Deep learning-based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features.

BACKGROUND: This study aimed to establish a dedicated deep-learning model (DLM) on routine magnetic resonance imaging (MRI) data to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentations. Another purpose of our work was to develop a radiomics model based on the radiomics features extracted from automatic segmentation to differentiate low- and high-grade meningiomas before surgery. MATERIALS: A total of 326 patients with pathologically confirmed meningiomas were enrolled. Samples were randomly split with a 6:2:2 ratio to the training set, validation set, and test set. Volumetric regions of interest (VOIs) were manually drawn on each slice using the ITK-SNAP software. An automatic segmentation model based on SegResNet was developed for the meningioma segmentation. Segmentation performance was evaluated by dice coefficient and 95% Hausdorff distance. Intra class correlation (ICC) analysis was applied to assess the agreement between radiomic features from manual and automatic segmentations. Radiomics features derived from automatic segmentation were extracted by pyradiomics. After feature selection, a model for meningiomas grading was built. RESULTS: The DLM detected meningiomas in all cases. For automatic segmentation, the mean dice coefficient and 95% Hausdorff distance were 0.881 (95% CI: 0.851-0.981) and 2.016 (95% CI:1.439-3.158) in the test set, respectively. Features extracted on manual and automatic segmentation are comparable: the average ICC value was 0.804 (range, 0.636-0.933). Features extracted on manual and automatic segmentation are comparable: the average ICC value was 0.804 (range, 0.636-0.933). For meningioma classification, the radiomics model based on automatic segmentation performed well in grading meningiomas, yielding a sensitivity, specificity, accuracy, and area under the curve (AUC) of 0.778 (95% CI: 0.701-0.856), 0.860 (95% CI: 0.722-0.908), 0.848 (95% CI: 0.715-0.903) and 0.842 (95% CI: 0.807-0.895) in the test set, respectively. CONCLUSIONS: The DLM yielded favorable automated detection and segmentation of meningioma and can help deploy radiomics for preoperative meningioma differentiation in clinical practice.

Dynamic evaluation of unruptured intracranial aneurysms by 4D-CT angiography: comparison with digital subtraction angiography (DSA) and surgical findings.

BACKGROUND: This study was to prospectively investigate the feasibility of four-dimensional computed tomography angiography (4D-CTA) with electrocardiogram-gated (ECG) reconstruction for preoperative evaluation of morphological parameters, and compared with digital subtraction angiography (DSA). We also aimed to detect pulsation in unruptured intracranial aneurysms (UIAs) by using 4D-CTA, as a potential predicting factor of growth or rupture. MATERIALS: 64 patients with 64 UIAs underwent ECG-gated dynamic 4D-CTA imaging before treatment, of which 46 patients additionally underwent DSA. Original scanning data were reconstructed to produce 20 data sets of cardiac cycles with 5%-time intervals. The extent of agreement on UIAs morphological features assessed with 4D-CTA and DSA was estimated using the k coefficient of the Kappa test. The radiation doses were also calculated and compared between 4D-CTA and DSA. In the aneurysmal surgically treated in our institution, we were able to compare the surgical findings of the aneurysm wall with 4D-CTA images. We performed long-term follow-up on untreated patients. RESULTS: The morphological characteristics detected by 4D-CTA and DSA were consistent in aneurysm location (k = 1.0), shape (k = 0.76), maximum diameter (k = 0.94), aneurysm neck (k = 0.79) and proximity to parent and branch vessels (k = 0.85). 4D-CTA required lower radiation doses (0.32 ± 0.11 mSv) than DSA (0.84 ± 0.37 mSv, P < 0.001). Pulsation was detected in 26 of the 64 unruptured aneurysms, and all underwent neurosurgical clipping or interventional embolization. In aneurysms surgically treated in our hospital, we observed a significant correlation between 4D-CTA findings and surgical evaluation of the aneurysmal wall, in particular the irregular pulsations detected on 4D-CTA have demonstrated to correspond to dark-reddish thinner wall at surgery. CONCLUSIONS: In this proof-of-concept study, 4D-CTA provided real-time, non-invasive preoperative assessments of UIAs comparable to DSA. Moreover, optimal correlation between the irregular pulsation detected by 4D-CTA and the surgical findings support a possible role of this technique to identify aneurysms with a higher risk of rupture.

[18F] AlF­NOTA­FAPI­04 PET/CT as a promising tool for imaging fibroblast activation protein in gastrointestinal system cancers: a prospective investigation of comparative analysis with 18F-FDG.

PURPOSE: The radiopharmaceutical [18F]AlF-NOTA-FAPI-04 presents a promising alternative to 68 Ga-FAPI owing to its relatively longer half-life. This study aimed to evaluate the clinical usefulness of [18F]AlF-NOTA-FAPI-04 PET/CT for the diagnosis of primary and metastatic lesions in various types of gastrointestinal system cancers, compared with 18F-FDG PET/CT. METHODS: Patients diagnosed with gastrointestinal system malignancies were prospectively enrolled. All patients underwent both 18F-FDG and 18F-FAPI-04 PET/CT scans within one week, with 44 (73.3%) for cancer staging and 16 (26.7%) for tumor restaging. Diagnostic efficacy of the primary tumor, as well as the presence and number of lymph nodes and distant metastases, were assessed. Tumor uptake was quantified by the maximum standard uptake value (SUVmax). RESULTS: For detection of primary tumor, the diagnostic sensitivity of 18F-FDG PET/CT was 72.7%, while it was 97.7% for 18F-FAPI-04 PET/CT. Based on per-lymph node analysis, the sensitivity, specificity, and accuracy of 18F-FAPI-04 PET/CT in diagnosing metastatic lymph nodes were 91.89%, 92.00%, and 91.96%, respectively. These values were notably higher than those 18F-FDG PET/CT (79.72%, 81.33% and 80.80%, respectively). The 18F-FAPI-04 PET/CT surpassed 18F-FDG PET/CT in detecting suspected metastases in the brain (7 vs. 3), liver (39 vs. 20), bone (79 vs. 51), lung (11 vs. 4), and peritoneal carcinoma (48 vs. 22). Based on per-patient analysis, differential diagnostic accuracies (18F-FAPI-04 vs. 18F-FDG PET/CT) were observed in all patients (91.7% vs. 76.7%), the initial staging group (90.9% vs. 79.5%), and the re-staging group (93.8% vs. 68.7%). Additionally, 18F-FAPI-04 PET/CT revised final diagnosis in 31.7% of patients, contrasting with 18F-FDG PET/CT, and prompted changes in clinical management for 21.7% of the patients. CONCLUSION: 18F-FAPI-04 PET/CT outperforms 18F-FDG PET/CT in delineating the primary gastrointestinal tumors and detecting suspected metastatic lesions due to a higher target-to-background ratio (TBR). Moreover, 18F-FAPI-04 PET/CT could provide valuable guidance for tumor staging, thereby having a potential impact on patient management.