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Familiar AM, Mahtabfar A, Fathi Kazerooni A, Kiani M, Vossough A, Viaene A, Storm PB, Resnick AC, Nabavizadeh A. Radio-pathomic approaches in pediatric neuro-oncology: Opportunities and challenges. Neurooncol Adv 2023; 5:vdad119. [PMID: 37841693 PMCID: PMC10576517 DOI: 10.1093/noajnl/vdad119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2023] Open
Abstract
With medical software platforms moving to cloud environments with scalable storage and computing, the translation of predictive artificial intelligence (AI) models to aid in clinical decision-making and facilitate personalized medicine for cancer patients is becoming a reality. Medical imaging, namely radiologic and histologic images, has immense analytical potential in neuro-oncology, and models utilizing integrated radiomic and pathomic data may yield a synergistic effect and provide a new modality for precision medicine. At the same time, the ability to harness multi-modal data is met with challenges in aggregating data across medical departments and institutions, as well as significant complexity in modeling the phenotypic and genotypic heterogeneity of pediatric brain tumors. In this paper, we review recent pathomic and integrated pathomic, radiomic, and genomic studies with clinical applications. We discuss current challenges limiting translational research on pediatric brain tumors and outline technical and analytical solutions. Overall, we propose that to empower the potential residing in radio-pathomics, systemic changes in cross-discipline data management and end-to-end software platforms to handle multi-modal data sets are needed, in addition to embracing modern AI-powered approaches. These changes can improve the performance of predictive models, and ultimately the ability to advance brain cancer treatments and patient outcomes through the development of such models.
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Affiliation(s)
- Ariana M Familiar
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Aria Mahtabfar
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Anahita Fathi Kazerooni
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mahsa Kiani
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arastoo Vossough
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Angela Viaene
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Phillip B Storm
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Adam C Resnick
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ali Nabavizadeh
- Center for Data-Driven Discovery in Biomedicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Abstract
PURPOSE OF REVIEW Pathomics, the fusion of digitalized pathology and artificial intelligence, is currently changing the landscape of medical pathology and biologic disease classification. In this review, we give an overview of Pathomics and summarize its most relevant applications in urology. RECENT FINDINGS There is a steady rise in the number of studies employing Pathomics, and especially deep learning, in urology. In prostate cancer, several algorithms have been developed for the automatic differentiation between benign and malignant lesions and to differentiate Gleason scores. Furthermore, several applications have been developed for the automatic cancer cell detection in urine and for tumor assessment in renal cancer. Despite the explosion in research, Pathomics is not fully ready yet for widespread clinical application. SUMMARY In prostate cancer and other urologic pathologies, Pathomics is avidly being researched with commercial applications on the close horizon. Pathomics is set to improve the accuracy, speed, reliability, cost-effectiveness and generalizability of pathology, especially in uro-oncology.
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Homeyer A, Lotz J, Schwen LO, Weiss N, Romberg D, Höfener H, Zerbe N, Hufnagl P. Artificial Intelligence in Pathology: From Prototype to Product. J Pathol Inform 2021; 12:13. [PMID: 34012717 PMCID: PMC8112352 DOI: 10.4103/jpi.jpi_84_20] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 12/28/2020] [Accepted: 01/18/2021] [Indexed: 12/13/2022] Open
Abstract
Modern image analysis techniques based on artificial intelligence (AI) have great potential to improve the quality and efficiency of diagnostic procedures in pathology and to detect novel biomarkers. Despite thousands of published research papers on applications of AI in pathology, hardly any research implementations have matured into commercial products for routine use. Bringing an AI solution for pathology to market poses significant technological, business, and regulatory challenges. In this paper, we provide a comprehensive overview and advice on how to meet these challenges. We outline how research prototypes can be turned into a product-ready state and integrated into the IT infrastructure of clinical laboratories. We also discuss business models for profitable AI solutions and reimbursement options for computer assistance in pathology. Moreover, we explain how to obtain regulatory approval so that AI solutions can be launched as in vitro diagnostic medical devices. Thus, this paper offers computer scientists, software companies, and pathologists a road map for transforming prototypes of AI solutions into commercial products.
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Affiliation(s)
| | | | | | | | | | | | - Norman Zerbe
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Pathology, Berlin, Germany
- HTW University of Applied Sciences Berlin, Berlin, Germany
| | - Peter Hufnagl
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Pathology, Berlin, Germany
- HTW University of Applied Sciences Berlin, Berlin, Germany
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Meng Y, Sun J, Qu N, Zhang G, Yu T, Piao H. Application of Radiomics for Personalized Treatment of Cancer Patients. Cancer Manag Res 2019; 11:10851-10858. [PMID: 31920394 PMCID: PMC6941598 DOI: 10.2147/cmar.s232473] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 12/16/2019] [Indexed: 12/14/2022] Open
Abstract
Radiomics is a novel concept that relies on obtaining image data from examinations such as computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET). With the appropriate algorithm, the extracted results have broad applicability and potential for a massive positive impact in radiology. For example, clinicians can verify treatment efficiency, predict the location of tumor metastasis, correlate results with a histopathological examination, or more accurately define the type of cancer. Combining radiomics with other testing techniques allows every patient to have a personalized treatment plan that is essential for advanced examination and treatment. This article explains the process of radiomics, including data collection mechanisms, combined use with genomics, and artificial intelligence and immunology techniques, which may solve many of the challenges faced by doctors in diagnosing and treating their patients.
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Affiliation(s)
- Yiming Meng
- Central Laboratory, Cancer Hospital of China Medical University, Liaoning Province Cancer Hospital, Shenyang 110042, People's Republic of China
| | - Jing Sun
- Central Laboratory, Cancer Hospital of China Medical University, Liaoning Province Cancer Hospital, Shenyang 110042, People's Republic of China
| | - Na Qu
- Department of Gynecology, Cancer Hospital of China Medical University, Liaoning Province Cancer Hospital, Shenyang 110042, People's Republic of China
| | - Guirong Zhang
- Central Laboratory, Cancer Hospital of China Medical University, Liaoning Province Cancer Hospital, Shenyang 110042, People's Republic of China
| | - Tao Yu
- Department of Medical Image, Cancer Hospital of China Medical University, Liaoning Province Cancer Hospital, Shenyang 110042, People's Republic of China
| | - Haozhe Piao
- Central Laboratory, Cancer Hospital of China Medical University, Liaoning Province Cancer Hospital, Shenyang 110042, People's Republic of China.,Department of Neurosurgery, Cancer Hospital of China Medical University, Liaoning Province Cancer Hospital, Shenyang 110042, People's Republic of China
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