Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
European Radiology
Published in: European Radiology 11/2022

08-04-2022 | Metastasis | Imaging Informatics and Artificial Intelligence

Deep learning–based algorithm improved radiologists’ performance in bone metastases detection on CT

Authors: Shunjiro Noguchi, Mizuho Nishio, Ryo Sakamoto, Masahiro Yakami, Koji Fujimoto, Yutaka Emoto, Takeshi Kubo, Yoshio Iizuka, Keita Nakagomi, Kazuhiro Miyasa, Kiyohide Satoh, Yuji Nakamoto

Published in: European Radiology | Issue 11/2022

Login to get access

Abstract

Objectives

To develop and evaluate a deep learning–based algorithm (DLA) for automatic detection of bone metastases on CT.

Methods

This retrospective study included CT scans acquired at a single institution between 2009 and 2019. Positive scans with bone metastases and negative scans without bone metastasis were collected to train the DLA. Another 50 positive and 50 negative scans were collected separately from the training dataset and were divided into validation and test datasets at a 2:3 ratio. The clinical efficacy of the DLA was evaluated in an observer study with board-certified radiologists. Jackknife alternative free-response receiver operating characteristic analysis was used to evaluate observer performance.

Results

A total of 269 positive scans including 1375 bone metastases and 463 negative scans were collected for the training dataset. The number of lesions identified in the validation and test datasets was 49 and 75, respectively. The DLA achieved a sensitivity of 89.8% (44 of 49) with 0.775 false positives per case for the validation dataset and 82.7% (62 of 75) with 0.617 false positives per case for the test dataset. With the DLA, the overall performance of nine radiologists with reference to the weighted alternative free-response receiver operating characteristic figure of merit improved from 0.746 to 0.899 (p < .001). Furthermore, the mean interpretation time per case decreased from 168 to 85 s (p = .004).

Conclusion

With the aid of the algorithm, the overall performance of radiologists in bone metastases detection improved, and the interpretation time decreased at the same time.

Key Points

• A deep learning–based algorithm for automatic detection of bone metastases on CT was developed.
• In the observer study, overall performance of radiologists in bone metastases detection improved significantly with the aid of the algorithm.
• Radiologists’ interpretation time decreased at the same time.
Appendix
Available only for authorised users
Literature
1.
Coleman RE (2001) Metastatic bone disease: clinical features, pathophysiology and treatment strategies. Cancer Treat Rev 27:165–176CrossRef
2.
Macedo F, Ladeira K, Pinho F et al (2017) Bone metastases: an overview. Oncol Rev 11:321
3.
D’Oronzo S, Coleman R, Brown J, Silvestris F (2019) Metastatic bone disease: pathogenesis and therapeutic options: up-date on bone metastasis management. J Bone Oncol 15:100205CrossRef
4.
O’Sullivan GJ, Carty FL, Cronin CG (2015) Imaging of bone metastasis: an update. World J Radiol 7:202–211CrossRef
5.
Heindel W, Gübitz R, Vieth V, Weckesser M, Schober O, Schäfers M (2014) The diagnostic imaging of bone metastases. Dtsch Arztebl Int 111:741–747
6.
Kalogeropoulou C, Karachaliou A, Zampakis P (2009) Radiologic evaluation of skeletal metastases: role of plain radiographs and computed tomography. In: Cancer metastasis – biology and treatment, 12:119–136. Springer, Dordrecht
7.
Groves AM, Beadsmoore CJ, Cheow HK et al (2006) Can 16-detector multislice CT exclude skeletal lesions during tumour staging? Implications for the cancer patient. Eur Radiol 16:1066–1073CrossRef
8.
Chmelik J, Jakubicek R, Walek P et al (2018) Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data. Med Image Anal 49:76–88CrossRef
9.
Hammon M, Dankerl P, Tsymbal A et al (2013) Automatic detection of lytic and blastic thoracolumbar spine metastases on computed tomography. Eur Radiol 23:1862–1870CrossRef
10.
Vandemark RM, Shpall EJ, Lou AM (1992) Bone metastases from breast cancer: value of CT bone windows. J Comput Assist Tomogr 16:608–614CrossRef
11.
Pomerantz SM, White CS, Krebs TL et al (2000) Liver and bone window settings for soft-copy interpretation of chest and abdominal CT. AJR Am J Roentgenol 174:311–314
12.
Burns JE, Yao J, Wiese TS, Muñoz HE, Jones EC, Summers RM (2013) Automated detection of sclerotic metastases in the thoracolumbar spine at CT. Radiology 268:69–78CrossRef
13.
Choy G, Khalilzadeh O, Michalski M et al (2018) Current applications and future impact of machine learning in radiology. Radiology 288:318–328CrossRef
14.
Roth HR, Lu L, Liu J et al (2016) Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE Trans Med Imaging 35:1170–1181CrossRef
15.
Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: MICCAI 2015. Lecture Notes in Computer Science, 9351:234–241. Springer, Cham
16.
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) p770–778. Las Vegas
17.
Noguchi S, Nishio M, Yakami M, Nakagomi K, Togashi K (2020) Bone segmentation on whole-body CT using convolutional neural network with novel data augmentation techniques. Comput Biol Med 121:103767CrossRef
18.
Zou KH, Warfield SK, Bharatha A et al (2004) Statistical validation of image segmentation quality based on a spatial overlap index. Acad Radiol 11:178–189CrossRef
19.
Chakraborty DP, Zhai X (2016) On the meaning of the weighted alternative free-response operating characteristic figure of merit. Med Phys 43:2548–2557CrossRef
20.
Chakraborty DP (2017) Observer performance methods for diagnostic imaging: foundations, modeling, and applications with R-based examples. CRC Press, Boca RatonCrossRef
21.
22.
Sakamoto R, Yakami M, Fujimoto K et al (2017) Temporal subtraction of serial CT images with large deformation diffeomorphic metric mapping in the identification of bone metastases. Radiology 285:629–639CrossRef
23.
Nakamoto Y, Osman M, Wahl RL (2003) Prevalence and patterns of bone metastases detected with positron emission tomography using F-18 FDG. Clin Nucl Med 28:302–307CrossRef
24.
Kakhki VRD, Anvari K, Sadeghi R, Mahmoudian AS, Torabian-Kakhki M (2013) Pattern and distribution of bone metastases in common malignant tumors. Nucl Med Rev 16:66–69CrossRef
25.
Kobatake H (2007) Future CAD in multi-dimensional medical images: - project on multi-organ, multi-disease CAD system -. Comput Med Imaging Graph 31:258–266
26.
Liu K, Li Q, Ma J et al (2019) Evaluating a fully automated pulmonary nodule detection approach and its impact on radiologist performance. Radiol Artif Intell 1:e180084CrossRef
27.
Xie H, Yang D, Sun N, Chen Z, Zhang Y (2019) Automated pulmonary nodule detection in CT images using deep convolutional neural networks. Pattern Recognit 85:109–119CrossRef
28.
Pehrson LM, Nielsen MB, Lauridsen CA (2019) Automatic pulmonary nodule detection applying deep learning or machine learning algorithms to the LIDC-IDRI database: a systematic review. Diagnostics 9:29CrossRef
29.
Chlebus G, Schenk A, Moltz JH, van Ginneken B, Hahn HK, Meine H (2018) Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing. Sci Rep 8:15497CrossRef
30.
Vorontsov E, Cerny M, Régnier P et al (2019) Deep learning for automated segmentation of liver lesions at ct in patients with colorectal cancer liver metastases. Radiol Artif Intell 1:e180014CrossRef
31.
Azer SA (2019) Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: a systematic review. World J Gastrointest Oncol 11:1218–1230CrossRef
32.
van Leeuwen KG, Schalekamp S, Rutten MJCM, van Ginneken B, de Rooij M (2021) Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol 31:3797–3804CrossRef
33.
Çiray I, Åström G, Sundström C, Hagberg H, Ahlström H (1997) Assessment of suspected bone metastases: CT with and without clinical information compared to CT-guided bone biopsy. Acta Radiol 38:890–895
Metadata
Title
Deep learning–based algorithm improved radiologists’ performance in bone metastases detection on CT
Authors
Shunjiro Noguchi
Mizuho Nishio
Ryo Sakamoto
Masahiro Yakami
Koji Fujimoto
Yutaka Emoto
Takeshi Kubo
Yoshio Iizuka
Keita Nakagomi
Kazuhiro Miyasa
Kiyohide Satoh
Yuji Nakamoto
Publication date
08-04-2022
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 11/2022
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-022-08741-3

Other articles of this Issue 11/2022

European Radiology 11/2022 Go to the issue

Musculoskeletal

Kinematic 4D CT case-control study of wrist in dart throwing motion “in vivo”: comparison with other maneuvers

Forensic Medicine

Forensic age assessment by 3.0 T MRI of the wrist: adaption of the Vieth classification

Chest

Incremental benefits of size-zone matrix-based radiomics features for the prognosis of lung adenocarcinoma: advantage of spatial partitioning on tumor evaluation

Musculoskeletal

A handbook for beginners in skeletal muscle diffusion tensor imaging: physical basis and technical adjustments

Imaging Informatics and Artificial Intelligence

Radiomics can differentiate high-grade glioma from brain metastasis: a systematic review and meta-analysis

Head and Neck

CT-based radiomics signature analysis for sevaluation of response to induction chemotherapy and progression-free survival in locally advanced hypopharyngeal carcinoma