Fractional tumor burden maps increase the confidence of reading brain MR perfusion.

RATIONALE AND OBJECTIVES: Various methods exist to perform and post-process perfusion weighted MR imaging in the post-treatment imaging of glioma patients to differentiate tumor progression from tumor-related abnormalities. One of these post-processing methods produces 'fractional tumor burden' maps. This multi-reader study investigated the clinical feasibility of fractional tumor burden maps on real world data from radiological follow-up of high-grade astrocytoma patients. METHODS: Five readers with background in radiology and varying levels of experience were tasked with assessing 30 astrocytoma and glioblastoma patients during one reader session. First, they were provided with a dataset of conventional MRI sequences, including perfusion MRI with regional cerebral blood volume maps. Then the dataset was expanded with a corresponding fractional tumor burden maps. Diagnostic accuracy, duration of post-processing, duration of the assessment of the fractional tumor burden maps, the diagnostic confidence reported by the readers and their diagnoses were recorded. Final diagnosis was determined by clinical and radiological follow-up and/or histopathological data used as gold standard. RESULTS: A mean sensitivity of 83.3 % and mean specificity of 55.1 % was obtained without the use of fractional tumor burden maps, whereas their additional of fractional tumor burden maps resulted in a mean sensitivity and specificity of 79.5 % and 54.2 %, respectively. Diagnostic accuracies with and without fractional tumor burden maps were not significantly different (Z = 0.76, p = 0.450). The median time spent post-processing was 313 s, while the median duration of the assessment of the FTB maps was 19 s. Interestingly, reader confidence increased significantly after adding the fractional tumor burden-maps to the assessment (Z = 454, p < 0.01). CONCLUSIONS: While the use of fractional tumor burden maps does not carry additional value in the radiological follow-up of post-operative high-grade astrocytoma and glioblastoma patients, it does give readers more confidence in their diagnosis.

Clinical applicability of signal heterogeneity and tumor border assessment on T2-weighted MR images to distinguish astrocytic from oligodendroglial origin of gliomas.

BACKGROUND AND PURPOSE: Radiological features on magnetic resonance imaging (MRI) were attributed to oligodendroglioma, although the diagnostic accuracy in a real-world clinical setting remains partially elusive. This study investigated the accuracy and robustness of tumor heterogeneity and tumor border delineation on T2-weighted MRI to distinguish oligodendroglioma from astrocytoma. MATERIALS AND METHODS: Eight readers from three different specialties (radiology, neurology, neurosurgery) with varying levels of experience blindly rated 79 T2-weighted MR images of patients with either oligodendroglioma or astrocytoma. After the first reading session, all readers were re-invited for a second reading session within three weeks. Diagnostic accuracy, including area under the receiver operator characteristics curve (AUC), and intra-observer variability and inter-observer variability were used as outcome measures. RESULTS: Pooled sensitivity and specificity to distinguish oligodendroglioma from astrocytoma for the use of tumor heterogeneity were 59.9 % respectively 74.5 %, and 85.7 % respectively 40.1 % for tumor border. A second reading session did not result in a significant change in sensitivity or specificity for tumor heterogeneity (P = 0.752 and P = 0.733, respectively) or tumor border (P = 0.309 and P = 0.271, respectively). An AUC of 0.825 was achieved with regard to predicting oligodendroglial origin of gliomas. Intra-observer agreement ranged from moderate to very good for tumor heterogeneity (kappa-value 0.43-0.87) and tumor border (0.40-0.84). A moderate inter-oberserver agreement was achieved for tumor heterogeneity and tumor border (kappa-value of 0.50 and 0.45, respectively). CONCLUSION: This study demonstrates that tumor heterogeneity and tumor borders on T2-weighted MRI could be used with moderate Finter-observer agreement to non-invasively distinguish oligodendroglioma from astrocytoma.

Artificial intelligence software for diagnosing intracranial arterial occlusion in patients with acute ischemic stroke.

PURPOSE: To evaluate the diagnostic performance of AI software in diagnosing intracranial arterial occlusions in the proximal anterior circulation at CT angiography (CTA) and to compare it to manual reading performed in clinical practice. METHODS: Patients with acute ischemic stroke underwent CTA to detect arterial occlusion in the proximal anterior circulation. Retrospective review of CTA scans by two neuroradiologists served as reference standard. Sensitivity and specificity of AI software (StrokeViewer) were compared to those of manual reading using the McNemar test. The proportions of correctly detected occlusions in the distal internal carotid artery and/or M1 segment of the middle cerebral artery (large vessel occlusion [LVO]) and in the M2 segment of the middle cerebral artery (medium vessel occlusion [MeVO]) were calculated. RESULTS: Of the 474 patients, 75 (15.8%) had an arterial occlusion in the proximal anterior circulation according to the reference standard. Sensitivity of StrokeViewer software was not significantly different compared to that of manual reading (77.3% vs. 78.7%, P = 1.000). Specificity of StrokeViewer software was significantly lower than that of manual reading (88.5% vs. 100%, P < 0.001). StrokeViewer software correctly identified 40 of 42 LVOs (95.2%) and 18 of 33 MeVOs (54.5%). StrokeViewer software detected 8 of 16 (50%) intracranial arterial occlusions which were missed by manual reading. CONCLUSION: The current AI software detected intracranial arterial occlusion with moderate sensitivity and fairly high specificity. The AI software may detect additional occlusions which are missed by manual reading. As such, the use of AI software may be of value in clinical stroke care.

Diagnostic performance of diffusion-weighted MR neurography as an adjunct to conventional MRI for the assessment of brachial plexus pathology.

OBJECTIVE: To investigate the diagnostic performance of diffusion-weighted (DW) MR neurography as an adjunct to conventional MRI for the assessment of brachial plexus pathology. METHODS: DW MR neurography scans (short tau inversion recovery fat suppression and b-value of 800 s/mm2) of 15 consecutive patients with and 45 randomly selected patients without brachial plexus abnormalities were independently and blindly reviewed by a 5th year radiology resident, a junior neuroradiologist, and a senior neuroradiologist. RESULTS: Median interpretation times ranged between 20 and 30 s. Interobserver agreement was substantial (κ coefficients of 0.715-0.739). For the 5th year radiology resident, sensitivity was 53.3% (95% CI, 30.1-75.2%) and specificity was 100% (95% CI, 92.1-100%). For the junior neuroradiologist, sensitivity was 66.7% (95% CI, 41.7-84.8%) and specificity was 100% (95% CI, 92.1-100%). For the senior neuroradiologist, sensitivity was 73.3% (95% CI, 48.1-89.1%) and specificity was 95.6% (95% CI, 85.2-98.8%). Traumatic injury, metastases, radiation-induced plexopathy, schwannoma, and inflammatory process of unknown cause could be detected by the majority of readers (100% detection rate for each disease entity by at least two readers). Neuralgic amyotrophy, iatrogenic injury after first rib resection, and cervical disc herniation causing root compression were not detected by the majority of readers (0% detection rate for each disease entity by at least two readers). CONCLUSION: DW MR neurography may be a useful adjunct when assessing for brachial plexus abnormalities, because interpretation time is relatively short and the majority of abnormalities can be detected. KEY POINTS: • DW MR neurography interpretation time of the brachial plexus is relatively short (median interpretation times of 20 to 30 s). • Interobserver agreement between three readers with different levels of experience is substantial (κ coefficients of 0.715 to 0.739). • DW MR neurography can detect brachial plexus abnormalities with moderate sensitivity (53.3 to 73.3%) and high specificity (95.6 to 100%).

Reliability and accuracy of 3-mm and 2-mm maximum intensity projection CT angiography to detect intracranial large vessel occlusion in patients with acute anterior cerebral circulation stroke.

PURPOSE: To evaluate the reliability and accuracy of thick maximum intensity projection (MIP) CTA images to detect large-vessel occlusion (LVO) in the anterior circulation in patients with acute stroke. METHODS: A total of 140 acute stroke patients (41 with and 99 without LVO) were evaluated by two neuroradiologists for LVO using axial 3-mm and 2-mm MIPs. RESULTS: Interobserver agreement was substantial using 3-mm MIPs (ĸ = 0.67) and almost perfect using 2-mm MIPs (ĸ = 0.82). Using 3-mm MIPs, sensitivities were 80.5% and 68.3%, with specificities of 98.0% and 96.0%. Using 2-mm MIPs, sensitivities were 82.9% and 73.2%, with specificities of 98.0% and 99.0%. Sensitivity and specificity of 3 mm and 2 mm MIPs were not statistically significantly different (P ≥ 0.375). The majority of LVOs in the distal intracranial carotid artery, and/or M1-segment were correctly identified: 96.0% (observer 1, 3-mm MIPs), 88.0% (observer 2, 3-mm MIPs), 96.0% (observer 1, 2-mm MIPs), and 96.0% (observer 2, 2 mm MIPs). Using 3-mm MIP images, observers 1 and 2 missed 7/15 (46.7%) and 9/15 (60.0%) of isolated M2-segment occlusions, respectively. Using 2-mm MIP images, observers 1 and 2 missed 5/15 (33.3%) and 6/15 (40.0%) of isolated M2-segment occlusions, respectively. CONCLUSION: Thick (2-3 mm) axial MIPs are not useful to detect proximal LVO in the anterior circulation.

Diagnostic performance of single-phase CT angiography in detecting large vessel occlusion in ischemic stroke: A systematic review.

PURPOSE: To systematically review the diagnostic performance of single-phase CT angiography (CTA) in detecting intracranial large vessel occlusion (LVO). METHOD: MEDLINE and Embase were searched for studies investigating the diagnostic performance of single-phase CTA in detecting LVO. Study quality was assessed. Sensitivity and specificity were calculated and meta-analyzed with a bivariate random-effects model. Heterogeneity was assessed with a chi-squared test. RESULTS: Eleven studies were included. High risk of bias with regard to "patient selection", "reference standard", and "flow and timing" was present in 4, 1, and 2 studies, respectively. In 7 studies, it was unclear whether reference tests were interpreted blinded to CTA readings. There was variability in types of vessel segments analyzed, resulting in heterogeneous sensitivity and specificity (P < 0.05). Two studies provided data for the proximal anterior circulation (distal intracranial carotid artery, A1-, A2-, M1- and M2-segments), with pooled sensitivity of 88.4 % (95 % CI: 62.2-97.2 %) and pooled specificity of 98.5 % (95 % CI: 33.2-100 %). One study suggested that multiphase CTA improved agreement between nonexperts and an expert in detecting A1-, A2-, M1-, M2-, and M3-segment occlusions compared to single-phase CTA (ĸ = 0.72-0.76 vs. ĸ = 0.32-0.45). No other included study reported added value of advanced CTA (CT perfusion, 4D-CTA, or multiphase CTA) compared to single-phase CTA in detecting proximal anterior circulation LVO. CONCLUSION: There is lack of high-quality studies on the diagnostic performance of single-phase CTA for LVO detection in the proximal anterior circulation. The added value of advanced CTA techniques in detecting proximal anterior circulation LVO is not completely clear yet.

Radiology workload in clinical implementation of thrombectomy for acute ischemic stroke: experience from The Netherlands.

PURPOSE: To investigate the number of acute stroke patients undergoing CT angiography (CTA) for suspected large vessel occlusion (LVO) and those eligible for thrombectomy in relation to the population. METHODS: Consecutive patients in a Western population who underwent CTA for suspected LVO of the proximal anterior circulation between January and August 2019 were included. The date and time of CTA and the number of patients eligible for thrombectomy were assessed. Our hospital's service area population was estimated using the Central Bureau for Statistics data. One-way analysis of variance with post-hoc tests and chi-squared tests were used for statistical analyses. RESULTS: Of 520 patients (49% males, mean age of 72 years) undergoing CTA, 84 (16.2%) were eligible for thrombectomy. Our hospital's service area population was estimated at 420,000. Therefore, 3.6 CTA scans were performed and 0.6 patients were eligible for thrombectomy per 100,000 people per week. The number of patients undergoing CTA and the number of patients eligible for thrombectomy both did not significantly differ between any days of the week (P > 0.05). A total of 236 (45%) and 284 patients (55%) underwent CTA during office and on-call hours, respectively. The percentage of patients eligible for thrombectomy did not significantly differ between office and on-call hours (P = 0.834). CONCLUSION: Our study estimated the number of stroke patients undergoing CTA for suspected LVO and those eligible for thrombectomy in relation to the population. Numbers were essentially the same throughout the week, and during office and on-call hours. Our data can be used to make adequate staffing plans.

Radiological Society of North America Chest CT Classification System for Reporting COVID-19 Pneumonia: Interobserver Variability and Correlation with Reverse-Transcription Polymerase Chain Reaction.

PURPOSE: To evaluate the Radiological Society of North America (RSNA) chest CT classification system for reporting coronavirus disease 2019 (COVID-19) pneumonia. MATERIALS AND METHODS: Chest CT scans of consecutive patients suspected of having COVID-19 were retrospectively and independently evaluated by two chest radiologists and a 5th-year radiology resident using the RSNA chest CT classification system for reporting COVID-19 pneumonia. Interobserver agreement was evaluated by calculating weighted κ coefficients. The proportion of patients with real-time reverse-transcription polymerase chain reaction (RT-PCR)-confirmed COVID-19 in each of the four chest CT categories (typical, indeterminate, atypical, and negative features for COVID-19) was calculated. RESULTS: In total, 96 patients (61 men; median age, 70 years [range, 29-94]) were included, of whom 45 had RT-PCR-confirmed COVID-19. The number of patients assigned to chest CT categories typical, indeterminate, atypical, and negative by the three readers ranged from 18 to 29, 26 to 43, 19 to 31, and 5 to 8, respectively. The κ coefficient among the chest radiologists was 0.663 (95% confidence interval [CI]: 0.565, 0.761). κ coefficients among the chest radiologists and the 5th-year radiology resident were 0.570 (95% CI: 0.443, 0.696) and 0.564 (95% CI: 0.451, 0.678), respectively. The proportion of patients with RT-PCR-confirmed COVID-19 in the chest CT categories typical, indeterminate, atypical, and negative for the three readers ranged from 76.9% to 96.6%, 51.2% to 64.1%, 2.8% to 5.3%, and 20% to 25%, respectively. CONCLUSION: The RSNA chest CT classification system for reporting COVID-19 pneumonia has moderate-to-substantial interobserver agreement. However, the proportion of RT-PCR-confirmed COVID-19 cases in the categories atypical appearance and negative for pneumonia is nonnegligible.Supplemental material is available for this article.© RSNA, 2020.