A study of MRI-based radiomics biomarkers for sacroiliitis and spondyloarthritis.

PURPOSE: To evaluate the performance of texture-based biomarkers by radiomic analysis using magnetic resonance imaging (MRI) of patients with sacroiliitis secondary to spondyloarthritis (SpA). RELEVANCE: The determination of sacroiliac joints inflammatory activity supports the drug management in these diseases. METHODS: Sacroiliac joints (SIJ) MRI examinations of 47 patients were evaluated. Thirty-seven patients had SpA diagnoses (27 axial SpA and ten peripheral SpA) which was established previously after clinical and laboratory follow-up. To perform the analysis, the SIJ MRI was first segmented and warped. Second, radiomics biomarkers were extracted from the warped MRI images for associative analysis with sacroiliitis and the SpA subtypes. Finally, statistical and machine learning methods were applied to assess the associations of the radiomics texture-based biomarkers with clinical outcomes. RESULTS: All diagnostic performances obtained with individual or combined biomarkers reached areas under the receiver operating characteristic curves ≥ 0.80 regarding SpA related sacroiliitis and and SpA subtypes classification. Radiomics texture-based analysis showed significant differences between the positive and negative SpA groups and differentiated the axial and peripheral subtypes (P < 0.001). In addition, the radiomics analysis was also able to correctly identify the disease even in the absence of active inflammation. CONCLUSION: We concluded that the application of the radiomic approach constitutes a potential noninvasive tool to aid the diagnosis of sacroiliitis and for SpA subclassifications based on MRI of sacroiliac joints.

CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms.

PURPOSE: As some of the most important factors for treatment decision of lung cancer (which is the deadliest neoplasm) are staging and histology, this work aimed to associate quantitative contrast-enhanced computed tomography (CT) features from malignant lung tumors with distant and nodal metastases (according to clinical TNM staging) and histopathology (according to biopsy and surgical resection) using radiomics assessment. METHODS: A local cohort of 85 patients were retrospectively (2010-2017) analyzed after approval by the institutional research review board. CT images acquired with the same protocol were semiautomatically segmented by a volumetric segmentation method. Tumors were characterized by quantitative CT features of shape, first-order, second-order, and higher-order textures. Statistical and machine learning analyses assessed the features individually and combined with clinical data. RESULTS: Univariate and multivariate analyses identified 40, 2003, and 45 quantitative features associated with distant metastasis, nodal metastasis, and histopathology (adenocarcinoma and squamous cell carcinoma), respectively. A machine learning model yielded the highest areas under the receiver operating characteristic curves of 0.92, 0.84, and 0.88 to predict the same previous patterns. CONCLUSION: Several radiomic features (including wavelet energies, information measures of correlation and maximum probability from co-occurrence matrix, busyness from neighborhood intensity-difference matrix, directionalities from Tamura's texture, and fractal dimension estimation) significantly associated with distant metastasis, nodal metastasis, and histology were discovered in this work, presenting great potential as imaging biomarkers for pathological diagnosis and target therapy decision.

Emergence of HA mutants during influenza virus pneumonia.

During the influenza pandemic of 2009, the number of viral pneumonia cases showed a marked increase in comparison with seasonal influenza viruses. Mutations at amino acid 222 (D222G mutations) in the virus hemagglutinin (HA) molecule, known to alter the receptor-recognition properties of the virus, were detected in a number of the more severely-affected patients in the early phases of the pandemic. To understand the background for the emergence of the mutant amino acid D222G in human lungs, we conducted histological examinations on lung specimens of patients from Mexico who had succumbed in the pandemic. Prominent regenerative and hyperplastic changes in the alveolar type II pneumocytes, which express avian-type sialoglycan receptors in the respiratory tract of severely affected individuals, were observed in the Mexican patients. An infection model utilizing guinea pigs, which was chosen in order to best simulate the sialic acid distribution of severe pneumonia in human patients, demonstrated an increase of D222G mutants and a delay in the diminution of mutants in the lower respiratory tract in comparison to the upper respiratory tract. Our data suggests that the predominance of avian-type sialoglycan receptors in the pneumonic lungs may contribute to the emergence of viral HA mutants. This data comprehensively illustrates the mechanisms for the emergence of mutants in the clinical samples.