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Pudakalakatti S, Raj P, Salzillo TC, Enriquez JS, Bourgeois D, Dutta P, Titus M, Shams S, Bhosale P, Kim M, McAllister F, Bhattacharya PK. Metabolic Imaging Using Hyperpolarization for Assessment of Premalignancy. Methods Mol Biol 2022; 2435:169-180. [PMID: 34993946 PMCID: PMC9352438 DOI: 10.1007/978-1-0716-2014-4_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
There is an unmet need for noninvasive surrogate markers that can help identify premalignant lesions across different tumor types. Here we describe the methodology and technical details of protocols employed for in vivo 13C pyruvate metabolic imaging experiments. The goal of the method described is to identify and understand metabolic changes, to enable detection of pancreatic premalignant lesions, as a proof of concept of the high sensitivity of this imaging modality.
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Affiliation(s)
- Shivanand Pudakalakatti
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Priyank Raj
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- MD Anderson Cancer Center UT Health Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Travis C Salzillo
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- MD Anderson Cancer Center UT Health Graduate School of Biomedical Sciences, Houston, TX, USA
| | - José S Enriquez
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- MD Anderson Cancer Center UT Health Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Dontrey Bourgeois
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Statistics, Rice University, Houston, TX, USA
| | - Prasanta Dutta
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mark Titus
- Department of Genitourinary Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Shayan Shams
- Department of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, USA
| | - Priya Bhosale
- MD Anderson Cancer Center UT Health Graduate School of Biomedical Sciences, Houston, TX, USA
- Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Michael Kim
- MD Anderson Cancer Center UT Health Graduate School of Biomedical Sciences, Houston, TX, USA
- Department of Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Florencia McAllister
- MD Anderson Cancer Center UT Health Graduate School of Biomedical Sciences, Houston, TX, USA
- Department of Clinical Cancer Prevention, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Pratip K Bhattacharya
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- MD Anderson Cancer Center UT Health Graduate School of Biomedical Sciences, Houston, TX, USA.
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Enriquez JS, Chu Y, Pudakalakatti S, Hsieh KL, Salmon D, Dutta P, Millward NZ, Lurie E, Millward S, McAllister F, Maitra A, Sen S, Killary A, Zhang J, Jiang X, Bhattacharya PK, Shams S. Hyperpolarized Magnetic Resonance and Artificial Intelligence: Frontiers of Imaging in Pancreatic Cancer. JMIR Med Inform 2021; 9:e26601. [PMID: 34137725 PMCID: PMC8277399 DOI: 10.2196/26601] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 02/24/2021] [Accepted: 04/03/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND There is an unmet need for noninvasive imaging markers that can help identify the aggressive subtype(s) of pancreatic ductal adenocarcinoma (PDAC) at diagnosis and at an earlier time point, and evaluate the efficacy of therapy prior to tumor reduction. In the past few years, there have been two major developments with potential for a significant impact in establishing imaging biomarkers for PDAC and pancreatic cancer premalignancy: (1) hyperpolarized metabolic (HP)-magnetic resonance (MR), which increases the sensitivity of conventional MR by over 10,000-fold, enabling real-time metabolic measurements; and (2) applications of artificial intelligence (AI). OBJECTIVE Our objective of this review was to discuss these two exciting but independent developments (HP-MR and AI) in the realm of PDAC imaging and detection from the available literature to date. METHODS A systematic review following the PRISMA extension for Scoping Reviews (PRISMA-ScR) guidelines was performed. Studies addressing the utilization of HP-MR and/or AI for early detection, assessment of aggressiveness, and interrogating the early efficacy of therapy in patients with PDAC cited in recent clinical guidelines were extracted from the PubMed and Google Scholar databases. The studies were reviewed following predefined exclusion and inclusion criteria, and grouped based on the utilization of HP-MR and/or AI in PDAC diagnosis. RESULTS Part of the goal of this review was to highlight the knowledge gap of early detection in pancreatic cancer by any imaging modality, and to emphasize how AI and HP-MR can address this critical gap. We reviewed every paper published on HP-MR applications in PDAC, including six preclinical studies and one clinical trial. We also reviewed several HP-MR-related articles describing new probes with many functional applications in PDAC. On the AI side, we reviewed all existing papers that met our inclusion criteria on AI applications for evaluating computed tomography (CT) and MR images in PDAC. With the emergence of AI and its unique capability to learn across multimodal data, along with sensitive metabolic imaging using HP-MR, this knowledge gap in PDAC can be adequately addressed. CT is an accessible and widespread imaging modality worldwide as it is affordable; because of this reason alone, most of the data discussed are based on CT imaging datasets. Although there were relatively few MR-related papers included in this review, we believe that with rapid adoption of MR imaging and HP-MR, more clinical data on pancreatic cancer imaging will be available in the near future. CONCLUSIONS Integration of AI, HP-MR, and multimodal imaging information in pancreatic cancer may lead to the development of real-time biomarkers of early detection, assessing aggressiveness, and interrogating early efficacy of therapy in PDAC.
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Affiliation(s)
- José S Enriquez
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States.,Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Yan Chu
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Shivanand Pudakalakatti
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Kang Lin Hsieh
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Duncan Salmon
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States
| | - Prasanta Dutta
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Niki Zacharias Millward
- Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center, Houston, TX, United States.,Department of Urology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Eugene Lurie
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Steven Millward
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States.,Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Florencia McAllister
- Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center, Houston, TX, United States.,Department of Clinical Cancer Prevention, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Anirban Maitra
- Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center, Houston, TX, United States.,Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Subrata Sen
- Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center, Houston, TX, United States.,Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Ann Killary
- Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center, Houston, TX, United States.,Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jian Zhang
- Division of Computer Science and Engineering, Louisiana State University, Baton Rouge, LA, United States
| | - Xiaoqian Jiang
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Pratip K Bhattacharya
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States.,Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Shayan Shams
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States
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