Recent trends in AI applications for pelvic MRI: a comprehensive review

doi:10.1007/s11547-024-01861-4

Review

. 2024 Sep;129(9):1275-1287.

doi: 10.1007/s11547-024-01861-4. Epub 2024 Aug 3.

Recent trends in AI applications for pelvic MRI: a comprehensive review

Takahiro Tsuboyama¹, Masahiro Yanagawa², Tomoyuki Fujioka³, Shohei Fujita⁴, Daiju Ueda⁵, Rintaro Ito⁶, Akira Yamada⁷, Yasutaka Fushimi⁸, Fuminari Tatsugami⁹, Takeshi Nakaura¹⁰, Taiki Nozaki¹¹, Koji Kamagata¹², Yusuke Matsui¹³, Kenji Hirata¹⁴, Noriyuki Fujima¹⁵, Mariko Kawamura⁶, Shinji Naganawa⁶

Affiliations

¹ Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe-City, Hyogo, 650-0017, Japan. ttsuboyama@gmail.com.
² Department of Radiology, Osaka University Graduate School of Medicine, Suita-City, Osaka, 565-0871, Japan.
³ Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan.
⁴ Department of Radiology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
⁵ Department of Artificial Intelligence, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan.
⁶ Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan.
⁷ Medical Data Science Course, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan.
⁸ Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawaharacho, Sakyoku, Kyoto, 606-8507, Japan.
⁹ Department of Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
¹⁰ Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, 1-1-1 Honjo Chuo-ku, Kumamoto, 860-8556, Japan.
¹¹ Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-0016, Japan.
¹² Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, 113-8421, Japan.
¹³ Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama, 700-8558, Japan.
¹⁴ Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15 Nishi 7, Kita-ku, Sapporo, Hokkaido, 060-8648, Japan.
¹⁵ Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N15, W5, Kita-ku, Sapporo, 060-8638, Japan.

PMID: 39096356
DOI: 10.1007/s11547-024-01861-4

Review

Recent trends in AI applications for pelvic MRI: a comprehensive review

Takahiro Tsuboyama et al. Radiol Med. 2024 Sep.

. 2024 Sep;129(9):1275-1287.

doi: 10.1007/s11547-024-01861-4. Epub 2024 Aug 3.

Authors

Affiliations

¹ Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe-City, Hyogo, 650-0017, Japan. ttsuboyama@gmail.com.
² Department of Radiology, Osaka University Graduate School of Medicine, Suita-City, Osaka, 565-0871, Japan.
³ Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan.
⁴ Department of Radiology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
⁵ Department of Artificial Intelligence, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan.
⁶ Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan.
⁷ Medical Data Science Course, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan.
⁸ Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawaharacho, Sakyoku, Kyoto, 606-8507, Japan.
⁹ Department of Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
¹⁰ Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, 1-1-1 Honjo Chuo-ku, Kumamoto, 860-8556, Japan.
¹¹ Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-0016, Japan.
¹² Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, 113-8421, Japan.
¹³ Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama, 700-8558, Japan.
¹⁴ Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15 Nishi 7, Kita-ku, Sapporo, Hokkaido, 060-8648, Japan.
¹⁵ Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N15, W5, Kita-ku, Sapporo, 060-8638, Japan.

PMID: 39096356
DOI: 10.1007/s11547-024-01861-4

Abstract

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.

Keywords: Artificial intelligence; Bladder; Magnetic resonance imaging; Ovary; Prostate; Rectum; Uterus.

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References

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1. 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 - PubMed - PMC
1. 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 - PubMed
1. 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 - PubMed
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[1] 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 - PubMed

[2] 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 - PubMed

[3] 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 - PubMed - PMC

[4] 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 - PubMed - PMC

[5] 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 - PubMed

[6] 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 - PubMed

[7] 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 - PubMed

[8] 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 - PubMed

[9] 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 - PubMed - PMC

[10] 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 - PubMed - PMC

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Recent trends in AI applications for pelvic MRI: a comprehensive review

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Recent trends in AI applications for pelvic MRI: a comprehensive review

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