Current Issue : January-March Volume : 2022 Issue Number : 1 Articles : 5 Articles
Background: Accurate measurement of hemorrhage volume is critical for both the prediction of prognosis and the selection of appropriate clinical treatment after spontaneous intracerebral hemorrhage (ICH). This study aimed to evaluate the performance and accuracy of a deep learning-based automated segmentation algorithm in segmenting spontaneous intracerebral hemorrhage (ICH) volume either with or without intraventricular hemorrhage (IVH) extension. We compared this automated pipeline with two manual segmentation techniques. Methods: We retrospectively reviewed 105 patients with acute spontaneous ICH. Depending on the presence of IVH extension, patients were divided into two groups: ICH without (n = 56) and with IVH (n = 49). ICH volume of the two groups were segmented and measured using a deep learning-based artificial intelligence (AI) diagnostic system and computed tomography-based planimetry (CTP), and the ABC/2 score were used to measure hemorrhage volume in the ICH without IVH group. Correlations and agreement analyses were used to analyze the differences in volume and length of processing time among the three segmentation approaches. Results: In the ICH without IVH group, the ICH volumes measured using AI and the ABC/2 score were comparable to CTP segmentation. Strong correlations were observed among the three segmentation methods (r = 0.994, 0.976, 0.974; P < 0.001; concordance correlation coefficient [CCC] = 0.993, 0.968, 0.967). But the absolute error of the ICH volume measured by the ABC/2 score was greater than that of the algorithm (P < 0.05). In the ICH with IVH group, there is no significant differences were found between algorithm and CTP(P = 0.614). The correlation and agreement between CTP and AI were strong (r = 0.996, P < 0.001; CCC = 0.996). The AI segmentation took a significantly shorter amount of time than CTP (P < 0.001), but was slightly longer than ABC/2 score technique (P = 0.002). Conclusions: The deep learning-based AI diagnostic system accurately quantified volumes of acute spontaneous ICH with high fidelity and greater efficiency compared to the CTP measurement and more accurately than the ABC/2 scores. We believe this is a promising tool to help physicians achieve precise ICH quantification in practice....
Purpose: To review the literature on the value of basivertebral nerve ablation in the treatment of chronic low back pain. Materials and Method: A systematic review and meta-analysis of the English literature to March 2020 was undertaken. The inclusion criteria were patients with discogenic back pain of more than 3 months duration with modic type 1 or 2 change and successful disc block or discogram. Primary outcomes were VAS pain, ODI, EQ-5D and SF36 improvement. Secondary outcomes were complications. Results: 6 studies were included, all funded by the same company, but otherwise of low bias. All studies showed significant improvement in all scores over the first 3 months with evidence these would be maintained over the longer term. There was one reported compression fracture, but otherwise no significant adverse events. Conclusion: This study supports the conclusion that radiofrequency ablation of the basivertebral nerve is a safe and effective treatment for discogenic chronic low back pain....
Background: This study aimed to describe the results of mammography done during breast cancer awareness campaigns in Lomé. Methods: This was a retrospective multicenter study which focused on the analysis of mammographic examinations, with or without breast ultrasound, carried out in three (3) clinics in Lomé over a period of five (5) years during the breast cancer awareness month (Pink October) campaigns. We included in our study women of all ages who underwent a mammography during the study period. Additional ultrasound was performed as needed in some women to better characterize a lesion. The parameters studied were socio-demographic data, and aspects of breast lesions. We classified the lesions in order of severity according to the BI-RADS classification. Results: During the study we counted one thousand and seventy-four (1074) women who underwent mammography examinations, corresponding to an average of 214.8 women per year. The median age of the women was 46 years. The most represented age group was 40 - 49, constituting 30% of cases. Mammography was performed on all women and ultrasound was performed on 51.3% of women. Lesions suspicious for malignancy (BI-RADS IV) and lesions highly suggestive of malignancy (BI-RADS V) represented 3.5% and 1.9% of cases respectively, amounting to a prevalence of 5.4%. They occurred more frequently from the age of 30 years with a statistically significant difference (p = 0.02). These lesions could be identified on mammograms as masses with irregular shapes and spiculated margins representing 16.1% and 9.7% of masses respectively. On ultrasound, these were solid masses with irregular and ill-defined borders, representing 25.2% and 5.2% of solid masses respectively. Lesions suspicious for malignancy were most often found in the UOQ (upper outer quadrant) in 70% of cases. Conclusion: Mammography screening for breast cancer remains a necessity in our community, even if the rate of cancer detected remains low. It allows for early diagnosis of cancers, promoting better management....
In women at high/intermediate lifetime risk of breast cancer (BC-LTR), contrast-enhanced magnetic resonance imaging (MRI) added to mammography ± ultrasound (MX ± US) increases sensitivity but decreases specificity. Screening with MRI alone is an alternative and potentially more cost-effective strategy. Here, we describe the study protocol and the characteristics of enrolled patients for MRIB feasibility, multicenter, randomized, controlled trial, which aims to compare MRI alone versus MX+US in women at intermediate breast cancer risk (aged 40–59, with a 15–30% BC-LTR and/or extremely dense breasts). Two screening rounds per woman were planned in ten centers experienced in MRI screening, the primary endpoint being the rate of cancers detected in the 2 arms after 5 years of follow-up. From July 2013 to November 2015, 1254 women (mean age 47 years) were enrolled: 624 were assigned to MX+US and 630 to MRI. Most of them were aged below 50 (72%) and premenopausal (45%), and 52% used oral contraceptives. Among postmenopausal women, 15% had used hormone replacement therapy. Breast and/or ovarian cancer in mothers and/or sisters were reported by 37% of enrolled women, 79% had extremely dense breasts, and 41% had a 15–30% BC-LTR. The distribution of the major determinants of breast cancer risk profiles (breast density and family history of breast and ovarian cancer) of enrolled women varied across centers....
Background: To assess the potential of radiomic features to quantify components of blood in intraaortic vessels to non-invasively predict moderate-to-severe anemia in non-contrast enhanced CT scans. Methods: One hundred patients (median age, 69 years; range, 19–94 years) who received CT scans of the thoracolumbar spine and blood-testing for hemoglobin and hematocrit levels ± 24 h between 08/2018 and 11/2019 were retrospectively included. Intraaortic blood was segmented using a spherical volume of interest of 1 cm diameter with consecutive radiomic analysis applying PyRadiomics software. Feature selection was performed applying analysis of correlation and collinearity. The final feature set was obtained to differentiate moderate-to-severe anemia. Random forest machine learning was applied and predictive performance was assessed. A decision-tree was obtained to propose a cut-off value of CT Hounsfield units (HU). Results: High correlation with hemoglobin and hematocrit levels was shown for first-order radiomic features (p < 0.001 to p = 0.032). The top 3 features showed high correlation to hemoglobin values (p) and minimal collinearity (r) to the top ranked feature Median (p < 0.001), Energy (p = 0.002, r = 0.387), Minimum (p = 0.032, r = 0.437). Median (p < 0.001) and Minimum (p = 0.003) differed in moderate-to-severe anemia compared to non-anemic state. Median yielded superiority to the combination of Median and Minimum (p(AUC) = 0.015, p(precision) = 0.017, p(accuracy) = 0.612) in the predictive performance employing random forest analysis. A Median HU value ≤ 36.5 indicated moderate-to-severe anemia (accuracy = 0.90, precision = 0.80). Conclusions: First-order radiomic features correlate with hemoglobin levels and may be feasible for the prediction of moderate-to-severe anemia. High dimensional radiomic features did not aid augmenting the data in our exemplary use case of intraluminal blood component assessment. Trial registration Retrospectively registered....
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