MRI in differential diagnosis between tuberculous and pyogenic spondylodiscitis.

PURPOSE: The aim of the study was to investigate whether MRI findings together with epidemiological data could help in differentiating between tuberculous and pyogenic spondylodiscitis. METHODS: Clinical records of 260 patients with a suspicion of spondylodiscitis were analysed. Patients were selected using the following inclusion criteria: confirmed diagnosis of spondylodiscitis either from pyogenic bacteria or from Mycobacterium tuberculosis and contrast-enhanced MRI performed before treatment. Clinical data concerning age, sex and country-of-origin were also collected. For each patient, several MRI-features were evaluated by two-expert musculoskeletal radiologists. A chi-squared test and a multiple logistic regression were used to find the best predictors of tuberculous or pyogenic spondylodiscitis. RESULTS: 114 patients were retrospectively enrolled, 30 with tuberculous and 84 with pyogenic spondylodiscitis. We found 18 MRI-features, significantly different between the two groups. Among these, the most strongly associated with tuberculous spondylodiscitis were: heterogeneous vertebral signal on T1w-sequences (Odds Ratio(OR) = 205.759-p < 0.001), presence of epidural abscess (OR = 86.221-p < 0.001), severe vertebral destruction (OR = 10.017-p < 0.001) and absence of epidural phlegmon (OR = 86.221-p < 0.001). Moreover, patients coming from countries with a middle-high prevalence of tuberculosis were more frequently affected by tuberculous spondylodiscitis than others were (OR = 229.136-p < 0.001). The best prediction model demonstrated a correct classification rate of 94.7%. CONCLUSION: To the best of our knowledge this is the largest study comparing MRI-features of tuberculous and pyogenic spondylodiscitis. The above-mentioned MRI-features and epidemiological data are crucial in the differential diagnosis between these two entities, guiding the choice of the appropriate therapy, especially when a pathogen cannot be clearly identified with other modalities.

Prognostic value of posttreatment 18F-FDG PET/CT and predictors of metabolic response to therapy in patients with locally advanced cervical cancer treated with concomitant chemoradiation therapy: an analysis of intensity- and volume-based PET parameters.

PURPOSE: To investigate the prognostic value of posttreatment 18F-FDG PET/CT in patients with locally advanced cervical cancer (LACC) treated with concomitant chemoradiation therapy (CCRT). The secondary aim was to assess the possible role of intensity-based and volume-based PET parameters including SUVmax, SUVmean, MTV and TLG, and clinical parameters including age, pathology, FIGO stage and nodal involvement as factors predicting response to treatment. METHODS: This retrospective study included 82 patients affected by LACC treated with CCRT. All patients underwent 18F-FDG PET/CT both before and after treatment. The posttreatment PET/CT scans were used to classify patients as complete metabolic responders (CMR) or non-complete metabolic responders (N-CMR) according to the EORTC criteria. Kaplan-Meier analysis was used to evaluate differences in overall survival (OS) between the CMR and N-CMR groups. Student's t test, Pearson's chi-squared test and logistic regression were used to investigate the possible value of PET and clinical parameters as predictors of metabolic response to therapy. RESULTS: Kaplan-Meier analysis showed a highly significant difference in OS between the CMR and N-CMR groups (log-rank test p < 0.0001). Significant independent predictors of response to therapy were MTV (p = 0.019, odds ratio = 1.015, 95% CI = 1.002-1.028, Nagelkerke R2 = 0.110), TLG (p = 0.045, odds ratio = 1.001, 95% CI = 1.000-1.002, Nagelkerke R2 = 0.081) and nodal involvement (p = 0.088, odds ratio = 2.361, 95% CI = 0.879-6.343, Nagelkerke R2 = 0.051). CONCLUSION: 18F-FDG PET/CT-based response assessment using the EORTC criteria reliably predicts OS in LACC patients treated with CCRT. In our cohort of patients, pretreatment MTV and TLG and nodal involvement were predictors of response to therapy. MTV was the best predictor of response. However, its additional risk value seems to be low (MTV odds ratio = 1.015).