Pseudoprogression (PsP) is a diagnostic clinical dilemma in cancer. In this study, we retrospectively analyse glioblastoma patients, and using their dynamic susceptibility contrast and dynamic contrast-enhanced perfusion MRI images we build a classifier using radiomic features obtained from both Ktrans and rCBV maps coupled with support vector machines. We achieve an accuracy of 90.82% (area under the curve (AUC) = 89.10%, sensitivity = 91.36%, 67 specificity = 88.24%, p = 0.017) in differentiating between pseudoprogression (PsP) and progressive disease (PD). The diagnostic performances of the models built using radiomic features from Ktrans and rCBV separately were equally high (Ktrans: AUC = 94%, 69 p = 0.012; rCBV: AUC = 89.8%, p = 0.004). Thus, this MR perfusion-based radiomic model demonstrates high accuracy, sensitivity and specificity in discriminating PsP from PD, thus provides a reliable alternative for noninvasive identification of PsP versus PD at the time of clinical/radiologic question. This study also illustrates the successful application of radiomic analysis as an advanced processing step on different MR perfusion maps.

High tumor markers were frequently found in RA patients, even with controlled disease and were not related to actual cancer. Therefore, small increases of these markers in RA cases probably do not warrant a search for an occult neoplasm.

2016 Background: To differentiate between pseudoprogression and true progression in patients with glioblastoma using MR perfusion radiomic texture analysis (TA). Methods: 98 patients with pathologically-proven diagnosis of GBM were retrospectively included in this IRB approved HIPAA compliant study. All patients underwent DSC and DCE Perfusion MRI as part of their routine clinical care. Images were analyzed using Nordic ICE 2.3 (NordicNeuroLab) ; rCBV and ktrans maps were obtained. Subsequently, 3D slicer 4.3.1(http://www.slicer.org) was used to segment the entire tumor on the different processed maps to create a volume of interest (VOI) for Radiomic TA. Multiple invariant texture features where then extracted from each VOI. 475 invariant texture features were applied to each map. Leave-one-out cross-validation (LOOCV), receiver operating characteristic (ROC), Kaplan Meier, and multivariate Cox proportional hazards regression analyses were used to assess the relationship between texture feature and pseudoprogression and true progression. Results: Variance and sum entropy were the two most significant radiomic features that discriminated between pseudoprogression and true progression. P value, AUC, specificity and sensitivity were 0.03, 89.26%, 81.82%, and 100% respectively. Conclusions: Radiomic TA derived from perfusion images can be helpful in determining true versus pseudoprogression in GBM. Further, this study illustrates successful application of radiomic TA as an advanced processing step for different MRI perfusion maps (DCE, DSC).

No abstract

The Sjögren's syndrome (SS) is an autoimmune disease characterized by a lymphocytic infi ltration of salivary and lacrimal glands. Hematological manifestations of primary SS (pSS) usually consist of mild anemia, thrombocytopenia, moderate neutropenia, and lymphopenia. Agranulocytosis is rarely reported and usually responds to immunosuppression. We report the case of a pSS patient who presented with refractory agranulocytosis. Bone marrow biopsy disclosed a hypocellular bone marrow with normal maturation of the granulocytic series. The patient was successively treated with high-dose prednisone, granulocyte-macrophage colony stimulation factor, and cyclosporine, with no hematological response. Mycophenolate mofetil (MMF) was initiated and after two months there was a rise on the white blood cell count. After one year of follow-up, she had neither further neutropenia episodes, nor infectious complications. We conclude that, in pSS refractory agranulocytosis, MMF can be an effective and well-tolerated treatment option.

Gout is an inflammatory arthritis characterized by the deposition of monosodium urate crystals in the synovial membrane, articular cartilage and periarticular tissues leading to inflammation. Men are more commonly affected, mainly after the 5th decade of life. Its incidence has been growing with the population aging. In the majority of the cases, the diagnosis is made by clinical criteria and synovial fluid analysis, in search for monosodium urate crystals. Nonetheless, gout may sometimes have atypical presentations, complicating the diagnosis. In these situations, imaging methods have a fundamental role, aiding in the diagnostic confirmation or excluding other possible differential diagnosis. Conventional radiographs are still the most commonly used method in gout patients' evaluation; nevertheless, this is not a sensitive method, since it detect only late alterations. In the last years, there have been several advances in imaging methods for gout patients. Ultrasound has shown a great accuracy in the diagnosis of gout, identifying monosodium urate deposits in the synovial membrane and articular cartilage, in detecting and characterizing tophi and in identifying tophaceous tendinopathy and enthesopathy. Ultrasound has also been able to show crystal deposition in patients with articular pain in the absence of a classical gout crisis. Computed tomography is an excellent method for detecting bone erosions, being useful in spine involvement. Dual-energy CT is a new method able to provide information about the chemical composition of tissues, with high accuracy in the identification of monosodium urate deposits, even in the early stages of the disease and in cases of difficult characterization. Magnetic resonance imaging is useful in the evaluation of deep tissues not accessible by ultrasound. Besides the diagnosis, with the emergence of new drugs that aim to reduce tophaceous burden, imaging methods have become useful tools in monitoring the treatment of patients with gout.

No abstract

BACKGROUND: Treatment-related imaging changes are often difficult to distinguish from true tumor progression. Treatment-related changes or pseudoprogression (PsP) subsequently subside or stabilize without any further treatment, whereas progressive tumor requires a more aggressive approach in patient management. Pseudoprogression can mimic true progression radiographically and may potentially alter the physician's judgment about the recurrent disease. Hence, it can predispose a patient to overtreatment or be categorized as a non-responder and exclude him from the clinical trials. This study aims at assessing the potential of radiomics to discriminate PsP from progressive disease (PD) in glioblastoma (GBM) patients. METHODS: We retrospectively evaluated 304 GBM patients with new or increased enhancement on conventional MRI after treatment, of which it was uncertain for PsP versus PD. 149 patients had the histopathological evidence of PD and 27 of PsP. Remaining 128 patients were categorized into PD and PsP based on RANO criteria performed by a board-certified radiologist. Volumetrics using 3D slicer 4.3.1 and radiomics texture analysis were performed of the enhancing lesion(s) in question. RESULTS: Using the MRMR feature selection method, we identified 100 significant features that were used to build a SVM model. Five texture features (E, CS, SA, MP, CP) were found to be most predictive of pseudoprogression. On Leave One Out Cross-Validation (LOOCV), sensitivity, specificity and accuracy were 97%, 72%, and 90%, respectively. Using 70% of the patient data for training and 30% for validation, an AUC of 94% was achieved, with the sensitivity of 97% and specificity of 75%. CONCLUSION: 3D radiomic texture features of conventional MRI successfully discriminated pseudoprogression from true progression in a large cohort of GBM patients. Citation Format: Srishti Abrol, Aikaterini Kotrotsou, Ahmed Hassan, Nabil Elshafeey, Tagwa Idris, Naveen Manohar, Anand Agarwal, Islam Hassan, Kamel Salek, Nikdokht Farid, Carrie McDonald, Shiao-Pei Weathers, Naeim Bahrami, Samuel Bergamaschi, Ahmed Elakkad, Kristin Alfaro-Munoz, Fanny Moron, Jason Huse, Jeffrey Weinberg, Sherise Ferguson, Evangelos Kogias, Amy Heimberger, Raymond Sawaya, Ashok Kumar, John de Groot, Meng Law, Pascal Zinn, Rivka R. Colen. Radiomics discriminates pseudo-progression from true progression in glioblastoma patients: A large-scale multi-institutional study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3040.