Search

Your search keyword '"Brisset, Jean-Christophe"' showing total 45 results
Start Over Author "Brisset, Jean-Christophe"
45 results on '"Brisset, Jean-Christophe"'

1. 3D-Morphomics, Morphological Features on CT scans for lung nodule malignancy diagnosis

Author

Munoz, Elias, Baudot, Pierre, Le, Van-Khoa, Voyton, Charles, Renoust, Benjamin, Francis, Danny, Groza, Vladimir, Brisset, Jean-Christophe, Geremia, Ezequiel, Iannessi, Antoine, Liu, Yan, and Huet, Benoit

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract

Pathologies systematically induce morphological changes, thus providing a major but yet insufficiently quantified source of observables for diagnosis. The study develops a predictive model of the pathological states based on morphological features (3D-morphomics) on Computed Tomography (CT) volumes. A complete workflow for mesh extraction and simplification of an organ's surface is developed, and coupled with an automatic extraction of morphological features given by the distribution of mean curvature and mesh energy. An XGBoost supervised classifier is then trained and tested on the 3D-morphomics to predict the pathological states. This framework is applied to the prediction of the malignancy of lung's nodules. On a subset of NLST database with malignancy confirmed biopsy, using 3D-morphomics only, the classification model of lung nodules into malignant vs. benign achieves 0.964 of AUC. Three other sets of classical features are trained and tested, (1) clinical relevant features gives an AUC of 0.58, (2) 111 radiomics gives an AUC of 0.976, (3) radiologist ground truth (GT) containing the nodule size, attenuation and spiculation qualitative annotations gives an AUC of 0.979. We also test the Brock model and obtain an AUC of 0.826. Combining 3D-morphomics and radiomics features achieves state-of-the-art results with an AUC of 0.978 where the 3D-morphomics have some of the highest predictive powers. As a validation on a public independent cohort, models are applied to the LIDC dataset, the 3D-morphomics achieves an AUC of 0.906 and the 3D-morphomics+radiomics achieves an AUC of 0.958, which ranks second in the challenge among deep models. It establishes the curvature distributions as efficient features for predicting lung nodule malignancy and a new method that can be applied directly to arbitrary computer aided diagnosis task., Comment: 10 pages, 7 figures

Published
2022

3. Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks

Author

Gros, Charley, De Leener, Benjamin, Badji, Atef, Maranzano, Josefina, Eden, Dominique, Dupont, Sara M., Talbott, Jason, Zhuoquiong, Ren, Liu, Yaou, Granberg, Tobias, Ouellette, Russell, Tachibana, Yasuhiko, Hori, Masaaki, Kamiya, Kouhei, Chougar, Lydia, Stawiarz, Leszek, Hillert, Jan, Bannier, Elise, Kerbrat, Anne, Edan, Gilles, Labauge, Pierre, Callot, Virginie, Pelletier, Jean, Audoin, Bertrand, Rasoanandrianina, Henitsoa, Brisset, Jean-Christophe, Valsasina, Paola, Rocca, Maria A., Filippi, Massimo, Bakshi, Rohit, Tauhid, Shahamat, Prados, Ferran, Yiannakas, Marios, Kearney, Hugh, Ciccarelli, Olga, Smith, Seth, Treaba, Constantina Andrada, Mainero, Caterina, Lefeuvre, Jennifer, Reich, Daniel S., Nair, Govind, Auclair, Vincent, McLaren, Donald G., Martin, Allan R., Fehlings, Michael G., Vahdat, Shahabeddin, Khatibi, Ali, Doyon, Julien, Shepherd, Timothy, Charlson, Erik, Narayanan, Sridar, and Cohen-Adad, Julien

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. The goal of this study was to develop a fully-automatic framework, robust to variability in both image parameters and clinical condition, for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data. Scans of 1,042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n=30). Data spanned three contrasts (T1-, T2-, and T2*-weighted) for a total of 1,943 volumes. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg, a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. The proposed framework is open-source and readily available in the Spinal Cord Toolbox., Comment: 38 pages, 7 figures, 2 tables

Published
2018

4. Spatial distribution of multiple sclerosis lesions in the cervical spinal cord.

Author

Eden, Dominique, Gros, Charley, Badji, Atef, Dupont, Sara, De Leener, Benjamin, Maranzano, Josefina, Zhuoquiong, Ren, Liu, Yaou, Granberg, Tobias, Ouellette, Russell, Stawiarz, Leszek, Hillert, Jan, Talbott, Jason, Bannier, Elise, Kerbrat, Anne, Edan, Gilles, Labauge, Pierre, Callot, Virginie, Pelletier, Jean, Audoin, Bertrand, Rasoanandrianina, Henitsoa, Brisset, Jean-Christophe, Valsasina, Paola, Rocca, Maria, Filippi, Massimo, Bakshi, Rohit, Tauhid, Shahamat, Prados, Ferran, Yiannakas, Marios, Kearney, Hugh, Ciccarelli, Olga, Smith, Seth, Andrada Treaba, Constantina, Mainero, Caterina, Lefeuvre, Jennifer, Reich, Daniel, Nair, Govind, Shepherd, Timothy, Charlson, Erik, Tachibana, Yasuhiko, Hori, Masaaki, Kamiya, Kouhei, Chougar, Lydia, Narayanan, Sridar, and Cohen-Adad, Julien

Subjects
MRI, lesions, multicentre, multiple sclerosis, spinal cord, Adult, Brain, Cervical Cord, Disability Evaluation, Disease Progression, Female, Gray Matter, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Multiple Sclerosis, Multiple Sclerosis, Chronic Progressive, Multiple Sclerosis, Relapsing-Remitting, Spatial Analysis, Spinal Cord, Spinal Cord Diseases, White Matter
Abstract

Spinal cord lesions detected on MRI hold important diagnostic and prognostic value for multiple sclerosis. Previous attempts to correlate lesion burden with clinical status have had limited success, however, suggesting that lesion location may be a contributor. Our aim was to explore the spatial distribution of multiple sclerosis lesions in the cervical spinal cord, with respect to clinical status. We included 642 suspected or confirmed multiple sclerosis patients (31 clinically isolated syndrome, and 416 relapsing-remitting, 84 secondary progressive, and 73 primary progressive multiple sclerosis) from 13 clinical sites. Cervical spine lesions were manually delineated on T2- and T2*-weighted axial and sagittal MRI scans acquired at 3 or 7 T. With an automatic publicly-available analysis pipeline we produced voxelwise lesion frequency maps to identify predilection sites in various patient groups characterized by clinical subtype, Expanded Disability Status Scale score and disease duration. We also measured absolute and normalized lesion volumes in several regions of interest using an atlas-based approach, and evaluated differences within and between groups. The lateral funiculi were more frequently affected by lesions in progressive subtypes than in relapsing in voxelwise analysis (P < 0.001), which was further confirmed by absolute and normalized lesion volumes (P < 0.01). The central cord area was more often affected by lesions in primary progressive than relapse-remitting patients (P < 0.001). Between white and grey matter, the absolute lesion volume in the white matter was greater than in the grey matter in all phenotypes (P < 0.001); however when normalizing by each region, normalized lesion volumes were comparable between white and grey matter in primary progressive patients. Lesions appearing in the lateral funiculi and central cord area were significantly correlated with Expanded Disability Status Scale score (P < 0.001). High lesion frequencies were observed in patients with a more aggressive disease course, rather than long disease duration. Lesions located in the lateral funiculi and central cord area of the cervical spine may influence clinical status in multiple sclerosis. This work shows the added value of cervical spine lesions, and provides an avenue for evaluating the distribution of spinal cord lesions in various patient groups.

Published
2019

5. Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks

Author

Gros, Charley, De Leener, Benjamin, Badji, Atef, Maranzano, Josefina, Eden, Dominique, Dupont, Sara M, Talbott, Jason, Zhuoquiong, Ren, Liu, Yaou, Granberg, Tobias, Ouellette, Russell, Tachibana, Yasuhiko, Hori, Masaaki, Kamiya, Kouhei, Chougar, Lydia, Stawiarz, Leszek, Hillert, Jan, Bannier, Elise, Kerbrat, Anne, Edan, Gilles, Labauge, Pierre, Callot, Virginie, Pelletier, Jean, Audoin, Bertrand, Rasoanandrianina, Henitsoa, Brisset, Jean-Christophe, Valsasina, Paola, Rocca, Maria A, Filippi, Massimo, Bakshi, Rohit, Tauhid, Shahamat, Prados, Ferran, Yiannakas, Marios, Kearney, Hugh, Ciccarelli, Olga, Smith, Seth, Treaba, Constantina Andrada, Mainero, Caterina, Lefeuvre, Jennifer, Reich, Daniel S, Nair, Govind, Auclair, Vincent, McLaren, Donald G, Martin, Allan R, Fehlings, Michael G, Vahdat, Shahabeddin, Khatibi, Ali, Doyon, Julien, Shepherd, Timothy, Charlson, Erik, Narayanan, Sridar, and Cohen-Adad, Julien

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Spinal Cord Injury, Neurodegenerative, Bioengineering, Autoimmune Disease, Traumatic Head and Spine Injury, Physical Injury - Accidents and Adverse Effects, Brain Disorders, Biomedical Imaging, Multiple Sclerosis, Neurosciences, Neurological, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Neural Networks, Computer, Observer Variation, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Spinal Cord, MRI, Segmentation, Spinal cord, Multiple sclerosis, Convolutional neural networks, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Abstract

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability in both image parameters and clinical condition - for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T1-, T2-, and T2∗-weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p ≤ 0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.

Published
2019

9. Cerebral perfusion using ASL in patients with COVID-19 and neurological manifestations: A retrospective multicenter observational study

Author

Ardellier, François-Daniel, primary, Baloglu, Seyyid, additional, Sokolska, Magdalena, additional, Noblet, Vincent, additional, Lersy, François, additional, Collange, Olivier, additional, Ferré, Jean-Christophe, additional, Maamar, Adel, additional, Carsin-Nicol, Béatrice, additional, Helms, Julie, additional, Schenck, Maleka, additional, Khalil, Antoine, additional, Gaudemer, Augustin, additional, Caillard, Sophie, additional, Pottecher, Julien, additional, Lefèbvre, Nicolas, additional, Zorn, Pierre-Emmanuel, additional, Matthieu, Muriel, additional, Brisset, Jean Christophe, additional, Boulay, Clotilde, additional, Mutschler, Véronique, additional, Hansmann, Yves, additional, Mertes, Paul-Michel, additional, Schneider, Francis, additional, Fafi-Kremer, Samira, additional, Ohana, Mickael, additional, Meziani, Ferhat, additional, Meyer, Nicolas, additional, Yousry, Tarek, additional, Anheim, Mathieu, additional, Cotton, François, additional, Jäger, Hans Rolf, additional, Kremer, Stéphane, additional, Bonneville, Fabrice, additional, Adam, Gilles, additional, Martin-Blondel, Guillaume, additional, Pariente, Jérémie, additional, Geeraerts, Thomas, additional, Oesterlé, Hélène, additional, Bolognini, Federico, additional, Messie, Julien, additional, Hmeydia, Ghazi, additional, Benzakoun, Joseph, additional, Oppenheim, Catherine, additional, Constans, Jean-Marc, additional, Metanbou, Serge, additional, Heintz, Adrien, additional, Bapst, Blanche, additional, Megdiche, Imen, additional, Jager, Lavinia, additional, Nesser, Patrick, additional, Mba, Yannick Talla, additional, Tourdias, Thomas, additional, Coutureau, Juliette, additional, Hemmert, Céline, additional, Feuerstein, Philippe, additional, Sebag, Nathan, additional, Carre, Sophie, additional, Alleg, Manel, additional, Lecocq, Claire, additional, Schmitt, Emmanuel, additional, Anxionnat, René, additional, Zhu, François, additional, Forestier, Géraud, additional, Rouchaud, Aymeric, additional, Comby, Pierre-Olivier, additional, Ricolfi, Frederic, additional, Thouant, Pierre, additional, Grand, Sylvie, additional, Krainik, Alexandre, additional, de Beaurepaire, Isaure, additional, Bornet, Grégoire, additional, Lacalm, Audrey, additional, Miailhes, Patrick, additional, Pique, Julie, additional, Boutet, Claire, additional, Fabre, Xavier, additional, Claise, Béatrice, additional, Mirafzal, Sonia, additional, Calvet, Laure, additional, Desal, Hubert, additional, Berge, Jérome, additional, Boulouis, Grégoire, additional, Kazemi, Apolline, additional, Pyatigorskaya, Nadya, additional, Lecler, Augustin, additional, Saleme, Suzana, additional, Edjlali-Goujon, Myriam, additional, Kerleroux, Basile, additional, Brisset, Jean-Christophe, additional, and Chenaf, Samir, additional

Published
2023
Full Text
View/download PDF

13. Oncogenic Kras is required for both the initiation and maintenance of pancreatic cancer in mice

Author

Collins, Meredith A., Bednar, Filip, Zhang, Yaqing, Brisset, Jean-Christophe, Galban, Stefanie, Galban, Craig J., Rakshit, Sabita, Flannagan, Karen S., Adsay, N. Volkan, and Magliano, Marina Pasca di

Subjects
Tumor proteins -- Physiological aspects -- Genetic aspects -- Research, Pancreatic cancer -- Risk factors -- Genetic aspects -- Development and progression -- Diagnosis -- Care and treatment, Ras genes -- Health aspects -- Research, Health care industry
Abstract

Pancreatic cancer is almost invariably associated with mutations in the KRAS gene, most commonly [KRAS.sup.G12D], that result in a dominantactive form of the KRAS GTPase. However, how KRAS mutations promote pancreatic carcinogenesis is not fully understood, and whether oncogenic KRAS is required for the maintenance of pancreatic cancer has not been established. To address these questions, we generated two mouse models of pancreatic tumorigenesis: mice transgenic for inducible [Kras.sup.G12D], which allows for inducible, pancreasspecific, and reversible expression of the oncogenic [Kras.sup.G12D], with or without inactivation of one allele of the tumor suppressor gene p53. Here, we report that, early in tumorigenesis, induction of oncogenic [Kras.sup.G12D] reversibly altered normal epithelial differentiation following tissue damage, leading to precancerous lesions. Inactivation of [Kras.sup.G12D] in established precursor lesions and during progression to cancer led to regression of the lesions, indicating that [Kras.sup.G12D] was required for tumor cell survival. Strikingly, during all stages of carcinogenesis, [Kras.sup.G12D] upregulated Hedgehog signaling, inflammatory pathways, and several pathways known to mediate paracrine interactions between epithelial cells and their surrounding microenvironment, thus promoting formation and maintenance of the fibroinflammatory stroma that plays a pivotal role in pancreatic cancer. Our data establish that epithelial [Kras.sup.G12D] influences multiple cell types to drive pancreatic tumorigenesis and is essential for tumor maintenance. They also strongly support the notion that inhibiting [Kras.sup.G12D], or its downstream effectors, could provide a new approach for the treatment of pancreatic cancer., Introduction Pancreatic ductal adenocarcinoma (PDA), the most common form of pancreatic cancer, has among the worst prognoses of all human malignancies. Annually, the number of victims of the disease approaches [...]

Published
2012
Full Text
View/download PDF

17. Cerebrospinal fluid features in COVID-19 patients with neurologic manifestations: correlation with brain MRI findings in 58 patients

Author

Lersy, François, Benotmane, Ilies, Helms, Julie, Collange, Olivier, Schenck, Maleka, Brisset, Jean-Christophe, Chammas, Agathe, Willaume, Thibault, Lefebvre, Nicolas, Solis, Morgane, Hansmann, Yves, Fabacher, Thibaut, Caillard, Sophie, Mertes, Paul Michel, Pottecher, Julien, Schneider, Francis, Meziani, Ferhat, Fafi-Kremer, Samira, and Kremer, Stéphane

Subjects
Inflammation, Male, Brain Diseases, COVID-19, Brain, Middle Aged, Leptomeningeal enhancement, Magnetic Resonance Imaging, AcademicSubjects/MED00290, Cerebrospinal fluid, Blood-Brain Barrier, Major Article, Humans, Female, France, Biomarkers, Aged, Retrospective Studies
Abstract

Neurological manifestations are common in patients with coronavirus disease 2019 (COVID-19), but little is known about pathophysiological mechanisms. In this single-center study, we examined neurological manifestations in 58 patients, including cerebrospinal fluid (CSF) analysis and neuroimaging findings.The study included 58 patients with COVID-19 and neurological manifestations in whom severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reverse-transcription polymerase chain reaction screening and on CSF analysis were performed. Clinical, laboratory, and brain magnetic resonance (MR) imaging data were retrospectively collected and analyzed.Patients were mostly men (66%), with a median age of 62 years. Encephalopathy was frequent (81%), followed by pyramidal dysfunction (16%), seizures (10%), and headaches (5%). CSF protein and albumin levels were increased in 38% and 23%, respectively. A total of 40% of patients displayed an elevated albumin quotient, suggesting impaired blood-brain barrier integrity. CSF-specific immunoglobulin G oligoclonal band was found in 5 patients (11%), suggesting an intrathecal synthesis of immunoglobulin G, and 26 patients (55%) presented identical oligoclonal bands in serum and CSF. Four patients (7%) had a positive CSF SARS-CoV-2 reverse-transcription polymerase chain reaction. Leptomeningeal enhancement was present on brain MR images in 20 patients (38%).Brain MR imaging abnormalities, especially leptomeningeal enhancement, and increased inflammatory markers in CSF are frequent in patients with neurological manifestations related to COVID-19, whereas SARS-CoV-2 detection in CSF remained scanty.

Published
2020

18. Cerebrospinal Fluid Features in Patients With Coronavirus Disease 2019 and Neurological Manifestations: Correlation with Brain Magnetic Resonance Imaging Findings in 58 Patients

Author

Lersy, François, primary, Benotmane, Ilies, additional, Helms, Julie, additional, Collange, Olivier, additional, Schenck, Maleka, additional, Brisset, Jean-Christophe, additional, Chammas, Agathe, additional, Willaume, Thibault, additional, Lefebvre, Nicolas, additional, Solis, Morgane, additional, Hansmann, Yves, additional, Fabacher, Thibaut, additional, Caillard, Sophie, additional, Mertes, Paul Michel, additional, Pottecher, Julien, additional, Schneider, Francis, additional, Meziani, Ferhat, additional, Fafi-Kremer, Samira, additional, and Kremer, Stéphane, additional

Published
2020
Full Text
View/download PDF

19. Critical illness-associated cerebral microbleeds for patients with severe COVID-19: etiologic hypotheses

Author

Lersy, François, primary, Willaume, Thibault, additional, Brisset, Jean-Christophe, additional, Collange, Olivier, additional, Helms, Julie, additional, Schneider, Francis, additional, Chammas, Agathe, additional, Willaume, Alexandre, additional, Meyer, Nicolas, additional, Anheim, Mathieu, additional, Cotton, François, additional, and Kremer, Stéphane, additional

Published
2020
Full Text
View/download PDF

20. Neurologic and neuroimaging findings in patients with COVID-19

Author

Kremer, Stéphane, primary, Lersy, François, additional, Anheim, Mathieu, additional, Merdji, Hamid, additional, Schenck, Maleka, additional, Oesterlé, Hélène, additional, Bolognini, Federico, additional, Messie, Julien, additional, Khalil, Antoine, additional, Gaudemer, Augustin, additional, Carré, Sophie, additional, Alleg, Manel, additional, Lecocq, Claire, additional, Schmitt, Emmanuelle, additional, Anxionnat, René, additional, Zhu, François, additional, Jager, Lavinia, additional, Nesser, Patrick, additional, Mba, Yannick Talla, additional, Hmeydia, Ghazi, additional, Benzakoun, Joseph, additional, Oppenheim, Catherine, additional, Ferré, Jean-Christophe, additional, Maamar, Adel, additional, Carsin-Nicol, Béatrice, additional, Comby, Pierre-Olivier, additional, Ricolfi, Frédéric, additional, Thouant, Pierre, additional, Boutet, Claire, additional, Fabre, Xavier, additional, Forestier, Géraud, additional, de Beaurepaire, Isaure, additional, Bornet, Grégoire, additional, Desal, Hubert, additional, Boulouis, Grégoire, additional, Berge, Jérome, additional, Kazémi, Apolline, additional, Pyatigorskaya, Nadya, additional, Lecler, Augustin, additional, Saleme, Suzana, additional, Edjlali-Goujon, Myriam, additional, Kerleroux, Basile, additional, Constans, Jean-Marc, additional, Zorn, Pierre-Emmanuel, additional, Mathieu, Muriel, additional, Baloglu, Seyyid, additional, Ardellier, François-Daniel, additional, Willaume, Thibault, additional, Brisset, Jean-Christophe, additional, Caillard, Sophie, additional, Collange, Olivier, additional, Mertes, Paul Michel, additional, Schneider, Francis, additional, Fafi-Kremer, Samira, additional, Ohana, Mickael, additional, Meziani, Ferhat, additional, Meyer, Nicolas, additional, Helms, Julie, additional, and Cotton, François, additional

Published
2020
Full Text
View/download PDF

21. New OFSEP recommendations for MRI assessment of multiple sclerosis patients: Special consideration for gadolinium deposition and frequent acquisitions

Author

Brisset, Jean-Christophe, primary, Kremer, Stephane, additional, Hannoun, Salem, additional, Bonneville, Fabrice, additional, Durand-Dubief, Francoise, additional, Tourdias, Thomas, additional, Barillot, Christian, additional, Guttmann, Charles, additional, Vukusic, Sandra, additional, Dousset, Vincent, additional, Cotton, Francois, additional, Ameli, R., additional, Anxionnat, R., additional, Audoin, B., additional, Attye, A., additional, Bannier, E., additional, Barillot, C., additional, Ben Salem, D., additional, Boncoeur-Martel, M.-P., additional, Bonhomme, G., additional, Bonneville, F., additional, Boutet, C., additional, Brisset, J.C., additional, Cervenanski, F., additional, Claise, B., additional, Commowick, O., additional, Constans, J.-M., additional, Cotton, F., additional, Dardel, P., additional, Desal, H., additional, Dousset, V., additional, Durand-Dubief, F., additional, Ferre, J.-C., additional, Gaultier, A., additional, Gerardin, E., additional, Glattard, T., additional, Grand, S., additional, Grenier, T., additional, Guillevin, R., additional, Guttmann, C., additional, Krainik, A., additional, Kremer, S., additional, Lion, S., additional, Champfleur, N. Menjot De, additional, Mondot, L., additional, Outteryck, O., additional, Pyatigorskaya, N., additional, Pruvo, J.-P., additional, Rabaste, S., additional, Ranjeva, J.-P., additional, Roch, J.-A., additional, Sadik, J.-C., additional, Sappey-Marinier, D., additional, Savatovsky, J., additional, Stankoff, B., additional, Tanguy, J.-Y., additional, Tourbah, A., additional, Tourdias, T., additional, Brochet, B., additional, Casey, R., additional, De Sèze, J., additional, Douek, P., additional, Guillemin, F., additional, Laplaud, D., additional, Lebrun-Frenay, C., additional, Mansuy, L., additional, Moreau, T., additional, Olaiz, J., additional, Pelletier, J., additional, Rigaud-Bully, C., additional, Vukusic, S., additional, Debouverie, M., additional, Edan, G., additional, Ciron, J., additional, Lubetzki, C., additional, Vermersch, P., additional, Labauge, P., additional, Defer, G., additional, Berger, E., additional, Clavelou, P., additional, Gout, O., additional, Thouvenot, E., additional, Heinzlef, O., additional, Al-Khedr, A., additional, Bourre, B., additional, Casez, O., additional, Cabre, P., additional, Montcuquet, A., additional, Créange, A., additional, Camdessanché, J.-P., additional, Bakchine, S., additional, Maurousset, A., additional, Patry, I., additional, De Broucker, T., additional, Pottier, C., additional, Neau, J.-P., additional, Labeyrie, C., additional, and Nifle, C., additional

Published
2020
Full Text
View/download PDF

23. Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks

Author

Gros, Charley, primary, De Leener, Benjamin, additional, Badji, Atef, additional, Maranzano, Josefina, additional, Eden, Dominique, additional, Dupont, Sara M., additional, Talbott, Jason, additional, Zhuoquiong, Ren, additional, Liu, Yaou, additional, Granberg, Tobias, additional, Ouellette, Russell, additional, Tachibana, Yasuhiko, additional, Hori, Masaaki, additional, Kamiya, Kouhei, additional, Chougar, Lydia, additional, Stawiarz, Leszek, additional, Hillert, Jan, additional, Bannier, Elise, additional, Kerbrat, Anne, additional, Edan, Gilles, additional, Labauge, Pierre, additional, Callot, Virginie, additional, Pelletier, Jean, additional, Audoin, Bertrand, additional, Rasoanandrianina, Henitsoa, additional, Brisset, Jean-Christophe, additional, Valsasina, Paola, additional, Rocca, Maria A., additional, Filippi, Massimo, additional, Bakshi, Rohit, additional, Tauhid, Shahamat, additional, Prados, Ferran, additional, Yiannakas, Marios, additional, Kearney, Hugh, additional, Ciccarelli, Olga, additional, Smith, Seth, additional, Treaba, Constantina Andrada, additional, Mainero, Caterina, additional, Lefeuvre, Jennifer, additional, Reich, Daniel S., additional, Nair, Govind, additional, Auclair, Vincent, additional, McLaren, Donald G., additional, Martin, Allan R., additional, Fehlings, Michael G., additional, Vahdat, Shahabeddin, additional, Khatibi, Ali, additional, Doyon, Julien, additional, Shepherd, Timothy, additional, Charlson, Erik, additional, Narayanan, Sridar, additional, and Cohen-Adad, Julien, additional

Published
2019
Full Text
View/download PDF

24. Cerebrospinal Fluid Features in Patients With Coronavirus Disease 2019 and Neurological Manifestations: Correlation with Brain Magnetic Resonance Imaging Findings in 58 Patients.

Author

Lersy, François, Benotmane, Ilies, Helms, Julie, Collange, Olivier, Schenck, Maleka, Brisset, Jean-Christophe, Chammas, Agathe, Willaume, Thibault, Lefebvre, Nicolas, Solis, Morgane, Hansmann, Yves, Fabacher, Thibaut, Caillard, Sophie, Mertes, Paul Michel, Pottecher, Julien, Schneider, Francis, Meziani, Ferhat, Fafi-Kremer, Samira, and Kremer, Stéphane

Subjects
COVID-19, NEUROLOGIC manifestations of general diseases, MAGNETIC resonance imaging, CEREBROSPINAL fluid, NEUROLOGICAL disorders, MENINGEAL cancer
Abstract

Background: Neurological manifestations are common in patients with coronavirus disease 2019 (COVID-19), but little is known about pathophysiological mechanisms. In this single-center study, we examined neurological manifestations in 58 patients, including cerebrospinal fluid (CSF) analysis and neuroimaging findings.Methods: The study included 58 patients with COVID-19 and neurological manifestations in whom severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reverse-transcription polymerase chain reaction screening and on CSF analysis were performed. Clinical, laboratory, and brain magnetic resonance (MR) imaging data were retrospectively collected and analyzed.Results: Patients were mostly men (66%), with a median age of 62 years. Encephalopathy was frequent (81%), followed by pyramidal dysfunction (16%), seizures (10%), and headaches (5%). CSF protein and albumin levels were increased in 38% and 23%, respectively. A total of 40% of patients displayed an elevated albumin quotient, suggesting impaired blood-brain barrier integrity. CSF-specific immunoglobulin G oligoclonal band was found in 5 patients (11%), suggesting an intrathecal synthesis of immunoglobulin G, and 26 patients (55%) presented identical oligoclonal bands in serum and CSF. Four patients (7%) had a positive CSF SARS-CoV-2 reverse-transcription polymerase chain reaction. Leptomeningeal enhancement was present on brain MR images in 20 patients (38%).Conclusions: Brain MR imaging abnormalities, especially leptomeningeal enhancement, and increased inflammatory markers in CSF are frequent in patients with neurological manifestations related to COVID-19, whereas SARS-CoV-2 detection in CSF remained scanty. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
View/download PDF

25. Susceptibility Weighted Imaging and Quantitative Susceptibility Mapping of the Cerebral Vasculature Using Ferumoxytol

Author

Liu, Saifeng, Brisset, Jean-Christophe, Hu, Jiani, Haacke, E. Mark, and Ge, Yulin

Subjects
Adult, Phantoms, Imaging, Brain, Cerebral Arteries, Middle Aged, Image Enhancement, Magnetic Resonance Imaging, Article, Ferrosoferric Oxide, Imaging, Three-Dimensional, Reference Values, Image Processing, Computer-Assisted, Humans, Female
Abstract

To demonstrate the potential of imaging cerebral arteries and veins with ferumoxytol using susceptibility weighted imaging (SWI) and quantitative susceptibility mapping (QSM).The relationships between ferumoxytol concentration and the apparent susceptibility at 1.5T, 3T, and 7T were determined using phantom data; the ability of visualizing subvoxel vessels was evaluated using simulations; and the feasibility of using ferumoxytol to enhance the visibility of small vessels was confirmed in three healthy volunteers at 7T(with doses 1 mg/kg to 4 mg/kg). The visualization of the lenticulostriate arteries and the medullary veins was assessed by two raters and the contrast-to-noise ratios (CNRs) of these vessels were measured.The relationship between ferumoxytol concentration and susceptibility was linear with a slope 13.3 ± 0.2 ppm·mgThe CNR for cerebral vessels with ferumoxytol can be enhanced using SWI, and the apparent susceptibilities of the arteries can be reliably quantified using QSM. This approach improves the imaging of the entire vascular system outside the capillaries and may be valuable for a variety of neurodegenerative diseases which involve the microvasculature.1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:621-633.

Published
2017

27. Weekly enhanced T1-weighted MRI with Gadobutrol injections in MS patients: Is there a signal intensity increase in the dentate nucleus and the globus pallidus?

Author

Jaulent, Paul, primary, Hannoun, Salem, additional, Kocevar, Gabriel, additional, Rollot, Fabien, additional, Durand-Dubief, Francoise, additional, Vukusic, Sandra, additional, Brisset, Jean-Christophe, additional, Sappey-Marinier, Dominique, additional, and Cotton, Francois, additional

Published
2018
Full Text
View/download PDF

28. Diagnostic value of 3DFLAIR in clinical practice for the detection of infratentorial lesions in multiple sclerosis in regard to dual echo T2 sequences

Author

Hannoun, Salem, primary, Heidelberg, Damien, additional, Hourani, Roula, additional, Nguyen, Thi Thuy Trang, additional, Brisset, Jean-Christophe, additional, Grand, Sylvie, additional, Kremer, Stéphane, additional, Bonneville, Fabrice, additional, Guttmann, Charles R.G., additional, Dousset, Vincent, additional, and Cotton, François, additional

Published
2018
Full Text
View/download PDF

30. Integrated Multimodal Imaging of Dynamic Bone-Tumor Alterations Associated with Metastatic Prostate Cancer

Author

Brisset, Jean-Christophe, Hoff, Benjamin A., Chenevert, Thomas L., Jacobson, Jon A., Boes, Jennifer L., Galban, Stefanie, Rehemtulla, Alnawaz, Johnson, Timothy D., Pienta, Kenneth J., Galban, Craig J., Meyer, Charles R., Schakel, Timothy, Nicolay, Klaas, Alva, Ajjai S., Hussain, Maha, Ross, Brian D., and Schakel, Tim

Subjects
Male, Oncology, BIOMARKER, Pathology, Osteolysis, Bone density, Bone disease, FUNCTIONAL DIFFUSION MAP, lcsh:Medicine, PROGRESSION, Docetaxel, SDG 3 – Goede gezondheid en welzijn, Multimodal Imaging, 030218 nuclear medicine & medical imaging, Mice, Prostate cancer, 0302 clinical medicine, Bone Density, Prostate, PARAMETRIC RESPONSE MAP, lcsh:Science, Non-U.S. Gov't, Multidisciplinary, Diphosphonates, Research Support, Non-U.S. Gov't, Bone metastasis, Clinical Trial, Tumor Burden, 3. Good health, THERAPY RESPONSE, Treatment Outcome, medicine.anatomical_structure, 030220 oncology & carcinogenesis, ANDROGEN INDEPENDENCE, Taxoids, Research Article, medicine.medical_specialty, GLIOBLASTOMA, Antineoplastic Agents, Bone Neoplasms, Research Support, N.I.H, 03 medical and health sciences, Breast cancer, SDG 3 - Good Health and Well-being, Research Support, N.I.H., Extramural, DW-MRI, Osteoclast, Internal medicine, medicine, Journal Article, Animals, Humans, BREAST-CANCER, Osteoblasts, business.industry, lcsh:R, Prostatic Neoplasms, Extramural, WEIGHTED MRI, medicine.disease, Disease Models, Animal, Diffusion Magnetic Resonance Imaging, lcsh:Q, business
Abstract

Bone metastasis occurs for men with advanced prostate cancer which promotes osseous growth and destruction driven by alterations in osteoblast and osteoclast homeostasis. Patients can experience pain, spontaneous fractures and morbidity eroding overall quality of life. The complex and dynamic cellular interactions within the bone microenvironment limit current treatment options thus prostate to bone metastases remains incurable. This study uses voxel-based analysis of diffusion-weighted MRI and CT scans to simultaneously evaluate temporal changes in normal bone homeostasis along with prostate bone metatastsis to deliver an improved understanding of the spatiotemporal local microenvironment. Dynamic tumor-stromal interactions were assessed during treatment in mouse models along with a pilot prospective clinical trial with metastatic hormone sensitive and castration resistant prostate cancer patients with bone metastases. Longitudinal changes in tumor and bone imaging metrics during delivery of therapy were quantified. Studies revealed that voxel-based parametric response maps (PRM) of DW-MRI and CT scans could be used to quantify and spatially visualize dynamic changes during prostate tumor growth and in response to treatment thereby distinguishing patients with stable disease from those with progressive disease (p

Published
2015

31. Integrated Multimodal Imaging of Dynamic Bone-Tumor Alterations Associated with Metastatic Prostate Cancer

Author

Fysica Radiotherapie Research, Cancer, Brisset, Jean-Christophe, Hoff, Benjamin A., Chenevert, Thomas L., Jacobson, Jon A., Boes, Jennifer L., Galban, Stefanie, Rehemtulla, Alnawaz, Johnson, Timothy D., Pienta, Kenneth J., Galban, Craig J., Meyer, Charles R., Schakel, Timothy, Nicolay, Klaas, Alva, Ajjai S., Hussain, Maha, Ross, Brian D., Schakel, Tim, Fysica Radiotherapie Research, Cancer, Brisset, Jean-Christophe, Hoff, Benjamin A., Chenevert, Thomas L., Jacobson, Jon A., Boes, Jennifer L., Galban, Stefanie, Rehemtulla, Alnawaz, Johnson, Timothy D., Pienta, Kenneth J., Galban, Craig J., Meyer, Charles R., Schakel, Timothy, Nicolay, Klaas, Alva, Ajjai S., Hussain, Maha, Ross, Brian D., and Schakel, Tim

Published
2015

35. Abstract 4299: Evaluation of a new multimodality voxel-based imaging biomarker for therapeutic response assessment in GBM

Author

Lemasson, Benjamin, primary, Chenevert, Thomas L., additional, Lawrence, Theodore S., additional, Tsien, Christina, additional, Sundgren, Pia C., additional, Meyer, Charles R., additional, Junck, Larry, additional, Boes, Jennifer, additional, Brisset, Jean-Christophe, additional, Galbán, Stefanie, additional, Johnson, Timothy, additional, Rehemtulla, Alnawez, additional, Ross, Brian D., additional, and Galbán, Craig, additional

Published
2012
Full Text
View/download PDF

39. Quantification of Iron-Labeled Cells with Positive Contrast in Mouse Brains

Author

Brisset, Jean-Christophe, primary, Sigovan, Monica, additional, Chauveau, Fabien, additional, Riou, Adrien, additional, Devillard, Emilie, additional, Desestret, Virginie, additional, Touret, Monique, additional, Nataf, Serge, additional, Honnorat, J., additional, Canet-Soulas, Emmanuelle, additional, Nighoghossian, Norbert, additional, Berthezene, Yves, additional, and Wiart, Marlene, additional

Published
2010
Full Text
View/download PDF

41. Contribution of diffusion-weighted imaging to distinguish herpetic encephalitis from auto-immune encephalitis at an early stage.

Author

Bani-Sadr, Alexandre, Ruitton-Allinieu, Marie-Camille, Brisset, Jean-Christophe, Ducray, François, Joubert, Bastien, Picard, Géraldine, and Cotton, François

Abstract

•In this case-control study, the early brain MRIs of 49 patients with herpes encephalitis (n=30) or autoimmune encephalitis (n=19) were assessed retrospectively.•Twenty-six patients had restricted diffusion lesions in the medial temporal lobe, including 25/30 patients with herpetic encephalitis.•Restricted diffusion lesions in the medial temporal lobe are a hallmark of herpetic encephalitis and may help distinguish it from early-stage auto-immune encephalitis.

Published
2022
Full Text
View/download PDF

42. Spatial distribution of multiple sclerosis lesions in the cervical spinal cord

Author

Kouhei Kamiya, Massimo Filippi, Marios C. Yiannakas, Ferran Prados, Rohit Bakshi, Hugh Kearney, Sridar Narayanan, Pierre Labauge, Timothy M. Shepherd, Jean-Christophe Brisset, Russell Ouellette, Tobias Granberg, Ren Zhuoquiong, Caterina Mainero, Julien Cohen-Adad, Atef Badji, Jean Pelletier, Josefina Maranzano, Erik Charlson, Leszek Stawiarz, Seth A. Smith, Yasuhiko Tachibana, Yaou Liu, Charley Gros, Masaaki Hori, Dominique Eden, Olga Ciccarelli, Virginie Callot, Daniel S. Reich, Jennifer Lefeuvre, Gilles Edan, Sara M. Dupont, Govind Nair, Henitsoa Rasoanandrianina, Elise Bannier, Paola Valsasina, Jason F. Talbott, Shahamat Tauhid, Jan Hillert, Lydia Chougar, Anne Kerbrat, Constantina A. Treaba, Maria A. Rocca, Benjamin De Leener, Bertrand Audoin, École Polytechnique de Montréal (EPM), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada], Xuanwu Hospital of Capital, Deutsche Telekom Group, Department of Computer Science [Liverpool], University of Liverpool, Karolinska Institutet [Stockholm], Department of Radiology and Biomedical Imaging [San Francisco], University of California [San Francisco] (UC San Francisco), University of California (UC)-University of California (UC), Neuroimagerie: méthodes et applications (Empenn), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Service de Neurologie [Rennes] = Neurology [Rennes], CHU Pontchaillou [Rennes], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Centre de résonance magnétique biologique et médicale (CRMBM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - AP-HM] (CEMEREM), Hôpital de la Timone [CHU - APHM] (TIMONE)-Centre de résonance magnétique biologique et médicale (CRMBM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Observatoire Français de la Sclérose En Plaques [Lyon] (OFSEP), Vita-Salute San Raffaele University and Center for Translational Genomics and Bioinformatics, Brigham and Women's Hospital [Boston], Harvard Medical School [Boston] (HMS), Translational imaging group [London] (TIG), Centre for Medical Image Computing (CMIC), University College of London [London] (UCL)-University College of London [London] (UCL)-Department of Medical Physics and Biomedical Engineering (UCL), University College of London [London] (UCL), Vanderbilt University [Nashville], Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School [Boston] (HMS)-Massachusetts General Hospital [Boston], National Institutes of Health [Bethesda] (NIH), New York University Langone Medical Center (NYU Langone Medical Center), NYU System (NYU), National Institute of Radiological Sciences (NIRS), Juntendo University Hospital [Tokyo], McConnell Brain Imaging Centre (MNI), McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], Funded by the Canada Research Chair in Quantitative Magnetic Resonance Imaging, CIHR-FDN-143263, FRQS-28826, FRQNT-2015-PR-182754, NSERC-435897–2013, QBIN, NMSS RG-1501–02840 (S.A.S.), NIH/NINDS R21 NS087465–01 (S.A.S.), NIH/NEI R01EY023240 (S.A.S.), DoD W81XWH-13–0073 (S.A.S.), the Intramural Research Program of NIH/NINDS (J.L., D.S.R., G.N.), PHRC (EMISEP), NIH/NINDS R01NS078322–02 (C.M.), ClinicalTrials.gov (NCT02117375), CNRS, 'Fondation A*midex-Investissements d’Avenir', the Stockholm County Council (ALF grant 20150166), Swedish Society for Medical Research (T.G.), Guarantors of Brain (F.P.), ANR-10-COHO-002, OFSEP, EDMUS Foundation against multiple sclerosis, UK MS Society and the National Institute for Health Research University College London Hospitals Biomedical Research Centre, EMD Serono (C.M., R.B.)., ANR-10-COHO-0002,OFSEP,Observatoire Français de la Sclérose en Plaques(2010), Bannier, Elise, Cohortes - Observatoire Français de la Sclérose en Plaques - - OFSEP2010 - ANR-10-COHO-0002 - COHO - VALID, University of California [San Francisco] (UCSF), University of California-University of California, Empenn, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)- Hôpital de la Timone [CHU - APHM] (TIMONE), Massachusetts General Hospital [Boston]-Harvard Medical School [Boston] (HMS), Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - APHM] (CEMEREM), Eden, Dominique, Gros, Charley, Badji, Atef, Dupont, Sara M, De Leener, Benjamin, Maranzano, Josefina, Zhuoquiong, Ren, Liu, Yaou, Granberg, Tobia, Ouellette, Russell, Stawiarz, Leszek, Hillert, Jan, Talbott, Jason, Kerbrat, Anne, Edan, Gille, Labauge, Pierre, Callot, Virginie, Pelletier, Jean, Audoin, Bertrand, Rasoanandrianina, Henitsoa, Brisset, Jean-Christophe, Valsasina, Paola, Rocca, Maria A, Filippi, Massimo, Bakshi, Rohit, Tauhid, Shahamat, Prados, Ferran, Yiannakas, Mario, Kearney, Hugh, Ciccarelli, Olga, Smith, Seth A, Andrada Treaba, Constantina, Mainero, Caterina, Lefeuvre, Jennifer, Reich, Daniel S, Nair, Govind, Shepherd, Timothy M, Charlson, Erik, Tachibana, Yasuhiko, Hori, Masaaki, Kamiya, Kouhei, Chougar, Lydia, Narayanan, Sridar, and Cohen-Adad, Julien

Subjects
0301 basic medicine, Adult, Male, medicine.medical_specialty, Cord, multicentre, Grey matter, multiple sclerosis, Spinal Cord Diseases, lesions, White matter, Lesion, 03 medical and health sciences, Disability Evaluation, 0302 clinical medicine, Multiple Sclerosis, Relapsing-Remitting, medicine, Humans, Gray Matter, 10. No inequality, [SDV.IB] Life Sciences [q-bio]/Bioengineering, Spatial Analysis, Expanded Disability Status Scale, Clinically isolated syndrome, business.industry, Multiple sclerosis, Brain, Cervical Cord, spinal cord, Original Articles, Middle Aged, Multiple Sclerosis, Chronic Progressive, medicine.disease, Spinal cord, Magnetic Resonance Imaging, White Matter, 030104 developmental biology, medicine.anatomical_structure, Disease Progression, Female, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Neurology (clinical), Radiology, medicine.symptom, business, 030217 neurology & neurosurgery, MRI
Abstract

International audience; Spinal cord lesions detected on MRI hold important diagnostic and prognostic value for multiple sclerosis. Previous attempts to correlate lesion burden with clinical status have had limited success, however, suggesting that lesion location may be a contributor. Our aim was to explore the spatial distribution of multiple sclerosis lesions in the cervical spinal cord, with respect to clinical status. We included 642 suspected or confirmed multiple sclerosis patients (31 clinically isolated syndrome, and 416 relapsing-remitting, 84 secondary progressive, and 73 primary progressive multiple sclerosis) from 13 clinical sites. Cervical spine lesions were manually delineated on T2- and T2*-weighted axial and sagittal MRI scans acquired at 3 or 7 T. With an automatic publicly-available analysis pipeline we produced voxelwise lesion frequency maps to identify predilection sites in various patient groups characterized by clinical subtype, Expanded Disability Status Scale score and disease duration. We also measured absolute and normalized lesion volumes in several regions of interest using an atlas-based approach, and evaluated differences within and between groups. The lateral funiculi were more frequently affected by lesions in progressive subtypes than in relapsing in voxelwise analysis (P < 0.001), which was further confirmed by absolute and normalized lesion volumes (P < 0.01). The central cord area was more often affected by lesions in primary progressive than relapse-remitting patients (P < 0.001). Between white and grey matter, the absolute lesion volume in the white matter was greater than in the grey matter in all phenotypes (P < 0.001); however when normalizing by each region, normalized lesion volumes were comparable between white and grey matter in primary progressive patients. Lesions appearing in the lateral funiculi and central cord area were significantly correlated with Expanded Disability Status Scale score (P < 0.001). High lesion frequencies were observed in patients with a more aggressive disease course, rather than long disease duration. Lesions located in the lateral funiculi and central cord area of the cervical spine may influence clinical status in multiple sclerosis. This work shows the added value of cervical spine lesions, and provides an avenue for evaluating the distribution of spinal cord lesions in various patient groups.

Published
2019

43. Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks

Author

Vincent Auclair, Ferran Prados, Jean-Christophe Brisset, Josefina Maranzano, Sridar Narayanan, Kouhei Kamiya, Constantina A. Treaba, Atef Badji, Hugh Kearney, Jean Pelletier, Pierre Labauge, Elise Bannier, Paola Valsasina, Erik Charlson, Daniel S. Reich, Olga Ciccarelli, Dominique Eden, Ren Zhuoquiong, Henitsoa Rasoanandrianina, Virginie Callot, Gilles Edan, Seth A. Smith, Marios C. Yiannakas, Jennifer Lefeuvre, Govind Nair, Allan R. Martin, Julien Cohen-Adad, Timothy M. Shepherd, Jason F. Talbott, Donald G. McLaren, Charley Gros, Masaaki Hori, Shahabeddin Vahdat, Julien Doyon, Yasuhiko Tachibana, Leszek Stawiarz, Shahamat Tauhid, Jan Hillert, Massimo Filippi, Lydia Chougar, Anne Kerbrat, Russell Ouellette, Caterina Mainero, Sara M. Dupont, Tobias Granberg, Bertrand Audoin, Ali Khatibi, Rohit Bakshi, Maria A. Rocca, Michael G. Fehlings, Benjamin De Leener, Yaou Liu, École Polytechnique de Montréal (EPM), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada], Department of Radiology and Biomedical Imaging [San Francisco], University of California [San Francisco] (UC San Francisco), University of California (UC)-University of California (UC), Xuanwu Hospital of Capital, Deutsche Telekom Group, Department of Computer Science [Liverpool], University of Liverpool, National Institute of Radiological Sciences (NIRS), Juntendo University Hospital [Tokyo], Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Karolinska Institutet [Stockholm], CHU Pontchaillou [Rennes], Vision, Action et Gestion d'informations en Santé (VisAGeS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Centre d'Investigation Clinique [Rennes] (CIC), Université de Rennes (UR)-Hôpital Pontchaillou-Institut National de la Santé et de la Recherche Médicale (INSERM), Département de neurologie [Montpellier], Université Montpellier 1 (UM1)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Hôpital Gui de Chauliac [CHU Montpellier], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Université de Montpellier (UM), Centre de résonance magnétique biologique et médicale (CRMBM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - AP-HM] (CEMEREM), Hôpital de la Timone [CHU - APHM] (TIMONE)-Centre de résonance magnétique biologique et médicale (CRMBM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Observatoire Français de la Sclérose En Plaques [Lyon] (OFSEP), Vita-Salute San Raffaele University and Center for Translational Genomics and Bioinformatics, Brigham and Women's Hospital [Boston], Harvard Medical School [Boston] (HMS), NMR Research Unit [London], Institute of Neurology [London], University College of London [London] (UCL)-University College of London [London] (UCL), University College of London [London] (UCL), Vanderbilt University [Nashville], Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School [Boston] (HMS)-Massachusetts General Hospital [Boston], National Institutes of Health [Bethesda] (NIH), Biospective [Montréal], University of Toronto, Unité de Neuroimagerie Fonctionnelle [Montréal] (UNF-CRIUGM), Université de Montréal (UdeM)-Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Stanford University, McConnell Brain Imaging Centre (MNI), McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], New York University Langone Medical Center (NYU Langone Medical Center), NYU System (NYU), CIHR FDN-143263, Canadian Institutes of Health Research, International Society of Regulatory Toxicology and Pharmacology, Svenska Sällskapet för Medicinsk Forskning, IVADO, EDMUS Foundation, TransMedTech, R01 EY023240 (SAS), NIH/NEI, RG-1501-02840 (SAS), National Multiple Sclerosis Society, Intramural Research Program, Hospital Programme of Clinical Research (PHRC), NCT02117375, EMISEP project, Fondation A*midex-Investissements d'Avenir, 435897-2013, Natural Sciences and Engineering Research Council of Canada, Observatoire Français de la Sclérose en plaques (OFSEP), 13034, MOP, Canada Research Chairs, National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC), 2015-PR-182754, Fonds de Recherche du Québec - Nature et Technologies, W81XWH-13-0073 (SAS), U.S. Department of Defense, ANR-10-COHO-002, Agence Nationale de la Recherche, R21 NS087465-01 (SAS), National Institute of Neurological Disorders and Stroke, Fondation Aix-Marseille Universite, SensoriMotor Rehabilitation Research Team (SMRRT), Wings for Life, 20150166, Stockholms Läns Landsting, Centre National de la Recherche Scientifique, 28826, Fonds de Recherche du Québec - Santé, 32454, Canada Foundation for Innovation, Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - APHM] (CEMEREM), University of California [San Francisco] (UCSF), University of California-University of California, SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Hôpital Pontchaillou-Institut National de la Santé et de la Recherche Médicale (INSERM), Hôpital Gui de Chauliac [Montpellier]-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Université Montpellier 1 (UM1)-Université de Montpellier (UM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)- Hôpital de la Timone [CHU - APHM] (TIMONE), Massachusetts General Hospital [Boston]-Harvard Medical School [Boston] (HMS), Gros, Charley, De Leener, Benjamin, Badji, Atef, Maranzano, Josefina, Eden, Dominique, Dupont, Sara M., Talbott, Jason, Zhuoquiong, Ren, Liu, Yaou, Granberg, Tobia, Ouellette, Russell, Tachibana, Yasuhiko, Hori, Masaaki, Kamiya, Kouhei, Chougar, Lydia, Stawiarz, Leszek, Hillert, Jan, Bannier, Elise, Kerbrat, Anne, Edan, Gille, Labauge, Pierre, Callot, Virginie, Pelletier, Jean, Audoin, Bertrand, Rasoanandrianina, Henitsoa, Brisset, Jean-Christophe, Valsasina, Paola, Rocca, Maria A., Filippi, Massimo, Bakshi, Rohit, Tauhid, Shahamat, Prados, Ferran, Yiannakas, Mario, Kearney, Hugh, Ciccarelli, Olga, Smith, Seth, Treaba, Constantina Andrada, Mainero, Caterina, Lefeuvre, Jennifer, Reich, Daniel S., Nair, Govind, Auclair, Vincent, Mclaren, Donald G., Martin, Allan R., Fehlings, Michael G., Vahdat, Shahabeddin, Khatibi, Ali, Doyon, Julien, Shepherd, Timothy, Charlson, Erik, Narayanan, Sridar, and Cohen-Adad, Julien

Subjects
FOS: Computer and information sciences, Image Processing, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Neurodegenerative, computer.software_genre, Convolutional neural network, Medical and Health Sciences, Pattern Recognition, Automated, 0302 clinical medicine, Computer-Assisted, Segmentation, Voxel, Image Processing, Computer-Assisted, Medicine, Spinal Cord Injury, Observer Variation, Spinal cord, medicine.diagnostic_test, 05 social sciences, Magnetic Resonance Imaging, 3. Good health, medicine.anatomical_structure, Neurology, Neurological, Biomedical Imaging, Convolutional neural networks, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Radiology, medicine.symptom, MRI, Automated, medicine.medical_specialty, Cord, Multiple Sclerosis, Physical Injury - Accidents and Adverse Effects, Neural Networks, Cognitive Neuroscience, Bioengineering, Pattern Recognition, Sensitivity and Specificity, Autoimmune Disease, 050105 experimental psychology, Article, Lesion, Multiple sclerosis, 03 medical and health sciences, Computer, Humans, 0501 psychology and cognitive sciences, Multiple sclerosi, Traumatic Head and Spine Injury, Neurology & Neurosurgery, business.industry, Psychology and Cognitive Sciences, Neurosciences, Reproducibility of Results, Magnetic resonance imaging, medicine.disease, Brain Disorders, Neural Networks, Computer, business, computer, 030217 neurology & neurosurgery
Abstract

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. The goal of this study was to develop a fully-automatic framework, robust to variability in both image parameters and clinical condition, for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data. Scans of 1,042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n=30). Data spanned three contrasts (T1-, T2-, and T2*-weighted) for a total of 1,943 volumes. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg, a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. The proposed framework is open-source and readily available in the Spinal Cord Toolbox., Comment: 38 pages, 7 figures, 2 tables

Published
2019

44. Brain MRI Findings in Severe COVID-19: A Retrospective Observational Study.

Author

Kremer S, Lersy F, de Sèze J, Ferré JC, Maamar A, Carsin-Nicol B, Collange O, Bonneville F, Adam G, Martin-Blondel G, Rafiq M, Geeraerts T, Delamarre L, Grand S, Krainik A, Caillard S, Constans JM, Metanbou S, Heintz A, Helms J, Schenck M, Lefèbvre N, Boutet C, Fabre X, Forestier G, de Beaurepaire I, Bornet G, Lacalm A, Oesterlé H, Bolognini F, Messié J, Hmeydia G, Benzakoun J, Oppenheim C, Bapst B, Megdiche I, Henry Feugeas MC, Khalil A, Gaudemer A, Jager L, Nesser P, Talla Mba Y, Hemmert C, Feuerstein P, Sebag N, Carré S, Alleg M, Lecocq C, Schmitt E, Anxionnat R, Zhu F, Comby PO, Ricolfi F, Thouant P, Desal H, Boulouis G, Berge J, Kazémi A, Pyatigorskaya N, Lecler A, Saleme S, Edjlali-Goujon M, Kerleroux B, Zorn PE, Matthieu M, Baloglu S, Ardellier FD, Willaume T, Brisset JC, Boulay C, Mutschler V, Hansmann Y, Mertes PM, Schneider F, Fafi-Kremer S, Ohana M, Meziani F, David JS, Meyer N, Anheim M, and Cotton F

Subjects
Adolescent, Adult, Aged, COVID-19, Child, Cohort Studies, Female, Humans, Male, Middle Aged, Pandemics, Retrospective Studies, SARS-CoV-2, Young Adult, Betacoronavirus, Brain diagnostic imaging, Brain pathology, Coronavirus Infections diagnostic imaging, Coronavirus Infections pathology, Magnetic Resonance Imaging methods, Pneumonia, Viral diagnostic imaging, Pneumonia, Viral pathology
Abstract

Background Brain MRI parenchymal signal abnormalities have been associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Purpose To describe the neuroimaging findings (excluding ischemic infarcts) in patients with severe coronavirus disease 2019 (COVID-19) infection. Materials and Methods This was a retrospective study of patients evaluated from March 23, 2020, to April 27, 2020, at 16 hospitals. Inclusion criteria were ( a ) positive nasopharyngeal or lower respiratory tract reverse transcriptase polymerase chain reaction assays, ( b ) severe COVID-19 infection defined as a requirement for hospitalization and oxygen therapy, ( c ) neurologic manifestations, and ( d ) abnormal brain MRI findings. Exclusion criteria were patients with missing or noncontributory data regarding brain MRI or brain MRI showing ischemic infarcts, cerebral venous thrombosis, or chronic lesions unrelated to the current event. Categorical data were compared using the Fisher exact test. Quantitative data were compared using the Student t test or Wilcoxon test. P < .05 represented a significant difference. Results Thirty men (81%) and seven women (19%) met the inclusion criteria, with a mean age of 61 years ± 12 (standard deviation) (age range, 8-78 years). The most common neurologic manifestations were alteration of consciousness (27 of 37, 73%), abnormal wakefulness when sedation was stopped (15 of 37, 41%), confusion (12 of 37, 32%), and agitation (seven of 37, 19%). The most frequent MRI findings were signal abnormalities located in the medial temporal lobe in 16 of 37 patients (43%; 95% confidence interval [CI]: 27%, 59%), nonconfluent multifocal white matter hyperintense lesions seen with fluid-attenuated inversion recovery and diffusion-weighted sequences with variable enhancement, with associated hemorrhagic lesions in 11 of 37 patients (30%; 95% CI: 15%, 45%), and extensive and isolated white matter microhemorrhages in nine of 37 patients (24%; 95% CI: 10%, 38%). A majority of patients (20 of 37, 54%) had intracerebral hemorrhagic lesions with a more severe clinical presentation and a higher admission rate in intensive care units (20 of 20 patients [100%] vs 12 of 17 patients without hemorrhage [71%], P = .01) and development of the acute respiratory distress syndrome (20 of 20 patients [100%] vs 11 of 17 patients [65%], P = .005). Only one patient had SARS-CoV-2 RNA in the cerebrospinal fluid. Conclusion Patients with severe coronavirus disease 2019 and without ischemic infarcts had a wide range of neurologic manifestations that were associated with abnormal brain MRI scans. Eight distinctive neuroradiologic patterns were described. © RSNA, 2020.

Published
2020
Full Text
View/download PDF

45. Multimodal imaging provides insight into targeted therapy response in metastatic prostate cancer to the bone.

Author

Hoff BA, Brisset JC, Galbán S, Van Dort M, Smith DC, Reichert ZR, Jacobson JA, Luker GD, Chenevert TL, and Ross BD

Abstract

Metastatic prostate cancer to bone remains incurable, driving efforts to develop individualized, targeted therapies to improve clinical outcomes while limiting adverse side-effects. Due to the complexity in cellular signaling pathways and the interaction between cancer and its microenvironment, multiparametric imaging approaches for treatment response may improve understanding of the biological effects of therapy. An orthotopic model of castration resistant prostate cancer (CRPC) bone metastasis was treated with the tyrosine kinase inhibitor Cabozantinib (CABO). Response was assessed using CT to monitor bone volumes, 99m Tc-MDP SPECT for bone metabolism, and anatomical and diffusion MRI for tumor volume and cell death. A concurrent clinical trial of CABO for CRPC patients also evaluated multimodality imaging in correlation with standard response criteria. Response in the preclinical study found significant slowing in tumor growth rate (P<0.01), rise in tumor apparent diffusion coefficient (ADC, P<0.001), and drop in 99m Tc-MDP adsorption (P<0.05). Loss of bone volume did not slow with treatment, attributed to the highly aggressive and osteolytic nature of the PC3 cell line. Clinical trial analysis found only a single subject who progressed after 12 weeks of therapy. Imaging at 6 weeks corroborated the 12-week radiological assessment with positive response visible as increased ADC and decreased vascular metrics. Conversely, the subject who progressed at 12 weeks had no change in ADC, and substantial drops in vascular metrics. These results showcase a multifaceted translational imaging approach for detecting targeted treatment response with effective blockade of tumor vascularization, tumor cell kill, and reduced proliferation., Competing Interests: None.

Published
2018

Catalog

Books, media, physical & digital resources
See catalog results