"Accelerated" chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL): unraveling the biological gray zone of CLL/SLL in the era of novel therapies.

Accelerated chronic lymphocytic leukemia/small lymphocytic lymphoma (A-CLL/SLL) is a histologically aggressive subtype of CLL/SLL that lies in between conventional CLL/SLL (C-CLL/SLL) and Richter transformation (RT) on the biological spectrum. Although the histologic criteria for A-CLL/SLL were defined 14 years ago, the clinical and genetic characteristics and survival outcomes of these patients have yet to be studied in the era of novel therapies. We retrospectively analyzed the clinicopathologic, genetic, and survival characteristics of 34 patients with confirmed tissue diagnosis of A-CLL/SLL and compared them with 120 patients with C-CLL/SLL. Patients with A-CLL/SLL had significantly higher frequencies of B-symptoms, anemia and thrombocytopenia, splenomegaly, higher LDH, and more advanced Rai stages. A-CLL/SLL showed a significantly higher frequency of TP53 mutations (55.0% vs. 11.5%;p < 0.0001) and deletions (38.2% vs. 8.3%;p < 0.0001), lower isolated del(13q) (5.8% vs. 27.5%;p < 0.0001), and increased incidence of RT (11.76% vs. 0.83%;p = 0.0025). The overall survival of patients with A-CLL/SLL was significantly lower than C-CLL/SLL (median survival: 6.17 years vs. not reached; 2 and 5-year survival rates: 75.5% vs. 94.7% and 53.3% vs. 93.7%, respectively; p < 0.0001); however, novel agents have improved the outcomes dramatically compared to the previously published data in the pre-BTKi era. Our results support the categorization of A-CLL/SLL as a distinct biologically aggressive subtype of CLL/SLL and highlight the need to revise the diagnostic criteria utilizing a multifaceted approach that integrates the overall pathobiological profile of the disease, in addition to the histology.

CNN-based evaluation of bone density improves diagnostic performance to detect osteopenia and osteoporosis in patients with non-contrast chest CT examinations.

PURPOSE: As osteoporosis is still underdiagnosed by clinicians and radiologists, the aim of the present study was to assess the performance of an Artificial intelligence (AI)-based Convolutional Neuronal Network (CNN)-Algorithm for the detection of low bone density on routine non-contrast chest CT in comparison to clinical reports using DEXA scans as reference. METHOD: This retrospective cross-sectional study included patients who underwent non-contrast chest CT and DEXA between April 2018 and June 2018 (n = 109, 19 men, mean age: 67.7 years). CT studies were evaluated for thoracic vertebral bone pathologies using a CNN-Algorithm, which calculates the attenuation profile of the spine. The content of the radiological reports was evaluated for the description of osteoporosis or osteopenia. DEXA was used as the reference standard. To estimate correlation the Spearman test was used and the comparison of the different groups was performed using the Wilcoxon rank sum test. Diagnostic was evaluated by performing a receiver operating characteristic curve analysis. RESULTS: The DEXA examination revealed normal bone density in 42 patients, while 49 patients had osteopenia and 7 osteoporosis. There was a statistically significant correlation between the mean CNN-based attenuation of the thoracic spine and the bone density measured on the DEXA in the hip (r = 0.51, p < 0.001) and lumbar spine (r = 0.34, p = 0.01). The mean attenuation was significantly higher in patients with normal bone density (172 ± 44.5 HU) compared to those with osteopenia or osteoporosis (125.2 ± 33.8 HU), (p < 0.0001). Diagnostic performance in distinguishing normal from abnormal bone density was higher using the CNN-based vertebral attenuation (accuracy 0.75, sensitivity: 0.93, specificity: 0.61) compared to clinical reports (accuracy 0.51, sensitivity: 0.14, specificity: 0.53). CONCLUSION: CNN-based evaluation of bone density may provide additional value over standard clinical reports for the detection of osteopenia and osteoporosis in patients undergoing routine non-contrast chest CT scans. This additional value could improve identification of fracture risk and subsequent treatment.

Quiescent-Interval Slice-Selective MRA Accurately Estimates Intravascular Stent Dimensions Prior to Intervention in Patients With Peripheral Artery Disease.

BACKGROUND: Quiescent-interval slice-selective (QISS) magnetic resonance angiography (MRA) is a non-contrast alternative for the pre-procedural assessment of patients with peripheral artery disease (PAD). However, the feasibility of pre-procedural stent size estimation using QISS MRA would merit investigation. PURPOSE: To evaluate the feasibility of QISS MRA for pre-procedural stent size estimation in PAD patients compared to computed tomography angiography (CTA). STUDY TYPE: Retrospective. SUBJECTS: Thirty-three PAD patients (68 ± 9 years, 18 men, 15 women). FIELD STRENGTH/SEQUENCE: Two-dimensional balanced steady-state free precession QISS MRA at 1.5 T and 3 T. ASSESSMENT: All patients received QISS MRA and CTA of the lower extremity run-off followed by interventional digital subtraction angiography (DSA). Stenotic lesion length and diameter were quantified (AMF and AVS with 3 and 13 years of experience in cardiovascular imaging, respectively) to estimate the dimensions of the stent necessary to restore blood flow in the treated arteries. Measured dimensions were adjusted to the closest stent size available. STATISTICAL TESTS: The Friedman test with subsequent pairwise Wilcoxon signed-rank test was used to compare the estimated stent dimensions between QISS MRA, CTA, and the physical stent size used for intervention. Intra-class correlation (ICC) analysis was performed to assess inter-reader agreement. Significant differences were considered at P < 0.05. RESULTS: No significant difference was observed between estimated stent diameter by QISS MRA or CTA compared to physical stent diameter (8.9 ± 2.9 mm, 8.8 ± 3.0 mm, and 8.8 ± 3.8 mm, respectively; χ2  = 1.45, P = 0.483). There was a significant underestimation of stent length for both QISS MRA and CTA, compared to physical stent length (45.8 ± 27.8 mm, 46.4 ± 29.3 mm, and 50.4 ± 34.0 mm, respectively; χ2  = 11.96) which could be corrected when measurements were adjusted to the next available stent length (χ2  = 2.38, P = 0.303). Inter-reader assessment showed good to excellent agreement between the readers (all ICC ≥0.81). DATA CONCLUSION: QISS MRA represents a reliable method for pre-procedural lesion assessment and stent diameter and length estimation in PAD patients. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.