BMC Medical Imaging最新文献

Recent improvements in artificial intelligence and computer vision make it possible to automatically detect abnormalities in medical images. Skin lesions are one broad class of them. There are types of lesions that cause skin cancer, again with several types. Melanoma is one of the deadliest types of skin cancer. Its early diagnosis is at utmost importance. The treatments are greatly aided with artificial intelligence by the quick and precise diagnosis of these conditions. The identification and delineation of boundaries inside skin lesions have shown promise when using the basic image processing approaches for edge detection. Further enhancements regarding edge detections are possible. In this paper, the use of fractional differentiation for improved edge detection is explored on the application of skin lesion detection. A framework based on fractional differential filters for edge detection in skin lesion images is proposed that can improve automatic detection rate of malignant melanoma. The derived images are used to enhance the input images. Obtained images then undergo a classification process based on deep learning. A well-studied dataset of HAM10000 is used in the experiments. The system achieves 81.04% accuracy with EfficientNet model using the proposed fractional derivative based enhancements whereas accuracies are around 77.94% when using original images. In almost all the experiments, the enhanced images improved the accuracy. The results show that the proposed method improves the recognition performance.

Objectives: To investigate the value of conventional ultrasonography (US) combined with quantitative shear wave elastography (SWE) in evaluating and identifying target axillary lymph node (TALN) for fine needle aspiration biopsy (FNAB) of patients with early breast cancer.

Materials and methods: A total of 222 patients with 223 ALNs were prospectively recruited from January 2018 to December 2021. All TALNs were evaluated by US, SWE and subsequently underwent FNAB. The diagnostic performances of US, SWE, UE_or (either US or SWE was positive) and UE_and (both US and SWE were positive), and FNAB guided by the above four methods for evaluating ALN status were assessed using receiver operator characteristic curve (ROC) analyses. Univariate and multivariate logistic regression analyses used to determine the independent predictors of axillary burden.

Results: The area under the ROC curve (AUC) for diagnosing ALNs using conventional US and SWE were 0.69 and 0.66, respectively, with sensitivities of 78.00% and 65.00% and specificities of 60.98% and 66.67%. The combined method, UE_or, demonstrated significantly improved sensitivity of 86.00% (p < 0.001 when compared with US and SWE alone). The AUC of the UE_or-guided FNAB [0.85 (95% CI, 0.80-0.90)] was significantly higher than that of US-guided FNAB [0.83 (95% CI, 0.78-0.88), p = 0.042], SWE-guided FNAB [0.79 (95% CI, 0.72-0.84), p = 0.001], and UE_and-guided FNAB [0.77 (95% CI, 0.71-0.82), p < 0.001]. Multivariate logistic regression showed that FNAB and number of suspicious ALNs were found independent predictors of axillary burden in patients with early breast cancer.

Conclusion: The UE_or had superior sensitivity compared to US or SWE alone in ALN diagnosis. The UE_or-guided FNAB achieved a lower false-negative rate compared to FNAB guided solely by US or SWE, which may be a promising tool for the preoperative diagnosis of ALNs in early breast cancer, and had the potential implication for the selection of axillary surgical modality.

Background: The presence of lateral lymph node metastases (LNM) in paediatric patients with papillary thyroid cancer (PTC) is an independent risk factor for recurrence. We aimed to identify risk factors and establish a prediction model for lateral LNM before surgery in children and adolescents with PTC.

Methods: We developed a prediction model based on data obtained from 63 minors with PTC between January 2014 and June 2023. We collected and analysed clinical factors, ultrasound (US) features of the primary tumour, and pathology records of the patients. Multivariate logistic regression analysis was used to determine independent predictors and build a prediction model. We evaluated the predictive performance of risk factors and the prediction model using the area under the receiver operating characteristic (ROC) curve. We assessed the clinical usefulness of the predicting model using decision curve analysis.

Results: Among the minors with PTC, 21 had lateral LNM (33.3%). Logistic regression revealed that independent risk factors for lateral LNM were multifocality, tumour size, sex, and age. The area under the ROC curve for multifocality, tumour size, sex, and age was 0.62 (p = 0.049), 0.61 (p = 0.023), 0.66 (p = 0.003), and 0.58 (p = 0.013), respectively. Compared to a single risk factor, the combined predictors had a significantly higher area under the ROC curve (0.842), with a sensitivity and specificity of 71.4% and 81.0%, respectively (cutoff value = 0.524). Decision curve analysis showed that the prediction model was clinically useful, with threshold probabilities between 2% and 99%.

Conclusions: The independent risk factors for lateral LNM in paediatric PTC patients were multifocality and tumour size on US imaging, as well as sex and age. Our model outperformed US imaging and clinical features alone in predicting the status of lateral LNM.

Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME) are vision related complications prominently found in diabetic patients. The early identification of DR/DME grades facilitates the devising of an appropriate treatment plan, which ultimately prevents the probability of visual impairment in more than 90% of diabetic patients. Thereby, an automatic DR/DME grade detection approach is proposed in this work by utilizing image processing. In this work, the retinal fundus image provided as input is pre-processed using Discrete Wavelet Transform (DWT) with the aim of enhancing its visual quality. The precise detection of DR/DME is supported further with the application of suitable Artificial Neural Network (ANN) based segmentation technique. The segmented images are subsequently subjected to feature extraction using Adaptive Gabor Filter (AGF) and the feature selection using Random Forest (RF) technique. The former has excellent retinal vein recognition capability, while the latter has exceptional generalization capability. The RF approach also assists with the improvement of classification accuracy of Deep Convolutional Neural Network (CNN) classifier. Moreover, Chicken Swarm Algorithm (CSA) is used for further enhancing the classifier performance by optimizing the weights of both convolution and fully connected layer. The entire approach is validated for its accuracy in determination of grades of DR/DME using MATLAB software. The proposed DR/DME grade detection approach displays an excellent accuracy of 97.91%.

Background: Although endometrial cancer (EC) is staged surgically, magnetic resonance imaging (MRI) plays a critical role in assessing and selecting the most appropriate treatment planning. We aimed to assess the diagnostic performance of quantitative analysis of diffusion-weighted imaging (DWI) in preoperative assessment of EC.

Methods: Prospective analysis was done for sixty-eight patients with pathology-proven endometrial cancer who underwent MRI and DWI. Apparent diffusion coefficient (ADC) values were measured by two independent radiologists and compared with the postoperative pathological results.

Results: There was excellent inter-observer reliability in measuring ADCmean values. There were statistically significant lower ADCmean values in patients with deep myometrial invasion (MI), cervical stromal invasion (CSI), type II EC, and lympho-vascular space involvement (LVSI) (AUC = 0.717, 0.816, 0.999, and 0.735 respectively) with optimal cut-off values of ≤ 0.84, ≤ 0.84, ≤ 0.78 and ≤ 0.82 mm²/s respectively. Also, there was a statistically significant negative correlation between ADC values and the updated 2023 FIGO stage and tumor grade (strong association), and the 2009 FIGO stage (medium association).

Conclusions: The preoperative ADCmean values of EC were significantly correlated with main prognostic factors including depth of MI, CSI, EC type, grade, nodal involvement, and LVSI.

Objective: To investigate the diagnostic value of combined ⁹⁹Tc^m-DX lymphoscintigraphy and CT lymphangiography (CTL) in primary chylopericardium.

Methods: Fifty-five patients diagnosed with primary chylopericardium clinically were retrospectively analyzed. ⁹⁹Tc^m-DX lymphoscintigraphy and CTL were performed in all patients. Primary chylopericardium was classified into three types, according to the ⁹⁹Tc^m-DX lymphoscintigraphy results. The evaluation indexes of CTL include: (1) abnormal contrast distribution in the neck, (2) abnormal contrast distribution in the chest, (3) dilated thoracic duct was defined as when the widest diameter of thoracic duct was > 3 mm, (4) abnormal contrast distribution in abdominal. CTL characteristics were analyzed between different groups, and P < 0.05 was considered a statistically significant difference.

Results: Primary chylopericardium showed 12 patients with type I, 14 patients with type II, and 22 patients with type III. The incidence of abnormal contrast distribution in the posterior mediastinum was greater in type I than type III (P = 0.003). The incidence of abnormal contrast distribution in the pericardial and aortopulmonary windows, type I was greater than type III (P = 0.008). And the incidence of abnormal distribution of contrast agent in the bilateral cervical or subclavian region was greater in type II than type III (P = 0.002).

Conclusion: The combined application of the ⁹⁹Tc^m-DX lymphoscintigraphy and CTL is of great value for the localized and qualitative diagnosis of primary chylopericardium and explore the pathogenesis of lesions.

Objective: To assess whether diffusion and perfusion MRI derived parameters could non-invasively predict PD-L1 and Ki-67 status in primary central nervous system diffuse large B-cell lymphoma (PCNS-DLBCL).

Methods: We retrospectively analyzed DWI, DSC-PWI, and morphological MRI (mMRI) in 88 patients with PCNS-DLBCL. The mMRI features were compared using chi-square tests or Fisher exact test. Minimum ADC (ADC_min), mean ADC(ADC_mean), relative minimum ADC (rADC_min), relative mean ADC (rADC_mean), and relative maximum CBV (rCBV_max) values were compared in PCNS-DLBCL with different molecular status by using the Mann-Whitney U test. The diagnostic performances were evaluated by receiver operating characteristic curves.

Results: PCNS-DLBCL with high PD-L1 expression demonstrated a significantly higher ADC_min value than those with low PD-L1. The ADC_mean and rADC_mean values were significantly lower in PCNS-DLBCL with high Ki-67 status compared with those in low Ki-67 status. Other ADC, CBV parameters, and mMRI features did not show any association with these molecular statuses The diagnostic efficacy of ADC values in assessing PD-L1 and Ki-67 status was relatively low, with area under the curves (AUCs) values less than 0.7.

Conclusions: DWI-derived ADC values can provide some relevant information about PD-L1 and Ki-67 status in PCNS-DLBCL, but may not be sufficient to predict their expression due to the rather low diagnostic performance.

Background: Non-Alcoholic Steatohepatitis (NASH) is a crucial stage in the progression of Non-Alcoholic Fatty Liver Disease(NAFLD). The purpose of this study is to explore the clinical value of ultrasound features and radiological analysis in predicting the diagnosis of Non-Alcoholic Steatohepatitis.

Method: An SD rat model of hepatic steatosis was established through a high-fat diet and subcutaneous injection of CCl₄. Liver ultrasound images and elastography were acquired, along with serum data and histopathological results of rat livers.The Pyradiomics software was used to extract radiomic features from 2D ultrasound images of rat livers. The rats were then randomly divided into a training set and a validation set, and feature selection was performed through dimensionality reduction. Various machine learning (ML) algorithms were employed to build clinical diagnostic models, radiomic models, and combined diagnostic models. The efficiency of each diagnostic model for diagnosing NASH was evaluated using Receiver Operating Characteristic (ROC) curves, Clinical Decision Curve Analysis (DCA), and calibration curves.

Results: In the machine learning radiomic model for predicting the diagnosis of NASH, the Area Under the Curve (AUC) of ROC curve for the clinical radiomic model in the training set and validation set were 0.989 and 0.885, respectively. The Decision Curve Analysis revealed that the clinical radiomic model had the highest net benefit within the probability threshold range of > 65%. The calibration curve in the validation set demonstrated that the clinical combined radiomic model is the optimal method for diagnosing Non-Alcoholic Steatohepatitis.

Conclusion: The combined diagnostic model constructed using machine learning algorithms based on ultrasound image radiomics has a high clinical predictive performance in diagnosing Non-Alcoholic Steatohepatitis.