Current Issue : July-September Volume : 2023 Issue Number : 3 Articles : 5 Articles
Background Head and neck squamous cell carcinoma (HNSCC) represents the 6th leading cancer worldwide. In most cases, patients present a locally advanced disease at diagnosis and non-surgical curative treatment is considered the standard of care. Nowadays, [18F]FDG PET/CT is a validated tool in post-treatment evaluation, with a high level of evidence. However, to standardize imaging response, several visual scales have been proposed with none of them approved yet. The study’s aim is a head-to-head comparison between the diagnostic performance of the Hopkins criteria, the Deauville score, and the new proposed Cuneo score, to establish their prognostic role. Secondly, we investigate the possible value of semiquantitative analysis, evaluating SUVmax and ΔSUVmax of the lymph node with the highest uptake on the restaging PET scan. Moreover, we also considered morphological features using the product of diameters measured on the co-registered CT images to assess the added value of hybrid imaging. Methods We performed a retrospective analysis on histologically proven HNSCC patients who underwent baseline and response assessment [18F]FDG PET/CT. Post-treatment scans were reviewed according to Hopkins, Deauville, and Cuneo criteria, assigning a score to the primary tumor site and lymph nodes. A per-patient final score for each scale was chosen, corresponding to the highest score between the two sites. Diagnostic performance was then calculated for each score considering any evidence of locoregional progression in the first 3 months as the gold standard. Survival analysis was performed using the Kaplan–Meier method. SUVmax and its delta, as well as the product of diameters of the lymph node with the highest uptake at post-treatment scan, if present, were calculated. Results A total of 43 patients were finally included in the study. Sensitivity, specificity, PPV, NPV, and accuracy were 87%, 86%, 76%, 92%, and 86% for the Hopkins score, whereas 93%, 79%, 70%, 96%, and 84% for the Deauville score, respectively. Conversely, the Cuneo score reached the highest specificity and PPV (93% and 78%, respectively) but the lowest sensitivity (47%), NPV (76%), and accuracy (77%). Each scale significantly correlated with PFS and OS. The ROC analysis of the combination of SUVmax and the product of diameters of the highest lymph node on the restaging PET scan reached an AUC of 0.822. The multivariate analysis revealed the Cuneo criteria and the product of diameters as prognostic factors for PFS. Conclusions Each visual score statistically correlated with prognosis thus demonstrating the reliability of point-scale criteria in HNSCC. The novel Cuneo score showed the highest specificity, but the lowest sensibility compared to Hopkins and Deauville criteria. Furthermore, the combination of PET data with morphological features could support the evaluation of equivocal cases....
Background Although the morphological changes of sella turcica have been drawing increasing attention, the acquirement of linear parameters of sella turcica relies on manual measurement. Manual measurement is laborious, time-consuming, and may introduce subjective bias. This paper aims to develop and evaluate a deep learning-based model for automatic segmentation and measurement of sella turcica in cephalometric radiographs. Methods 1129 images were used to develop a deep learning-based segmentation network for automatic sella turcica segmentation. Besides, 50 images were used to test the generalization ability of the model. The performance of the segmented network was evaluated by the dice coefficient. Images in the test datasets were segmented by the trained segmentation network, and the segmentation results were saved in binary images. Then the extremum points and corner points were detected by calling the function in the OpenCV library to obtain the coordinates of the four landmarks of the sella turcica. Finally, the length, diameter, and depth of the sella turcica can be obtained by calculating the distance between the two points and the distance from the point to the straight line. Meanwhile, images were measured manually using Digimizer. Intraclass correlation coefficients (ICCs) and Bland–Altman plots were used to analyze the consistency between automatic and manual measurements to evaluate the reliability of the proposed methodology. Results The dice coefficient of the segmentation network is 92.84%. For the measurement of sella turcica, there is excellent agreement between the automatic measurement and the manual measurement. In Test1, the ICCs of length, diameter and depth are 0.954, 0.953, and 0.912, respectively. In Test2, ICCs of length, diameter and depth are 0.906, 0.921, and 0.915, respectively. In addition, Bland–Altman plots showed the excellent reliability of the automated measurement method, with the majority measurements differences falling within ± 1.96 SDs intervals around the mean difference and no bias was apparent. Conclusions Our experimental results indicated that the proposed methodology could complete the automatic segmentation of the sella turcica efficiently, and reliably predict the length, diameter, and depth of the sella turcica. Moreover, the proposed method has generalization ability according to its excellent performance on Test2....
Objective To compare the diagnostic accuracy of diffusion-weighted imaging (DWI) and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) for differentiating pulmonary nodules and masses. Methods We systematically searched six databases, including PubMed, EMBASE, the Cochrane Library, and three Chinese databases, to identify studies that used both DWI and PET/CT to differentiate pulmonary nodules. The diagnostic performance of DWI and PET/CT was compared and pooled sensitivity and specificity were calculated along with 95% confidence intervals (CIs). The Quality Assessment of Diagnostic Accuracy Studies 2 was used to assess the quality of the included studies, and STATA 16.0 software was utilized to perform statistical analysis. Results Overall, 10 studies that enrolled a total of 871 patients with 948 pulmonary nodules were included in this meta-analysis. DWI had greater pooled sensitivity (0.85 [95% CI 0.77–0.90]) and specificity (0.91 [95% CI 0.82–0.96]) than PET/CT (sensitivity, 0.82 [95% CI 0.70–0.90]); specificity, (0.81, [95% CI 0.72–0.87]). The area under the curve of DWI and PET/CT were 0.94 (95% CI 0.91–0.96) and 0.87 (95% CI 0.84–0.90) (Z = 1.58, P > 0.05), respectively. The diagnostic odds ratio of DWI (54.46, [95% CI 17.98–164.99]) was superior to that of PET/CT (15.77, [95% CI 8.19–30.37]). The Deeks’ funnel plot asymmetry test showed no publication bias. The Spearman correlation coefficient test revealed no significant threshold effect. Lesion diameter and reference standard could be potential causes for the heterogeneity of both DWI and PET/CT studies, and quantitative or semi-quantitative parameters used would be a potential source of bias for PET/CT studies. Conclusion As a radiation-free technique, DWI may have similar performance compare with PET/CT in differentiating malignant pulmonary nodules or masses from benign ones....
Purpose To investigate the relationship between renal artery anatomical configuration and renal artery plaque (RAP) based on 320-row CT. Methods The abdominal contrast-enhanced CT data from 210 patients was retrospectively analyzed. Among 210 patients, there were 118 patients with RAP and 92 patients with no RAP. The anatomical parameters between lesion group and control group were compared and analyzed by using t-test, χ2-test and logistic regression analysis. Results (1) There were statistical differences on age, hypertension, diabetes, hypertriglyceridemia and hypercholesterolemia between lesion group and control group. (2) The differences on the distribution and type and of RAP between lesion group and control group were statistically significant. The most common position was the proximal, and the most common type was calcified plaque. (3)There were significant statistical differences on the proximal diameter of renal artery and renal artery-aorta angle A between lesion group and control group. The differences on the other anatomical factors between two groups were not statistically significant. (4) The result of logistic regression analysis showed that right RAP was related to age, hypertension and right renal artery angle A (the AUC of ROC = 0.82), and left RAP was related to high serum cholesterol, age and left renal artery angle A(the AUC of ROC = 0.83). (5) The RAP was associated with renal artery-aorta angle A, but the differences on distribution, type stability of RAP between R1 (L1) group and R2 (L2) group were not statistically significant. Conclusions The RAP was associated with age, hypertension, hypercholesterolemia and renal artery-aorta angle A. Adults which had the greater renal artery-aorta angle A and the other above risk factors may be at increased risk for RAP....
Background Manual microscopic examination remains the golden standard for malaria diagnosis. But it is laborious, and pathologists with experience are needed for accurate diagnosis. The need for computer-aided diagnosis methods is driven by the enormous workload and difficulties associated with manual microscopy based examination. While the importance of computer-aided diagnosis is increasing at an enormous pace, fostered by the advancement of deep learning algorithms, there are still challenges in detecting small objects such as malaria parasites in microscopic images of blood films. The state-of-the-art (SOTA) deep learning-based object detection models are inefficient in detecting small objects accurately because they are underrepresented on benchmark datasets. The performance of these models is affected by the loss of detailed spatial information due to in-network feature map downscaling. This is due to the fact that the SOTA models cannot directly process high-resolution images due to their low-resolution network input layer. Methods In this study, an efficient and robust tile-based image processing method is proposed to enhance the performance of malaria parasites detection SOTA models. Three variants of YOLOV4-based object detectors are adopted considering their detection accuracy and speed. These models were trained using tiles generated from 1780 high-resolution P. falciparum-infected thick smear microscopic images. The tiling of high-resolution images improves the performance of the object detection models. The detection accuracy and the generalization capability of these models have been evaluated using three datasets acquired from different regions. Results The best-performing model using the proposed tile-based approach outperforms the baseline method significantly (Recall, [95.3%] vs [57%] and Average Precision, [87.1%] vs [76%]). Furthermore, the proposed method has outperformed the existing approaches that used different machine learning techniques evaluated on similar datasets. Conclusions The experimental results show that the proposed method significantly improves P. falciparum detection from thick smear microscopic images while maintaining real-time detection speed. Furthermore, the proposed method has the potential to assist and reduce the workload of laboratory technicians in malaria-endemic remote areas of developing countries where there is a critical skill gap and a shortage of experts....
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