-3 Castillo, 2020, Automated classification of significant prostate cancer on MRI: a systematic review on the performance of machine Learning applications, Cancers, 12 Salameh, 2020, Preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA): explanation, elaboration, and checklist, BMJ, 370, m2632, 10.1136/bmj.m2632 QUADAS-2. Bristol Medical School: Population Health Sciences, University of Bristol; Available from: https://www. bristol. ac. uk/ population-health-sciences/ projects/ quadas/ quadas- 2. Bonekamp, 2018, Radiomic machine learning for characterization of prostate lesions with MRI: comparison to ADC Values, Radiology, 289, 128, 10.1148/radiol.2018173064 Dinh, 2018, Characterization of prostate cancer with Gleason score of at least 7 by using quantitative multiparametric MR imaging: validation of a computer-aided diagnosis system in patients referred for prostate biopsy, Radiology, 287, 525, 10.1148/radiol.2017171265 Dikaios, 2019, Multi-parametric MRI zone-specific diagnostic model performance compared with experienced radiologists for detection of prostate cancer, Eur Radiol, 29, 4150, 10.1007/s00330-018-5799-y Transin, 2019, Computer-aided diagnosis system for characterizing ISUP grade>/=2 prostate cancers at multiparametric MRI: a cross-vendor evaluation, Diagn Interv Imaging, 100, 801, 10.1016/j.diii.2019.06.012 Hiremath, 2021, An integrated nomogram combining deep learning, Prostate Imaging-Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study, Lancet Digit Health, 3, e445, 10.1016/S2589-7500(21)00082-0 Ji, 2021, Bi-parametric magnetic resonance imaging based radiomics for the identification of benign and malignant prostate lesions: cross-vendor validation, Phys Eng Sci Med, 44, 745, 10.1007/s13246-021-01022-1 Peng, 2021, Can machine learning-based analysis of multiparameter MRI and clinical parameters improve the performance of clinically significant prostate cancer diagnosis?, Int J Comput Assist Radiol Surg, 16, 2235, 10.1007/s11548-021-02507-w Montoya Perez, 2022, Detection of prostate cancer using biparametric prostate MRI, radiomics, and kallikreins: a retrospective multicenter study of men with a clinical suspicion of prostate cancer, J Magn Reson Imaging, 55, 465, 10.1002/jmri.27811 Jing, 2022, Prediction of clinically significant prostate cancer with a multimodal MRI-based radiomics nomogram, Front Oncol, 12, 10.3389/fonc.2022.918830 Hu, 2022, Adversarial training for prostate cancer classification using magnetic resonance imaging, Quant Imaging Med Surg, 12, 3276, 10.21037/qims-21-1089 Li, 2022, Development and validation of a radiomics nomogram for predicting clinically significant prostate cancer in PI-RADS 3 lesions, Front Oncol, 11, 10.3389/fonc.2021.825429 Zhang, 2020, Development of a novel, multi-parametric, MRI-based radiomic nomogram for differentiating between clinically significant and insignificant prostate cancer, Front Oncol, 10, 888, 10.3389/fonc.2020.00888 Bleker, 2021, Single-center versus multi-center biparametric MRI radiomics approach for clinically significant peripheral zone prostate cancer, Insights Imaging, 12, 150, 10.1186/s13244-021-01099-y Castillo, 2021, A multi-center, multi-vendor study to evaluate the generalizability of a radiomics model for classifying prostate cancer: high grade vs. low grade, Diagnostics, 11, 369, 10.3390/diagnostics11020369 Yang, 2022, Radiomic machine learning and external validation based on 3.0 T mpMRI for prediction of intraductal carcinoma of prostate with different proportion, Front Oncol, 12 Campa, 2019, Improvement of prostate cancer detection combining a computer-aided diagnostic system with TRUS-MRI targeted biopsy, Abdom Radiol, 44, 264, 10.1007/s00261-018-1712-z Cao, 2021, Performance of deep learning and genitourinary radiologists in detection of prostate cancer using 3-T multiparametric magnetic resonance imaging, J Magn Reson Imaging, 54, 474, 10.1002/jmri.27595 Netzer, 2021, Fully automatic deep learning in bi-institutional prostate magnetic resonance imaging: effects of cohort size and heterogeneity, Invest Radiol, 56, 799, 10.1097/RLI.0000000000000791 Saha, 2021, End-to-end prostate cancer detection in bpMRI via 3D CNNs: effects of attention mechanisms, clinical priori and decoupled false positive reduction, Med Image Anal, 73, 10.1016/j.media.2021.102155 Schelb, 2021, Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment, Eur Radiol, 31, 302, 10.1007/s00330-020-07086-z Youn, 2021, Detection and PI-RADS classification of focal lesions in prostate MRI: performance comparison between a deep learning-based algorithm and radiologists with various levels of experience, Eur J Radiol, 142, 10.1016/j.ejrad.2021.109894 Bhattacharya, 2022, Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA: an MRI-pathology correlation and deep learning framework, Med Image Anal, 75, 10.1016/j.media.2021.102288 Dai, 2020, Segmentation of the prostatic gland and the intraprostatic lesions on multiparametic magnetic resonance imaging using mask region-based convolutional neural networks, Adv Radiat Oncol, 5, 473, 10.1016/j.adro.2020.01.005 Adams, 2022, Prostate158: an expert-annotated 3T MRI dataset and algorithm for prostate cancer detection, Comput Biol Med, 148, 10.1016/j.compbiomed.2022.105817 Gaur, 2018, Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? A multi-center, multi-reader investigation, Oncotarget, 9, 33804, 10.18632/oncotarget.26100 Mehralivand, 2020, Multicenter multireader evaluation of an artificial intelligence-based attention mapping system for the detection of prostate cancer with multiparametric MRI, AJR Am J Roentgenol, 215, 903, 10.2214/AJR.19.22573 Ferriero, 2021, Fusion US/MRI prostate biopsy using a computer aided diagnostic (CAD) system, Minerva Urol Nephrol, 73, 616, 10.23736/S2724-6051.20.04008-4 Li, 2022, Deep learning in prostate cancer diagnosis using multiparametric magnetic resonance imaging with whole-mount histopathology referenced delineations, Front Med, 8, 10.3389/fmed.2021.810995 Duran, 2022, ProstAttention-Net: a deep attention model for prostate cancer segmentation by aggressiveness in MRI, Med Image Anal, 77, 10.1016/j.media.2021.102347 Zhang, 2022, Pseudoprospective paraclinical Interaction of radiology residents with a deep learning system for prostate cancer detection: experience, performance, and Identification of the need for intermittent recalibration, Invest Radiol, 57, 601, 10.1097/RLI.0000000000000878 Labus, 2022, A concurrent, deep learning-based computer-aided detection system for prostate multiparametric MRI: a performance study involving experienced and less-experienced radiologists, Eur Radiol, 10.1007/s00330-022-08978-y Varghese, 2019, Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images, Sci Rep, 9, 1570, 10.1038/s41598-018-38381-x Mehta, 2021, Computer-aided diagnosis of prostate cancer using multiparametric MRI and clinical features: a patient-level classification framework, Med Image Anal, 73, 10.1016/j.media.2021.102153 Shao, 2020, Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy, Theranostics, 10, 10200, 10.7150/thno.48706 Castillo, 2021, Classification of clinically significant prostate cancer on multi-parametric MRI: a validation study comparing deep learning and radiomics, Cancers, 14, 12, 10.3390/cancers14010012 Bleker, 2020, Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer, Eur Radiol, 30, 1313, 10.1007/s00330-019-06488-y Cao, 2019, Joint prostate cancer detection and Gleason score prediction in mpMRI via FocalNet, IEEE Trans Med Imaging, 38, 2496, 10.1109/TMI.2019.2901928 Mongan, 2020, Jr. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers, Radiol Artif Intell, 2, 10.1148/ryai.2020200029 Lambin, 2017, Radiomics: the bridge between medical imaging and personalized medicine, Nat Rev Clin Oncol, 14, 749, 10.1038/nrclinonc.2017.141 Penzkofer, 2022, Assessing the clinical performance of artificial intelligence software for prostate cancer detection on MRI, Eur Radiol, 32, 2221, 10.1007/s00330-022-08609-6 Schelb, 2021, Comparison of prostate MRI lesion segmentation agreement between multiple radiologists and a fully automatic deep learning system, Rofo, 193, 559, 10.1055/a-1290-8070 Armato, 2018, PROSTATEx challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images, J Med Imaging, 5, 10.1117/1.JMI.5.4.044501 Sunoqrot, 2022, Artificial intelligence for prostate MRI: open datasets, available applications, and grand challenges, Eur Radiol Exp, 6, 35, 10.1186/s41747-022-00288-8 van, 2011, Computer-aided diagnosis: how to move from the laboratory to the clinic, Radiology, 261, 719, 10.1148/radiol.11091710 Surowiecki, 2004 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 Winkel, 2020, High spatiotemporal resolution dynamic contrast-enhanced MRI improves the image-based discrimination of histopathology risk groups of peripheral zone prostate cancer: a supervised machine learning approach, Eur Radiol, 30, 4828, 10.1007/s00330-020-06849-y Aldoj, 2020, Semi-automatic classification of prostate cancer on multi-parametric MR imaging using a multi-channel 3D convolutional neural network, Eur Radiol, 30, 1243, 10.1007/s00330-019-06417-z Wang, 2020, Selecting proper combination of mpMRI sequences for prostate cancer classification using multi-input convolutional neuronal network, Phys Med, 80, 92, 10.1016/j.ejmp.2020.10.013 Breit, 2021, Revisiting DCE-MRI: classification of prostate tissue using descriptive signal enhancement features derived from DCE-MRI acquisition with high spatiotemporal resolution, Invest Radiol, 56, 553, 10.1097/RLI.0000000000000772 Pellicer-Valero, 2022, Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of prostate cancer in multiparametric magnetic resonance images, Sci Rep, 12, 2975, 10.1038/s41598-022-06730-6 Brancato, 2021, Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions, Sci Rep, 11, 643, 10.1038/s41598-020-80749-5 Zong, 2020, A deep dive into understanding tumor foci classification using multiparametric MRI based on convolutional neural network, Med Phys, 47, 4077, 10.1002/mp.14255 Chen, 2021, MRI-based radiomics compared with the PI-RADS V2.1 in the prediction of clinically significant prostate cancer: biparametric vs multiparametric MRI, Front Oncol, 11 Belue, 2022, Current status of biparametric MRI in prostate cancer diagnosis: literature analysis, Life, 12, 804, 10.3390/life12060804 Schoots, 2021, PI-RADS Committee Position on MRI without contrast medium in biopsy-naive men with suspected prostate cancer: narrative review, AJR Am J Roentgenol, 216, 3, 10.2214/AJR.20.24268 Cornud, 2020, Bi-parametric prostate MRI before biopsy: yes, but only if you deserve it, Diagn Interv Imaging, 101, 191, 10.1016/j.diii.2020.03.001 Han, 2020, MRI combined with PSA density in detecting clinically significant prostate cancer in patients with PSA serum levels of 4 approximately 10ng/mL: biparametric versus multiparametric MRI, Diagn Interv Imaging, 101, 235, 10.1016/j.diii.2020.01.014 Hotker, 2021, Improving workflow in prostate MRI: AI-based decision-making on biparametric or multiparametric MRI, Insights Imaging, 12, 112, 10.1186/s13244-021-01058-7