BMC Medical Imaging最新文献

Background: This study was designed to develop a combined radiomics nomogram to preoperatively predict the risk categorization of thymomas based on contrast-enhanced computed tomography (CE-CT) images.

Materials: The clinical and CT data of 178 patients with thymoma (100 patients with low-risk thymomas and 78 patients with high-risk thymomas) collected in our hospital from March 2018 to July 2023 were retrospectively analyzed. The patients were randomly divided into a training set (n = 125) and a validation set (n = 53) in a 7:3 ratio. Qualitative radiological features were recorded, including (a) tumor diameter, (b) location, (c) shape, (d) capsule integrity, (e) calcification, (f) necrosis, (g) fatty infiltration, (h) lymphadenopathy, and (i) enhanced CT value. Radiomics features were extracted from each CE-CT volume of interest (VOI), and the least absolute shrinkage and selection operator (LASSO) algorithm was performed to select the optimal discriminative ones. A combined radiomics nomogram was further established based on the clinical factors and radiomics scores. The differentiating efficacy was determined using receiver operating characteristic (ROC) analysis.

Results: Only one clinical factor (incomplete capsule) and seven radiomics features were found to be independent predictors and were used to establish the radiomics nomogram. In differentiating low-risk thymomas (types A, AB, and B1) from high-risk ones (types B2 and B3), the nomogram demonstrated better diagnostic efficacy than any single model, with the respective area under the curve (AUC), accuracy, sensitivity, and specificity of 0.974, 0.921, 0.962 and 0.900 in the training cohort, 0.960, 0.892, 0923 and 0.897 in the validation cohort, respectively. The calibration curve showed good agreement between the prediction probability and actual clinical findings.

Conclusions: The nomogram incorporating clinical factors and radiomics features provides additional value in differentiating the risk categorization of thymomas, which could potentially be useful in clinical practice for planning personalized treatment strategies.

Purpose: In pediatric medicine, precise estimation of bone age is essential for skeletal maturity evaluation, growth disorder diagnosis, and therapeutic intervention planning. Conventional techniques for determining bone age depend on radiologists' subjective judgments, which may lead to non-negligible differences in the estimated bone age. This study proposes a deep learning-based model utilizing a fully connected convolutional neural network(CNN) to predict bone age from left-hand radiographs.

Methods: The data set used in this study, consisting of 473 patients, was retrospectively retrieved from the PACS (Picture Achieving and Communication System) of a single institution. We developed a fully connected CNN consisting of four convolutional blocks, three fully connected layers, and a single neuron as output. The model was trained and validated on 80% of the data using the mean-squared error as a cost function to minimize the difference between the predicted and reference bone age values through the Adam optimization algorithm. Data augmentation was applied to the training and validation sets yielded in doubling the data samples. The performance of the trained model was evaluated on a test data set (20%) using various metrics including, the mean absolute error (MAE), median absolute error (MedAE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). The code of the developed model for predicting the bone age in this study is available publicly on GitHub at https://github.com/afiosman/deep-learning-based-bone-age-estimation .

Results: Experimental results demonstrate the sound capabilities of our model in predicting the bone age on the left-hand radiographs as in the majority of the cases, the predicted bone ages and reference bone ages are nearly close to each other with a calculated MAE of 2.3 [1.9, 2.7; 0.95 confidence level] years, MedAE of 2.1 years, RMAE of 3.0 [1.5, 4.5; 0.95 confidence level] years, and MAPE of 0.29 (29%) on the test data set.

Conclusion: These findings highlight the usability of estimating the bone age from left-hand radiographs, helping radiologists to verify their own results considering the margin of error on the model. The performance of our proposed model could be improved with additional refining and validation.

The objective of this study was to evaluate the intramammary distribution of MRI-detected mass and focus lesions that were difficult to identify with conventional B-mode ultrasound (US) alone. Consecutive patients with lesions detected with MRI but not second-look conventional B-mode US were enrolled between May 2015 and June 2023. Following an additional supine MRI examination, we performed third-look US using real-time virtual sonography (RVS), an MRI/US image fusion technique. We divided the distribution of MRI-detected mammary gland lesions as follows: center of the mammary gland versus other (superficial fascia, deep fascia, and atrophic mammary gland). We were able to detect 27 (84%) of 32 MRI-detected lesions using third-look US with RVS. Of these 27 lesions, 5 (19%) were in the center of the mammary gland and 22 (81%) were located in other areas. We were able to biopsy all 27 lesions; 8 (30%) were malignant and 19 (70%) were benign. Histopathologically, three malignant lesions were invasive ductal carcinoma (IDC; luminal A), one was IDC (luminal B), and four were ductal carcinoma in situ (low-grade). Malignant lesions were found in all areas. During this study period, 132 MRI-detected lesions were identified and 43 (33%) were located in the center of the mammary gland and 87 (64%) were in other areas. Also, we were able to detect 105 of 137 MRI-detected lesions by second-look conventional-B mode US and 38 (36%) were located in the center of the mammary gland and 67 (64%) were in other areas. In this study, 81% of the lesions identified using third-look US with RVS and 64% lesions detected by second-look conventional-B mode US were located outside the center of the mammary gland. We consider that adequate attention should be paid to the whole mammary gland when we perform third-look US using MRI/US fusion technique.

In the realm of disease prognosis and diagnosis, a plethora of medical images are utilized. These images are typically stored either within the local on-premises servers of healthcare providers or within cloud storage infrastructures. However, this conventional storage approach often incurs high infrastructure costs and results in sluggish information retrieval, ultimately leading to delays in diagnosis and consequential wastage of valuable time for patients. The methodology proposed in this paper offers a pioneering solution to expedite the diagnosis of medical conditions while simultaneously reducing infrastructure costs associated with data storage. Through this study, a high-speed biomedical image processing approach is designed to facilitate rapid prognosis and diagnosis. The proposed framework includes Deep learning QR code technique using an optimized database design aimed at alleviating the burden of intensive on-premises database requirements. The work includes medical dataset from Crawford Image and Data Archive and Duke CIVM for evaluating the proposed work suing different performance metrics, The work has also been compared from the previous research further enhancing the system's efficiency. By providing healthcare providers with high-speed access to medical records, this system enables swift retrieval of comprehensive patient details, thereby improving accuracy in diagnosis and supporting informed decision-making.

Background: To investigate the value of ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) semi-quantitative parameters, including the lesion diameter, maximum standardized uptake value (SUVmax), maximum standardized uptake value corrected for lean body mass (SULmax), metabolic lesion volume (MLV), and total lesion glycolysis (TLG), for classifying hepatic echinococcosis.

Methods: In total, 20 patients with 36 hepatic echinococcosis lesions were included in the study. Overall, these lesions were categorized as hepatic cystic echinococcosis (HCE) or hepatic alveolar echinococcosis (HAE) according to the pathological results. Multiple semi-parameters including the maximum diameter, SUVmax, SULmax, MLV, and TLG were measured to classify HCE and HAE compared with the pathological results. The receiver operator characteristic curve and area under the curve (AUC) of each quantitative parameter were calculated. The Mann-Whitney U test was used to compare data between the two groups.

Results: In total, 12 cystic lesions and 24 alveolar lesions were identified after surgery. There were significant differences in SUV max, SUL max, MLV, and TLG between the HAE and HCE groups (Z =  - 4.70, - 4.77, - 3.36, and - 4.23, respectively, all P < 0.05). There was no significant difference in the maximum lesion diameter between the two groups (Z =  - 0.77, P > 0.05). The best cutoffs of SUV max, SUL max, MLV, and TLG for the differential diagnosis of HAE and HCE were 2.09, 2.67, 27.12, and 18.79, respectively. The AUCs of the four parameters were 0.99, 0.99, 0.85, and 0.94, respectively. The sensitivities were 91.7%, 87.5%, 66.7%, and 85.6%, respectively, and the specificities were 90.1%, 91.7%, 83.3%, and 90.9%, respectively.

Conclusion: ¹⁸F-FDG PET/CT semi-quantitative parameters had significant clinical value in the diagnosis and pathological classification of hepatic echinococcosis and evaluation of clinical treatment.

Background: Programmed cell death ligand 1 (PD-L1), as a reliable predictive biomarker, plays an important role in guiding immunotherapy of lung cancer. To investigate the value of CT-based deep learning radiomics signature to predict PD-L1 expression in non-small cell lung cancers(NSCLCs).

Methods: 259 consecutive patients with pathological confirmed NSCLCs were retrospectively collected and divided into the training cohort and validation cohort according to the chronological order. The univariate and multivariate analyses were used to build the clinical model. Radiomics and deep learning features were extracted from preoperative non-contrast CT images. After feature selection, Radiomics score (Rad-score) and deep learning radiomics score (DLR-score) were calculated through a linear combination of the selected features and their coefficients. Predictive performance for PD-L1 expression was evaluated via the area under the curve (AUC) of receiver operating characteristic, the calibration curves, and the decision curve analysis.

Results: The clinical model based on Cytokeratin 19 fragment and lobulated shape obtained an AUC of 0.767(95% CI: 0.673-0.860) in the training cohort and 0.604 (95% CI:0.477-0.731) in the validation cohort. 11 radiomics features and 15 deep learning features were selected by LASSO regression. AUCs of the Rad-score were 0.849 (95%CI: 0.783-0.914) and 0.717 (95%CI: 0.607-0.826) in the training cohort and validation cohort, respectively. AUCs of DLR-score were 0.938 (95%CI: 0.899-0.977) and 0.818(95%CI:0.727-0.910) in the training cohort and validation cohort, respectively. AUCs of the DLR-score were significantly higher than those of the Rad-score and the clinical model.

Conclusion: The CT-based deep learning radiomics signature could achieve clinically acceptable predictive performance for PD-L1 expression, which showed potential to be a surrogate imaging biomarker or a complement of immunohistochemistry assessment.

Purpose: To evaluate the difference in the diagnostic efficacy of ¹⁸F-PSMA-1007 PET/CT and pelvic MRI in primary prostate cancer, as well as the correlation between the two methods and histopathological parameters and serum PSA levels.

Methods: A total of 41 patients with suspected prostate cancer who underwent ¹⁸F-PSMA-1007 PET/CT imaging in our department from 2018 to 2023 were retrospectively collected. All patients underwent ¹⁸F-PSMA-1007 PET/CT and MRI scans. The sensitivity, PPV and diagnostic accuracy of MRI and ¹⁸F-PSMA-1007 PET/CT in the diagnosis of prostate cancer were calculated after comparing the results of MRI and ¹⁸F-PSMA-1007 PET/CT with biopsy. The Spearman test was used to calculate the correlation between ¹⁸F-PSMA-1007 PET/CT, MRI parameters, histopathological indicators, and serum PSA levels.

Results: Compared with histopathological results, the sensitivity, PPV and diagnostic accuracy of ¹⁸F-PSMA-1007 PET/CT in the diagnosis of prostate cancer were 95.1%, 100.0% and 95.1%, respectively. The sensitivity, PPV and diagnostic accuracy of MRI in the diagnosis of prostate cancer were 82.9%, 100.0% and 82.9%, respectively. There was a mild to moderately positive correlation between Gleason (Gs) score, Ki-67 index, serum PSA level and ¹⁸F-PSMA-1007 PET/CT parameters (p < 0.05). There was a moderately negative correlation between the expression of AMACR (P504S) and ¹⁸F-PSMA-1007 PET/CT parameters (p < 0.05). The serum PSA level and the Gs score were moderately positively correlated with the MRI parameters (p < 0.05). There was no correlation between histopathological parameters and MRI parameters (p > 0.05).

Conclusion: Compared with MRI, ¹⁸F-PSMA-1007 PET/CT has higher sensitivity and diagnostic accuracy in the detection of malignant prostate tumors. In addition, the Ki-67 index and AMACR (P504S) expression were only correlated with ¹⁸F-PSMA-1007 PET/CT parameters. Gs score and serum PSA level were correlated with ¹⁸F-PSMA-1007 PET/CT and MRI parameters. ¹⁸F-PSMA-1007 PET/CT examination can provide certain reference values for the clinical diagnosis, evaluation, and treatment of malignant prostate tumors.

Breast cancer is a prevalent disease and the second leading cause of death in women globally. Various imaging techniques, including mammography, ultrasonography, X-ray, and magnetic resonance, are employed for detection. Thermography shows significant promise for early breast disease detection, offering advantages such as being non-ionizing, non-invasive, cost-effective, and providing real-time results. Medical image segmentation is crucial in image analysis, and this study introduces a thermographic image segmentation algorithm using the improved Black Widow Optimization Algorithm (IBWOA). While the standard BWOA is effective for complex optimization problems, it has issues with stagnation and balancing exploration and exploitation. The proposed method enhances exploration with Levy flights and improves exploitation with quasi-opposition-based learning. Comparing IBWOA with other algorithms like Harris Hawks Optimization (HHO), Linear Success-History based Adaptive Differential Evolution (LSHADE), and the whale optimization algorithm (WOA), sine cosine algorithm (SCA), and black widow optimization (BWO) using otsu and Kapur's entropy method. Results show IBWOA delivers superior performance in both qualitative and quantitative analyses including visual inspection and metrics such as fitness value, threshold values, peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity index (FSIM). Experimental results demonstrate the outperformance of the proposed IBWOA, validating its effectiveness and superiority.

Rationale and objective: To investigate the impact of the contrast enhancement boost (CE-boost) technique on the image quality of CT angiography (CTA) derived from 80-kVp cerebral CT perfusion (CTP) data, and to compare it with conventional CTA_peak as well as other currently employed methods for enhancing CTA images, such as CTA_tMIP and CTA_tAve extracted from CTP.

Materials and methods: The data of forty-seven patients who underwent CTP at 80 kVp were retrospectively collected. Four sets of images: CTA_peak, CTA_tMIP, CTA_tAve, and CE-boost images. The CTA_peak image represents the arterial phase at its peak value, captured as a single time point. CTA_tMIP and CTA_tAve are 4D CTA images that provide maximum density projection and average images from the three most prominent time points. CE-boost is a postprocessing technique used to enhance contrast in the arterial phase at its peak value. We compared the average CT value, standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of the internal carotid artery (ICA) and basilar artery (BA) among the four groups. Image quality was evaluated using a 5-point scale.

Results: The CE-boost demonstrated and CNR in the ICA and BA (all p < 0.001). Compared with the other three CTA reconstructed images, the CE-boost images had the best subjective image quality, with the highest scores of 4.77 ± 0.43 and 4.87 ± 0.34 for each reader (all p < 0.001).

Conclusion: Compared with other currently used techniques,CE-boost enhances the image quality of CTA derived from 80-kVp CTP data, leading to improved visualization of intracranial arteries.