Automated weight-bearing foot measurements using an artificial intelligence-based software.

OBJECTIVE: To assess the accuracy of an artificial intelligence (AI) software (BoneMetrics, Gleamer) in performing automated measurements on weight-bearing forefoot and lateral foot radiographs. METHODS: Consecutive forefoot and lateral foot radiographs were retrospectively collected from three imaging institutions. Two senior musculoskeletal radiologists independently annotated key points to measure the hallux valgus, first-second metatarsal, and first-fifth metatarsal angles on forefoot radiographs and the talus-first metatarsal, medial arch, and calcaneus inclination angles on lateral foot radiographs. The ground truth was defined as the mean of their measurements. Statistical analysis included mean absolute error (MAE), bias assessed with Bland-Altman analysis between the ground truth and AI prediction, and intraclass coefficient (ICC) between the manual ratings. RESULTS: Eighty forefoot radiographs were included (53 ± 17 years, 50 women), and 26 were excluded. Ninety-seven lateral foot radiographs were included (51 ± 20 years, 46 women), and 21 were excluded. MAE for the hallux valgus, first-second metatarsal, and first-fifth metatarsal angles on forefoot radiographs were respectively 1.2° (95% CI [1; 1.4], bias = - 0.04°, ICC = 0.98), 0.7° (95% CI [0.6; 0.9], bias = - 0.19°, ICC = 0.91) and 0.9° (95% CI [0.7; 1.1], bias = 0.44°, ICC = 0.96). MAE for the talus-first, medial arch, and calcaneal inclination angles on the lateral foot radiographs were respectively 3.9° (95% CI [3.4; 4.5], bias = 0.61° ICC = 0.88), 1.5° (95% CI [1.2; 1.8], bias = - 0.18°, ICC = 0.95) and 1° (95% CI [0.8; 1.2], bias = 0.74°, ICC = 0.99). Bias and MAE between the ground truth and the AI prediction were low across all measurements. ICC between the two manual ratings was excellent, except for the talus-first metatarsal angle. CONCLUSION: AI demonstrated potential for accurate and automated measurements on weight-bearing forefoot and lateral foot radiographs.

Predictive Factors for Recurrent Hemoptysis after Bronchial Artery Embolization in Patients with Lung Cancer.

PURPOSE: To identify clinical, radiological, and angiographic characteristics associated with recurrent hemoptysis after bronchial artery embolization (BAE) in patients with lung cancer and severe hemoptysis admitted to the intensive care unit (ICU). MATERIALS AND METHODS: A total of 144 consecutive patients with lung cancer who underwent BAE for life-threatening hemoptysis admitted in the ICU between 2014 and 2022 were retrospectively included. Demographics, laboratory values, clinical course, and radiological/angiographic features were compared between those with and without recurrent hemoptysis within 1 month after embolization. RESULTS: Of the 144 patients (mean age, 60.2 years [SD ± 10.9]; females, 15.3%), 34.7% (50/144) experienced clinically relevant recurrent hemoptysis within 1 month; among them, 29 of 50 (58.0%) cases necessitated a second embolization. Massive hemoptysis was observed in 54.2%, with 16.7% receiving the vasopressin analog terlipressin. The mean volume of hemoptysis and simplified acute physiology score II (SAPS II) were 235 mL (SD ± 214.3) and 31.2 (SD ± 18.6), respectively. Computed tomography (CT) angiography revealed pulmonary artery (PA) injury (11.5%) and necrosis/cavitation (25.8%), and PA embolization was performed in 15.3% of cases. Technical success rate was 92%. SAPS II (P = .01), massive hemoptysis (P < .001), terlipressin use (P = .01), necrosis/cavitation (P = .01), and PA injury on CT angiography (P < .001) were associated with recurrent hemoptysis. Independent predictors on multivariate analysis were massive hemoptysis (P = .016) and PA injury on CT angiography (P = .001). CONCLUSIONS: In patients with lung cancer and life-threatening hemoptysis treated by BAE, massive hemoptysis and PA injury identified on CT angiography are independent predictors of recurrent hemoptysis.

Virtual Injection Software Reduces Radiation Exposure and Procedural Time of Prostatic Artery Embolization Performed with Cone-Beam CT.

PURPOSE: To evaluate the impact of virtual injection software (VIS) use during cone-beam computed tomography (CT)-guided prostatic artery embolization (PAE) on both patient radiation exposure and procedural time. MATERIALS AND METHODS: This institutional review board (IRB)-approved comparative retrospective study analyzed the treatment at a single institution of 131 consecutive patients from January 2020 to May 2022. Cone-beam CT was used with (Group 1, 77/131; 58.8%) or without VIS (Group 2, 54/131, 41.2%). Radiation exposure (number of digital subtraction angiography [DSA] procedures), dose area product (DAP), total air kerma (AK), peak skin dose (PSD), fluoroscopy time (FT), and procedure time (PT) were recorded. The influences of age, body mass index, radial access, and use of VIS were assessed. RESULTS: In bivariate analysis, VIS use (Group 1) showed reduction in the number of DSA procedures (8.6 ± 3.7 vs 16.8 ± 4.3; P < .001), DAP (110.4 Gy·cm2 ± 46.8 vs 140.5 Gy·cm2 ± 61; P < .01), AK (642 mGy ± 451 vs 1,150 mGy ± 637; P = .01), PSD (358 mGy ± 251 vs 860 mGy ± 510; P = .001), FT (35.6 minutes ± 15.4 vs 46.6 minutes ± 20; P = .001), and PT (94.6 minutes ± 41.3 vs 115.2 minutes ± 39.6, P = .005) compared to those in Group 2. In multivariate analysis, AK, PSD, FT, and PT reductions were associated with VIS use (P < .001, P < .001, P = .001, and P = .006, respectively). CONCLUSIONS: The use of VIS during PAE performed under cone-beam CT guidance led to significant reduction in patient radiation exposure and procedural time.

Using AI to Improve Radiologist Performance in Detection of Abnormalities on Chest Radiographs.

Background Chest radiography remains the most common radiologic examination, and interpretation of its results can be difficult. Purpose To explore the potential benefit of artificial intelligence (AI) assistance in the detection of thoracic abnormalities on chest radiographs by evaluating the performance of radiologists with different levels of expertise, with and without AI assistance. Materials and Methods Patients who underwent both chest radiography and thoracic CT within 72 hours between January 2010 and December 2020 in a French public hospital were screened retrospectively. Radiographs were randomly included until reaching 500 radiographs, with about 50% of radiographs having abnormal findings. A senior thoracic radiologist annotated the radiographs for five abnormalities (pneumothorax, pleural effusion, consolidation, mediastinal and hilar mass, lung nodule) based on the corresponding CT results (ground truth). A total of 12 readers (four thoracic radiologists, four general radiologists, four radiology residents) read half the radiographs without AI and half the radiographs with AI (ChestView; Gleamer). Changes in sensitivity and specificity were measured using paired t tests. Results The study included 500 patients (mean age, 54 years ± 19 [SD]; 261 female, 239 male), with 522 abnormalities visible on 241 radiographs. On average, for all readers, AI use resulted in an absolute increase in sensitivity of 26% (95% CI: 20, 32), 14% (95% CI: 11, 17), 12% (95% CI: 10, 14), 8.5% (95% CI: 6, 11), and 5.9% (95% CI: 4, 8) for pneumothorax, consolidation, nodule, pleural effusion, and mediastinal and hilar mass, respectively (P < .001). Specificity increased with AI assistance (3.9% [95% CI: 3.2, 4.6], 3.7% [95% CI: 3, 4.4], 2.9% [95% CI: 2.3, 3.5], and 2.1% [95% CI: 1.6, 2.6] for pleural effusion, mediastinal and hilar mass, consolidation, and nodule, respectively), except in the diagnosis of pneumothorax (-0.2%; 95% CI: -0.36, -0.04; P = .01). The mean reading time was 81 seconds without AI versus 56 seconds with AI (31% decrease, P < .001). Conclusion AI-assisted chest radiography interpretation resulted in absolute increases in sensitivity for all radiologists of various levels of expertise and reduced the reading times; specificity increased with AI, except in the diagnosis of pneumothorax. © RSNA, 2023 Supplemental material is available for this article.

Performance of Sonoelastography for predicting malignancy in soft tissue.

BACKGROUND: Separating benign from malignant soft-tissue masses often requires a biopsy. The objective of this study was to assess whether shear-wave elastography (SWE) helped to separate benign from malignant soft-tissue masses. METHODS: In 2015-2016, we prospectively included patients with soft-tissue masses deemed by our multidisciplinary sarcoma board to require a diagnostic biopsy. All patients underwent ultrasonography (US) followed by SWE to measure elasticity. We compared benign and malignant tumors, overall and after separating tumors with vs. without a fatty component. The biopsy findings, and surgical-specimen histology when available, served as the reference standard. RESULTS: We included 136 patients, 99 with non-fatty and 37 with fatty soft-tissue masses. Mean elasticity and tumor-to-fat elasticity ratio (T/F) values were significantly lower for the benign than the malignant soft-tissue masses in the overall cohort (30.9 vs. 50.0 kilopascals (kPa), P = 0.03; and 2.55 vs. 4.30, P = 0.046) and in the non-fatty subgroup (37.8 ± 31.9 vs. 58.9 ± 39.1 kPa, P = 0.049 and 2.89 ± 5.25 vs. 5.07 ± 5.41, P = 0.046). Data for fatty tumors were non relevant due to lack of conclusive results. By receiver operating characteristics curve analysis, a T/F cutoff of 3.5 had 46% sensitivity and 84% specificity for separating benign and malignant soft-tissue masses. CONCLUSIONS: SWE had good specificity and poor sensitivity for separating benign from malignant soft-tissue masses.

Assessment of performances of a deep learning algorithm for the detection of limbs and pelvic fractures, dislocations, focal bone lesions, and elbow effusions on trauma X-rays.

PURPOSE: To appraise the performances of an AI trained to detect and localize skeletal lesions and compare them to the routine radiological interpretation. METHODS: We retrospectively collected all radiographic examinations with the associated radiologists' reports performed after a traumatic injury of the limbs and pelvis during 3 consecutive months (January to March 2017) in a private imaging group of 14 centers. Each examination was analyzed by an AI (BoneView, Gleamer) and its results were compared to those of the radiologists' reports. In case of discrepancy, the examination was reviewed by a senior skeletal radiologist to settle on the presence of fractures, dislocations, elbow effusions, and focal bone lesions (FBL). The lesion-wise sensitivity of the AI and the radiologists' reports was compared for each lesion type. This study received IRB approval (CRM-2106-177). RESULTS: A total of 4774 exams were included in the study. Lesion-wise sensitivity was 73.7% for the radiologists' reports vs. 98.1% for the AI (+24.4 points) for fracture detection, 63.3% vs. 89.9% (+26.6 points) for dislocation detection, 84.7% vs. 91.5% (+6.8 points) for elbow effusion detection, and 16.1% vs. 98.1% (+82 points) for FBL detection. The specificity of the radiologists' reports was always 100% whereas AI specificity was 88%, 99.1%, 99.8%, 95.6% for fractures, dislocations, elbow effusions, and FBL respectively. The NPV was measured at 99.5% for fractures, 99.8% for dislocations, and 99.9% for elbow effusions and FBL. CONCLUSION: AI has the potential to prevent diagnosis errors by detecting lesions that were initially missed in the radiologists' reports.

Deep learning to detect anterior cruciate ligament tear on knee MRI: multi-continental external validation.

OBJECTIVES: To develop a deep-learning algorithm for anterior cruciate ligament (ACL) tear detection and to compare its accuracy using two external datasets. METHODS: A database of 19,765 knee MRI scans (17,738 patients) issued from different manufacturers and magnetic fields was used to build a deep learning-based ACL tear detector. Fifteen percent showed partial or complete ACL rupture. Coronal and sagittal fat-suppressed proton density or T2-weighted sequences were used. A Natural Language Processing algorithm was used to automatically label reports associated with each MRI exam. We compared the accuracy of our model on two publicly available external datasets: MRNet, Bien et al, USA (PLoS Med 15:e1002699, 2018); and KneeMRI, Stajduhar et al, Croatia (Comput Methods Prog Biomed 140:151-164, 2017). Receptor operating characteristics (ROC) curves, area under the curve (AUC), sensitivity, specificity, and accuracy were used to evaluate our model. RESULTS: Our neural networks achieved an AUC value of 0.939 for detection of ACL tears, with a sensitivity of 87% (0.875) and a specificity of 91% (0.908). After retraining our model on Bien dataset and Stajduhar dataset, our algorithm achieved AUC of 0.962 (95% CI 0.930-0.988) and 0.922 (95% CI 0.875, 0.962) respectively. Sensitivity, specificity, and accuracy were respectively 85% (95% CI 75-94%, 0.852), 89% (95% CI 82-97%, 0.894), 0.875 (95% CI 0.817-0.933) for Bien dataset, and 68% (95% CI 54-81%, 0.681), 93% (95% CI 89-97%, 0.934), and 0.870 (95% CI 0.821-0.913) for Stajduhar dataset. CONCLUSION: Our algorithm showed high performance in the detection of ACL tears with AUC on two external datasets, demonstrating its generalizability on different manufacturers and populations. This study shows the performance of an algorithm for detecting anterior cruciate ligament tears with an external validation on populations from countries and continents different from the study population. KEY POINTS: • An algorithm for detecting anterior cruciate ligament ruptures was built from a large dataset of nearly 20,000 MRI with AUC values of 0.939, sensitivity of 87%, and specificity of 91%. • This algorithm was tested on two external populations from different other countries: a dataset from an American population and a dataset from a Croatian population. Performance remains high on these two external validation populations (AUC of 0.962 and 0.922 respectively).

Prevalence of Enthesopathies in Adults With X-linked Hypophosphatemia: Analysis of Risk Factors.

CONTEXT: Enthesopathies are the determinant of a poor quality of life in adults with X-linked hypophosphatemia (XLH). OBJECTIVE: To describe the prevalence of patients with enthesopathies and to identify the risk factors of having enthesopathies. METHODS: Retrospective study in the French Reference Center for Rare Diseases of the Calcium and Phosphate Metabolism between June 2011 and December 2020. Adult XLH patients with full body X-rays performed using the EOS® low-dose radiation system and clinical data collected from medical records. The main outcome measures were demographics, PHEX mutation, conventional treatment, and dental disease with the presence of enthesopathies. RESULTS: Of the 114 patients included (68% women, mean age 42.2 ± 14.3 years), PHEX mutation was found in 105 patients (94.6%), 86 (77.5%) had been treated during childhood. Enthesopathies (spine and/or pelvis) were present in 67% of the patients (n = 76). Patients with enthesopathies were significantly older (P = .001) and more frequently reported dental disease collected from medical records (P = .03). There was no correlation between the PHEX mutations and the presence of enthesopathies. Sixty-two patients had a radiographic dental examination in a reference center. Severe dental disease (number of missing teeth, number of teeth endodontically treated, alveolar bone loss, and proportion of patients with 5 abscesses or more) was significantly higher in patients with enthesopathies. CONCLUSION: Adult XLH patients have a high prevalence of enthesopathies in symptomatic adults patients with XLH seen in a reference center. Age and severe dental disease were significantly associated with the presence of enthesopathies.

Clinical guidelines and care pathway for management of low back pain with or without radicular pain.

OBJECTIVE: To develop guidelines for low back pain management according to previous international guidelines and the updated literature. METHODS: A report was compiled from a review of systematic reviews of guidelines published between 2013 and 2018 and meta-analysis of the management of low back pain published between 2015 and 2018. This report summarized the state-of-the-art scientific knowledge for each predefined area of the guidelines from a critical review of selected literature. A multidisciplinary panel of experts including 17 health professionals involved in low back pain management and 2 patient representatives formulated preliminary guidelines based on the compilation report and a care pathway. The compilation report and preliminary guidelines were submitted to 25 academic institutions and stakeholders for the consultation phase. From responses of academic institutions and stakeholders, the final guidelines were developed. For each area of the guidelines, agreement between experts was assessed by the RAND/UCLA method. RESULTS: The expert panel drafted 32 preliminary recommendations including a care pathway, which was amended after academic institution and stakeholder consultation. The consensus of the multidisciplinary expert panel was assessed for each final guideline: 32 recommendations were assessed as appropriate; none was assessed as uncertain or inappropriate. Strong approval was obtained for 27 recommendations and weak for 5. CONCLUSION: These new guidelines introduce several concepts, including the need to early identify low back pain at risk of chronicity to provide quicker intensive and multidisciplinary management if necessary.

Can T2-weighted Dixon fat-only images replace T1-weighted images in degenerative disc disease with Modic changes on lumbar spine MRI?

OBJECTIVES: To evaluate the diagnostic performance and interobserver agreement of a magnetic resonance imaging (MRI) protocol that only includes sagittal T2-weighted Dixon fat and water images as an alternative to a standard protocol that includes both sagittal T1-weighted sequence and T2-weighted Dixon water images as reference standard in lumbar degenerative disc disease with Modic changes. METHODS: From February 2017 to March 2019, 114 patients who underwent lumbar spine MRI for low back pain were included in this retrospective study. All MRI showed Modic changes at least at one vertebral level. Two radiologists read the standard protocol and 1 month later the alternative protocol. All MRI were assessed for Modic changes (types, location, extension) as well as structural changes (endplate defects, facet arthropathy, spinal stenosis, foraminal stenosis, Schmorl nodes, spondylolisthesis, disc bulges, and degeneration). Interobserver agreement was assessed, as well as diagnostic performance using the standard protocol as reference standard. RESULTS: Interobserver agreement was moderate to excellent (kappa ranging from 0.51 to 0.92). Diagnostic performance of the alternative protocol was good for detection of any Modic change (sensitivity = 100.00% [95% CI, 99.03-100.00]; specificity = 98.89% [95% CI, 98.02-99.44]), as well as for detection of each Modic subtype and structural variables (sensitivity respectively 100% and ranging from 88.43 to 99.75% ; specificity ranging respectively from 97.62 to 100% and 99.58 to 99.91% ). CONCLUSIONS: Combined with T2-weighted Dixon water images, T2-weighted Dixon fat images provide good diagnostic performance compared to T1-weighted images in lumbar degenerative disc disease with Modic changes, and could therefore allow for a shortened protocol. KEY POINTS: • Combined with T2-weighted Dixon water images, T2-weighted Dixon fat images (in comparison to T1-weighted sequence) can provide good diagnostic performance in lumbar degenerative disc disease with Modic changes. • Interobserver agreement of the alternative protocol including sagittal T2-weighted Dixon fat and water images was substantial to excellent for every studied variable except for facet arthropathy. • A shortened MRI protocol including T2-weighted Dixon sequence without T1-weighted sequence could be proposed in this clinical setting.

Assessment of an AI Aid in Detection of Adult Appendicular Skeletal Fractures by Emergency Physicians and Radiologists: A Multicenter Cross-sectional Diagnostic Study.

Background The interpretation of radiographs suffers from an ever-increasing workload in emergency and radiology departments, while missed fractures represent up to 80% of diagnostic errors in the emergency department. Purpose To assess the performance of an artificial intelligence (AI) system designed to aid radiologists and emergency physicians in the detection and localization of appendicular skeletal fractures. Materials and Methods The AI system was previously trained on 60 170 radiographs obtained in patients with trauma. The radiographs were randomly split into 70% training, 10% validation, and 20% test sets. Between 2016 and 2018, 600 adult patients in whom multiview radiographs had been obtained after a recent trauma, with or without one or more fractures of shoulder, arm, hand, pelvis, leg, and foot, were retrospectively included from 17 French medical centers. Radiographs with quality precluding human interpretation or containing only obvious fractures were excluded. Six radiologists and six emergency physicians were asked to detect and localize fractures with (n = 300) and fractures without (n = 300) the aid of software highlighting boxes around AI-detected fractures. Aided and unaided sensitivity, specificity, and reading times were compared by means of paired Student t tests after averaging of performances of each reader. Results A total of 600 patients (mean age ± standard deviation, 57 years ± 22; 358 women) were included. The AI aid improved the sensitivity of physicians by 8.7% (95% CI: 3.1, 14.2; P = .003 for superiority) and the specificity by 4.1% (95% CI: 0.5, 7.7; P < .001 for noninferiority) and reduced the average number of false-positive fractures per patient by 41.9% (95% CI: 12.8, 61.3; P = .02) in patients without fractures and the mean reading time by 15.0% (95% CI: -30.4, 3.8; P = .12). Finally, stand-alone performance of a newer release of the AI system was greater than that of all unaided readers, including skeletal expert radiologists, with an area under the receiver operating characteristic curve of 0.94 (95% CI: 0.92, 0.96). Conclusion The artificial intelligence aid provided a gain of sensitivity (8.7% increase) and specificity (4.1% increase) without loss of reading speed. © RSNA, 2021 Online supplemental material is available for this article.

Limiting radiation exposure during prostatic arteries embolization: influence of patient characteristics, anatomical conditions, and technical factors.

OBJECTIVE: To assess the influence of patient characteristics, anatomical conditions, and technical factors on radiation exposure during prostatic arteries embolization (PAE) performed for benign prostatic hyperplasia. MATERIALS AND METHODS: Patient characteristics (age, body mass index (BMI)), anatomical conditions (number of prostatic arteries, anastomosis), and technical factors (use of cone beam computed tomography (CBCT), large display monitor (LDM), and magnification) were recorded as well as total air kerma (AK), dose area product (DAP), fluoroscopy time (FT), and number of acquisitions (NAcq). Associations between potential dose-influencing factors and AK using univariate analysis and a multiple linear regression model were assessed. RESULTS: Forty-one consecutive men (68 ± 8 years, min-max: 40-76) were included. LDM and CBCT decreased the use of small field of view with 13.9 and 3.8% respectively, both p < 0.001. The use of a LDM significantly reduced AK (1006.6 ± 471.7 vs. 1412 ± 754.6 mGy, p = 0.02), DAP (119.4 ± 64.4 vs. 167.9 ± 99.2, p = 0.04), FT (40.4 ± 11.5 vs. 53.6 ± 25.5 min, p = 0.01), and NAcq (16.3 ± 6.3 vs. 18.2 ± 7, p = 0.04). In multivariate analysis, AK reduction was associated with lower patient BMI (ß = 0.359, p = 0.002), shorter FT (ß = 0.664, p < 0.001) and CBCT use (ß = - 0.223, p = 0.03), and decreased NAcq (ß = 0.229, p = 0.04). CONCLUSION: LDM and CBCT are important technical dose-related factors to help reduce radiation exposure during PAE, and should be considered in standard practice. KEY POINTS: • The use of large display monitor (LDM) and cone beam computed tomography (CBCT) both decreased the need for magnification during prostatic arteries embolization (PAE). • The use of LDM reduces radiation exposure during PAE. • Total air kerma is associated with patient's body mass index, fluoroscopy time, CBCT, and the number of acquisitions.

Predictors of favorable outcome after mechanical thrombectomy for anterior circulation acute ischemic stroke in octogenarians.

INTRODUCTION: Mechanical thrombectomy for anterior circulation large vessel occlusion (LVO) improves functional outcome at three months. This therapeutic approach is the new gold standard, with a benefit being also observed in elderly patients. However, data are limited in this heterogeneous and fragile population. The objectives of this study were, first, to describe outcome after mechanical thrombectomy in a representative group of patients over 80. Second, to evaluate factors associated with a favorable functional outcome after thrombectomy for anterior circulation LVO in elderly patients (aged≥80 years). METHODS: A total of 169 patients with anterior circulation LVO referred for an endovascular treatment were included. Primary outcome evaluated functional outcome at three months. Multivariable analysis was performed to identify prognostic factors in elderly patients with pre-stroke mRS≤3. RESULTS: Overall, 25.34% of patients (43/169) were functionally independent at three months (mRS≤2) and 16.57% (28/169) had a moderate functional disability (mRS=3). Mortality rate was 33.14% (56/169). At 24h, 7.1% of patients (12/169) had symptomatic hemorrhage. Male gender (P=0.033), low initial NIHSS (P=0.037), higher DWI-ASPECTS (P=0.022) and use of intravenous thrombolysis (IVT) (P=0.0193) were associated with a better functional outcome. CONCLUSIONS: There is no reason to withhold mechanical thrombectomy on the basis of age alone. Small infarct core, low NIHSS, male gender and use of IVT are associated with a better functional outcome.

Therapeutic outcome of CT-guided radiofrequency ablation in patients with osteoid osteoma.

OBJECTIVE: To assess the long-term outcome of computed tomography-guided radiofrequency ablation (CT-guided RFA) in patients with suspected osteoid osteoma (OO). MATERIALS AND METHODS: Single-center retrospective study. Patients with clinical suspicion and imaging diagnosis of osteoid osteoma were treated by CT-guided RFA using the same device with either a 7- or 10-mm active tip electrode. Specific precautions were applied in case of articular or spinal OO. Patients were contacted by phone to evaluate the long-term outcome in terms of pain, ability to perform daily activities (including sports), and long-term complications. Success was defined as the absence of residual pain and ability to perform daily activities normally. RESULTS: From 2008 to 2015, 126 patients were treated by CT-guided RFA for OO in our institution. Mean patient age was 26.1 years (SD = 11, range 1-53); mean delay to diagnosis was 16.9 months (SD = 15.2, range 1-120). Among patients who answered the follow-up call (n = 88), the overall success rate was 94.3%: 79/88 (89.8%) had primary success of the procedure, and 4/88 (4.5%) had a secondary success (repeat-RFA after pain recurrence). Mean follow-up time was 34.6 months (SD = 24.7, range 3-90). Few complications occurred: two mild reversible peripheral nerve injuries, one brachial plexus neuropathy, one broken electrode tip fragment, and one muscular hematoma. CONCLUSION: Osteoid osteoma can be effectively and safely treated by CT-guided RFA using the presented ablation protocol. Beneficial effects of the treatment persist at long-term follow-up.

ASPECTS (Alberta Stroke Program Early CT Score) Assessment of the Perfusion-Diffusion Mismatch.

BACKGROUND AND PURPOSE: Rapid and reliable assessment of the perfusion-weighted imaging (PWI)/diffusion-weighted imaging (DWI) mismatch is required to promote its wider application in both acute stroke clinical routine and trials. We tested whether an evaluation based on the Alberta Stroke Program Early CT Score (ASPECTS) reliably identifies the PWI/DWI mismatch. METHODS: A total of 232 consecutive patients with acute middle cerebral artery stroke who underwent pretreatment magnetic resonance imaging (PWI and DWI) were retrospectively evaluated. PWI-ASPECTS and DWI-ASPECTS were determined blind from manually segmented PWI and DWI volumes. Mismatch-ASPECTS was defined as the difference between PWI-ASPECTS and DWI-ASPECTS (a high score indicates a large mismatch). We determined the mismatch-ASPECTS cutoff that best identified the volumetric mismatch, defined as VolumeTmax>6s/VolumeDWI≥1.8, a volume difference≥15 mL, and a VolumeDWI<70 mL. RESULTS: Inter-reader agreement was almost perfect for PWI-ASPECTS (κ=0.95 [95% confidence interval, 0.90-1]), and DWI-ASPECTS (κ=0.96 [95% confidence interval, 0.91-1]). There were strong negative correlations between volumetric and ASPECTS-based assessments of DWI lesions (ρ=-0.84, P<0.01) and PWI lesions (ρ=-0.90, P<0.01). Receiver operating characteristic curve analysis showed that a mismatch-ASPECTS ≥2 best identified a volumetric mismatch, with a sensitivity of 0.93 (95% confidence interval, 0.89-0.98) and a specificity of 0.82 (95% confidence interval, 0.74-0.89). CONCLUSIONS: The mismatch-ASPECTS method can detect a true mismatch in patients with acute middle cerebral artery stroke. It could be used for rapid screening of patients with eligible mismatch, in centers not equipped with ultrafast postprocessing software.

Progressive development of renal cysts in glycogen storage disease type I.

Glycogen storage disease type I (GSDI) is a rare metabolic disease due to glucose-6 phosphatase deficiency, characterized by fasting hypoglycemia. Patients also develop chronic kidney disease whose mechanisms are poorly understood. To decipher the process, we generated mice with a kidney-specific knockout of glucose-6 phosphatase (K.G6pc-/- mice) that exhibited the first signs of GSDI nephropathy after 6 months of G6pc deletion. We studied the natural course of renal deterioration in K.G6pc-/- mice for 18 months and observed the progressive deterioration of renal functions characterized by early tubular dysfunction and a later destruction of the glomerular filtration barrier. After 15 months, K.G6pc-/- mice developed tubular-glomerular fibrosis and podocyte injury, leading to the development of cysts and renal failure. On the basis of these findings, we were able to detect the development of cysts in 7 out of 32 GSDI patients, who developed advanced renal impairment. Of these 7 patients, 3 developed renal failure. In addition, no renal cysts were detected in six patients who showed early renal impairment. In conclusion, renal pathology in GSDI is characterized by progressive tubular dysfunction and the development of polycystic kidneys that probably leads to the development of irreversible renal failure in the late stages. Systematic observations of cyst development by kidney imaging should improve the evaluation of the disease's progression, independently of biochemical markers.