MATERIALS AND METHODS: Seven hundred fifty-four radiomics-based features were extracted from 1732 scans derived from the TICH-2 multicentre clinical trial. Features were harmonised and a correlation-based feature selection was applied. Different elastic-net parameterisations were tested to assess the predictive performance of the selected radiomics-based features using grid optimisation. For comparison, the same procedure was run using radiological signs and clinical factors separately. Models trained with radiomics-based features combined with radiological signs or clinical factors were tested. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC) score.
RESULTS: The optimal radiomics-based model showed an AUC of 0.693 for haematoma expansion and an AUC of 0.783 for poor functional outcome. Models with radiological signs alone yielded substantial reductions in sensitivity. Combining radiomics-based features and radiological signs did not provide any improvement over radiomics-based features alone. Models with clinical factors had similar performance compared to using radiomics-based features, albeit with low sensitivity for haematoma expansion. Performance of radiomics-based features was boosted by incorporating clinical factors, with time from onset to scan and age being the most important contributors for haematoma expansion and poor functional outcome prediction, respectively.
CONCLUSION: Radiomics-based features perform better than radiological signs and similarly to clinical factors on the prediction of haematoma expansion and poor functional outcome. Moreover, combining radiomics-based features with clinical factors improves their performance.
KEY POINTS: • Linear models based on CT radiomics-based features perform better than radiological signs on the prediction of haematoma expansion and poor functional outcome in the context of intracerebral haemorrhage. • Linear models based on CT radiomics-based features perform similarly to clinical factors known to be good predictors. However, combining these clinical factors with radiomics-based features increases their predictive performance.
PURPOSE: To evaluate the accuracy, safety, and diagnostic outcome of fluoroscopic guided and CT transpedicular biopsy techniques.
STUDY DESIGN: Prospective randomized trial.
PATIENT SAMPLE: Sixty consecutive patients with clinical symptoms and radiological features suggestive of spinal infection or malignancy were recruited and randomized into fluoroscopic or CT guided spinal biopsy groups. Both groups were similar in terms of patient demographics, distribution of spinal infections and malignancy cases, and the level of biopsies.
OUTCOME MEASURES: The primary outcome measure was diagnostic accuracy of both methods, determined based on true positive, true negative, false positive, and false negative biopsy findings. Secondary outcome measures included radiation exposure to patients and doctors, complications, and postbiopsy pain score.
METHODS: A transpedicular approach was performed with an 8G core biopsy needle. Specimens were sent for histopathological and microbiological examinations. Diagnosis was made based on biopsy results, clinical criteria and monitoring of disease progression during a 6-month follow up duration. Clinical criteria included presence of risk factors, level of inflammatory markers and magnetic resonance imaging findings. Radiation exposure to patients and doctors was measured with dosimeters.
RESULTS: There was no significant difference between the diagnostic accuracy of fluoroscopic and CT guided spinal biopsy (p=0.67) or between the diagnostic accuracy of spinal infection and spinal tumor in both groups (p=0.402 for fluoroscopy group and p=0.223 for CT group). Radiation exposure to patients was approximately 26 times higher in the CT group. Radiation exposure to doctors in the CT group was approximately 2 times higher compared to the fluoroscopic group if a lead shield was not used. Lead shields significantly reduced radiation exposure to doctors anywhere from 2 to 8 times. No complications were observed for either group and the differences in postbiopsy pain scores were not significant.
CONCLUSIONS: The accuracy, procedure time, complication rate and pain score for both groups were similar. However, radiation exposure to patients and doctors were significantly higher in the CT group without lead protection. With lead protection, radiation to doctors reduced significantly.
METHOD: Two large datasets, including 1110 3D CT images, were split into five segments of 20% each. Each dataset's first 20% segment was separated as a holdout test set. 3D-CNN training was performed with the remaining 80% from each dataset. Two small external datasets were also used to independently evaluate the trained models.
RESULTS: The total combination of 80% of each dataset has an accuracy of 91% on Iranmehr and 83% on Moscow holdout test datasets. Results indicated that 80% of the primary datasets are adequate for fully training a model. The additional fine-tuning using 40% of a secondary dataset helps the model generalize to a third, unseen dataset. The highest accuracy achieved through transfer learning was 85% on LDCT dataset and 83% on Iranmehr holdout test sets when retrained on 80% of Iranmehr dataset.
CONCLUSION: While the total combination of both datasets produced the best results, different combinations and transfer learning still produced generalizable results. Adopting the proposed methodology may help to obtain satisfactory results in the case of limited external datasets.
DESIGN: Systematic review and meta-analysis.
METHODS: Using PRISMA guidelines, SCOPUS and PUBMED databases were searched from inception until 1 March 2022. The regions and populations identified were from Europe, Asia, Middle East, Australia-New Zealand-Oceania, South America, North America and Africa. Random-effects model was used to estimate the pooled prevalence with 95% confidence intervals (CIs). Heterogeneity was assessed using the I2 statistic and Cochran's Q test.
MAIN OUTCOME MEASURES: Anatomical variations of the lateral nasal wall and anterior skull base confirmed by computed tomography scan.
RESULTS: Fifty-six articles were included with a total of 11 805 persons. The most common anatomical variation of the ostiomeatal complex was pneumatization of the agger nasi (84.1%), olfactory fossa was Keros type 2 (53.8%) and ethmoids was asymmetry of the roof (42.8%). Sphenoethmoidal and suprabullar cells have a higher prevalence in North Americans (53.7%, 95% CI: 46.00-61.33) while asymmetry of ethmoid roof more common in Middle Easterns (85.5%, 95% CI: .00-100). Bent uncinate process has greater prevalence in Asians while supraorbital ethmoid cells and Keros type 3 more common in non-Asians. The overall studies have substantial heterogeneity and publication bias.
CONCLUSION: Certain anatomic variants are more common in a specific population. The 'approach of analysis' plays a role in the prevalence estimates and consensus should be made in future studies regarding the most appropriate 'approach of analysis' either by persons or by sides.
METHOD: Prevalence of the anterior ethmoid genu, its morphology and its relationship with the frontal sinus drainage pathway was assessed. Computed tomography scans with multiplanar reconstruction were used to study non-diseased sinonasal complexes.
RESULTS: The anterior ethmoidal genu was present in all 102 anatomical sides studied, independent of age, gender and race. Its position was within the frontal sinus drainage pathway, and the drainage pathway was medial to it in 98 of 102 cases. The anterior ethmoidal genu sometimes extended laterally and formed a recess bounded by the lamina papyracea laterally, by the uncinate process anteriorly and by the bulla ethmoidalis posteriorly. Distance of the anterior ethmoidal genu to frontal ostia can be determined by the height of the posterior wall of the agger nasi cell rather than its volume or other dimensions.
CONCLUSION: This study confirmed that the anterior ethmoidal genu is a constant anatomical structure positioned within frontal sinus drainage pathway. The description of anterior ethmoidal genu found in this study explained the anatomical connection between the agger nasi cell, uncinate process and bulla ethmoidalis and its structural organisation.
METHODS: In total, 154 patients (wild-type EGFR, 72 patients; Del19 mutation, 45 patients; and L858R mutation, 37 patients) were retrospectively enrolled and randomly divided into 92 training and 62 test cases. Two support vector machine (SVM) models to distinguish between wild-type and mutant EGFR (mutation [M] classification) as well as between the Del19 and L858R subtypes (subtype [S] classification) were trained using 3DBN features. These features were computed from 3DBN maps by using histogram and texture analyses. The 3DBN maps were generated using computed tomography (CT) images based on the Čech complex constructed on sets of points in the images. These points were defined by coordinates of voxels with CT values higher than several threshold values. The M classification model was built using image features and demographic parameters of sex and smoking status. The SVM models were evaluated by determining their classification accuracies. The feasibility of the 3DBN model was compared with those of conventional radiomic models based on pseudo-3D BN (p3DBN), two-dimensional BN (2DBN), and CT and wavelet-decomposition (WD) images. The validation of the model was repeated with 100 times random sampling.
RESULTS: The mean test accuracies for M classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.810, 0.733, 0.838, 0.782, and 0.799, respectively. The mean test accuracies for S classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.773, 0.694, 0.657, 0.581, and 0.696, respectively.
CONCLUSION: 3DBN features, which showed a radiogenomic association with the characteristics of the EGFR Del19/L858R mutation subtypes, yielded higher accuracy for subtype classifications in comparison with conventional features.
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