Artificial Intelligence for Clinical Flow Cytometry.

In this review, the authors discuss the fundamental principles of machine learning. They explore recent studies and approaches in implementing machine learning into flow cytometry workflows. These applications are promising but not without their shortcomings. Explainability may be the biggest barrier to adoption, as they contain "black boxes" in which a complex network of mathematical processes learns features of data that are not translatable into real language. The authors discuss the current limitations of machine learning models and the possibility that, without a multiinstitutional development process, these applications could have poor generalizability. They also discuss widespread deployment of augmented decision-making.

Diagnostic Utility of Radiomics in Thyroid and Head and Neck Cancers.

Radiomics is an emerging field in radiology that utilizes advanced statistical data characterizing algorithms to evaluate medical imaging and objectively quantify characteristics of a given disease. Due to morphologic heterogeneity and genetic variation intrinsic to neoplasms, radiomics have the potential to provide a unique insight into the underlying tumor and tumor microenvironment. Radiomics has been gaining popularity due to potential applications in disease quantification, predictive modeling, treatment planning, and response assessment - paving way for the advancement of personalized medicine. However, producing a reliable radiomic model requires careful evaluation and construction to be translated into clinical practices that have varying software and/or medical equipment. We aim to review the diagnostic utility of radiomics in otorhinolaryngology, including both cancers of the head and neck as well as the thyroid.

Radiogenomics and Its Role in Lymphoma.

PURPOSE OF REVIEW: Imaging features of lymphoma vary regionally. Awareness of site-specific key imaging characteristics of lymphoma can aid in rapid staging and assist in prompt treatment. FDG PET/CT and conventional MRI are readily available diagnostic modalities with excellent sensitivity and good specificity. Diagnostic specificity can be enhanced using emerging PET radiotracers, e.g., FLT and FET. RECENT FINDINGS: Emerging research has shown higher dimensional analysis (radiomics and radiogenomics) of imaging data can yield information of the underlying genetic aberrations in lymphoma, which can aid in assessing real-time evolution of tumor. CT, PET/CT, MRI, and ultrasound accentuate the intrinsic qualities of lymphoma (e.g., FDG PET/CT for increased metabolic activity, FLT PET/CT for increased proliferation index, and DWI for increased cellularity) and play an essential role in its diagnosis and examination. Advanced radiogenomic analyses use radiomic parameters to deduce genetic variations of lymphoma, providing noninvasive, repeatable, and real-time surveillance of its genetic progression.