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Ramakrishnan D, Jekel L, Chadha S, Janas A, Moy H, Maleki N, Sala M, Kaur M, Petersen GC, Merkaj S, von Reppert M, Baid U, Bakas S, Kirsch C, Davis M, Bousabarah K, Holler W, Lin M, Westerhoff M, Aneja S, Memon F, Aboian MS. A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information. Sci Data 2024; 11:254. [PMID: 38424079 PMCID: PMC10904366 DOI: 10.1038/s41597-024-03021-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 01/29/2024] [Indexed: 03/02/2024] Open
Abstract
Resection and whole brain radiotherapy (WBRT) are standard treatments for brain metastases (BM) but are associated with cognitive side effects. Stereotactic radiosurgery (SRS) uses a targeted approach with less side effects than WBRT. SRS requires precise identification and delineation of BM. While artificial intelligence (AI) algorithms have been developed for this, their clinical adoption is limited due to poor model performance in the clinical setting. The limitations of algorithms are often due to the quality of datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and peritumoral edema 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging information. We used a streamlined approach to database-building through a PACS-integrated segmentation workflow.
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Affiliation(s)
- Divya Ramakrishnan
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA.
| | - Leon Jekel
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
- University of Essen School of Medicine, Essen, Germany
| | - Saahil Chadha
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
| | - Anastasia Janas
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
- Charité University School of Medicine, Berlin, Germany
| | - Harrison Moy
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
- Wesleyan University, Middletown, CT, USA
| | - Nazanin Maleki
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
| | - Matthew Sala
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
- Tulane University School of Medicine, New Orleans, LA, USA
| | - Manpreet Kaur
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
- Ludwig Maximilian University School of Medicine, Munich, Germany
| | - Gabriel Cassinelli Petersen
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
- University of Göttingen School of Medicine, Göttingen, Germany
| | - Sara Merkaj
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
- Ulm University School of Medicine, Ulm, Germany
| | - Marc von Reppert
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
- University of Leipzig School of Medicine, Leipzig, Germany
| | - Ujjwal Baid
- Division of Computational Pathology, Department of Pathology & Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology and Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Spyridon Bakas
- Division of Computational Pathology, Department of Pathology & Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology and Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Claudia Kirsch
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
- School of Clinical Dentistry, University of Sheffield, Sheffield, England
- Diagnostic, Molecular and Interventional Radiology, Biomedical Engineering Imaging, Mount Sinai Hospital, New York City, NY, USA
| | - Melissa Davis
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
| | | | | | - MingDe Lin
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
- Visage Imaging, Inc., San Diego, CA, USA
| | | | - Sanjay Aneja
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale School of Medicine, New Haven, CT, USA
| | - Fatima Memon
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
| | - Mariam S Aboian
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
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von Reppert M, Ramakrishnan D, Brüningk SC, Memon F, Abi Fadel S, Maleki N, Bahar R, Avesta AE, Jekel L, Sala M, Lost J, Tillmanns N, Kaur M, Aneja S, Fathi Kazerooni A, Nabavizadeh A, Lin M, Hoffmann KT, Bousabarah K, Swanson KR, Haas-Kogan D, Mueller S, Aboian MS. Comparison of volumetric and 2D-based response methods in the PNOC-001 pediatric low-grade glioma clinical trial. Neurooncol Adv 2024; 6:vdad172. [PMID: 38221978 PMCID: PMC10785766 DOI: 10.1093/noajnl/vdad172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024] Open
Abstract
Background Although response in pediatric low-grade glioma (pLGG) includes volumetric assessment, more simplified 2D-based methods are often used in clinical trials. The study's purpose was to compare volumetric to 2D methods. Methods An expert neuroradiologist performed solid and whole tumor (including cyst and edema) volumetric measurements on MR images using a PACS-based manual segmentation tool in 43 pLGG participants (213 total follow-up images) from the Pacific Pediatric Neuro-Oncology Consortium (PNOC-001) trial. Classification based on changes in volumetric and 2D measurements of solid tumor were compared to neuroradiologist visual response assessment using the Brain Tumor Reporting and Data System (BT-RADS) criteria for a subset of 65 images using receiver operating characteristic (ROC) analysis. Longitudinal modeling of solid tumor volume was used to predict BT-RADS classification in 54 of the 65 images. Results There was a significant difference in ROC area under the curve between 3D solid tumor volume and 2D area (0.96 vs 0.78, P = .005) and between 3D solid and 3D whole volume (0.96 vs 0.84, P = .006) when classifying BT-RADS progressive disease (PD). Thresholds of 15-25% increase in 3D solid tumor volume had an 80% sensitivity in classifying BT-RADS PD included in their 95% confidence intervals. The longitudinal model of solid volume response had a sensitivity of 82% and a positive predictive value of 67% for detecting BT-RADS PD. Conclusions Volumetric analysis of solid tumor was significantly better than 2D measurements in classifying tumor progression as determined by BT-RADS criteria and will enable more comprehensive clinical management.
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Affiliation(s)
- Marc von Reppert
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Neuroradiology, Leipzig University Hospital, Leipzig, Germany
| | - Divya Ramakrishnan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Sarah C Brüningk
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- Swiss Institute for Bioinformatics (SIB), Lausanne, Switzerland
| | - Fatima Memon
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Sandra Abi Fadel
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Nazanin Maleki
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Ryan Bahar
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Arman E Avesta
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, Connecticut, USA
- Center for Outcomes Research and Evaluation (CORE), Yale School of Medicine, New Haven, Connecticut, USA
- Department of Neuroradiology, Harvard Medical School—Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Leon Jekel
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- University of Duisburg-Essen, Essen, Germany
| | - Matthew Sala
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Tulane School of Medicine, New Orleans, Louisiana, USA
| | - Jan Lost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
| | - Niklas Tillmanns
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
| | - Manpreet Kaur
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Ludwig Maximilian University, Munich, Germany
| | - Sanjay Aneja
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, Connecticut, USA
- Center for Outcomes Research and Evaluation (CORE), Yale School of Medicine, New Haven, Connecticut, USA
| | - Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ali Nabavizadeh
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - MingDe Lin
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Visage Imaging, Inc., San Diego, California, USA
| | | | | | - Kristin R Swanson
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, Arizona, USA
| | - Daphne Haas-Kogan
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Sabine Mueller
- Department of Neurology, Neurosurgery, and Pediatrics, UCSF, San Francisco, California, USA
- Children’s University Hospital Zürich, Zürich, Switzerland
| | - Mariam S Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
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Tillmanns N, Lost J, Tabor J, Vasandani S, Vetsa S, Marianayagam N, Yalcin K, Erson-Omay EZ, von Reppert M, Jekel L, Merkaj S, Ramakrishnan D, Avesta A, de Oliveira Santo ID, Jin L, Huttner A, Bousabarah K, Ikuta I, Lin M, Aneja S, Turowski B, Aboian M, Moliterno J. Application of novel PACS-based informatics platform to identify imaging based predictors of CDKN2A allelic status in glioblastomas. Sci Rep 2023; 13:22942. [PMID: 38135704 PMCID: PMC10746716 DOI: 10.1038/s41598-023-48918-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 12/01/2023] [Indexed: 12/24/2023] Open
Abstract
Gliomas with CDKN2A mutations are known to have worse prognosis but imaging features of these gliomas are unknown. Our goal is to identify CDKN2A specific qualitative imaging biomarkers in glioblastomas using a new informatics workflow that enables rapid analysis of qualitative imaging features with Visually AcceSAble Rembrandtr Images (VASARI) for large datasets in PACS. Sixty nine patients undergoing GBM resection with CDKN2A status determined by whole-exome sequencing were included. GBMs on magnetic resonance images were automatically 3D segmented using deep learning algorithms incorporated within PACS. VASARI features were assessed using FHIR forms integrated within PACS. GBMs without CDKN2A alterations were significantly larger (64 vs. 30%, p = 0.007) compared to tumors with homozygous deletion (HOMDEL) and heterozygous loss (HETLOSS). Lesions larger than 8 cm were four times more likely to have no CDKN2A alteration (OR: 4.3; 95% CI 1.5-12.1; p < 0.001). We developed a novel integrated PACS informatics platform for the assessment of GBM molecular subtypes and show that tumors with HOMDEL are more likely to have radiographic evidence of pial invasion and less likely to have deep white matter invasion or subependymal invasion. These imaging features may allow noninvasive identification of CDKN2A allele status.
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Affiliation(s)
- Niklas Tillmanns
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, 40225, Dusseldorf, Germany
| | - Jan Lost
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
| | - Joanna Tabor
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Sagar Vasandani
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Shaurey Vetsa
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | | | - Kanat Yalcin
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | | | - Marc von Reppert
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
| | - Leon Jekel
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
| | - Sara Merkaj
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
| | - Divya Ramakrishnan
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
| | - Arman Avesta
- Department of Radiation Oncology, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
| | - Irene Dixe de Oliveira Santo
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
| | - Lan Jin
- R&D, Sema4, 333 Ludlow Street, North Tower, 8th Floor, Stamford, CT, 06902, USA
| | - Anita Huttner
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | | | - Ichiro Ikuta
- Department of Radiology, Mayo Clinic Arizona, 5711 E Mayo Blvd, Phoenix, AZ, 85054, USA
| | - MingDe Lin
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
- Visage Imaging, Inc., 12625 High Bluff Dr, Suite 205, San Diego, CA, 92130, USA
| | - Sanjay Aneja
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Bernd Turowski
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, 40225, Dusseldorf, Germany
| | - Mariam Aboian
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA.
- , New Haven, USA.
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4
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Lost J, Verma T, Jekel L, von Reppert M, Tillmanns N, Merkaj S, Petersen GC, Bahar R, Gordem A, Haider MA, Subramanian H, Brim W, Ikuta I, Omuro A, Conte GM, Marquez-Nostra BV, Avesta A, Bousabarah K, Nabavizadeh A, Kazerooni AF, Aneja S, Bakas S, Lin M, Sabel M, Aboian M. Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction. AJNR Am J Neuroradiol 2023; 44:1126-1134. [PMID: 37770204 PMCID: PMC10549943 DOI: 10.3174/ajnr.a8000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 08/01/2023] [Indexed: 10/03/2023]
Abstract
BACKGROUND The molecular profile of gliomas is a prognostic indicator for survival, driving clinical decision-making for treatment. Pathology-based molecular diagnosis is challenging because of the invasiveness of the procedure, exclusion from neoadjuvant therapy options, and the heterogeneous nature of the tumor. PURPOSE We performed a systematic review of algorithms that predict molecular subtypes of gliomas from MR Imaging. DATA SOURCES Data sources were Ovid Embase, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, Web of Science. STUDY SELECTION Per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 12,318 abstracts were screened and 1323 underwent full-text review, with 85 articles meeting the inclusion criteria. DATA ANALYSIS We compared prediction results from different machine learning approaches for predicting molecular subtypes of gliomas. Bias analysis was conducted for each study, following the Prediction model Risk Of Bias Assessment Tool (PROBAST) guidelines. DATA SYNTHESIS Isocitrate dehydrogenase mutation status was reported with an area under the curve and accuracy of 0.88 and 85% in internal validation and 0.86 and 87% in limited external validation data sets, respectively. For the prediction of O6-methylguanine-DNA methyltransferase promoter methylation, the area under the curve and accuracy in internal validation data sets were 0.79 and 77%, and in limited external validation, 0.89 and 83%, respectively. PROBAST scoring demonstrated high bias in all articles. LIMITATIONS The low number of external validation and studies with incomplete data resulted in unequal data analysis. Comparing the best prediction pipelines of each study may introduce bias. CONCLUSIONS While the high area under the curve and accuracy for the prediction of molecular subtypes of gliomas are reported in internal and external validation data sets, limited use of external validation and the increased risk of bias in all articles may present obstacles for clinical translation of these techniques.
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Affiliation(s)
- Jan Lost
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
- Department of Neurosurgery (J.L., M.S.), Heinrich-Heine-University, Duesseldorf, Germany
| | - Tej Verma
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Leon Jekel
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Marc von Reppert
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Niklas Tillmanns
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Sara Merkaj
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Gabriel Cassinelli Petersen
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Ryan Bahar
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Ayyüce Gordem
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Muhammad A Haider
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Harry Subramanian
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Waverly Brim
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Ichiro Ikuta
- Department of Radiology (I.I.), Mayo Clinic Arizona, Phoenix, Arizona
| | - Antonio Omuro
- Department of Neurology and Yale Cancer Center (A.O.), Yale School of Medicine, New Haven, Connecticut
| | - Gian Marco Conte
- Department of Radiology (G.M.C.), Mayo Clinic, Rochester, Minesotta
| | - Bernadette V Marquez-Nostra
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Arman Avesta
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | | | - Ali Nabavizadeh
- Department of Radiology (A.N.), Perelman School of Medicine, Hospital of University of Pennsylvania, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Anahita Fathi Kazerooni
- Department of Neurosurgery (A.F.K.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Division of Neurosurgery (A.F.K.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Center for Data-Driven Discovery (A.F.K.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Sanjay Aneja
- Department of Therapeutic Radiology (S.A), Yale School of Medicine, New Haven, Connecticut
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (S.B.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Richards Medical Research Laboratories (S.B.), Philadelphia, Pennsylvania
- Department of Radiology (S.B.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - MingDe Lin
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
- Visage Imaging Inc (K.B., M.L.), San Diego, California
| | - Michael Sabel
- Department of Neurosurgery (J.L., M.S.), Heinrich-Heine-University, Duesseldorf, Germany
| | - Mariam Aboian
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
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Kaur M, Cassinelli Petersen G, Jekel L, von Reppert M, Varghese S, Dixe de Oliveira Santo I, Avesta A, Aneja S, Omuro A, Chiang V, Aboian M. PACS-Integrated Tools for Peritumoral Edema Volumetrics Provide Additional Information to RANO-BM-Based Assessment of Lung Cancer Brain Metastases after Stereotactic Radiotherapy: A Pilot Study. Cancers (Basel) 2023; 15:4822. [PMID: 37835516 PMCID: PMC10571649 DOI: 10.3390/cancers15194822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 09/18/2023] [Accepted: 09/20/2023] [Indexed: 10/15/2023] Open
Abstract
Stereotactic radiotherapy (SRT) is the standard of care treatment for brain metastases (METS) today. Nevertheless, there is limited understanding of how posttreatment lesional volumetric changes may assist prediction of lesional outcome. This is partly due to the paucity of volumetric segmentation tools. Edema alone can cause significant clinical symptoms and, therefore, needs independent study along with standard measurements of contrast-enhancing tumors. In this study, we aimed to compare volumetric changes of edema to RANO-BM-based measurements of contrast-enhancing lesion size. Patients with NSCLC METS ≥10 mm on post-contrast T1-weighted image and treated with SRT had measurements for up to seven follow-up scans using a PACS-integrated tool segmenting the peritumoral FLAIR hyperintense volume. Two-dimensional contrast-enhancing and volumetric edema changes were compared by creating treatment response curves. Fifty NSCLC METS were included in the study. The initial median peritumoral edema volume post-SRT relative to pre-SRT baseline was 37% (IQR 8-114%). Most of the lesions with edema volume reduction post-SRT experienced no increase in edema during the study. In over 50% of METS, the pattern of edema volume change was different than the pattern of contrast-enhancing lesion change at different timepoints, which was defined as incongruent. Lesions demonstrating incongruence at the first follow-up were more likely to progress subsequently. Therefore, edema assessment of METS post-SRT provides critical additional information to RANO-BM.
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Affiliation(s)
- Manpreet Kaur
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA; (M.K.); (L.J.)
- Medical Faculty, Ludwig-Maximilians-University of Munich, 80336 Munich, Germany
| | - Gabriel Cassinelli Petersen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA; (M.K.); (L.J.)
| | - Leon Jekel
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA; (M.K.); (L.J.)
| | - Marc von Reppert
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA; (M.K.); (L.J.)
| | - Sunitha Varghese
- Department of Neurosurgery, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Irene Dixe de Oliveira Santo
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA; (M.K.); (L.J.)
| | - Arman Avesta
- Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, CT 06510, USA (S.A.)
| | - Sanjay Aneja
- Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, CT 06510, USA (S.A.)
| | - Antonio Omuro
- Department of Neurology, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Veronica Chiang
- Department of Neurosurgery, Yale University School of Medicine, New Haven, CT 06510, USA
- Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, CT 06510, USA (S.A.)
| | - Mariam Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA; (M.K.); (L.J.)
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6
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Ramakrishnan D, Jekel L, Chadha S, Janas A, Moy H, Maleki N, Sala M, Kaur M, Petersen GC, Merkaj S, von Reppert M, Baid U, Bakas S, Kirsch C, Davis M, Bousabarah K, Holler W, Lin M, Westerhoff M, Aneja S, Memon F, Aboian MS. A Large Open Access Dataset of Brain Metastasis 3D Segmentations with Clinical and Imaging Feature Information. ArXiv 2023:arXiv:2309.05053v2. [PMID: 37744461 PMCID: PMC10516117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Resection and whole brain radiotherapy (WBRT) are the standards of care for the treatment of patients with brain metastases (BM) but are often associated with cognitive side effects. Stereotactic radiosurgery (SRS) involves a more targeted treatment approach and has been shown to avoid the side effects associated with WBRT. However, SRS requires precise identification and delineation of BM. While many AI algorithms have been developed for this purpose, their clinical adoption has been limited due to poor model performance in the clinical setting. Major reasons for non-generalizable algorithms are the limitations in the datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models to improve generalizability. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and whole tumor (including peritumoral edema) 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging feature information. We used a streamlined approach to database-building leveraging a PACS-integrated segmentation workflow.
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Affiliation(s)
- Divya Ramakrishnan
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
| | - Leon Jekel
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
- University of Essen School of Medicine, Essen, Germany
| | - Saahil Chadha
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
| | - Anastasia Janas
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
- Charité University School of Medicine, Berlin, Germany
| | - Harrison Moy
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
- Wesleyan University, Middletown, CT, USA
| | - Nazanin Maleki
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
| | - Matthew Sala
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
- Tulane University School of Medicine, New Orleans, LA, USA
| | - Manpreet Kaur
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
- Ludwig Maximilian University School of Medicine, Munich, Germany
| | - Gabriel Cassinelli Petersen
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
- University of Göttingen School of Medicine, Göttingen, Germany
| | - Sara Merkaj
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
- Ulm University School of Medicine, Ulm, Germany
| | - Marc von Reppert
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
- University of Leipzig School of Medicine, Leipzig, Germany
| | - Ujjwal Baid
- Division of Computational Pathology, Department of Pathology & Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology and Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Spyridon Bakas
- Division of Computational Pathology, Department of Pathology & Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology and Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Claudia Kirsch
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
- School of Clinical Dentistry, University of Sheffield, Sheffield, England
- Diagnostic, Molecular and Interventional Radiology, Biomedical Engineering Imaging, Mount Sinai Hospital, New York City, NY, USA
| | - Melissa Davis
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
| | | | | | - MingDe Lin
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
- Visage Imaging, Inc., San Diego, CA, USA
| | | | - Sanjay Aneja
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale School of Medicine, New Haven, CT, USA
| | - Fatima Memon
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
| | - Mariam S Aboian
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
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7
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Moawad AW, Janas A, Baid U, Ramakrishnan D, Jekel L, Krantchev K, Moy H, Saluja R, Osenberg K, Wilms K, Kaur M, Avesta A, Pedersen GC, Maleki N, Salimi M, Merkaj S, von Reppert M, Tillmans N, Lost J, Bousabarah K, Holler W, Lin M, Westerhoff M, Maresca R, Link KE, Tahon NH, Marcus D, Sotiras A, LaMontagne P, Chakrabarty S, Teytelboym O, Youssef A, Nada A, Velichko YS, Gennaro N, Cramer J, Johnson DR, Kwan BY, Petrovic B, Patro SN, Wu L, So T, Thompson G, Kam A, Perez-Carrillo GG, Lall N, Albrecht J, Anazodo U, Lingaru MG, Menze BH, Wiestler B, Adewole M, Anwar SM, Labella D, Li HB, Iglesias JE, Farahani K, Eddy J, Bergquist T, Chung V, Shinohara RT, Dako F, Wiggins W, Reitman Z, Wang C, Liu X, Jiang Z, Van Leemput K, Piraud M, Ezhov I, Johanson E, Meier Z, Familiar A, Kazerooni AF, Kofler F, Calabrese E, Aneja S, Chiang V, Ikuta I, Shafique U, Memon F, Conte GM, Bakas S, Rudie J, Aboian M. The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI. ArXiv 2023:arXiv:2306.00838v1. [PMID: 37396600 PMCID: PMC10312806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Clinical monitoring of metastatic disease to the brain can be a laborious and timeconsuming process, especially in cases involving multiple metastases when the assessment is performed manually. The Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) guideline, which utilizes the unidimensional longest diameter, is commonly used in clinical and research settings to evaluate response to therapy in patients with brain metastases. However, accurate volumetric assessment of the lesion and surrounding peri-lesional edema holds significant importance in clinical decision-making and can greatly enhance outcome prediction. The unique challenge in performing segmentations of brain metastases lies in their common occurrence as small lesions. Detection and segmentation of lesions that are smaller than 10 mm in size has not demonstrated high accuracy in prior publications. The brain metastases challenge sets itself apart from previously conducted MICCAI challenges on glioma segmentation due to the significant variability in lesion size. Unlike gliomas, which tend to be larger on presentation scans, brain metastases exhibit a wide range of sizes and tend to include small lesions. We hope that the BraTS-METS dataset and challenge will advance the field of automated brain metastasis detection and segmentation.
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Affiliation(s)
| | - Anastasia Janas
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
- Charité - Universitatsmedizin, Berlin, Germany
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania School of Medicine, Philadelphia, PA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Divya Ramakrishnan
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
| | - Leon Jekel
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
- DKFZ Division of Translational Neurooncology at the WTZ, German Cancer Consortium, DKTK Partner Site, University Hospital Essen, Essen, Germany
- German Cancer Research Center, Heidelberg, Germany
- University of Ulm, Ulm, Germany
| | - Kiril Krantchev
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
- Charité - Universitatsmedizin, Berlin, Germany
| | - Harrison Moy
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
| | | | - Klara Osenberg
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
- University of Leipzig, Leipzig, Germany
| | - Klara Wilms
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
- University of Leipzig, Leipzig, Germany
| | - Manpreet Kaur
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
- Ludwig Maximillian University, Munich, Germany
| | - Arman Avesta
- Yale University School of Medicine, Department of Radiology, New Haven, CT
| | - Gabriel Cassinelli Pedersen
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
| | - Nazanin Maleki
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
| | - Mahdi Salimi
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
| | - Sarah Merkaj
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
- University of Ulm, Ulm, Germany
| | - Marc von Reppert
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
- University of Leipzig, Leipzig, Germany
| | - Niklas Tillmans
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
- University of Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Dusseldorf, Germany
| | - Jan Lost
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
- University of Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Dusseldorf, Germany
| | | | | | - MingDe Lin
- Visage Imaging, Inc, San Diego, California, USA
| | | | - Ryan Maresca
- Yale University School of Medicine, Department of Therapeutic Radiology, New Haven, CT
| | | | | | | | | | | | | | | | - Ayda Youssef
- Yale University School of Medicine, Department of Radiology, New Haven, CT
| | | | - Yuri S. Velichko
- Northwestern University, Department of Radiology, Feinberg School of Medicine, Chicago, IL
| | - Nicolo Gennaro
- Northwestern University, Department of Radiology, Feinberg School of Medicine, Chicago, IL
| | - Connectome Students
- Connectome – Student Association for Neurosurgery, Neurology and Neurosciences E.V
| | | | | | | | - Benjamin Y.M. Kwan
- Queen’s University, Department of Diagnostic Radiology, Kingston, Canada
| | | | - Satya N. Patro
- University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Lei Wu
- University of Washington Department of Radiology, Seattle, WA
| | - Tiffany So
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong
| | | | - Anthony Kam
- Loyola University Medical Center, Chicago, IL
| | | | - Neil Lall
- Children’s Healthcare of Atlanta, Atlanta, GA
| | - Group of Approvers
- Connectome – Student Association for Neurosurgery, Neurology and Neurosciences E.V
| | | | - Udunna Anazodo
- Montreal Neurological Institute (MNI), McGill University, Montreal, CA
| | | | - Bjoern H Menze
- Biomedical Image Analysis & Machine Learning, Department of Quantitative Biomedicine, University of Zurich, Switzerland
| | - Benedikt Wiestler
- Department of Neuroradiology, Technical University of Munich, Munich, Germany
| | - Maruf Adewole
- Medical Artificial Intelligence (MAI) Lab, Crestview Radiology, Lagos, Nigeria
| | | | | | - Hongwei Bran Li
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA
| | - Juan Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA
| | - Keyvan Farahani
- Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | | | | | | | - Russel Takeshi Shinohara
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA
| | - Farouk Dako
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | | | | | | | - Xinyang Liu
- Children’s National Hospital, Washington DC, USA
| | - Zhifan Jiang
- Children’s National Hospital, Washington DC, USA
| | - Koen Van Leemput
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark
| | | | - Ivan Ezhov
- Department of Informatics, Technical University Munich, Germany
| | - Elaine Johanson
- PrecisionFDA, U.S. Food and Drug Administration, Silver Spring, MD
| | | | - Ariana Familiar
- Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA
| | | | | | | | - Sanjay Aneja
- Yale University School of Medicine, Department of Therapeutic Radiology, New Haven, CT
| | - Veronica Chiang
- Yale University School of Medicine, Department of Neurosurgery, New Haven, CT
| | | | | | - Fatima Memon
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
| | | | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania School of Medicine, Philadelphia, PA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Jeffrey Rudie
- University of California San Diego, San Diego, CA
- University of California San Francisco, San Francisco, CA
| | - Mariam Aboian
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
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8
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Jekel L, Bousabarah K, Lin M, Merkaj S, Kaur M, Avesta A, Aneja S, Omuro A, Chiang V, Scheffler B, Aboian M. NIMG-02. PACS-INTEGRATED AUTO-SEGMENTATION WORKFLOW FOR BRAIN METASTASES USING NNU-NET. Neuro Oncol 2022. [PMCID: PMC9661012 DOI: 10.1093/neuonc/noac209.622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Abstract
PURPOSE
Monitoring metastatic disease to the brain is laborious and time-consuming, especially in the setting of multiple metastases and when performed manually. Response assessment in brain metastases based on maximal unidimensional diameter as per the RANO-BM guideline is commonly performed1, however, accurate volumetric lesion estimates can be crucial for clinical decision-making2 and enhance outcome prediction3. We propose a deep learning (DL)-based auto-segmentation approach with the potential for improvement of time-efficiency, reproducibility and robustness against inter-rater variability. Materials and
METHODS
We retrospectively retrieved 259 patients with a total number of 916 lesions from our institutional database from 2014 - 2021. Patients with prior history of local radiation therapy or surgery were excluded. Manually generated trainee segmentations were revised and adjusted by a board-certified radiologist and served as ground truth for evaluation of segmentation accuracy. Model performance was tested via dice-similarity-coefficient (DSC). Volumetric measurements were then obtained within our PACS-integrated workflow on Visage 7 (Visage Imaging, Inc., San Diego, CA) at the click of one button.
RESULTS
For model training and evaluation, a train-test split of 90:10 on patient-level was performed (n= 234:25 (Patients), n= 861:55 (Lesions). A DL-algorithm (nnUNet) was incrementally trained on 10 batches of 23 patients. The DSC of the U-Net gradually increased throughout the training process and heuristically reached a plateau of 0.85. The sensitivity of the algorithm was 83% with detection of 46 out of 55 lesions in the testing dataset. The lesions that were not detected by the algorithm were below 5 mm in size. The false positive rate was 8% (n=4/50).
CONCLUSION
Our study demonstrates the feasibility of PACS-based integration of automatized segmentation workflows of brain metastases. An incremental-training approach is recommended to adapt DL algorithms to specific hospital settings.
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Affiliation(s)
- Leon Jekel
- Yale School of Medicine , New Haven , USA
| | | | - MingDe Lin
- Yale School of Medicine , New Haven , USA
| | | | | | - Arman Avesta
- Yale University School of Medicine , New Haven, CT , USA
| | - Sanjay Aneja
- Yale University School of Medicine , New Haven , USA
| | | | - Veronica Chiang
- Yale School of Medicine, Department of Neurosurgery , New Haven, CT , USA
| | - Björn Scheffler
- DKFZ-Division Translational Neurooncology at the West German Cancer Center (WTZ), DKTK Partner Site, University Medicine Essen; German Cancer Consortium (DKTK) , Essen , Germany
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9
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Kaur M, Petersen GC, von Reppert M, Jekel L, de Santo IDO, Varghese S, Chiang V, Aboian M. NIMG-04. LONGITUDINAL TRACKING OF PERITUMORAL EDEMA VOLUME USING PACS-INTEGRATED TOOLS PROVIDES CRITICAL INFORMATION IN TREATMENT ASSESSMENT OF NSCLC BRAIN METASTASES AFTER RADIOSURGERY. Neuro Oncol 2022. [PMCID: PMC9661156 DOI: 10.1093/neuonc/noac209.624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Abstract
PURPOSE
Brain metastases (BM) are the most common intracranial malignancies in adults and mostly originate from lung cancer. Gamma Knife (GK) has become standard of care for BM, however there is insufficient knowledge of the posttreatment volumetric changes of peritumoral edema of BM due to paucity of tools for volumetric segmentation in neuroradiology practice. Recently PACS-integrated tools have become available in select clinical practices facilitating the comparison of posttreatment peritumoral edema volume changes and 2D-based measurements of CE tumor core.
METHODS
Patients with NSCLC BM ≥ 10 mm on T1c+ treated with GK had volumetric measurements for up to 7 follow-ups using a PACS-integrated tool that segments the FLAIR hyperintense region surrounding and including the CE lesion. The 2D and volumetric measurements were compared by creating treatment response curves with incorporation of clinical information including steroid timing.
RESULTS
50 NSCLC BM were included. The median pretreatment peritumoral volume was 8.5 cm3 (IQR 1–47 cm3, n= 36). The volume significantly decreased at 0–90 days (median 1.2 cm3, IQR 0.5–6.1 cm3, n= 31) and between 0-90 and 91-180 days (median 0.8 cm3, IQR 0.3-2.3 cm3, n= 26) post-GK. The time of peak median peritumoral volume increase was at > 365 days (median 1.4 cm3, IQR 0.4–8.1 cm3, n= 19). There was a positive correlation between longest diameter (LD) and peritumoral edema volume (rs= .75, p< .05). At 181–270 days post-GK 50% of BM showed incongruent response course for LD and peritumoral edema volume. The congruence/incongruence ratio of edema/enhancing portion of BM changed over follow-up time.
CONCLUSION
Half of the BM in our study did not show congruent response when comparing posttreatment peritumoral edema volume course to CE lesions in longitudinal assessment. Therefore, there is a critical need for quantitative tools that are incorporated into clinical practice to assess peritumoral edema treatment response.
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Affiliation(s)
| | | | | | - Leon Jekel
- Yale School of Medicine , New Haven , USA
| | | | | | - Veronica Chiang
- Yale School of Medicine, Department of Neurosurgery , New Haven, CT , USA
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10
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Kaur M, Varghese S, Jekel L, Tillmanns N, Merkaj S, Bousabarah K, Lin M, Bhawnani J, Chiang V, Aboian M. NIMG-07. APPLYING A GLIOMA-TRAINED DEEP LEARNING AUTO-SEGMENTATION TOOL ON BM PRE- AND POST-RADIOSURGERY. Neuro Oncol 2022. [PMCID: PMC9660643 DOI: 10.1093/neuonc/noac209.626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Abstract
PURPOSE
Stereotactic radiosurgery (SRS) has become the mainstay to treat BM. Follow-up MRI provides important information on lesion treatment response and guides future therapy planning. Volumetric measurements of BM have shown promise over traditional uni- and two-dimensional measurements in more accurate and repeatable assessment. However, routine clinical use has yet to be achieved because the workflow is laborious. In previous work, we developed a PACS-integrated deep learning algorithm for automatic high- and low-grade glioma 3D segmentation. In this work, we applied this U-Net to segment BM on pre- and post-Gamma Knife (GK) MRI and evaluated the performance.
METHODS
10 pre- and post-GK studies were autosegmented in five randomly selected patients (melanoma n= 3, breast n= 2). The glioma trained algorithm segmented the “Whole Tumor” (tumor core+peritumoral edema on T2w-FLAIR) and “Tumor Core” (CE tumor core+necrosis on SPGR). The AI generated segmentation was then revised as needed by a board-certified neuroradiologist and the dice-similarity-coefficient (DSC) between the revised and automatic volumetric segmentations were calculated.
RESULTS
Four patients had multicentric (2-4 BM) lesions. The mean± SD DSC for Whole Tumor and Tumor Core were 0.92±0.06 and 0.46±0.30 for pretreatment, 0.84±0.09 and 0.41±0.25 for posttreatment BM, respectively. The tool detected lesions with a sensitivity of 45% (5/11) for pretreatment and 50% (3/6) for posttreatment lesions. Three pretreatment and all posttreatment lesions that were not detected by the autosegmentation tool showed a very faint hyperintense peritumoral edema in T2w-FLAIR.
CONCLUSION
Volumetric segmentation of edema on FLAIR using the glioma-trained segmentation algorithm on pre- and post-GK BM did not require major adjustment of segmentation if it detects the lesion. On the other hand, with low sensitivity of lesion detection and low DSC for enhancing component, dedicated training of the algorithm on annotated BM data will be needed.
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Affiliation(s)
| | | | - Leon Jekel
- Yale School of Medicine , New Haven , USA
| | | | | | | | - MingDe Lin
- Yale School of Medicine , New Haven , USA
| | | | - Veronica Chiang
- Yale School of Medicine, Department of Neurosurgery , New Haven, CT , USA
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11
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Tillmanns N, Lum AE, Cassinelli G, Merkaj S, Verma T, Zeevi T, Staib L, Subramanian H, Bahar RC, Brim W, Lost J, Jekel L, Brackett A, Payabvash S, Ikuta I, Lin M, Bousabarah K, Johnson MH, Cui J, Malhotra A, Omuro A, Turowski B, Aboian MS. Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries. Neurooncol Adv 2022; 4:vdac093. [PMID: 36071926 PMCID: PMC9446682 DOI: 10.1093/noajnl/vdac093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Background While there are innumerable machine learning (ML) research algorithms used for segmentation of gliomas, there is yet to be a US FDA cleared product. The aim of this study is to explore the systemic limitations of research algorithms that have prevented translation from concept to product by a review of the current research literature. Methods We performed a systematic literature review on 4 databases. Of 11 727 articles, 58 articles met the inclusion criteria and were used for data extraction and screening using TRIPOD. Results We found that while many articles were published on ML-based glioma segmentation and report high accuracy results, there were substantial limitations in the methods and results portions of the papers that result in difficulty reproducing the methods and translation into clinical practice. Conclusions In addition, we identified that more than a third of the articles used the same publicly available BRaTS and TCIA datasets and are responsible for the majority of patient data on which ML algorithms were trained, which leads to limited generalizability and potential for overfitting and bias.
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Affiliation(s)
- Niklas Tillmanns
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA,University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Dusseldorf, Germany
| | - Avery E Lum
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Gabriel Cassinelli
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Sara Merkaj
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Tej Verma
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Tal Zeevi
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Lawrence Staib
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Harry Subramanian
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Ryan C Bahar
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Waverly Brim
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Jan Lost
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Leon Jekel
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Alexandria Brackett
- Harvey Cushing/John Hay Whitney Medical Library, Yale University, New Haven, Connecticut, USA
| | - Sam Payabvash
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Ichiro Ikuta
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - MingDe Lin
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA,Visage Imaging, Inc., San Diego, California, USA
| | | | - Michele H Johnson
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Jin Cui
- Department of Pathology, Boston Children’s Hospital, Boston, Massachusetts, USA
| | - Ajay Malhotra
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Antonio Omuro
- Department of Neurology and Yale Cancer Center, Yale School of Medicine, New Haven, Connecticut, USA
| | - Bernd Turowski
- University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Dusseldorf, Germany
| | - Mariam S Aboian
- Corresponding Author: Mariam S. Aboian, MD, PhD, 789 Howard Avenue (CB30), PO Box 208042, New Haven, CT 06520, USA ()
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12
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Cassinelli Petersen G, Bousabarah K, Verma T, von Reppert M, Jekel L, Gordem A, Jang B, Merkaj S, Abi Fadel S, Owens R, Omuro A, Chiang V, Ikuta I, Lin M, Aboian MS. Real-time PACS-integrated longitudinal brain metastasis tracking tool provides comprehensive assessment of treatment response to radiosurgery. Neurooncol Adv 2022; 4:vdac116. [PMID: 36043121 PMCID: PMC9412827 DOI: 10.1093/noajnl/vdac116] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Background
Treatment of brain metastases can be tailored to individual lesions with treatments such as stereotactic radiosurgery. Accurate surveillance of lesions is a prerequisite but challenging in patients with multiple lesions and prior imaging studies, in a process that is laborious and time consuming. We aimed to longitudinally track several lesions using a PACS-integrated lesion tracking tool (LTT) to evaluate the efficiency of a PACS-integrated lesion tracking workflow, and characterize the prevalence of heterogenous response (HeR) to treatment after Gamma Knife (GK).
Methods
We selected a group of brain metastases patients treated with GK at our institution. We used a PACS-integrated LTT to track the treatment response of each lesion after first GK intervention to maximally seven diagnostic follow-up scans. We evaluated the efficiency of this tool by comparing the number of clicks necessary to complete this task with and without the tool and examined the prevalence of HeR in treatment.
Results
A cohort of eighty patients was selected and 494 lesions were measured and tracked longitudinally for a mean follow-up time of 374 days after first GK. Use of LTT significantly decreased number of necessary clicks. 81.7% of patients had HeR to treatment at the end of follow-up. The prevalence increased with increasing number of lesions.
Conclusions
Lesions in a single patient often differ in their response to treatment, highlighting the importance of individual lesion size assessments for further treatment planning. PACS-integrated lesion tracking enables efficient lesion surveillance workflow and specific and objective result reports to treating clinicians.
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Affiliation(s)
- Gabriel Cassinelli Petersen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine , New Haven, Connecticut , USA
- University of Göttingen Medical Faculty , Göttingen , Germany
| | | | - Tej Verma
- New York University , New York City, New York , USA
| | - Marc von Reppert
- Department of Radiology and Biomedical Imaging, Yale School of Medicine , New Haven, Connecticut , USA
| | - Leon Jekel
- Department of Radiology and Biomedical Imaging, Yale School of Medicine , New Haven, Connecticut , USA
| | - Ayyuce Gordem
- Department of Radiology and Biomedical Imaging, Yale School of Medicine , New Haven, Connecticut , USA
| | - Benjamin Jang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine , New Haven, Connecticut , USA
| | - Sara Merkaj
- Department of Radiology and Biomedical Imaging, Yale School of Medicine , New Haven, Connecticut , USA
| | - Sandra Abi Fadel
- Department of Radiology and Biomedical Imaging, Yale School of Medicine , New Haven, Connecticut , USA
| | - Randy Owens
- Visage Imaging Inc. , San Diego, California , USA
| | - Antonio Omuro
- Department of Neurology, Yale School of Medicine , New Haven, Connecticut , USA
| | - Veronica Chiang
- Department of Neurosurgery, Yale School of Medicine , New Haven, Connecticut , USA
| | - Ichiro Ikuta
- Department of Radiology and Biomedical Imaging, Yale School of Medicine , New Haven, Connecticut , USA
- Yale Program for Innovation in Imaging Informatics, Yale School of Medicine , New Haven, Connecticut , USA (M.S.A., I.I.)
| | - MingDe Lin
- Department of Radiology and Biomedical Imaging, Yale School of Medicine , New Haven, Connecticut , USA
- Visage Imaging Inc. , San Diego, California , USA
| | - Mariam S Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine , New Haven, Connecticut , USA
- Yale Program for Innovation in Imaging Informatics, Yale School of Medicine , New Haven, Connecticut , USA
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13
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Brim WR, Jekel L, Petersen GC, Subramanian H, Zeevi T, Payabvash S, Bousabarah K, Lin M, Cui J, Brackett A, Mahajan A, Johnson M, Mahajan A, Aboian M. OTHR-12. The development of machine learning algorithms for the differentiation of glioma and brain metastases – a systematic review. Neurooncol Adv 2021. [PMCID: PMC8351249 DOI: 10.1093/noajnl/vdab071.067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Purpose Medical staging, surgical planning, and therapeutic decisions are significantly different for brain metastases versus gliomas. Machine learning (ML) algorithms have been developed to differentiate these pathologies. We performed a systematic review to characterize ML methods and to evaluate their accuracy. Methods Studies on the application of machine learning in neuro-oncology were searched in Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL) and Web of science core-collection. A search strategy was designed in compliance with a clinical librarian and confirmed by a second librarian. The search strategy comprised of controlled vocabulary including artificial intelligence, machine learning, deep learning, magnetic resonance imaging, and glioma. The initial search was performed in October 2020 and then updated in February 2021. Candidate articles were screened in Covidence by at least two reviewers each. A bias analysis was conducted in agreement with TRIPOD, a bias assessment tool similar to CLAIM. Results Twenty-nine articles were used for data extraction. Four articles specified model development for solitary brain metastases. Classical ML (cML) algorithms represented 85% of models used, while deep learning (DL) accounted for 15%. cML algorithms performed with an average accuracy, sensitivity, and specificity of 82%, 78%, 88%, respectively; DL performed 84%, 79%, 81%. The support vector machine (SVM) algorithm was the most common used cML model in the literature and convolutional neural networks (CNN) were standard for DL models. We also found T1, T1 post-gadolinium and T2 sequences were most commonly used for feature extraction. Preliminary TRIPOD analysis yielded an average score of 14.25 (range 8–18). Conclusion ML algorithms that can accurately classify glioma from brain metastases have been developed. SVM and CNN are leading approaches with high accuracy. Standardized algorithm performance reporting is a clear limitation to be addressed in future studies.
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Affiliation(s)
- Waverly Rose Brim
- Brain Tumor Research Group - Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Leon Jekel
- Brain Tumor Research Group - Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Gabriel Cassinelli Petersen
- Brain Tumor Research Group - Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Harry Subramanian
- Brain Tumor Research Group - Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Tal Zeevi
- Brain Tumor Research Group - Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Sam Payabvash
- Brain Tumor Research Group - Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | | | - MingDe Lin
- Brain Tumor Research Group - Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Jin Cui
- Brain Tumor Research Group - Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Alexandria Brackett
- Harvey Cushing/John Hay Whitney Medical Library, Yale University, New Haven, CT, USA
| | - Ajay Mahajan
- Brain Tumor Research Group - Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Michele Johnson
- Brain Tumor Research Group - Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Amit Mahajan
- Brain Tumor Research Group - Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Mariam Aboian
- Brain Tumor Research Group - Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
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14
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Jekel L, Brim WR, Petersen GC, Subramanian H, Zeevi T, Payabvash S, Bousabarah K, Lin M, Cui J, Brackett A, Johnson M, Malhotra A, Aboian M. OTHR-15. Assessment of TRIPOD adherence in articles developing machine learning models for differentiation of glioma from brain metastasis. Neurooncol Adv 2021. [PMCID: PMC8351195 DOI: 10.1093/noajnl/vdab071.070] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Purpose Machine learning (ML) applications in predictive models in neuro-oncology have become an increasingly investigated subject of research. For their incorporation into clinical practice, rigorous assessment is needed to reduce bias. Several reports have indicated utility of ML applications in differentiation of glioma from brain metastasis. However, a systematic assessment of quality of methodology and reporting in these studies has not been done yet. We examined the adherence of 29 published reports in this field to the TRIPOD statement, which is similar to CLAIM checklist. Materials and Methods Our systematic review was conducted in accordance with PRISMA guidelines. Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL) and Web of science core-collection were searched. Keywords included artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. Assessment of TRIPOD adherence in 29 eligible studies was performed. Individual item performance was assessed by adherence index (ADI), the ratio of mean achieved score to maximum score per TRIPOD item. Results In a preliminary analysis of 8 studies, the average TRIPOD adherence score was 0.48 (14.25/30 items fulfilled) with individual scores ranging from 0.27 (8/30) to 0.60 (18/30). Best overall item performance, with an ADI of 1, was seen in item 3 (Background/Objectives), 16 (Model performance) and 19 (Interpretation). Poorest performance was detected in item 1 (Title) and 2 (Abstract), followed by item 9 (Missing Data) with ADI of 0, 0 and 0.13, respectively. Conclusion Preliminary results underline the lack of reproducibility in ML studies on distinction between glioma and brain metastasis. An average TRIPOD adherence score of 0.48 indicates insufficient quality of reporting and outlines the need for increased utilization of quality scoring systems in study documentation. Systematic evaluation of quality score adherence will allow us to identify common flaws in this field for enabling translation of models into clinical workflow.
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Affiliation(s)
- Leon Jekel
- Brain Tumor Research Group, Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Waverly Rose Brim
- Brain Tumor Research Group, Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Gabriel Cassinelli Petersen
- Brain Tumor Research Group, Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Harry Subramanian
- Brain Tumor Research Group, Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Tal Zeevi
- Brain Tumor Research Group, Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Seyedmehdi Payabvash
- Brain Tumor Research Group, Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | | | - MingDe Lin
- Brain Tumor Research Group, Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Jin Cui
- Brain Tumor Research Group, Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Alexandria Brackett
- Harvey Cushing/John Hay Whitney Medical Library, Yale University, New Haven, CT, USA
| | - Michele Johnson
- Brain Tumor Research Group, Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Ajay Malhotra
- Brain Tumor Research Group, Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Mariam Aboian
- Brain Tumor Research Group, Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
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15
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Binnema R, van der Wal A, Visser C, Schepp R, Jekel L, Schröder P. Treatment of accidental hypothermia with cardiopulmonary bypass: a case report. Perfusion 2009; 23:193-6. [PMID: 19029271 DOI: 10.1177/0267659108099651] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This case report describes the successful treatment of severe accidental hypothermia of a 40-year-old woman. At arrival in the operating theatre her rectal temperature was 23 degrees C, her nasal temperature 21 degrees C and her periferal temperature 14 degrees C. The patient presented with a severe respiratory and metabolic acidosis which was corrected during cardiopulmonary bypass (CPB). She was rewarmed to obtain a rectal and nasal temperature of 34 degrees C. After 272 minutes, the patient was weaned successfully from CPB. The patient remained at mild hypothermia (34 degrees C) for 24 hours in the intensive care unit (ICU). The chest X-ray showed some signs of acute respiratory distress syndrome (ARDS) in spite of normal blood gas values. This improved within a few days and, after five days, she was transferred to the nursing department. On the seventh day, the patient was discharged from hospital without physical or neurological complaints.
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Affiliation(s)
- R Binnema
- Department of Extracorporeal Circulation, Medical Centre Leeuwarden, the Netherlands.
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16
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Affiliation(s)
- B P van Putte
- Department of Cardiothoracic Surgery, University Medical Center Utrecht, The Netherlands
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17
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Abstract
Absent pulmonary valve syndrome (APVS); the combination of tetralogy of Fallot (TOF) with agenesis of the pulmonary valve, is a relatively rare cardiac malformation. Despite the anatomic similarity with classic TOF, the pathophysiology is strikingly different. Data on 10 patients (3 male, 7 female) with APVS, treated between January 1978 and December 1995, were retrospectively reviewed. During this period a total of 2920 children underwent correction of a variety of congenital cardiac anomalies, of which 246 patients (8%) had a correction for TOF. Two patients with APVS presented within the first four months of life with severe cardiorespiratory distress and required several operative procedures. The remaining eight patients had only mild to moderate respiratory and/or cardiac symptoms and elective intracardiac repair was performed on those between the ages of 10 months and 9.5 years. Associated cardiac anomalies seen in five patients included aberrant coronary artery, absent or interrupted left pulmonary artery, partial AVSD and aberrant azygos continuation. In those electively corrected, the strategies used were ventriculotomy (7), pulmonary homograft (3) and aneurysmorrhaphy (2). There were two deaths, one in each group of patients, as a result of progressive respiratory insufficiency and cardiac tamponade, respectively. The follow-up of the eight survivors ranged from 2 to 11 years (median 6.75). All have a normal effort tolerance; only one child is on digoxin therapy, and one child continues to suffer bronchospastis episodes. Our experience with infants with this lesion is limited but underlines the different approaches required, depending on the age of presentation.
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Affiliation(s)
- L Jekel
- Paediatric Heart Centre, Wilhelmina Children's Hospital, Utrecht University, The Netherlands
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18
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Abstract
OBJECTIVES Evaluation of an aggressive policy for the treatment of phrenic nerve palsy (PNP), following cardiac operations, with emphasis on early diaphragmatic plication. Attention was given to the incidence and predisposing factors for PNP and the potential for recovery following plication. METHODS From 1 June 1991 to 1 January 1996 we prospectively screened patients for PNP following cardiac surgery. The diagnosis was suspected if difficulty was experienced in weaning the child from the ventilator. If abnormal elevation of the hemidiaphragm was present diaphragmatic plication was performed. Echocardiography was used to assess subsequent return of diaphragmatic function. RESULTS Seventeen children (nine boys, eight girls), out of 867 (1.9%) children younger than 16 years of age, undergoing cardiac operations were found to have PNP. The mean age was 66 days (range 1-17 months) with 16 patients below 1 year out of a total of 285 patients (incidence 5.6%) and one patient 17 months old. The incidence following open procedures was 11/190, following closed procedures 2/95 and following reoperation 4/83. PNP was diagnosed from 2 to 44 days (mean 14 days) following surgery. It was present on the right side in seven cases, the left in nine and was bilateral in one patient. Two patients were extubated at the time of diagnosis, one patient could be extubated shortly thereafter. Fourteen children underwent diaphragmatic plication, at a median 5 days post diagnosis. Extubation was possible 1-60 days (mean 4 days) after plication. Mean follow-up was 19 +/- 5 months. Subsequent recovery of diaphragmatic movement was documented in seven (41%) children. Time to recovery following plication was 16 months, without plication 38 months. CONCLUSION Prospective screening for PNP revealed an incidence in children younger than 1 year of 6%. Early plication substantially reduces the duration of ventilation, with its associated reduced morbidity and ICU stay.
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Affiliation(s)
- I E van Onna
- Paediatric Heart Center, Wilhelmina Children's Hospital, Utrecht University, The Netherlands
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19
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Jekel L, Benatar A, Woolley S, van de Wal HJ. Diaphragmatic paralysis after cardiac surgery in infants: prolonged medical management or surgical plication? Eur J Cardiothorac Surg 1994; 8:225. [PMID: 8031569 DOI: 10.1016/1010-7940(94)90121-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
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20
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Jekel L. The challenge of efficient clinical engineering. Calif Hosp 1993; 7:25-6, 28. [PMID: 10124844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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