[en] Short communication

No abstract available

[en] Short communication

No abstract available

[en] Short communication

[en] We searched for recurrence of breast cancer after a curative treatment by means of [18F]-FDG imaging using a 'hybrid' gamma-camera with coincidence detection (CDET). The 44 patients whose examinations are currently evaluable were referred either for occult disease (rising blood levels of tumor markers CEA or CA 15.3 without detectable lesions at conventional work-up) in 20 cases or for a recurrence suspected at imaging or assessed histologically in 24 cases. In the first clinical setting FDG-CDET had a 100 % sensitivity (13/13) and a 71% specificity (5/7); in the second clinical setting, the corresponding values were 90% (18/20) and 100% (4/4); globally the values were respectively 94% (31/33) and 82% (9/11). In 11 cases (35%), the recurrence appeared at CDET as an isolated focus, that left open the opportunity of a targeted therapy by radiotherapy or surgery. CDET detection of FDG did allow localisation of occult disease and of foci that were smaller than 10 mm. This modality could develop rapidly (as long as the number of patients referred is not too large) and would allow the clinician to prescribe it as a first line examination in case of rising tumor marker levels, in order to avoid useless imaging procedures in those patients. Its excellent sensitivity and positive predictive value (94%) favours the detection of recurrences at an early stage when they can be treated by a targeted therapy. Concerning detection of bone metastases, FDG-CDET appeared more efficient than conventional scintigraphy (sensitivity at lesion level = 75% versus 44%). (author)

[en] Complete text of publication follows: Purpose: PET tumour segmentation is currently an active research topic in the field of radiotherapy planning and multi-parametric data quantification. Although being efficient on homogeneous spheroid-shaped tumours, classical threshold-based approaches are of limited value for heterogeneous or complex-shaped tumours, still making expert-based manual delineation the reference standard for tumour imaging, despite several limitations. In this context, the aim of this study was to assess the performances of an active contour-based approach for the PET segmentation of complex-shaped lung tumours, in comparison to an optimized expert-based manual reference standard. Subjects and Methods: Seventy-five thoracic tumours were segmented using the same graphical user interface (GUI) ITK snap software. For each tumour, an optimized expert-based reference standard was generated from the set of six independent expert-based manual segmentation results using the Simultaneous Truth And Performance Level Estimate (STAPLE) algorithm. In addition, four raters semi-automatically segmented the 75 PET tumours twice using the active contour based-procedure of the GUI software, with a delay time of one week between two segmentation sessions. For the 75 tumours, accuracy of the semi-automatic segmentations against the optimized expert-based reference standard was assessed using the DICE similarity coefficient (DSC). Inter-rater and intra-rater reliability analyses were performed using the intra class correlation coefficients (ICC) estimates of the output volumes, along with their 95% confidence intervals (two-way mixed-model, individual-rating, absolute-agreement). For all tumours segmentation procedures, average time per procedure was also estimated. Results: Overall accuracy of the semi-automatic procedure was excellent with a DSC of 0.835 (95%CI = 0.775-0.895). Inter-rater reliabilities provided the following results: ICC = 0.941 (95%CI = 0.913-0.961) for the first session and ICC = 0.935 (95%CI = 0.906- 0.956) for the second session. Intra-rater reliabilities provided the following results: ICC = 0.993 (95%CI = 0.990-0.996) for the rater 1; ICC = 0.987 (95%CI = 0.976-0.993) for the rater 2; ICC = 0.972 (95%CI = 0.956-0.982) for the rater 3; and ICC = 0.977 (95%CI = 0.964-0.985) for the rater 4. Average time was 631 seconds for manual segmentation procedure and 130 seconds for active contour-based. Conclusions: Compared to the state of the art expert-based manual segmentation, the GUI-based active contour procedure provided excellent accuracy and reliability, with a mean procedure duration almost five-times faster than the manual reference procedure. ITK snap software is robust, fast, and easy enough to be routinely applied as a powerful alternative to the manual reference standard in this setting

[en] Complete text of publication follows: Aim/Introduction: To assess the feasibility of a fully integrated dynamic PET-MRI approach applied to the characterization of lung lesions specifically. Materials and Methods: A total of 9 patients underwent a one-hour dynamic PET-MRI imaging protocol for suspected lung cancer. FDG PET and DCE MRI full kinetic analyses (Sokoloff and extended Toft models respectively), together with DWI-ADC and T1/T2-mapping were performed. All the PET-MRI data were warped into the same isotropic reference space before analyses. For each lung lesion, voxel-wise 3D maps of 14 biological features / 4 main categories were computed using an in-house fully integrative multimodal post-processing pipeline developed for this purpose specifically : perfusion/vascularization (K_trans, K_ep, V_e and V_p); metabolism (SUV, k1, k2, k3, Ki, V_b, and MRGlu); diffusion (ADC); and tissular characterization (T1 and T2 mapping). For each lesion, voxel-wise monotonic relationships between all the PET-MRI features were explored (Spearman correlations). Finally, all the lesions were partitioned by using multidimensional unsupervised gaussian mixture approach, and features profiles/relationships were explored at the supervoxel regional level. Results: Relationships between the perfusion/vascularization, metabolism, diffusion and tissular feature categories differed among the lesions. At the feature level, strong correlations (absolute r value superior to 0.5) were observed for the perfusion/vascularization features pairs and the metabolic feature pairs. At the category level, absolute r values were inferior to 0.5 for the vast majority of the feature pairs. Combining the 14 features together, unsupervised clustering provided 2 to 4 supervoxels depending on the lesion. At the supervoxel regional levels, feature profiles differed, as well as their monotonic relationships. Conclusion: Our one-stop-shop fully integrative dynamic PETMRI protocol provided 14 biological features (4 main categories) in the same examination. For all lesions, the heterogeneity of features profiles/relationships at the regional supervoxel level highlights the unique capability of PET-MRI to get better insight of the biological characterization of lung masses