The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.

Quantitative research, especially in the field of radio(geno)mics, has helped us understand fundamental mechanisms of neurologic diseases. Such research is integrally based on advanced algorithms to derive extensive radiomic features and integrate them into diagnostic and predictive models. To exploit the benefit of such complex algorithms, their swift translation into clinical practice is required, currently hindered by their complicated nature. brain-CaPTk is a modular platform, with components spanning across image processing, segmentation, feature extraction, and machine learning, that facilitates such translation, enabling quantitative analyses without requiring substantial computational background. Thus, brain-CaPTk can be seamlessly integrated into the typical quantification, analysis and reporting workflow of a radiologist, underscoring its clinical potential. This paper describes currently available components of brain-CaPTk and example results from their application in glioblastoma.

Optoacoustic imaging relies on the detection of optically induced acoustic waves to offer new possibilities in morphological and functional imaging. As the modality matures towards clinical application, research efforts aim to address multifactorial limitations that negatively impact the resulting image quality. In an endeavor to obtain a clear view on the limitations and their effects, as well as the status of this progressive refinement process, we conduct an extensive search for optoacoustic image quality improvement approaches that have been evaluated with humans in vivo, thus focusing on clinically relevant outcomes. We query six databases (PubMed, Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and Google Scholar) for articles published from 1 January 2010 to 31 October 2021, and identify 45 relevant research works through a systematic screening process. We review the identified approaches, describing their primary objectives, targeted limitations, and key technical implementation details. Moreover, considering comprehensive and objective quality assessment as an essential prerequisite for the adoption of such approaches in clinical practice, we subject 36 of the 45 papers to a further in-depth analysis of the reported quality evaluation procedures, and elicit a set of criteria with the intent to capture key evaluation aspects. Through a comparative criteria-wise rating process, we seek research efforts that exhibit excellence in quality assessment of their proposed methods, and discuss features that distinguish them from works with similar objectives. Additionally, informed by the rating results, we highlight areas with improvement potential, and extract recommendations for designing quality assessment pipelines capable of providing rich evidence.

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