A comprehensive analysis of patients with cerebral arteriovenous malformation with headache: assessment of risk factors and treatment effectiveness.

BACKGROUND: Due to the high mortality and disability rate of intracranial hemorrhage, headache is not the main focus of research on cerebral arteriovenous malformation (AVM), so research on headaches in AVM is still scarce, and the clinical understanding is shallow. This study aims to delineate the risk factors associated with headaches in AVM and to compare the effectiveness of various intervention treatments versus conservative treatment in alleviating headache symptoms. METHODS: This study conducted a retrospective analysis of AVMs who were treated in our institution from August 2011 to December 2021. Multivariable logistic regression analysis was employed to assess the risk factors for headaches in AVMs with unruptured, non-epileptic. Additionally, the effectiveness of different intervention treatments compared to conservative management in alleviating headaches was evaluated through propensity score matching (PSM). RESULTS: A total of 946 patients were included in the analysis of risk factors for headaches. Multivariate logistic regression analysis identified that female (OR 1.532, 95% CI 1.173-2.001, p = 0.002), supply artery dilatation (OR 1.423, 95% CI 1.082-1.872, p = 0.012), and occipital lobe (OR 1.785, 95% CI 1.307-2.439, p < 0.001) as independent risk factors for the occurrence of headaches. There were 443 AVMs with headache symptoms. After propensity score matching, the microsurgery group (OR 7.27, 95% CI 2.82-18.7 p < 0.001), stereotactic radiosurgery group(OR 9.46, 95% CI 2.26-39.6, p = 0.002), and multimodality treatment group (OR 8.34 95% CI 2.87-24.3, p < 0.001) demonstrate significant headache relief compared to the conservative group. However, there was no significant difference between the embolization group (OR 2.24 95% CI 0.88-5.69, p = 0.091) and the conservative group. CONCLUSIONS: This study identified potential risk factors for headaches in AVMs and found that microsurgery, stereotactic radiosurgery, and multimodal therapy had significant benefits in headache relief compared to conservative treatment. These findings provide important guidance for clinicians when developing treatment options that can help improve overall treatment outcomes and quality of life for patients.

Noninvasive Assessment of HER2 Expression Status in Gastric Cancer Using 18F-FDG Positron Emission Tomography/Computed Tomography-Based Radiomics: A Pilot Study.

Purpose: Immunohistochemistry (IHC) is the main method to detect human epidermal growth factor receptor 2 (HER2) expression levels. However, IHC is invasive and cannot reflect HER2 expression status in real time. The aim of this study was to construct and verify three types of radiomics models based on 18F-fuorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging and to evaluate the predictive ability of these radiomics models for the expression status of HER2 in patients with gastric cancer (GC). Patients and Methods: A total of 118 patients with GC were enrolled in this study. 18F-FDG PET/CT imaging was performed prior to surgery. The LIFEx software package was applied to extract PET and CT radiomics features. The minimum absolute contraction and selection operator (least absolute shrinkage and selection operator [LASSO]) algorithm was used to select the best radiomics features. Three machine learning methods, logistic regression (LR), support vector machine (SVM), and random forest (RF) models, were constructed and verified. The Synthetic Minority Oversampling Technique (SMOTE) was applied to address data imbalance. Results: In the training and test sets, the area under the curve (AUC) values of the LR, SVM, and RF models were 0.809, 0.761, 0.861 and 0.628, 0.993, 0.717, respectively, and the Brier scores were 0.118, 0.214, and 0.143, respectively. Among the three models, the LR and RF models exhibited extremely good prediction performance. The AUC values of the three models significantly improved after SMOTE balanced the data. Conclusions: 18F-FDG PET/CT-based radiomics models, especially LR and RF models, demonstrate good performance in predicting HER2 expression status in patients with GC and can be used to preselect patients who may benefit from HER2-targeted therapy.

Application of Quantitative Computed Tomographic Perfusion in the Prognostic Assessment of Patients with Aneurysmal Subarachnoid Hemorrhage Coexistent Intracranial Atherosclerotic Stenosis.

The comorbidity of aneurysmal subarachnoid hemorrhage (aSAH) with intracranial atherosclerotic stenosis (ICAS) has been suggested to increase the risk of postoperative ischemic stroke. Logistic regression models were established to explore the association between computed tomography perfusion (CTP) parameters and 3-month neurological outcomes and delayed cerebral ischemia (DCI). Prognostic-related perfusion parameters were added to the existing prognostic prediction models to evaluate model performance improvement. Tmax > 4.0 s volume > 0 mL was significantly associated with 3-month unfavorable neurological outcomes after adjusting for potential confounders (OR 3.90, 95% CI 1.11-13.73), whereas the stenosis degree of ICAS was not. Although the cross-validated area under the curve (AUC) was similar after the addition of the Tmax > 4.0 s volume > 0 mL (SAHIT: p = 0.591; TAPS: p = 0.379), the continuous net reclassification index (cNRI) and integrated discrimination index (IDI) showed that the perfusion parameters significantly improved the performance of the two models (p < 0.001 for all comparisons). Patients with coexistent aSAH and ICAS, Tmax > 4.0 s volume > 0 mL is an independent factor of 3-month neurological outcomes. A quantitative assessment of cerebral perfusion may help accurately screen patients with poor outcomes due to the coexistence of aSAH and ICAS.

An intelligent identification system combining image and DNA sequence methods for fruit flies with economic importance (Diptera: Tephritidae).

BACKGROUND: Images and DNA sequences are two important methods for identifying fruit fly species. In addition, the identification of insect species complexes is highly problematic when attempting to utilize automatic identification methods in an actual environment. We integrated the image and DNA sequence identification methods into a single system for the first time and explored an open interactive multi-image comparison function for solving the problem of species complexes. The Automated Fruit Fly Identification System 1.0 (AFIS1.0) was updated to AFIS2.0 by employing different models and developing the system under a novel framework. RESULTS: AFIS2.0 was developed using 83 species belonging to eight genera in the Tephritidae, which includes most pests of this family. The system applies the Mask Region Convolutional Neural Network (Mask R-CNN) and discriminative deep metric learning (AlexNet based) methods for image identification, integrates Blast+ for DNA sequence comparison and specific weighting for the fusion result. At the species level, the best classification success rate for wing images (as the Top 1 species in the species list of outcomes) reached 90%, and the average classification success rate for wing, thorax, and abdomen images (as the Top 5 species in the species list of outcomes) was 94%. CONCLUSION: AFIS2.0 is more accurate and convenient than AFIS1.0 and can be beneficial for users with or without specific expertise regarding Tephritidae. It also provides a more compact and fluent computer system for fruit fly identification, and can be easily applied in practice. © 2021 Society of Chemical Industry.

Morbidity After Symptomatic Hemorrhage of Cerebral Cavernous Malformation: A Nomogram Approach to Risk Assessment.

BACKGROUND AND PURPOSE: Symptomatic hemorrhage contributes to an increased risk of repeated bleeding and morbidity in cerebral cavernous malformation (CCM). A better understanding of morbidity after CCM hemorrhage would be helpful to identify patients of higher risk for unfavorable outcome and tailor individualized management. METHODS: We identified 282 consecutive patients who referred to our institute from 2014 to 2018 for CCM with symptomatic hemorrhage and had an untreated follow-up period over 6 months after the first hemorrhage. The morbidity after hemorrhage was described in CCM of different features. Nomogram to predict morbidity was formulated based on the multivariable model of risk factors. The predictive accuracy and discriminative ability of nomogram were determined with concordance index (C-index) and calibration curve, and further validated in an independent CCM cohort of a prospective multicenter study from 2019 to 2020. RESULTS: The overall morbidity of CCM was 26.2% after a mean follow-up of 1.9 years (range 0.5-3.5 years) since the first hemorrhage. The morbidity during untreated follow-up was associated with hemorrhage ictus (adjusted odds ratio per ictus increase, 4.17 [95% CI, 1.86-9.33]), modified Rankin Scale score at initial hemorrhage (adjusted odds ratio per point increase, 2.57 [95% CI, 1.82-3.63]), brainstem location (adjusted odds ratio, 2.93 [95% CI, 1.28-6.68]), and associated developmental venous anomaly (adjusted odds ratio, 2.21 [95% CI, 1.01-4.83]). Subgroup analysis revealed similar findings in brainstem and non-brainstem CCM. Nomogram was contracted based on these features. The calibration curve showed good agreement between nomogram prediction and actual observation. The C-index of nomogram predicting morbidity was 0.83 (95% CI, 0.77-0.88). In validation cohort, the nomogram maintained the discriminative ability (C-index, 0.87 [95% CI, 0.78-0.96]). CONCLUSIONS: Multiple symptomatic hemorrhages, initial neurological function after hemorrhage, brainstem location, and associated developmental venous anomaly were associated with morbidity of CCM hemorrhage. The nomogram represented a practical approach to provide individualized risk assessment for CCM patients. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT04076449.

Construction, implementation and testing of an image identification system using computer vision methods for fruit flies with economic importance (Diptera: Tephritidae).

BACKGROUND: Many species of Tephritidae are damaging to fruit, which might negatively impact international fruit trade. Automatic or semi-automatic identification of fruit flies are greatly needed for diagnosing causes of damage and quarantine protocols for economically relevant insects. RESULTS: A fruit fly image identification system named AFIS1.0 has been developed using 74 species belonging to six genera, which include the majority of pests in the Tephritidae. The system combines automated image identification and manual verification, balancing operability and accuracy. AFIS1.0 integrates image analysis and expert system into a content-based image retrieval framework. In the the automatic identification module, AFIS1.0 gives candidate identification results. Afterwards users can do manual selection based on comparing unidentified images with a subset of images corresponding to the automatic identification result. The system uses Gabor surface features in automated identification and yielded an overall classification success rate of 87% to the species level by Independent Multi-part Image Automatic Identification Test. CONCLUSION: The system is useful for users with or without specific expertise on Tephritidae in the task of rapid and effective identification of fruit flies. It makes the application of computer vision technology to fruit fly recognition much closer to production level. © 2016 Society of Chemical Industry.