Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI

Papp, László; Spielvogel, Clemens P.; Grubmüller, Bernhard; Grahovac, Marko; Krajnc, Denis; Ecsedi, Boglarka; Sareshgi, Reza Agha Mohammadi; Mohamad, D; Hamboeck, Martina; Rausch, Ivo; Mitterhauser, Markus; Wadsak, Wolfgang; Haug, Alexander; Kenner, Lukas; Mazal, Peter; Susani, Martin; Hartenbach, Sabrina; Baltzer, Pascal; Helbich, Thomas H.; Kramer, Gero; Shariat, Shahrokh F.; Beyer, Thomas; Hartenbach, Markus; Hacker, Marcus

doi:10.1007/s00259-020-05140-y

Cited by 97 publications

(88 citation statements)

References 40 publications

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“…Consequently, this study demonstrates that multi-imaging features can guide to the genomically most aggressive region within the prostate. Papp et al [ 72 ] aimed to investigate the diagnostic performance of PSMA PET/MRI in vivo models for predicting low vs. high lesion risk, together with BCR and overall patient risk, with machine learning. They demonstrated the potential to enhance risk classification in primary prostate cancer patients built on PET/MRI radiomics and machine learning without biopsy sampling.…”

Section: Future Perspectivementioning

confidence: 99%

Radiomics and Prostate MRI: Current Role and Future Applications

et al. 2021

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Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer.

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Section: Future Perspectivementioning

confidence: 99%

Radiomics and Prostate MRI: Current Role and Future Applications

et al. 2021

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“…2.0 [37] was used to extract a total number of 234 radiomic features from each sample as of optimized radiomic principles [38]. The resulting radiomic dataset underwent redundancy reduction with covariance matrix analysis where a Pearson correlation of 0.85 was selected as threshold [37]. This step resulted in 98 features.…”

Section: Statistical and Radiomic Analysismentioning

confidence: 99%

“…In each fold, feature ranking and selection of the highest-ranking 10 features was performed via R-squared ranking in the training set of each MC fold. The selected features were then also selected for the validation subset [37,39]. This step was necessary to minimize the chances of overfitting.…”

Section: Statistical and Radiomic Analysismentioning

confidence: 99%

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Morpho-Molecular Metabolic Analysis and Classification of Human Pituitary Gland and Adenoma Biopsies Based on Multimodal Optical Imaging

Giardina

Micko

Bovenkamp

et al. 2021

Cancers

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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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“…Each dataset underwent redundancy reduction by correlation matrix analysis 27 followed by a 10-fold cross-validation split with a training-validation ratio of 80%-20% 16 . Training sets of the folds were subjects of feature ranking analysis 28 and the highestranking eight as well as 16 (if available) features were selected for further analysis. The resulted dataset configurations were analyzed by class imbalance ratios and the quantum advantage score (a.k.a.…”

Section: Datasetmentioning

confidence: 99%

Clinical Data Classification With Noisy Intermediate Scale Quantum Computers

Moradi

Brandner

Spielvogel

et al. 2021

Preprint

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Quantum machine learning has experienced a 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 (\({\text{log}}_{2}N\)) for embedding classical data into quantum states and estimating the inner product on 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 \({\text{log}}_{2}N\) 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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Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI

Cited by 97 publications

References 40 publications

Radiomics and Prostate MRI: Current Role and Future Applications

Radiomics and Prostate MRI: Current Role and Future Applications

Morpho-Molecular Metabolic Analysis and Classification of Human Pituitary Gland and Adenoma Biopsies Based on Multimodal Optical Imaging

Clinical Data Classification With Noisy Intermediate Scale Quantum Computers

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