Design and application of a customizable relational DataBase to assess clinicopathological correlations and concomitant pathology in neurodegenerative diseases.

The diagnosis of neurodegenerative diseases is made complex by the heterogenous phenotype of the patients and the regular occurrence of concomitant pathology. Studying clinicopathological correlations in autopsy series is a central approach to improve pathological prediction in clinical practice. However, such method requires a wealth of information, and the use of standard spreadsheet software is hardly suitable. To overcome this constraint, we designed a customizable and freely available neuropathology form with 456 data entry fields driven by an open-source DataBase Management Systems (DBMS) using Structured Query Language (SQL). This approach allowed us to optimize the compilation of clinical and pathological data from our brain collection (264 autopsied patients, 22,885 data points). Information was then easily retrieved using general and specific queries, facilitating the analysis of demographics, clinicopathological correlations, and incidental and concomitant proteinopathies. Tau, amyloid-ß and α-synuclein incidental pathology was observed in respectively 78.1%, 42.8%, and 10.7% of all the patients. These proportions increased with age, reaching 100% for Tau pathology after 80. Concomitant proteinopathy was observed in 46.4% of the patients diagnosed with neurodegenerative diseases and prion disease. We observed a particularly high rate of co-pathology in patients with Dementia with Lewy bodies (81.3% of associated Tau and amyloid-ß pathology) and Creutzfeldt-Jakob disease (68.4% of associated Tau pathology). Finally, we used specific queries to identify old cases that could meet newly defined neuropathological criteria and revised the diagnosis of a 90-year-old patient to LATE Stage 2. Increasing our understanding of clinicopathological correlations in neurodegenerative diseases is crucial given the implications in clinical diagnosis, biomarker identification and targeted therapies assessment. The precise characterization of clinical and pathological data of autopsy series remains a central approach but the large amount of generated data should encourage a more systematic use of DBMS.

A semi-automated method for measuring the evolution of both lumen area and blood flow in carotid from Phase Contrast MRI.

Phase-Contrast (PC) velocimetry Magnetic Resonance Imaging (MRI) is a useful modality to explore cardiovascular pathologies, but requires the automatic segmentation of vessels and the measurement of both lumen area and blood flow evolutions. In this paper, we propose a semi-automated method for extracting lumen boundaries of the carotid artery and compute both lumen area and blood flow evolutions over the cardiac cycle. This method uses narrow band region-based active contours in order to correctly capture the lumen boundary without being corrupted by surrounding structures. This approach is compared to traditional edge-based active contours, considered in related works, which significantly underestimate lumen area and blood flow. Experiments are performed using both a sequence of a homemade phantom and sequences of 20 real carotids, including a comparison with manual segmentation performed by a radiologist expert. Results obtained on the phantom sequence show that the edge-based approach leads to an underestimate of carotid lumen area and related flows of respectively 18.68% and 4.95%. This appears significantly larger than weak errors obtained using the region-based approach (respectively 2.73% and 1.23%). Benefits appear even better on the real sequences. The edge-based approach leads to underestimates of 40.88% for areas and 13.39% for blood flows, compared to limited errors of 7.41% and 4.6% with our method. Experiments also illustrate the high variability and therefore the lack of reliability of manual segmentation.