ATD: a multiplatform for semiautomatic 3-D detection of kidneys and their pathology in real time

This research presents a novel multi-functional system for medical Imaging-enabled Assistive Diagnosis (IAD). Although the IAD demonstrator has focused on abdominal images and supports the clinical diagnosis of kidneys using CT/MRI imaging, it can be adapted to work on image delineation, annotation and 3D real-size volumetric modelling of other organ structures such as the brain, spine, etc. The IAD provides advanced real-time 3D visualisation and measurements with fully automated functionalities as developed in two stages. In the first stage, via the clinically driven user interface, specialist clinicians use CT/MRI imaging datasets to accurately delineate and annotate the kidneys and their possible abnormalities, thus creating “3D Golden Standard Models”. Based on these models, in the second stage, clinical support staff i.e. medical technicians interactively define model-based rules and parameters for the integrated “Automatic Recognition Framework” to achieve results which are closest to that of the clinicians. These specific rules and parameters are stored in “Templates” and can later be used by any clinician to automatically identify organ structures i.e. kidneys and their possible abnormalities. The system also supports the transmission of these “Templates” to another expert for a second opinion. A 3D model of the body, the organs and their possible pathology with real metrics is also integrated. The automatic functionality was tested on eleven MRI datasets (comprising of 286 images) and the 3D models were validated by comparing them with the metrics from the corresponding “3D Golden Standard Models”. The system provides metrics for the evaluation of the results, in terms of Accuracy, Precision, Sensitivity, Specificity and Dice Similarity Coefficient (DSC) so as to enable benchmarking of its performance. The first IAD prototype has produced promising results as its performance accuracy based on the most widely deployed evaluation metric, DSC, yields 97% for the recognition of kidneys and 96% for their abnormalities; whilst across all the above evaluation metrics its performance ranges between 96% and 100%. Further development of the IAD system is in progress to extend and evaluate its clinical diagnostic support capability through development and integration of additional algorithms to offer fully computer-aided identification of other organs and their abnormalities based on CT/MRI/Ultra-sound Imaging

Urothelial Carcinoma of the Urinary Bladder in Young Adults: Presentation, Clinical behavior and Outcome

Introduction. There is not much evidence regarding clinical behavior of bladder cancer in younger patients. We evaluated clinical characteristics, tumor recurrence and progression in patients younger than 40 years old with urothelial bladder carcinoma. Methods. We retrospectively reviewed the medical records of 31 patients less than 40 years old who were firstly managed with bladder urothelial carcinoma in our department. Data were analysed with the Chi-square test. Results. Mean age was 31.7 years. Mean followup was 38.52 months (11–72 months). Nineteen (61%) patients were diagnosed with GII and 2 (6%) patients with GIII disease. Five (16%) patients presented with T1 disease. Three (9%) patients with invasive disease underwent cystectomy and adjuvant chemotherapy and one developed metastatic disease. Ten (32%) patients recurred during followup with a disease free recurrence rate of 65% the first 2 years after surgery. From those, 1 patient progressed to higher stage and three to higher grade disease. No patient died during followup. Conclusions. Bladder urothelial carcinoma in patients younger than 40 years is usually low stage and low grade. Management of these patients should be according to clinical characteristics and no different from older patients with the same disease