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Moradi S, Spielvogel C, Krajnc D, Brandner C, Hillmich S, Wille R, Traub-Weidinger T, Li X, Hacker M, Drexler W, Papp L. Error mitigation enables PET radiomic cancer characterization on quantum computers. Eur J Nucl Med Mol Imaging 2023; 50:3826-3837. [PMID: 37540237 PMCID: PMC10611844 DOI: 10.1007/s00259-023-06362-6] [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] [Received: 03/21/2023] [Accepted: 07/24/2023] [Indexed: 08/05/2023]
Abstract
BACKGROUND Cancer is a leading cause of death worldwide. While routine diagnosis of cancer is performed mainly with biopsy sampling, it is suboptimal to accurately characterize tumor heterogeneity. Positron emission tomography (PET)-driven radiomic research has demonstrated promising results when predicting clinical endpoints. This study aimed to investigate the added value of quantum machine learning both in simulator and in real quantum computers utilizing error mitigation techniques to predict clinical endpoints in various PET cancer patients. METHODS Previously published PET radiomics datasets including 11C-MET PET glioma, 68GA-PSMA-11 PET prostate and lung 18F-FDG PET with 3-year survival, low-vs-high Gleason risk and 2-year survival as clinical endpoints respectively were utilized in this study. Redundancy reduction with 0.7, 0.8, and 0.9 Spearman rank thresholds (SRT), followed by selecting 8 and 16 features from all cohorts, was performed, resulting in 18 dataset variants. Quantum advantage was estimated by Geometric Difference (GDQ) score in each dataset variant. Five classic machine learning (CML) and their quantum versions (QML) were trained and tested in simulator environments across the dataset variants. Quantum circuit optimization and error mitigation were performed, followed by training and testing selected QML methods on the 21-qubit IonQ Aria quantum computer. Predictive performances were estimated by test balanced accuracy (BACC) values. RESULTS On average, QML outperformed CML in simulator environments with 16-features (BACC 70% and 69%, respectively), while with 8-features, CML outperformed QML with + 1%. The highest average QML advantage was + 4%. The GDQ scores were ≤ 1.0 in all the 8-feature cases, while they were > 1.0 when QML outperformed CML in 9 out of 11 cases. The test BACC of selected QML methods and datasets in the IonQ device without error mitigation (EM) were 69.94% BACC, while EM increased test BACC to 75.66% (76.77% in noiseless simulators). CONCLUSIONS We demonstrated that with error mitigation, quantum advantage can be achieved in real existing quantum computers when predicting clinical endpoints in clinically relevant PET cancer cohorts. Quantum advantage can already be achieved in simulator environments in these cohorts when relying on QML.
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Affiliation(s)
- S Moradi
- Applied Quantum Computing Group, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, T1090, Vienna, Austria
| | - Clemens Spielvogel
- Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Denis Krajnc
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - C Brandner
- Applied Quantum Computing Group, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, T1090, Vienna, Austria
| | - S Hillmich
- Institute for Integrated Circuits, Johannes Kepler University Linz, Linz, Austria
| | - R Wille
- Chair for Design Automation, Technical University of Munich, Munich, Germany
| | - T Traub-Weidinger
- Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - X Li
- Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - M Hacker
- Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - W Drexler
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - L Papp
- Applied Quantum Computing Group, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, T1090, Vienna, Austria.
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Papp L, Haberl D, Ecsedi B, Spielvogel CP, Krajnc D, Grahovac M, Moradi S, Drexler W. DEBI-NN: Distance-encoding biomorphic-informational neural networks for minimizing the number of trainable parameters. Neural Netw 2023; 167:517-532. [PMID: 37690213 DOI: 10.1016/j.neunet.2023.08.026] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 08/11/2023] [Accepted: 08/17/2023] [Indexed: 09/12/2023]
Abstract
Modern artificial intelligence (AI) approaches mainly rely on neural network (NN) or deep NN methodologies. However, these approaches require large amounts of data to train, given, that the number of their trainable parameters has a polynomial relationship to their neuron counts. This property renders deep NNs challenging to apply in fields operating with small, albeit representative datasets such as healthcare. In this paper, we propose a novel neural network architecture which trains spatial positions of neural soma and axon pairs, where weights are calculated by axon-soma distances of connected neurons. We refer to this method as distance-encoding biomorphic-informational (DEBI) neural network. This concept significantly minimizes the number of trainable parameters compared to conventional neural networks. We demonstrate that DEBI models can yield comparable predictive performance in tabular and imaging datasets, where they require a fraction of trainable parameters compared to conventional NNs, resulting in a highly scalable solution.
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Affiliation(s)
- Laszlo Papp
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.
| | - David Haberl
- Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Boglarka Ecsedi
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria; Georgia Institute of Technology, Atlanta, GA, USA
| | | | - Denis Krajnc
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Marko Grahovac
- Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Sasan Moradi
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Drexler
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
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Grahovac M, Spielvogel CP, Krajnc D, Ecsedi B, Traub-Weidinger T, Rasul S, Kluge K, Zhao M, Li X, Hacker M, Haug A, Papp L. Machine learning predictive performance evaluation of conventional and fuzzy radiomics in clinical cancer imaging cohorts. Eur J Nucl Med Mol Imaging 2023; 50:1607-1620. [PMID: 36738311 PMCID: PMC10119059 DOI: 10.1007/s00259-023-06127-1] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 01/25/2023] [Indexed: 02/05/2023]
Abstract
BACKGROUND Hybrid imaging became an instrumental part of medical imaging, particularly cancer imaging processes in clinical routine. To date, several radiomic and machine learning studies investigated the feasibility of in vivo tumor characterization with variable outcomes. This study aims to investigate the effect of recently proposed fuzzy radiomics and compare its predictive performance to conventional radiomics in cancer imaging cohorts. In addition, lesion vs. lesion+surrounding fuzzy and conventional radiomic analysis was conducted. METHODS Previously published 11C Methionine (MET) positron emission tomography (PET) glioma, 18F-FDG PET/computed tomography (CT) lung, and 68GA-PSMA-11 PET/magneto-resonance imaging (MRI) prostate cancer retrospective cohorts were included in the analysis to predict their respective clinical endpoints. Four delineation methods including manually defined reference binary (Ref-B), its smoothed, fuzzified version (Ref-F), as well as extended binary (Ext-B) and its fuzzified version (Ext-F) were incorporated to extract imaging biomarker standardization initiative (IBSI)-conform radiomic features from each cohort. Machine learning for the four delineation approaches was performed utilizing a Monte Carlo cross-validation scheme to estimate the predictive performance of the four delineation methods. RESULTS Reference fuzzy (Ref-F) delineation outperformed its binary delineation (Ref-B) counterpart in all cohorts within a volume range of 938-354987 mm3 with relative cross-validation area under the receiver operator characteristics curve (AUC) of +4.7-10.4. Compared to Ref-B, the highest AUC performance difference was observed by the Ref-F delineation in the glioma cohort (Ref-F: 0.74 vs. Ref-B: 0.70) and in the prostate cohort by Ref-F and Ext-F (Ref-F: 0.84, Ext-F: 0.86 vs. Ref-B: 0.80). In addition, fuzzy radiomics decreased feature redundancy by approx. 20%. CONCLUSIONS Fuzzy radiomics has the potential to increase predictive performance particularly in small lesion sizes compared to conventional binary radiomics in PET. We hypothesize that this effect is due to the ability of fuzzy radiomics to model partial volume effects and delineation uncertainties at small lesion boundaries. In addition, we consider that the lower redundancy of fuzzy radiomic features supports the identification of imaging biomarkers in future studies. Future studies shall consider systematically analyzing lesions and their surroundings with fuzzy and binary radiomics.
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Affiliation(s)
- M Grahovac
- Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - C P Spielvogel
- Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, Vienna, Austria
| | - D Krajnc
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, AT-1090, Vienna, Austria
| | - B Ecsedi
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, AT-1090, Vienna, Austria
| | - T Traub-Weidinger
- Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - S Rasul
- Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - K Kluge
- Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - M Zhao
- Department of Nuclear Medicine, Peking University Third Hospital, Beijing, People's Republic of China
| | - X Li
- Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - M Hacker
- Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - A Haug
- Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, Vienna, Austria
| | - Laszlo Papp
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, AT-1090, Vienna, Austria.
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Papp L, Rasul S, Spielvogel CP, Krajnc D, Poetsch N, Woehrer A, Patronas EM, Ecsedi B, Furtner J, Mitterhauser M, Rausch I, Widhalm G, Beyer T, Hacker M, Traub-Weidinger T. Sex-specific radiomic features of L-[S-methyl- 11C] methionine PET in patients with newly-diagnosed gliomas in relation to IDH1 predictability. Front Oncol 2023; 13:986788. [PMID: 36816966 PMCID: PMC9936222 DOI: 10.3389/fonc.2023.986788] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 01/23/2023] [Indexed: 02/05/2023] Open
Abstract
Introduction Amino-acid positron emission tomography (PET) is a validated metabolic imaging approach for the diagnostic work-up of gliomas. This study aimed to evaluate sex-specific radiomic characteristics of L-[S-methyl-11Cmethionine (MET)-PET images of glioma patients in consideration of the prognostically relevant biomarker isocitrate dehydrogenase (IDH) mutation status. Methods MET-PET of 35 astrocytic gliomas (13 females, mean age 41 ± 13 yrs. and 22 males, mean age 46 ± 17 yrs.) and known IDH mutation status were included. All patients underwent radiomic analysis following imaging biomarker standardization initiative (IBSI)-conform guidelines both from standardized uptake value (SUV) and tumor-to-background ratio (TBR) PET values. Aligned Monte Carlo (MC) 100-fold split was utilized for SUV and TBR dataset pairs for both sex and IDH-specific analysis. Borderline and outlier scores were calculated for both sex and IDH-specific MC folds. Feature ranking was performed by R-squared ranking and Mann-Whitney U-test together with Bonferroni correction. Correlation of SUV and TBR radiomics in relation to IDH mutational status in male and female patients were also investigated. Results There were no significant features in either SUV or TBR radiomics to distinguish female and male patients. In contrast, intensity histogram coefficient of variation (ih.cov) and intensity skewness (stat.skew) were identified as significant to predict IDH +/-. In addition, IDH+ females had significant ih.cov deviation (0.031) and mean stat.skew (-0.327) differences compared to IDH+ male patients (0.068 and -0.123, respectively) with two-times higher standard deviations of the normal brain background MET uptake as well. Discussion We demonstrated that female and male glioma patients have significantly different radiomic profiles in MET PET imaging data. Future IDH prediction models shall not be built on mixed female-male cohorts, but shall rely on sex-specific cohorts and radiomic imaging biomarkers.
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Affiliation(s)
- Laszlo Papp
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Sazan Rasul
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Clemens P. Spielvogel
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria,Christian Doppler Laboratory for Applied Metabolomics , Medical University of Vienna, Vienna, Austria
| | - Denis Krajnc
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Nina Poetsch
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Adelheid Woehrer
- Clinical Institute of Neurology, Medical University of Vienna, Vienna, Austria
| | - Eva-Maria Patronas
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria,Division of Pharmaceutical Technology and Biopharmaceutics, Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
| | - Boglarka Ecsedi
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Julia Furtner
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Markus Mitterhauser
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria,Ludwig Boltzmann Institute Applied Diagnostics, Medical University of Vienna, Vienna, Austria
| | - Ivo Rausch
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Georg Widhalm
- Clinical University of Neuro-Surgery, Medical University of Vienna, Vienna, Austria
| | - Thomas Beyer
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Tatjana Traub-Weidinger
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria,*Correspondence: Tatjana Traub-Weidinger,
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Spielvogel CP, Stoiber S, Papp L, Krajnc D, Grahovac M, Gurnhofer E, Trachtova K, Bystry V, Leisser A, Jank B, Schnoell J, Kadletz L, Heiduschka G, Beyer T, Hacker M, Kenner L, Haug AR. Radiogenomic markers enable risk stratification and inference of mutational pathway states in head and neck cancer. Eur J Nucl Med Mol Imaging 2023; 50:546-558. [PMID: 36161512 PMCID: PMC9816299 DOI: 10.1007/s00259-022-05973-9] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 09/15/2022] [Indexed: 01/11/2023]
Abstract
PURPOSE Head and neck squamous cell carcinomas (HNSCCs) are a molecularly, histologically, and clinically heterogeneous set of tumors originating from the mucosal epithelium of the oral cavity, pharynx, and larynx. This heterogeneous nature of HNSCC is one of the main contributing factors to the lack of prognostic markers for personalized treatment. The aim of this study was to develop and identify multi-omics markers capable of improved risk stratification in this highly heterogeneous patient population. METHODS In this retrospective study, we approached this issue by establishing radiogenomics markers to identify high-risk individuals in a cohort of 127 HNSCC patients. Hybrid in vivo imaging and whole-exome sequencing were employed to identify quantitative imaging markers as well as genetic markers on pathway-level prognostic in HNSCC. We investigated the deductibility of the prognostic genetic markers using anatomical and metabolic imaging using positron emission tomography combined with computed tomography. Moreover, we used statistical and machine learning modeling to investigate whether a multi-omics approach can be used to derive prognostic markers for HNSCC. RESULTS Radiogenomic analysis revealed a significant influence of genetic pathway alterations on imaging markers. A highly prognostic radiogenomic marker based on cellular senescence was identified. Furthermore, the radiogenomic biomarkers designed in this study vastly outperformed the prognostic value of markers derived from genetics and imaging alone. CONCLUSION Using the identified markers, a clinically meaningful stratification of patients is possible, guiding the identification of high-risk patients and potentially aiding in the development of effective targeted therapies.
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Affiliation(s)
- Clemens P Spielvogel
- Christian Doppler Laboratory for Applied Metabolomics, Vienna, Austria
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Stefan Stoiber
- Christian Doppler Laboratory for Applied Metabolomics, Vienna, Austria
- Clinical Institute of Pathology, Medical University of Vienna, Vienna, Austria
| | - Laszlo Papp
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Denis Krajnc
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Marko Grahovac
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Elisabeth Gurnhofer
- Clinical Institute of Pathology, Medical University of Vienna, Vienna, Austria
| | - Karolina Trachtova
- Christian Doppler Laboratory for Applied Metabolomics, Vienna, Austria
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
- Centre for Molecular Medicine, Central European Institute of Technology, Brno, Czech Republic
| | - Vojtech Bystry
- Centre for Molecular Medicine, Central European Institute of Technology, Brno, Czech Republic
| | - Asha Leisser
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Bernhard Jank
- Department of Otorhinolaryngology, Head and Neck Surgery, Medical University of Vienna, Vienna, Austria
| | - Julia Schnoell
- Department of Otorhinolaryngology, Head and Neck Surgery, Medical University of Vienna, Vienna, Austria
| | - Lorenz Kadletz
- Department of Otorhinolaryngology, Head and Neck Surgery, Medical University of Vienna, Vienna, Austria
| | - Gregor Heiduschka
- Department of Otorhinolaryngology, Head and Neck Surgery, Medical University of Vienna, Vienna, Austria
| | - Thomas Beyer
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Lukas Kenner
- Christian Doppler Laboratory for Applied Metabolomics, Vienna, Austria.
- Clinical Institute of Pathology, Medical University of Vienna, Vienna, Austria.
| | - Alexander R Haug
- Christian Doppler Laboratory for Applied Metabolomics, Vienna, Austria
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
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Krajnc D, Spielvogel CP, Grahovac M, Ecsedi B, Rasul S, Poetsch N, Traub-Weidinger T, Haug AR, Ritter Z, Alizadeh H, Hacker M, Beyer T, Papp L. Automated data preparation for in vivo tumor characterization with machine learning. Front Oncol 2022; 12:1017911. [PMID: 36303841 PMCID: PMC9595446 DOI: 10.3389/fonc.2022.1017911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 09/23/2022] [Indexed: 11/23/2022] Open
Abstract
Background This study proposes machine learning-driven data preparation (MLDP) for optimal data preparation (DP) prior to building prediction models for cancer cohorts. Methods A collection of well-established DP methods were incorporated for building the DP pipelines for various clinical cohorts prior to machine learning. Evolutionary algorithm principles combined with hyperparameter optimization were employed to iteratively select the best fitting subset of data preparation algorithms for the given dataset. The proposed method was validated for glioma and prostate single center cohorts by 100-fold Monte Carlo (MC) cross-validation scheme with 80-20% training-validation split ratio. In addition, a dual-center diffuse large B-cell lymphoma (DLBCL) cohort was utilized with Center 1 as training and Center 2 as independent validation datasets to predict cohort-specific clinical endpoints. Five machine learning (ML) classifiers were employed for building prediction models across all analyzed cohorts. Predictive performance was estimated by confusion matrix analytics over the validation sets of each cohort. The performance of each model with and without MLDP, as well as with manually-defined DP were compared in each of the four cohorts. Results Sixteen of twenty established predictive models demonstrated area under the receiver operator characteristics curve (AUC) performance increase utilizing the MLDP. The MLDP resulted in the highest performance increase for random forest (RF) (+0.16 AUC) and support vector machine (SVM) (+0.13 AUC) model schemes for predicting 36-months survival in the glioma cohort. Single center cohorts resulted in complex (6-7 DP steps) DP pipelines, with a high occurrence of outlier detection, feature selection and synthetic majority oversampling technique (SMOTE). In contrast, the optimal DP pipeline for the dual-center DLBCL cohort only included outlier detection and SMOTE DP steps. Conclusions This study demonstrates that data preparation prior to ML prediction model building in cancer cohorts shall be ML-driven itself, yielding optimal prediction models in both single and multi-centric settings.
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Affiliation(s)
- Denis Krajnc
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Clemens P. Spielvogel
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, Vienna, Austria
| | - Marko Grahovac
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Boglarka Ecsedi
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Sazan Rasul
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Nina Poetsch
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Tatjana Traub-Weidinger
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Alexander R. Haug
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, Vienna, Austria
| | - Zsombor Ritter
- Department of Medical Imaging, University of Pécs, Medical School, Pécs, Hungary
| | - Hussain Alizadeh
- 1st Department of Internal Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Thomas Beyer
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
- *Correspondence: Thomas Beyer,
| | - Laszlo Papp
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
- Applied Quantum Computing group, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
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Zhao M, Kluge K, Papp L, Grahovac M, Yang S, Jiang C, Krajnc D, Spielvogel CP, Ecsedi B, Haug A, Wang S, Hacker M, Zhang W, Li X. Multi-lesion radiomics of PET/CT for non-invasive survival stratification and histologic tumor risk profiling in patients with lung adenocarcinoma. Eur Radiol 2022; 32:7056-7067. [PMID: 35896836 DOI: 10.1007/s00330-022-08999-7] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/15/2022] [Accepted: 06/27/2022] [Indexed: 12/01/2022]
Abstract
OBJECTIVES This study investigates the ability of machine learning (ML) models trained on clinical data and 2-deoxy-2-[18F]fluoro-D-glucose(FDG) positron emission tomography/computed tomography (PET/CT) radiomics to predict overall survival (OS), tumor grade (TG), and histologic growth pattern risk (GPR) in lung adenocarcinoma (LUAD) patients. METHODS A total of 421 treatment-naive patients with histologically-proven LUAD and available FDG PET/CT imaging were retrospectively included. Four cohorts were assessed for predicting 4-year OS (n = 276), 3-year OS (n = 280), TG (n = 298), and GPR (n = 265). FDG-avid lesions were delineated, and 2082 radiomics features were extracted and combined with endpoint-specific clinical parameters. ML models were built for the prediction of 4-year OS (M4OS), 3-year OS (M3OS), tumor grading (MTG), and histologic growth pattern risk (MGPR). A 100-fold Monte Carlo cross-validation with 80:20 training to validation split was employed as a performance evaluation for all models. The association between the M4OS and M3OS predictions with OS was assessed by the Kaplan-Meier survival analysis. RESULTS The area under the receiver operator characteristics curve (AUC) was the highest for M4OS (AUC 0.88, 95% confidence interval (CI) 86.7-88.7), followed by M3OS (AUC 0.84, CI 82.9-84.9), while MTG and MGPR performed equally well (AUC 0.76, CI 74.4-77.9, CI 74.6-78, respectively). Predictions of M4OS (hazard ratio (HR) -2.4, CI -2.47 to -1.64, p < 0.05) and M3OS (HR -2.36, CI -2.79 to -1.93, p < 0.05) were independently associated with OS. CONCLUSION ML models are able to predict long-term survival outcomes in LUAD patients with high accuracy. Furthermore, histologic grade and predominant growth pattern risk can be predicted with satisfactory accuracy. KEY POINTS • Machine learning models trained on pre-therapeutic PET/CT radiomics enable highly accurate long-term survival prediction of patients with lung adenocarcinoma. • Highly accurate survival predictions are achieved in lung adenocarcinoma patients despite heterogenous histologies and treatment regimens. • Radiomic machine learning models are able to predict lung adenocarcinoma tumor grade and histologic growth pattern risk with satisfactory accuracy.
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Affiliation(s)
- Meixin Zhao
- Department of Nuclear Medicine, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Kilian Kluge
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, Floor 3L, 1090, Vienna, Austria.,Christian Doppler Laboratory for Applied Metabolomics (CDLAM), Vienna, Austria
| | - Laszlo Papp
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Marko Grahovac
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, Floor 3L, 1090, Vienna, Austria
| | - Shaomin Yang
- Department of Pathology, Peking University Health Science Center, Beijing, China
| | - Chunting Jiang
- Department of Nuclear Medicine, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Denis Krajnc
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Clemens P Spielvogel
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, Floor 3L, 1090, Vienna, Austria.,Christian Doppler Laboratory for Applied Metabolomics (CDLAM), Vienna, Austria
| | - Boglarka Ecsedi
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Alexander Haug
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, Floor 3L, 1090, Vienna, Austria.,Christian Doppler Laboratory for Applied Metabolomics (CDLAM), Vienna, Austria
| | - Shiwei Wang
- Evomics Medical Technology Co., Ltd., Shanghai, China
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, Floor 3L, 1090, Vienna, Austria
| | - Weifang Zhang
- Department of Nuclear Medicine, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China.
| | - Xiang Li
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, Floor 3L, 1090, Vienna, Austria.
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Moradi S, Brandner C, Spielvogel C, Krajnc D, Hillmich S, Wille R, Drexler W, Papp L. Clinical data classification with noisy intermediate scale quantum computers. Sci Rep 2022; 12:1851. [PMID: 35115630 PMCID: PMC8814029 DOI: 10.1038/s41598-022-05971-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 01/21/2022] [Indexed: 11/09/2022] Open
Abstract
Quantum machine learning has experienced significant progress in both software and hardware development in the recent years and has emerged as an applicable area of near-term quantum computers. In this work, we investigate the feasibility of utilizing quantum machine learning (QML) on real clinical datasets. We propose two QML algorithms for data classification on IBM quantum hardware: a quantum distance classifier (qDS) and a simplified quantum-kernel support vector machine (sqKSVM). We utilize these different methods using the linear time quantum data encoding technique ([Formula: see text]) for embedding classical data into quantum states and estimating the inner product on the 15-qubit IBMQ Melbourne quantum computer. We match the predictive performance of our QML approaches with prior QML methods and with their classical counterpart algorithms for three open-access clinical datasets. Our results imply that the qDS in small sample and feature count datasets outperforms kernel-based methods. In contrast, quantum kernel approaches outperform qDS in high sample and feature count datasets. We demonstrate that the [Formula: see text] encoding increases predictive performance with up to + 2% area under the receiver operator characteristics curve across all quantum machine learning approaches, thus, making it ideal for machine learning tasks executed in Noisy Intermediate Scale Quantum computers.
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Affiliation(s)
- S Moradi
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - C Brandner
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - C Spielvogel
- Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - D Krajnc
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - S Hillmich
- Institute for Integrated Circuits, Johannes Kepler University Linz, Linz, Austria
| | - R Wille
- Institute for Integrated Circuits, Johannes Kepler University Linz, Linz, Austria.,Software Competence Center Hagenberg GmbH, Hagenberg, Austria
| | - W Drexler
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - L Papp
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
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9
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Hasimbegovic E, Papp L, Grahovac M, Krajnc D, Poschner T, Hasan W, Andreas M, Gross C, Strouhal A, Delle-Karth G, Grabenwöger M, Adlbrecht C, Mach M. A Sneak-Peek into the Physician's Brain: A Retrospective Machine Learning-Driven Investigation of Decision-Making in TAVR versus SAVR for Young High-Risk Patients with Severe Symptomatic Aortic Stenosis. J Pers Med 2021; 11:jpm11111062. [PMID: 34834414 PMCID: PMC8622882 DOI: 10.3390/jpm11111062] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 10/10/2021] [Accepted: 10/16/2021] [Indexed: 12/22/2022] Open
Abstract
Transcatheter aortic valve replacement (TAVR) has rapidly become a viable alternative to the conventional isolated surgical aortic valve replacement (iSAVR) for treating severe symptomatic aortic stenosis. However, data on younger patients is scarce and a gap exists between data-based recommendations and the clinical use of TAVR. In our study, we utilized a machine learning (ML) driven approach to model the complex decision-making process of Heart Teams when treating young patients with severe symptomatic aortic stenosis with either TAVR or iSAVR and to identify the relevant considerations. Out of the considered factors, the variables most prominently featured in our ML model were congestive heart failure, established risk assessment scores, previous cardiac surgeries, a reduced left ventricular ejection fraction and peripheral vascular disease. Our study demonstrates a viable application of ML-based approaches for studying and understanding complex clinical decision-making processes.
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Affiliation(s)
- Ena Hasimbegovic
- Division of Cardiac Surgery, Department of Surgery, Medical University of Vienna, 1090 Vienna, Austria; (E.H.); (T.P.); (M.A.); (C.G.)
- Division of Cardiology, Department of Internal Medicine II, Medical University of Vienna, 1090 Vienna, Austria
| | - Laszlo Papp
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (L.P.); (D.K.)
| | - Marko Grahovac
- Division of Nuclear Medicine, Medical University of Vienna, 1090 Vienna, Austria;
| | - Denis Krajnc
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (L.P.); (D.K.)
| | - Thomas Poschner
- Division of Cardiac Surgery, Department of Surgery, Medical University of Vienna, 1090 Vienna, Austria; (E.H.); (T.P.); (M.A.); (C.G.)
| | - Waseem Hasan
- Faculty of Medicine, Imperial College London, London SW7 2AZ, UK;
| | - Martin Andreas
- Division of Cardiac Surgery, Department of Surgery, Medical University of Vienna, 1090 Vienna, Austria; (E.H.); (T.P.); (M.A.); (C.G.)
| | - Christoph Gross
- Division of Cardiac Surgery, Department of Surgery, Medical University of Vienna, 1090 Vienna, Austria; (E.H.); (T.P.); (M.A.); (C.G.)
- Vienna North Hospital—Floridsdorf Clinic and the Karl Landsteiner Institute for Cardiovascular and Critical Care Research, 1090 Vienna, Austria
| | - Andreas Strouhal
- Department of Cardiovascular Surgery, Hospital Hietzing and the Karl Landsteiner Institute for Cardiovascular and Critical Care Research, 1090 Vienna, Austria; (A.S.); (G.D.-K.); (C.A.)
| | - Georg Delle-Karth
- Department of Cardiovascular Surgery, Hospital Hietzing and the Karl Landsteiner Institute for Cardiovascular and Critical Care Research, 1090 Vienna, Austria; (A.S.); (G.D.-K.); (C.A.)
| | - Martin Grabenwöger
- Faculty of Medicine, Sigmund Freud University, 1090 Vienna, Austria;
- Imed19—Internal Medicine Doebling, 1090 Vienna, Austria
| | - Christopher Adlbrecht
- Department of Cardiovascular Surgery, Hospital Hietzing and the Karl Landsteiner Institute for Cardiovascular and Critical Care Research, 1090 Vienna, Austria; (A.S.); (G.D.-K.); (C.A.)
- Imed19—Internal Medicine Doebling, 1090 Vienna, Austria
| | - Markus Mach
- Division of Cardiac Surgery, Department of Surgery, Medical University of Vienna, 1090 Vienna, Austria; (E.H.); (T.P.); (M.A.); (C.G.)
- Department of Cardiovascular Surgery, Hospital Hietzing and the Karl Landsteiner Institute for Cardiovascular and Critical Care Research, 1090 Vienna, Austria; (A.S.); (G.D.-K.); (C.A.)
- Correspondence: ; Tel.: +43-40400-52620
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Giardina G, Micko A, Bovenkamp D, Krause A, Placzek F, Papp L, Krajnc D, Spielvogel CP, Winklehner M, Höftberger R, Vila G, Andreana M, Leitgeb R, Drexler W, Wolfsberger S, Unterhuber A. Morpho-Molecular Metabolic Analysis and Classification of Human Pituitary Gland and Adenoma Biopsies Based on Multimodal Optical Imaging. Cancers (Basel) 2021; 13:3234. [PMID: 34209497 PMCID: PMC8267638 DOI: 10.3390/cancers13133234] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 06/23/2021] [Accepted: 06/24/2021] [Indexed: 12/12/2022] Open
Abstract
Pituitary adenomas count among the most common intracranial tumors. During pituitary oncogenesis structural, textural, metabolic and molecular changes occur which can be revealed with our integrated ultrahigh-resolution multimodal imaging approach including optical coherence tomography (OCT), multiphoton microscopy (MPM) and line scan Raman microspectroscopy (LSRM) on an unprecedented cellular level in a label-free manner. We investigated 5 pituitary gland and 25 adenoma biopsies, including lactotroph, null cell, gonadotroph, somatotroph and mammosomatotroph as well as corticotroph. First-level binary classification for discrimination of pituitary gland and adenomas was performed by feature extraction via radiomic analysis on OCT and MPM images and achieved an accuracy of 88%. Second-level multi-class classification was performed based on molecular analysis of the specimen via LSRM to discriminate pituitary adenomas subtypes with accuracies of up to 99%. Chemical compounds such as lipids, proteins, collagen, DNA and carotenoids and their relation could be identified as relevant biomarkers, and their spatial distribution visualized to provide deeper insight into the chemical properties of pituitary adenomas. Thereby, the aim of the current work was to assess a unique label-free and non-invasive multimodal optical imaging platform for pituitary tissue imaging and to perform a multiparametric morpho-molecular metabolic analysis and classification.
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Affiliation(s)
- Gabriel Giardina
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria; (G.G.); (D.B.); (A.K.); (F.P.); (R.L.); (W.D.); (A.U.)
| | - Alexander Micko
- Department of Neurosurgery, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria; (A.M.); (S.W.)
| | - Daniela Bovenkamp
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria; (G.G.); (D.B.); (A.K.); (F.P.); (R.L.); (W.D.); (A.U.)
| | - Arno Krause
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria; (G.G.); (D.B.); (A.K.); (F.P.); (R.L.); (W.D.); (A.U.)
| | - Fabian Placzek
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria; (G.G.); (D.B.); (A.K.); (F.P.); (R.L.); (W.D.); (A.U.)
| | - Laszlo Papp
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria; (L.P.); (D.K.)
| | - Denis Krajnc
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria; (L.P.); (D.K.)
| | - Clemens P. Spielvogel
- Christian Doppler Laboratory for Applied Metabolomics, Division of Nuclear Medicine, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria;
| | - Michael Winklehner
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria; (M.W.); (R.H.)
| | - Romana Höftberger
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria; (M.W.); (R.H.)
| | - Greisa Vila
- Department of Internal Medicine III, Division of Endocrinology and Metabolism, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria;
| | - Marco Andreana
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria; (G.G.); (D.B.); (A.K.); (F.P.); (R.L.); (W.D.); (A.U.)
| | - Rainer Leitgeb
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria; (G.G.); (D.B.); (A.K.); (F.P.); (R.L.); (W.D.); (A.U.)
| | - Wolfgang Drexler
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria; (G.G.); (D.B.); (A.K.); (F.P.); (R.L.); (W.D.); (A.U.)
| | - Stefan Wolfsberger
- Department of Neurosurgery, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria; (A.M.); (S.W.)
| | - Angelika Unterhuber
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria; (G.G.); (D.B.); (A.K.); (F.P.); (R.L.); (W.D.); (A.U.)
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11
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Papp L, Spielvogel CP, Grubmüller B, Grahovac M, Krajnc D, Ecsedi B, Sareshgi RAM, Mohamad D, Hamboeck M, Rausch I, Mitterhauser M, Wadsak W, Haug AR, Kenner L, Mazal P, Susani M, Hartenbach S, Baltzer P, Helbich TH, Kramer G, Shariat SF, Beyer T, Hartenbach M, Hacker M. Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [ 68Ga]Ga-PSMA-11 PET/MRI. Eur J Nucl Med Mol Imaging 2021; 48:1795-1805. [PMID: 33341915 PMCID: PMC8113201 DOI: 10.1007/s00259-020-05140-y] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 11/29/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE Risk classification of primary prostate cancer in clinical routine is mainly based on prostate-specific antigen (PSA) levels, Gleason scores from biopsy samples, and tumor-nodes-metastasis (TNM) staging. This study aimed to investigate the diagnostic performance of positron emission tomography/magnetic resonance imaging (PET/MRI) in vivo models for predicting low-vs-high lesion risk (LH) as well as biochemical recurrence (BCR) and overall patient risk (OPR) with machine learning. METHODS Fifty-two patients who underwent multi-parametric dual-tracer [18F]FMC and [68Ga]Ga-PSMA-11 PET/MRI as well as radical prostatectomy between 2014 and 2015 were included as part of a single-center pilot to a randomized prospective trial (NCT02659527). Radiomics in combination with ensemble machine learning was applied including the [68Ga]Ga-PSMA-11 PET, the apparent diffusion coefficient, and the transverse relaxation time-weighted MRI scans of each patient to establish a low-vs-high risk lesion prediction model (MLH). Furthermore, MBCR and MOPR predictive model schemes were built by combining MLH, PSA, and clinical stage values of patients. Performance evaluation of the established models was performed with 1000-fold Monte Carlo (MC) cross-validation. Results were additionally compared to conventional [68Ga]Ga-PSMA-11 standardized uptake value (SUV) analyses. RESULTS The area under the receiver operator characteristic curve (AUC) of the MLH model (0.86) was higher than the AUC of the [68Ga]Ga-PSMA-11 SUVmax analysis (0.80). MC cross-validation revealed 89% and 91% accuracies with 0.90 and 0.94 AUCs for the MBCR and MOPR models respectively, while standard routine analysis based on PSA, biopsy Gleason score, and TNM staging resulted in 69% and 70% accuracies to predict BCR and OPR respectively. CONCLUSION Our results demonstrate the potential to enhance risk classification in primary prostate cancer patients built on PET/MRI radiomics and machine learning without biopsy sampling.
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Affiliation(s)
- L Papp
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - C P Spielvogel
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Christian Doppler Laboratory for Applied Metabolomics, Vienna, Austria
| | - B Grubmüller
- Department of Urology, Medical University of Vienna, Vienna, Austria
| | - M Grahovac
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - D Krajnc
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - B Ecsedi
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - R A M Sareshgi
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - D Mohamad
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - M Hamboeck
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - I Rausch
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - M Mitterhauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Ludwig Boltzmann Institute Applied Diagnostics, Vienna, Austria
| | - W Wadsak
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - A R Haug
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Christian Doppler Laboratory for Applied Metabolomics, Vienna, Austria
| | - L Kenner
- Christian Doppler Laboratory for Applied Metabolomics, Vienna, Austria
- Clinical Institute of Pathology, Medical University of Vienna, Vienna, Austria
| | - P Mazal
- Clinical Institute of Pathology, Medical University of Vienna, Vienna, Austria
| | - M Susani
- Clinical Institute of Pathology, Medical University of Vienna, Vienna, Austria
| | | | - P Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Common General and Pediatric Radiology, Medical University of Vienna, Vienna, Austria
| | - T H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of Common General and Pediatric Radiology, Medical University of Vienna, Vienna, Austria
| | - G Kramer
- Department of Urology, Medical University of Vienna, Vienna, Austria
| | - S F Shariat
- Department of Urology, Medical University of Vienna, Vienna, Austria
| | - T Beyer
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - M Hartenbach
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - M Hacker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
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12
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Krajnc D, Papp L, Nakuz TS, Magometschnigg HF, Grahovac M, Spielvogel CP, Ecsedi B, Bago-Horvath Z, Haug A, Karanikas G, Beyer T, Hacker M, Helbich TH, Pinker K. Breast Tumor Characterization Using [ 18F]FDG-PET/CT Imaging Combined with Data Preprocessing and Radiomics. Cancers (Basel) 2021; 13:cancers13061249. [PMID: 33809057 PMCID: PMC8000810 DOI: 10.3390/cancers13061249] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 03/06/2021] [Accepted: 03/09/2021] [Indexed: 12/20/2022] Open
Abstract
Simple Summary Breast cancer is the second most common diagnosed malignancy in women worldwide. In this study, we examine the feasibility of breast tumor characterization based on [18F]FDG-PET/CT images using machine learning (ML) approaches in combination with data-preprocessing techniques. ML prediction models for breast cancer detection and the identification of breast cancer receptor status, proliferation rate, and molecular subtypes were established and evaluated. Furthermore, the importance of most repeatable features was investigated. Results displayed high performance of malignant/benign tumor differentiation and triple negative tumor subtype ML models. We observed high repeatability of radiomic features for both high performing predictive models. Abstract Background: This study investigated the performance of ensemble learning holomic models for the detection of breast cancer, receptor status, proliferation rate, and molecular subtypes from [18F]FDG-PET/CT images with and without incorporating data pre-processing algorithms. Additionally, machine learning (ML) models were compared with conventional data analysis using standard uptake value lesion classification. Methods: A cohort of 170 patients with 173 breast cancer tumors (132 malignant, 38 benign) was examined with [18F]FDG-PET/CT. Breast tumors were segmented and radiomic features were extracted following the imaging biomarker standardization initiative (IBSI) guidelines combined with optimized feature extraction. Ensemble learning including five supervised ML algorithms was utilized in a 100-fold Monte Carlo (MC) cross-validation scheme. Data pre-processing methods were incorporated prior to machine learning, including outlier and borderline noisy sample detection, feature selection, and class imbalance correction. Feature importance in each model was assessed by calculating feature occurrence by the R-squared method across MC folds. Results: Cross validation demonstrated high performance of the cancer detection model (80% sensitivity, 78% specificity, 80% accuracy, 0.81 area under the curve (AUC)), and of the triple negative tumor identification model (85% sensitivity, 78% specificity, 82% accuracy, 0.82 AUC). The individual receptor status and luminal A/B subtype models yielded low performance (0.46–0.68 AUC). SUVmax model yielded 0.76 AUC in cancer detection and 0.70 AUC in predicting triple negative subtype. Conclusions: Predictive models based on [18F]FDG-PET/CT images in combination with advanced data pre-processing steps aid in breast cancer diagnosis and in ML-based prediction of the aggressive triple negative breast cancer subtype.
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Affiliation(s)
- Denis Krajnc
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (D.K.); (L.P.); (B.E.)
| | - Laszlo Papp
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (D.K.); (L.P.); (B.E.)
| | - Thomas S. Nakuz
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
| | - Heinrich F. Magometschnigg
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (H.F.M.); (T.H.H.); or (K.P.)
| | - Marko Grahovac
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, 1090 Vienna, Austria
| | - Clemens P. Spielvogel
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, 1090 Vienna, Austria
| | - Boglarka Ecsedi
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (D.K.); (L.P.); (B.E.)
| | | | - Alexander Haug
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, 1090 Vienna, Austria
| | - Georgios Karanikas
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
| | - Thomas Beyer
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (D.K.); (L.P.); (B.E.)
- Correspondence:
| | - Marcus Hacker
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
| | - Thomas H. Helbich
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (H.F.M.); (T.H.H.); or (K.P.)
| | - Katja Pinker
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (H.F.M.); (T.H.H.); or (K.P.)
- Memorial Sloan Kettering Cancer Center, Breast Imaging Service, Department of Radiology, New York, NY 10065, USA
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Abstract
Exposing 7-day-old rat pups to hypoxia, 8% oxygen/92% nitrogen, for 3 h alters glutamate (GLU), glutamine and glutamine synthetase (GS) activity in the striatum, frontal cortex and hippocampus. Immediately following the hypoxic insult there is a rapid transient elevation of GLU followed by a fall and then recovery to control values within 6 h. Glutamine content initially decreased after the termination of the insult, rose thereafter and approached control values within 6 h. GS activity was depressed after hypoxia and gradually returned to normal levels within 6 h. GS mRNA was increased in the three brain regions studied after hypoxia and returned to control values within 24 h. These results suggest that hypoxia alters GLU metabolism in the immature brain.
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Affiliation(s)
- D Krajnc
- Department of Psychiatry, Ohio State University College of Medicine, Colombus 43210, USA
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14
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Krajnc D, Wemlinger TA, Neff NH, Hadjiconstantinou M. Neonatal hypoxia: early neurotransmitter responses and the consequences of treatment with GM1 ganglioside. J Pharmacol Exp Ther 1994; 271:1299-305. [PMID: 7996438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Brain neurotransmitter content and uptake activity were assessed in the brains of 7-day-old rats 15 min after exposure to hypoxia (8% O2-92% N2) for 3 hr. Glutamate, dopamine and choline uptake were decreased in the striatum, hippocampus and frontal cortex of the hypoxic animals. Moreover, the content of glutamate, dopamine and serotonin as well as the acidic metabolites of the two biogenic amines increased in the same tissues. Acetylcholine content was decreased in all three brain regions as well. Treating the animals with GM1 ganglioside before the insult prevented all neurochemical changes in the hypoxic neonatal brain. GM1 also prevented an hypoxia-induced decrease in phorbol ester binding. Finally, GM1 ganglioside reduced the mortality rate resulting from the hypoxic insult. Our results along with those in the literature suggest that GM1 might be useful for combating the pathology associated with perinatal hypoxia.
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Affiliation(s)
- D Krajnc
- Department of Pharmacology, Ohio State University College of Medicine, Columbus 43210
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15
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Hadjiconstantinou M, Rossetti ZL, Sylvia CP, Krajnc D, Neff NH. Aromatic L-amino acid decarboxylase activity of retina is modulated via aminergic receptors. Neurochem Int 1992; 20 Suppl:161S-163S. [PMID: 1365417 DOI: 10.1016/0197-0186(92)90231-f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Affiliation(s)
- M Hadjiconstantinou
- Department of Pharmacology, Ohio State University, College of Medicine, Columbus 43210
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Abstract
When rats are placed in a lighted environment from the dark retinal DOPAC increases. There is no significant change of retinal dopamine (DA) under either lighting condition. Blockade of aromatic L-amino acid decarboxylase results in a more rapid accumulation of DOPA in the retina of animals in the light than in the dark implying that DA synthesis and metabolism are more rapid in the light than in the dark. Retinal DOPAC increases in the dark and in the light when rats are treated with the DA D2 antagonists sulpiride and spiperone. Treatment with the D2 agonist, quinpirole, lowers the content of DA in the retina of rats kept in the dark or exposed to light. D1 receptor drugs induce only limited changes in DA metabolism. We conclude that D2 receptors play a principal role for modulating DA synthesis and metabolism in the rat retina.
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Affiliation(s)
- M Hadjiconstantinou
- Department of Pharmacology, Ohio State University College of Medicine, Columbus 43210
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Hadjiconstantinou M, Rossetti Z, Silvia C, Krajnc D, Neff N. Receptor mediated modulation of aromatic L-amino acid decarboxylase activity of retinal and nigrostriatal dopaminergic neurons. Eur J Pharmacol 1990. [DOI: 10.1016/0014-2999(90)93311-d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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18
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Abstract
Aromatic L-amino acid decarboxylase (AAAD) activity of rat retina increases when animals are placed in a lighted environment from the dark. The increase of activity can be inhibited by administering the selective dopamine D1 receptor agonist SKF 38393, but not the selective D2 agonist quinpirole, or apomorphine. Conversely, in the dark, enzyme activity can be enhanced by administering the selective D1 antagonist SCH 23390 or haloperidol, but not the selective D2 antagonist (-)-sulpiride. Furthermore, in animals exposed to room light for 3 h, the D1 agonist SKF 38393 reduced retinal AAAD activity, and this effect was prevented by prior administration of SCH 23390. In contrast, quinpirole had little or no effect when administered to animals in the light. Kinetic analysis indicated that the apparent Vmax for the enzyme increases with little change in the apparent Km for the substrate 3,4-dihydroxyphenylalanine or the cofactor pyridoxal-5'-phosphate. We suggest that dopamine released in the dark tonically occupies D1 receptors and suppresses AAAD activity. When the room light is turned on, D1 receptors are vacated and selective D1 agonists can either prevent the rise of AAAD or reverse light-enhanced AAAD activity.
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Affiliation(s)
- Z L Rossetti
- Department of Pharmacology, Ohio State University College of Medicine, Columbus 43210
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19
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Abstract
Aromatic L-amino acid decarboxylase (AAAD) activity of the rat retina increases when animals are placed in a lighted environment from the dark. The rise of activity can be inhibited by administering alpha 2 adrenoceptor agonists. In the dark, the enzyme activity can be made to increase by administering alpha 2 adrenoceptor antagonist drugs. Kinetic analysis indicates that the maximum velocity of the enzyme increases with little change of the Km for the substrate L-3,4-dihydroxyphenylalanine or the cofactor pyridoxal-5'-phosphate. The rise of activity in the light and in the dark after alpha 2 antagonists can be blocked by administering cycloheximide, suggesting that protein synthesis is needed for the response. We speculate that epinephrine released in the dark from a subpopulation of retinal amacrine cells onto alpha 2 receptors suppresses AAAD activity that is associated with dopaminergic amacrines.
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Affiliation(s)
- Z Rossetti
- Department of Pharmacology, Ohio State University College of Medicine, Columbus 43210-1239
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20
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Hadjiconstantinou M, Rossetti Z, Silvia C, Krajnc D, Neff NH. Aromatic L-amino acid decarboxylase activity of the rat retina is modulated in vivo by environmental light. J Neurochem 1988; 51:1560-4. [PMID: 3139836 DOI: 10.1111/j.1471-4159.1988.tb01125.x] [Citation(s) in RCA: 49] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Aromatic L-amino acid decarboxylase (AAAD) activity of rat retina is low in animals placed in the dark. When the room lights are turned on, activity rises for almost 3 h and reaches values that are about twice the values found in the dark. A study of the kinetics of the enzyme revealed that the apparent Km values for L-3,4-dihydroxyphenylalanine and pyridoxal-5'-phosphate were unchanged in light- and dark-exposed animals, whereas the Vmax increased in the light. Treating the animals with cycloheximide before exposure to light prevented the increase of enzyme activity. Immunotitration with antibodies to AAAD suggested that more enzyme molecules are present in the light than in the dark. When the room lights are turned off AAAD activity drops rapidly at first and then more slowly, suggesting that at least two processes are responsible for the fall of enzyme activity. Exposure to short periods of dark followed by light results in a rapid increase of AAAD activity. Mixing homogenates from light- and dark-exposed rats results in activity values that are less than expected, suggesting the presence of an endogenous inhibitor(s). These studies demonstrate that AAAD activity is modulated in vivo.
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Affiliation(s)
- M Hadjiconstantinou
- Department of Pharmacology, Ohio State University College of Medicine, Columbus 43210-1239
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