There has been recent interest in the application of machine learning techniques to neuroimaging-based diagnosis. These methods promise fully automated, standard PC-based clinical decisions, unbiased by variable radiological expertise. We recently used support vector machines (SVMs) to separate sporadic Alzheimer's disease from normal ageing and from fronto-temporal lobar degeneration (FTLD). In this study, we compare the results to those obtained by radiologists. A binary diagnostic classification was made by six radiologists with different levels of experience on the same scans and information that had been previously analysed with SVM. SVMs correctly classified 95% (sensitivity/specificity: 95/95) of sporadic Alzheimer's disease and controls into their respective groups. Radiologists correctly classified 65–95% (median 89%; sensitivity/specificity: 88/90) of scans. SVM correctly classified another set of sporadic Alzheimer's disease in 93% (sensitivity/specificity: 100/86) of cases, whereas radiologists ranged between 80% and 90% (median 83%; sensitivity/specificity: 80/85). SVMs were better at separating patients with sporadic Alzheimer's disease from those with FTLD (SVM 89%; sensitivity/specificity: 83/95; compared to radiological range from 63% to 83%; median 71%; sensitivity/specificity: 64/76). Radiologists were always accurate when they reported a high degree of diagnostic confidence. The results show that well-trained neuroradiologists classify typical Alzheimer's disease-associated scans comparable to SVMs. However, SVMs require no expert knowledge and trained SVMs can readily be exchanged between centres for use in diagnostic classification. These results are encouraging and indicate a role for computerized diagnostic methods in clinical practice.

Osteoid osteomas can be imaged with greater conspicuity by using dynamic gadolinium-enhanced instead of nonenhanced MR imaging and with conspicuity equal to or better than that obtained with thin-section CT.

T cell repertoire diversity is generated in part by recombination of variable (V), diversity (D), and joining (J) segments in the T cell receptor β (TCR) locus. T cell clonal frequency distribution determined by high-throughput sequencing of TCR β in 10 stem cell transplantation (SCT) donors revealed a fractal, self-similar frequency distribution of unique TCR bearing clones with respect to V, D, and J segment usage in the T cell repertoire of these individuals. Further, ranking of T cell clones by frequency of gene segment usage in the observed sequences revealed an ordered distribution of dominant clones conforming to a power law, with a fractal dimension of 1.6 and 1.8 in TCR β DJ and VDJ containing clones in healthy stem cell donors. This self-similar distribution was perturbed in the recipients after SCT, with patients demonstrating a lower level of complexity in their TCR repertoire at day 100 followed by a modest improvement by 1 year post-SCT. A large shift was observed in the frequency distribution of the dominant T cell clones compared to the donor, with fewer than one third of the VDJ-containing clones shared in the top 4 ranks. In conclusion, the normal T cell repertoire is highly ordered with a TCR gene segment usage that results in a fractal self-similar motif of pattern repetition across levels of organization. Fractal analysis of high-throughput TCR β sequencing data provides a comprehensive measure of immune reconstitution after SCT.

Diagnostic image-guided needle biopsy plays a vital role in the work-up and treatment of patients with extremity bone tumors. The radiologist and the orthopedic oncologic surgeon should take a team approach to this procedure, especially when the bone lesion might be a primary sarcoma for which limb-sparing surgery (LSS) would be considered. A set of anatomically based guidelines were developed that can be used by the radiologist, in combination with case-by-case consultation with the surgeon, to plan image-guided core needle biopsies of extremity long bone lesions that may be treatable with LSS. By using these guidelines, along with the aforementioned consultation, the radiologist will be able to preserve the patient's chances of receiving optimal surgical treatment.

This article describes the anatomy, pathophysiology, microbiology, and diagnosis of sinonasal disease, including chronic and fungal sinusitis, juvenile nasopharyngeal angiofibroma, inverted papilloma, and chondrosarcoma.