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Article ; Online: DEBI-NN: Distance-encoding biomorphic-informational neural networks for minimizing the number of trainable parameters.

Papp, Laszlo / Haberl, David / Ecsedi, Boglarka / Spielvogel, Clemens P / Krajnc, Denis / Grahovac, Marko / Moradi, Sasan / Drexler, Wolfgang

Neural networks : the official journal of the International Neural Network Society

2023 Volume 167, Page(s) 517–532

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 ... ...

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.
MeSH term(s)	Artificial Intelligence ; Neural Networks, Computer ; Algorithms ; Diagnostic Imaging ; Neurons
Language	English
Publishing date	2023-08-25
Publishing country	United States
Document type	Journal Article
ZDB-ID	740542-x
ISSN	1879-2782 ; 0893-6080
ISSN (online)	1879-2782
ISSN	0893-6080
DOI	10.1016/j.neunet.2023.08.026
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Article: Sex-specific radiomic features of L-[S-methyl-

Papp, Laszlo / Rasul, Sazan / Spielvogel, Clemens P / Krajnc, Denis / Poetsch, Nina / Woehrer, Adelheid / Patronas, Eva-Maria / Ecsedi, Boglarka / Furtner, Julia / Mitterhauser, Markus / Rausch, Ivo / Widhalm, Georg / Beyer, Thomas / Hacker, Marcus / Traub-Weidinger, Tatjana

Frontiers in oncology

2023 Volume 13, Page(s) 986788

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-: Methods: MET-PET of 35 ... ...

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- 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.
Language	English
Publishing date	2023-02-03
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2649216-7
ISSN	2234-943X
ISSN	2234-943X
DOI	10.3389/fonc.2023.986788
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Article ; Online: Error mitigation enables PET radiomic cancer characterization on quantum computers.

Moradi, S / Spielvogel, Clemens / Krajnc, Denis / Brandner, C / Hillmich, S / Wille, R / Traub-Weidinger, T / Li, X / Hacker, M / Drexler, W / Papp, L

European journal of nuclear medicine and molecular imaging

2023 Volume 50, Issue 13, Page(s) 3826–3837

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 ... ...

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 (GD 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 GD 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.
MeSH term(s)	Male ; Humans ; Fluorodeoxyglucose F18 ; Positron-Emission Tomography/methods ; Lung Neoplasms/pathology ; Lung/pathology ; Computers ; Positron Emission Tomography Computed Tomography/methods ; Retrospective Studies
Chemical Substances	Fluorodeoxyglucose F18 (0Z5B2CJX4D)
Language	English
Publishing date	2023-08-04
Publishing country	Germany
Document type	Journal Article
ZDB-ID	8236-3
ISSN	1619-7089 ; 0340-6997 ; 1619-7070
ISSN (online)	1619-7089
ISSN	0340-6997 ; 1619-7070
DOI	10.1007/s00259-023-06362-6
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Article: Automated data preparation for

Krajnc, Denis / Spielvogel, Clemens P / Grahovac, Marko / Ecsedi, Boglarka / Rasul, Sazan / Poetsch, Nina / Traub-Weidinger, Tatjana / Haug, Alexander R / Ritter, Zsombor / Alizadeh, Hussain / Hacker, Marcus / Beyer, Thomas / Papp, Laszlo

Frontiers in oncology

2022 Volume 12, Page(s) 1017911

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 ... ...

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.
Language	English
Publishing date	2022-10-11
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2649216-7
ISSN	2234-943X
ISSN	2234-943X
DOI	10.3389/fonc.2022.1017911
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Article ; Online: Multi-lesion radiomics of PET/CT for non-invasive survival stratification and histologic tumor risk profiling in patients with lung adenocarcinoma.

Zhao, Meixin / Kluge, Kilian / Papp, Laszlo / Grahovac, Marko / Yang, Shaomin / Jiang, Chunting / Krajnc, Denis / Spielvogel, Clemens P / Ecsedi, Boglarka / Haug, Alexander / Wang, Shiwei / Hacker, Marcus / Zhang, Weifang / Li, Xiang

European radiology

2022 Volume 32, Issue 10, Page(s) 7056–7067

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 ...

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.
MeSH term(s)	Adenocarcinoma of Lung/diagnostic imaging ; Adenocarcinoma of Lung/pathology ; Fluorodeoxyglucose F18 ; Humans ; Lung Neoplasms/pathology ; Positron Emission Tomography Computed Tomography ; Retrospective Studies
Chemical Substances	Fluorodeoxyglucose F18 (0Z5B2CJX4D)
Language	English
Publishing date	2022-07-28
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1085366-2
ISSN	1432-1084 ; 0938-7994 ; 1613-3749
ISSN (online)	1432-1084
ISSN	0938-7994 ; 1613-3749
DOI	10.1007/s00330-022-08999-7
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Article: Breast Tumor Characterization Using [

Krajnc, Denis / Papp, Laszlo / Nakuz, Thomas S / Magometschnigg, Heinrich F / Grahovac, Marko / Spielvogel, Clemens P / Ecsedi, Boglarka / Bago-Horvath, Zsuzsanna / Haug, Alexander / Karanikas, Georgios / Beyer, Thomas / Hacker, Marcus / Helbich, Thomas H / Pinker, Katja

Cancers

2021 Volume 13, Issue 6

Abstract: ... ...

Abstract	Background
Language	English
Publishing date	2021-03-12
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2527080-1
ISSN	2072-6694
ISSN	2072-6694
DOI	10.3390/cancers13061249
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Article: 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.

Hasimbegovic, Ena / Papp, Laszlo / Grahovac, Marko / Krajnc, Denis / Poschner, Thomas / Hasan, Waseem / Andreas, Martin / Gross, Christoph / Strouhal, Andreas / Delle-Karth, Georg / Grabenwöger, Martin / Adlbrecht, Christopher / Mach, Markus

Journal of personalized medicine

2021 Volume 11, Issue 11

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 ... ...

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.
Language	English
Publishing date	2021-10-22
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662248-8
ISSN	2075-4426
ISSN	2075-4426
DOI	10.3390/jpm11111062
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Article ; Online: Radiogenomic markers enable risk stratification and inference of mutational pathway states in head and neck cancer.

Spielvogel, Clemens P / Stoiber, Stefan / Papp, Laszlo / Krajnc, Denis / Grahovac, Marko / Gurnhofer, Elisabeth / Trachtova, Karolina / Bystry, Vojtech / Leisser, Asha / Jank, Bernhard / Schnoell, Julia / Kadletz, Lorenz / Heiduschka, Gregor / Beyer, Thomas / Hacker, Marcus / Kenner, Lukas / Haug, Alexander R

European journal of nuclear medicine and molecular imaging

2022 Volume 50, Issue 2, Page(s) 546–558

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 ...

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.
MeSH term(s)	Humans ; Squamous Cell Carcinoma of Head and Neck/diagnostic imaging ; Squamous Cell Carcinoma of Head and Neck/genetics ; Carcinoma, Squamous Cell/pathology ; Retrospective Studies ; Genetic Markers ; Head and Neck Neoplasms/diagnostic imaging ; Head and Neck Neoplasms/genetics ; Prognosis ; Risk Assessment
Chemical Substances	Genetic Markers
Language	English
Publishing date	2022-09-26
Publishing country	Germany
Document type	Journal Article ; Comment
ZDB-ID	8236-3
ISSN	1619-7089 ; 0340-6997 ; 1619-7070
ISSN (online)	1619-7089
ISSN	0340-6997 ; 1619-7070
DOI	10.1007/s00259-022-05973-9
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Article: Morpho-Molecular Metabolic Analysis and Classification of Human Pituitary Gland and Adenoma Biopsies Based on Multimodal Optical Imaging.

Giardina, Gabriel / Micko, Alexander / Bovenkamp, Daniela / Krause, Arno / Placzek, Fabian / Papp, Laszlo / Krajnc, Denis / Spielvogel, Clemens P / Winklehner, Michael / Höftberger, Romana / Vila, Greisa / Andreana, Marco / Leitgeb, Rainer / Drexler, Wolfgang / Wolfsberger, Stefan / Unterhuber, Angelika

Cancers

2021 Volume 13, Issue 13

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 ... ...

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.
Language	English
Publishing date	2021-06-29
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2527080-1
ISSN	2072-6694
ISSN	2072-6694
DOI	10.3390/cancers13133234
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