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Construction and evaluation of a gated high-resolution neural network for automatic brain metastasis detection and segmentation

  • Oncology
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To construct and evaluate a gated high-resolution convolutional neural network for detecting and segmenting brain metastasis (BM).

Methods

This retrospective study included craniocerebral MRI scans of 1392 patients with 14,542 BMs and 200 patients with no BM between January 2012 and April 2022. A primary dataset including 1000 cases with 11,686 BMs was employed to construct the model, while an independent dataset including 100 cases with 1069 BMs from other hospitals was used to examine the generalizability. The potential of the model for clinical use was also evaluated by comparing its performance in BM detection and segmentation to that of radiologists, and comparing radiologists’ lesion detecting performances with and without model assistance.

Results

Our model yielded a recall of 0.88, a dice similarity coefficient (DSC) of 0.90, a positive predictive value (PPV) of 0.93 and a false positives per patient (FP) of 1.01 in the test set, and a recall of 0.85, a DSC of 0.89, a PPV of 0.93, and a FP of 1.07 in dataset from other hospitals. With the model’s assistance, the BM detection rates of 4 radiologists improved significantly, ranging from 5.2 to 15.1% (all p < 0.001), and also for detecting small BMs with diameter ≤ 5 mm (ranging from 7.2 to 27.0%, all p < 0.001).

Conclusions

The proposed model enables accurate BM detection and segmentation with higher sensitivity and less time consumption, showing the potential to augment radiologists’ performance in detecting BM.

Clinical relevance statement

This study offers a promising computer-aided tool to assist the brain metastasis detection and segmentation in routine clinical practice for cancer patients.

Key Points

• The GHR-CNN could accurately detect and segment BM on contrast-enhanced 3D-T1W images.

• The GHR-CNN improved the BM detection rate of radiologists, including the detection of small lesions.

• The GHR-CNN enabled automated segmentation of BM in a very short time.

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Abbreviations

BM:

Brain metastasis

CAD:

Computer-assisted diagnosis

CNN:

Convolutional neural network

DL:

Deep learning

DSC:

Dice similarity coefficient

GHR-CNN:

Gated high-resolution convolutional neural network

ML:

Machine learning

PPV:

Positive predictive value

SRT:

Stereotactic radiotherapy

References

  1. Owonikoko TK, Arbiser J, Zelnak A et al (2014) Current approaches to the treatment of metastatic brain tumours. Nat Rev Clin Oncol 11:203–222

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Suh JH, Kotecha R, Chao ST, Ahluwalia MS, Sahgal A, Chang EL (2020) Current approaches to the management of brain metastases. Nat Rev Clin Oncol 17:279–299

    Article  PubMed  Google Scholar 

  3. Carden CP, Agarwal R, Saran F, Judson IR (2008) Eligibility of patients with brain metastases for phase I trials: time for a rethink? Lancet Oncol 9:1012–1017

    Article  PubMed  Google Scholar 

  4. Olson JJ, Kalkanis SN, Ryken TC (2019) Congress of Neurological Surgeons Systematic Review and Evidence-Based Guidelines for the Treatment of Adults With Metastatic Brain Tumors: Executive Summary. Neurosurgery 84:550–552

    Article  PubMed  Google Scholar 

  5. Pope WB (2018) Brain metastases: neuroimaging. Handb Clin Neurol 149:89–112

    Article  PubMed  PubMed Central  Google Scholar 

  6. National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology. Available via https://www.nccn.org/professionals/physician_gls/. Accessed 1 Jun 2020

  7. Lin NU, Lee EQ, Aoyama H et al (2015) Response assessment criteria for brain metastases: proposal from the RANO group. Lancet Oncol 16:e270–278

    Article  PubMed  Google Scholar 

  8. Schellinger PD, Meinck HM, Thron A (1999) Diagnostic accuracy of MRI compared to CCT in patients with brain metastases. J Neurooncol 44:275–281

    Article  CAS  PubMed  Google Scholar 

  9. Yin S, Luo X, Yang Y et al (2022) Development and validation of a deep-learning model for detecting brain metastases on 3D post-contrast MRI: a multi-center multi-reader evaluation study. Neuro Oncol 24:1559–1570

    Article  PubMed  PubMed Central  Google Scholar 

  10. Zhang M, Young GS, Chen H et al (2020) Deep-learning detection of cancer metastases to the brain on MRI. J Magn Reson Imaging 52:1227–1236

    Article  PubMed  PubMed Central  Google Scholar 

  11. Growcott S, Dembrey T, Patel R, Eaton D, Cameron A (2020) Inter-observer variability in target volume delineations of benign and metastatic brain tumours for stereotactic radiosurgery: results of a national quality assurance programme. Clin Oncol (R Coll Radiol) 32:13–25

    Article  CAS  PubMed  Google Scholar 

  12. Gudigar A, Raghavendra U, Hegde A, Kalyani M, Ciaccio EJ, Rajendra Acharya U (2020) Brain pathology identification using computer aided diagnostic tool: a systematic review. Comput Methods Programs Biomed 187:105205

    Article  PubMed  Google Scholar 

  13. Titano JJ, Badgeley M, Schefflein J et al (2018) Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat Med 24:1337–1341

    Article  CAS  PubMed  Google Scholar 

  14. Yang J, Xie M, Hu C et al (2021) Deep learning for detecting cerebral aneurysms with CT angiography. Radiology 298:155–163

    Article  PubMed  Google Scholar 

  15. Larson DB, Chen MC, Lungren MP, Halabi SS, Stence NV, Langlotz CP (2018) Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology 287:313–322

    Article  PubMed  Google Scholar 

  16. Zheng Q, Yang L, Zeng B et al (2021) Artificial intelligence performance in detecting tumor metastasis from medical radiology imaging: a systematic review and meta-analysis. EClinicalMedicine 31:100669

    Article  PubMed  Google Scholar 

  17. Sunwoo L, Kim YJ, Choi SH et al (2017) Computer-aided detection of brain metastasis on 3D MR imaging: Observer performance study. PLoS One 12:e0178265

  18. Shi Z, Miao C, Schoepf UJ et al (2020) A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images. Nat Commun 11:6090

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Bousabarah K, Ruge M, Brand JS et al (2020) Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data. Radiat Oncol 15:87

    Article  PubMed  PubMed Central  Google Scholar 

  20. Cao Y, Vassantachart A, Ye JC et al (2021) Automatic detection and segmentation of multiple brain metastases on magnetic resonance image using asymmetric UNet architecture. Phys Med Biol 66:015003

    Article  CAS  PubMed  Google Scholar 

  21. Charron O, Lallement A, Jarnet D, Noblet V, Clavier JB, Meyer P (2018) Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network. Comput Biol Med 95:43–54

    Article  PubMed  Google Scholar 

  22. Cho J, Kim YJ, Sunwoo L et al (2021) Deep learning-based computer-aided detection system for automated treatment response assessment of brain metastases on 3D MRI. Front Oncol 11:739639

    Article  PubMed  PubMed Central  Google Scholar 

  23. Dikici E, Ryu JL, Demirer M et al (2020) Automated brain metastases detection framework for T1-weighted contrast-enhanced 3D MRI. IEEE J Biomed Health Inform 24:2883–2893

    Article  PubMed  Google Scholar 

  24. Deike-Hofmann K, Dancs D, Paech D et al (2021) Pre-examinations improve automated metastases detection on cranial MRI. Invest Radiol 56:320–327

    Article  CAS  PubMed  Google Scholar 

  25. Grøvik E, Yi D, Iv M, Tong E, Rubin D, Zaharchuk G (2020) Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI. J Magn Reson Imaging 51:175–182

    Article  PubMed  Google Scholar 

  26. Jünger ST, Hoyer UCI, Schaufler D et al (2021) Fully Automated MR detection and segmentation of brain metastases in non-small cell lung cancer using deep learning. J Magn Reson Imaging 54:1608–1622

    Article  PubMed  Google Scholar 

  27. Kikuchi Y, Togao O, Kikuchi K et al (2022) A deep convolutional neural network-based automatic detection of brain metastases with and without blood vessel suppression. Eur Radiol 32:2998–3005

    Article  PubMed  Google Scholar 

  28. Liu Y, Stojadinovic S, Hrycushko B et al (2017) A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery. PLoS One 12:e0185844

  29. Losch M (2015) Detection and segmentation of brain metastases with deep convolutional networks. Master’s Thesis, KTH Royal Institute of Technology in Stockholm.

  30. Park YW, Jun Y, Lee Y et al (2021) 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 31:6686–6695

    Article  PubMed  Google Scholar 

  31. Pennig L, Shahzad R, Caldeira L et al (2021) Automated detection and segmentation of brain metastases in malignant melanoma: evaluation of a dedicated deep learning model. AJNR Am J Neuroradiol 42:655–662

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Xue J, Wang B, Ming Y et al (2020) Deep learning-based detection and segmentation-assisted management of brain metastases. Neuro Oncol 22:505–514

    Article  PubMed  Google Scholar 

  33. Yoo SK, Kim TH, Chun J et al (2022) Deep-learning-based automatic detection and segmentation of brain metastases with small volume for stereotactic ablative radiotherapy. Cancers (Basel) 14:2555

    Article  PubMed  Google Scholar 

  34. Zhou Z, Sanders JW, Johnson JM et al (2020) Computer-aided detection of brain metastases in T1-weighted MRI for stereotactic radiosurgery using deep learning single-shot detectors. Radiology 295:407–415

    Article  PubMed  Google Scholar 

  35. Yang X, Shi J, Zhang J, Li Q (2022) Gated channel attention mechanism YOLOv3 network for small target detection. Advances in Multimedia 2022:1–10

    Article  Google Scholar 

  36. Isensee F, Petersen J, Kohl SAA, Jäger PF, Maier-Hein KH (2019) nnU-Net: breaking the spell on successful medical image segmentation, arXiv preprint arXiv:1904.08128

  37. Chen C, Liu X, Ding M, Zheng J, Li J (2019) 3D Dilated multi-fiber network for real-time brain tumor segmentation in MRI. in: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer.2019:184-192

  38. Luo Z, Jia Z, Yuan Z, Peng J (2021) HDC-Net: Hierarchical Decoupled Convolution Network for Brain Tumor Segmentation. IEEE J Biomed Health Inform 25:737–745

  39. Cho SJ, Sunwoo L, Baik SH, Bae YJ, Choi BS, Kim JH (2021) Brain metastasis detection using machine learning: a systematic review and meta-analysis. Neuro Oncol 23:214–225

    Article  PubMed  Google Scholar 

  40. Farjam R, Parmar HA, Noll DC, Tsien CI, Cao Y (2012) An approach for computer-aided detection of brain metastases in post-Gd T1-W MRI. Magn Reson Imaging 30:824–36

    Article  PubMed  PubMed Central  Google Scholar 

  41. Yang S, Nam Y, Kim MO, Kim EY, Park J, Kim DH (2013) Computer-aided detection of metastatic brain tumors using magnetic resonance black-blood imaging. Invest Radiol 48:113–119

    Article  PubMed  Google Scholar 

  42. Perez-Ramirez U, Arana E, Moratal D (2016) Brain metastases detection on MR by means of three-dimensional tumor-appearance template matching. J Magn Reson Imaging 44:642–652

    Article  PubMed  Google Scholar 

  43. James CA, Wachter RM, Woolliscroft JO (2022) Preparing clinicians for a clinical world influenced by artificial intelligence. JAMA 327:1333–1334

    Article  PubMed  Google Scholar 

  44. Kumawat S, Verma M, Nakashima Y, Raman S (2021) Depthwise spatio-temporal STFT convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 44:4839–4851

    Google Scholar 

Download references

Funding

This work was supported by National Key R&D Program of China (Project Nos. 2022YFC2009901, 2022YFC2009900), the National Natural Science Foundation of China (Grant Nos. 82120108014, 82071908 and 82101998), CAMS Innovation Fund for Medical Sciences (CIFMS) (Project No. 2021-I2M-C&T-A-022), Chengdu Science and Technology Office, major technology application demonstration project (Project Nos. 2022-YF09-00062-SN, 2022-GH03-00017-HZ) and Sichuan Science and Technology Program (Grant Nos. 2021JDTD0002). Dr. Su Lui acknowledges the support from Humboldt Foundation Friedrich Wilhelm Bessel Research Award and Chang Jiang Scholars (Program No. T2019069).

Author information

Author notes

  1. Jiao Qu and Wenjing Zhang contributed equally to the current study and shared first authorship.

Authors and Affiliations

  1. Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China

    Jiao Qu, Wenjing Zhang, Ying Wang, Mengyuan Xu, Li Yao, Na Hu, Biqiu Tang & Su Lui

  2. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China

    Xin Shu, Lituan Wang & Lei Zhang

  3. Department of Nuclear Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, China

    Ying Wang

Authors
  1. Jiao Qu
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Corresponding author

Correspondence to Su Lui.

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Guarantor

The scientific guarantor of this publication is Professor Su Lui, MD, PhD, from Department of Radiology, West China Hospital, Sichuan University.

Conflict of interest

The authors have no relevant conflicts of interest to disclose.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

The institutional review board waived the requirement to obtain informed patient consent for this retrospective study.

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Institutional Review Board approval was obtained.

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• retrospective

• diagnostic or prognostic study

• performed at multiple institutions

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Qu, J., Zhang, W., Shu, X. et al. Construction and evaluation of a gated high-resolution neural network for automatic brain metastasis detection and segmentation. Eur Radiol 33, 6648–6658 (2023). https://doi.org/10.1007/s00330-023-09648-3

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