Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications

Visvikis, Dimitris; Rest, Catherine Cheze Le; Jaouen, Vincent; Hatt, Mathieu

doi:10.1007/s00259-019-04373-w

Cited by 112 publications

(89 citation statements)

References 69 publications

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“…In AI/ML, computers can learn features from observations (e.g., medical imaging data) based on a mathematical model and cost function by training unknown variables (e.g., weights in ANN and SVM), and further make statistical inferences from learning [96]. For more information on AI/ML, readers are referred to the aforementioned reviews by Visvikis et al [95] and Uribe et al [96]. For more information on DL, readers are referred to reviews by Visvikis et al [95] and Choi [98].…”

Section: Nmp In Precision Medicinementioning

confidence: 99%

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Nuclear Medicine Physics: Review of Advanced Technology

2020

Progress in Medical Physics

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This review aims to provide a brief, comprehensive overview of advanced technologies of nuclear medicine physics, with a focus on recent developments from both hardware and software perspectives. Developments in image acquisition/reconstruction, especially the time-of-flight and point spread function, have potential advantages in the image signal-to-noise ratio and spatial resolution. Modern detector materials and devices (including lutetium oxyorthosilicate, cadmium zinc tellurium, and silicon photomultiplier) as well as modern nuclear medicine imaging systems (including positron emission tomography [PET]/computerized tomography [CT], whole-body PET, PET/magnetic resonance [MR], and digital PET) enable not only high-quality digital image acquisition, but also subsequent image processing, including image reconstruction and postreconstruction methods. Moreover, theranostics in nuclear medicine extend the usefulness of nuclear medicine physics far more than quantitative image-based diagnosis, playing a key role in personalized/precision medicine by raising the importance of internal radiation dosimetry in nuclear medicine. Now that deep-learning-based image processing can be incorporated in nuclear medicine image acquisition/processing, the aforementioned fields of nuclear medicine physics face the new era of Industry 4.0. Ongoing technological developments in nuclear medicine physics are leading to enhanced image quality and decreased radiation exposure as well as quantitative and personalized healthcare.

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Section: Nmp In Precision Medicinementioning

confidence: 99%

“…For more information on AI/ML, readers are referred to the aforementioned reviews by Visvikis et al [95] and Uribe et al [96]. For more information on DL, readers are referred to reviews by Visvikis et al [95] and Choi [98].…”

Section: Nmp In Precision Medicinementioning

confidence: 99%

Nuclear Medicine Physics: Review of Advanced Technology

2020

Progress in Medical Physics

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“…However, in the context of medical imaging, we specifically define AI as a system that "correctly interprets external data and extracts information from these data, using it flexibly to achieve specific goals and tasks. 45 " AI systems are used for analysis, personal inspiration, or personified AI. In the twenty-first century, artificial intelligence technology benefits from improved theoretical understanding (such as in neural network mathematics), advances in computer functions (such as graphics processing units [GPU]), and high-end data processing platforms (such as cloud storage and computing), as well as the operability of the algorithm and the database itself.…”

Section: Combining Radiomics With Artificial Intelligencementioning

confidence: 99%

“…The combination of AI and radiomics will produce dramatic social and economic benefits, and rapidly improve the level of diagnosis and treatments by grassroots doctors, with the accumulation of more data and the continued maturity of the technology. [52][53][54]…”

Section: Combining Radiomics With Artificial Intelligencementioning

confidence: 99%

<p>Application of Radiomics for Personalized Treatment of Cancer Patients</p>

Meng¹,

Sun²,

Qu³

et al. 2019

CMAR

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Radiomics is a novel concept that relies on obtaining image data from examinations such as computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET). With the appropriate algorithm, the extracted results have broad applicability and potential for a massive positive impact in radiology. For example, clinicians can verify treatment efficiency, predict the location of tumor metastasis, correlate results with a histopathological examination, or more accurately define the type of cancer. Combining radiomics with other testing techniques allows every patient to have a personalized treatment plan that is essential for advanced examination and treatment. This article explains the process of radiomics, including data collection mechanisms, combined use with genomics, and artificial intelligence and immunology techniques, which may solve many of the challenges faced by doctors in diagnosing and treating their patients.

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“…Radiomics, a branch of quantitative image analysis, can capture heterogeneity characteristics of regions of interest (ROIs) by extracting relevant features from medical images (CT, MR, PET) has been widely explored in the literature and shown to provide predictive capability of treatment response in different cancers [3][4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Tumor Response Prediction in 90Y Radioembolization with PET-based Radiomics Features and Absorbed Dose Metrics

Wei

Cui

et al. 2020

Preprint

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Purpose Evaluate whether lesion radiomics features and absorbed dose metrics extracted from post-therapy 90Y PET can be integrated to better predict outcome in microsphere radioembolization of liver malignancies. Methods Given the noisy nature of 90Y PET, first, a liver phantom study with repeated acquisitions and varying reconstruction parameters was used to identify a subset of robust radiomics features for the patient analysis. In 36 radioembolization procedures, 90Y PET/CT was performed within a couple of hours to extract 46 radiomics features and estimate absorbed dose in 105 primary and metastatic liver lesions. Robust radiomics modeling was based on bootstrapped multivariate logistic regression with shrinkage regularization (LASSO) and Cox regression with LASSO. Nested cross-validation and bootstrap resampling were used for optimal parameter/feature selection and for guarding against overfitting risks. Spearman rank correlation was used to analyze feature associations. Area under the receiver-operating characteristics curve (AUC) was used for lesion response (at first follow-up) analysis while Kaplan-Meier plots and c-index were used to assess progression model performance. Models with absorbed dose only, radiomics only and combined models were developed to predict lesion outcome.Results The phantom study identified 15/46 reproducible and robust radiomics features that were subsequently used in the patient models. A lesion response model with zone percentage (ZP) and mean absorbed dose achieved an AUC of 0.729 (95%CI: 0.702-0.758) and a progression model with zone size nonuniformity (ZSN) and absorbed dose achieved a c-index of 0.803 (95% CI: 0.790-0.815) on nested cross validation (CV). The combined models outperformed the radiomics only and absorbed dose only models, but statistical significance was not achieved with this limited data set.Conclusion We have developed new lesion-level response and progression models using textural radiomics features, derived from 90Y PET combined with mean absorbed dose for predicting outcome in radioembolization. These encouraging results will need further validation in independent datasets prior to clinical adoption.

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Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications

Cited by 112 publications

References 69 publications

Nuclear Medicine Physics: Review of Advanced Technology

Nuclear Medicine Physics: Review of Advanced Technology

<p>Application of Radiomics for Personalized Treatment of Cancer Patients</p>

Tumor Response Prediction in 90Y Radioembolization with PET-based Radiomics Features and Absorbed Dose Metrics

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