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Liu J, Cao B, Luo Y, Chen X, Han H, Li L, Zeng J. Risk factors of major bleeding detected by machine learning method in patients undergoing liver resection with controlled low central venous pressure technique. Postgrad Med J 2023; 99:1280-1286. [PMID: 37794600 DOI: 10.1093/postmj/qgad087] [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: 06/22/2023] [Revised: 08/18/2023] [Accepted: 09/01/2023] [Indexed: 10/06/2023]
Abstract
BACKGROUND Controlled low central venous pressure (CLCVP) technique has been extensively validated in clinical practices to decrease intraoperative bleeding during liver resection process; however, no studies to date have attempted to propose a scoring method to better understand what risk factors might still be responsible for bleeding when CLCVP technique was implemented. METHODS We aimed to use machine learning to develop a model for detecting the risk factors of major bleeding in patients who underwent liver resection using CLCVP technique. We reviewed the medical records of 1077 patients who underwent liver surgery between January 2017 and June 2020. We evaluated the XGBoost model and logistic regression model using stratified K-fold cross-validation (K = 5), and the area under the receiver operating characteristic curve, the recall rate, precision rate, and accuracy score were calculated and compared. The SHapley Additive exPlanations was employed to identify the most influencing factors and their contribution to the prediction. RESULTS The XGBoost classifier with an accuracy of 0.80 and precision of 0.89 outperformed the logistic regression model with an accuracy of 0.76 and precision of 0.79. According to the SHapley Additive exPlanations summary plot, the top six variables ranked from most to least important included intraoperative hematocrit, surgery duration, intraoperative lactate, preoperative hemoglobin, preoperative aspartate transaminase, and Pringle maneuver duration. CONCLUSIONS Anesthesiologists should be aware of the potential impact of increased Pringle maneuver duration and lactate levels on intraoperative major bleeding in patients undergoing liver resection with CLCVP technique. What is already known on this topic-Low central venous pressure technique has already been extensively validated in clinical practices, with no prediction model for major bleeding. What this study adds-The XGBoost classifier outperformed logistic regression model for the prediction of major bleeding during liver resection with low central venous pressure technique. How this study might affect research, practice, or policy-anesthesiologists should be aware of the potential impact of increased PM duration and lactate levels on intraoperative major bleeding in patients undergoing liver resection with CLCVP technique.
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Affiliation(s)
- Jing Liu
- Department of Anesthesiology, the Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
| | - Bingbing Cao
- Department of Anesthesiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510000, China
| | - Yuelian Luo
- Department of Anesthesiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510000, China
| | - Xianqing Chen
- Department of Hepatobiliary and Pancreatic Surgery, the Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
| | - Hong Han
- Department of Anesthesiology, the Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
| | - Li Li
- Department of Anesthesiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510000, China
| | - Jianfeng Zeng
- Department of Anesthesiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510000, China
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Duff LM, Scarsbrook AF, Ravikumar N, Frood R, van Praagh GD, Mackie SL, Bailey MA, Tarkin JM, Mason JC, van der Geest KSM, Slart RHJA, Morgan AW, Tsoumpas C. An Automated Method for Artifical Intelligence Assisted Diagnosis of Active Aortitis Using Radiomic Analysis of FDG PET-CT Images. Biomolecules 2023; 13:343. [PMID: 36830712 PMCID: PMC9953018 DOI: 10.3390/biom13020343] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/30/2023] [Accepted: 02/01/2023] [Indexed: 02/12/2023] Open
Abstract
The aim of this study was to develop and validate an automated pipeline that could assist the diagnosis of active aortitis using radiomic imaging biomarkers derived from [18F]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET-CT) images. The aorta was automatically segmented by convolutional neural network (CNN) on FDG PET-CT of aortitis and control patients. The FDG PET-CT dataset was split into training (43 aortitis:21 control), test (12 aortitis:5 control) and validation (24 aortitis:14 control) cohorts. Radiomic features (RF), including SUV metrics, were extracted from the segmented data and harmonized. Three radiomic fingerprints were constructed: A-RFs with high diagnostic utility removing highly correlated RFs; B used principal component analysis (PCA); C-Random Forest intrinsic feature selection. The diagnostic utility was evaluated with accuracy and area under the receiver operating characteristic curve (AUC). Several RFs and Fingerprints had high AUC values (AUC > 0.8), confirmed by balanced accuracy, across training, test and external validation datasets. Good diagnostic performance achieved across several multi-centre datasets suggests that a radiomic pipeline can be generalizable. These findings could be used to build an automated clinical decision tool to facilitate objective and standardized assessment regardless of observer experience.
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Affiliation(s)
- Lisa M. Duff
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Institute of Medical and Biological Engineering, University of Leeds, Leeds LS2 9JT, UK
| | - Andrew F. Scarsbrook
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Department of Radiology, St. James University Hospital, Leeds LS9 7TF, UK
| | - Nishant Ravikumar
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Leeds, Leeds LS2 9JT, UK
| | - Russell Frood
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Department of Radiology, St. James University Hospital, Leeds LS9 7TF, UK
| | - Gijs D. van Praagh
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
| | - Sarah L. Mackie
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- NIHR Leeds Biomedical Research Centre and NIHR Leeds MedTech and In Vitro Diagnostics Co-Operative, Leeds Teaching Hospitals NHS Trust, Leeds LS7 4SA, UK
| | - Marc A. Bailey
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- The Leeds Vascular Institute, Leeds General Infirmary, Leeds LS2 9NS, UK
| | - Jason M. Tarkin
- Division of Cardiovascular Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Justin C. Mason
- National Heart and Lung Institute, Imperial College London, London SW3 6LY, UK
| | - Kornelis S. M. van der Geest
- Department of Rheumatology and Clinical Immunology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
| | - Riemer H. J. A. Slart
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
- Department of Biomedical Photonic Imaging, Faculty of Science and Technology, University of Twente, 7522 NB Enschede, The Netherlands
| | - Ann W. Morgan
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- NIHR Leeds Biomedical Research Centre and NIHR Leeds MedTech and In Vitro Diagnostics Co-Operative, Leeds Teaching Hospitals NHS Trust, Leeds LS7 4SA, UK
| | - Charalampos Tsoumpas
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
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Duff L, Scarsbrook AF, Mackie SL, Frood R, Bailey M, Morgan AW, Tsoumpas C. A methodological framework for AI-assisted diagnosis of active aortitis using radiomic analysis of FDG PET-CT images: Initial analysis. J Nucl Cardiol 2022; 29:3315-3331. [PMID: 35322380 PMCID: PMC9834376 DOI: 10.1007/s12350-022-02927-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 01/05/2022] [Indexed: 02/01/2023]
Abstract
BACKGROUND The aim of this study was to explore the feasibility of assisted diagnosis of active (peri-)aortitis using radiomic imaging biomarkers derived from [18F]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET-CT) images. METHODS The aorta was manually segmented on FDG PET-CT in 50 patients with aortitis and 25 controls. Radiomic features (RF) (n = 107), including SUV (Standardized Uptake Value) metrics, were extracted from the segmented data and harmonized using the ComBat technique. Individual RFs and groups of RFs (i.e., signatures) were used as input in Machine Learning classifiers. The diagnostic utility of these classifiers was evaluated with area under the receiver operating characteristic curve (AUC) and accuracy using the clinical diagnosis as the ground truth. RESULTS Several RFs had high accuracy, 84% to 86%, and AUC scores 0.83 to 0.97 when used individually. Radiomic signatures performed similarly, AUC 0.80 to 1.00. CONCLUSION A methodological framework for a radiomic-based approach to support diagnosis of aortitis was outlined. Selected RFs, individually or in combination, showed similar performance to the current standard of qualitative assessment in terms of AUC for identifying active aortitis. This framework could support development of a clinical decision-making tool for a more objective and standardized assessment of aortitis.
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Affiliation(s)
- Lisa Duff
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, 8.49b Worsley Building, Clarendon Way, Leeds, LS2 9JT, UK.
- Institute of Medical and Biological Engineering, University of Leeds, Leeds, UK.
| | - Andrew F Scarsbrook
- Leeds Institute of Medical Research - St James's, University of Leeds, Leeds, UK
- Department of Radiology, St. James University Hospital, Leeds, UK
| | - Sarah L Mackie
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Biomedical Research Centre, NIHR Leeds, Leeds, UK
| | - Russell Frood
- Leeds Institute of Medical Research - St James's, University of Leeds, Leeds, UK
- Department of Radiology, St. James University Hospital, Leeds, UK
| | - Marc Bailey
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, 8.49b Worsley Building, Clarendon Way, Leeds, LS2 9JT, UK
- The Leeds Vascular Institute, Leeds General Infirmary, Leeds, UK
| | - Ann W Morgan
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, 8.49b Worsley Building, Clarendon Way, Leeds, LS2 9JT, UK
- Leeds Teaching Hospitals NHS Trust, Biomedical Research Centre, NIHR Leeds, Leeds, UK
| | - Charalampos Tsoumpas
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, 8.49b Worsley Building, Clarendon Way, Leeds, LS2 9JT, UK
- Icahn School of Medicine at Mount Sinai, Biomedical Engineering and Imaging Institute, New York, USA
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center of Groningen, University of Groningen, 9700 RB, Groningen, Netherlands
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Piri R, Edenbrandt L, Larsson M, Enqvist O, Skovrup S, Iversen KK, Saboury B, Alavi A, Gerke O, Høilund-Carlsen PF. "Global" cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation in 18F-sodium fluoride PET/CT scans: Head-to-head comparison. J Nucl Cardiol 2022; 29:2531-2539. [PMID: 34386861 DOI: 10.1007/s12350-021-02758-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 07/13/2021] [Indexed: 01/06/2023]
Abstract
BACKGROUND Artificial intelligence (AI) is known to provide effective means to accelerate and facilitate clinical and research processes. So in this study it was aimed to compare a AI-based method for cardiac segmentation in positron emission tomography/computed tomography (PET/CT) scans with manual segmentation to assess global cardiac atherosclerosis burden. METHODS A trained convolutional neural network (CNN) was used for cardiac segmentation in 18F-sodium fluoride PET/CT scans of 29 healthy volunteers and 20 angina pectoris patients and compared with manual segmentation. Parameters for segmented volume (Vol) and mean, maximal, and total standardized uptake values (SUVmean, SUVmax, SUVtotal) were analyzed by Bland-Altman Limits of Agreement. Repeatability with AI-based assessment of the same scans is 100%. Repeatability (same conditions, same operator) and reproducibility (same conditions, two different operators) of manual segmentation was examined by re-segmentation in 25 randomly selected scans. RESULTS Mean (± SD) values with manual vs. CNN-based segmentation were Vol 617.65 ± 154.99 mL vs 625.26 ± 153.55 mL (P = .21), SUVmean 0.69 ± 0.15 vs 0.69 ± 0.15 (P = .26), SUVmax 2.68 ± 0.86 vs 2.77 ± 1.05 (P = .34), and SUVtotal 425.51 ± 138.93 vs 427.91 ± 132.68 (P = .62). Limits of agreement were - 89.42 to 74.2, - 0.02 to 0.02, - 1.52 to 1.32, and - 68.02 to 63.21, respectively. Manual segmentation lasted typically 30 minutes vs about one minute with the CNN-based approach. The maximal deviation at manual re-segmentation was for the four parameters 0% to 0.5% with the same and 0% to 1% with different operators. CONCLUSION The CNN-based method was faster and provided values for Vol, SUVmean, SUVmax, and SUVtotal comparable to the manually obtained ones. This AI-based segmentation approach appears to offer a more reproducible and much faster substitute for slow and cumbersome manual segmentation of the heart.
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Affiliation(s)
- Reza Piri
- Department of Nuclear Medicine, Odense University Hospital, 5000, Odense C, Denmark.
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
| | - Lars Edenbrandt
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Physiology, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
| | | | - Olof Enqvist
- Eigenvision AB, Malmö, Sweden
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Sofie Skovrup
- Department of Nuclear Medicine, Odense University Hospital, 5000, Odense C, Denmark
| | - Kasper Karmark Iversen
- Department of Cardiology, Herlev and Gentofte Hospital, Copenhagen, Denmark
- Department of Emergency Medicine, Herlev and Gentofte Hospital, Copenhagen, Denmark
| | - Babak Saboury
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USA
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health (NIH), Bethesda, MD, USA
| | - Abass Alavi
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Oke Gerke
- Department of Nuclear Medicine, Odense University Hospital, 5000, Odense C, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Poul Flemming Høilund-Carlsen
- Department of Nuclear Medicine, Odense University Hospital, 5000, Odense C, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
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Cuocolo A, Nappi C, Acampa W, Petretta M. Nuclear cardiac imaging between implementation and globalization: The key role of integration. J Nucl Cardiol 2021; 28:793-795. [PMID: 33939159 PMCID: PMC8090522 DOI: 10.1007/s12350-021-02633-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 04/08/2021] [Indexed: 11/26/2022]
Affiliation(s)
- Alberto Cuocolo
- Department of Advanced Biomedical Sciences, University Federico II, Via Pansini 5, 80131, Naples, Italy.
| | - Carmela Nappi
- Department of Advanced Biomedical Sciences, University Federico II, Via Pansini 5, 80131, Naples, Italy
| | - Wanda Acampa
- Department of Advanced Biomedical Sciences, University Federico II, Via Pansini 5, 80131, Naples, Italy
- Institute of Biostructure and Bioimaging, National Council of Research, Naples, Italy
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Cuocolo A, Petretta M. PET and SPECT Specialty Grand Challenge. When Knowledge Travels at the Speed of Light, Photons Take to the Field. FRONTIERS IN NUCLEAR MEDICINE (LAUSANNE, SWITZERLAND) 2021; 1:671914. [PMID: 39355642 PMCID: PMC11440946 DOI: 10.3389/fnume.2021.671914] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 03/24/2021] [Indexed: 10/03/2024]
Affiliation(s)
- Alberto Cuocolo
- Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy
| | - Mario Petretta
- Department of Nuclear Medicine, Scientific Institute for Research and Health Care IRCCS SDN, Naples, Italy
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Nappi C, Gaudieri V, Petretta M. Simultaneous dual-tracer 99mTc-tetrofosmin and 123I-BMIPP acquisition with CZT for ischemic memory: The future approaches to image the past. J Nucl Cardiol 2021; 28:196-198. [PMID: 30701444 DOI: 10.1007/s12350-019-01614-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 01/16/2019] [Indexed: 12/01/2022]
Affiliation(s)
- Carmela Nappi
- Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy
| | - Valeria Gaudieri
- Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy
| | - Mario Petretta
- Department of Translational Medical Sciences, University Federico II, Naples, Italy.
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Nappi C, Gaudieri V, Cuocolo A. Behind Traditional Semi-quantitative Scores of Myocardial Perfusion Imaging: An Eye on Niche Parameters. Eur Cardiol 2019; 14:13-17. [PMID: 31131032 PMCID: PMC6523048 DOI: 10.15420/ecr.2019.5.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
The evaluation of stress-induced myocardial perfusion defects by non-invasive myocardial perfusion imaging (MPI) modalities has a leading role in the identification of coronary artery disease, and has excellent diagnostic and prognostic value. Non-invasive MPI can be performed using conventional and novel gamma cameras or by PET/CT. New software has allowed novel parameters that may have a role in the identification of early marks of cardiac impairment to be evaluated. We aim to give an overview of niche parameters obtainable by single photon emission CT (SPECT) and PET/CT MPI that may help practitioners to detect initial signs of cardiac damage and identify new therapy targets. In particular, we summarise the role of left ventricular geometry indices for remodelling, phase analysis parameters to evaluate mechanical dyssynchrony, the concept of relative flow reserve in the evaluation of flow-limiting epicardial stenosis, vascular age and epicardial adipose tissue as early markers of atherosclerotic burden, and emerging parameters for the evaluation of myocardial innervation, such as the total defect score.
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Affiliation(s)
- Carmela Nappi
- Department of Advanced Biomedical Sciences, University Federico II Naples, Italy
| | - Valeria Gaudieri
- Department of Advanced Biomedical Sciences, University Federico II Naples, Italy
| | - Alberto Cuocolo
- Department of Advanced Biomedical Sciences, University Federico II Naples, Italy
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