Artificial Intelligence-assisted Volume Isotropic Simultaneous Interleaved Bright- and Black-blood Examination for Brain Metastases

Overview

Journal Neuroradiology

Specialties Neurology
Radiology

Date 2024 Aug 22

PMID 39172167

Authors

Kazufumi Kikuchi

Osamu Togao

Yoshitomo Kikuchi

Koji Yamashita

Daichi Momosaka

Kazunori Fukasawa

Shunsuke Nishimura

Hiroyuki Toyoda

Makoto Obara

Akio Hiwatashi

Kousei Ishigami

Affiliations

Soon will be listed here.

Abstract

Purpose: To verify the effectiveness of artificial intelligence-assisted volume isotropic simultaneous interleaved bright-/black-blood examination (AI-VISIBLE) for detecting brain metastases.

Methods: This retrospective study was approved by our institutional review board and the requirement for written informed consent was waived. Forty patients were included: 20 patients with and without brain metastases each. Seven independent observers (three radiology residents and four neuroradiologists) participated in two reading sessions: in the first, brain metastases were detected using VISIBLE only; in the second, the results of the first session were comprehensively evaluated by adding AI-VISIBLE information. Sensitivity, diagnostic performance, and false positives/case were evaluated. Diagnostic performance was assessed using a figure-of-merit (FOM). Sensitivity and false positives/case were evaluated using McNemar and paired t-tests, respectively.

Results: The McNemar test revealed a significant difference between VISIBLE with/without AI information (P < 0.0001). Significantly higher sensitivity (94.9 ± 1.7% vs. 88.3 ± 5.1%, P = 0.0028) and FOM (0.983 ± 0.009 vs. 0.972 ± 0.013, P = 0.0063) were achieved using VISIBLE with AI information vs. without. No significant difference was observed in false positives/case with and without AI information (0.23 ± 0.19 vs. 0.18 ± 0.15, P = 0.250). AI-assisted results of radiology residents became comparable to results of neuroradiologists (sensitivity, FOM: 85.9 ± 3.4% vs. 90.0 ± 5.9%, 0.969 ± 0.016 vs. 0.974 ± 0.012 without AI information; 94.8 ± 1.3% vs. 95.0 ± 2.1%, 0.977 ± 0.010 vs. 0.988 ± 0.005 with AI information, respectively).

Conclusion: AI-VISIBLE improved the sensitivity and performance for diagnosing brain metastases.

References

Lakhani P, Sundaram B . Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. Radiology. 2017; 284(2):574-582. DOI: 10.1148/radiol.2017162326. View

Savenije M, Maspero M, Sikkes G, van der Voort van Zyp J, Kotte A, Bol G . Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy. Radiat Oncol. 2020; 15(1):104. PMC: 7216473. DOI: 10.1186/s13014-020-01528-0. View

Kamnitsas K, Ledig C, Newcombe V, Simpson J, Kane A, Menon D . Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal. 2016; 36:61-78. DOI: 10.1016/j.media.2016.10.004. View

Lippitz B, Lindquist C, Paddick I, Peterson D, ONeill K, Beaney R . Stereotactic radiosurgery in the treatment of brain metastases: the current evidence. Cancer Treat Rev. 2013; 40(1):48-59. DOI: 10.1016/j.ctrv.2013.05.002. View

Posner J, Chernik N . Intracranial metastases from systemic cancer. Adv Neurol. 1978; 19:579-92. View

Serizawa T, Hirai T, Nagano O, Higuchi Y, Matsuda S, Ono J . Gamma knife surgery for 1-10 brain metastases without prophylactic whole-brain radiation therapy: analysis of cases meeting the Japanese prospective multi-institute study (JLGK0901) inclusion criteria. J Neurooncol. 2010; 98(2):163-7. DOI: 10.1007/s11060-010-0169-x. View

Bousabarah K, Ruge M, Brand J, Hoevels M, Ruess D, Borggrefe J . Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data. Radiat Oncol. 2020; 15(1):87. PMC: 7171921. DOI: 10.1186/s13014-020-01514-6. View

Monaco 3rd E, Faraji A, Berkowitz O, Parry P, Hadelsberg U, Kano H . Leukoencephalopathy after whole-brain radiation therapy plus radiosurgery versus radiosurgery alone for metastatic lung cancer. Cancer. 2012; 119(1):226-32. DOI: 10.1002/cncr.27504. View

Kikuchi Y, Togao O, Kikuchi K, Momosaka D, Obara M, Van Cauteren M . A deep convolutional neural network-based automatic detection of brain metastases with and without blood vessel suppression. Eur Radiol. 2022; 32(5):2998-3005. DOI: 10.1007/s00330-021-08427-2. View

10.

Chakraborty D . Analysis of location specific observer performance data: validated extensions of the jackknife free-response (JAFROC) method. Acad Radiol. 2006; 13(10):1187-93. DOI: 10.1016/j.acra.2006.06.016. View

11.

Linskey M, Andrews D, Asher A, Burri S, Kondziolka D, Robinson P . The role of stereotactic radiosurgery in the management of patients with newly diagnosed brain metastases: a systematic review and evidence-based clinical practice guideline. J Neurooncol. 2009; 96(1):45-68. PMC: 2808519. DOI: 10.1007/s11060-009-0073-4. View

12.

Charron O, Lallement A, Jarnet D, Noblet V, Clavier J, Meyer P . Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network. Comput Biol Med. 2018; 95:43-54. DOI: 10.1016/j.compbiomed.2018.02.004. View

13.

Russell E, Geremia G, Johnson C, Huckman M, Ramsey R, Turner D . Multiple cerebral metastases: detectability with Gd-DTPA-enhanced MR imaging. Radiology. 1987; 165(3):609-17. DOI: 10.1148/radiology.165.3.3317495. View

14.

Park Y, Jun Y, Lee Y, Han K, An C, Ahn S . Robust performance of deep learning for automatic detection and segmentation of brain metastases using three-dimensional black-blood and three-dimensional gradient echo imaging. Eur Radiol. 2021; 31(9):6686-6695. DOI: 10.1007/s00330-021-07783-3. View

15.

Pope W . Brain metastases: neuroimaging. Handb Clin Neurol. 2018; 149:89-112. PMC: 6118134. DOI: 10.1016/B978-0-12-811161-1.00007-4. View

16.

Zhou Z, Sanders J, Johnson J, Gule-Monroe M, Chen M, Briere T . Computer-aided Detection of Brain Metastases in T1-weighted MRI for Stereotactic Radiosurgery Using Deep Learning Single-Shot Detectors. Radiology. 2020; 295(2):407-415. PMC: 8287889. DOI: 10.1148/radiol.2020191479. View

17.

Nagao E, Yoshiura T, Hiwatashi A, Obara M, Yamashita K, Kamano H . 3D turbo spin-echo sequence with motion-sensitized driven-equilibrium preparation for detection of brain metastases on 3T MR imaging. AJNR Am J Neuroradiol. 2011; 32(4):664-70. PMC: 7965899. DOI: 10.3174/ajnr.A2343. View

18.

Akeson P, Larsson E, Kristoffersen D, Jonsson E, Holtas S . Brain metastases--comparison of gadodiamide injection-enhanced MR imaging at standard and high dose, contrast-enhanced CT and non-contrast-enhanced MR imaging. Acta Radiol. 1995; 36(3):300-6. View

19.

Zhang M, Young G, Chen H, Li J, Qin L, McFaline-Figueroa J . Deep-Learning Detection of Cancer Metastases to the Brain on MRI. J Magn Reson Imaging. 2020; 52(4):1227-1236. PMC: 7487020. DOI: 10.1002/jmri.27129. View

20.

Dikici E, Ryu J, Demirer M, Bigelow M, White R, Slone W . Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI. IEEE J Biomed Health Inform. 2020; 24(10):2883-2893. DOI: 10.1109/JBHI.2020.2982103. View