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Merkler B, Masson A, Ferré JC, Bajeux E, Edan G, Michel L, Page EL, Leclercq M, Pegat B, Lamy S, Corre GL, Ahrweiler K, Zagnoli F, Maréchal D, Combès B, Kerbrat A. Impact of automatic tools for detecting new lesions on therapeutic strategies offered to patients with MS by neurologists. Mult Scler Relat Disord 2023; 80:105064. [PMID: 37866026 DOI: 10.1016/j.msard.2023.105064] [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: 07/19/2023] [Revised: 09/16/2023] [Accepted: 10/08/2023] [Indexed: 10/24/2023]
Abstract
BACKGROUND Automatic tools for detecting new lesions in patients with MS between two MRI scans are now available to clinicians. They have been assessed from the radiologist's point of view, but their impact on the therapeutic strategies that neurologists offer their patients has not yet been documented. OBJECTIVES To compare neurologist's decisions according to whether a lesion detection support system had been used and describe variability between neurologists on decision-making for the same clinical cases. METHODS We submitted 28 clinical cases associated with pairs of MRI images and radiological reports (produced by the same radiologist without vs. with the help of a system to detect new lesions) to 10 neurologists who regularly follow patients with MS. They examined each clinical case twice (without vs. with support system) in two sessions several weeks apart, and their patient management decisions were recorded. RESULTS There was considerable variability between neurologists on decision-making (both with and without support system). When the support system had been used, neurologists more often made changes to patient management (75 % vs. 68 % of cases, p = 0.01) and spent significantly less time analyzing the clinical cases (249 s vs. 216 s, p == 3.10-4). CONCLUSION The use of a lesion detection support system has an impact not only on radiologists' reports, but also on neurologists' subsequent decision-making. This observation constitutes another strong argument for promoting the wider use of such systems in clinical routine. However, despite their use, there is still considerable variability in decision-making across neurologists, which should encourage us to refine the guidelines.
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Affiliation(s)
| | - Arthur Masson
- EMPENN research team, IRISA, CNRS‑INSERM‑INRIA, Rennes University, France
| | - Jean Christophe Ferré
- EMPENN research team, IRISA, CNRS‑INSERM‑INRIA, Rennes University, France; Radiology Department, Rennes University Hospital, Rennes, France
| | - Emma Bajeux
- Public Health and Epidemiology Department, Rennes University Hospital, Rennes, France
| | - Gilles Edan
- EMPENN research team, IRISA, CNRS‑INSERM‑INRIA, Rennes University, France; Neurology Department, Rennes University Hospital, Rennes, France
| | - Laure Michel
- Neurology Department, Rennes University Hospital, Rennes, France
| | | | - Marion Leclercq
- Neurology Department, Rennes University Hospital, Rennes, France
| | - Benoit Pegat
- Neurology Department, Vannes Hospital, Vannes, France
| | - Simon Lamy
- Neurology Department, Rennes University Hospital, Rennes, France
| | | | - Kevin Ahrweiler
- Neurology Department, Saint Malo Hospital, Saint Malo, France
| | - Fabien Zagnoli
- Private neurology office, 22 Rue d'Aiguillon Brest, France
| | - Denis Maréchal
- Neurology Department, Brest University Hospital, Brest, France
| | - Benoît Combès
- EMPENN research team, IRISA, CNRS‑INSERM‑INRIA, Rennes University, France
| | - Anne Kerbrat
- EMPENN research team, IRISA, CNRS‑INSERM‑INRIA, Rennes University, France; Neurology Department, Rennes University Hospital, Rennes, France.
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Spagnolo F, Depeursinge A, Schädelin S, Akbulut A, Müller H, Barakovic M, Melie-Garcia L, Bach Cuadra M, Granziera C. How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review. Neuroimage Clin 2023; 39:103491. [PMID: 37659189 PMCID: PMC10480555 DOI: 10.1016/j.nicl.2023.103491] [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: 08/02/2023] [Accepted: 08/04/2023] [Indexed: 09/04/2023]
Abstract
INTRODUCTION Over the past few years, the deep learning community has developed and validated a plethora of tools for lesion detection and segmentation in Multiple Sclerosis (MS). However, there is an important gap between validating models technically and clinically. To this end, a six-step framework necessary for the development, validation, and integration of quantitative tools in the clinic was recently proposed under the name of the Quantitative Neuroradiology Initiative (QNI). AIMS Investigate to what extent automatic tools in MS fulfill the QNI framework necessary to integrate automated detection and segmentation into the clinical neuroradiology workflow. METHODS Adopting the systematic Cochrane literature review methodology, we screened and summarised published scientific articles that perform automatic MS lesions detection and segmentation. We categorised the retrieved studies based on their degree of fulfillment of QNI's six-steps, which include a tool's technical assessment, clinical validation, and integration. RESULTS We found 156 studies; 146/156 (94%) fullfilled the first QNI step, 155/156 (99%) the second, 8/156 (5%) the third, 3/156 (2%) the fourth, 5/156 (3%) the fifth and only one the sixth. CONCLUSIONS To date, little has been done to evaluate the clinical performance and the integration in the clinical workflow of available methods for MS lesion detection/segmentation. In addition, the socio-economic effects and the impact on patients' management of such tools remain almost unexplored.
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Affiliation(s)
- Federico Spagnolo
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland; MedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
| | - Adrien Depeursinge
- MedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland; Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Sabine Schädelin
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Clinical Trial Unit, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Aysenur Akbulut
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Ankara University School of Medicine, Ankara, Turkey
| | - Henning Müller
- MedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland; The Sense Research and Innovation Center, Lausanne and Sion, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Lester Melie-Garcia
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Meritxell Bach Cuadra
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland; Radiology Department, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland.
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Ed‐driouch C, Chéneau F, Simon F, Pasquier G, Combès B, Kerbrat A, Le Page E, Limou S, Vince N, Laplaud D, Mars F, Dumas C, Edan G, Gourraud P. Multiple sclerosis clinical decision support system based on projection to reference datasets. Ann Clin Transl Neurol 2022; 9:1863-1873. [PMID: 36412095 PMCID: PMC9735373 DOI: 10.1002/acn3.51649] [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] [Received: 04/07/2022] [Revised: 07/08/2022] [Accepted: 07/27/2022] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE Multiple sclerosis (MS) is a multifactorial disease with increasingly complicated management. Our objective is to use on-demand computational power to address the challenges of dynamically managing MS. METHODS A phase 3 clinical trial data (NCT00906399) were used to contextualize the medication efficacy of peg-interferon beta-1a vs placebo on patients with relapsing-remitting MS (RRMS). Using a set of reference patients (PORs), selected based on adequate features similar to those of an individual patient, we visualize disease activity by measuring the percentage of relapses, accumulation of new T2 lesions on MRI, and worsening EDSS during the clinical trial. RESULTS We developed MS Vista, a functional prototype of clinical decision support system (CDSS), with a user-centered design and distributed infrastructure. MS Vista shows the medication efficacy of peginterferon beta-1a versus placebo for each individual patient with RRMS. In addition, MS Vista initiated the integration of a longitudinal magnetic resonance imaging (MRI) viewer and interactive dual physician-patient data display to facilitate communication. INTERPRETATION The pioneer use of PORs for each individual patient enables personalized analytics sustaining the dialog between neurologists, patients and caregivers with quantified evidence.
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Affiliation(s)
- Chadia Ed‐driouch
- Nantes Université, INSERM, CR2TI ‐ Center for Research in Transplantation and Translational ImmunologyF‐44000NantesFrance,Département Automatique, Productique et Informatique, IMT AtlantiqueCNRS, LS2N, UMR CNRS6004NantesFrance
| | - Florent Chéneau
- Département Automatique, Productique et Informatique, IMT AtlantiqueCNRS, LS2N, UMR CNRS6004NantesFrance
| | - Françoise Simon
- Nantes Université, INSERM, CR2TI ‐ Center for Research in Transplantation and Translational ImmunologyF‐44000NantesFrance,Mount Sinai School of Medicine and Columbia UniversityNew YorkNYUSA
| | | | - Benoit Combès
- Université de Rennes, Inria, CNRS, Inserm IRISA UMR 6074, Empenn ERL U 1228F‐35000RennesFrance
| | - Anne Kerbrat
- Université de Rennes, Inria, CNRS, Inserm IRISA UMR 6074, Empenn ERL U 1228F‐35000RennesFrance,CRC‐SEP, CICP 1414 INSERM, CHU Pontchaillou RennesRennesFrance
| | | | - Sophie Limou
- Nantes Université, INSERM, CR2TI ‐ Center for Research in Transplantation and Translational ImmunologyF‐44000NantesFrance,Ecole Centrale Nantes, Department of MathematicsComputer Sciences and BiologyF-44000NantesFrance
| | - Nicolas Vince
- Nantes Université, INSERM, CR2TI ‐ Center for Research in Transplantation and Translational ImmunologyF‐44000NantesFrance
| | - David‐Axel Laplaud
- Nantes Université, CRC‐SEP, CHU Nantes, CIC 1413, Centre de Recherche en Transplantation et Immunologie UMR 1064, INSERMNantesFrance
| | - Franck Mars
- Nantes Université, Centrale NantesCNRS, LS2N, UMR 6004F‐44000NantesFrance
| | - Cédric Dumas
- Département Automatique, Productique et Informatique, IMT AtlantiqueCNRS, LS2N, UMR CNRS6004NantesFrance
| | - Gilles Edan
- Université de Rennes, Inria, CNRS, Inserm IRISA UMR 6074, Empenn ERL U 1228F‐35000RennesFrance,CRC‐SEP, CICP 1414 INSERM, CHU Pontchaillou RennesRennesFrance
| | - Pierre‐Antoine Gourraud
- Nantes Université, CHU Nantes, Pôle Hospitalo‐Universitaire 11: Santé Publique, Clinique des données, INSERM CIC 1413F‐44000NantesFrance
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Aboian M, Bousabarah K, Kazarian E, Zeevi T, Holler W, Merkaj S, Cassinelli Petersen G, Bahar R, Subramanian H, Sunku P, Schrickel E, Bhawnani J, Zawalich M, Mahajan A, Malhotra A, Payabvash S, Tocino I, Lin M, Westerhoff M. Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction. Front Neurosci 2022; 16:860208. [PMID: 36312024 PMCID: PMC9606757 DOI: 10.3389/fnins.2022.860208] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 07/13/2022] [Indexed: 11/18/2022] Open
Abstract
Purpose Personalized interpretation of medical images is critical for optimum patient care, but current tools available to physicians to perform quantitative analysis of patient’s medical images in real time are significantly limited. In this work, we describe a novel platform within PACS for volumetric analysis of images and thus development of large expert annotated datasets in parallel with radiologist performing the reading that are critically needed for development of clinically meaningful AI algorithms. Specifically, we implemented a deep learning-based algorithm for automated brain tumor segmentation and radiomics extraction, and embedded it into PACS to accelerate a supervised, end-to- end workflow for image annotation and radiomic feature extraction. Materials and methods An algorithm was trained to segment whole primary brain tumors on FLAIR images from multi-institutional glioma BraTS 2021 dataset. Algorithm was validated using internal dataset from Yale New Haven Health (YHHH) and compared (by Dice similarity coefficient [DSC]) to radiologist manual segmentation. A UNETR deep-learning was embedded into Visage 7 (Visage Imaging, Inc., San Diego, CA, United States) diagnostic workstation. The automatically segmented brain tumor was pliable for manual modification. PyRadiomics (Harvard Medical School, Boston, MA) was natively embedded into Visage 7 for feature extraction from the brain tumor segmentations. Results UNETR brain tumor segmentation took on average 4 s and the median DSC was 86%, which is similar to published literature but lower than the RSNA ASNR MICCAI BRATS challenge 2021. Finally, extraction of 106 radiomic features within PACS took on average 5.8 ± 0.01 s. The extracted radiomic features did not vary over time of extraction or whether they were extracted within PACS or outside of PACS. The ability to perform segmentation and feature extraction before radiologist opens the study was made available in the workflow. Opening the study in PACS, allows the radiologists to verify the segmentation and thus annotate the study. Conclusion Integration of image processing algorithms for tumor auto-segmentation and feature extraction into PACS allows curation of large datasets of annotated medical images and can accelerate translation of research into development of personalized medicine applications in the clinic. The ability to use familiar clinical tools to revise the AI segmentations and natively embedding the segmentation and radiomic feature extraction tools on the diagnostic workstation accelerates the process to generate ground-truth data.
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Affiliation(s)
- Mariam Aboian
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
- *Correspondence: Mariam Aboian,
| | | | - Eve Kazarian
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Tal Zeevi
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT, United States
| | | | - Sara Merkaj
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Gabriel Cassinelli Petersen
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Ryan Bahar
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Harry Subramanian
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Pranay Sunku
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Elizabeth Schrickel
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Jitendra Bhawnani
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Mathew Zawalich
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Amit Mahajan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Ajay Malhotra
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Sam Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Irena Tocino
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - MingDe Lin
- Department of Radiology, Yale University and Visage Imaging, New Haven, CT, United States
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Andresen J, Uzunova H, Ehrhardt J, Kepp T, Handels H. Image registration and appearance adaptation in non-correspondent image regions for new MS lesions detection. Front Neurosci 2022; 16:981523. [PMID: 36161180 PMCID: PMC9490269 DOI: 10.3389/fnins.2022.981523] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/18/2022] [Indexed: 11/25/2022] Open
Abstract
Manual detection of newly formed lesions in multiple sclerosis is an important but tedious and difficult task. Several approaches for automating the detection of new lesions have recently been proposed, but they tend to either overestimate the actual amount of new lesions or to miss many lesions. In this paper, an image registration convolutional neural network (CNN) that adapts the baseline image to the follow-up image by spatial deformations and simulation of new lesions is proposed. Simultaneously, segmentations of new lesions are generated, which are shown to reliably estimate the real new lesion load and to separate stable and progressive patients. Several applications of the proposed network emerge: image registration, detection and segmentation of new lesions, and modeling of new MS lesions. The modeled lesions offer the possibility to investigate the intensity profile of new lesions.
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Affiliation(s)
- Julia Andresen
- Institute of Medical Informatics, University of Lübeck, Lübeck, Germany
- *Correspondence: Julia Andresen
| | - Hristina Uzunova
- German Research Center for Artificial Intelligence, Lübeck, Germany
| | - Jan Ehrhardt
- Institute of Medical Informatics, University of Lübeck, Lübeck, Germany
- German Research Center for Artificial Intelligence, Lübeck, Germany
| | - Timo Kepp
- Institute of Medical Informatics, University of Lübeck, Lübeck, Germany
| | - Heinz Handels
- Institute of Medical Informatics, University of Lübeck, Lübeck, Germany
- German Research Center for Artificial Intelligence, Lübeck, Germany
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Sarica B, Seker DZ. New MS lesion segmentation with deep residual attention gate U-Net utilizing 2D slices of 3D MR images. Front Neurosci 2022; 16:912000. [PMID: 35968389 PMCID: PMC9365701 DOI: 10.3389/fnins.2022.912000] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 06/27/2022] [Indexed: 11/13/2022] Open
Abstract
Multiple sclerosis (MS) is an autoimmune disease that causes lesions in the central nervous system of humans due to demyelinating axons. Magnetic resonance imaging (MRI) is widely used for monitoring and measuring MS lesions. Automated methods for MS lesion segmentation have usually been performed on individual MRI scans. Recently, tracking lesion activity for quantifying and monitoring MS disease progression, especially detecting new lesions, has become an important biomarker. In this study, a unique pipeline with a deep neural network that combines U-Net, attention gate, and residual learning is proposed to perform better new MS lesion segmentation using baseline and follow-up 3D FLAIR MR images. The proposed network has a similar architecture to U-Net and is formed from residual units which facilitate the training of deep networks. Networks with fewer parameters are designed with better performance through the skip connections of U-Net and residual units, which facilitate information propagation without degradation. Attention gates also learn to focus on salient features of the target structures of various sizes and shapes. The MSSEG-2 dataset was used for training and testing the proposed pipeline, and the results were compared with those of other proposed pipelines of the challenge and experts who participated in the same challenge. According to the results over the testing set, the lesion-wise F1 and dice scores were obtained as a mean of 48 and 44.30%. For the no-lesion cases, the number of tested and volume of tested lesions were obtained as a mean of 0.148 and 1.488, respectively. The proposed pipeline outperformed 22 proposed pipelines and ranked 8th in the challenge.
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Affiliation(s)
- Beytullah Sarica
- Department of Applied Informatics, Graduate School, Istanbul Technical University, Istanbul, Turkey
| | - Dursun Zafer Seker
- Department of Geomatics Engineering, Faculty of Civil Engineering, Istanbul Technical University, Istanbul, Turkey
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