Recent trends in AI applications for pelvic MRI: a comprehensive review.
Artificial intelligence
Bladder
Magnetic resonance imaging
Ovary
Prostate
Rectum
Uterus
Journal
La Radiologia medica
ISSN: 1826-6983
Titre abrégé: Radiol Med
Pays: Italy
ID NLM: 0177625
Informations de publication
Date de publication:
03 Aug 2024
03 Aug 2024
Historique:
received:
22
03
2024
accepted:
25
07
2024
medline:
4
8
2024
pubmed:
4
8
2024
entrez:
3
8
2024
Statut:
aheadofprint
Résumé
Magnetic resonance imaging (MRI) is an essential tool for evaluating pelvic disorders affecting the prostate, bladder, uterus, ovaries, and/or rectum. Since the diagnostic pathway of pelvic MRI can involve various complex procedures depending on the affected organ, the Reporting and Data System (RADS) is used to standardize image acquisition and interpretation. Artificial intelligence (AI), which encompasses machine learning and deep learning algorithms, has been integrated into both pelvic MRI and the RADS, particularly for prostate MRI. This review outlines recent developments in the use of AI in various stages of the pelvic MRI diagnostic pathway, including image acquisition, image reconstruction, organ and lesion segmentation, lesion detection and classification, and risk stratification, with special emphasis on recent trends in multi-center studies, which can help to improve the generalizability of AI.
Identifiants
pubmed: 39096356
doi: 10.1007/s11547-024-01861-4
pii: 10.1007/s11547-024-01861-4
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. Italian Society of Medical Radiology.
Références
Turkbey B, Rosenkrantz AB, Haider MA et al (2019) Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2. Eur Urol 76:340–351. https://doi.org/10.1016/j.eururo.2019.02.033
doi: 10.1016/j.eururo.2019.02.033
pubmed: 30898406
Panebianco V, Narumi Y, Altun E et al (2018) Multiparametric magnetic resonance imaging for bladder cancer: development of VI-RADS (vesical imaging-reporting and data system). Eur Urol 74:294–306. https://doi.org/10.1016/j.eururo.2018.04.029
doi: 10.1016/j.eururo.2018.04.029
pubmed: 29755006
pmcid: 6690492
Nougaret S, Horta M, Sala E et al (2019) Endometrial cancer MRI staging: updated guidelines of the european society of urogenital radiology. Eur Radiol 29:792–805. https://doi.org/10.1007/s00330-018-5515-y
doi: 10.1007/s00330-018-5515-y
pubmed: 29995239
Manganaro L, Lakhman Y, Bharwani N et al (2021) Staging, recurrence and follow-up of uterine cervical cancer using MRI: updated guidelines of the European society of urogenital radiology after revised FIGO staging 2018. Eur Radiol 31:7802–7816. https://doi.org/10.1007/s00330-020-07632-9
doi: 10.1007/s00330-020-07632-9
pubmed: 33852049
Kubik-Huch RA, Weston M, Nougaret S et al (2018) European society of urogenital radiology (ESUR) guidelines: MR imaging of leiomyomas. Eur Radiol 28:3125–3137. https://doi.org/10.1007/s00330-017-5157-5
doi: 10.1007/s00330-017-5157-5
pubmed: 29492599
pmcid: 6028852
Jha P, Pōder L, Bourgioti C et al (2020) Society of abdominal radiology (SAR) and European society of urogenital radiology (ESUR) joint consensus statement for MR imaging of placenta accreta spectrum disorders. Eur Radiol 30:2604–2615. https://doi.org/10.1007/s00330-019-06617-7
doi: 10.1007/s00330-019-06617-7
pubmed: 32040730
Sadowski EA, Thomassin-Naggara I, Rockall A et al (2022) O-RADS MRI risk stratification system: guide for assessing adnexal lesions from the ACR O-RADS committee. Radiology 303:35–47. https://doi.org/10.1148/radiol.204371
doi: 10.1148/radiol.204371
pubmed: 35040672
Beets-Tan RGH, Lambregts DMJ, Maas M et al (2018) Magnetic resonance imaging for clinical management of rectal cancer: updated recommendations from the 2016 European society of gastrointestinal and abdominal radiology (ESGAR) consensus meeting. Eur Radiol 28:1465–1475. https://doi.org/10.1007/s00330-017-5026-2
doi: 10.1007/s00330-017-5026-2
pubmed: 29043428
Shibuki S, Saida T, Hoshiai S et al (2024) Imaging findings in inflammatory disease of the genital organs. Jpn J Radiol. https://doi.org/10.1007/s11604-023-01518-8
doi: 10.1007/s11604-023-01518-8
pubmed: 38165529
pmcid: 10980613
Ohya A, Fujinaga Y (2022) Magnetic resonance imaging findings of cystic ovarian tumors: major differential diagnoses in five types frequently encountered in daily clinical practice. Jpn J Radiol 40:1213–1234. https://doi.org/10.1007/s11604-022-01321-x
doi: 10.1007/s11604-022-01321-x
pubmed: 35916971
pmcid: 9719891
Fujii S, Mukuda N, Ochiai R et al (2021) MR imaging findings of unusual leiomyoma and malignant uterine myometrial tumors: what the radiologist should know. Jpn J Radiol 39:527–539. https://doi.org/10.1007/s11604-021-01096-7
doi: 10.1007/s11604-021-01096-7
pubmed: 33517507
Matsuura K, Inoue K, Hoshino E et al (2022) Utility of magnetic resonance imaging for differentiating malignant mesenchymal tumors of the uterus from T2-weighted hyperintense leiomyomas. Jpn J Radiol 40:385–395. https://doi.org/10.1007/s11604-021-01217-2
doi: 10.1007/s11604-021-01217-2
pubmed: 34750737
Inoue A, Tanabe M, Ihara K et al (2023) Evaluation of diffusion-weighted magnetic resonance imaging of the rectal cancers: comparison between modified reduced field-of-view single-shot echo-planar imaging with tilted two-dimensional radiofrequency excitation pulses and conventional full field-of-view readout-segmented echo-planar imaging. Radiol Med 128:1192–1198. https://doi.org/10.1007/s11547-023-01699-2
doi: 10.1007/s11547-023-01699-2
pubmed: 37606795
Albano D, Bruno F, Agostini A et al (2022) Dynamic contrast-enhanced (DCE) imaging: state of the art and applications in whole-body imaging. Jpn J Radiol 40:341–366. https://doi.org/10.1007/s11604-021-01223-4
doi: 10.1007/s11604-021-01223-4
pubmed: 34951000
Meng S, Gan W, Chen L et al (2023) Intravoxel incoherent motion predicts positive surgical margins and Gleason score upgrading after radical prostatectomy for prostate cancer. Radiol Med 128:668–678. https://doi.org/10.1007/s11547-023-01645-2
doi: 10.1007/s11547-023-01645-2
pubmed: 37277573
Nakanishi K, Tanaka J, Nakaya Y et al (2022) Whole-body MRI: detecting bone metastases from prostate cancer. Jpn J Radiol 40:229–244. https://doi.org/10.1007/s11604-021-01205-6
doi: 10.1007/s11604-021-01205-6
pubmed: 34693502
Voicu IP, Pravatà E, Panara V et al (2022) Differentiating solitary brain metastases from high-grade gliomas with MR: comparing qualitative versus quantitative diagnostic strategies. Radiol Med 127:891–898. https://doi.org/10.1007/s11547-022-01516-2
doi: 10.1007/s11547-022-01516-2
pubmed: 35763250
pmcid: 9349158
Yuan G, Qu W, Li S et al (2023) Noninvasive assessment of renal function and fibrosis in CKD patients using histogram analysis based on diffusion kurtosis imaging. Jpn J Radiol 41:180–193. https://doi.org/10.1007/s11604-022-01346-2
doi: 10.1007/s11604-022-01346-2
pubmed: 36255600
Brembilla G, Lavalle S, Parry T et al (2023) Impact of prostate imaging quality (PI-QUAL) score on the detection of clinically significant prostate cancer at biopsy. Eur J Radiol 164:110849. https://doi.org/10.1016/j.ejrad.2023.110849
doi: 10.1016/j.ejrad.2023.110849
pubmed: 37141845
Higaki A, Tamada T, Kido A et al (2023) Short repetition time diffusion-weighted imaging improves visualization of prostate cancer. Jpn J Radiol. https://doi.org/10.1007/s11604-023-01519-7
doi: 10.1007/s11604-023-01519-7
pubmed: 38123889
pmcid: 11056335
Qian W-L, Chen Q, Zhang J-B et al (2023) RESOLVE-based radiomics in cervical cancer: improved image quality means better feature reproducibility? Clin Radiol 78:e469–e476. https://doi.org/10.1016/j.crad.2023.03.001
doi: 10.1016/j.crad.2023.03.001
pubmed: 37029000
Maciel C, Bharwani N, Kubik-Huch RA et al (2020) MRI of female genital tract congenital anomalies: European society of urogenital radiology (ESUR) guidelines. Eur Radiol 30:4272–4283. https://doi.org/10.1007/s00330-020-06750-8
doi: 10.1007/s00330-020-06750-8
pubmed: 32221681
pmcid: 7338830
Ueda D, Shimazaki A, Miki Y (2019) Technical and clinical overview of deep learning in radiology. Jpn J Radiol 37:15–33. https://doi.org/10.1007/s11604-018-0795-3
doi: 10.1007/s11604-018-0795-3
pubmed: 30506448
Nakata N (2019) Recent technical development of artificial intelligence for diagnostic medical imaging. Jpn J Radiol 37:103–108. https://doi.org/10.1007/s11604-018-0804-6
doi: 10.1007/s11604-018-0804-6
pubmed: 30706381
Penzkofer T, Padhani AR, Turkbey B et al (2021) ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging. Eur Radiol 31:9567–9578. https://doi.org/10.1007/s00330-021-08021-6
doi: 10.1007/s00330-021-08021-6
pubmed: 33991226
pmcid: 8589789
Yasaka K, Akai H, Sugawara H et al (2022) Impact of deep learning reconstruction on intracranial 1.5 T magnetic resonance angiography. Jpn J Radiol 40:476–483. https://doi.org/10.1007/s11604-021-01225-2
doi: 10.1007/s11604-021-01225-2
pubmed: 34851499
Fujima N, Kamagata K, Ueda D et al (2023) Current state of artificial intelligence in clinical applications for head and neck MR imaging. Magn Reson Med Sci 22:401–414. https://doi.org/10.2463/mrms.rev.2023-0047
doi: 10.2463/mrms.rev.2023-0047
pubmed: 37532584
pmcid: 10552661
Yanagawa M, Ito R, Nozaki T et al (2023) New trend in artificial intelligence-based assistive technology for thoracic imaging. Radiol Med 128:1236–1249. https://doi.org/10.1007/s11547-023-01691-w
doi: 10.1007/s11547-023-01691-w
pubmed: 37639191
pmcid: 10547663
Goto M, Sakai K, Toyama Y et al (2023) Use of a deep learning algorithm for non-mass enhancement on breast MRI: comparison with radiologists’ interpretations at various levels. Jpn J Radiol 41:1094–1103. https://doi.org/10.1007/s11604-023-01435-w
doi: 10.1007/s11604-023-01435-w
pubmed: 37071250
pmcid: 10543141
Barat M, Pellat A, Hoeffel C et al (2024) CT and MRI of abdominal cancers: current trends and perspectives in the era of radiomics and artificial intelligence. Jpn J Radiol 42:246–260. https://doi.org/10.1007/s11604-023-01504-0
doi: 10.1007/s11604-023-01504-0
pubmed: 37926780
Yamada A, Kamagata K, Hirata K et al (2023) Clinical applications of artificial intelligence in liver imaging. Radiol Med 128:655–667. https://doi.org/10.1007/s11547-023-01638-1
doi: 10.1007/s11547-023-01638-1
pubmed: 37165151
Tsang B, Gupta A, Takahashi MS et al (2023) Applications of artificial intelligence in magnetic resonance imaging of primary pediatric cancers: a scoping review and CLAIM score assessment. Jpn J Radiol 41:1127–1147. https://doi.org/10.1007/s11604-023-01437-8
doi: 10.1007/s11604-023-01437-8
pubmed: 37395982
Tatsugami F, Nakaura T, Yanagawa M et al (2023) Recent advances in artificial intelligence for cardiac CT: enhancing diagnosis and prognosis prediction. Diagn Interv Imaging. https://doi.org/10.1016/j.diii.2023.06.011
doi: 10.1016/j.diii.2023.06.011
pubmed: 37407346
Hirata K, Kamagata K, Ueda D et al (2023) From FDG and beyond: the evolving potential of nuclear medicine. Ann Nucl Med 37:583–595. https://doi.org/10.1007/s12149-023-01865-6
doi: 10.1007/s12149-023-01865-6
pubmed: 37749301
Kawamura M, Kamomae T, Yanagawa M et al (2024) Revolutionizing radiation therapy: the role of AI in clinical practice. J Radiat Res 65:1–9. https://doi.org/10.1093/jrr/rrad090
doi: 10.1093/jrr/rrad090
pubmed: 37996085
Toyama Y, Harigai A, Abe M et al (2024) Performance evaluation of ChatGPT, GPT-4, and Bard on the official board examination of the Japan radiology society. Jpn J Radiol 42:201–207. https://doi.org/10.1007/s11604-023-01491-2
doi: 10.1007/s11604-023-01491-2
pubmed: 37792149
Nakaura T, Yoshida N, Kobayashi N et al (2024) Preliminary assessment of automated radiology report generation with generative pre-trained transformers: comparing results to radiologist-generated reports. Jpn J Radiol 42:190–200. https://doi.org/10.1007/s11604-023-01487-y
doi: 10.1007/s11604-023-01487-y
pubmed: 37713022
Fusco R, Granata V, Grazzini G et al (2022) Radiomics in medical imaging: pitfalls and challenges in clinical management. Jpn J Radiol 40:919–929. https://doi.org/10.1007/s11604-022-01271-4
doi: 10.1007/s11604-022-01271-4
pubmed: 35344132
Galluzzo A, Boccioli S, Danti G et al (2023) Radiomics in gastrointestinal stromal tumours: an up-to-date review. Jpn J Radiol 41:1051–1061. https://doi.org/10.1007/s11604-023-01441-y
doi: 10.1007/s11604-023-01441-y
pubmed: 37171755
Granata V, Fusco R, De Muzio F et al (2022) Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of liver mucinous colorectal metastases. Radiol Med 127:763–772. https://doi.org/10.1007/s11547-022-01501-9
doi: 10.1007/s11547-022-01501-9
pubmed: 35653011
Zhong J, Frood R, McWilliam A et al (2023) Prediction of prostate tumour hypoxia using pre-treatment MRI-derived radiomics: preliminary findings. Radiol Med 128:765–774. https://doi.org/10.1007/s11547-023-01644-3
doi: 10.1007/s11547-023-01644-3
pubmed: 37198374
pmcid: 10264289
Ueda D, Kakinuma T, Fujita S et al (2024) Fairness of artificial intelligence in healthcare: review and recommendations. Jpn J Radiol 42:3–15. https://doi.org/10.1007/s11604-023-01474-3
doi: 10.1007/s11604-023-01474-3
pubmed: 37540463
Higaki T, Nakamura Y, Tatsugami F et al (2019) Improvement of image quality at CT and MRI using deep learning. Jpn J Radiol 37:73–80. https://doi.org/10.1007/s11604-018-0796-2
doi: 10.1007/s11604-018-0796-2
pubmed: 30498876
Lei K, Syed AB, Zhu X et al (2023) Automated MRI field of view prescription from region of interest prediction by intra-stack attention neural network. Bioengineering 10:92. https://doi.org/10.3390/bioengineering10010092
doi: 10.3390/bioengineering10010092
pubmed: 36671663
pmcid: 9854842
Hausmann D, Lerch A, Hitziger S et al (2023) AI-supported autonomous uterus reconstructions: first application in MRI using 3D SPACE with iterative denoising. Acad Radiol. https://doi.org/10.1016/j.acra.2023.09.035
doi: 10.1016/j.acra.2023.09.035
pubmed: 37925344
Hoffmann M, Turk EA, Gagoski B et al (2021) Rapid head-pose detection for automated slice prescription of fetal-brain MRI. Int J Imaging Syst Technol 31:1136–1154. https://doi.org/10.1002/ima.22563
doi: 10.1002/ima.22563
pubmed: 34421216
pmcid: 8372849
Cipollari S, Guarrasi V, Pecoraro M et al (2022) Convolutional neural networks for automated classification of prostate multiparametric magnetic resonance imaging based on image quality. J Magn Reson Imaging 55:480–490. https://doi.org/10.1002/jmri.27879
doi: 10.1002/jmri.27879
pubmed: 34374181
Alis D, Kartal MS, Seker ME et al (2023) Deep learning for assessing image quality in bi-parametric prostate MRI: a feasibility study. Eur J Radiol 165:110924. https://doi.org/10.1016/j.ejrad.2023.110924
doi: 10.1016/j.ejrad.2023.110924
pubmed: 37354768
Thijssen LCP, de Rooij M, Barentsz JO, Huisman HJ (2023) Radiomics based automated quality assessment for T2W prostate MR images. Eur J Radiol 165:110928. https://doi.org/10.1016/j.ejrad.2023.110928
doi: 10.1016/j.ejrad.2023.110928
pubmed: 37354769
Hötker AM, Da Mutten R, Tiessen A et al (2021) Improving workflow in prostate MRI: AI-based decision-making on biparametric or multiparametric MRI. Insights Imaging 12:112. https://doi.org/10.1186/s13244-021-01058-7
doi: 10.1186/s13244-021-01058-7
pubmed: 34370164
pmcid: 8353049
Gagoski B, Xu J, Wighton P et al (2022) Automated detection and reacquisition of motion-degraded images in fetal HASTE imaging at 3 T. Magn Reson Med 87:1914–1922. https://doi.org/10.1002/mrm.29106
doi: 10.1002/mrm.29106
pubmed: 34888942
Bischoff LM, Peeters JM, Weinhold L et al (2023) Deep learning super-resolution reconstruction for fast and motion-robust T2-weighted prostate MRI. Radiology 308:e230427. https://doi.org/10.1148/radiol.230427
doi: 10.1148/radiol.230427
pubmed: 37750774
Lee K-L, Kessler DA, Dezonie S et al (2023) Assessment of deep learning-based reconstruction on T2-weighted and diffusion-weighted prostate MRI image quality. Eur J Radiol 166:111017. https://doi.org/10.1016/j.ejrad.2023.111017
doi: 10.1016/j.ejrad.2023.111017
pubmed: 37541181
Park JC, Park KJ, Park MY et al (2022) Fast T2-weighted imaging with deep learning-based reconstruction: evaluation of image quality and diagnostic performance in patients undergoing radical prostatectomy. J Magn Reson Imaging 55:1735–1744. https://doi.org/10.1002/jmri.27992
doi: 10.1002/jmri.27992
pubmed: 34773449
Ursprung S, Herrmann J, Joos N et al (2023) Accelerated diffusion-weighted imaging of the prostate using deep learning image reconstruction: A retrospective comparison with standard diffusion-weighted imaging. Eur J Radiol 165:110953. https://doi.org/10.1016/j.ejrad.2023.110953
doi: 10.1016/j.ejrad.2023.110953
pubmed: 37399667
Ren J, Li Y, Liu F-S et al (2022) Comparison of a deep learning-accelerated T2-weighted turbo spin echo sequence and its conventional counterpart for female pelvic MRI: reduced acquisition times and improved image quality. Insights Imaging 13:193. https://doi.org/10.1186/s13244-022-01321-5
doi: 10.1186/s13244-022-01321-5
pubmed: 36512158
pmcid: 9747993
Tong A, Bagga B, Petrocelli R et al (2023) Comparison of a deep learning-accelerated vs. conventional T2-Weighted Sequence in biparametric MRI of the prostate. J Magn Reson Imaging 58:1055–1064. https://doi.org/10.1002/jmri.28602
doi: 10.1002/jmri.28602
pubmed: 36651358
pmcid: 10352465
Lee EJ, Hwang J, Park S et al (2023) Utility of accelerated T2-weighted turbo spin-echo imaging with deep learning reconstruction in female pelvic MRI: a multi-reader study. Eur Radiol 33:7697–7706. https://doi.org/10.1007/s00330-023-09781-z
doi: 10.1007/s00330-023-09781-z
pubmed: 37314472
Jung W, Kim EH, Ko J et al (2022) Convolutional neural network-based reconstruction for acceleration of prostate T2 weighted MR imaging: a retro- and prospective study. Br J Radiol 95:20211378. https://doi.org/10.1259/bjr.20211378
doi: 10.1259/bjr.20211378
pubmed: 35148172
pmcid: 10993971
Johnson PM, Tong A, Donthireddy A et al (2022) Deep learning reconstruction enables highly accelerated biparametric MR imaging of the prostate. J Magn Reson Imaging 56:184–195. https://doi.org/10.1002/jmri.28024
doi: 10.1002/jmri.28024
pubmed: 34877735
Ueda T, Ohno Y, Yamamoto K et al (2021) Compressed sensing and deep learning reconstruction for women’s pelvic MRI denoising: utility for improving image quality and examination time in routine clinical practice. Eur J Radiol 134:109430. https://doi.org/10.1016/j.ejrad.2020.109430
doi: 10.1016/j.ejrad.2020.109430
pubmed: 33276249
Kim EH, Choi MH, Lee YJ et al (2021) Deep learning-accelerated T2-weighted imaging of the prostate: impact of further acceleration with lower spatial resolution on image quality. Eur J Radiol 145:110012. https://doi.org/10.1016/j.ejrad.2021.110012
doi: 10.1016/j.ejrad.2021.110012
pubmed: 34753082
Tsuboyama T, Onishi H, Nakamoto A et al (2022) Impact of deep learning reconstruction combined with a sharpening filter on single-shot fast spin-echo T2-weighted magnetic resonance imaging of the uterus. Invest Radiol 57:379–386. https://doi.org/10.1097/RLI.0000000000000847
doi: 10.1097/RLI.0000000000000847
pubmed: 34999668
Yang R, Zou Y, Liu WV et al (2023) High-resolution single-shot fast spin-echo MR imaging with deep learning reconstruction algorithm can improve repeatability and reproducibility of follicle counting. J Clin Med Res 12:3234. https://doi.org/10.3390/jcm12093234
doi: 10.3390/jcm12093234
Ueda T, Ohno Y, Yamamoto K et al (2022) Deep learning reconstruction of diffusion-weighted MRI improves image quality for prostatic imaging. Radiology 303:373–381. https://doi.org/10.1148/radiol.204097
doi: 10.1148/radiol.204097
pubmed: 35103536
Watanabe M, Taguchi S, Machida H et al (2022) Clinical validity of non-contrast-enhanced VI-RADS: prospective study using 3-T MRI with high-gradient magnetic field. Eur Radiol 32:7513–7521. https://doi.org/10.1007/s00330-022-08813-4
doi: 10.1007/s00330-022-08813-4
pubmed: 35554648
pmcid: 9668777
Gassenmaier S, Warm V, Nickel D et al (2023) Thin-slice prostate MRI enabled by deep learning image reconstruction. Cancers 15:573. https://doi.org/10.3390/cancers15030578
doi: 10.3390/cancers15030578
Kim M, Kim SH, Hong S et al (2024) Evaluation of extra-prostatic extension on deep learning-reconstructed high-resolution thin-slice T2-weighted images in patients with prostate cancer. Cancers 16:413. https://doi.org/10.3390/cancers16020413
doi: 10.3390/cancers16020413
pubmed: 38254901
pmcid: 10814256
Matsumoto S, Tsuboyama T, Onishi H et al (2024) Ultra-high-resolution T2-weighted PROPELLER MRI of the rectum with deep learning reconstruction: assessment of image quality and diagnostic performance. Invest Radiol. 59:479–488. https://doi.org/10.1097/RLI.0000000000001047
doi: 10.1097/RLI.0000000000001047
pubmed: 37975732
Wu C, Montagne S, Hamzaoui D et al (2022) Automatic segmentation of prostate zonal anatomy on MRI: a systematic review of the literature. Insights Imaging 13:202. https://doi.org/10.1186/s13244-022-01340-2
doi: 10.1186/s13244-022-01340-2
pubmed: 36543901
pmcid: 9772373
Jimenez-Pastor A, Lopez-Gonzalez R, Fos-Guarinos B et al (2023) Automated prostate multi-regional segmentation in magnetic resonance using fully convolutional neural networks. Eur Radiol 33:5087–5096. https://doi.org/10.1007/s00330-023-09410-9
doi: 10.1007/s00330-023-09410-9
pubmed: 36690774
Xu L, Zhang G, Zhang D et al (2023) Development and clinical utility analysis of a prostate zonal segmentation model on T2-weighted imaging: a multicenter study. Insights Imaging 14:44. https://doi.org/10.1186/s13244-023-01394-w
doi: 10.1186/s13244-023-01394-w
pubmed: 36928683
pmcid: 10020392
Meglič J, Sunoqrot MRS, Bathen TF, Elschot M (2023) Label-set impact on deep learning-based prostate segmentation on MRI. Insights Imaging 14:157. https://doi.org/10.1186/s13244-023-01502-w
doi: 10.1186/s13244-023-01502-w
pubmed: 37749333
pmcid: 10519913
Abdulkadir Y, Luximon D, Morris E et al (2023) Human factors in the clinical implementation of deep learning-based automated contouring of pelvic organs at risk for MRI-guided radiotherapy. Med Phys 50:5969–5977. https://doi.org/10.1002/mp.16676
doi: 10.1002/mp.16676
pubmed: 37646527
Trigui R, Adel M, Di Bisceglie M et al (2022) Bladder wall segmentation and characterization on MR images: computer-aided spina bifida diagnosis. J Imaging Sci Technol 8:151. https://doi.org/10.3390/jimaging8060151
doi: 10.3390/jimaging8060151
Pang X, Wang F, Zhang Q et al (2021) A pipeline for predicting the treatment response of neoadjuvant chemoradiotherapy for locally advanced rectal cancer using single MRI modality: combining deep segmentation network and radiomics analysis based on “suspicious region.” Front Oncol 11:711747. https://doi.org/10.3389/fonc.2021.711747
doi: 10.3389/fonc.2021.711747
pubmed: 34422664
pmcid: 8371269
Netzer N, Eith C, Bethge O et al (2023) Application of a validated prostate MRI deep learning system to independent same-vendor multi-institutional data: demonstration of transferability. Eur Radiol 33:7463–7476. https://doi.org/10.1007/s00330-023-09882-9
doi: 10.1007/s00330-023-09882-9
pubmed: 37507610
pmcid: 10598076
Simeth J, Jiang J, Nosov A et al (2023) Deep learning-based dominant index lesion segmentation for MR-guided radiation therapy of prostate cancer. Med Phys 50:4854–4870. https://doi.org/10.1002/mp.16320
doi: 10.1002/mp.16320
pubmed: 36856092
Mehta P, Antonelli M, Singh S et al (2021) AutoProstate: towards automated reporting of prostate MRI for prostate cancer assessment using deep learning. Cancers 13:6138. https://doi.org/10.3390/cancers13236138
doi: 10.3390/cancers13236138
pubmed: 34885246
pmcid: 8656605
Yu R, Jiang K-W, Bao J et al (2023) PI-RADSAI: introducing a new human-in-the-loop AI model for prostate cancer diagnosis based on MRI. Br J Cancer 128:1019–1029. https://doi.org/10.1038/s41416-022-02137-2
doi: 10.1038/s41416-022-02137-2
pubmed: 36599915
pmcid: 10006083
Mehralivand S, Yang D, Harmon SA et al (2022) Deep learning-based artificial intelligence for prostate cancer detection at biparametric MRI. Abdom Radiol 47:1425–1434. https://doi.org/10.1007/s00261-022-03419-2
doi: 10.1007/s00261-022-03419-2
Netzer N, Weißer C, Schelb P et al (2021) Fully automatic deep learning in bi-institutional prostate magnetic resonance imaging: effects of cohort size and heterogeneity. Invest Radiol 56:799–808. https://doi.org/10.1097/RLI.0000000000000791
doi: 10.1097/RLI.0000000000000791
pubmed: 34049336
Saha A, Hosseinzadeh M, Huisman H (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 73:102155. https://doi.org/10.1016/j.media.2021.102155
doi: 10.1016/j.media.2021.102155
pubmed: 34245943
Adams LC, Makowski MR, Engel G et al (2022) Prostate158—an expert-annotated 3T MRI dataset and algorithm for prostate cancer detection. Comput Biol Med 148:105817. https://doi.org/10.1016/j.compbiomed.2022.105817
doi: 10.1016/j.compbiomed.2022.105817
pubmed: 35841780
Mehralivand S, Yang D, Harmon SA et al (2022) A cascaded deep learning-based artificial intelligence algorithm for automated lesion detection and classification on biparametric prostate magnetic resonance imaging. Acad Radiol 29:1159–1168. https://doi.org/10.1016/j.acra.2021.08.019
doi: 10.1016/j.acra.2021.08.019
pubmed: 34598869
Pellicer-Valero OJ, Marenco Jiménez JL, Gonzalez-Perez V et al (2022) Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of prostate cancer in multiparametric magnetic resonance images. Sci Rep 12:2975. https://doi.org/10.1038/s41598-022-06730-6
doi: 10.1038/s41598-022-06730-6
pubmed: 35194056
pmcid: 8864013
Liu Y, Zhu Y, Wang W et al (2022) Multi-scale discriminative network for prostate cancer lesion segmentation in multiparametric MR images. Med Phys 49:7001–7015. https://doi.org/10.1002/mp.15861
doi: 10.1002/mp.15861
pubmed: 35851482
Moribata Y, Kurata Y, Nishio M et al (2023) Automatic segmentation of bladder cancer on MRI using a convolutional neural network and reproducibility of radiomics features: a two-center study. Sci Rep 13:628. https://doi.org/10.1038/s41598-023-27883-y
doi: 10.1038/s41598-023-27883-y
pubmed: 36635425
pmcid: 9837183
Ye Y, Luo Z, Qiu Z et al (2023) Radiomics prediction of muscle invasion in bladder cancer using semi-automatic lesion segmentation of MRI compared with manual segmentation. Bioengineering 10:1355. https://doi.org/10.3390/bioengineering10121355
doi: 10.3390/bioengineering10121355
pubmed: 38135946
pmcid: 10740947
Hu D, Jian J, Li Y, Gao X (2023) Deep learning-based segmentation of epithelial ovarian cancer on T2-weighted magnetic resonance images. Quant Imaging Med Surg 13:1464–1477. https://doi.org/10.21037/qims-22-494
doi: 10.21037/qims-22-494
pubmed: 36915355
pmcid: 10006162
Lin Y-C, Lin Y, Huang Y-L et al (2023) Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights Imaging 14:14. https://doi.org/10.1186/s13244-022-01356-8
doi: 10.1186/s13244-022-01356-8
pubmed: 36690870
pmcid: 9871146
Ma S, Lu H, Jing G et al (2023) Deep learning-based clinical-radiomics nomogram for preoperative prediction of lymph node metastasis in patients with rectal cancer: a two-center study. Front Med 10:1276672. https://doi.org/10.3389/fmed.2023.1276672
doi: 10.3389/fmed.2023.1276672
Ke J, Jin C, Tang J et al (2023) A longitudinal MRI-based artificial intelligence system to predict pathological complete response after neoadjuvant therapy in rectal cancer: a multicenter validation study. Dis Colon Rectum 66:e1195–e1206. https://doi.org/10.1097/DCR.0000000000002931
doi: 10.1097/DCR.0000000000002931
pubmed: 37682775
Li L, Xu B, Zhuang Z et al (2023) Accurate tumor segmentation and treatment outcome prediction with DeepTOP. Radiother Oncol 183:109550. https://doi.org/10.1016/j.radonc.2023.109550
doi: 10.1016/j.radonc.2023.109550
pubmed: 36813177
Defeudis A, Mazzetti S, Panic J et al (2022) MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study. Eur Radiol Exp 6:19. https://doi.org/10.1186/s41747-022-00272-2
doi: 10.1186/s41747-022-00272-2
pubmed: 35501512
pmcid: 9061921
Knuth F, Adde IA, Huynh BN et al (2022) MRI-based automatic segmentation of rectal cancer using 2D U-Net on two independent cohorts. Acta Oncol 61:255–263. https://doi.org/10.1080/0284186X.2021.2013530
doi: 10.1080/0284186X.2021.2013530
pubmed: 34918621
Song K, Zhao Z, Ma Y et al (2022) A multitask dual-stream attention network for the identification of KRAS mutation in colorectal cancer. Med Phys 49:254–270. https://doi.org/10.1002/mp.15361
doi: 10.1002/mp.15361
pubmed: 34806195
Bleker J, Kwee TC, Rouw D et al (2022) A deep learning masked segmentation alternative to manual segmentation in biparametric MRI prostate cancer radiomics. Eur Radiol 32:6526–6535. https://doi.org/10.1007/s00330-022-08712-8
doi: 10.1007/s00330-022-08712-8
pubmed: 35420303
pmcid: 9381625
Rouvière O, Jaouen T, Baseilhac P et al (2023) Artificial intelligence algorithms aimed at characterizing or detecting prostate cancer on MRI: how accurate are they when tested on independent cohorts?— a systematic review. Diagn Interv Imaging 104:221–234. https://doi.org/10.1016/j.diii.2022.11.005
doi: 10.1016/j.diii.2022.11.005
pubmed: 36517398
Jiang K-W, Song Y, Hou Y et al (2023) Performance of artificial intelligence-aided diagnosis system for clinically significant prostate cancer with MRI: a diagnostic comparison study. J Magn Reson Imaging 57:1352–1364. https://doi.org/10.1002/jmri.28427
doi: 10.1002/jmri.28427
pubmed: 36222324
Sun Z, Wang K, Kong Z et al (2023) A multicenter study of artificial intelligence-aided software for detecting visible clinically significant prostate cancer on mpMRI. Insights Imaging 14:72. https://doi.org/10.1186/s13244-023-01421-w
doi: 10.1186/s13244-023-01421-w
pubmed: 37121983
pmcid: 10149551
Labus S, Altmann MM, Huisman H et al (2023) A concurrent, deep learning-based computer-aided detection system for prostate multiparametric MRI: a performance study involving experienced and less-experienced radiologists. Eur Radiol 33:64–76. https://doi.org/10.1007/s00330-022-08978-y
doi: 10.1007/s00330-022-08978-y
pubmed: 35900376
Liu G, Pan S, Zhao R et al (2023) The added value of AI-based computer-aided diagnosis in classification of cancer at prostate MRI. Eur Radiol 33:5118–5130. https://doi.org/10.1007/s00330-023-09433-2
doi: 10.1007/s00330-023-09433-2
pubmed: 36725719
Matsuoka Y, Ueno Y, Uehara S et al (2023) Deep-learning prostate cancer detection and segmentation on biparametric versus multiparametric magnetic resonance imaging: added value of dynamic contrast-enhanced imaging. Int J Urol 30:1103–1111. https://doi.org/10.1111/iju.15280
doi: 10.1111/iju.15280
pubmed: 37605627
Kurata Y, Nishio M, Moribata Y et al (2021) Automatic segmentation of uterine endometrial cancer on multi-sequence MRI using a convolutional neural network. Sci Rep 11:14440. https://doi.org/10.1038/s41598-021-93792-7
doi: 10.1038/s41598-021-93792-7
pubmed: 34262088
pmcid: 8280152
Shen L, Du L, Hu Y et al (2023) MRI-based radiomics model for distinguishing stage I endometrial carcinoma from endometrial polyp: a multicenter study. Acta radiol 64:2651–2658. https://doi.org/10.1177/02841851231175249
doi: 10.1177/02841851231175249
pubmed: 37291882
Wei M, Zhang Y, Bai G et al (2022) T2-weighted MRI-based radiomics for discriminating between benign and borderline epithelial ovarian tumors: a multicenter study. Insights Imaging 13:130. https://doi.org/10.1186/s13244-022-01264-x
doi: 10.1186/s13244-022-01264-x
pubmed: 35943620
pmcid: 9363551
Li Y, Jian J, Pickhardt PJ et al (2020) MRI-based machine learning for differentiating borderline from malignant epithelial ovarian tumors: a multicenter study. J Magn Reson Imaging 52:897–904. https://doi.org/10.1002/jmri.27084
doi: 10.1002/jmri.27084
pubmed: 32045064
Jian J, Li Y, Pickhardt PJ et al (2021) MR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancers. Eur Radiol 31:403–410. https://doi.org/10.1007/s00330-020-07091-2
doi: 10.1007/s00330-020-07091-2
pubmed: 32743768
Ponsiglione A, Gambardella M, Stanzione A et al (2023) Radiomics for the identification of extraprostatic extension with prostate MRI: a systematic review and meta-analysis. Eur Radiol. https://doi.org/10.1007/s00330-023-10427-3
doi: 10.1007/s00330-023-10427-3
pubmed: 37955670
pmcid: 11166859
Calimano-Ramirez LF, Virarkar MK, Hernandez M et al (2023) MRI-based nomograms and radiomics in presurgical prediction of extraprostatic extension in prostate cancer: a systematic review. Abdom Radiol (NY) 48:2379–2400. https://doi.org/10.1007/s00261-023-03924-y
doi: 10.1007/s00261-023-03924-y
pubmed: 37142824
Petrila O, Stefan A-E, Gafitanu D et al (2023) The applicability of artificial intelligence in predicting the depth of myometrial invasion on MRI studies–a systematic review. Diagnostics 13:2592. https://doi.org/10.3390/diagnostics131525925
doi: 10.3390/diagnostics131525925
pubmed: 37568955
pmcid: 10416838
Gollub MJ, Costello JR, Ernst RD et al (2023) A primer on rectal MRI in patients on watch-and-wait treatment for rectal cancer. Abdom Radiol 48:2836–2873. https://doi.org/10.1007/s00261-023-03900-6
doi: 10.1007/s00261-023-03900-6
Yardimci AH, Kocak B, Sel I et al (2023) Radiomics of locally advanced rectal cancer: machine learning-based prediction of response to neoadjuvant chemoradiotherapy using pre-treatment sagittal T2-weighted MRI. Jpn J Radiol 41:71–82. https://doi.org/10.1007/s11604-022-01325-7
doi: 10.1007/s11604-022-01325-7
pubmed: 35962933
Wei Q, Chen Z, Tang Y et al (2023) External validation and comparison of MR-based radiomics models for predicting pathological complete response in locally advanced rectal cancer: a two-centre, multi-vendor study. Eur Radiol 33:1906–1917. https://doi.org/10.1007/s00330-022-09204-5
doi: 10.1007/s00330-022-09204-5
pubmed: 36355199
Horvat N, Veeraraghavan H, Nahas CSR et al (2022) Combined artificial intelligence and radiologist model for predicting rectal cancer treatment response from magnetic resonance imaging: an external validation study. Abdom Radiol 47:2770–2782. https://doi.org/10.1007/s00261-022-03572-8
doi: 10.1007/s00261-022-03572-8
Prabhakaran S, Choong KWK, Prabhakaran S et al (2023) Accuracy of deep neural learning models in the imaging prediction of pathological complete response after neoadjuvant chemoradiotherapy for locally advanced rectal cancer: a systematic review. Langenbecks Arch Surg 408:321. https://doi.org/10.1007/s00423-023-03039-4
doi: 10.1007/s00423-023-03039-4
pubmed: 37594552
Wang K, Xing Z, Kong Z et al (2023) Artificial intelligence as diagnostic aiding tool in cases of prostate imaging reporting and data system category 3: the results of retrospective multi-center cohort study. Abdom Radiol 48:3757–3765. https://doi.org/10.1007/s00261-023-03989-9
doi: 10.1007/s00261-023-03989-9
Jin P, Shen J, Yang L et al (2023) Machine learning-based radiomics model to predict benign and malignant PI-RADS v2.1 category 3 lesions: a retrospective multi-center study. BMC Med Imaging 23:47. https://doi.org/10.1186/s12880-023-01002-9
doi: 10.1186/s12880-023-01002-9
pubmed: 36991347
pmcid: 10053087
Li T, Sun L, Li Q et al (2021) Development and validation of a radiomics nomogram for predicting clinically significant prostate cancer in PI-RADS 3 lesions. Front Oncol 11:825429. https://doi.org/10.3389/fonc.2021.825429
doi: 10.3389/fonc.2021.825429
pubmed: 35155214
Lim CS, Abreu-Gomez J, Thornhill R et al (2021) Utility of machine learning of apparent diffusion coefficient (ADC) and T2-weighted (T2W) radiomic features in PI-RADS version 2.1 category 3 lesions to predict prostate cancer diagnosis. Abdom Radiol (NY) 46:5647–5658. https://doi.org/10.1007/s00261-021-03235-0
doi: 10.1007/s00261-021-03235-0
pubmed: 34467426
Zhao Y-Y, Xiong M-L, Liu Y-F et al (2023) Magnetic resonance imaging radiomics-based prediction of clinically significant prostate cancer in equivocal PI-RADS 3 lesions in the transitional zone. Front Oncol 13:1247682. https://doi.org/10.3389/fonc.2023.1247682
doi: 10.3389/fonc.2023.1247682
pubmed: 38074651
pmcid: 10701731
Li J, Cao K, Lin H et al (2023) Predicting muscle invasion in bladder cancer by deep learning analysis of MRI: comparison with vesical imaging-reporting and data system. Eur Radiol 33:2699–2709. https://doi.org/10.1007/s00330-022-09272-7
doi: 10.1007/s00330-022-09272-7
pubmed: 36434397
Youn SY, Choi MH, Kim DH et al (2021) Detection and PI-RADS classification of focal lesions in prostate MRI: performance comparison between a deep learning-based algorithm (DLA) and radiologists with various levels of experience. Eur J Radiol 142:109894. https://doi.org/10.1016/j.ejrad.2021.109894
doi: 10.1016/j.ejrad.2021.109894
pubmed: 34388625
Arslan A, Alis D, Erdemli S et al (2023) Does deep learning software improve the consistency and performance of radiologists with various levels of experience in assessing bi-parametric prostate MRI? Insights Imaging 14:48. https://doi.org/10.1186/s13244-023-01386-w
doi: 10.1186/s13244-023-01386-w
pubmed: 36939953
pmcid: 10027972
Bosma JS, Saha A, Hosseinzadeh M et al (2023) Semisupervised learning with report-guided pseudo labels for deep learning-based prostate cancer detection using biparametric MRI. Radiol Artif Intell 5:e230031. https://doi.org/10.1148/ryai.230031
doi: 10.1148/ryai.230031
pubmed: 37795142
pmcid: 10546362
Ying J, Huang W, Fu L et al (2023) Weakly supervised segmentation of uterus by scribble labeling on endometrial cancer MR images. Comput Biol Med 167:107582. https://doi.org/10.1016/j.compbiomed.2023.107582
doi: 10.1016/j.compbiomed.2023.107582
pubmed: 37922606
Patel BN, Rosenberg L, Willcox G et al (2019) Human-machine partnership with artificial intelligence for chest radiograph diagnosis. NPJ Digit Med 2:111. https://doi.org/10.1038/s41746-019-0189-7
doi: 10.1038/s41746-019-0189-7
pubmed: 31754637
pmcid: 6861262