BMC Medical Imaging最新文献

Background: This study evaluated the impact of contrast-enhanced ultrasonography (CEUS) combined with CT or MRI fusion imaging on percutaneous radiofrequency ablation (RFA) outcomes for hepatocellular carcinoma (HCC) inconspicuous on conventional ultrasonography (US).

Methods: Patients were categorized into US-inconspicuous (USI) and US-conspicuous (USC) groups based on US imaging. The parameters of viable HCCs ⎯ including diameter, location, and RFA efficacy ⎯ were compared between USI and USC groups. Moreover, the breathing fusion imaging errors were measured. The differences in technical success, technical efficacy, local tumor progression, new tumor occurrence, and overall survival rate between USI and USC groups were analyzed.

Results: Sixty-five patients with 106 lesions were included. CEUS showed high consistency with CT/MRI but revealed larger diameters (p < 0.001) and more feeding arteries (p = 0.019) than CT/MRI. Breathing fusion imaging errors averaged 17 ± 4 mm, significantly affecting lesions in segments II, III, V, and VI (p < 0.001). The USI group had more lesions ablated per patient in a single RFA procedure (p = 0.001) than the USC group. No significant differences were observed in technical success rate, technical efficacy rate, local tumor progression rate, and overall survival rate between the two groups.

Conclusions: CEUS combined with fusion imaging provides detailed information on viable HCCs and their feeding arteries. CEUS-guided RFA avoids fusion imaging errors and achieves comparable efficacy in both US-conspicuous and US-inconspicuous HCCs.

Objective: Lymphovascular invasion (LVI) is critical for the effective treatment and prognosis of breast cancer (BC). This study aimed to investigate the value of eight machine learning models based on MRI radiomic features for the preoperative prediction of LVI status in BC.

Methods: A total of 454 patients with BC with known LVI status who underwent breast MRI were enrolled and randomly assigned to the training and validation sets at a ratio of 7:3. Radiomic features were extracted from T2WI and dynamic contrast-enhanced (DCE) of MRI sequences, the optimal feature filter and LASSO algorithm were used to obtain the optimal features, and eight machine learning algorithms, including LASSO, logistic regression, random forest, k-nearest neighbor (KNN), support vector machine, gradient boosting decision tree, extreme gradient boosting, and light gradient boosting machine, were used to construct models for predicating LVI status in BC. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to evaluate the performance of the models.

Results: Eighteen radiomic features were retained to construct the radiomic signature. Among the eight machine learning algorithms, the KNN model demonstrated superior performance to the other models in assessing the LVI status of patients with BC, with an accuracy of 0.696 and 0.642 in training and validation sets, respectively.

Conclusion: The eight machine learning models based on MRI radiomics serve as reliable indicators for identifying LVI status, and the KNN model demonstrated superior performance.This model offers substantial clinical utility, facilitating timely intervention in invasive BC and ultimately aiming to enhance patient survival rates.

Background: This study aims to develop and validate an integrated predictive model combining CT radiomics and clinical parameters for early assessment of acute pancreatitis severity.

Methods: A retrospective cohort of 246 patients with acute pancreatitis was analyzed, with a 70%-30% split for training and validation groups. CT image segmentation was performed using ITK-SNAP, followed by the extraction of radiomics features. The stability of the radiomics features was assessed through inter-observer Intraclass Correlation Coefficient analysis. Feature selection was carried out using univariate analysis and least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation. A radiomics model was constructed through logistic regression to compute the radiomics score. Concurrently, univariate and multivariate logistic regression were employed to identify independent clinical risk factors for the clinical model. The radiomics score and clinical variables were integrated into a combined model, which was visualized with a nomogram. Model performance and net clinical benefit were evaluated through the area under the receiver operating characteristic curve (AUC), the DeLong test, and decision curve analysis.

Results: A total of 913 radiomics features demonstrated satisfactory consistency. Eight features were selected for the radiomics model. Serum calcium, C-reactive protein, and white blood cell count were identified as independent clinical predictors. The AUC of the radiomics model was 0.871 (95% CI, 0.793-0.949) in the training cohort and 0.859 (95% CI, 0.751-0.967) in the validation cohort. The clinical model achieved AUCs of 0.833 (95% CI, 0.756-0.910) and 0.810 (95% CI, 0.692-0.929) for the training and validation cohorts, respectively. The combined model outperformed both the radiomics and clinical models, with an AUC of 0.905 (95% CI, 0.837-0.973) in the training cohort and 0.908 (95% CI, 0.824-0.992) in the validation cohort. The DeLong test confirmed superior predictive performance of the combined model over both the radiomics and clinical models in the training cohort, and over the clinical model in the validation cohort. Decision curve analysis further demonstrated that the combined model provided greater net clinical benefit than the radiomics or clinical models alone.

Conclusion: The clinical-radiomics model offers a novel tool for the early prediction of acute pancreatitis severity, providing valuable support for clinical decision-making.

Background: This study aimed to perform a comprehensive morphometric analysis of the epiglottis using cone-beam computed tomography (CBCT) images, including the determination of epiglottis dimensions, the investigation of shape variations, and the assessment of their relationship with gender and age.

Methods: A retrospective analysis was conducted on high-quality CBCT images from 100 patients, obtained using the NewTom 5G system. In CBCT images, epiglottis thicknesses (right, midline, left) and horizontal angle at three levels (suprahyoid, hyoid, infrahyoid) were measured in axial sections, while the length and vertical angle of epiglottis were measured in midsagittal view. Epiglottis shapes were identified through 3D visualization.

Results: The midline epiglottis thicknesses were 4.68 mm at the suprahyoid level, 5.51 mm at the hyoid level, and 6.80 mm at the infrahyoid levels. Epiglottis thicknesses and length were statistically significantly greater in males. Of the 100 patients, 51 had a normal curvature, 41 had a flat epiglottis, and 8 had an omega epiglottis. The omega-shaped epiglottis was significantly longer compared to both the flat and normal curvature types (p = 0.011). There was a positive correlation between age and epiglottis thicknesses at the suprahyoid level and horizontal angles at three levels.

Conclusions: This study visualizes epiglottis morphology and uncovers significant morphometric differences. Males exhibit greater epiglottis thickness and length compared to females, while the omega-shaped epiglottis is notably longer than other types. These findings underscore the need for further investigation into the clinical relevance of these morphometric differences, particularly in improving airway management and refining approaches to swallowing function.

Background: A deep learning (DL) model that can automatically detect and classify cervical canal and neural foraminal stenosis using cervical spine magnetic resonance imaging (MRI) can improve diagnostic accuracy and efficiency.

Methods: A method comprising region-of-interest (ROI) detection and cascade prediction was formulated for diagnosing cervical spinal stenosis based on a DL model. First, three part-specific convolutional neural networks were employed to detect the ROIs in different parts of the cervical MR images. Cascade prediction of the stenosis categories was subsequently performed to record the stenosis level and position on each patient slice. Finally, the results were combined to obtain a patient-level diagnostic report. Performance was evaluated based on the accuracy (ACC), area under the curve (AUC), sensitivity, specificity, F1 Score, diagnosis time of the DL model, and recall rate for ROI detection localization.

Results: The average recall rate of the ROI localization was 89.3% (neural foramen) and 99.7% (central canal) under the five-fold cross-validation of the DL model. In the dichotomous classification (normal or mild vs. moderate or severe), the ACC and AUC of the DL model were comparable to those of the radiologists, and the F1 score (84.8%) of the DL model was slightly higher than that of the radiologists (83.8%) for the central canal. Diagnosing whether the central canal or neural foramen of a slice is narrowed in the cervical MRI scan required an average of 15 and 0.098 s for the radiologists and DL model, respectively.

Conclusions: The DL model demonstrated comparable performance with subspecialist radiologists for the detection and classification of central canal and neural foraminal stenosis on cervical spine MRI. Moreover, the DL model demonstrated significant timesaving ability.

Virtual histopathology is an emerging technology in medical imaging that utilizes advanced computational methods to analyze tissue images for more precise disease diagnosis. Traditionally, histopathology relies on manual techniques and expertise, often resulting in time-consuming processes and variability in diagnoses. Virtual histopathology offers a more consistent, and automated approach, employing techniques like machine learning, deep learning, and image processing to simulate staining and enhance tissue analysis. This review explores the strengths, limitations, and clinical applications of these methods, highlighting recent advancements in virtual histopathological approaches. In addition, important areas are identified for future research to improve diagnostic accuracy and efficiency in clinical settings.

Objective: Differentiating intramedullary spinal cord tumor (IMSCT) from spinal cord tumefactive demyelinating lesion (scTDL) remains challenging with standard diagnostic approaches. This study aims to develop and evaluate the effectiveness of a magnetic resonance imaging (MRI)-based radiomics model for distinguishing scTDL from IMSCT before treatment initiation.

Methods: A total of 75 patients were analyzed in this retrospective study, comprising 55 with IMSCT and 20 with scTDL. Radiomics features were extracted from T1- and T2-weighted imaging (T1&T2WI) scans upon admission. Ten classification algorithms were employed: logistic regression (LR); naive bayes (NaiveBayes); support vector machine (SVM); k nearest neighbors (KNN); random forest (RF); extra trees (ExtraTrees); eXtreme gradient boosting (XGBoost); light gradient boosting machine (LightGBM); gradient boosting (GradientBoosting); and multi-Layer perceptron (MLP). The performance of the optimal model was then compared to radiologists' assessments.

Results: This study developed 30 predictive models using ten classifiers across two imaging sequences. The MLP model with two sequences (T1&T2WI) emerged as the most effective one, showing superior accuracy in MRI analysis with an area under the curve (AUC) of 0.991 in training and 0.962 in testing. Moreover, statistical analyses highlighted the radiomics model significantly outperformed radiologists' assessments (p < 0.05) in distinguishing between IMSCT and scTDL.

Conclusion: We present an MRI-based radiomics model with high diagnostic accuracy in differentiating IMSCT from scTDL. The model's performance was comparable to junior radiologists, highlighting its potential as an effective diagnostic aid in clinical practice.

Introduction: The treatment of preinvasive lesions is more effective than treating invasive disease, hence detecting cancer at its early stages is crucial. However, currently, available screening methods show various limitations in terms of sensitivity, specificity, and practicality, thus novel markers complementing traditional cyto/histopathological assessments are needed. Alteration in choline metabolism is a hallmark of many malignancies, including cervical and breast cancers. Choline radiotracers are widely used for imaging purposes, even though many risks are associated with their radioactivity. Therefore, this work aimed to synthesise and characterise a non-radioactive choline tracer based on a fluorinated acridine scaffold (CFA) for the in vitro detection of cervical and breast cancer cells by fluorescence imaging.

Methods: CFA was fully characterised and tested for its cytotoxicity on breast (MCF-7), cervical (HeLa), glioblastoma (U-87 MG) and hepatoblastoma (HepG2) cancer cell lines and in normal cell lines (epithelial, HEK-293 and human dermal fibroblasts, HDFs). The cellular uptake of CFA was investigated by a confocal microscope and its accumulation was quantified over time. The specificity of CFA over mesenchymal origin cells (HDFs), as a model of cancer-associated fibroblasts was investigated by fluorescence microscopy.

Results: CFA was toxic at much higher concentrations (HeLa IC₅₀ = 200 ± 18 µM and MCF-7 IC₅₀ = 105 ± 3 µM) than needed for its detection in cancer cells (5 µM). CFA was not toxic in the other cell lines tested. The intensity of CFA in breast and cervical cancer cells was not significantly different at any time point, yet it was greater than HepG2 and U-87 MG (p ≤ 0.01 and p ≤ 0.0001, respectively) after 24 h incubation. A very weak signal intensity was recorded in HEK-293 and HDFs (p ≤ 0.001 and p ≤ 0.0001, respectively). A selective ability of CFA to accumulate in HeLa and MCF-7 was recorded upon co-culture with fibroblasts.

Conclusions: The results showed that CFA preferentially accumulated in cancer cells rather than in normal cells. These findings suggest that CFA may be a potential diagnostic probe for discriminating healthy tissues from malignant tissues due to its specific and highly sensitive features; CFA may also represent a useful tool for in vitro/ex vivo investigations of choline metabolism in patients with cervical and breast cancers.

Background and purpose: Tumor bed (TB) is the residual cavity of resected tumor after surgery. Delineating TB from CT is crucial in generating clinical target volume for radiotherapy. Due to multiple surgical effects and low image contrast, segmenting TB from soft tissue is challenging. In clinical practice, titanium clips were used as marks to guide the searching of TB. However, this information is limited and may cause large error. To provide more prior location information, the tumor regions on both pre-operative and post-operative CTs are both used by the deep learning model in segmenting TB from surrounding tissues.

Materials and methods: For breast cancer patient after surgery and going to be treated by radiotherapy, it is important to delineate the target volume for treatment planning. In clinical practice, the target volume is usually generated from TB by adding a certain margin. Therefore, it is crucial to identify TB from soft tissue. To facilitate this process, a deep learning model is developed to segment TB from CT with the guidance of prior tumor location. Initially, the tumor contour on the pre-operative CT is delineated by physician for surgical planning purpose. Then this contour is transformed to the post-operative CT via the deformable image registration between paired pre-operative and post-operative CTs. The original and transformed tumor regions are both used as inputs for predicting the possible region of TB by the deep-learning model.

Results: Compared to the one without prior tumor contour information, the dice similarity coefficient of the deep-learning model with the prior tumor contour information is improved significantly (0.812 vs. 0.520, P = 0.001). Compared to the traditional gray-level thresholding method, the dice similarity coefficient of the deep-learning model with the prior tumor contour information is improved significantly (0.812 vs.0.633, P = 0.0005).

Conclusions: The prior tumor contours on both pre-operative and post-operative CTs provide valuable information in searching for the precise location of TB on post-operative CT. The proposed method provided a feasible way to assist auto-segmentation of TB in treatment planning of radiotherapy after breast-conserving surgery.

Background: Esophageal fistula (EF), a rare and potentially fatal complication, can be better managed with predictive models for personalized treatment plans in esophageal cancers. We aim to develop a clinical-deep learning radiomics model for effectively predicting the occurrence of EF.

Methods: The study involved esophageal cancer patients undergoing radiotherapy or chemoradiotherapy. Arterial phase enhanced CT images were used to extract handcrafted and deep learning radiomic features. Along with clinical information, a 3-step feature selection method (statistical tests, Least Absolute Shrinkage and Selection Operator, and Recursive Feature Elimination) was used to identify five feature sets in training cohort for constructing random forest EF prediction models. Model performance was compared and validated in both retrospective and prospective test cohorts.

Results: One hundred seventy five patients (122 in training and 53 in test cohort)were retrospectively collected from April 2018 to June 2022. An additional 27 patients were enrolled as a prospective test cohort from June 2022 to December 2023. Post-selection in the training cohort, five feature sets were used for model construction: clinical, handcrafted radiomic, deep learning radiomic, clinical-handcrafted radiomic, and clinical-deep learning radiomic. The clinical-deep learning radiomic model excelled with AUC of 0.89 (95% Confidence Interval: 0.83-0.95) in the training cohort, 0.81 (0.65-0.94) in the test cohort, and 0.85 (0.71-0.97) in the prospective test cohort. Brier-score and calibration curve analyses validated its predictive ability.

Conclusions: The clinical-deep learning radiomic model can effectively predict EF in patients with advanced esophageal cancer undergoing radiotherapy or chemoradiotherapy.