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Al-Rahbi A, Al-Mahrouqi O, Al-Saadi T. Uses of artificial intelligence in glioma: A systematic review. MEDICINE INTERNATIONAL 2024; 4:40. [PMID: 38827949 PMCID: PMC11140312 DOI: 10.3892/mi.2024.164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 04/26/2024] [Indexed: 06/05/2024]
Abstract
Glioma is the most prevalent type of primary brain tumor in adults. The use of artificial intelligence (AI) in glioma is increasing and has exhibited promising results. The present study performed a systematic review of the applications of AI in glioma as regards diagnosis, grading, prediction of genotype, progression and treatment response using different databases. The aim of the present study was to demonstrate the trends (main directions) of the recent applications of AI within the field of glioma, and to highlight emerging challenges in integrating AI within clinical practice. A search in four databases (Scopus, PubMed, Wiley and Google Scholar) yielded a total of 42 articles specifically using AI in glioma and glioblastoma. The articles were retrieved and reviewed, and the data were summarized and analyzed. The majority of the articles were from the USA (n=18) followed by China (n=11). The number of articles increased by year reaching the maximum number in 2022. The majority of the articles studied glioma as opposed to glioblastoma. In terms of grading, the majority of the articles were about both low-grade glioma (LGG) and high-grade glioma (HGG) (n=23), followed by HGG/glioblastoma (n=13). Additionally, three articles were about LGG only; two articles did not specify the grade. It was found that one article had the highest sample size among the other studies, reaching 897 samples. Despite the limitations and challenges that face AI, the use of AI in glioma has increased in recent years with promising results, with a variety of applications ranging from diagnosis, grading, prognosis prediction, and reaching to treatment and post-operative care.
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Affiliation(s)
- Adham Al-Rahbi
- College of Medicine and Health Sciences, Sultan Qaboos University, Muscat 123, Sultanate of Oman
| | - Omar Al-Mahrouqi
- College of Medicine and Health Sciences, Sultan Qaboos University, Muscat 123, Sultanate of Oman
| | - Tariq Al-Saadi
- Department of Neurosurgery, Khoula Hospital, Muscat 123, Sultanate of Oman
- Department of Neurology and Neurosurgery-Montreal Neurological Institute, Faculty of Medicine, McGill University, Montreal, QC H3A 2B4, Canada
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Mohammadi S, Ghaderi S, Jouzdani AF, Azinkhah I, Alibabaei S, Azami M, Omrani V. Differentiation Between High-Grade Glioma and Brain Metastasis Using Cerebral Perfusion-Related Parameters (Cerebral Blood Volume and Cerebral Blood Flow): A Systematic Review and Meta-Analysis of Perfusion-weighted MRI Techniques. J Magn Reson Imaging 2024. [PMID: 38899965 DOI: 10.1002/jmri.29473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 05/21/2024] [Accepted: 05/22/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Distinguishing high-grade gliomas (HGGs) from brain metastases (BMs) using perfusion-weighted imaging (PWI) remains challenging. PWI offers quantitative measurements of cerebral blood flow (CBF) and cerebral blood volume (CBV), but optimal PWI parameters for differentiation are unclear. PURPOSE To compare CBF and CBV derived from PWIs in HGGs and BMs, and to identify the most effective PWI parameters and techniques for differentiation. STUDY TYPE Systematic review and meta-analysis. POPULATION Twenty-four studies compared CBF and CBV between HGGs (n = 704) and BMs (n = 488). FIELD STRENGTH/SEQUENCE Arterial spin labeling (ASL), dynamic susceptibility contrast (DSC), dynamic contrast-enhanced (DCE), and dynamic susceptibility contrast-enhanced (DSCE) sequences at 1.5 T and 3.0 T. ASSESSMENT Following the PRISMA guidelines, four major databases were searched from 2000 to 2024 for studies evaluating CBF or CBV using PWI in HGGs and BMs. STATISTICAL TESTS Standardized mean difference (SMD) with 95% CIs was used. Risk of bias (ROB) and publication bias were assessed, and I2 statistic was used to assess statistical heterogeneity. A P-value<0.05 was considered significant. RESULTS HGGs showed a significant modest increase in CBF (SMD = 0.37, 95% CI: 0.05-0.69) and CBV (SMD = 0.26, 95% CI: 0.01-0.51) compared with BMs. Subgroup analysis based on region, sequence, ROB, and field strength for CBF (HGGs: 375 and BMs: 222) and CBV (HGGs: 493 and BMs: 378) values were conducted. ASL showed a considerable moderate increase (50% overlapping CI) in CBF for HGGs compared with BMs. However, no significant difference was found between ASL and DSC (P = 0.08). DATA CONCLUSION ASL-derived CBF may be more useful than DSC-derived CBF in differentiating HGGs from BMs. This suggests that ASL may be used as an alternative to DSC when contrast medium is contraindicated or when intravenous injection is not feasible. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Sana Mohammadi
- Neuromuscular Research Center, Department of Neurology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Sadegh Ghaderi
- Neuromuscular Research Center, Department of Neurology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Fathi Jouzdani
- Neuroscience and Artificial Intelligence Research Group (NAIRG), Department of Neuroscience, School of Science and Advanced Technologies in Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Iman Azinkhah
- Medical Physics Department, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Sanaz Alibabaei
- Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Mobin Azami
- Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Vida Omrani
- School Medical Physics Department, School of paramedical Sciences, Bushehr University of Medical Sciences, Bushehr, Iran
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3
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Moawad AW, Janas A, Baid U, Ramakrishnan D, Saluja R, Ashraf N, Jekel L, Amiruddin R, Adewole M, Albrecht J, Anazodo U, Aneja S, Anwar SM, Bergquist T, Calabrese E, Chiang V, Chung V, Conte GMM, Dako F, Eddy J, Ezhov I, Familiar A, Farahani K, Iglesias JE, Jiang Z, Johanson E, Kazerooni AF, Kofler F, Krantchev K, LaBella D, Van Leemput K, Li HB, Linguraru MG, Link KE, Liu X, Maleki N, Meier Z, Menze BH, Moy H, Osenberg K, Piraud M, Reitman Z, Shinohara RT, Tahon NH, Nada A, Velichko YS, Wang C, Wiestler B, Wiggins W, Shafique U, Willms K, Avesta A, Bousabarah K, Chakrabarty S, Gennaro N, Holler W, Kaur M, LaMontagne P, Lin M, Lost J, Marcus DS, Maresca R, Merkaj S, Nada A, Pedersen GC, von Reppert M, Sotiras A, Teytelboym O, Tillmans N, Westerhoff M, Youssef A, Godfrey D, Floyd S, Rauschecker A, Villanueva-Meyer J, Pflüger I, Cho J, Bendszus M, Brugnara G, Cramer J, Perez-Carillo GJG, Johnson DR, Kam A, Kwan BYM, Lai L, Lall NU, Memon F, Patro SN, Petrovic B, So TY, Thompson G, Wu L, Schrickel EB, Bansal A, Barkhof F, Besada C, Chu S, Druzgal J, Dusoi A, Farage L, Feltrin F, Fong A, Fung SH, Gray RI, Ikuta I, Iv M, Postma AA, Mahajan A, Joyner D, Krumpelman C, Letourneau-Guillon L, Lincoln CM, Maros ME, Miller E, Morón F, Nimchinsky EA, Ozsarlak O, Patel U, Rohatgi S, Saha A, Sayah A, Schwartz ED, Shih R, Shiroishi MS, Small JE, Tanwar M, Valerie J, Weinberg BD, White ML, Young R, Zohrabian VM, Azizova A, Brüßeler MMT, Fehringer P, Ghonim M, Ghonim M, Gkampenis A, Okar A, Pasquini L, Sharifi Y, Singh G, Sollmann N, Soumala T, Taherzadeh M, Yordanov N, Vollmuth P, Foltyn-Dumitru M, Malhotra A, Abayazeed AH, Dellepiane F, Lohmann P, Pérez-García VM, Elhalawani H, Al-Rubaiey S, Armindo RD, Ashraf K, Asla MM, Badawy M, Bisschop J, Lomer NB, Bukatz J, Chen J, Cimflova P, Corr F, Crawley A, Deptula L, Elakhdar T, Shawali IH, Faghani S, Frick A, Gulati V, Haider MA, Hierro F, Dahl RH, Jacobs SM, Hsieh KCJ, Kandemirli SG, Kersting K, Kida L, Kollia S, Koukoulithras I, Li X, Abouelatta A, Mansour A, Maria-Zamfirescu RC, Marsiglia M, Mateo-Camacho YS, McArthur M, McDonnell O, McHugh M, Moassefi M, Morsi SM, Muntenu A, Nandolia KK, Naqvi SR, Nikanpour Y, Alnoury M, Nouh AMA, Pappafava F, Patel MD, Petrucci S, Rawie E, Raymond S, Roohani B, Sabouhi S, Sanchez-Garcia LM, Shaked Z, Suthar PP, Altes T, Isufi E, Dhermesh Y, Gass J, Thacker J, Tarabishy AR, Turner B, Vacca S, Vilanilam GK, Warren D, Weiss D, Willms K, Worede F, Yousry S, Lerebo W, Aristizabal A, Karargyris A, Kassem H, Pati S, Sheller M, Bakas S, Rudie JD, Aboian M. The Brain Tumor Segmentation - Metastases (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI. ARXIV 2024:arXiv:2306.00838v2. [PMID: 37396600 PMCID: PMC10312806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment.
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Affiliation(s)
| | - Anastasia Janas
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Ujjwal Baid
- Division of Computational Pathology, Department of Pathology and Laboratory Medicine, School of Medicine, Indiana University, Indianapolis, IN, USA
| | - Divya Ramakrishnan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Rachit Saluja
- Department of Electical and Computer Engineering, Cornell University and Cornell Tech, New York, NY, USA
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Nader Ashraf
- ImagineQuant, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Leon Jekel
- DKFZ Division of Translational Neurooncology at the WTZ, German Cancer Consortium, DKTK Partner Site, University Hospital Essen, Essen, Germany
| | - Raisa Amiruddin
- ImagineQuant, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Maruf Adewole
- Medical Artificial Intelligence Lab, Crestview Radiology, Lagos, Nigeria
| | | | - Udunna Anazodo
- Montreal Neurological Institute, McGill University, Montreal, Canada
- Medical Artificial Intelligence (MAI) lab, Crestview Radiology, Lagos, Nigeria
| | - Sanjay Aneja
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT, USA
| | - Syed Muhammad Anwar
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, D.C., USA
| | | | - Evan Calabrese
- Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - Veronica Chiang
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | | | | | - Farouk Dako
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, PA, USA
| | | | - Ivan Ezhov
- Department of Informatics, Technical University Munich, Germany
| | - Ariana Familiar
- Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - Keyvan Farahani
- Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Juan Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Zhifan Jiang
- Children’s National Hospital, Washington, D.C., USA
| | - Elaine Johanson
- PrecisionFDA, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Anahita Fathi Kazerooni
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA
- Division of Neurosurgery, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Data-Driven Discovery in Biomedicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Kiril Krantchev
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Dominic LaBella
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Koen Van Leemput
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark
| | - Hongwei Bran Li
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Marius George Linguraru
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, D.C., USA
- Departments of Radiology and Pediatrics, George Washington University School of Medicine and Health Sciences, Washington, D.C., USA
| | | | - Xinyang Liu
- Children’s National Hospital, Washington, D.C., USA
| | - Nazanin Maleki
- ImagineQuant, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Bjoern H Menze
- Biomedical Image Analysis & Machine Learning, Department of Quantitative Biomedicine, University of Zurich, Switzerland
| | - Harrison Moy
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Klara Osenberg
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | | | | | - Russel Takeshi Shinohara
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Yuri S. Velichko
- Northwestern University, Department of Radiology, Feinberg School of Medicine, Chicago, IL, USA
| | - Chunhao Wang
- Duke University School of Medicine, Durham, NC, USA
| | - Benedikt Wiestler
- Department of Neuroradiology, Technical University of Munich, Munich, Germany
| | | | - Umber Shafique
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA
| | - Klara Willms
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Arman Avesta
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | | | - Satrajit Chakrabarty
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA
- GE HealthCare, San Ramon, CA, USA
| | - Nicolo Gennaro
- Northwestern University, Department of Radiology, Feinberg School of Medicine, Chicago, IL, USA
| | | | - Manpreet Kaur
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Pamela LaMontagne
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Jan Lost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Daniel S. Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Ryan Maresca
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT, USA
| | - Sarah Merkaj
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | | | | | - Marc von Reppert
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Aristeidis Sotiras
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Niklas Tillmans
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | | | | | | | - Scott Floyd
- Duke University Medical Center, Durham, NC, USA
| | - Andreas Rauschecker
- Department of Radiology and Biomedical Imaging, University of California San Francisco, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology and Biomedical Imaging, University of California San Francisco, CA, USA
| | - Irada Pflüger
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Jaeyoung Cho
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Justin Cramer
- Department of Radiology, Mayo Clinic, Phoenix, AZ, USA
| | | | | | - Anthony Kam
- Loyola University Medical Center, Hines, IL, USA
| | | | - Lillian Lai
- Department of Radiology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | | | - Fatima Memon
- Carolina Radiology Associates, Myrtle Beach, SC, USA
- McLeod Regional Medical Center, Florence, SC, USA
- Medical University of South Carolina, Charleston, SC, USA
| | | | | | - Tiffany Y. So
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR
| | - Gerard Thompson
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Department of Clinical Neurosciences, NHS Lothian, Edinburgh, United Kingdom
| | - Lei Wu
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - E. Brooke Schrickel
- Department of Radiology, Ohio State University College of medicine, Columbus, OH, USA
| | - Anu Bansal
- Albert Einstein Medical Center, Hartford, CT, USA
| | - Frederik Barkhof
- Amsterdam UMC, location Vrije Universiteir, the Netherlands
- University College London, United Kingdom
| | | | - Sammy Chu
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Jason Druzgal
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA
| | | | - Luciano Farage
- Centro Universitario Euro-Americana (UNIEURO), Brasília, DF, Brazil
| | - Fabricio Feltrin
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Amy Fong
- Southern District Health Board, Dunedin, New Zealand
| | - Steve H. Fung
- Department of Radiology, Houston Methodist, Houston, TX, USA
| | - R. Ian Gray
- University of Tennessee medical Center, Knoxville, TN, USA
| | - Ichiro Ikuta
- Mayo Clinic, Department of Radiology, Section of Neuroradiology, Phoenix, AZ, USA
| | - Michael Iv
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Alida A. Postma
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- Mental Health and Neuroscience research institute, Maastricht University, Maastricht, the Netherlands
| | - Amit Mahajan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - David Joyner
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, USA
| | - Chase Krumpelman
- Department of Radiology, University of Northwestern, Chicago, IL, USA
| | | | | | - Mate E. Maros
- Departments of Neuroradiology & Biomedical Informatics, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Elka Miller
- Department of Diagnostic and Interventional Radiology, SickKids Hospital, University of Toronto, Canada
| | - Fanny Morón
- Department of Radiology, Baylor College of medicine, Houston, TX, USA
| | | | - Ozkan Ozsarlak
- Department of Radiology, AZ Monica, Antwerp Area, Belgium
| | - Uresh Patel
- Medicolegal Imaging Experts LLC, Mercer Island, WA, USA
| | - Saurabh Rohatgi
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Atin Saha
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell Medical College, New York, NY, USA
| | - Anousheh Sayah
- MedStar Georgetown University Hospital, Washington, D.C., USA
| | - Eric D. Schwartz
- Department of Radiology, St.Elizabeth’s Medical Center, Boston, MA, USA
- Department of Radiology, Tufts University School of Medicine, Boston, MA, USA
| | - Robert Shih
- Walter Reed National Military Medical Center, Bethesda, MD, USA
| | | | | | | | - Jewels Valerie
- Department of Radiology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Brent D. Weinberg
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
| | | | - Robert Young
- George Washington University, Washington, D.C., USA
| | - Vahe M. Zohrabian
- Northwell Health, Zucker Hofstra School of Medicine at Northwell, North Shore University Hospital, Hempstead, New York, NY, USA
| | - Aynur Azizova
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | | | - Pascal Fehringer
- Faculty of Medicine, Jena University Hospital, Friedrich Schiller University Jena, Jena, Germany
| | - Mohanad Ghonim
- Department of Radiology, Ain Shams University, Cairo, Egypt
| | - Mohamed Ghonim
- Department of Radiology, Ain Shams University, Cairo - Egypt
| | | | | | - Luca Pasquini
- Radiology Department, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | | | - Gagandeep Singh
- Columbia University Irving Medical Center, New York, NY, USA
| | - Nico Sollmann
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | | | | | - Nikolay Yordanov
- Faculty of Medicine, Medical University - Sofia, Sofia, Bulgaria
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Department of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Ajay Malhotra
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | | | - Francesco Dellepiane
- Functional and Interventional Neuroradiology Unit, Bambino Gesù Children’s Hospital, Rome, Italy
| | - Philipp Lohmann
- Institute of Neuroscience and Medicine (INM-4), Research Center Juelich, Juelich, Germany
- Department of Nuclear Medicine, University Hospital RWTH Aachen, Aachen, Germany
| | - Víctor M. Pérez-García
- Mathematical Oncology Laboratory & Department of Mathematics, University of Castilla-La Mancha, Spain
| | - Hesham Elhalawani
- Department of Radiation Oncology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Sanaria Al-Rubaiey
- Charité-Universitätsmedizin Berlin (Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Berlin, Germany
| | - Rui Duarte Armindo
- Department of Neuroradiology, Western Lisbon Hospital Centre (CHLO), Portugal
| | | | | | - Mohamed Badawy
- Diagnostic Radiology Department, Wayne State University, Detroit, MI
| | - Jeroen Bisschop
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | | | - Jan Bukatz
- Charité-Universitätsmedizin Berlin (Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Berlin, Germany
| | - Jim Chen
- Department of Radiology/Division of Neuroradiology, San Diego Veterans Administration Medical Center/UC San Diego Health System, San Diego, CA, USA
| | - Petra Cimflova
- Department of Radiology, University of Calgary, Calgary, Canada
| | - Felix Corr
- EDU Institute of Higher Education, Villa Bighi, Chaplain’s House, Kalkara, Malta
| | | | - Lisa Deptula
- Ross University School of Medicine, Bridgetown, Barbados
| | | | | | | | - Alexandra Frick
- Department of Neurosurgery, Vivantes Klinikum Neukölln, Berlin, Germany
| | | | | | - Fátima Hierro
- Neuroradiology Department, Pedro Hispano Hospital, Matosinhos, Portugal
| | - Rasmus Holmboe Dahl
- Department of Radiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Sarah Maria Jacobs
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | | | - Sedat G. Kandemirli
- Department of Radiology, University of Iowa Hospital and Clinics, Iowa City, IA, USA
| | - Katharina Kersting
- Charité-Universitätsmedizin Berlin (Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Berlin, Germany
| | - Laura Kida
- Charité-Universitätsmedizin Berlin (Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Berlin, Germany
| | - Sofia Kollia
- National and Kapodistrian University of Athens, School of Medicine, Athens, Greece
| | | | - Xiao Li
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
| | - Ahmed Abouelatta
- Department of Diagnostic and Interventional Radiology, Cairo University, Cairo, Egypt
| | | | - Ruxandra-Catrinel Maria-Zamfirescu
- Charité-Universitätsmedizin Berlin (Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Berlin, Germany
| | - Marcela Marsiglia
- Department of Radiology, Brigham and Women’s Hospital, Massachusetts General Hospital, Boston, MA, USA
| | | | - Mark McArthur
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | | | - Maire McHugh
- Department of Radiology Manchester NHS Foundation Trust, North West School of Radiology, Manchester, United Kingdom
| | - Mana Moassefi
- Artificial Intelligence Lab, Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Khanak K. Nandolia
- Department of Radiodiagnosis, All India Institute of Medical Sciences Rishikesh, India
| | - Syed Raza Naqvi
- Windsor Regional Hospital, Western University, Ontario, Canada
| | - Yalda Nikanpour
- Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA
| | - Mostafa Alnoury
- Department of Radiology, University of Pennsylvania, PA, USA
| | | | - Francesca Pappafava
- Department of Medicine and Surgery, Università degli Studi di Perugia, Italy
| | - Markand D. Patel
- Department of Neuroradiology, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Samantha Petrucci
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Eric Rawie
- Department of Radiology, Michigan Medicine, Ann Arbor, MI, USA
| | - Scott Raymond
- Department of Radiology, University of Vermont Medical Center, Burlington, VT, USA
| | - Borna Roohani
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Sadeq Sabouhi
- Isfahan University of Medical Sciences, Isfahan, Iran
| | | | - Zoe Shaked
- Charité-Universitätsmedizin Berlin (Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Berlin, Germany
| | | | - Talissa Altes
- Radiology Department, University of Missouri, Columbia, MO, USA
| | | | | | | | | | - Abdul Rahman Tarabishy
- Department of NeuroRadiology, Rockefeller Neuroscience Institute, West Virginia University. Morgantown, WV, USA
| | | | - Sebastiano Vacca
- University of Cagliari, School of Medicine and Surgery, Cagliari, Italy
| | - George K. Vilanilam
- Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Daniel Warren
- Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - David Weiss
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Klara Willms
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Fikadu Worede
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Wondwossen Lerebo
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | | | | | | | - Sarthak Pati
- Division of Computational Pathology, Department of Pathology and Laboratory Medicine, School of Medicine, Indiana University, Indianapolis, IN, USA
- Center For Federated Learning in Medicine, Indiana University, Indianapolis, IN, USA
- Medical Working Group, MLCommons, San Fransisco, CA, USA
| | | | - Spyridon Bakas
- Division of Computational Pathology, Department of Pathology and Laboratory Medicine, School of Medicine, Indiana University, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA
- Department of Neurological Surgery, School of Medicine, Indiana University, Indianapolis, IN, USA
| | - Jeffrey D. Rudie
- Department of Radiology, University of California San Diego, CA, USA
- Department of Radiology, Scripps Clinic Medical Group, CA, USA
| | - Mariam Aboian
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
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4
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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] [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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5
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Zhu FY, Sun YF, Yin XP, Zhang Y, Xing LH, Ma ZP, Xue LY, Wang JN. Using machine learning-based radiomics to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes. Discov Oncol 2023; 14:224. [PMID: 38055122 DOI: 10.1007/s12672-023-00837-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 11/22/2023] [Indexed: 12/07/2023] Open
Abstract
OBJECTIVE To establish a machine learning-based radiomics model to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes, thereby achieving accurate preoperative classification. MATERIALS AND METHODS A retrospective analysis was conducted on MRI T1WI-enhanced images of 105 patients with glioma and 172 patients with solitary brain metastasis from lung cancer, which were confirmed pathologically. The patients were divided into the training group and validation group in an 8:2 ratio for image segmentation, extraction, and filtering; multiple layer perceptron (MLP), support vector machine (SVM), random forest (RF), and logistic regression (LR) were used for modeling; fivefold cross-validation was used to train the model; the validation group was used to evaluate and assess the predictive performance of the model, ROC curve was used to calculate the accuracy, sensitivity, and specificity of the model, and the area under curve (AUC) was used to assess the predictive performance of the model. RESULTS The accuracy and AUC of the MLP differentiation model for high-grade glioma and solitary brain metastasis in the validation group was 0.992, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.968, respectively. The accuracy and AUC for the MLP and SVM differentiation model for high-grade glioma and small cell lung cancer brain metastasis in the validation group was 0.966, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.929, respectively. The accuracy and AUC for the MLP differentiation model for high-grade glioma and non-small cell lung cancer brain metastasis in the validation group was 0.982, 0.999, respectively, while the sensitivity and specificity were 0.958, 1.000, respectively. CONCLUSION The application of machine learning-based radiomics has a certain clinical value in differentiating glioma from solitary brain metastasis from lung cancer and its subtypes. In the HGG/SBM and HGG/NSCLC SBM validation groups, the MLP model had the best diagnostic performance, while in the HGG/SCLC SBM validation group, the MLP and SVM models had the best diagnostic performance.
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Affiliation(s)
- Feng-Ying Zhu
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Yu-Feng Sun
- College of Electronic Information Engineering, Hebei University, Baoding, 071002, China
| | - Xiao-Ping Yin
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Yu Zhang
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Li-Hong Xing
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Ze-Peng Ma
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China
| | - Lin-Yan Xue
- College of Quality and Technical Supervision, Hebei University, No.180 of Wusi Road, Lianchi District, Baoding, 071002, China.
| | - Jia-Ning Wang
- Department of Radiology, Affiliated Hospital of Hebei University, No.212 of Yuhua Road, Lianchi District, Baoding, 071000, China.
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6
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Felefly T, Roukoz C, Fares G, Achkar S, Yazbeck S, Meyer P, Kordahi M, Azoury F, Nasr DN, Nasr E, Noël G, Francis Z. An Explainable MRI-Radiomic Quantum Neural Network to Differentiate Between Large Brain Metastases and High-Grade Glioma Using Quantum Annealing for Feature Selection. J Digit Imaging 2023; 36:2335-2346. [PMID: 37507581 PMCID: PMC10584786 DOI: 10.1007/s10278-023-00886-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/11/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Solitary large brain metastases (LBM) and high-grade gliomas (HGG) are sometimes hard to differentiate on MRI. The management differs significantly between these two entities, and non-invasive methods that help differentiate between them are eagerly needed to avoid potentially morbid biopsies and surgical procedures. We explore herein the performance and interpretability of an MRI-radiomics variational quantum neural network (QNN) using a quantum-annealing mutual-information (MI) feature selection approach. We retrospectively included 423 patients with HGG and LBM (> 2 cm) who had a contrast-enhanced T1-weighted (CE-T1) MRI between 2012 and 2019. After exclusion, 72 HGG and 129 LBM were kept. Tumors were manually segmented, and a 5-mm peri-tumoral ring was created. MRI images were pre-processed, and 1813 radiomic features were extracted. A set of best features based on MI was selected. MI and conditional-MI were embedded into a quadratic unconstrained binary optimization (QUBO) formulation that was mapped to an Ising-model and submitted to D'Wave's quantum annealer to solve for the best combination of 10 features. The 10 selected features were embedded into a 2-qubits QNN using PennyLane library. The model was evaluated for balanced-accuracy (bACC) and area under the receiver operating characteristic curve (ROC-AUC) on the test set. The model performance was benchmarked against two classical models: dense neural networks (DNN) and extreme gradient boosting (XGB). Shapley values were calculated to interpret sample-wise predictions on the test set. The best 10-feature combination included 6 tumor and 4 ring features. For QNN, DNN, and XGB, respectively, training ROC-AUC was 0.86, 0.95, and 0.94; test ROC-AUC was 0.76, 0.75, and 0.79; and test bACC was 0.74, 0.73, and 0.72. The two most influential features were tumor Laplacian-of-Gaussian-GLRLM-Entropy and sphericity. We developed an accurate interpretable QNN model with quantum-informed feature selection to differentiate between LBM and HGG on CE-T1 brain MRI. The model performance is comparable to state-of-the-art classical models.
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Affiliation(s)
- Tony Felefly
- Radiation Oncology Department, Hôtel-Dieu de France Hospital, Saint Joseph University, Beirut, Lebanon.
- ICube Laboratory, University of Strasbourg, Strasbourg, France.
- Radiation Oncology Department, Hôtel-Dieu de Lévis, Lévis, QC, Canada.
| | - Camille Roukoz
- Radiation Oncology Department, Hôtel-Dieu de France Hospital, Saint Joseph University, Beirut, Lebanon
| | - Georges Fares
- Radiation Oncology Department, Hôtel-Dieu de France Hospital, Saint Joseph University, Beirut, Lebanon
- Physics Department, Saint Joseph University, Beirut, Lebanon
| | - Samir Achkar
- Radiation Oncology Department, Gustave Roussy Cancer Campus, 94805, Villejuif, France
| | - Sandrine Yazbeck
- Department of Radiology, University of Maryland School of Medicine, 655 W Baltimore St S, Baltimore, MD, 21201, USA
| | - Philippe Meyer
- Medical Physics Department, Institut de Cancérologie de Strasbourg (ICANS), 67200, Strasbourg, France
- IMAGeS Unit, IRIS Platform, ICube, University of Strasbourg, 67085, Strasbourg Cedex, France
| | | | - Fares Azoury
- Radiation Oncology Department, Hôtel-Dieu de France Hospital, Saint Joseph University, Beirut, Lebanon
| | - Dolly Nehme Nasr
- Radiation Oncology Department, Hôtel-Dieu de France Hospital, Saint Joseph University, Beirut, Lebanon
| | - Elie Nasr
- Radiation Oncology Department, Hôtel-Dieu de France Hospital, Saint Joseph University, Beirut, Lebanon
| | - Georges Noël
- Radiotherapy Department, Institut de Cancérologie de Strasbourg (ICANS), 67200, Strasbourg, France
- Radiobiology Department, IMIS Unit, IRIS Platform, ICube, University of Strasbourg, 67085, Strasbourg Cedex, France
- Faculty of Medicine, University of Strasbourg, 67000, Strasbourg, France
| | - Ziad Francis
- Physics Department, Saint Joseph University, Beirut, Lebanon
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7
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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] [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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8
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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] [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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9
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Miller DM, Yadanapudi K, Rai V, Rai SN, Chen J, Frieboes HB, Masters A, McCallum A, Williams BJ. Untangling the web of glioblastoma treatment resistance using a multi-omic and multidisciplinary approach. Am J Med Sci 2023; 366:185-198. [PMID: 37330006 DOI: 10.1016/j.amjms.2023.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 05/01/2023] [Accepted: 06/13/2023] [Indexed: 06/19/2023]
Abstract
Glioblastoma (GBM), the most common human brain tumor, has been notoriously resistant to treatment. As a result, the dismal overall survival of GBM patients has not changed over the past three decades. GBM has been stubbornly resistant to checkpoint inhibitor immunotherapies, which have been remarkably effective in the treatment of other tumors. It is clear that GBM resistance to therapy is multifactorial. Although therapeutic transport into brain tumors is inhibited by the blood brain barrier, there is evolving evidence that overcoming this barrier is not the predominant factor. GBMs generally have a low mutation burden, exist in an immunosuppressed environment and they are inherently resistant to immune stimulation, all of which contribute to treatment resistance. In this review, we evaluate the contribution of multi-omic approaches (genomic and metabolomic) along with analyzing immune cell populations and tumor biophysical characteristics to better understand and overcome GBM multifactorial resistance to treatment.
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Affiliation(s)
- Donald M Miller
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Medicine, School of Medicine, University of Louisville, Louisville, KY, USA.
| | - Kavitha Yadanapudi
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Medicine, School of Medicine, University of Louisville, Louisville, KY, USA
| | - Veeresh Rai
- Brown Cancer Center, University of Louisville, Louisville, KY, USA
| | - Shesh N Rai
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Biostatistics and Informatics Shared Resources, University of Cincinnati Cancer Center, Cincinnati, OH, USA; Cancer Data Science Center of University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Joseph Chen
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY, USA
| | - Hermann B Frieboes
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY, USA; Center for Preventative Medicine, University of Louisville, Louisville, KY, USA
| | - Adrianna Masters
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Radiation Oncology, University of Louisville, Louisville, KY, USA
| | - Abigail McCallum
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Neurosurgery, University of Louisville, Louisville, KY, USA
| | - Brian J Williams
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Neurosurgery, University of Louisville, Louisville, KY, USA
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10
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Raslan O, Ozturk A, Oguz KK, Sen F, Aboud O, Ivanovic V, Assadsangabi R, Hacein-Bey L. Imaging Cancer in Neuroradiology. Curr Probl Cancer 2023:100965. [PMID: 37349190 DOI: 10.1016/j.currproblcancer.2023.100965] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 05/22/2023] [Accepted: 05/25/2023] [Indexed: 06/24/2023]
Abstract
Neuroimaging plays a pivotal role in the diagnosis, management, and prognostication of brain tumors. Recently, the World Health Organization published the fifth edition of the WHO Classification of Tumors of the Central Nervous System (CNS5), which places greater emphasis on tumor genetics and molecular markers to complement the existing histological and immunohistochemical approaches. Recent advances in computational power allowed modern neuro-oncological imaging to move from a strictly morphology-based discipline to advanced neuroimaging techniques with quantifiable tissue characteristics such as tumor cellularity, microstructural organization, hemodynamic, functional, and metabolic features, providing more precise tumor diagnosis and management. The aim of this review is to highlight the key imaging features of the recently published CNS5, outlining the current imaging standards and summarizing the latest advances in neuro-oncological imaging techniques and their role in complementing traditional brain tumor imaging and management.
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Affiliation(s)
- Osama Raslan
- Department of Radiology, Division of Neuroradiology, University of California Davis Medical Center, Sacramento, CA.
| | - Arzu Ozturk
- Department of Radiology, Division of Neuroradiology, University of California Davis Medical Center, Sacramento, CA
| | - Kader Karli Oguz
- Department of Radiology, Division of Neuroradiology, University of California Davis Medical Center, Sacramento, CA
| | - Fatma Sen
- Department of Radiology, Division of Nuclear Medicine, University of California Davis Medical Center, Sacramento, CA
| | - Orwa Aboud
- Department of Neurology and Neurological Surgery, UC Davis Comprehensive Cancer Center, CA
| | - Vladimir Ivanovic
- Department of Radiology, Division of Neuroradiology, Medical College of Wisconsin., Milwaukee, WI
| | - Reza Assadsangabi
- Department of Radiology, Keck School of Medicine of USC University of Southern California, Sacramento, CA
| | - Lotfi Hacein-Bey
- Department of Radiology, Division of Neuroradiology, University of California Davis Medical Center, Sacramento, CA
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11
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Ramakrishnan D, von Reppert M, Krycia M, Sala M, Mueller S, Aneja S, Nabavizadeh A, Galldiks N, Lohmann P, Raji C, Ikuta I, Memon F, Weinberg BD, Aboian MS. Evolution and implementation of radiographic response criteria in neuro-oncology. Neurooncol Adv 2023; 5:vdad118. [PMID: 37860269 PMCID: PMC10584081 DOI: 10.1093/noajnl/vdad118] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023] Open
Abstract
Radiographic response assessment in neuro-oncology is critical in clinical practice and trials. Conventional criteria, such as the MacDonald and response assessment in neuro-oncology (RANO) criteria, rely on bidimensional (2D) measurements of a single tumor cross-section. Although RANO criteria are established for response assessment in clinical trials, there is a critical need to address the complexity of brain tumor treatment response with multiple new approaches being proposed. These include volumetric analysis of tumor compartments, structured MRI reporting systems like the Brain Tumor Reporting and Data System, and standardized approaches to advanced imaging techniques to distinguish tumor response from treatment effects. In this review, we discuss the strengths and limitations of different neuro-oncology response criteria and summarize current research findings on the role of novel response methods in neuro-oncology clinical trials and practice.
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Affiliation(s)
- Divya Ramakrishnan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Marc von Reppert
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Mark Krycia
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Matthew Sala
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Tulane University School of Medicine, New Orleans, Louisiana, USA
| | - Sabine Mueller
- Department of Neurology, Neurosurgery, and Pediatrics, University of California San Francisco, San Francisco, California, USA
| | - Sanjay Aneja
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Ali Nabavizadeh
- Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA
| | - Norbert Galldiks
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Juelich, Germany
- Center for Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne, and Duesseldorf, Cologne, Germany
| | - Philipp Lohmann
- Institute of Neuroscience and Medicine (INM-4), Research Center Juelich, Juelich, Germany
| | - Cyrus Raji
- Department of Radiology, Washington University in St. Louis School of Medicine, St. Louis, Missouri, USA
| | - Ichiro Ikuta
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, USA
| | - Fatima Memon
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Brent D Weinberg
- Department of Radiology, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Mariam S Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
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12
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Aboian M, Bousabarah K, Kazarian E, Zeevi T, Holler W, Merkaj S, Cassinelli Petersen G, Bahar R, Subramanian H, Sunku P, Schrickel E, Bhawnani J, Zawalich M, Mahajan A, Malhotra A, Payabvash S, Tocino I, Lin M, Westerhoff M. Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction. Front Neurosci 2022; 16:860208. [PMID: 36312024 PMCID: PMC9606757 DOI: 10.3389/fnins.2022.860208] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 07/13/2022] [Indexed: 11/18/2022] Open
Abstract
Purpose Personalized interpretation of medical images is critical for optimum patient care, but current tools available to physicians to perform quantitative analysis of patient’s medical images in real time are significantly limited. In this work, we describe a novel platform within PACS for volumetric analysis of images and thus development of large expert annotated datasets in parallel with radiologist performing the reading that are critically needed for development of clinically meaningful AI algorithms. Specifically, we implemented a deep learning-based algorithm for automated brain tumor segmentation and radiomics extraction, and embedded it into PACS to accelerate a supervised, end-to- end workflow for image annotation and radiomic feature extraction. Materials and methods An algorithm was trained to segment whole primary brain tumors on FLAIR images from multi-institutional glioma BraTS 2021 dataset. Algorithm was validated using internal dataset from Yale New Haven Health (YHHH) and compared (by Dice similarity coefficient [DSC]) to radiologist manual segmentation. A UNETR deep-learning was embedded into Visage 7 (Visage Imaging, Inc., San Diego, CA, United States) diagnostic workstation. The automatically segmented brain tumor was pliable for manual modification. PyRadiomics (Harvard Medical School, Boston, MA) was natively embedded into Visage 7 for feature extraction from the brain tumor segmentations. Results UNETR brain tumor segmentation took on average 4 s and the median DSC was 86%, which is similar to published literature but lower than the RSNA ASNR MICCAI BRATS challenge 2021. Finally, extraction of 106 radiomic features within PACS took on average 5.8 ± 0.01 s. The extracted radiomic features did not vary over time of extraction or whether they were extracted within PACS or outside of PACS. The ability to perform segmentation and feature extraction before radiologist opens the study was made available in the workflow. Opening the study in PACS, allows the radiologists to verify the segmentation and thus annotate the study. Conclusion Integration of image processing algorithms for tumor auto-segmentation and feature extraction into PACS allows curation of large datasets of annotated medical images and can accelerate translation of research into development of personalized medicine applications in the clinic. The ability to use familiar clinical tools to revise the AI segmentations and natively embedding the segmentation and radiomic feature extraction tools on the diagnostic workstation accelerates the process to generate ground-truth data.
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Affiliation(s)
- Mariam Aboian
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
- *Correspondence: Mariam Aboian,
| | | | - Eve Kazarian
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Tal Zeevi
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT, United States
| | | | - Sara Merkaj
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Gabriel Cassinelli Petersen
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Ryan Bahar
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Harry Subramanian
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Pranay Sunku
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Elizabeth Schrickel
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Jitendra Bhawnani
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Mathew Zawalich
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Amit Mahajan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Ajay Malhotra
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Sam Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Irena Tocino
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - MingDe Lin
- Department of Radiology, Yale University and Visage Imaging, New Haven, CT, United States
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13
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A Systematic Review of the Current Status and Quality of Radiomics for Glioma Differential Diagnosis. Cancers (Basel) 2022; 14:cancers14112731. [PMID: 35681711 PMCID: PMC9179305 DOI: 10.3390/cancers14112731] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/26/2022] [Accepted: 05/30/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Gliomas can be difficult to discern clinically and radiologically from other brain lesions (either neoplastic or non-neoplastic) since their clinical manifestations as well as preoperative imaging features often overlap and appear misleading. Radiomics could be extremely helpful for non-invasive glioma differential diagnosis (DDx). However, implementation in clinical practice is still distant and concerns have been raised regarding the methodological quality of radiomic studies. In this context, we aimed to summarize the current status and quality of radiomic studies concerning glioma DDx in a systematic review. In total, 42 studies were selected and examined in our work. Our study revealed that, despite promising and encouraging results, current studies on radiomics for glioma DDx still lack the quality required to allow its introduction into clinical practice. This work could provide new insights and help to reach a consensus on the use of the radiomic approach for glioma DDx. Abstract Radiomics is a promising tool that may increase the value of imaging in differential diagnosis (DDx) of glioma. However, implementation in clinical practice is still distant and concerns have been raised regarding the methodological quality of radiomic studies. Therefore, we aimed to systematically review the current status of radiomic studies concerning glioma DDx, also using the radiomics quality score (RQS) to assess the quality of the methodology used in each study. A systematic literature search was performed to identify original articles focused on the use of radiomics for glioma DDx from 2015. Methodological quality was assessed using the RQS tool. Spearman’s correlation (ρ) analysis was performed to explore whether RQS was correlated with journal metrics and the characteristics of the studies. Finally, 42 articles were selected for the systematic qualitative analysis. Selected articles were grouped and summarized in terms of those on DDx between glioma and primary central nervous system lymphoma, those aiming at differentiating glioma from brain metastases, and those based on DDx of glioma and other brain diseases. Median RQS was 8.71 out 36, with a mean RQS of all studies of 24.21%. Our study revealed that, despite promising and encouraging results, current studies on radiomics for glioma DDx still lack the quality required to allow its introduction into clinical practice. This work could provide new insights and help to reach a consensus on the use of the radiomic approach for glioma DDx.
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