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.

Measuring plant disease severity in R: introducing and evaluating the pliman package

Image analysis based on color thresholding is the reference method for measuring severity as percent area affected. It is deemed to produce accurate results, usually considered the "true" severity value. More than a dozen applications have been used for the task in phytopathometry studies, but none was coded in R language. Here we introduced and evaluated pliman, a suite for the analysis of plant images. In particular, we show functions for computing percent severity based on RGB information contained in image palettes prepared by the user. Six image collections, totaling 249 images, from different diseases (wheat tan spot, soybean rust, olive leaf spot, rice brown spot, bean angular spot, and Xyllela fastidiosa on tobacco) exhibiting a range of symptomatic patterns and severity were used to evaluate the agreement of pliman predictions with APS Assess, LeafDoctor and ImageJ. Three users independently prepared three image palettes (each representing leaf background, symptomatic or healthy leaf tissue) by manually inspecting and subsetting these target areas in the images. Pliman predictions by a joint palette (by joining images by the three users into one) were highly concordant (ρc > 0.98) with measures by the other software for all but Xylella fastidiosa on Tobacco (ρc = 0.49). The error for the latter may be due to the low contrast between symptomatic and healthy tobacco tissues. Users showed to be a source of variation in the overall concordance depending on the disease. Reduction in the image resolution (< 1 megapixel) did not impact the results. Combined with parallel processing, the use of low image resolution sped up the processing time resulting in pliman being ~170 to ~430 times faster than existing tools for disease quantification. Pliman showed great potential to produce accurate measures and accelerate studies involving plant disease severity measurements, especially for the batch processing of large sets of image collections.

Plant disease severity estimated visually: a century of research, best practices, and opportunities for improving methods and practices to maximize accuracy

Plant disease quantification, mainly the intensity of disease symptoms on individual units (severity), is the basis for a plethora of research and applied purposes in plant pathology and related disciplines. These include evaluating treatment effect, monitoring epidemics, understanding yield loss, and phenotyping for host resistance. Although sensor technology has been available to measure disease severity using the visible spectrum or other spectral range imaging, it is visual sensing and perception that still dominates, especially in field research. Awareness of the importance of accuracy of visual estimates of severity began in 1892, when Cobb developed a set of diagrams as an aid to guide estimates of rust severity in wheat. Since that time, various approaches, some of them based on principles of psychophysics, have provided a foundation to understand sources of error during the estimation process as well as to develop different disease scales and disease-specific illustrations indicating the diseased area on specimens, similar to that developed by Cobb, and known as standard area diagrams (SADs). Several rater-related (experience, inherent ability, training) and technology-related (instruction, scales, and SADs) characteristics have been shown to affect accuracy. This review provides a historical perspective of visual severity assessment, accounting for concepts, tools, changing paradigms, and methods to maximize accuracy of estimates. A list of best-operating practices in plant disease quantification and future research on the topic is presented based on the current knowledge.

Induction of the endogenous sialoglycan ligand for eosinophil death receptor Siglec-8 in chronic rhinosinusitis with hyperplastic nasal polyposis

Siglec-8, an immune-inhibitory sialoglycan binding lectin (S8), is expressed on the surface of eosinophils and mast cells, which are potent mediators of allergic inflammation. When S8 engages endogenous sialoglycan ligands, eosinophils undergo apoptosis and mast cell mediator release is inhibited. In the human airway, Siglec-8 ligands (S8L) are sialylated keratan sulfate chains carried on isoforms of the protein Deleted in Malignant Brain Tumors-1 (DMBT1), an immunoregulatory protein that we recently identified as the endogenous ligand for S8, DMBT1S8. We herein report that S8L is overexpressed in chronic rhinosinusitis with nasal polyposis (CRSwNP), a prevalent eosinophilic laden airway disease. Quantification and comparison of the degree to which DMBT1 carries the S8L by immunoblot analysis and lectin blot overlay, respectively, from nasal lavage showed that the S8L/DMBT1 ratio was significantly increased in CRSwNP vs. control or CRS patients. We identified the histological sites of S8L and DMBT1 expression in fresh surgically resected human nasal polyps. Histochemistry of diseased polyps and adjacent nondiseased middle turbinate (MT) tissue from CRSwNP demonstrated colocalization of S8L and DMBT1 with highest staining in submucosal glands >> epithelium > stoma. S8L expression was specifically elevated in the submucosal glands and epithelium of polyp tissue compared to MT. We hypothesize that expression of the isoform of DMBT1 carrying the Siglec-8 binding sialoglycan, DMBT1S8, is induced in polyps of CRSwNP specifically at the site of disease, is produced in the submucosal glands of polyps and secreted into the lumen of the sinonasal cavity as a host response to mitigate eosinophil-mediated inflammation.

Plant disease severity estimated visually: a century of research, best practices and opportunities for improving methods and practices to maximize accuracy

Plant disease quantification, mainly the intensity of disease symptoms on individual units (severity) is the basis for a plethora of research and applied purposes in plant pathology and related disciplines. These include evaluating treatment effect, monitoring epidemics, understanding yield loss, and phenotyping for host resistance. Although sensor technology has been available to measure disease severity using the visible spectrum or other spectral range imaging, it is visual sensing and perception that still dominates, especially in field research. Awareness of the importance of accuracy of visual estimates of severity began in 1892, when Cobb developed a set of diagrams as an aid to guide estimates of rust severity in wheat. Since that time, various approaches, some of them based on principles of psychophysics, have provided a foundation to understand sources of error during the estimation process as well as to develop different disease scales and disease-specific illustrations indicating the diseased area on specimens, similar to that developed by Cobb, and known as standard area diagrams (SADs). Several rater-related (experience, inherent ability, training) and technology-related (instruction, scales and SADs) characteristics have been shown to affect accuracy. This review provides a historical perspective of visual severity assessment, accounting for concepts, tools, changing paradigms, and methods to maximize accuracy of estimates. A list of best operating practices in plant disease quantification and future research on the topic is presented based on the current knowledge.