Insights into Imaging最新文献

Objectives: This study aimed to evaluate the efficacy of two combined ultrafast breast MRI kinetic parameters, combination 1 including time to enhancement [TTE], maximum slope [MS], and initial enhancement phase [IE phase] compared to combination 2 including relative enhancement [RE], maximum enhancement [ME], maximum relative enhancement [MRE], time to peak [TTP], and wash in rate in characterizing benign and malignant breast lesions.

Materials and methods: This prospective study included 264 female patients with 273 breast lesions. The ultrafast protocol was done using the TWIST sequence. The parameters for combination 1 were generated manually; however, the parameters for combination 2 were generated semi-automatically. The overall performance of the ultrafast protocol was compared to the conventional dynamic MRI protocol.

Results: The ultrafast protocol was obtained in 77 s. The mean interpretation time was 5 ± 2.7 and 1 ± 0.5 min for combinations 1 and 2, respectively. Combination 1 showed an AUC of 0.910, a sensitivity of 76.5% and a specificity of 90%, while combination 2 showed an AUC of 0.869, a sensitivity of 76.5%, and a specificity of 85% in differentiating benign from malignant lesions. Upon combining all parameters, the AUC, sensitivity, and specificity in discriminating between the two groups increased to 0.944, 80.4%, and 85%, respectively. Both ultrafast techniques and conventional MRI demonstrated excellent performance in discriminating between benign and malignant lesions (AUC = 0.921 vs 0.940, respectively).

Conclusion: Adding the semiautomatically generated parameters derived from ultrafast breast MRI can improve the performance in characterizing breast lesions.

Critical relevance statement: By studying ultrafast-derived semiautomatic, easily applicable parameters, we aim to reduce the acquisition and interpretation times of breast MRI without compromising performance, when used as a problem-solving modality in indeterminate breast lesions to characterize them as either benign or malignant.

Key points: Adding semiautomatic ultrafast parameters to the MS and TTE improves the overall performance in characterizing breast lesions. The combined ultrafast parameters provide the highest discriminating power between benign and malignant breast lesions. Ultrafast MRI showed comparable performance to conventional dynamic contrast-enhanced MRI in the discrimination between benign and malignant breast lesions.

Objectives: To develop multivariate models for preoperative detection of occult lymph node (LN) metastasis in gastric cancer (GC), by integrating CT-based extracellular volume (ECV) fraction and clinicopathological features, and further evaluate the prognostic value of the combined model.

Materials and methods: This retrospective study included 129 GCs with the N (-) group (n = 49) and the N (+) group (n = 80). The preoperative CT parameters (including ECV fraction), WHO types and differentiation degree based on endoscopic pathological, and 4 hematological indices were assessed. The diagnostic performance of multivariate models was evaluated by receiver operating characteristic curve analysis.

Results: The N (+) group demonstrated significantly higher proportions of poorly cohesive carcinoma and poor differentiation based on endoscope (both p < 0.001). Significantly higher CT-measured tumor area and ECV fraction were seen in the N (+) group (p < 0.001 and p = 0.008, respectively), and a significantly higher proportion of ECV value > 50% in the N (+) group (p = 0.001). The clinicopathological model, CT parameters model, and combined model yielded areas under the curves of 0.768, 0.774, and 0.843, respectively. The combined model with the high-risk group revealed a significantly shorter median recurrence-free survival compared to the low-risk group (p = 0.008).

Conclusion: The proposed preoperative combined model exhibited a promising performance for early predicting occult LN metastasis and stratifying postoperative recurrence risk in GC, by integrating CT-based ECV fraction and clinicopathological features.

Critical relevance statement: The CT-based ECV preoperative model could potentially provide valuable clinical reference for making clinical strategies in GC.

Key points: It is a great challenge for clinicians to evaluate occult lymph node (LN) status in gastric cancer (GC). The N (+) group demonstrated higher CT-based extracellular volume (ECV) fractions and tumor area, and higher proportions of poorly cohesive carcinoma and poor differentiation. This model helped preoperative detection of occult LN metastasis and stratifying postoperative recurrence risk in GC.

Objective: The study sought to develop and validate an MRI-based deep learning radiomics (DLR) nomogram for preoperative prediction of vessels encapsulating tumor clusters (VETC) and recurrence-free survival (RFS) in hepatocellular carcinoma (HCC).

Materials and methods: The dual-center study retrospectively enrolled 625 HCC patients who underwent preoperative Gd-EOB-DTPA-enhanced MRI, including training (n = 296), internal (n = 126), and external (n = 203) test sets. Clinical-radiologic characteristics were selected to develop a clinical-radiologic model. Habitat radiomics and deep learning (DL) features were extracted and selected to develop the habitat radiomics and DL models using the machine learning classifiers. The DLR nomogram model was ultimately constructed by integrating univariate-selected clinical-radiologic characteristics with habitat radiomics and DL scores. Both univariable and multivariable Cox regression analyses were performed to identify independent prognostic factors and develop a prognostic model for RFS.

Results: In the external test set, the DLR nomogram model yielded a higher area under the curve (AUC) than the clinical-radiologic model (0.752 vs 0.678; p = 0.004), while habitat radiomics (0.750) and DL models (0.748) showed comparable performance (both p > 0.05). The DLR nomogram consistently demonstrated the higher F1-scores across all three sets. The prognostic model incorporating AFP (hazard ratio (HR), 1.628 [95% CI: 1.113-2.380]; p = 0.012) and DLR score (1.279 [1.051-1.557]; p = 0.014) achieved C-indexes of 0.679 and 0.642 for RFS in the internal and external test sets.

Conclusion: The DLR nomogram model helps predict VETC in HCC and assess the risk for RFS.

Critical relevance statement: Interpretable deep learning radiomics nomogram model provides clinicians with more precise technical support for preoperative prediction of VETC status and RFS in HCC, potentially aiding in clinical decision-making and follow-up strategies.

Key points: Vessels encapsulating tumor clusters (VETC) is a critical predictor of aggressive hepatocellular carcinoma. The deep learning radiomics (DLR) nomogram model helps predict VETC, and the DLR score serves as an independent prognostic factor for recurrence-free survival. The model demonstrated favorable interpretability through the SHAP method.

Magnetic resonance imaging (MRI) of the neuroaxis is prone to a variety of artifacts. Familiarity with these artifacts and their respective mitigation techniques is essential for accurate neuroradiological interpretation. In this educational review, we focus on artifacts caused by the physiological flow of cerebrospinal fluid (CSF), which are encountered commonly and, depending on the context, may be beneficial or detrimental in diagnostic decision-making. The pictorial examples provided will illustrate key cases with their practical implications. CRITICAL RELEVANCE STATEMENT: This paper highlights common CSF flow artifacts, including phase encoding artifacts, time-of-flight signal loss, entry slice phenomenon, and intravoxel dephasing, emphasizing their impact on diagnosis interpretation and mitigation strategies. KEY POINTS: CSF artifacts stem from flow dynamics, phase differences, or magnetic field interactions. Artifacts obscure or mimic pathologies, degrade image quality, or occasionally aid in diagnostic decision-making. Mitigation strategies are simple and intuitive, including modification of phase directions, employing alternate imaging sequences, and altering MRI parameters.

Objectives: To investigate normal and pathologic values of fatty infiltration (FI) and muscle volume through volumetric quantification of the main hip abductors of patients with unilateral greater trochanteric pain syndrome (GTPS) using 2-point-Dixon MRI.

Materials and methods: Patients prospectively underwent MRI of both hips: FI of the gluteus minimus (Gmin) and medius (Gmed) muscles were quantified by volumetric fat fractions (3D FF) using 2-point-Dixon MRI. Whole (WMV) and lean muscle volumes (LMV) were calculated for both muscles. 3D FF and volumes were compared between asymptomatic and GTPS hips, using the Wilcoxon signed-rank test. Gender-specific differences were assessed using the Mann-Whitney U test.

Results: Forty-one patients (mean age 65.0 ± 13.6 years, 27 females) were analyzed. 3D FF in asymptomatic hips was lower than in symptomatic hips (Gmin: 17.8% vs. 19.8%; Gmed: 12.7% vs. 15.9% (all p ≤ 0.02)). Gmin had a higher 3D FF than Gmed (p < 0.001). Females had higher FF (asymptomatic and symptomatic Gmin: 19.4%, 21.8%; asymptomatic and symptomatic Gmed: 13.2%, 16.3%) than males (asymptomatic and symptomatic Gmin: 14.7%, 16.1%; asymptomatic and symptomatic Gmed: 11.8%, 14.9%) for both sides and muscles. Average WMV in asymptomatic hips for Gmin and Gmed were 77.2 cm³, 270.1 cm³ in females, and lower in males (both p < 0.001) with 107.1 cm³, 408.1 cm³, respectively.

Conclusion: This study offers reference values for 3D FF and volumes of the Gmin and Gmed muscles in asymptomatic elderly hips, which are significantly lower than their GTPS counterparts, with succinctly higher fat fractions in females than males. Women showed significantly lower muscle volume for both muscles than men.

Critical relevance statement: Volumetric fat fractions of gluteal muscles show significant symptoms and gender related differences, indicating their potential as an imaging biomarker in the common GTPS patient.

Key points: In females, asymptomatic hips showed average volumetric fat fractions of 19% for Gmin and 13% for Gmed; with lower values in males, of 15% and 12%, respectively. Whole muscle volumes in asymptomatic hips for Gmin and Gmed were 77.2 cm³, 270.1 cm³ in females, and 107.1 cm³, 408.1 cm³ in males. Using volumetric fat fractions, abductor muscle fat content was significantly higher in symptomatic GTPS hips compared to asymptomatic hips.

Objectives: High classification accuracy of Alzheimer's disease (AD) from structural MRI has been achieved using deep neural networks, yet the specific image features contributing to these decisions remain unclear. In this study, the contributions of T1-weighted (T1w) gray-white matter texture, volumetric information, and preprocessing-particularly skull-stripping-were systematically assessed.

Materials and methods: A dataset of 990 matched T1w MRIs from AD patients and cognitively normal controls from the ADNI database was used. Preprocessing was varied through skull-stripping and intensity binarization to isolate texture and shape contributions. A 3D convolutional neural network was trained on each configuration, and classification performance was compared using exact McNemar tests with discrete Bonferroni-Holm correction. Feature relevance was analyzed using Layer-wise Relevance Propagation, image similarity metrics, and spectral clustering of relevance maps.

Results: Despite substantial differences in image content, classification accuracy, sensitivity, and specificity remained stable across preprocessing conditions. Models trained on binarized images preserved performance, indicating minimal reliance on gray-white matter texture. Instead, volumetric features-particularly brain contours introduced through skull-stripping-were consistently used by the models.

Conclusion: This behavior reflects a shortcut learning phenomenon, where preprocessing artifacts act as potentially unintended cues. The resulting Clever Hans effect emphasizes the critical importance of interpretability tools to reveal hidden biases and to ensure robust and trustworthy deep learning in medical imaging.

Critical relevance statement: We investigated the mechanisms underlying deep learning-based disease classification using a widely utilized Alzheimer's disease dataset, and our findings reveal a reliance on features induced through skull-stripping, highlighting the need for careful preprocessing to ensure clinically relevant and interpretable models.

Key points: Shortcut learning is induced by skull-stripping applied to T1-weighted MRIs. Explainable deep learning and spectral clustering estimate the bias. Highlights the importance of understanding the dataset, image preprocessing and deep learning model, for interpretation and validation.

Magnetic resonance imaging (MRI) is considered the gold standard for staging penile squamous cell carcinoma and assessing its extent. However, due to the rarity of this pathology, few medical centers have regular experience with penis carcinoma imaging. The purpose of this article is to provide a comprehensive update on the role of MRI in penile cancer by reviewing the MRI anatomy of a normal penis, outlining the recommended MRI techniques for penis assessment, and discussing the benefits and drawbacks of artificial erection. We will also highlight how MRI can serve the purpose of tumor staging and its therapeutic consequences. CRITICAL RELEVANCE STATEMENT: To provide a comprehensive and practical review of penile cancer based on imaging, including epidemiology, prognosis, treatment, penile MRI protocol, anatomy, and key points for accurate analysis. KEY POINTS: Penile carcinoma affects the glans and/or the foreskin in 98% of cases. MRI is the most accurate imaging modality for staging penile carcinoma and assessing its extent. T2-weighted using thin section is the best sequence to identify the tumor. Accurate treatment depends on the depth of local invasion and lymph node involvement.

Objectives: To evaluate the adjunctive diagnostic value of three-dimensional MR cholangiopancreatography (3D MRCP) for identifying biliary atresia (BA) in infants with cholestasis.

Materials and methods: This retrospective two-center study evaluated the adjunctive diagnostic performance of 3D MRCP beyond ultrasound (US) using receiver operating characteristic (ROC) analysis. The cohort from center 1 was divided into training (n = 770) and validation (n = 330) sets, with center 2 as the test set (n = 252). The optimal cut-off for the MR triangular cord thickness (MR-TCT) was derived from the area under the ROC curve (AUC) calculated from all cases. Extrahepatic bile ducts visualization on 3D MRCP was validated against surgical findings. Image quality metrics were assessed for their diagnostic value on BA detection.

Results: One thousand three hundred fifty-two eligible cholestatic infants undergoing 3D MRCP (February 2012 to June 2023) were enrolled, including 363 BA and 989 non-BA. ROC analysis identified 3.75 mm as the optimal cut-off MR-TCT for BA diagnosis (AUC = 0.828). The combination of MR-TCT, 3D MRCP, and US yielded superior diagnostic performance, achieving AUCs of 0.967 in the training set, 0.958 in the validation set, and 0.972 in the test set (all p < 0.001). Image quality scores (p < 0.001), signal-to-noise ratio (SNR) (p < 0.001), contrast ratio (p = 0.012), and contrast-to-noise ratio (CNR) (p < 0.001) of 3D MRCP significantly differed between correct and incorrect diagnosis groups.

Conclusions: 3D MRCP is a valuable diagnostic adjunct tool in diagnosing BA, particularly when combined with MR-TCT and US. Optimizing 3D MRCP image quality enhances diagnostic accuracy.

Critical relevance statement: 3D MRCP enhances BA diagnosis when combined with MR-TCT and US. Importantly, in cases with strong clinical suspicion but negative US findings, MRCP should be utilized as an adjunct diagnostic modality to reduce false-negative rates.

Key points: The diagnostic efficacy of 3D-MRCP in BA remains to be fully characterized. MR-TCT, 3D-MRCP, and US combined achieved optimal diagnostic accuracy for BA. For high-suspicion BA with negative US, adjunct 3D-MRCP reduces false-negative diagnoses.