Novel Quantitative PET Techniques for Clinical Decision Support in Oncology

Abstract

Quantitative image analysis has deep roots in the usage of positron emission tomography (PET) in clinical and research settings to address a wide variety of diseases. It has been extensively employed to assess molecular and physiological biomarkers in vivo in healthy and disease states, in oncology, cardiology, neurology, and psychiatry. Quantitative PET allows relating the time-varying activity concentration in tissues/organs of interest and the basic functional parameters governing the biological processes being studied. Yet, quantitative PET is challenged by a number of degrading physical factors related to the physics of PET imaging, the limitations of the instrumentation used, and the physiological status of the patient. Moreover, there is no consensus on the most reliable and robust image-derived PET metric(s) that can be used with confidence in clinical oncology owing to the discrepancies between the conclusions reported in the literature. There is also increasing interest in the use of artificial intelligence based techniques, particularly machine learning and deep learning techniques in a variety of applications to extract quantitative features (radiomics) from PET including image segmentation and outcome prediction in clinical oncology. These novel techniques are revolutionizing clinical practice and are now offering unique capabilities to the clinical molecular imaging community and biomedical researchers at large. In this report, we summarize recent developments and future tendencies in quantitative PET imaging and present example applications in clinical decision support to illustrate its potential in the context of clinical oncology.

Positron emission tomography and magnetic resonance imaging in primary central nervous system lymphoma—a narrative review

“…Radiomics, or textural feature extraction, is an emerging technique for assessing intra-tumoral heterogeneity based on complex mathematical modeling of the spatial relationship between multiple image voxels ( 95 , 96 ). One recent retrospective study in 77 patients (primary CNS lymphoma: n=24; GBM: n=53) suggested that this analysis may help distinguish primary CNS lymphoma from GBM ( 97 ).…”

What can artificial intelligence teach us about the molecular mechanisms underlying disease?

“…Indeed, radiomics has progressed from direct selection of predefined features that can be used alone or in combination as inputs into ML classifiers, to obtaining indirect learned features without a priori definition using deep-learning datadriven methodology. The latter methods may be more generalizable, as they do not require preprocessing, for example segmentation [12][13][14], complex patterns may be 'discovered' in an objective and automated way, and multimodality and multiparametric scales may therefore be more easily accommodated. They may also overcome statistical issues of overfitting, collinearity and redundancy of features, although these advantages need to be more clearly determined in molecular imaging techniques and are dependent on the size of the training dataset, molecular imaging datasets usually being relatively small [15,16].…”

“…These biological processes can be crudely determined using functional and molecular imaging methods on a global scale, and there is some evidence that several of these adverse biological features may be reflected in medical images. However, it is likely that measurement of their heterogeneous expression will require more sophisticated analysis to which radiomic and AI methodology could contribute [6,[11][12][13].…”

Does the beta regularization parameter of bayesian penalized likelihood reconstruction always affect the quantification accuracy and image quality of positron emission tomography computed tomography?

“…1 A range of image-derived numerical metrics, such as the standardized uptake value (SUV) or the kinetic attributes are used in PET for quantitative analysis, which is better than visual assessment for distinguishing effective early treatment response from ineffective one in oncotherapy. 2 Recently, progress and innovative developments of new probes targeting different biological features (cell proliferation, amino acid transport/metabolism, integrin receptor expression in angiogenesis and metastasis), 3 as well as the use of artificial intelligence-based techniques (machine learning and deep learning of radiomics from PET imaging), 4 are revolutionizing clinical practice in oncology. As a result, the quantitative accuracy is extremely important when quantitative PET is still challenged by several degrading physical factors related to the physics of PET imaging.…”