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Machura B, Kucharski D, Bozek O, Eksner B, Kokoszka B, Pekala T, Radom M, Strzelczak M, Zarudzki L, Gutiérrez-Becker B, Krason A, Tessier J, Nalepa J. Deep learning ensembles for detecting brain metastases in longitudinal multi-modal MRI studies. Comput Med Imaging Graph 2024; 116:102401. [PMID: 38795690 DOI: 10.1016/j.compmedimag.2024.102401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 05/13/2024] [Accepted: 05/13/2024] [Indexed: 05/28/2024]
Abstract
Metastatic brain cancer is a condition characterized by the migration of cancer cells to the brain from extracranial sites. Notably, metastatic brain tumors surpass primary brain tumors in prevalence by a significant factor, they exhibit an aggressive growth potential and have the capacity to spread across diverse cerebral locations simultaneously. Magnetic resonance imaging (MRI) scans of individuals afflicted with metastatic brain tumors unveil a wide spectrum of characteristics. These lesions vary in size and quantity, spanning from tiny nodules to substantial masses captured within MRI. Patients may present with a limited number of lesions or an extensive burden of hundreds of them. Moreover, longitudinal studies may depict surgical resection cavities, as well as areas of necrosis or edema. Thus, the manual analysis of such MRI scans is difficult, user-dependent and cost-inefficient, and - importantly - it lacks reproducibility. We address these challenges and propose a pipeline for detecting and analyzing brain metastases in longitudinal studies, which benefits from an ensemble of various deep learning architectures originally designed for different downstream tasks (detection and segmentation). The experiments, performed over 275 multi-modal MRI scans of 87 patients acquired in 53 sites, coupled with rigorously validated manual annotations, revealed that our pipeline, built upon open-source tools to ensure its reproducibility, offers high-quality detection, and allows for precisely tracking the disease progression. To objectively quantify the generalizability of models, we introduce a new data stratification approach that accommodates the heterogeneity of the dataset and is used to elaborate training-test splits in a data-robust manner, alongside a new set of quality metrics to objectively assess algorithms. Our system provides a fully automatic and quantitative approach that may support physicians in a laborious process of disease progression tracking and evaluation of treatment efficacy.
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Affiliation(s)
| | - Damian Kucharski
- Graylight Imaging, Gliwice, Poland; Silesian University of Technology, Gliwice, Poland.
| | - Oskar Bozek
- Department of Radiodiagnostics and Invasive Radiology, School of Medicine in Katowice, Medical University of Silesia in Katowice, Katowice, Poland.
| | - Bartosz Eksner
- Department of Radiology and Nuclear Medicine, ZSM Chorzów, Chorzów, Poland.
| | - Bartosz Kokoszka
- Department of Radiodiagnostics and Invasive Radiology, School of Medicine in Katowice, Medical University of Silesia in Katowice, Katowice, Poland.
| | - Tomasz Pekala
- Department of Radiodiagnostics, Interventional Radiology and Nuclear Medicine, University Clinical Centre, Katowice, Poland.
| | - Mateusz Radom
- Department of Radiology and Diagnostic Imaging, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland.
| | - Marek Strzelczak
- Department of Radiology and Diagnostic Imaging, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland.
| | - Lukasz Zarudzki
- Department of Radiology and Diagnostic Imaging, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland.
| | - Benjamín Gutiérrez-Becker
- Roche Pharma Research and Early Development, Informatics, Roche Innovation Center Basel, Basel, Switzerland.
| | - Agata Krason
- Roche Pharma Research and Early Development, Early Clinical Development Oncology, Roche Innovation Center Basel, Basel, Switzerland.
| | - Jean Tessier
- Roche Pharma Research and Early Development, Early Clinical Development Oncology, Roche Innovation Center Basel, Basel, Switzerland.
| | - Jakub Nalepa
- Graylight Imaging, Gliwice, Poland; Silesian University of Technology, Gliwice, Poland.
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Weller M, Remon J, Rieken S, Vollmuth P, Ahn MJ, Minniti G, Le Rhun E, Westphal M, Brastianos PK, Soo RA, Kirkpatrick JP, Goldberg SB, Öhrling K, Hegi-Johnson F, Hendriks LEL. Central nervous system metastases in advanced non-small cell lung cancer: A review of the therapeutic landscape. Cancer Treat Rev 2024; 130:102807. [PMID: 39151281 DOI: 10.1016/j.ctrv.2024.102807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 07/19/2024] [Accepted: 07/29/2024] [Indexed: 08/19/2024]
Abstract
Up to 40% of patients with non-small cell lung cancer (NSCLC) develop central nervous system (CNS) metastases. Current treatments for this subgroup of patients with advanced NSCLC include local therapies (surgery, stereotactic radiosurgery, and, less frequently, whole-brain radiotherapy), targeted therapies for oncogene-addicted NSCLC (small molecules, such as tyrosine kinase inhibitors, and antibody-drug conjugates), and immune checkpoint inhibitors (as monotherapy or combination therapy), with multiple new drugs in development. However, confirming the intracranial activity of these treatments has proven to be challenging, given that most lung cancer clinical trials exclude patients with untreated and/or progressing CNS metastases, or do not include prespecified CNS-related endpoints. Here we review progress in the treatment of patients with CNS metastases originating from NSCLC, examining local treatment options, systemic therapies, and multimodal therapeutic strategies. We also consider challenges regarding assessment of treatment response and provide thoughts around future directions for managing CNS disease in patients with advanced NSCLC.
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Affiliation(s)
- Michael Weller
- Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland.
| | - Jordi Remon
- Paris-Saclay University, Department of Cancer Medicine, Gustave Roussy, Villejuif, France.
| | - Stefan Rieken
- Department of Radiation Oncology, University Hospital Göttingen (UMG), Göttingen, Germany; Comprehensive Cancer Center Lower Saxony (CCC-N), University Hospital Göttingen (UMG), Göttingen, Germany.
| | - Philipp Vollmuth
- Division for Computational Radiology & Clinical AI, Clinic for Neuroradiology, University Hospital Bonn, Bonn, Germany; Division for Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Myung-Ju Ahn
- Division of Hematology-Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
| | - Giuseppe Minniti
- Department of Radiological Sciences, Oncology and Anatomical Pathology, Sapienza University of Rome, Rome, Italy; IRCCS Neuromed, Pozzilli, Italy.
| | - Emilie Le Rhun
- Departments of Neurosurgery and Neurology, University Hospital and University of Zurich, Zurich, Switzerland.
| | - Manfred Westphal
- Department of Neurosurgery and Institute for Tumor Biology, University Hospital Hamburg-Eppendorf, Hamburg, Germany.
| | | | - Ross A Soo
- Department of Hematology-Oncology, National University Hospital, Singapore, Singapore.
| | - John P Kirkpatrick
- Departments of Radiation Oncology and Neurosurgery, Duke University, Durham, NC, USA.
| | - Sarah B Goldberg
- Department of Medicine (Medical Oncology), Yale School of Medicine, Yale Cancer Center, New Haven, CT, USA.
| | | | - Fiona Hegi-Johnson
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Australia; Sir Peter MacCallum Department of Clinical Oncology, University of Melbourne, Melbourne, Australia.
| | - Lizza E L Hendriks
- Department of Respiratory Medicine, Maastricht University Medical Centre, GROW School for Oncology and Reproduction, Maastricht, Netherlands.
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3
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Ghaderi S, Mohammadi S, Mohammadi M, Pashaki ZNA, Heidari M, Khatyal R, Zafari R. A systematic review of brain metastases from lung cancer using magnetic resonance neuroimaging: Clinical and technical aspects. J Med Radiat Sci 2024; 71:269-289. [PMID: 38234262 PMCID: PMC11177032 DOI: 10.1002/jmrs.756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 01/06/2024] [Indexed: 01/19/2024] Open
Abstract
INTRODUCTION Brain metastases (BMs) are common in lung cancer (LC) and are associated with poor prognosis. Magnetic resonance imaging (MRI) plays a vital role in the detection, diagnosis and management of BMs. This review summarises recent advances in MRI techniques for BMs from LC. METHODS This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was conducted in three electronic databases: PubMed, Scopus and the Web of Science. The search was limited to studies published between January 2000 and March 2023. The quality of the included studies was evaluated using appropriate tools for different study designs. A narrative synthesis was carried out to describe the key findings of the included studies. RESULTS Sixty-five studies were included. Standard MRI sequences such as T1-weighted (T1w), T2-weighted (T2w) and fluid-attenuated inversion recovery (FLAIR) were commonly used. Advanced techniques included perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and radiomics analysis. DWI and PWI parameters could distinguish tumour recurrence from radiation necrosis. Radiomics models predicted genetic mutations and the risk of BMs. Diagnostic accuracy was improved with deep learning (DL) approaches. Prognostic factors such as performance status and concurrent chemotherapy impacted survival. CONCLUSION Advanced MRI techniques and specialised MRI methods have emerging roles in managing BMs from LC. PWI and DWI improve diagnostic accuracy in treated BMs. Radiomics and DL facilitate personalised prognosis and treatment. Magnetic resonance imaging plays a key role in the continuum of care for BMs of patients with LC, from screening to treatment monitoring.
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Affiliation(s)
- Sadegh Ghaderi
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in MedicineTehran University of Medical SciencesTehranIran
| | - Sana Mohammadi
- Department of Medical Sciences, School of MedicineIran University of Medical SciencesTehranIran
| | - Mahdi Mohammadi
- Department of Medical Physics and Biomedical Engineering, School of MedicineTehran University of Medical SciencesTehranIran
| | | | - Mehrsa Heidari
- Department of Medical Science, School of MedicineAhvaz Jundishapur University of Medical SciencesAhvazIran
| | - Rahim Khatyal
- Department of Radiology, Faculty of Allied Medical SciencesTabriz University of Medical SciencesTabrizIran
| | - Rasa Zafari
- School of MedicineTehran University of Medical SciencesTehranIran
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Kim M, Wang JY, Lu W, Jiang H, Stojadinovic S, Wardak Z, Dan T, Timmerman R, Wang L, Chuang C, Szalkowski G, Liu L, Pollom E, Rahimy E, Soltys S, Chen M, Gu X. Where Does Auto-Segmentation for Brain Metastases Radiosurgery Stand Today? Bioengineering (Basel) 2024; 11:454. [PMID: 38790322 PMCID: PMC11117895 DOI: 10.3390/bioengineering11050454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 04/26/2024] [Accepted: 04/30/2024] [Indexed: 05/26/2024] Open
Abstract
Detection and segmentation of brain metastases (BMs) play a pivotal role in diagnosis, treatment planning, and follow-up evaluations for effective BM management. Given the rising prevalence of BM cases and its predominantly multiple onsets, automated segmentation is becoming necessary in stereotactic radiosurgery. It not only alleviates the clinician's manual workload and improves clinical workflow efficiency but also ensures treatment safety, ultimately improving patient care. Recent strides in machine learning, particularly in deep learning (DL), have revolutionized medical image segmentation, achieving state-of-the-art results. This review aims to analyze auto-segmentation strategies, characterize the utilized data, and assess the performance of cutting-edge BM segmentation methodologies. Additionally, we delve into the challenges confronting BM segmentation and share insights gleaned from our algorithmic and clinical implementation experiences.
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Affiliation(s)
- Matthew Kim
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| | - Jen-Yeu Wang
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| | - Weiguo Lu
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Hao Jiang
- NeuralRad LLC, Madison, WI 53717, USA
| | | | - Zabi Wardak
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Tu Dan
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Robert Timmerman
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Lei Wang
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| | - Cynthia Chuang
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| | - Gregory Szalkowski
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| | - Lianli Liu
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| | - Erqi Pollom
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| | - Elham Rahimy
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| | - Scott Soltys
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| | - Mingli Chen
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Xuejun Gu
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
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Park YW, Park JE, Ahn SS, Han K, Kim N, Oh JY, Lee DH, Won SY, Shin I, Kim HS, Lee SK. Deep learning-based metastasis detection in patients with lung cancer to enhance reproducibility and reduce workload in brain metastasis screening with MRI: a multi-center study. Cancer Imaging 2024; 24:32. [PMID: 38429843 PMCID: PMC10905821 DOI: 10.1186/s40644-024-00669-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 01/29/2024] [Indexed: 03/03/2024] Open
Abstract
OBJECTIVES To assess whether a deep learning-based system (DLS) with black-blood imaging for brain metastasis (BM) improves the diagnostic workflow in a multi-center setting. MATERIALS AND METHODS In this retrospective study, a DLS was developed in 101 patients and validated on 264 consecutive patients (with lung cancer) having newly developed BM from two tertiary university hospitals, which performed black-blood imaging between January 2020 and April 2021. Four neuroradiologists independently evaluated BM either with segmented masks and BM counts provided (with DLS) or not provided (without DLS) on a clinical trial imaging management system (CTIMS). To assess reading reproducibility, BM count agreement between the readers and the reference standard were calculated using limits of agreement (LoA). Readers' workload was assessed with reading time, which was automatically measured on CTIMS, and were compared between with and without DLS using linear mixed models considering the imaging center. RESULTS In the validation cohort, the detection sensitivity and positive predictive value of the DLS were 90.2% (95% confidence interval [CI]: 88.1-92.2) and 88.2% (95% CI: 85.7-90.4), respectively. The difference between the readers and the reference counts was larger without DLS (LoA: -0.281, 95% CI: -2.888, 2.325) than with DLS (LoA: -0.163, 95% CI: -2.692, 2.367). The reading time was reduced from mean 66.9 s (interquartile range: 43.2-90.6) to 57.3 s (interquartile range: 33.6-81.0) (P <.001) in the with DLS group, regardless of the imaging center. CONCLUSION Deep learning-based BM detection and counting with black-blood imaging improved reproducibility and reduced reading time, on multi-center validation.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, 03722, Seoul, Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, 05505, Seoul, Korea.
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, 03722, Seoul, Korea.
| | - Kyunghwa Han
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, 03722, Seoul, Korea
| | | | - Joo Young Oh
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, 05505, Seoul, Korea
| | - Da Hyun Lee
- Department of Radiology, Ajou University Medical Center, Suwon, Korea
| | - So Yeon Won
- Department of Radiology, Samsung Seoul Hospital, Seoul, Korea
| | - Ilah Shin
- Department of Radiology, The Catholic University of Korea, Seoul St. Mary's hospital, Seoul, Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, 05505, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, 03722, Seoul, Korea
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Son S, Joo B, Park M, Suh SH, Oh HS, Kim JW, Lee S, Ahn SJ, Lee JM. Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment. Front Oncol 2024; 13:1273013. [PMID: 38288101 PMCID: PMC10823345 DOI: 10.3389/fonc.2023.1273013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 12/27/2023] [Indexed: 01/31/2024] Open
Abstract
Purpose/objectives Previous deep learning (DL) algorithms for brain metastasis (BM) detection and segmentation have not been commonly used in clinics because they produce false-positive findings, require multiple sequences, and do not reflect physiological properties such as necrosis. The aim of this study was to develop a more clinically favorable DL algorithm (RLK-Unet) using a single sequence reflecting necrosis and apply it to automated treatment response assessment. Methods and materials A total of 128 patients with 1339 BMs, who underwent BM magnetic resonance imaging using the contrast-enhanced 3D T1 weighted (T1WI) turbo spin-echo black blood sequence, were included in the development of the DL algorithm. Fifty-eight patients with 629 BMs were assessed for treatment response. The detection sensitivity, precision, Dice similarity coefficient (DSC), and agreement of treatment response assessments between neuroradiologists and RLK-Unet were assessed. Results RLK-Unet demonstrated a sensitivity of 86.9% and a precision of 79.6% for BMs and had a DSC of 0.663. Segmentation performance was better in the subgroup with larger BMs (DSC, 0.843). The agreement in the response assessment for BMs between the radiologists and RLK-Unet was excellent (intraclass correlation, 0.84). Conclusion RLK-Unet yielded accurate detection and segmentation of BM and could assist clinicians in treatment response assessment.
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Affiliation(s)
- Seungyeon Son
- Department of Artificial Intelligence, Hanyang University, Seoul, Republic of Korea
| | - Bio Joo
- Department of Radiology, Gangnam Severance Hospital, Yonsei University, College of Medicine, Seoul, Republic of Korea
| | - Mina Park
- Department of Radiology, Gangnam Severance Hospital, Yonsei University, College of Medicine, Seoul, Republic of Korea
| | - Sang Hyun Suh
- Department of Radiology, Gangnam Severance Hospital, Yonsei University, College of Medicine, Seoul, Republic of Korea
| | - Hee Sang Oh
- Department of Radiology, Gangnam Severance Hospital, Yonsei University, College of Medicine, Seoul, Republic of Korea
| | - Jun Won Kim
- Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University, College of Medicine, Seoul, Republic of Korea
| | - Seoyoung Lee
- Division of Medical Oncology, Department of Internal Medicine, Gangnam Severance Hospital, College of Medicine, Yonsei University, Seoul, Republic of Korea
| | - Sung Jun Ahn
- Department of Radiology, Gangnam Severance Hospital, Yonsei University, College of Medicine, Seoul, Republic of Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
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Wang TW, Hsu MS, Lee WK, Pan HC, Yang HC, Lee CC, Wu YT. Brain metastasis tumor segmentation and detection using deep learning algorithms: A systematic review and meta-analysis. Radiother Oncol 2024; 190:110007. [PMID: 37967585 DOI: 10.1016/j.radonc.2023.110007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 10/15/2023] [Accepted: 11/08/2023] [Indexed: 11/17/2023]
Abstract
BACKGROUND Manual detection of brain metastases is both laborious and inconsistent, driving the need for more efficient solutions. Accordingly, our systematic review and meta-analysis assessed the efficacy of deep learning algorithms in detecting and segmenting brain metastases from various primary origins in MRI images. METHODS We conducted a comprehensive search of PubMed, Embase, and Web of Science up to May 24, 2023, which yielded 42 relevant studies for our analysis. We assessed the quality of these studies using the QUADAS-2 and CLAIM tools. Using a random-effect model, we calculated the pooled lesion-wise dice score as well as patient-wise and lesion-wise sensitivity. We performed subgroup analyses to investigate the influence of factors such as publication year, study design, training center of the model, validation methods, slice thickness, model input dimensions, MRI sequences fed to the model, and the specific deep learning algorithms employed. Additionally, meta-regression analyses were carried out considering the number of patients in the studies, count of MRI manufacturers, count of MRI models, training sample size, and lesion number. RESULTS Our analysis highlighted that deep learning models, particularly the U-Net and its variants, demonstrated superior segmentation accuracy. Enhanced detection sensitivity was observed with an increased diversity in MRI hardware, both in terms of manufacturer and model variety. Furthermore, slice thickness was identified as a significant factor influencing lesion-wise detection sensitivity. Overall, the pooled results indicated a lesion-wise dice score of 79%, with patient-wise and lesion-wise sensitivities at 86% and 87%, respectively. CONCLUSIONS The study underscores the potential of deep learning in improving brain metastasis diagnostics and treatment planning. Still, more extensive cohorts and larger meta-analysis are needed for more practical and generalizable algorithms. Future research should prioritize these areas to advance the field. This study was funded by the Gen. & Mrs. M.C. Peng Fellowship and registered under PROSPERO (CRD42023427776).
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Affiliation(s)
- Ting-Wei Wang
- Institute of Biophotonics, National Yang Ming Chiao Tung University, 155, Sec. 2, Li-Nong St. Beitou Dist., Taipei 112304, Taiwan; School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ming-Sheng Hsu
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Wei-Kai Lee
- Institute of Biophotonics, National Yang Ming Chiao Tung University, 155, Sec. 2, Li-Nong St. Beitou Dist., Taipei 112304, Taiwan
| | - Hung-Chuan Pan
- Department of Neurosurgery, Taichung Veterans General Hospital, Taichung, Taiwan; Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Huai-Che Yang
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Cheng-Chia Lee
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, 155, Sec. 2, Li-Nong St. Beitou Dist., Taipei 112304, Taiwan; National Yang Ming Chiao Tung University, Brain Research Center, Taiwan; National Yang Ming Chiao Tung University, College Medical Device Innovation and Translation Center, Taiwan.
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Qu J, Zhang W, Shu X, Wang Y, Wang L, Xu M, Yao L, Hu N, Tang B, Zhang L, Lui S. Construction and evaluation of a gated high-resolution neural network for automatic brain metastasis detection and segmentation. Eur Radiol 2023; 33:6648-6658. [PMID: 37186214 DOI: 10.1007/s00330-023-09648-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/23/2023] [Accepted: 02/08/2023] [Indexed: 05/17/2023]
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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Affiliation(s)
- Jiao Qu
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China
| | - Wenjing Zhang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China
| | - Xin Shu
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China
| | - Ying Wang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China
- Department of Nuclear Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Lituan Wang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China
| | - Mengyuan Xu
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China
| | - Li Yao
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China
| | - Na Hu
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China
| | - Biqiu Tang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China
| | - Lei Zhang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China
| | - Su Lui
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China.
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9
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Zhu D, Shao Y, Yang Z, Cheng A, Xi Q, Liang X, Chu S. Magnetic resonance imaging characteristics of brain metastases in small cell lung cancer. Cancer Med 2023; 12:15199-15206. [PMID: 37288842 PMCID: PMC10417172 DOI: 10.1002/cam4.6206] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 05/13/2023] [Accepted: 05/23/2023] [Indexed: 06/09/2023] Open
Abstract
BACKGROUND Lung is the most common primary site of brain metastases (BMs). For different pathological types of BMs have some similar characteristics, it is still a challenge to confirm the origin based on their characteristics directly. BMs of small cell lung cancer (SCLC) have favorable therapeutic expectations due to their high sensitivity to radiotherapy. This study sought to identify unique characteristics of BMs in SCLC, aiming to assist in clinical decision-making. METHODS Patients diagnosed with BMs of lung cancer who received radiotherapy from January 2017 to January 2022 were reviewed (N = 284). Definitive diagnosis of BMs of SCLC was reached for 36 patients. All patients underwent head examination using magnetic resonance imaging. The number, size, location, and signal characteristics of lesions were analyzed. RESULTS There were 7 and 29 patients with single focus and non-single focus, respectively. Ten patients had diffuse lesions, and the remaining 26 patients had a total of 90 lesions. These lesions were divided into three groups according to size: <1, 1-3, and >3 cm (43.33%, 53.34%, and 3.33%, respectively). Sixty-six lesions were located in the supratentorial area, primarily including cortical and subcortical lesions (55.56%) and deep brain lesions (20%). Moreover, 22 lesions were located in the infratentorial area. According to diffusion-weighted imaging and T1-weighted contrast enhancement, the imaging characteristics were classified into six patterns. Hyperintensity in diffusion-weighted imaging and homogeneous enhancement was the most common pattern of BMs in SCLC (46.67%), while partial lesions showed hyperintensity in diffusion-weighted imaging without enhancement (7.78%). CONCLUSIONS The manifestations of BMs in SCLC were multiple lesions (diameter: 1-3 cm), hyperintensity in diffusion-weighted imaging, and homogeneous enhancement. Interestingly, hyperintensity in diffusion-weighted imaging without enhancement was also one of the characteristics.
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Affiliation(s)
- Dan Zhu
- Department of Radiology, Shanghai East HospitalTongji University School of MedicineShanghaiChina
| | - Yongjia Shao
- Department of Radiology, Shanghai East HospitalTongji University School of MedicineShanghaiChina
| | - Zhangwei Yang
- Department of Radiology, Shanghai East HospitalTongji University School of MedicineShanghaiChina
| | - Ailan Cheng
- Department of Radiology, Shanghai East HospitalTongji University School of MedicineShanghaiChina
| | - Qian Xi
- Department of Radiology, Shanghai East HospitalTongji University School of MedicineShanghaiChina
| | - Xiaohua Liang
- Department of Oncology, Shanghai Huashan HospitalFudan University School of MedicineShanghaiChina
| | - Shuguang Chu
- Department of Radiology, Shanghai East HospitalTongji University School of MedicineShanghaiChina
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10
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Heidari A, Javaheri D, Toumaj S, Navimipour NJ, Rezaei M, Unal M. A new lung cancer detection method based on the chest CT images using Federated Learning and blockchain systems. Artif Intell Med 2023; 141:102572. [PMID: 37295902 DOI: 10.1016/j.artmed.2023.102572] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/16/2023] [Accepted: 04/27/2023] [Indexed: 06/12/2023]
Abstract
With an estimated five million fatal cases each year, lung cancer is one of the significant causes of death worldwide. Lung diseases can be diagnosed with a Computed Tomography (CT) scan. The scarcity and trustworthiness of human eyes is the fundamental issue in diagnosing lung cancer patients. The main goal of this study is to detect malignant lung nodules in a CT scan of the lungs and categorize lung cancer according to severity. In this work, cutting-edge Deep Learning (DL) algorithms were used to detect the location of cancerous nodules. Also, the real-life issue is sharing data with hospitals around the world while bearing in mind the organizations' privacy issues. Besides, the main problems for training a global DL model are creating a collaborative model and maintaining privacy. This study presented an approach that takes a modest amount of data from multiple hospitals and uses blockchain-based Federated Learning (FL) to train a global DL model. The data were authenticated using blockchain technology, and FL trained the model internationally while maintaining the organization's anonymity. First, we presented a data normalization approach that addresses the variability of data obtained from various institutions using various CT scanners. Furthermore, using a CapsNets method, we classified lung cancer patients in local mode. Finally, we devised a way to train a global model cooperatively utilizing blockchain technology and FL while maintaining anonymity. We also gathered data from real-life lung cancer patients for testing purposes. The suggested method was trained and tested on the Cancer Imaging Archive (CIA) dataset, Kaggle Data Science Bowl (KDSB), LUNA 16, and the local dataset. Finally, we performed extensive experiments with Python and its well-known libraries, such as Scikit-Learn and TensorFlow, to evaluate the suggested method. The findings showed that the method effectively detects lung cancer patients. The technique delivered 99.69 % accuracy with the smallest possible categorization error.
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Affiliation(s)
- Arash Heidari
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
| | - Danial Javaheri
- Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea
| | - Shiva Toumaj
- Urmia University of Medical Sciences, Urmia, Iran
| | - Nima Jafari Navimipour
- Department of Computer Engineering, Kadir Has University, Istanbul, Turkiye; Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Yunlin 64002, Taiwan.
| | - Mahsa Rezaei
- Tabriz University of Medical Sciences, Faculty of Surgery, Tabriz, Iran
| | - Mehmet Unal
- Department of Computer Engineering, Nisantasi University, Istanbul, Turkiye
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11
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Luo X, Yang Y, Yin S, Li H, Zhang W, Xu G, Fan W, Zheng D, Li J, Shen D, Gao Y, Shao Y, Ban X, Li J, Lian S, Zhang C, Ma L, Lin C, Luo Y, Zhou F, Wang S, Sun Y, Zhang R, Xie C. False-negative and false-positive outcomes of computer-aided detection on brain metastasis: Secondary analysis of a multicenter, multireader study. Neuro Oncol 2023; 25:544-556. [PMID: 35943350 PMCID: PMC10013637 DOI: 10.1093/neuonc/noac192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Errors have seldom been evaluated in computer-aided detection on brain metastases. This study aimed to analyze false negatives (FNs) and false positives (FPs) generated by a brain metastasis detection system (BMDS) and by readers. METHODS A deep learning-based BMDS was developed and prospectively validated in a multicenter, multireader study. Ad hoc secondary analysis was restricted to the prospective participants (148 with 1,066 brain metastases and 152 normal controls). Three trainees and 3 experienced radiologists read the MRI images without and with the BMDS. The number of FNs and FPs per patient, jackknife alternative free-response receiver operating characteristic figure of merit (FOM), and lesion features associated with FNs were analyzed for the BMDS and readers using binary logistic regression. RESULTS The FNs, FPs, and the FOM of the stand-alone BMDS were 0.49, 0.38, and 0.97, respectively. Compared with independent reading, BMDS-assisted reading generated 79% fewer FNs (1.98 vs 0.42, P < .001); 41% more FPs (0.17 vs 0.24, P < .001) but 125% more FPs for trainees (P < .001); and higher FOM (0.87 vs 0.98, P < .001). Lesions with small size, greater number, irregular shape, lower signal intensity, and located on nonbrain surface were associated with FNs for readers. Small, irregular, and necrotic lesions were more frequently found in FNs for BMDS. The FPs mainly resulted from small blood vessels for the BMDS and the readers. CONCLUSIONS Despite the improvement in detection performance, attention should be paid to FPs and small lesions with lower enhancement for radiologists, especially for less-experienced radiologists.
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Affiliation(s)
- Xiao Luo
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Yadi Yang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Shaohan Yin
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Hui Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Weijing Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Guixiao Xu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Weixiong Fan
- Department of Radiology, Meizhou People's Hospital, Meizhou, China
| | - Dechun Zheng
- Department of Radiology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, Fujian Province, China
| | - Jianpeng Li
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Guangzhou, China
| | - Dinggang Shen
- R&D Department, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.,School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Yaozong Gao
- R&D Department, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Ying Shao
- R&D Department, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.,Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Xiaohua Ban
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Jing Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Shanshan Lian
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Cheng Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Lidi Ma
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Cuiping Lin
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Yingwei Luo
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Fan Zhou
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Shiyuan Wang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Ying Sun
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Rong Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Chuanmiao Xie
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
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12
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Akasaka T, Okada T. Editorial for "Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning". J Magn Reson Imaging 2023; 57:882-883. [PMID: 35778673 DOI: 10.1002/jmri.28329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 06/16/2022] [Indexed: 11/11/2022] Open
Affiliation(s)
- Thai Akasaka
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tomohisa Okada
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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13
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Ottesen JA, Yi D, Tong E, Iv M, Latysheva A, Saxhaug C, Jacobsen KD, Helland Å, Emblem KE, Rubin DL, Bjørnerud A, Zaharchuk G, Grøvik E. 2.5D and 3D segmentation of brain metastases with deep learning on multinational MRI data. Front Neuroinform 2023; 16:1056068. [PMID: 36743439 PMCID: PMC9889663 DOI: 10.3389/fninf.2022.1056068] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 12/26/2022] [Indexed: 01/20/2023] Open
Abstract
Introduction Management of patients with brain metastases is often based on manual lesion detection and segmentation by an expert reader. This is a time- and labor-intensive process, and to that end, this work proposes an end-to-end deep learning segmentation network for a varying number of available MRI available sequences. Methods We adapt and evaluate a 2.5D and a 3D convolution neural network trained and tested on a retrospective multinational study from two independent centers, in addition, nnU-Net was adapted as a comparative benchmark. Segmentation and detection performance was evaluated by: (1) the dice similarity coefficient, (2) a per-metastases and the average detection sensitivity, and (3) the number of false positives. Results The 2.5D and 3D models achieved similar results, albeit the 2.5D model had better detection rate, whereas the 3D model had fewer false positive predictions, and nnU-Net had fewest false positives, but with the lowest detection rate. On MRI data from center 1, the 2.5D, 3D, and nnU-Net detected 79%, 71%, and 65% of all metastases; had an average per patient sensitivity of 0.88, 0.84, and 0.76; and had on average 6.2, 3.2, and 1.7 false positive predictions per patient, respectively. For center 2, the 2.5D, 3D, and nnU-Net detected 88%, 86%, and 78% of all metastases; had an average per patient sensitivity of 0.92, 0.91, and 0.85; and had on average 1.0, 0.4, and 0.1 false positive predictions per patient, respectively. Discussion/Conclusion Our results show that deep learning can yield highly accurate segmentations of brain metastases with few false positives in multinational data, but the accuracy degrades for metastases with an area smaller than 0.4 cm2.
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Affiliation(s)
- Jon André Ottesen
- CRAI, Division of Radiology and Nuclear Medicine, Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway,Department of Physics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway,*Correspondence: Jon André Ottesen ✉
| | - Darvin Yi
- Department of Ophthalmology, University of Illinois, Chicago, IL, United States
| | - Elizabeth Tong
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Michael Iv
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Anna Latysheva
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Cathrine Saxhaug
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | | | - Åslaug Helland
- Department of Oncology, Oslo University Hospital, Oslo, Norway
| | - Kyrre Eeg Emblem
- Division of Radiology and Nuclear Medicine, Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway
| | - Daniel L. Rubin
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
| | - Atle Bjørnerud
- CRAI, Division of Radiology and Nuclear Medicine, Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway,Department of Physics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
| | - Greg Zaharchuk
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Endre Grøvik
- Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway,Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
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14
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Yu H, Zhang Z, Xia W, Liu Y, Liu L, Luo W, Zhou J, Zhang Y. DeSeg: auto detector-based segmentation for brain metastases. Phys Med Biol 2023; 68. [PMID: 36535028 DOI: 10.1088/1361-6560/acace7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
Delineation of brain metastases (BMs) is a paramount step in stereotactic radiosurgery treatment. Clinical practice has specific expectation on BM auto-delineation that the method is supposed to avoid missing of small lesions and yield accurate contours for large lesions. In this study, we propose a novel coarse-to-fine framework, named detector-based segmentation (DeSeg), to incorporate object-level detection into pixel-wise segmentation so as to meet the clinical demand. DeSeg consists of three components: a center-point-guided single-shot detector to localize the potential lesion regions, a multi-head U-Net segmentation model to refine contours, and a data cascade unit to connect both tasks smoothly. Performance on tiny lesions is measured by the object-based sensitivity and positive predictive value (PPV), while that on large lesions is quantified by dice similarity coefficient (DSC), average symmetric surface distance (ASSD) and 95% Hausdorff distance (HD95). Besides, computational complexity is also considered to study the potential of method in real-time processing. This study retrospectively collected 240 BM patients with Gadolinium injected contrast-enhanced T1-weighted magnetic resonance imaging (T1c-MRI), which were randomly split into training, validating and testing datasets (192, 24 and 24 scans, respectively). The lesions in the testing dataset were further divided into two groups based on the volume size (smallS: ≤1.5 cc,N= 88; largeL: > 1.5 cc,N= 15). On average, DeSeg yielded a sensitivity of 0.91 and a PPV of 0.77 on S group, and a DSC of 0.86, an ASSD 0f 0.76 mm and a HD95 of 2.31 mm onLgroup. The results indicated that DeSeg achieved leading sensitivity and PPV for tiny lesions as well as segmentation metrics for large ones. After our clinical validation, DeSeg showed competitive segmentation performance while kept faster processing speed comparing with existing 3D models.
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Affiliation(s)
- Hui Yu
- College of Computer Science, Sichuan University, Chengdu, 610065, People's Republic of China
| | - Zhongzhou Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, People's Republic of China
| | - Wenjun Xia
- College of Computer Science, Sichuan University, Chengdu, 610065, People's Republic of China
| | - Yan Liu
- College of Electrical Engineering, Sichuan University, Chengdu, 610065, People's Republic of China
| | - Lunxin Liu
- Department of Neurosurgery, West China Hospital of Sichuan University, Chengdu, 610044, People's Republic of China
| | - Wuman Luo
- School of Applied Sciences, Macao Polytechnic University, Macao, 999078, People's Republic of China
| | - Jiliu Zhou
- College of Computer Science, Sichuan University, Chengdu, 610065, People's Republic of China
| | - Yi Zhang
- School of Cyber Science and Engineering, Sichuan University, Chengdu, 610065, People's Republic of China
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15
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Deep Learning for Detecting Brain Metastases on MRI: A Systematic Review and Meta-Analysis. Cancers (Basel) 2023; 15:cancers15020334. [PMID: 36672286 PMCID: PMC9857123 DOI: 10.3390/cancers15020334] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 12/31/2022] [Accepted: 12/31/2022] [Indexed: 01/06/2023] Open
Abstract
Since manual detection of brain metastases (BMs) is time consuming, studies have been conducted to automate this process using deep learning. The purpose of this study was to conduct a systematic review and meta-analysis of the performance of deep learning models that use magnetic resonance imaging (MRI) to detect BMs in cancer patients. A systematic search of MEDLINE, EMBASE, and Web of Science was conducted until 30 September 2022. Inclusion criteria were: patients with BMs; deep learning using MRI images was applied to detect the BMs; sufficient data were present in terms of detective performance; original research articles. Exclusion criteria were: reviews, letters, guidelines, editorials, or errata; case reports or series with less than 20 patients; studies with overlapping cohorts; insufficient data in terms of detective performance; machine learning was used to detect BMs; articles not written in English. Quality Assessment of Diagnostic Accuracy Studies-2 and Checklist for Artificial Intelligence in Medical Imaging was used to assess the quality. Finally, 24 eligible studies were identified for the quantitative analysis. The pooled proportion of patient-wise and lesion-wise detectability was 89%. Articles should adhere to the checklists more strictly. Deep learning algorithms effectively detect BMs. Pooled analysis of false positive rates could not be estimated due to reporting differences.
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16
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Buchner JA, Kofler F, Etzel L, Mayinger M, Christ SM, Brunner TB, Wittig A, Menze B, Zimmer C, Meyer B, Guckenberger M, Andratschke N, El Shafie RA, Debus J, Rogers S, Riesterer O, Schulze K, Feldmann HJ, Blanck O, Zamboglou C, Ferentinos K, Wolff R, Eitz KA, Combs SE, Bernhardt D, Wiestler B, Peeken JC. Development and external validation of an MRI-based neural network for brain metastasis segmentation in the AURORA multicenter study. Radiother Oncol 2023; 178:109425. [PMID: 36442609 DOI: 10.1016/j.radonc.2022.11.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND Stereotactic radiotherapy is a standard treatment option for patients with brain metastases. The planning target volume is based on gross tumor volume (GTV) segmentation. The aim of this work is to develop and validate a neural network for automatic GTV segmentation to accelerate clinical daily routine practice and minimize interobserver variability. METHODS We analyzed MRIs (T1-weighted sequence ± contrast-enhancement, T2-weighted sequence, and FLAIR sequence) from 348 patients with at least one brain metastasis from different cancer primaries treated in six centers. To generate reference segmentations, all GTVs and the FLAIR hyperintense edematous regions were segmented manually. A 3D-U-Net was trained on a cohort of 260 patients from two centers to segment the GTV and the surrounding FLAIR hyperintense region. During training varying degrees of data augmentation were applied. Model validation was performed using an independent international multicenter test cohort (n = 88) including four centers. RESULTS Our proposed U-Net reached a mean overall Dice similarity coefficient (DSC) of 0.92 ± 0.08 and a mean individual metastasis-wise DSC of 0.89 ± 0.11 in the external test cohort for GTV segmentation. Data augmentation improved the segmentation performance significantly. Detection of brain metastases was effective with a mean F1-Score of 0.93 ± 0.16. The model performance was stable independent of the center (p = 0.3). There was no correlation between metastasis volume and DSC (Pearson correlation coefficient 0.07). CONCLUSION Reliable automated segmentation of brain metastases with neural networks is possible and may support radiotherapy planning by providing more objective GTV definitions.
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Affiliation(s)
- Josef A Buchner
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
| | - Florian Kofler
- Department of Informatics, Technical University of Munich, Munich, Germany; Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany; Helmholtz AI, Helmholtz Zentrum Munich, Munich, Germany
| | - Lucas Etzel
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany
| | - Michael Mayinger
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Sebastian M Christ
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Thomas B Brunner
- Department of Radiation Oncology, University Hospital Magdeburg, Magdeburg, Germany
| | - Andrea Wittig
- Department of Radiotherapy and Radiation Oncology, University Hospital Jena, Friedrich-Schiller University, Jena, Germany
| | - Björn Menze
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Bernhard Meyer
- Department of Neurosurgery, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Rami A El Shafie
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Oncology (NCRO), Heidelberg, Germany; Department of Radiation Oncology, University Medical Center Göttingen, Göttingen, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Oncology (NCRO), Heidelberg, Germany
| | - Susanne Rogers
- Radiation Oncology Center KSA-KSB, Kantonsspital Aarau, Aarau, Switzerland
| | - Oliver Riesterer
- Radiation Oncology Center KSA-KSB, Kantonsspital Aarau, Aarau, Switzerland
| | - Katrin Schulze
- Department of Radiation Oncology, General Hospital Fulda, Fulda, Germany
| | - Horst J Feldmann
- Department of Radiation Oncology, General Hospital Fulda, Fulda, Germany
| | - Oliver Blanck
- Department of Radiation Oncology, University Medical Center Schleswig Holstein, Kiel, Germany
| | - Constantinos Zamboglou
- Department of Radiation Oncology, University of Freiburg - Medical Center, Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany; Department of Radiation Oncology, German Oncology Center, European University of Cyprus, Limassol, Cyprus
| | - Konstantinos Ferentinos
- Department of Radiation Oncology, German Oncology Center, European University of Cyprus, Limassol, Cyprus
| | - Robert Wolff
- Saphir Radiosurgery Center Frankfurt and Northern Germany, Guestrow, Germany; Department of Neurosurgery, University Hospital Frankfurt, Frankfurt, Germany
| | - Kerstin A Eitz
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Jan C Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
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17
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Chartrand G, Emiliani RD, Pawlowski SA, Markel DA, Bahig H, Cengarle-Samak A, Rajakesari S, Lavoie J, Ducharme S, Roberge D. Automated Detection of Brain Metastases on T1-Weighted MRI Using a Convolutional Neural Network: Impact of Volume Aware Loss and Sampling Strategy. J Magn Reson Imaging 2022; 56:1885-1898. [PMID: 35624544 DOI: 10.1002/jmri.28274] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 05/13/2022] [Accepted: 05/13/2022] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Detection of brain metastases (BM) and segmentation for treatment planning could be optimized with machine learning methods. Convolutional neural networks (CNNs) are promising, but their trade-offs between sensitivity and precision frequently lead to missing small lesions. HYPOTHESIS Combining volume aware (VA) loss function and sampling strategy could improve BM detection sensitivity. STUDY TYPE Retrospective. POPULATION A total of 530 radiation oncology patients (55% women) were split into a training/validation set (433 patients/1460 BM) and an independent test set (97 patients/296 BM). FIELD STRENGTH/SEQUENCE 1.5 T and 3 T, contrast-enhanced three-dimensional (3D) T1-weighted fast gradient echo sequences. ASSESSMENT Ground truth masks were based on radiotherapy treatment planning contours reviewed by experts. A U-Net inspired model was trained. Three loss functions (Dice, Dice + boundary, and VA) and two sampling methods (label and VA) were compared. Results were reported with Dice scores, volumetric error, lesion detection sensitivity, and precision. A detected voxel within the ground truth constituted a true positive. STATISTICAL TESTS McNemar's exact test to compare detected lesions between models. Pearson's correlation coefficient and Bland-Altman analysis to compare volume agreement between predicted and ground truth volumes. Statistical significance was set at P ≤ 0.05. RESULTS Combining VA loss and VA sampling performed best with an overall sensitivity of 91% and precision of 81%. For BM in the 2.5-6 mm estimated sphere diameter range, VA loss reduced false negatives by 58% and VA sampling reduced it further by 30%. In the same range, the boundary loss achieved the highest precision at 81%, but a low sensitivity (24%) and a 31% Dice loss. DATA CONCLUSION Considering BM size in the loss and sampling function of CNN may increase the detection sensitivity regarding small BM. Our pipeline relying on a single contrast-enhanced T1-weighted MRI sequence could reach a detection sensitivity of 91%, with an average of only 0.66 false positives per scan. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
| | | | | | - Daniel A Markel
- Department of Radiation Oncology, Centre Hospitalier de l'Université de Montréal, Montréal, Québec, Canada
| | - Houda Bahig
- Department of Radiation Oncology, Centre Hospitalier de l'Université de Montréal, Montréal, Québec, Canada
| | | | - Selvan Rajakesari
- Department of Radiation Oncology, Hopital Charles Lemoyne, Greenfield Park, Québec, Canada
| | | | - Simon Ducharme
- AFX Medical Inc., Montréal, Canada.,Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montréal, Canada.,McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montréal, Canada
| | - David Roberge
- Department of Radiation Oncology, Centre Hospitalier de l'Université de Montréal, Montréal, Québec, Canada
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18
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Liew A, Lee CC, Subramaniam V, Lan BL, Tan M. Gradual Self-Training via Confidence and Volume Based Domain Adaptation for Multi Dataset Deep Learning-Based Brain Metastases Detection Using Nonlocal Networks on MRI Images. J Magn Reson Imaging 2022; 57:1728-1740. [PMID: 36208095 DOI: 10.1002/jmri.28456] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 09/20/2022] [Accepted: 09/20/2022] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Research suggests that treatment of multiple brain metastases (BMs) with stereotactic radiosurgery shows improvement when metastases are detected early, providing a case for BM detection capabilities on small lesions. PURPOSE To demonstrate automatic detection of BM on three MRI datasets using a deep learning-based approach. To improve the performance of the network is iteratively co-trained with datasets from different domains. A systematic approach is proposed to prevent catastrophic forgetting during co-training. STUDY TYPE Retrospective. POPULATION A total of 156 patients (105 ground truth and 51 pseudo labels) with 1502 BM (BrainMetShare); 121 patients with 722 BM (local); 400 patients with 447 primary gliomas (BrATS). Training/pseudo labels/validation data were distributed 84/51/21 (BrainMetShare). Training/validation data were split: 121/23 (local) and 375/25 (BrATS). FIELD STRENGTH/SEQUENCE A 5 T and 3 T/T1 spin-echo postcontrast (T1-gradient echo) (BrainMetShare), 3 T/T1 magnetization prepared rapid acquisition gradient echo postcontrast (T1-MPRAGE) (local), 0.5 T, 1 T, and 1.16 T/T1-weighted-fluid-attenuated inversion recovery (T1-FLAIR) (BrATS). ASSESSMENT The ground truth was manually segmented by two (BrainMetShare) and four (BrATS) radiologists and manually annotated by one (local) radiologist. Confidence and volume based domain adaptation (CAVEAT) method of co-training the three datasets on a 3D nonlocal convolutional neural network (CNN) architecture was implemented to detect BM. STATISTICAL TESTS The performance was evaluated using sensitivity and false positive rates per patient (FP/patient) and free receiver operating characteristic (FROC) analysis at seven predefined (1/8, 1/4, 1/2, 1, 2, 4, and 8) FPs per scan. RESULTS The sensitivity and FP/patient from a held-out set registered 0.811 at 2.952 FP/patient (BrainMetShare), 0.74 at 3.130 (local), and 0.723 at 2.240 (BrATS) using the CAVEAT approach with lesions as small as 1 mm being detected. DATA CONCLUSION Improved sensitivities at lower FP can be achieved by co-training datasets via the CAVEAT paradigm to address the problem of data sparsity. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Andrea Liew
- Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, Bandar Sunway, Malaysia
| | - Chun Cheng Lee
- Radiology Department, Sunway Medical Centre, Bandar Sunway, Malaysia
| | | | - Boon Leong Lan
- Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, Bandar Sunway, Malaysia.,Advanced Engineering Platform, School of Engineering, Monash University Malaysia, Bandar Sunway, Malaysia
| | - Maxine Tan
- Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, Bandar Sunway, Malaysia.,School of Electrical and Computer Engineering, The University of Oklahoma, Norman, Oklahoma, USA
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19
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Deep-learning 2.5-dimensional single-shot detector improves the performance of automated detection of brain metastases on contrast-enhanced CT. Neuroradiology 2022; 64:1511-1518. [PMID: 35064786 DOI: 10.1007/s00234-022-02902-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 01/15/2022] [Indexed: 10/19/2022]
Abstract
PURPOSE This study aims to develop a 2.5-dimensional (2.5D) deep-learning, object detection model for the automated detection of brain metastases, into which three consecutive slices were fed as the input for the prediction in the central slice, and to compare its performance with that of an ordinary 2-dimensional (2D) model. METHODS We analyzed 696 brain metastases on 127 contrast-enhanced computed tomography (CT) scans from 127 patients with brain metastases. The scans were randomly divided into training (n = 79), validation (n = 18), and test (n = 30) datasets. Single-shot detector (SSD) models with a feature fusion module were constructed, trained, and compared using the lesion-based sensitivity, positive predictive value (PPV), and the number of false positives per patient at a confidence threshold of 50%. RESULTS The 2.5D SSD model had a significantly higher PPV (t test, p < 0.001) and a significantly smaller number of false positives (t test, p < 0.001). The sensitivities of the 2D and 2.5D models were 88.1% (95% confidence interval [CI], 86.6-89.6%) and 88.7% (95% CI, 87.3-90.1%), respectively. The corresponding PPVs were 39.0% (95% CI, 36.5-41.4%) and 58.9% (95% CI, 55.2-62.7%), respectively. The numbers of false positives per patient were 11.9 (95% CI, 10.7-13.2) and 4.9 (95% CI, 4.2-5.7), respectively. CONCLUSION Our results indicate that 2.5D deep-learning, object detection models, which use information about the continuity between adjacent slices, may reduce false positives and improve the performance of automated detection of brain metastases compared with ordinary 2D models.
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20
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Pflüger I, Wald T, Isensee F, Schell M, Meredig H, Schlamp K, Bernhardt D, Brugnara G, Heußel CP, Debus J, Wick W, Bendszus M, Maier-Hein KH, Vollmuth P. Automated detection and quantification of brain metastases on clinical MRI data using artificial neural networks. Neurooncol Adv 2022; 4:vdac138. [PMID: 36105388 PMCID: PMC9466273 DOI: 10.1093/noajnl/vdac138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Background
Reliable detection and precise volumetric quantification of brain metastases (BM) on MRI are essential for guiding treatment decisions. Here we evaluate the potential of artificial neural networks (ANN) for automated detection and quantification of BM.
Methods
A consecutive series of 308 patients with BM was used for developing an ANN (with a 4:1 split for training/testing) for automated volumetric assessment of contrast-enhancing tumors (CE) and non-enhancing FLAIR signal abnormality including edema (NEE). An independent consecutive series of 30 patients was used for external testing. Performance was assessed case-wise for CE and NEE and lesion-wise for CE using the case-wise/lesion-wise DICE-coefficient (C/L-DICE), positive predictive value (L-PPV) and sensitivity (C/L-Sensitivity).
Results
The performance of detecting CE lesions on the validation dataset was not significantly affected when evaluating different volumetric thresholds (0.001–0.2 cm3; P = .2028). The median L-DICE and median C-DICE for CE lesions were 0.78 (IQR = 0.6–0.91) and 0.90 (IQR = 0.85–0.94) in the institutional as well as 0.79 (IQR = 0.67–0.82) and 0.84 (IQR = 0.76–0.89) in the external test dataset. The corresponding median L-Sensitivity and median L-PPV were 0.81 (IQR = 0.63–0.92) and 0.79 (IQR = 0.63–0.93) in the institutional test dataset, as compared to 0.85 (IQR = 0.76–0.94) and 0.76 (IQR = 0.68–0.88) in the external test dataset. The median C-DICE for NEE was 0.96 (IQR = 0.92–0.97) in the institutional test dataset as compared to 0.85 (IQR = 0.72–0.91) in the external test dataset.
Conclusion
The developed ANN-based algorithm (publicly available at www.github.com/NeuroAI-HD/HD-BM) allows reliable detection and precise volumetric quantification of CE and NEE compartments in patients with BM.
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Affiliation(s)
- Irada Pflüger
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg , Germany
| | - Tassilo Wald
- Medical Image Computing, German Cancer Research Center (DKFZ) , Heidelberg , Germany
| | - Fabian Isensee
- Medical Image Computing, German Cancer Research Center (DKFZ) , Heidelberg , Germany
| | - Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg , Germany
| | - Hagen Meredig
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg , Germany
| | - Kai Schlamp
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Clinic for Thoracic Diseases (Thoraxklinik), Heidelberg University Hospital , Heidelberg , Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University Munich , Munich , Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg , Germany
| | - Claus Peter Heußel
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Clinic for Thoracic Diseases (Thoraxklinik), Heidelberg University Hospital , Heidelberg , Germany
- Member of the Cerman Center for Lung Research (DZL), Translational Lung Research Center (TLRC) , Heidelberg , Germany
| | - Juergen Debus
- Department of Radiation Oncology, Heidelberg University Hospital , Heidelberg , Germany
- Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg University Hospital , Heidelberg , Germany
- German Cancer Consotium (DKTK), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ) , Heidelberg , Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ) , Heidelberg , Germany
| | - Wolfgang Wick
- Neurology Clinic, Heidelberg University Hospital , Heidelberg , Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ) , Heidelberg , Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg , Germany
| | - Klaus H Maier-Hein
- Medical Image Computing, German Cancer Research Center (DKFZ) , Heidelberg , Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg , Germany
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21
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Takao H, Amemiya S, Kato S, Yamashita H, Sakamoto N, Abe O. Deep-learning single-shot detector for automatic detection of brain metastases with the combined use of contrast-enhanced and non-enhanced computed tomography images. Eur J Radiol 2021; 144:110015. [PMID: 34742108 DOI: 10.1016/j.ejrad.2021.110015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/10/2021] [Accepted: 10/27/2021] [Indexed: 11/30/2022]
Abstract
PURPOSE To develop a deep-learning object detection model for automatic detection of brain metastases that simultaneously uses contrast-enhanced and non-enhanced images as inputs, and to compare its performance with that of a model that uses only contrast-enhanced images. METHOD A total of 116 computed tomography (CT) scans of 116 patients with brain metastases were included in this study. They showed a total of 659 metastases, 428 of which were used for training and validation (mean size, 11.3 ± 9.9 mm) and 231 were used for testing (mean size, 9.0 ± 7.0 mm). Single-shot detector (SSD) models were constructed with a feature fusion module, and their results were compared per lesion at a confidence threshold of 50%. RESULTS The sensitivity was 88.7% for the model that used both contrast-enhanced and non-enhanced CT images (the CE + NECT model) and 87.6% for the model that used only contrast-enhanced CT images (the CECT model). The positive predictive value (PPV) was 44.0% for the CE + NECT model and 37.2% for the CECT model. The number of false positives per patient was 9.9 for the CE + NECT model and 13.6 for the CECT model. The CE + NECT model had a significantly higher PPV (t test, p < 0.001), significantly fewer false positives (t test, p < 0.001), and a tendency to be more sensitive (t test, p = 0.14). CONCLUSIONS The results indicate that the information on true contrast enhancement obtained by comparing the contrast-enhanced and non-enhanced images may prevent the detection of pseudolesions, suppress false positives, and improve the performance of deep-learning object detection models.
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Affiliation(s)
- Hidemasa Takao
- Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
| | - Shiori Amemiya
- Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Shimpei Kato
- Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Hiroshi Yamashita
- Department of Radiology, Teikyo University Hospital, Mizonokuchi, 5-1-1 Futago, Takatsu-ku, Kawasaki, Kanagawa 213-8507, Japan
| | - Naoya Sakamoto
- Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
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22
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Herrmann P, Busana M, Cressoni M, Lotz J, Moerer O, Saager L, Meissner K, Quintel M, Gattinoni L. Using Artificial Intelligence for Automatic Segmentation of CT Lung Images in Acute Respiratory Distress Syndrome. Front Physiol 2021; 12:676118. [PMID: 34594233 PMCID: PMC8476971 DOI: 10.3389/fphys.2021.676118] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 08/17/2021] [Indexed: 01/17/2023] Open
Abstract
Knowledge of gas volume, tissue mass and recruitability measured by the quantitative CT scan analysis (CT-qa) is important when setting the mechanical ventilation in acute respiratory distress syndrome (ARDS). Yet, the manual segmentation of the lung requires a considerable workload. Our goal was to provide an automatic, clinically applicable and reliable lung segmentation procedure. Therefore, a convolutional neural network (CNN) was used to train an artificial intelligence (AI) algorithm on 15 healthy subjects (1,302 slices), 100 ARDS patients (12,279 slices), and 20 COVID-19 (1,817 slices). Eighty percent of this populations was used for training, 20% for testing. The AI and manual segmentation at slice level were compared by intersection over union (IoU). The CT-qa variables were compared by regression and Bland Altman analysis. The AI-segmentation of a single patient required 5–10 s vs. 1–2 h of the manual. At slice level, the algorithm showed on the test set an IOU across all CT slices of 91.3 ± 10.0, 85.2 ± 13.9, and 84.7 ± 14.0%, and across all lung volumes of 96.3 ± 0.6, 88.9 ± 3.1, and 86.3 ± 6.5% for normal lungs, ARDS and COVID-19, respectively, with a U-shape in the performance: better in the lung middle region, worse at the apex and base. At patient level, on the test set, the total lung volume measured by AI and manual segmentation had a R2 of 0.99 and a bias −9.8 ml [CI: +56.0/−75.7 ml]. The recruitability measured with manual and AI-segmentation, as change in non-aerated tissue fraction had a bias of +0.3% [CI: +6.2/−5.5%] and −0.5% [CI: +2.3/−3.3%] expressed as change in well-aerated tissue fraction. The AI-powered lung segmentation provided fast and clinically reliable results. It is able to segment the lungs of seriously ill ARDS patients fully automatically.
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Affiliation(s)
- Peter Herrmann
- Department of Anesthesiology, University Medical Center Göttingen, Göttingen, Germany
| | - Mattia Busana
- Department of Anesthesiology, University Medical Center Göttingen, Göttingen, Germany
| | | | - Joachim Lotz
- Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany
| | - Onnen Moerer
- Department of Anesthesiology, University Medical Center Göttingen, Göttingen, Germany
| | - Leif Saager
- Department of Anesthesiology, University Medical Center Göttingen, Göttingen, Germany
| | - Konrad Meissner
- Department of Anesthesiology, University Medical Center Göttingen, Göttingen, Germany
| | - Michael Quintel
- Department of Anesthesiology, University Medical Center Göttingen, Göttingen, Germany.,Department of Anesthesiology, DONAUISAR Klinikum Deggendorf, Deggendorf, Germany
| | - Luciano Gattinoni
- Department of Anesthesiology, University Medical Center Göttingen, Göttingen, Germany
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23
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Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11199180] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Semantic segmentation of medical images with deep learning models is rapidly being developed. In this study, we benchmarked state-of-the-art deep learning segmentation algorithms on our clinical stereotactic radiosurgery dataset. The dataset consists of 1688 patients with various brain lesions (pituitary tumors, meningioma, schwannoma, brain metastases, arteriovenous malformation, and trigeminal neuralgia), and we divided the dataset into a training set (1557 patients) and test set (131 patients). This study demonstrates the strengths and weaknesses of deep-learning algorithms in a fairly practical scenario. We compared the model performances concerning their sampling method, model architecture, and the choice of loss functions, identifying suitable settings for their applications and shedding light on the possible improvements. Evidence from this study led us to conclude that deep learning could be promising in assisting the segmentation of brain lesions even if the training dataset was of high heterogeneity in lesion types and sizes.
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