Biological and clinical implications of BIRC3 mutations in chronic lymphocytic leukemia.

BIRC3 is a recurrently mutated gene in chronic lymphocytic leukemia (CLL) but the functional implications of BIRC3 mutations are largely unexplored. Furthermore, little is known about the prognostic impact of BIRC3 mutations in CLL cohorts homogeneously treated with first-line fludarabine, cyclophosphamide, and rituximab (FCR). By immunoblotting analysis, we showed that the non-canonical nuclear factor-κB pathway is active in BIRC3-mutated cell lines and in primary CLL samples, as documented by the stabilization of MAP3K14 and by the nuclear localization of p52. In addition, BIRC3-mutated primary CLL cells are less sensitive to flu-darabine. In order to confirm in patients that BIRC3 mutations confer resistance to fludarabine-based chemoimmunotherapy, a retrospective multicenter cohort of 287 untreated patients receiving first-line FCR was analyzed by targeted next-generation sequencing of 24 recurrently mutated genes in CLL. By univariate analysis adjusted for multiple comparisons BIRC3 mutations identify a poor prognostic subgroup of patients in whom FCR treatment fails (median progression-free survival: 2.2 years, P<0.001) similar to cases harboring TP53 mutations (median progression-free survival: 2.6 years, P<0.0001). BIRC3 mutations maintained an independent association with an increased risk of progression with a hazard ratio of 2.8 (95% confidence interval 1.4-5.6, P=0.004) in multivariate analysis adjusted for TP53 mutation, 17p deletion and IGHV mutation status. If validated, BIRC3 mutations may be used as a new molecular predictor to select high-risk patients for novel frontline therapeutic approaches.

Validation of a Web-Based Planning Tool for Percutaneous Cryoablation of Renal Tumors.

PURPOSE: To validate a simulation environment for virtual planning of percutaneous cryoablation of renal tumors. MATERIALS AND METHODS: Prospectively collected data from 19 MR-guided procedures were used for validation of the simulation model. Volumetric overlap of the simulated ablation zone volume (Σ) and the segmented ablation zone volume (S; assessed on 1-month follow-up scan) was quantified. Validation metrics were DICE Similarity Coefficient (DSC; the ratio between twice the overlapping volume of both ablation zones divided by the sum of both ablation zone volumes), target overlap (the ratio between the overlapping volume of both ablation zones to the volume of S; low ratio means S is underestimated), and positive predictive value (the ratio between the overlapping volume of both ablation zones to the volume of Σ; low ratio means S is overestimated). Values were between 0 (no alignment) and 1 (perfect alignment), a value > 0.7 is considered good. RESULTS: Mean volumes of S and Σ were 14.8 cm3 (± 9.9) and 26.7 cm3 (± 15.0), respectively. Mean DSC value was 0.63 (± 0.2), and ≥ 0.7 in 9 cases (47%). Mean target overlap and positive predictive value were 0.88 (± 0.11) and 0.53 (± 0.24), respectively. In 17 cases (89%), target overlap was ≥ 0.7; positive predictive value was ≥ 0.7 in 4 cases (21%) and < 0.6 in 13 cases (68%). This indicates S is overestimated in the majority of cases. CONCLUSION: The validation results showed a tendency of the simulation model to overestimate the ablation effect. Model adjustments are necessary to make it suitable for clinical use.

Surgical Management of Primary Bone Lymphoma of the Hip: A Case Report and Review of the Literature.

INTRODUCTION: Primary bone lymphoma (PBL) is a rare bone malignancy which may present with atraumatic pain, swelling, or pathological fracture. Whilst the femur is the most commonly affected site, any bone may be involved. PBL should be distinguished from other bone lesions to determine clinical management. CASE REPORT: We report the case of an 89-year-old gentleman who presented to the local emergency department with atraumatic hip pain and inability to weight-bear. Multimodal imaging showed evidence of a tumor involving the proximal femur and adjacent acetabulum with an associated pathological intertrochanteric fracture. Biopsy specimens demonstrated this to be PBL of the diffuse large B-cell subtype. No other disease foci or nodal involvement was identified. The patient underwent proximal femoral replacement and acetabular reconstruction prior to commencing R-Mini-CHOP chemotherapy, during which time he has been permitted to fully weight-bear. CONCLUSION: To our knowledge, this is the first reported case of a patient having PBL with both femoral and acetabular involvements. Due to its infrequent occurrence, evidence remains limited to advise therapeutic guidelines. Our practice concurs with literature suggesting that surgery be reserved for cases of pathological fracture. However, the merits of undergoing surgical fixation prior to chemoradiation treatment have been considered.

GPU-based RFA simulation for minimally invasive cancer treatment of liver tumours.

PURPOSE: Radiofrequency ablation (RFA) is one of the most popular and well-standardized minimally invasive cancer treatments (MICT) for liver tumours, employed where surgical resection has been contraindicated. Less-experienced interventional radiologists (IRs) require an appropriate planning tool for the treatment to help avoid incomplete treatment and so reduce the tumour recurrence risk. Although a few tools are available to predict the ablation lesion geometry, the process is computationally expensive. Also, in our implementation, a few patient-specific parameters are used to improve the accuracy of the lesion prediction. METHODS: Advanced heterogeneous computing using personal computers, incorporating the graphics processing unit (GPU) and the central processing unit (CPU), is proposed to predict the ablation lesion geometry. The most recent GPU technology is used to accelerate the finite element approximation of Penne's bioheat equation and a three state cell model. Patient-specific input parameters are used in the bioheat model to improve accuracy of the predicted lesion. RESULTS: A fast GPU-based RFA solver is developed to predict the lesion by doing most of the computational tasks in the GPU, while reserving the CPU for concurrent tasks such as lesion extraction based on the heat deposition at each finite element node. The solver takes less than 3 min for a treatment duration of 26 min. When the model receives patient-specific input parameters, the deviation between real and predicted lesion is below 3 mm. CONCLUSION: A multi-centre retrospective study indicates that the fast RFA solver is capable of providing the IR with the predicted lesion in the short time period before the intervention begins when the patient has been clinically prepared for the treatment.