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Early diagnosis of sacroiliitis may lead to preventive treatment which can significantly improve the patient's quality of life in the long run. Oftentimes, a CT scan of the lower back or abdomen is acquired for suspected back pain. However, since the differences between a healthy and an inflamed sacroiliac joint in the early stages are subtle, the condition may be missed. We have developed a new automatic algorithm for the diagnosis and grading of sacroiliitis CT scans as incidental findings, for patients who underwent CT scanning as part of their lower back pain workout. The method is based on supervised machine and deep learning techniques. The input is a CT scan that includes the patient's pelvis. The output is a diagnosis for each sacroiliac joint. The algorithm consists of four steps: 1) computation of an initial region of interest (ROI) that includes the pelvic joints region using heuristics and a U-Net classifier; 2) refinement of the ROI to detect both sacroiliac joints using a four-tree random forest; 3) individual sacroiliitis grading of each sacroiliac joint in each CT slice with a custom slice CNN classifier, and; 4) sacroiliitis diagnosis and grading by combining the individual slice grades using a random forest. Experimental results on 484 sacroiliac joints yield a binary and a 3-class case classification accuracy of 91.9% and 86%, a sensitivity of 95% and 82%, and an Area-Under-the-Curve of 0.97 and 0.57, respectively. Automatic computer-based analysis of CT scans has the potential of being a useful method for the diagnosis and grading of sacroiliitis as an incidental finding.

Purpose We present a new algorithm for nearly automatic liver segmentation and volume estimation from abdominal Computed Tomography Angiography (CTA) images and its validation. Materials and methods Our hybrid algorithm uses a multiresolution iterative scheme. It starts from a single user-defined pixel seed inside the liver, and repeatedly applies smoothed Bayesian classification to identify the liver and other organs, followed by adaptive morphological operations and active contours refinement. We evaluate the algorithm with two retrospective studies on 56 validated CTA images. The first study compares it to ground-truth manual segmentation and semi-automatic and automatic commercial methods. The second study uses the public data-set SLIVER07 and its comparison methodology. Results We achieved for both studies, correlations of 0.98 and 0.99 for liver volume estimation, with mean volume differences of 5.36 and 2.68% with respect to manual groundtruth estimation, and mean volume variability for different initial seeds of 0.54 and 0.004%, respectively. For the second study, our algorithm scored 71.8 and 67.87 for the training and test datasets, which compares very favorably with other semi-automatic methods. Conclusions Our algorithm requires minimal interaction by a non-expert user, is accurate, efficient, and robust to initial seed selection. It can be effective for hepatic volume estimation and liver modeling in a clinical setup.

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BackgroundTo explore the activity of pazopanib (P) + sirolimus (S) in patients who progressed after previous clinical benefit on pazopanib.MethodsEight patients with progressing metastatic high grade soft tissue sarcoma (STS) whose disease advanced on P following a response duration of at least 4 months were offered re-challenge of P supplemented by off-label S and a single patient with progressing metastatic chondrosarcoma was offered the combination as compassionate treatment. Patients were treated in two centers: Hadassah Medical Center and Tel Aviv Medical Center. Patients received oral P 200–600 mg once a day supplemented by S 3–4 mg taken separately, 12 h after the P dose.ResultsPatients received treatment from December 2012 to February 2016. Four progressed on the combination and their treatment was terminated. Two patients were undergoing treatment when data was summarized. Best Response Evaluation Criteria in Solid Tumour (RECIST) responses were: one partial response (PR), four stable disease (SD), and four progressive disease (PD), corresponding to five PR and four PD on the Choi criteria. Median progression free survival was 5.5 months (range 4–17).ConclusionsOur series showed that the combination of P + S has activity in STS patients selected by previous response to P and in a patient with chondrosarcoma, suggesting this can serve as a mechanism to reverse resistance to P and extend the chemotherapy-free window.