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Huang X, Huang Y, Liu K, Zhang F, Zhu Z, Xu K, Li P. Predicting prognosis for epithelial ovarian cancer patients receiving bevacizumab treatment with CT-based deep learning. NPJ Precis Oncol 2024; 8:202. [PMID: 39271912 PMCID: PMC11399346 DOI: 10.1038/s41698-024-00688-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 08/28/2024] [Indexed: 09/15/2024] Open
Abstract
Epithelial ovarian cancer (EOC) presents considerable difficulties in prognostication and treatment strategy development. Bevacizumab, an anti-angiogenic medication, has demonstrated potential in enhancing progression-free survival (PFS) in EOC patients. Nevertheless, the identification of individuals at elevated risk of disease progression following treatment remains a challenging task. This study was to develop and validate a deep learning (DL) model using retrospectively collected computed tomography (CT) plain scans of inoperable and recurrent EOC patients receiving bevacizumab treatment diagnosed between January 2013 and January 2024. A total of 525 patients from three different institutions were retrospectively included in the study and divided into training set (N = 400), internal test set (N = 97) and external test set (N = 28). The model's performance was evaluated using Harrell's C-index. Patients were categorized into high-risk and low-risk group based on a predetermined cutoff in the training set. Additionally, a multimodal model was evaluated, incorporating the risk score generated by the DL model and the pretreatment level of carbohydrate antigen 125 as input variables. The Net Reclassification Improvement (NRI) metric quantified the reclassification performance of our optimal model in comparison to the International Federation of Gynecology and Obstetrics (FIGO) staging model. The results indicated that DL model achieved a PFS predictive C-index of 0.73 in the internal test set and a C-index of 0.61 in the external test set, along with hazard ratios of 34.24 in the training set (95% CI: 21.7, 54.1; P < 0.001) and 8.16 in the internal test set (95% CI: 2.5, 26.8; P < 0.001). The multimodal model demonstrated a C-index of 0.76 in the internal test set and a C-index of 0.64 in the external test set. Comparative analysis against FIGO staging revealed an NRI of 0.06 (P < 0.001) for the multimodal model. The model presents opportunities for prognostic assessment, treatment strategizing, and ongoing patient monitoring.
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Affiliation(s)
- Xiaoyu Huang
- Department of Chinese Integrative Medicine Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yong Huang
- Department of Medical Oncology, The Second People's Hospital of Hefei, Hefei, China
| | - Kexin Liu
- Department of Chinese Integrative Medicine Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Fenglin Zhang
- Department of Chinese Integrative Medicine Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Zhou Zhu
- Scholl of Internet, Anhui university, Hefei, China
| | - Kai Xu
- Scholl of Internet, Anhui university, Hefei, China.
| | - Ping Li
- Department of Chinese Integrative Medicine Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
- Graduate School of Anhui University of Traditional Chinese Medicine, Hefei, China.
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2
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Cheng H, Xu H, Peng B, Huang X, Hu Y, Zheng C, Zhang Z. Illuminating the future of precision cancer surgery with fluorescence imaging and artificial intelligence convergence. NPJ Precis Oncol 2024; 8:196. [PMID: 39251820 PMCID: PMC11385925 DOI: 10.1038/s41698-024-00699-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 08/29/2024] [Indexed: 09/11/2024] Open
Abstract
Real-time and accurate guidance for tumor resection has long been anticipated by surgeons. In the past decade, the flourishing material science has made impressive progress in near-infrared fluorophores that may fulfill this purpose. Fluorescence imaging-guided surgery shows great promise for clinical application and has undergone widespread evaluations, though it still requires continuous improvements to transition this technique from bench to bedside. Concurrently, the rapid progress of artificial intelligence (AI) has revolutionized medicine, aiding in the screening, diagnosis, and treatment of human doctors. Incorporating AI helps enhance fluorescence imaging and is poised to bring major innovations to surgical guidance, thereby realizing precision cancer surgery. This review provides an overview of the principles and clinical evaluations of fluorescence-guided surgery. Furthermore, recent endeavors to synergize AI with fluorescence imaging were presented, and the benefits of this interdisciplinary convergence were discussed. Finally, several implementation strategies to overcome technical hurdles were proposed to encourage and inspire future research to expedite the clinical application of these revolutionary technologies.
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Affiliation(s)
- Han Cheng
- Department of Oral and Maxillofacial-Head & Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, P. R. China
- College of Stomatology, Shanghai Jiao Tong University & National Center for Stomatology, Shanghai, 200011, P. R. China
- National Clinical Research Center for Oral Diseases & Shanghai Key Laboratory of Stomatology, Shanghai, 200011, P. R. China
- Shanghai Research Institute of Stomatology, Shanghai, 200011, P. R. China
- Research Unit of Oral and Maxillofacial Regenerative Medicine, Chinese Academy of Medical Sciences, Shanghai, 200011, P. R. China
| | - Hongtao Xu
- Department of Oral and Maxillofacial-Head & Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, P. R. China
- College of Stomatology, Shanghai Jiao Tong University & National Center for Stomatology, Shanghai, 200011, P. R. China
- National Clinical Research Center for Oral Diseases & Shanghai Key Laboratory of Stomatology, Shanghai, 200011, P. R. China
- Shanghai Research Institute of Stomatology, Shanghai, 200011, P. R. China
- Research Unit of Oral and Maxillofacial Regenerative Medicine, Chinese Academy of Medical Sciences, Shanghai, 200011, P. R. China
| | - Boyang Peng
- School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
| | - Xiaojuan Huang
- Department of Oral and Maxillofacial-Head & Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, P. R. China
- College of Stomatology, Shanghai Jiao Tong University & National Center for Stomatology, Shanghai, 200011, P. R. China
- National Clinical Research Center for Oral Diseases & Shanghai Key Laboratory of Stomatology, Shanghai, 200011, P. R. China
- Shanghai Research Institute of Stomatology, Shanghai, 200011, P. R. China
- Research Unit of Oral and Maxillofacial Regenerative Medicine, Chinese Academy of Medical Sciences, Shanghai, 200011, P. R. China
| | - Yongjie Hu
- Department of Oral and Maxillofacial-Head & Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, P. R. China
- College of Stomatology, Shanghai Jiao Tong University & National Center for Stomatology, Shanghai, 200011, P. R. China
- National Clinical Research Center for Oral Diseases & Shanghai Key Laboratory of Stomatology, Shanghai, 200011, P. R. China
- Shanghai Research Institute of Stomatology, Shanghai, 200011, P. R. China
- Research Unit of Oral and Maxillofacial Regenerative Medicine, Chinese Academy of Medical Sciences, Shanghai, 200011, P. R. China
| | - Chongyang Zheng
- Department of Oral and Maxillofacial-Head & Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, P. R. China.
- College of Stomatology, Shanghai Jiao Tong University & National Center for Stomatology, Shanghai, 200011, P. R. China.
- National Clinical Research Center for Oral Diseases & Shanghai Key Laboratory of Stomatology, Shanghai, 200011, P. R. China.
- Shanghai Research Institute of Stomatology, Shanghai, 200011, P. R. China.
- Research Unit of Oral and Maxillofacial Regenerative Medicine, Chinese Academy of Medical Sciences, Shanghai, 200011, P. R. China.
| | - Zhiyuan Zhang
- Department of Oral and Maxillofacial-Head & Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, P. R. China.
- College of Stomatology, Shanghai Jiao Tong University & National Center for Stomatology, Shanghai, 200011, P. R. China.
- National Clinical Research Center for Oral Diseases & Shanghai Key Laboratory of Stomatology, Shanghai, 200011, P. R. China.
- Shanghai Research Institute of Stomatology, Shanghai, 200011, P. R. China.
- Research Unit of Oral and Maxillofacial Regenerative Medicine, Chinese Academy of Medical Sciences, Shanghai, 200011, P. R. China.
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3
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Massaad E, Smith WJ, Bradley J, Esposito E, Gupta M, Burns E, Burns R, Velarde JK, Berglar IK, Gupta R, Martinez-Lage M, Dietrich J, Lennerz JK, Dunn GP, Jones PS, Choi BD, Kim AE, Frosch M, Barker FG, Curry WT, Carter BS, Nahed BV, Cahill DP, Shankar GM. Radical surgical resection with molecular margins is associated with improved survival in IDH wild-type glioblastoma. Neuro Oncol 2024; 26:1660-1669. [PMID: 38581292 PMCID: PMC11376447 DOI: 10.1093/neuonc/noae073] [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: 01/29/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND Survival is variable in patients with glioblastoma IDH wild-type (GBM), even after comparable surgical resection of radiographically detectable disease, highlighting the limitations of radiographic assessment of infiltrative tumor anatomy. The majority of postsurgical progressive events are failures within 2 cm of the resection margin, motivating supramaximal resection strategies to improve local control. However, which patients benefit from such radical resections remains unknown. METHODS We developed a predictive model to identify which IDH wild-type GBMs are amenable to radiographic gross-total resection (GTR). We then investigated whether GBM survival heterogeneity following GTR is correlated with microscopic tumor burden by analyzing tumor cell content at the surgical margin with a rapid qPCR-based method for detection of TERT promoter mutation. RESULTS Our predictive model for achievable GTR, developed on retrospective radiographic and molecular data of GBM patients undergoing resection, had an area under the curve of 0.83, sensitivity of 62%, and specificity of 90%. Prospective analysis of this model in 44 patients found that 89% of patients were correctly predicted to achieve a residual volume (RV) < 4.9cc. Of the 44 prospective patients undergoing rapid qPCR TERT promoter mutation analysis at the surgical margin, 7 had undetectable TERT mutation, of which 5 also had a GTR (RV < 1cc). In these 5 patients at 30 months follow-up, 75% showed no progression, compared to 0% in the group with TERT mutations detected at the surgical margin (P = .02). CONCLUSIONS These findings identify a subset of patients with GBM that may derive local control benefits from radical resection to undetectable molecular margins.
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Affiliation(s)
- Elie Massaad
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - William J Smith
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Joseph Bradley
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Eric Esposito
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Mihir Gupta
- Department of Neurosurgery, Yale New Heaven Health, New Haven, Connecticut, USA
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Evan Burns
- Jacobs School of Medicine, University of Buffalo, Buffalo, New York, USA
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Ryan Burns
- Department of Biology, Boston College, Newton, Massachusetts, USA
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - José K Velarde
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Inka K Berglar
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Rajiv Gupta
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Maria Martinez-Lage
- Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jorg Dietrich
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jochen K Lennerz
- Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Gavin P Dunn
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Pamela S Jones
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Bryan D Choi
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Albert E Kim
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Matthew Frosch
- Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Fred G Barker
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - William T Curry
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Bob S Carter
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Brian V Nahed
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Daniel P Cahill
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Ganesh M Shankar
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA
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4
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Reinecke D, Ruess D, Meissner AK, Fürtjes G, von Spreckelsen N, Ion-Margineanu A, Khalid F, Blau T, Stehle T, Al-Shugri A, Büttner R, Goldbrunner R, Ruge MI, Neuschmelting V. Streamlined Intraoperative Brain Tumor Classification and Molecular Subtyping in Stereotactic Biopsies Using Stimulated Raman Histology and Deep Learning. Clin Cancer Res 2024; 30:3824-3836. [PMID: 38976016 DOI: 10.1158/1078-0432.ccr-23-3842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 04/25/2024] [Accepted: 07/03/2024] [Indexed: 07/09/2024]
Abstract
PURPOSE Recent artificial intelligence algorithms aided intraoperative decision-making via stimulated Raman histology (SRH) during craniotomy. This study assesses deep learning algorithms for rapid intraoperative diagnosis from SRH images in small stereotactic-guided brain biopsies. It defines a minimum tissue sample size threshold to ensure diagnostic accuracy. EXPERIMENTAL DESIGN A prospective single-center study examined 121 SRH images from 84 patients with unclear intracranial lesions undergoing stereotactic brain biopsy. Unprocessed, label-free samples were imaged using a portable fiber laser Raman scattering microscope. Three deep learning models were tested to (i) identify tumorous/nontumorous tissue as qualitative biopsy control; (ii) subclassify into high-grade glioma (central nervous system World Health Organization grade 4), diffuse low-grade glioma (central nervous system World Health Organization grades 2-3), metastases, lymphoma, or gliosis; and (iii) molecularly subtype IDH and 1p/19q statuses of adult-type diffuse gliomas. Model predictions were evaluated against frozen section analysis and final neuropathologic diagnoses. RESULTS The first model identified tumorous/nontumorous tissue with 91.7% accuracy. Sample size on slides impacted accuracy in brain tumor subclassification (81.6%, κ = 0.72 frozen section; 73.9%, κ = 0.61 second model), with SRH images being smaller than hematoxylin and eosin images (4.1 ± 2.5 mm2 vs. 16.7 ± 8.2 mm2, P < 0.001). SRH images with more than 140 high-quality patches and a mean squeezed sample of 5.26 mm2 yielded 89.5% accuracy in subclassification and 93.9% in molecular subtyping of adult-type diffuse gliomas. CONCLUSIONS Artificial intelligence-based SRH image analysis is non-inferior to frozen section analysis in detecting and subclassifying brain tumors during small stereotactic-guided biopsies once a critical squeezed sample size is reached. Beyond frozen section analysis, it enables valid molecular glioma subtyping, allowing faster treatment decisions in the future; however, refinement is needed for long-term application.
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Affiliation(s)
- David Reinecke
- Department of Stereotactic and Functional Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Department of General Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Daniel Ruess
- Department of Stereotactic and Functional Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Centre for Integrated Oncology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Anna-Katharina Meissner
- Department of General Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Gina Fürtjes
- Department of General Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Niklas von Spreckelsen
- Department of General Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | | | | | - Tobias Blau
- Institute for Neuropathology, University of Essen, Essen, Germany
| | - Thomas Stehle
- Institute for Neuropathology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Abdulkader Al-Shugri
- Institute for Neuropathology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Reinhard Büttner
- Institute for Neuropathology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Institute of General Pathology and Pathological Anatomy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Roland Goldbrunner
- Department of General Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Centre for Integrated Oncology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Maximilian I Ruge
- Department of Stereotactic and Functional Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Centre for Integrated Oncology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Volker Neuschmelting
- Department of General Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Centre for Integrated Oncology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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5
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Reinecke D, Maroouf N, Smith A, Alber D, Markert J, Goff NK, Hollon TC, Chowdury A, Jiang C, Hou X, Meissner AK, Fürtjes G, Ruge MI, Ruess D, Stehle T, Al-Shughri A, Körner LI, Widhalm G, Roetzer-Pejrimovsky T, Golfinos JG, Snuderl M, Neuschmelting V, Orringer DA. Fast intraoperative detection of primary CNS lymphoma and differentiation from common CNS tumors using stimulated Raman histology and deep learning. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.25.24312509. [PMID: 39252932 PMCID: PMC11383472 DOI: 10.1101/2024.08.25.24312509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Accurate intraoperative diagnosis is crucial for differentiating between primary CNS lymphoma (PCNSL) and other CNS entities, guiding surgical decision-making, but represents significant challenges due to overlapping histomorphological features, time constraints, and differing treatment strategies. We combined stimulated Raman histology (SRH) with deep learning to address this challenge. We imaged unprocessed, label-free tissue samples intraoperatively using a portable Raman scattering microscope, generating virtual H&E-like images within less than three minutes. We developed a deep learning pipeline called RapidLymphoma based on a self-supervised learning strategy to (1) detect PCNSL, (2) differentiate from other CNS entities, and (3) test the diagnostic performance in a prospective international multicenter cohort and two additional independent test cohorts. We trained on 54,000 SRH patch images sourced from surgical resections and stereotactic-guided biopsies, including various CNS tumor/non-tumor lesions. Training and test data were collected from four tertiary international medical centers. The final histopathological diagnosis served as ground-truth. In the prospective test cohort of PCNSL and non-PCNSL entities (n=160), RapidLymphoma achieved an overall balanced accuracy of 97.81% ±0.91, non-inferior to frozen section analysis in detecting PCNSL (100% vs. 78.94%). The additional test cohorts (n=420, n=59) reached balanced accuracy rates of 95.44% ±0.74 and 95.57% ±2.47 in differentiating IDH-wildtype diffuse gliomas and various brain metastasis from PCNSL. Visual heatmaps revealed RapidLymphoma's capabilities to detect class-specific histomorphological key features. RapidLymphoma is valid and reliable in detecting PCNSL and differentiating from other CNS entities within three minutes, as well as visual feedback in an intraoperative setting. This leads to fast clinical decision-making and further treatment strategy planning.
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Affiliation(s)
- David Reinecke
- Department of Neurosurgery, New York University Grossman School of Medicine, New York, USA
- Department of General Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Nader Maroouf
- Department of Neurosurgery, New York University Grossman School of Medicine, New York, USA
| | - Andrew Smith
- Department of Neurosurgery, New York University Grossman School of Medicine, New York, USA
| | - Daniel Alber
- Department of Neurosurgery, New York University Grossman School of Medicine, New York, USA
| | - John Markert
- Department of Neurosurgery, New York University Grossman School of Medicine, New York, USA
- Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, USA
| | - Nicolas K Goff
- Department of Neurosurgery, New York University Grossman School of Medicine, New York, USA
- Department of Neurosurgery, University of Texas at Austin Dell Medical School, Austin, USA
| | - Todd C Hollon
- Machine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, Ann Arbor, USA
| | - Asadur Chowdury
- Machine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, Ann Arbor, USA
| | - Cheng Jiang
- Machine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, Ann Arbor, USA
| | - Xinhai Hou
- Machine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, Ann Arbor, USA
| | - Anna-Katharina Meissner
- Department of General Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Gina Fürtjes
- Department of General Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Maximilian I Ruge
- Center for Integrated Oncology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Department of Stereotactic and Functional Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Daniel Ruess
- Center for Integrated Oncology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Department of Stereotactic and Functional Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Thomas Stehle
- Institute for Neuropathology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Abdulkader Al-Shughri
- Institute for Neuropathology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Lisa I Körner
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Georg Widhalm
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Thomas Roetzer-Pejrimovsky
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences & Mental Health, Medical University of Vienna, Vienna, Austria
| | - John G Golfinos
- Department of Neurosurgery, New York University Grossman School of Medicine, New York, USA
| | - Matija Snuderl
- Department of Pathology, New York Grossman School of Medicine, New York, USA
| | - Volker Neuschmelting
- Department of General Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Integrated Oncology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Daniel A Orringer
- Department of Neurosurgery, New York University Grossman School of Medicine, New York, USA
- Department of Pathology, New York Grossman School of Medicine, New York, USA
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6
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Mehari M, Sibih Y, Dada A, Chang SM, Wen PY, Molinaro AM, Chukwueke UN, Budhu JA, Jackson S, McFaline-Figueroa JR, Porter A, Hervey-Jumper SL. Enhancing neuro-oncology care through equity-driven applications of artificial intelligence. Neuro Oncol 2024:noae127. [PMID: 39159285 DOI: 10.1093/neuonc/noae127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/21/2024] Open
Abstract
The disease course and clinical outcome for brain tumor patients depend not only on the molecular and histological features of the tumor but also on the patient's demographics and social determinants of health. While current investigations in neuro-oncology have broadly utilized artificial intelligence (AI) to enrich tumor diagnosis and more accurately predict treatment response, postoperative complications, and survival, equity-driven applications of AI have been limited. However, AI applications to advance health equity in the broader medical field have the potential to serve as practical blueprints to address known disparities in neuro-oncologic care. In this consensus review, we will describe current applications of AI in neuro-oncology, postulate viable AI solutions for the most pressing inequities in neuro-oncology based on broader literature, propose a framework for the effective integration of equity into AI-based neuro-oncology research, and close with the limitations of AI.
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Affiliation(s)
- Mulki Mehari
- Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA
| | - Youssef Sibih
- Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA
| | - Abraham Dada
- Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA
| | - Susan M Chang
- Division of Neuro-Oncology, University of California San Francisco and Weill Institute for Neurosciences, San Francisco, California, USA
| | - Patrick Y Wen
- Center for Neuro-Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Annette M Molinaro
- Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA
| | - Ugonma N Chukwueke
- Center for Neuro-Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Joshua A Budhu
- Department of Neurology, Memorial Sloan Kettering Cancer Center, Department of Neurology, Weill Cornell Medicine, Joan & Sanford I. Weill Medical College of Cornell University, New York, New York, USA
| | - Sadhana Jackson
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, Pediatric Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - J Ricardo McFaline-Figueroa
- Center for Neuro-Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Alyx Porter
- Division of Neuro-Oncology, Department of Neurology, Mayo Clinic, Phoenix, Arizona, USA
| | - Shawn L Hervey-Jumper
- Department of Neurosurgery, University of California, San Francisco, San Francisco, California, USA
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7
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Wu Z, Yang Y, Chen M, Zha Y. Matrix metalloproteinase 9 expression and glioblastoma survival prediction using machine learning on digital pathological images. Sci Rep 2024; 14:15065. [PMID: 38956384 PMCID: PMC11220146 DOI: 10.1038/s41598-024-66105-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: 04/18/2024] [Accepted: 06/27/2024] [Indexed: 07/04/2024] Open
Abstract
This study aimed to apply pathomics to predict Matrix metalloproteinase 9 (MMP9) expression in glioblastoma (GBM) and investigate the underlying molecular mechanisms associated with pathomics. Here, we included 127 GBM patients, 78 of whom were randomly allocated to the training and test cohorts for pathomics modeling. The prognostic significance of MMP9 was assessed using Kaplan-Meier and Cox regression analyses. PyRadiomics was used to extract the features of H&E-stained whole slide images. Feature selection was performed using the maximum relevance and minimum redundancy (mRMR) and recursive feature elimination (RFE) algorithms. Prediction models were created using support vector machines (SVM) and logistic regression (LR). The performance was assessed using ROC analysis, calibration curve assessment, and decision curve analysis. MMP9 expression was elevated in patients with GBM. This was an independent prognostic factor for GBM. Six features were selected for the pathomics model. The area under the curves (AUCs) of the training and test subsets were 0.828 and 0.808, respectively, for the SVM model and 0.778 and 0.754, respectively, for the LR model. The C-index and calibration plots exhibited effective estimation abilities. The pathomics score calculated using the SVM model was highly correlated with overall survival time. These findings indicate that MMP9 plays a crucial role in GBM development and prognosis. Our pathomics model demonstrated high efficacy for predicting MMP9 expression levels and prognosis of patients with GBM.
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Affiliation(s)
- Zijun Wu
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430000, China
| | - Yuan Yang
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430000, China
| | - Maojuan Chen
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430000, China
| | - Yunfei Zha
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430000, China.
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8
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Jin Y, Kondov B, Kondov G, Singhal S, Nie S, Gruev V. Convolutional neural network advances in demosaicing for fluorescent cancer imaging with color-near-infrared sensors. JOURNAL OF BIOMEDICAL OPTICS 2024; 29:076005. [PMID: 39045222 PMCID: PMC11265532 DOI: 10.1117/1.jbo.29.7.076005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 06/14/2024] [Accepted: 06/17/2024] [Indexed: 07/25/2024]
Abstract
Significance Single-chip imaging devices featuring vertically stacked photodiodes and pixelated spectral filters are advancing multi-dye imaging methods for cancer surgeries, though this innovation comes with a compromise in spatial resolution. To mitigate this drawback, we developed a deep convolutional neural network (CNN) aimed at demosaicing the color and near-infrared (NIR) channels, with its performance validated on both pre-clinical and clinical datasets. Aim We introduce an optimized deep CNN designed for demosaicing both color and NIR images obtained using a hexachromatic imaging sensor. Approach A residual CNN was fine-tuned and trained on a dataset of color images and subsequently assessed on a series of dual-channel, color, and NIR images to demonstrate its enhanced performance compared with traditional bilinear interpolation. Results Our optimized CNN for demosaicing color and NIR images achieves a reduction in the mean square error by 37% for color and 40% for NIR, respectively, and enhances the structural dissimilarity index by 37% across both imaging modalities in pre-clinical data. In clinical datasets, the network improves the mean square error by 35% in color images and 42% in NIR images while enhancing the structural dissimilarity index by 39% in both imaging modalities. Conclusions We showcase enhancements in image resolution for both color and NIR modalities through the use of an optimized CNN tailored for a hexachromatic image sensor. With the ongoing advancements in graphics card computational power, our approach delivers significant improvements in resolution that are feasible for real-time execution in surgical environments.
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Affiliation(s)
- Yifei Jin
- University of Illinois at Urbana-Champaign, Department of Electrical and Computer Engineering, Urbana, Illinois, United States
| | - Borislav Kondov
- Ss. Cyril and Methodius University of Skopje, Department of Thoracic and Vascular Surgery, Skopje, North Macedonia
| | - Goran Kondov
- Ss. Cyril and Methodius University of Skopje, Department of Thoracic and Vascular Surgery, Skopje, North Macedonia
| | - Sunil Singhal
- University of Pennsylvania, Perelman School of Medicine, Department of Thoracic Surgery, Philadelphia, Pennsylvania, United States
| | - Shuming Nie
- University of Illinois at Urbana-Champaign, Department of Electrical and Computer Engineering, Urbana, Illinois, United States
- University of Illinois at Urbana-Champaign, Department of Bioengineering, Urbana, Illinois, United States
- University of Illinois at Urbana-Champaign, Beckman Institute for Advanced Science and Technology, Urbana, Illinois, United States
| | - Viktor Gruev
- University of Illinois at Urbana-Champaign, Department of Electrical and Computer Engineering, Urbana, Illinois, United States
- University of Illinois at Urbana-Champaign, Department of Bioengineering, Urbana, Illinois, United States
- University of Illinois at Urbana-Champaign, Beckman Institute for Advanced Science and Technology, Urbana, Illinois, United States
- University of Illinois at Urbana-Champaign, Carle Illinois College of Medicine, Urbana, Illinois, United States
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9
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Yu D, Zhong Q, Xiao Y, Feng Z, Tang F, Feng S, Cai Y, Gao Y, Lan T, Li M, Yu F, Wang Z, Gao X, Li Z. Combination of MRI-based prediction and CRISPR/Cas12a-based detection for IDH genotyping in glioma. NPJ Precis Oncol 2024; 8:140. [PMID: 38951603 PMCID: PMC11217299 DOI: 10.1038/s41698-024-00632-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 05/30/2024] [Indexed: 07/03/2024] Open
Abstract
Early identification of IDH mutation status is of great significance in clinical therapeutic decision-making in the treatment of glioma. We demonstrate a technological solution to improve the accuracy and reliability of IDH mutation detection by combining MRI-based prediction and a CRISPR-based automatic integrated gene detection system (AIGS). A model was constructed to predict the IDH mutation status using whole slices in MRI scans with a Transformer neural network, and the predictive model achieved accuracies of 0.93, 0.87, and 0.84 using the internal and two external test sets, respectively. Additionally, CRISPR/Cas12a-based AIGS was constructed, and AIGS achieved 100% diagnostic accuracy in terms of IDH detection using both frozen tissue and FFPE samples in one hour. Moreover, the feature attribution of our predictive model was assessed using GradCAM, and the highest correlations with tumor cell percentages in enhancing and IDH-wildtype gliomas were found to have GradCAM importance (0.65 and 0.5, respectively). This MRI-based predictive model could, therefore, guide biopsy for tumor-enriched, which would ensure the veracity and stability of the rapid detection results. The combination of our predictive model and AIGS improved the early determination of IDH mutation status in glioma patients. This combined system of MRI-based prediction and CRISPR/Cas12a-based detection can be used to guide biopsy, resection, and radiation for glioma patients to improve patient outcomes.
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Affiliation(s)
- Donghu Yu
- Brain Glioma Center & Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Qisheng Zhong
- Department of Neurosurgery, 960 Hospital of PLA, Jinan, Shandong, China
| | - Yilei Xiao
- Department of Neurosurgery, Liaocheng People's Hospital, Liaocheng, China
| | - Zhebin Feng
- Department of Neurosurgery, PLA General Hospital, Beijing, China
| | - Feng Tang
- Brain Glioma Center & Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Shiyu Feng
- Department of Neurosurgery, PLA General Hospital, Beijing, China
| | - Yuxiang Cai
- Department of Pathology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yutong Gao
- Department of Prosthodontics, Wuhan University Hospital of Stomatology, Wuhan, China
| | - Tian Lan
- Brain Glioma Center & Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Mingjun Li
- Department of Radiology, Liaocheng People's Hospital, Liaocheng, China
| | - Fuhua Yu
- Department of Neurosurgery, Liaocheng People's Hospital, Liaocheng, China
| | - Zefen Wang
- Department of Physiology, Wuhan University School of Basic Medical Sciences, Wuhan, China.
| | - Xu Gao
- Department of Neurosurgery, General Hospital of Northern Theater Command, Shenyang, China.
| | - Zhiqiang Li
- Brain Glioma Center & Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China.
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10
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Sarri B, Chevrier V, Poizat F, Heuke S, Franchi F, De Franqueville L, Traversari E, Ratone JP, Caillol F, Dahel Y, Hoibian S, Giovannini M, de Chaisemartin C, Appay R, Guasch G, Rigneault H. In vivo organoid growth monitoring by stimulated Raman histology. NPJ IMAGING 2024; 2:18. [PMID: 38948153 PMCID: PMC11213706 DOI: 10.1038/s44303-024-00019-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 05/21/2024] [Indexed: 07/02/2024]
Abstract
Patient-derived tumor organoids have emerged as a crucial tool for assessing the efficacy of chemotherapy and conducting preclinical drug screenings. However, the conventional histological investigation of these organoids necessitates their devitalization through fixation and slicing, limiting their utility to a single-time analysis. Here, we use stimulated Raman histology (SRH) to demonstrate non-destructive, label-free virtual staining of 3D organoids, while preserving their viability and growth. This novel approach provides contrast similar to conventional staining methods, allowing for the continuous monitoring of organoids over time. Our results demonstrate that SRH transforms organoids from one-time use products into repeatable models, facilitating the efficient selection of effective drug combinations. This advancement holds promise for personalized cancer treatment, allowing for the dynamic assessment and optimization of chemotherapy treatments in patient-specific contexts.
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Affiliation(s)
- Barbara Sarri
- Aix Marseille Univ, CNRS, Centrale Med, Institut Fresnel, Marseille, France
- Ligthcore Technologies, Marseille, France
| | - Véronique Chevrier
- CRCM, Inserm, CNRS, Institut Paoli-Calmettes, Aix-Marseille Univ, Epithelial Stem Cells and Cancer Lab, Marseille, France
| | - Flora Poizat
- Department of Biopathology, Institut Paoli-Calmettes, Marseille, France
| | - Sandro Heuke
- Aix Marseille Univ, CNRS, Centrale Med, Institut Fresnel, Marseille, France
| | - Florence Franchi
- Department of Biopathology, Institut Paoli-Calmettes, Marseille, France
| | | | - Eddy Traversari
- Department of Surgical Oncology, Institut Paoli-Calmette, Marseille, France
| | | | - Fabrice Caillol
- Department of Gastro-enterology, Institut Paoli-Calmettes, Marseille, France
| | - Yanis Dahel
- Department of Gastro-enterology, Institut Paoli-Calmettes, Marseille, France
| | - Solène Hoibian
- Department of Gastro-enterology, Institut Paoli-Calmettes, Marseille, France
| | - Marc Giovannini
- Department of Gastro-enterology, Institut Paoli-Calmettes, Marseille, France
| | | | - Romain Appay
- Aix- Marseille Univ, CNRS, Neurophysiopathology Institute, Marseille, France
| | - Géraldine Guasch
- CRCM, Inserm, CNRS, Institut Paoli-Calmettes, Aix-Marseille Univ, Epithelial Stem Cells and Cancer Lab, Marseille, France
| | - Hervé Rigneault
- Aix Marseille Univ, CNRS, Centrale Med, Institut Fresnel, Marseille, France
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11
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Chow DJX, Tan TCY, Upadhya A, Lim M, Dholakia K, Dunning KR. Viewing early life without labels: optical approaches for imaging the early embryo†. Biol Reprod 2024; 110:1157-1174. [PMID: 38647415 PMCID: PMC11180623 DOI: 10.1093/biolre/ioae062] [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: 01/28/2024] [Revised: 03/26/2024] [Accepted: 04/18/2024] [Indexed: 04/25/2024] Open
Abstract
Embryo quality is an important determinant of successful implantation and a resultant live birth. Current clinical approaches for evaluating embryo quality rely on subjective morphology assessments or an invasive biopsy for genetic testing. However, both approaches can be inherently inaccurate and crucially, fail to improve the live birth rate following the transfer of in vitro produced embryos. Optical imaging offers a potential non-invasive and accurate avenue for assessing embryo viability. Recent advances in various label-free optical imaging approaches have garnered increased interest in the field of reproductive biology due to their ability to rapidly capture images at high resolution, delivering both morphological and molecular information. This burgeoning field holds immense potential for further development, with profound implications for clinical translation. Here, our review aims to: (1) describe the principles of various imaging systems, distinguishing between approaches that capture morphological and molecular information, (2) highlight the recent application of these technologies in the field of reproductive biology, and (3) assess their respective merits and limitations concerning the capacity to evaluate embryo quality. Additionally, the review summarizes challenges in the translation of optical imaging systems into routine clinical practice, providing recommendations for their future development. Finally, we identify suitable imaging approaches for interrogating the mechanisms underpinning successful embryo development.
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Affiliation(s)
- Darren J X Chow
- Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
- Centre of Light for Life, The University of Adelaide, Adelaide, Australia
| | - Tiffany C Y Tan
- Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
| | - Avinash Upadhya
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
- Centre of Light for Life, The University of Adelaide, Adelaide, Australia
- School of Biological Sciences, The University of Adelaide, Adelaide, Australia
| | - Megan Lim
- Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
- Centre of Light for Life, The University of Adelaide, Adelaide, Australia
- School of Biological Sciences, The University of Adelaide, Adelaide, Australia
| | - Kishan Dholakia
- Centre of Light for Life, The University of Adelaide, Adelaide, Australia
- School of Biological Sciences, The University of Adelaide, Adelaide, Australia
- Scottish Universities Physics Alliance, School of Physics and Astronomy, University of St Andrews, St Andrews, United Kingdom
| | - Kylie R Dunning
- Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
- Centre of Light for Life, The University of Adelaide, Adelaide, Australia
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12
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Raghunathan R, Vasquez M, Zhang K, Zhao H, Wong STC. Label-free optical imaging for brain cancer assessment. Trends Cancer 2024; 10:557-570. [PMID: 38575412 PMCID: PMC11168891 DOI: 10.1016/j.trecan.2024.03.005] [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: 01/01/2024] [Revised: 03/12/2024] [Accepted: 03/13/2024] [Indexed: 04/06/2024]
Abstract
Advances in label-free optical imaging offer a promising avenue for brain cancer assessment, providing high-resolution, real-time insights without the need for radiation or exogeneous agents. These cost-effective and intricately detailed techniques overcome the limitations inherent in magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) scans by offering superior resolution and more readily accessible imaging options. This comprehensive review explores a variety of such methods, including photoacoustic imaging (PAI), optical coherence tomography (OCT), Raman imaging, and IR microscopy. It focuses on their roles in the detection, diagnosis, and management of brain tumors. By highlighting recent advances in these imaging techniques, the review aims to underscore the importance of label-free optical imaging in enhancing early detection and refining therapeutic strategies for brain cancer.
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Affiliation(s)
- Raksha Raghunathan
- Department of Systems Medicine and Bioengineering and T.T. and W.F. Chao Center for BRAIN, Houston Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, TX 77030, USA; Advanced Cellular and Tissue Microscopy Core, Houston Methodist Neal Cancer Center and Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Matthew Vasquez
- Department of Systems Medicine and Bioengineering and T.T. and W.F. Chao Center for BRAIN, Houston Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, TX 77030, USA; Advanced Cellular and Tissue Microscopy Core, Houston Methodist Neal Cancer Center and Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Katherine Zhang
- Department of Systems Medicine and Bioengineering and T.T. and W.F. Chao Center for BRAIN, Houston Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, TX 77030, USA; Advanced Cellular and Tissue Microscopy Core, Houston Methodist Neal Cancer Center and Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Hong Zhao
- Department of Systems Medicine and Bioengineering and T.T. and W.F. Chao Center for BRAIN, Houston Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, TX 77030, USA; Advanced Cellular and Tissue Microscopy Core, Houston Methodist Neal Cancer Center and Houston Methodist Research Institute, Houston, TX 77030, USA; Department of Medicine, Weill Cornell Medicine, New York, NY 10065, USA.
| | - Stephen T C Wong
- Department of Systems Medicine and Bioengineering and T.T. and W.F. Chao Center for BRAIN, Houston Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, TX 77030, USA; Advanced Cellular and Tissue Microscopy Core, Houston Methodist Neal Cancer Center and Houston Methodist Research Institute, Houston, TX 77030, USA; Departments of Radiology, Pathology, and Laboratory Medicine and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
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13
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Jiang C, Gedeon A, Lyu Y, Landgraf E, Zhang Y, Hou X, Kondepudi A, Chowdury A, Lee H, Hollon T. Super-resolution of biomedical volumes with 2D supervision. CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS. IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION. WORKSHOPS 2024; 2024:6966-6977. [PMID: 39355755 PMCID: PMC11444667 DOI: 10.1109/cvprw63382.2024.00690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2024]
Abstract
Volumetric biomedical microscopy has the potential to increase the diagnostic information extracted from clinical tissue specimens and improve the diagnostic accuracy of both human pathologists and computational pathology models. Unfortunately, barriers to integrating 3-dimensional (3D) volumetric microscopy into clinical medicine include long imaging times, poor depth/z-axis resolution, and an insufficient amount of high-quality volumetric data. Leveraging the abundance of high-resolution 2D microscopy data, we introduce masked slice diffusion for super-resolution (MSDSR), which exploits the inherent equivalence in the data-generating distribution across all spatial dimensions of biological specimens. This intrinsic characteristic allows for super-resolution models trained on high-resolution images from one plane (e.g., XY) to effectively generalize to others (XZ, YZ), overcoming the traditional dependency on orientation. We focus on the application of MSDSR to stimulated Raman histology (SRH), an optical imaging modality for biological specimen analysis and intraoperative diagnosis, characterized by its rapid acquisition of high-resolution 2D images but slow and costly optical z-sectioning. To evaluate MSDSR's efficacy, we introduce a new performance metric, SliceFID, and demonstrate MSDSR's superior performance over baseline models through extensive evaluations. Our findings reveal that MSDSR not only significantly enhances the quality and resolution of 3D volumetric data, but also addresses major obstacles hindering the broader application of 3D volumetric microscopy in clinical diagnostics and biomedical research.
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14
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Liu C, Wang J, Shen J, Chen X, Ji N, Yue S. Accurate and rapid molecular subgrouping of high-grade glioma via deep learning-assisted label-free fiber-optic Raman spectroscopy. PNAS NEXUS 2024; 3:pgae208. [PMID: 38860145 PMCID: PMC11164103 DOI: 10.1093/pnasnexus/pgae208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 05/17/2024] [Indexed: 06/12/2024]
Abstract
Molecular genetics is highly related with prognosis of high-grade glioma. Accordingly, the latest WHO guideline recommends that molecular subgroups of the genes, including IDH, 1p/19q, MGMT, TERT, EGFR, Chromosome 7/10, CDKN2A/B, need to be detected to better classify glioma and guide surgery and treatment. Unfortunately, there is no preoperative or intraoperative technology available for accurate and comprehensive molecular subgrouping of glioma. Here, we develop a deep learning-assisted fiber-optic Raman diagnostic platform for accurate and rapid molecular subgrouping of high-grade glioma. Specifically, a total of 2,354 fingerprint Raman spectra was obtained from 743 tissue sites (astrocytoma: 151; oligodendroglioma: 150; glioblastoma (GBM): 442) of 44 high-grade glioma patients. The convolutional neural networks (ResNet) model was then established and optimized for molecular subgrouping. The mean area under receiver operating characteristic curves (AUC) for identifying the molecular subgroups of high-grade glioma reached 0.904, with mean sensitivity of 83.3%, mean specificity of 85.0%, mean accuracy of 83.3%, and mean time expense of 10.6 s. The diagnosis performance using ResNet model was shown to be superior to PCA-SVM and UMAP models, suggesting that high dimensional information from Raman spectra would be helpful. In addition, for the molecular subgroups of GBM, the mean AUC reached 0.932, with mean sensitivity of 87.8%, mean specificity of 83.6%, and mean accuracy of 84.1%. Furthermore, according to saliency maps, the specific Raman features corresponding to tumor-associated biomolecules (e.g. nucleic acid, tyrosine, tryptophan, cholesteryl ester, fatty acid, and collagen) were found to contribute to the accurate molecular subgrouping. Collectively, this study opens up new opportunities for accurate and rapid molecular subgrouping of high-grade glioma, which would assist optimal surgical resection and instant post-operative decision-making.
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Affiliation(s)
- Chang Liu
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Xueyuan Road 37, Beijing 100191, China
| | - Jiejun Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, South Fourth Ring West Road 119, Beijing 100050, China
| | - Jianghao Shen
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Xueyuan Road 37, Beijing 100191, China
| | - Xun Chen
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Xueyuan Road 37, Beijing 100191, China
- School of Engineering Medicine, Beihang University, Xueyuan Road 37, Beijing 100191, China
| | - Nan Ji
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, South Fourth Ring West Road 119, Beijing 100050, China
| | - Shuhua Yue
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Xueyuan Road 37, Beijing 100191, China
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15
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Bratash O, Buhot A, Leroy L, Engel E. Optical fiber biosensors toward in vivo detection. Biosens Bioelectron 2024; 251:116088. [PMID: 38335876 DOI: 10.1016/j.bios.2024.116088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 01/19/2024] [Accepted: 01/28/2024] [Indexed: 02/12/2024]
Abstract
This review takes stock of the various optical fiber-based biosensors that could be used for in vivo applications. We discuss the characteristics that biosensors must have to be suitable for such applications and the corresponding transduction modes. In particular, we focus on optical fiber biosensors based on fluorescence, evanescent wave, plasmonics, interferometry, and Raman phenomenon. The operational principles, implemented solutions, and performances are described and debated. The different sensing configurations, such as the side- and tip-based fiber biosensors, are illustrated, and their adaptation for in vivo measurements is discussed. The required implementation of multiplexed biosensing on optical fibers is shown. In particular, the use of multi-fiber assemblies, one of the most optimal configurations for multiplexed detection, is discussed. Different possibilities for multiple localized functionalizations on optical fibers are presented. A final section is devoted to the practical in vivo use of fiber-based biosensors, covering regulatory, sterilization, and packaging aspects. Finally, the trends and required improvements in this promising and emerging field are analyzed and discussed.
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Affiliation(s)
- Oleksii Bratash
- Univ. Grenoble Alpes, CEA, CNRS, Grenoble INP, IRIG, SyMMES, 38000, Grenoble, France
| | - Arnaud Buhot
- Univ. Grenoble Alpes, CEA, CNRS, Grenoble INP, IRIG, SyMMES, 38000, Grenoble, France
| | - Loïc Leroy
- Univ. Grenoble Alpes, CEA, CNRS, Grenoble INP, IRIG, SyMMES, 38000, Grenoble, France
| | - Elodie Engel
- Univ. Grenoble Alpes, CEA, CNRS, Grenoble INP, IRIG, SyMMES, 38000, Grenoble, France.
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16
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Tak D, Ye Z, Zapaischykova A, Zha Y, Boyd A, Vajapeyam S, Chopra R, Hayat H, Prabhu SP, Liu KX, Elhalawani H, Nabavizadeh A, Familiar A, Resnick AC, Mueller S, Aerts HJWL, Bandopadhayay P, Ligon KL, Haas-Kogan DA, Poussaint TY, Kann BH. Noninvasive Molecular Subtyping of Pediatric Low-Grade Glioma with Self-Supervised Transfer Learning. Radiol Artif Intell 2024; 6:e230333. [PMID: 38446044 PMCID: PMC11140508 DOI: 10.1148/ryai.230333] [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: 08/18/2023] [Revised: 01/11/2024] [Accepted: 02/14/2024] [Indexed: 03/07/2024]
Abstract
Purpose To develop and externally test a scan-to-prediction deep learning pipeline for noninvasive, MRI-based BRAF mutational status classification for pediatric low-grade glioma. Materials and Methods This retrospective study included two pediatric low-grade glioma datasets with linked genomic and diagnostic T2-weighted MRI data of patients: Dana-Farber/Boston Children's Hospital (development dataset, n = 214 [113 (52.8%) male; 104 (48.6%) BRAF wild type, 60 (28.0%) BRAF fusion, and 50 (23.4%) BRAF V600E]) and the Children's Brain Tumor Network (external testing, n = 112 [55 (49.1%) male; 35 (31.2%) BRAF wild type, 60 (53.6%) BRAF fusion, and 17 (15.2%) BRAF V600E]). A deep learning pipeline was developed to classify BRAF mutational status (BRAF wild type vs BRAF fusion vs BRAF V600E) via a two-stage process: (a) three-dimensional tumor segmentation and extraction of axial tumor images and (b) section-wise, deep learning-based classification of mutational status. Knowledge-transfer and self-supervised approaches were investigated to prevent model overfitting, with a primary end point of the area under the receiver operating characteristic curve (AUC). To enhance model interpretability, a novel metric, center of mass distance, was developed to quantify the model attention around the tumor. Results A combination of transfer learning from a pretrained medical imaging-specific network and self-supervised label cross-training (TransferX) coupled with consensus logic yielded the highest classification performance with an AUC of 0.82 (95% CI: 0.72, 0.91), 0.87 (95% CI: 0.61, 0.97), and 0.85 (95% CI: 0.66, 0.95) for BRAF wild type, BRAF fusion, and BRAF V600E, respectively, on internal testing. On external testing, the pipeline yielded an AUC of 0.72 (95% CI: 0.64, 0.86), 0.78 (95% CI: 0.61, 0.89), and 0.72 (95% CI: 0.64, 0.88) for BRAF wild type, BRAF fusion, and BRAF V600E, respectively. Conclusion Transfer learning and self-supervised cross-training improved classification performance and generalizability for noninvasive pediatric low-grade glioma mutational status prediction in a limited data scenario. Keywords: Pediatrics, MRI, CNS, Brain/Brain Stem, Oncology, Feature Detection, Diagnosis, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2024.
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Affiliation(s)
- Divyanshu Tak
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Zezhong Ye
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Anna Zapaischykova
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Yining Zha
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Aidan Boyd
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Sridhar Vajapeyam
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Rishi Chopra
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Hasaan Hayat
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Sanjay P Prabhu
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Kevin X Liu
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Hesham Elhalawani
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Ali Nabavizadeh
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Ariana Familiar
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Adam C Resnick
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Sabine Mueller
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Hugo J W L Aerts
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Pratiti Bandopadhayay
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Keith L Ligon
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Daphne A Haas-Kogan
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Tina Y Poussaint
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
| | - Benjamin H Kann
- From the Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., H.J.W.L.A., B.H.K.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (D.T., Z.Y., A.Z., Y.Z., A.B., R.C., H.H., K.X.L., H.E., H.J.W.L.A., D.A.H.K., B.H.K.); Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.V., S.P.P., T.Y.P.); Center for Data-Driven Discovery in Biomedicine (A.N., A.F.) and Department of Neurosurgery (A.F., A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California San Francisco, San Francisco, Calif (S.M.); Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (H.J.W.L.A.); Department of Radiology and Nuclear Medicine, CalifRIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.); and Department of Pediatric Oncology (P.B.) and Department of Pathology (K.L.L.), Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, Mass
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Pillar N, Li Y, Zhang Y, Ozcan A. Virtual Staining of Nonfixed Tissue Histology. Mod Pathol 2024; 37:100444. [PMID: 38325706 DOI: 10.1016/j.modpat.2024.100444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 01/19/2024] [Accepted: 01/29/2024] [Indexed: 02/09/2024]
Abstract
Surgical pathology workflow involves multiple labor-intensive steps, such as tissue removal, fixation, embedding, sectioning, staining, and microscopic examination. This process is time-consuming and costly and requires skilled technicians. In certain clinical scenarios, such as intraoperative consultations, there is a need for faster histologic evaluation to provide real-time surgical guidance. Currently, frozen section techniques involving hematoxylin and eosin (H&E) staining are used for intraoperative pathology consultations. However, these techniques have limitations, including a turnaround time of 20 to 30 minutes, staining artifacts, and potential tissue loss, negatively impacting accurate diagnosis. To address these challenges, researchers are exploring alternative optical imaging modalities for rapid microscopic tissue imaging. These modalities differ in optical characteristics, tissue preparation requirements, imaging equipment, and output image quality and format. Some of these imaging methods have been combined with computational algorithms to generate H&E-like images, which could greatly facilitate their adoption by pathologists. Here, we provide a comprehensive, organ-specific review of the latest advancements in emerging imaging modalities applied to nonfixed human tissue. We focused on studies that generated H&E-like images evaluated by pathologists. By presenting up-to-date research progress and clinical utility, this review serves as a valuable resource for scholars and clinicians, covering some of the major technical developments in this rapidly evolving field. It also offers insights into the potential benefits and drawbacks of alternative imaging modalities and their implications for improving patient care.
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Affiliation(s)
- Nir Pillar
- Electrical and Computer Engineering Department, University of California, Los Angeles, California; Bioengineering Department, University of California, Los Angeles, California; California NanoSystems Institute (CNSI), University of California, Los Angeles, California
| | - Yuzhu Li
- Electrical and Computer Engineering Department, University of California, Los Angeles, California; Bioengineering Department, University of California, Los Angeles, California; California NanoSystems Institute (CNSI), University of California, Los Angeles, California
| | - Yijie Zhang
- Electrical and Computer Engineering Department, University of California, Los Angeles, California; Bioengineering Department, University of California, Los Angeles, California; California NanoSystems Institute (CNSI), University of California, Los Angeles, California
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, California; Bioengineering Department, University of California, Los Angeles, California; California NanoSystems Institute (CNSI), University of California, Los Angeles, California.
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18
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Voigtlaender S, Pawelczyk J, Geiger M, Vaios EJ, Karschnia P, Cudkowicz M, Dietrich J, Haraldsen IRJH, Feigin V, Owolabi M, White TL, Świeboda P, Farahany N, Natarajan V, Winter SF. Artificial intelligence in neurology: opportunities, challenges, and policy implications. J Neurol 2024; 271:2258-2273. [PMID: 38367046 DOI: 10.1007/s00415-024-12220-8] [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: 12/20/2023] [Revised: 01/20/2024] [Accepted: 01/22/2024] [Indexed: 02/19/2024]
Abstract
Neurological conditions are the leading cause of disability and mortality combined, demanding innovative, scalable, and sustainable solutions. Brain health has become a global priority with adoption of the World Health Organization's Intersectoral Global Action Plan in 2022. Simultaneously, rapid advancements in artificial intelligence (AI) are revolutionizing neurological research and practice. This scoping review of 66 original articles explores the value of AI in neurology and brain health, systematizing the landscape for emergent clinical opportunities and future trends across the care trajectory: prevention, risk stratification, early detection, diagnosis, management, and rehabilitation. AI's potential to advance personalized precision neurology and global brain health directives hinges on resolving core challenges across four pillars-models, data, feasibility/equity, and regulation/innovation-through concerted pursuit of targeted recommendations. Paramount actions include swift, ethical, equity-focused integration of novel technologies into clinical workflows, mitigating data-related issues, counteracting digital inequity gaps, and establishing robust governance frameworks balancing safety and innovation.
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Affiliation(s)
- Sebastian Voigtlaender
- Systems Neuroscience Division, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany
- Virtual Diagnostics Team, QuantCo Inc., Cambridge, MA, USA
| | - Johannes Pawelczyk
- Faculty of Medicine, Ruprecht-Karls-University, Heidelberg, Germany
- Graduate Center of Medicine and Health, Technical University Munich, Munich, Germany
| | - Mario Geiger
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- NVIDIA, Zurich, Switzerland
| | - Eugene J Vaios
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Philipp Karschnia
- Department of Neurosurgery, Ludwig-Maximilians-University and University Hospital Munich, Munich, Germany
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Merit Cudkowicz
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jorg Dietrich
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ira R J Hebold Haraldsen
- Department of Neurology, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
| | - Valery Feigin
- National Institute for Stroke and Applied Neurosciences, Auckland University of Technology, Auckland, New Zealand
| | - Mayowa Owolabi
- Center for Genomics and Precision Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria
- Neurology Unit, Department of Medicine, University of Ibadan, Ibadan, Nigeria
- Blossom Specialist Medical Center, Ibadan, Nigeria
- Lebanese American University of Beirut, Beirut, Lebanon
| | - Tara L White
- Department of Behavioral and Social Sciences, Brown University, Providence, RI, USA
| | | | | | | | - Sebastian F Winter
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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19
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Dai C, Xiong H, He R, Zhu C, Li P, Guo M, Gou J, Mei M, Kong D, Li Q, Wee ATS, Fang X, Kong J, Liu Y, Wei D. Electro-Optical Multiclassification Platform for Minimizing Occasional Inaccuracy in Point-of-Care Biomarker Detection. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2312540. [PMID: 38288781 DOI: 10.1002/adma.202312540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/13/2024] [Indexed: 02/06/2024]
Abstract
On-site diagnostic tests that accurately identify disease biomarkers lay the foundation for self-healthcare applications. However, these tests routinely rely on single-mode signals and suffer from insufficient accuracy, especially for multiplexed point-of-care tests (POCTs) within a few minutes. Here, this work develops a dual-mode multiclassification diagnostic platform that integrates an electrochemiluminescence sensor and a field-effect transistor sensor in a microfluidic chip. The microfluidic channel guides the testing samples to flow across electro-optical sensor units, which produce dual-mode readouts by detecting infectious biomarkers of tuberculosis (TB), human rhinovirus (HRV), and group B streptococcus (GBS). Then, machine-learning classifiers generate three-dimensional (3D) hyperplanes to diagnose different diseases. Dual-mode readouts derived from distinct mechanisms enhance the anti-interference ability physically, and machine-learning-aided diagnosis in high-dimensional space reduces the occasional inaccuracy mathematically. Clinical validation studies with 501 unprocessed samples indicate that the platform has an accuracy approaching 99%, higher than the 77%-93% accuracy of rapid point-of-care testing technologies at 100% statistical power (>150 clinical tests). Moreover, the diagnosis time is 5 min without a trade-off of accuracy. This work solves the occasional inaccuracy issue of rapid on-site diagnosis, endowing POCT systems with the same accuracy as laboratory tests and holding unique prospects for complicated scenes of personalized healthcare.
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Affiliation(s)
- Changhao Dai
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai, 200433, China
- Laboratory of Molecular Materials and Devices, Fudan University, Shanghai, 200433, China
| | - Huiwen Xiong
- Department of Chemistry, Fudan University, Shanghai, 200433, China
| | - Rui He
- School of Nuclear Science and Technology, Lanzhou University, Lanzhou, 73000, China
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, 730000, China
| | - Chenxin Zhu
- Institute of Biomedical Sciences, Fudan University, Shanghai, 200032, China
| | - Pintao Li
- Department of Chemistry, Fudan University, Shanghai, 200433, China
| | - Mingquan Guo
- Department of Laboratory Medicine, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China
| | - Jian Gou
- Department of Physics, National University of Singapore, Singapore, 117542, Singapore
| | - Miaomiao Mei
- Yizheng Hospital of Traditional Chinese Medicine, Yangzhou, 211400, China
| | - Derong Kong
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai, 200433, China
- Laboratory of Molecular Materials and Devices, Fudan University, Shanghai, 200433, China
| | - Qiang Li
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, 730000, China
| | - Andrew Thye Shen Wee
- Department of Physics, National University of Singapore, Singapore, 117542, Singapore
| | - Xueen Fang
- Department of Chemistry, Fudan University, Shanghai, 200433, China
| | - Jilie Kong
- Department of Chemistry, Fudan University, Shanghai, 200433, China
| | - Yunqi Liu
- Laboratory of Molecular Materials and Devices, Fudan University, Shanghai, 200433, China
| | - Dacheng Wei
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai, 200433, China
- Laboratory of Molecular Materials and Devices, Fudan University, Shanghai, 200433, China
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Khalighi S, Reddy K, Midya A, Pandav KB, Madabhushi A, Abedalthagafi M. Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment. NPJ Precis Oncol 2024; 8:80. [PMID: 38553633 PMCID: PMC10980741 DOI: 10.1038/s41698-024-00575-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 03/13/2024] [Indexed: 04/02/2024] Open
Abstract
This review delves into the most recent advancements in applying artificial intelligence (AI) within neuro-oncology, specifically emphasizing work on gliomas, a class of brain tumors that represent a significant global health issue. AI has brought transformative innovations to brain tumor management, utilizing imaging, histopathological, and genomic tools for efficient detection, categorization, outcome prediction, and treatment planning. Assessing its influence across all facets of malignant brain tumor management- diagnosis, prognosis, and therapy- AI models outperform human evaluations in terms of accuracy and specificity. Their ability to discern molecular aspects from imaging may reduce reliance on invasive diagnostics and may accelerate the time to molecular diagnoses. The review covers AI techniques, from classical machine learning to deep learning, highlighting current applications and challenges. Promising directions for future research include multimodal data integration, generative AI, large medical language models, precise tumor delineation and characterization, and addressing racial and gender disparities. Adaptive personalized treatment strategies are also emphasized for optimizing clinical outcomes. Ethical, legal, and social implications are discussed, advocating for transparency and fairness in AI integration for neuro-oncology and providing a holistic understanding of its transformative impact on patient care.
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Affiliation(s)
- Sirvan Khalighi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Kartik Reddy
- Department of Radiology, Emory University, Atlanta, GA, USA
| | - Abhishek Midya
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Krunal Balvantbhai Pandav
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Anant Madabhushi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
- Atlanta Veterans Administration Medical Center, Atlanta, GA, USA.
| | - Malak Abedalthagafi
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA.
- The Cell and Molecular Biology Program, Winship Cancer Institute, Atlanta, GA, USA.
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21
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Wen J, Zhao B, Yang Z, Erus G, Skampardoni I, Mamourian E, Cui Y, Hwang G, Bao J, Boquet-Pujadas A, Zhou Z, Veturi Y, Ritchie MD, Shou H, Thompson PM, Shen L, Toga AW, Davatzikos C. The genetic architecture of multimodal human brain age. Nat Commun 2024; 15:2604. [PMID: 38521789 PMCID: PMC10960798 DOI: 10.1038/s41467-024-46796-6] [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: 05/13/2023] [Accepted: 03/06/2024] [Indexed: 03/25/2024] Open
Abstract
The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine .
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gyujoon Hwang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Zhen Zhou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yogasudha Veturi
- Department of Biobehavioral Health and Statistics, Penn State University, University Park, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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22
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Yim D, Khuntia J, Parameswaran V, Meyers A. Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review. JMIR Med Inform 2024; 12:e52073. [PMID: 38506918 PMCID: PMC10993141 DOI: 10.2196/52073] [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: 08/21/2023] [Revised: 10/12/2023] [Accepted: 01/30/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI. OBJECTIVE This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals. METHODS We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles. RESULTS Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care. CONCLUSIONS GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.
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Affiliation(s)
- Dobin Yim
- Loyola University, Maryland, MD, United States
| | - Jiban Khuntia
- University of Colorado Denver, Denver, CO, United States
| | | | - Arlen Meyers
- University of Colorado Denver, Denver, CO, United States
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23
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Hao J, Cai H, Gu L, Ma Y, Li Y, Liu B, Zhu H, Zeng F, Wu M. A transferrin receptor targeting dual-modal MR/NIR fluorescent imaging probe for glioblastoma diagnosis. Regen Biomater 2024; 11:rbae015. [PMID: 38487713 PMCID: PMC10939466 DOI: 10.1093/rb/rbae015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/22/2024] [Accepted: 02/02/2024] [Indexed: 03/17/2024] Open
Abstract
The prognosis of glioblastoma (GBM) remains challenging, primarily due to the lack of a precise, effective imaging technique for comprehensively characterization. Addressing GBM diagnostic challenges, our study introduces an innovative dual-modal imaging that merges near-infrared (NIR) fluorescent imaging with magnetic resonance imaging (MRI). This method employs superparamagnetic iron oxide nanoparticles coated with NIR fluorescent dyes, specifically Cyanine 7, and targeted peptides. This synthetic probe facilitates MRI functionality through superparamagnetic iron oxide nanoparticles, provides NIR imaging capability via Cyanine 7 and enhances tumor targeting trough peptide interactions, offering a comprehensive diagnostic tool for GBM. Notably, the probe traverses the blood-brain barrier, targeting GBM in vivo via peptides, producing clear and discernible images in both modalities. Cytotoxicity and histopathology assessments confirm the probe's favorable safety profile. These findings suggest that the dual-modal MR\NIR fluorescent imaging probe could revolutionize GBM prognosis and survival rates, which can also be extended to other tumors type.
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Affiliation(s)
- Jiaqi Hao
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610041, China
| | - Huawei Cai
- Department of Nuclear Medicine & Laboratory of Clinical Nuclear Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Lei Gu
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yiqi Ma
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yan Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Beibei Liu
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Hongyan Zhu
- State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Fanxin Zeng
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan 635000, China
| | - Min Wu
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610041, China
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24
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Drexler R, Khatri R, Schüller U, Eckhardt A, Ryba A, Sauvigny T, Dührsen L, Mohme M, Ricklefs T, Bode H, Hausmann F, Huber TB, Bonn S, Voß H, Neumann JE, Silverbush D, Hovestadt V, Suvà ML, Lamszus K, Gempt J, Westphal M, Heiland DH, Hänzelmann S, Ricklefs FL. Temporal change of DNA methylation subclasses between matched newly diagnosed and recurrent glioblastoma. Acta Neuropathol 2024; 147:21. [PMID: 38244080 PMCID: PMC10799798 DOI: 10.1007/s00401-023-02677-8] [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: 09/14/2023] [Revised: 12/08/2023] [Accepted: 12/24/2023] [Indexed: 01/22/2024]
Abstract
The longitudinal transition of phenotypes is pivotal in glioblastoma treatment resistance and DNA methylation emerged as an important tool for classifying glioblastoma phenotypes. We aimed to characterize DNA methylation subclass heterogeneity during progression and assess its clinical impact. Matched tissues from 47 glioblastoma patients were subjected to DNA methylation profiling, including CpG-site alterations, tissue and serum deconvolution, mass spectrometry, and immunoassay. Effects of clinical characteristics on temporal changes and outcomes were studied. Among 47 patients, 8 (17.0%) had non-matching classifications at recurrence. In the remaining 39 cases, 28.2% showed dominant DNA methylation subclass transitions, with 72.7% being a mesenchymal subclass. In general, glioblastomas with a subclass transition showed upregulated metabolic processes. Newly diagnosed glioblastomas with mesenchymal transition displayed increased stem cell-like states and decreased immune components at diagnosis and exhibited elevated immune signatures and cytokine levels in serum. In contrast, tissue of recurrent glioblastomas with mesenchymal transition showed increased immune components but decreased stem cell-like states. Survival analyses revealed comparable outcomes for patients with and without subclass transitions. This study demonstrates a temporal heterogeneity of DNA methylation subclasses in 28.2% of glioblastomas, not impacting patient survival. Changes in cell state composition associated with subclass transition may be crucial for recurrent glioblastoma targeted therapies.
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Affiliation(s)
- Richard Drexler
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Robin Khatri
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ulrich Schüller
- Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Pediatric Hematology and Oncology, Research Institute Children's Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Research Institute Children's Cancer Center Hamburg, Hamburg, Germany
| | - Alicia Eckhardt
- Department of Pediatric Hematology and Oncology, Research Institute Children's Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Research Institute Children's Cancer Center Hamburg, Hamburg, Germany
- Department of Radiation Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alice Ryba
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Thomas Sauvigny
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Lasse Dührsen
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Malte Mohme
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Tammo Ricklefs
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Helena Bode
- Research Institute Children's Cancer Center Hamburg, Hamburg, Germany
| | - Fabian Hausmann
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Tobias B Huber
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan Bonn
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Hannah Voß
- Section of Mass Spectrometric Proteomics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Julia E Neumann
- Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Dana Silverbush
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Volker Hovestadt
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Mario L Suvà
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Katrin Lamszus
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Jens Gempt
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Manfred Westphal
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Dieter H Heiland
- Department of Neurosurgery, Medical Center University of Freiburg, Freiburg, Germany
| | - Sonja Hänzelmann
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Franz L Ricklefs
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany.
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25
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Hayashi T, Tateishi K, Matsuyama S, Iwashita H, Miyake Y, Oshima A, Honma H, Sasame J, Takabayashi K, Sugino K, Hirata E, Udaka N, Matsushita Y, Kato I, Hayashi H, Nakamura T, Ikegaya N, Takayama Y, Sonoda M, Oka C, Sato M, Isoda M, Kato M, Uchiyama K, Tanaka T, Muramatsu T, Miyake S, Suzuki R, Takadera M, Tatezuki J, Ayabe J, Suenaga J, Matsunaga S, Miyahara K, Manaka H, Murata H, Yokoyama T, Tanaka Y, Shuto T, Ichimura K, Kato S, Yamanaka S, Cahill DP, Fujii S, Shankar GM, Yamamoto T. Intraoperative Integrated Diagnostic System for Malignant Central Nervous System Tumors. Clin Cancer Res 2024; 30:116-126. [PMID: 37851071 DOI: 10.1158/1078-0432.ccr-23-1660] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 08/19/2023] [Accepted: 10/16/2023] [Indexed: 10/19/2023]
Abstract
PURPOSE The 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors uses an integrated approach involving histopathology and molecular profiling. Because majority of adult malignant brain tumors are gliomas and primary CNS lymphomas (PCNSL), rapid differentiation of these diseases is required for therapeutic decisions. In addition, diffuse gliomas require molecular information on single-nucleotide variants (SNV), such as IDH1/2. Here, we report an intraoperative integrated diagnostic (i-ID) system to classify CNS malignant tumors, which updates legacy frozen-section (FS) diagnosis through incorporation of a qPCR-based genotyping assay. EXPERIMENTAL DESIGN FS evaluation, including GFAP and CD20 rapid IHC, was performed on adult malignant CNS tumors. PCNSL was diagnosed through positive CD20 and negative GFAP immunostaining. For suspected glioma, genotyping for IDH1/2, TERT SNV, and CDKN2A copy-number alteration was routinely performed, whereas H3F3A and BRAF SNV were assessed for selected cases. i-ID was determined on the basis of the 2021 WHO classification and compared with the permanent integrated diagnosis (p-ID) to assess its reliability. RESULTS After retrospectively analyzing 153 cases, 101 cases were prospectively examined using the i-ID system. Assessment of IDH1/2, TERT, H3F3AK27M, BRAFV600E, and CDKN2A alterations with i-ID and permanent genomic analysis was concordant in 100%, 100%, 100%, 100%, and 96.4%, respectively. Combination with FS and intraoperative genotyping assay improved diagnostic accuracy in gliomas. Overall, i-ID matched with p-ID in 80/82 (97.6%) patients with glioma and 18/19 (94.7%) with PCNSL. CONCLUSIONS The i-ID system provides reliable integrated diagnosis of adult malignant CNS tumors.
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Affiliation(s)
- Takahiro Hayashi
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
- Neurosurgical-Oncology Laboratory, Yokohama City University, Yokohama, Japan
| | - Kensuke Tateishi
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
- Neurosurgical-Oncology Laboratory, Yokohama City University, Yokohama, Japan
- Laboratory of Biopharmaceutical and Regenerative Science, Graduate School of Medical Science, Yokohama City University, Yokohama, Japan
| | - Shinichiro Matsuyama
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
- Neurosurgical-Oncology Laboratory, Yokohama City University, Yokohama, Japan
| | - Hiromichi Iwashita
- Department of Pathology, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
| | - Yohei Miyake
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
- Neurosurgical-Oncology Laboratory, Yokohama City University, Yokohama, Japan
| | - Akito Oshima
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
- Neurosurgical-Oncology Laboratory, Yokohama City University, Yokohama, Japan
| | - Hirokuni Honma
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
- Neurosurgical-Oncology Laboratory, Yokohama City University, Yokohama, Japan
| | - Jo Sasame
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
- Neurosurgical-Oncology Laboratory, Yokohama City University, Yokohama, Japan
| | - Katsuhiro Takabayashi
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
- Neurosurgical-Oncology Laboratory, Yokohama City University, Yokohama, Japan
| | - Kyoka Sugino
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
- Neurosurgical-Oncology Laboratory, Yokohama City University, Yokohama, Japan
- Laboratory of Biopharmaceutical and Regenerative Science, Graduate School of Medical Science, Yokohama City University, Yokohama, Japan
| | - Emi Hirata
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
- Neurosurgical-Oncology Laboratory, Yokohama City University, Yokohama, Japan
| | - Naoko Udaka
- Department of Diagnostic Pathology, Yokohama City University Hospital, Yokohama, Japan
| | - Yuko Matsushita
- Department of Brain Disease Translational Research, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Ikuma Kato
- Department of Molecular Pathology, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
| | - Hiroaki Hayashi
- Neurosurgical-Oncology Laboratory, Yokohama City University, Yokohama, Japan
- Department of Pediatrics, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
| | - Taishi Nakamura
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
- Neurosurgical-Oncology Laboratory, Yokohama City University, Yokohama, Japan
- Department of Neurosurgery, Yokohama City University Medical Center, Yokohama, Japan
| | - Naoki Ikegaya
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
| | - Yutaro Takayama
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
- Neurosurgical-Oncology Laboratory, Yokohama City University, Yokohama, Japan
| | - Masaki Sonoda
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
- Neurosurgical-Oncology Laboratory, Yokohama City University, Yokohama, Japan
| | - Chihiro Oka
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
- Neurosurgical-Oncology Laboratory, Yokohama City University, Yokohama, Japan
| | - Mitsuru Sato
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
| | - Masataka Isoda
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
- Neurosurgical-Oncology Laboratory, Yokohama City University, Yokohama, Japan
| | - Miyui Kato
- Neurosurgical-Oncology Laboratory, Yokohama City University, Yokohama, Japan
- Laboratory of Biopharmaceutical and Regenerative Science, Graduate School of Medical Science, Yokohama City University, Yokohama, Japan
| | - Kaho Uchiyama
- Neurosurgical-Oncology Laboratory, Yokohama City University, Yokohama, Japan
- Laboratory of Biopharmaceutical and Regenerative Science, Graduate School of Medical Science, Yokohama City University, Yokohama, Japan
| | - Tamon Tanaka
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
- Neurosurgical-Oncology Laboratory, Yokohama City University, Yokohama, Japan
| | - Toshiki Muramatsu
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
- Neurosurgical-Oncology Laboratory, Yokohama City University, Yokohama, Japan
| | - Shigeta Miyake
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
| | - Ryosuke Suzuki
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
- Department of Neurosurgery, Odawara Municipal Hospital, Odawara, Japan
| | - Mutsumi Takadera
- Department of Neurosurgery, Yokohama City Minato Red Cross Hospital, Yokohama, Japan
- Department of Neurosurgery, Yokosuka Kyosai Hospital, Yokosuka, Japan
| | - Junya Tatezuki
- Department of Neurosurgery, Yokohama City Minato Red Cross Hospital, Yokohama, Japan
| | - Junichi Ayabe
- Department of Neurosurgery, Yokosuka Kyosai Hospital, Yokosuka, Japan
| | - Jun Suenaga
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
| | - Shigeo Matsunaga
- Department of Neurosurgery, Yokohama Rosai Hospital, Yokohama, Japan
| | - Kosuke Miyahara
- Department of Neurosurgery, National Hospital Organization Yokohama Medical Center, Yokohama, Japan
| | - Hiroshi Manaka
- Department of Neurosurgery, Yokohama Minami Kyosai Hospital, Yokohama, Japan
| | - Hidetoshi Murata
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
| | | | - Yoshihide Tanaka
- Department of Neurosurgery, Yokosuka Kyosai Hospital, Yokosuka, Japan
| | - Takashi Shuto
- Department of Neurosurgery, Yokohama Rosai Hospital, Yokohama, Japan
| | - Koichi Ichimura
- Department of Brain Disease Translational Research, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Shingo Kato
- Department of Clinical Cancer Genomics, Yokohama City University, Yokohama, Japan
| | - Shoji Yamanaka
- Department of Diagnostic Pathology, Yokohama City University Hospital, Yokohama, Japan
| | - Daniel P Cahill
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Satoshi Fujii
- Department of Diagnostic Pathology, Yokohama City University Hospital, Yokohama, Japan
- Department of Molecular Pathology, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
| | - Ganesh M Shankar
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Tetsuya Yamamoto
- Department of Neurosurgery, Yokohama City University, Graduate School of Medicine, Yokohama, Japan
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26
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Banu MA, McKhann GM. Maximizing Extent of Resection for Noneloquent Glioblastoma: Fluorescent Dye or Intraoperative Magnetic Resonance Imaging? J Clin Oncol 2023; 41:5493-5496. [PMID: 37722089 DOI: 10.1200/jco.23.00963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 07/31/2023] [Accepted: 08/08/2023] [Indexed: 09/20/2023] Open
Affiliation(s)
- Matei A Banu
- Department of Neurological Surgery, Columbia University Irving Medical Center, New York Presbyterian Hospital, New York, NY
| | - Guy M McKhann
- Department of Neurological Surgery, Columbia University Irving Medical Center, New York Presbyterian Hospital, New York, NY
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27
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Nakhate V, Gonzalez Castro LN. Artificial intelligence in neuro-oncology. Front Neurosci 2023; 17:1217629. [PMID: 38161802 PMCID: PMC10755952 DOI: 10.3389/fnins.2023.1217629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 11/14/2023] [Indexed: 01/03/2024] Open
Abstract
Artificial intelligence (AI) describes the application of computer algorithms to the solution of problems that have traditionally required human intelligence. Although formal work in AI has been slowly advancing for almost 70 years, developments in the last decade, and particularly in the last year, have led to an explosion of AI applications in multiple fields. Neuro-oncology has not escaped this trend. Given the expected integration of AI-based methods to neuro-oncology practice over the coming years, we set to provide an overview of existing technologies as they are applied to the neuropathology and neuroradiology of brain tumors. We highlight current benefits and limitations of these technologies and offer recommendations on how to appraise novel AI-tools as they undergo consideration for integration into clinical workflows.
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Affiliation(s)
- Vihang Nakhate
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - L. Nicolas Gonzalez Castro
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- The Center for Neuro-Oncology, Dana–Farber Cancer Institute, Boston, MA, United States
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28
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Zhou QQ, Guo J, Wang Z, Li J, Chen M, Xu Q, Zhu L, Xu Q, Wang Q, Pan H, Pan J, Zhu Y, Song M, Liu X, Wang J, Zhang Z, Zhang L, Wang Y, Cai H, Chen X, Lu G. Rapid visualization of PD-L1 expression level in glioblastoma immune microenvironment via machine learning cascade-based Raman histopathology. J Adv Res 2023:S2090-1232(23)00377-6. [PMID: 38072311 DOI: 10.1016/j.jare.2023.12.002] [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: 08/04/2023] [Revised: 11/29/2023] [Accepted: 12/01/2023] [Indexed: 02/13/2024] Open
Abstract
INTRODUCTION Combination immunotherapy holds promise for improving survival in responsive glioblastoma (GBM) patients. Programmed death-ligand 1 (PD-L1) expression in immune microenvironment (IME) is the most important predictive biomarker for immunotherapy. Due to the heterogeneous distribution of PD-L1, post-operative histopathology fails to accurately capture its expression in residual tumors, making intra-operative diagnosis crucial for GBM treatment strategies. However, the current methods for evaluating the expression of PD-L1 are still time-consuming. OBJECTIVE To overcome the PD-L1 heterogeneity and enable rapid, accurate, and label-free imaging of PD-L1 expression level in GBM IME at the tissue level. METHODS We proposed a novel intra-operative diagnostic method, Machine Learning Cascade (MLC)-based Raman histopathology, which uses a coordinate localization system (CLS), hierarchical clustering analysis (HCA), support vector machine (SVM), and similarity analysis (SA). This method enables visualization of PD-L1 expression in glioma cells, CD8+ T cells, macrophages, and normal cells in addition to the tumor/normal boundary. The study quantified PD-L1 expression levels using the tumor proportion, combined positive, and cellular composition scores (TPS, CPS, and CCS, respectively) based on Raman data. Furthermore, the association between Raman spectral features and biomolecules was examined biochemically. RESULTS The entire process from signal collection to visualization could be completed within 30 min. In an orthotopic glioma mouse model, the MLC-based Raman histopathology demonstrated a high average accuracy (0.990) for identifying different cells and exhibited strong concordance with multiplex immunofluorescence (84.31 %) and traditional pathologists' scoring (R2 ≥ 0.9). Moreover, the peak intensities at 837 and 874 cm-1 showed a positive linear correlation with PD-L1 expression level. CONCLUSIONS This study introduced a new and extendable diagnostic method to achieve rapid and accurate visualization of PD-L1 expression in GBM IMB at the tissular level, leading to great potential in GBM intraoperative diagnosis for guiding surgery and post-operative immunotherapy.
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Affiliation(s)
- Qing-Qing Zhou
- Department of Radiology, Jinling Hospital, Affiliated Nanjing Medical University, Nanjing, China; Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, China
| | - Jingxing Guo
- School of Chemistry, Chemical Engineering and Life Sciences, Wuhan University of Technology, Wuhan, China.
| | - Ziyang Wang
- Department of Biomedical Engineering, College of Engineering and Applied Sciences, State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, China; Nanjing Nuoyuan Medical Devices Co. Ltd, Nanjing, China
| | - Jianrui Li
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Meng Chen
- Nanjing Nuoyuan Medical Devices Co. Ltd, Nanjing, China
| | - Qiang Xu
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Lijun Zhu
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Qing Xu
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Qiang Wang
- Department of Neurosurgery, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Hao Pan
- Department of Neurosurgery, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Jing Pan
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Yong Zhu
- School of Science, China Pharmaceutical University, Nanjing, China
| | - Ming Song
- Department of Mathmatical Sciences, The University of Texas at Dallas, Richardson, USA
| | - Xiaoxue Liu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jiandong Wang
- Department of Pathology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Zhiqiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Longjiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Yiqing Wang
- Department of Biomedical Engineering, College of Engineering and Applied Sciences, State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, China
| | - Huiming Cai
- Department of Biomedical Engineering, College of Engineering and Applied Sciences, State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, China; Nanjing Nuoyuan Medical Devices Co. Ltd, Nanjing, China
| | - Xiaoyuan Chen
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore; Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Guangming Lu
- Department of Radiology, Jinling Hospital, Affiliated Nanjing Medical University, Nanjing, China; Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China; State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, China.
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Xie X, Shen C, Zhang X, Wu G, Yang B, Qi Z, Tang Q, Wang Y, Ding H, Shi Z, Yu J. Rapid intraoperative multi-molecular diagnosis of glioma with ultrasound radio frequency signals and deep learning. EBioMedicine 2023; 98:104899. [PMID: 38041959 PMCID: PMC10711390 DOI: 10.1016/j.ebiom.2023.104899] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 11/16/2023] [Accepted: 11/17/2023] [Indexed: 12/04/2023] Open
Abstract
BACKGROUND Molecular diagnosis is crucial for biomarker-assisted glioma resection and management. However, some limitations of current molecular diagnostic techniques prevent their widespread use intraoperatively. With the unique advantages of ultrasound, this study developed a rapid intraoperative molecular diagnostic method based on ultrasound radio-frequency signals. METHODS We built a brain tumor ultrasound bank with 169 cases enrolled since July 2020, of which 43483 RF signal patches from 67 cases with a pathological diagnosis of glioma were a retrospective cohort for model training and validation. IDH1 and TERT promoter (TERTp) mutations and 1p/19q co-deletion were detected by next-generation sequencing. We designed a spatial-temporal integration model (STIM) to diagnose the three molecular biomarkers, thus establishing a rapid intraoperative molecular diagnostic system for glioma, and further analysed its consistency with the fifth edition of the WHO Classification of Tumors of the Central Nervous System (WHO CNS5). We tested STIM in 16-case prospective cohorts, which contained a total of 10384 RF signal patches. Two other RF-based classical models were used for comparison. Further, we included 20 cases additional prospective data for robustness test (ClinicalTrials.govNCT05656053). FINDINGS In the retrospective cohort, STIM achieved a mean accuracy and AUC of 0.9190 and 0.9650 (95% CI, 0.94-0.99) respectively for the three molecular biomarkers, with a total time of 3 s and a 96% match to WHO CNS5. In the prospective cohort, the diagnostic accuracy of STIM is 0.85 ± 0.04 (mean ± SD) for IDH1, 0.84 ± 0.05 for TERTp, and 0.88 ± 0.04 for 1p/19q. The AUC is 0.89 ± 0.02 (95% CI, 0.84-0.94) for IDH1, 0.80 ± 0.04 (95% CI, 0.71-0.89) for TERTp, and 0.85 ± 0.06 (95% CI, 0.73-0.98) for 1p/19q. Compared to the second best available method based on RF signal, the diagnostic accuracy of STIM is improved by 16.70% and the AUC is improved by 19.23% on average. INTERPRETATION STIM is a rapid, cost-effective, and easy-to-manipulate AI method to perform real-time intraoperative molecular diagnosis. In the future, it may help neurosurgeons designate personalized surgical plans and predict survival outcomes. FUNDING A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.
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Affiliation(s)
- Xuan Xie
- School of Information Science and Technology, Fudan University, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China
| | - Chao Shen
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Shanghai, China; Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China
| | - Xiandi Zhang
- Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China
| | - Guoqing Wu
- School of Information Science and Technology, Fudan University, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China
| | - Bojie Yang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Shanghai, China; Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China
| | - Zengxin Qi
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Shanghai, China; Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China
| | - Qisheng Tang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Shanghai, China; Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China
| | - Yuanyuan Wang
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Hong Ding
- Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China.
| | - Zhifeng Shi
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Shanghai, China; Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China.
| | - Jinhua Yu
- School of Information Science and Technology, Fudan University, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China.
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Soffietti R. Editorial: Advances in basic science and technology are bringing new flavor in neuro-oncology. Curr Opin Neurol 2023; 36:541-543. [PMID: 37973022 DOI: 10.1097/wco.0000000000001215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Affiliation(s)
- Riccardo Soffietti
- Division of Neuro-Oncology, Department of Neuroscience "Rita Levi Montalcini", University of Turin, Italy
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Zhang C, Xu J, Tang R, Yang J, Wang W, Yu X, Shi S. Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment. J Hematol Oncol 2023; 16:114. [PMID: 38012673 PMCID: PMC10680201 DOI: 10.1186/s13045-023-01514-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 11/20/2023] [Indexed: 11/29/2023] Open
Abstract
Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.
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Affiliation(s)
- Chaoyi Zhang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jin Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Rong Tang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jianhui Yang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Wei Wang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Xianjun Yu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
| | - Si Shi
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
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Preusser M, Geurts M, Hainfellner JA, van den Bent MJ. What is an isocitrate dehydrogenase-mutated central nervous system World Health Organization grade 2 glioma, or who should receive vorasidenib? Neuro Oncol 2023; 25:1915-1917. [PMID: 37477610 PMCID: PMC10628922 DOI: 10.1093/neuonc/noad113] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2023] Open
Affiliation(s)
- Matthias Preusser
- Division of Oncology, Department of Medicine 1, Medical University, Vienna, Austria
| | - Marjolein Geurts
- The Brain Tumour Center at the Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Johannes A Hainfellner
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Martin J van den Bent
- The Brain Tumour Center at the Erasmus MC Cancer Institute, Rotterdam, The Netherlands
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Wen J, Zhao B, Yang Z, Erus G, Skampardoni I, Mamourian E, Cui Y, Hwang G, Bao J, Boquet-Pujadas A, Zhou Z, Veturi Y, Ritchie MD, Shou H, Thompson PM, Shen L, Toga AW, Davatzikos C. The Genetic Architecture of Multimodal Human Brain Age. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.13.536818. [PMID: 37333190 PMCID: PMC10274645 DOI: 10.1101/2023.04.13.536818] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
The complex biological mechanisms underlying human brain aging remain incompletely understood, involving multiple body organs and chronic diseases. In this study, we used multimodal magnetic resonance imaging and artificial intelligence to examine the genetic architecture of the brain age gap (BAG) derived from gray matter volume (GM-BAG, N=31,557 European ancestry), white matter microstructure (WM-BAG, N=31,674), and functional connectivity (FC-BAG, N=32,017). We identified sixteen genomic loci that reached genome-wide significance (P-value<5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG showed the highest heritability enrichment for genetic variants in conserved regions, whereas WM-BAG exhibited the highest heritability enrichment in the 5' untranslated regions; oligodendrocytes and astrocytes, but not neurons, showed significant heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several exposure variables on brain aging, such as type 2 diabetes on GM-BAG (odds ratio=1.05 [1.01, 1.09], P-value=1.96×10-2) and AD on WM-BAG (odds ratio=1.04 [1.02, 1.05], P-value=7.18×10-5). Overall, our results provide valuable insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at the MEDICINE knowledge portal: https://labs.loni.usc.edu/medicine.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Gyujoon Hwang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | | | - Zhen Zhou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yogasudha Veturi
- Department of Biobehavioral Health and Statistics, Penn State University, University Park, PA, USA
| | - Marylyn D. Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, California
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Arthur W. Toga
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
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Orringer DA, Hollon TC. Unlocking glioma genetics with deep learning. MED 2023; 4:493-494. [PMID: 37572648 DOI: 10.1016/j.medj.2023.07.008] [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: 06/11/2023] [Revised: 07/15/2023] [Accepted: 07/17/2023] [Indexed: 08/14/2023]
Abstract
The AI era in medicine has ushered in new opportunities to improve the diagnosis and treatment of human disease. CHARM, an AI algorithm described in this issue,1 has the potential to streamline molecular classification, intraoperative diagnosis, surgical decision making, and trial enrollment for glioma patients.
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Affiliation(s)
- Daniel A Orringer
- Departments of Neurosurgery and Pathology, Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA.
| | - Todd C Hollon
- Departments of Neurosurgery, Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
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Nasrallah MP, Zhao J, Tsai CC, Meredith D, Marostica E, Ligon KL, Golden JA, Yu KH. Machine learning for cryosection pathology predicts the 2021 WHO classification of glioma. MED 2023; 4:526-540.e4. [PMID: 37421953 PMCID: PMC10527821 DOI: 10.1016/j.medj.2023.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 04/17/2023] [Accepted: 06/06/2023] [Indexed: 07/10/2023]
Abstract
BACKGROUND Timely and accurate intraoperative cryosection evaluations remain the gold standard for guiding surgical treatments for gliomas. However, the tissue-freezing process often generates artifacts that make histologic interpretation difficult. In addition, the 2021 WHO Classification of Tumors of the Central Nervous System incorporates molecular profiles in the diagnostic categories, so standard visual evaluation of cryosections alone cannot completely inform diagnoses based on the new classification system. METHODS To address these challenges, we develop the context-aware Cryosection Histopathology Assessment and Review Machine (CHARM) using samples from 1,524 glioma patients from three different patient populations to systematically analyze cryosection slides. FINDINGS Our CHARM models successfully identified malignant cells (AUROC = 0.98 ± 0.01 in the independent validation cohort), distinguished isocitrate dehydrogenase (IDH)-mutant tumors from wild type (AUROC = 0.79-0.82), classified three major types of molecularly defined gliomas (AUROC = 0.88-0.93), and identified the most prevalent subtypes of IDH-mutant tumors (AUROC = 0.89-0.97). CHARM further predicts clinically important genetic alterations in low-grade glioma, including ATRX, TP53, and CIC mutations, CDKN2A/B homozygous deletion, and 1p/19q codeletion via cryosection images. CONCLUSIONS Our approaches accommodate the evolving diagnostic criteria informed by molecular studies, provide real-time clinical decision support, and will democratize accurate cryosection diagnoses. FUNDING Supported in part by the National Institute of General Medical Sciences grant R35GM142879, the Google Research Scholar Award, the Blavatnik Center for Computational Biomedicine Award, the Partners' Innovation Discovery Grant, and the Schlager Family Award for Early Stage Digital Health Innovations.
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Affiliation(s)
- MacLean P Nasrallah
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Junhan Zhao
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Cheng Che Tsai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - David Meredith
- Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Eliana Marostica
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Division of Health Sciences and Technology, Harvard-Massachusetts Institute of Technology, Boston, MA 02139, USA
| | - Keith L Ligon
- Department of Pathology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Jeffrey A Golden
- Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Pathology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA.
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Bhargav AG, Domino JS, Alvarado AM, Tuchek CA, Akhavan D, Camarata PJ. Advances in computational and translational approaches for malignant glioma. Front Physiol 2023; 14:1219291. [PMID: 37405133 PMCID: PMC10315500 DOI: 10.3389/fphys.2023.1219291] [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] [Received: 05/08/2023] [Accepted: 06/05/2023] [Indexed: 07/06/2023] Open
Abstract
Gliomas are the most common primary brain tumors in adults and carry a dismal prognosis for patients. Current standard-of-care for gliomas is comprised of maximal safe surgical resection following by a combination of chemotherapy and radiation therapy depending on the grade and type of tumor. Despite decades of research efforts directed towards identifying effective therapies, curative treatments have been largely elusive in the majority of cases. The development and refinement of novel methodologies over recent years that integrate computational techniques with translational paradigms have begun to shed light on features of glioma, previously difficult to study. These methodologies have enabled a number of point-of-care approaches that can provide real-time, patient-specific and tumor-specific diagnostics that may guide the selection and development of therapies including decision-making surrounding surgical resection. Novel methodologies have also demonstrated utility in characterizing glioma-brain network dynamics and in turn early investigations into glioma plasticity and influence on surgical planning at a systems level. Similarly, application of such techniques in the laboratory setting have enhanced the ability to accurately model glioma disease processes and interrogate mechanisms of resistance to therapy. In this review, we highlight representative trends in the integration of computational methodologies including artificial intelligence and modeling with translational approaches in the study and treatment of malignant gliomas both at the point-of-care and outside the operative theater in silico as well as in the laboratory setting.
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Affiliation(s)
- Adip G. Bhargav
- Department of Neurological Surgery, University of Kansas Medical Center, Kansas City, KS, United States
| | - Joseph S. Domino
- Department of Neurological Surgery, University of Kansas Medical Center, Kansas City, KS, United States
| | - Anthony M. Alvarado
- Department of Neurological Surgery, Rush University Medical Center, Chicago, IL, United States
| | - Chad A. Tuchek
- Department of Neurological Surgery, University of Kansas Medical Center, Kansas City, KS, United States
| | - David Akhavan
- Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, KS, United States
- Department of Cancer Biology, University of Kansas Medical Center, Kansas City, KS, United States
- Bioengineering Program, University of Kansas Medical Center, Kansas City, KS, United States
| | - Paul J. Camarata
- Department of Neurological Surgery, University of Kansas Medical Center, Kansas City, KS, United States
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Fürtjes G, Reinecke D, von Spreckelsen N, Meißner AK, Rueß D, Timmer M, Freudiger C, Ion-Margineanu A, Khalid F, Watrinet K, Mawrin C, Chmyrov A, Goldbrunner R, Bruns O, Neuschmelting V. Intraoperative microscopic autofluorescence detection and characterization in brain tumors using stimulated Raman histology and two-photon fluorescence. Front Oncol 2023; 13:1146031. [PMID: 37234975 PMCID: PMC10207900 DOI: 10.3389/fonc.2023.1146031] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 04/21/2023] [Indexed: 05/28/2023] Open
Abstract
Introduction The intrinsic autofluorescence of biological tissues interferes with the detection of fluorophores administered for fluorescence guidance, an emerging auxiliary technique in oncological surgery. Yet, autofluorescence of the human brain and its neoplasia is sparsely examined. This study aims to assess autofluorescence of the brain and its neoplasia on a microscopic level by stimulated Raman histology (SRH) combined with two-photon fluorescence. Methods With this experimentally established label-free microscopy technique unprocessed tissue can be imaged and analyzed within minutes and the process is easily incorporated in the surgical workflow. In a prospective observational study, we analyzed 397 SRH and corresponding autofluorescence images of 162 samples from 81 consecutive patients that underwent brain tumor surgery. Small tissue samples were squashed on a slide for imaging. SRH and fluorescence images were acquired with a dual wavelength laser (790 nm and 1020 nm) for excitation. In these images tumor and non-tumor regions were identified by a convolutional neural network that reliably differentiates between tumor, healthy brain tissue and low quality SRH images. The identified areas were used to define regions.of- interests (ROIs) and the mean fluorescence intensity was measured. Results In healthy brain tissue, we found an increased mean autofluorescence signal in the gray (11.86, SD 2.61, n=29) compared to the white matter (5.99, SD 5.14, n=11, p<0.01) and in the cerebrum (11.83, SD 3.29, n=33) versus the cerebellum (2.82, SD 0.93, n=7, p<0.001), respectively. The signal of carcinoma metastases, meningiomas, gliomas and pituitary adenomas was significantly lower (each p<0.05) compared to the autofluorescence in the cerebrum and dura, and significantly higher (each p<0.05) compared to the cerebellum. Melanoma metastases were found to have a higher fluorescent signal (p<0.01) compared to cerebrum and cerebellum. Discussion In conclusion we found that autofluorescence in the brain varies depending on the tissue type and localization and differs significantly among various brain tumors. This needs to be considered for interpreting photon signal during fluorescence-guided brain tumor surgery.
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Affiliation(s)
- Gina Fürtjes
- Department of General Neurosurgery, Center of Neurosurgery, University Hospital Cologne, Cologne, Germany
- Helmholtz Zentrum München, Neuherberg, Germany
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Medizinische Fakultät and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
| | - David Reinecke
- Department of General Neurosurgery, Center of Neurosurgery, University Hospital Cologne, Cologne, Germany
| | - Niklas von Spreckelsen
- Department of General Neurosurgery, Center of Neurosurgery, University Hospital Cologne, Cologne, Germany
| | - Anna-Katharina Meißner
- Department of General Neurosurgery, Center of Neurosurgery, University Hospital Cologne, Cologne, Germany
| | - Daniel Rueß
- Department of Stereotaxy and Functional Neurosurgery, Center of Neurosurgery, University Hospital Cologne, Cologne, Germany
| | - Marco Timmer
- Department of General Neurosurgery, Center of Neurosurgery, University Hospital Cologne, Cologne, Germany
| | | | | | | | | | - Christian Mawrin
- University Hospital Magdeburg, Institute of Neuropathology, Magdeburg, Germany
| | - Andriy Chmyrov
- Helmholtz Zentrum München, Neuherberg, Germany
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Medizinische Fakultät and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
| | - Roland Goldbrunner
- Department of General Neurosurgery, Center of Neurosurgery, University Hospital Cologne, Cologne, Germany
| | - Oliver Bruns
- Helmholtz Zentrum München, Neuherberg, Germany
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Medizinische Fakultät and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
| | - Volker Neuschmelting
- Department of General Neurosurgery, Center of Neurosurgery, University Hospital Cologne, Cologne, Germany
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