Contrast-enhanced Imaging Research Articles

Pictorial review of the diagnosis of muscle-invasive bladder cancer using vesical imaging-reporting and data system (VI-RADS).

The Vesical Imaging-Reporting and Data System (VI-RADS) is a standard magnetic resonance imaging (MRI) and diagnostic method for muscle-invasive bladder cancer that was published in 2018. Several studies have demonstrated that VI-RADS has high diagnostic power and reproducibility. However, reading VI-RADS requires a certain amount of expertise, and radiologists need to be aware of the various pitfalls. MRI of the bladder includes T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced imaging (DCEI). T2WI is excellent for understanding anatomy. DWI and DCEI show high contrast between the tumor and normal anatomical structures and are suitable for staging local tumors. Bladder tumors are classified into five categories according to their size and morphology and their positional relationship to the bladder wall based on the VI-RADS diagnostic criteria. If the T2WI, DWI, and DCEI categories are the same, the category is the VI-RADS category. If the categories do not match, the DWI category is the VI-RADS category. If image quality of DWI is not evaluable, the DCEI category is the final category. In many cases, DWI is dominant, but this does not mean that T2WI and DCEI can be omitted from the reading of the bladder. In this educational review, typical and atypical teaching cases are demonstrated, and how to resolve misdiagnosis and the limitations of VI-RADS are discussed. The most important aspect of VI-RADS reading is to practice multiparametric reading with a solid understanding of the characteristics and role of each sequence and an awareness of the various pitfalls.

PI-RADS 2.1: A Practical Overview

AbstractThe Prostate Imaging Reporting and Data System (PI-RADS) is an essential tool for standardizing the interpretation of multiparametric magnetic resonance imaging (mp-MRI) of the prostate for detecting prostate cancer. This article provides a comprehensive overview of the latest version, PI-RADS 2.1, with clear pictures to guide radiologists in its practical application for improved diagnostic accuracy. The article explores the key modifications, emphasizing the changes in scoring criteria and their impact on clinical decision-making. It discusses the importance of mp-MRI sequences, such as T2-weighted imaging, diffusion-weighted imaging, and dynamic contrast-enhanced imaging, and clarifies their significance in the PI-RADS 2.1 framework. The article highlights practical insights to help radiologists integrate this updated system into their day-to-day practice, promoting consistency and reliability in reporting.

An Automated Assessment Method for Chronic Kidney Disease–Mineral and Bone Disorder (CKD-MBD) Utilizing Metacarpal Cortical Percentage

Chronic kidney disease–mineral and bone disorder (CKD-MBD) frequently occurs in hemodialysis patients and is a common cause of osteoporosis. Regular dual-energy X-ray absorptiometry (DXA) scans are used to monitor these patients, but frequent, cost-effective, and low-dose alternatives are needed. This study proposes an automatic CKD-MBD assessment model using histogram equalization and a squeeze-and-excitation block-based residual U-Net (SER-U-Net) with hand diagnostic radiography for preliminary classification. The process involves enhancing image contrast with histogram equalization, extracting features with the SE-ResNet model, and segmenting metacarpal bones using U-Net. Ultimately, a correlation analysis is carried out between the calculated dual metacarpal cortical percentage (dMCP) and DXA T-scores. The model’s performance was validated by analyzing clinical data from 30 individuals, achieving a 93.33% accuracy in classifying bone density compared to DXA results. This automated method provides a rapid, effective tool for CKD-MBD assessment in clinical settings.

Predicting histological grade in pediatric glioma using multiparametric radiomics and conventional MRI features

Prediction of glioma is crucial to provide a precise treatment plan to optimize the prognosis of children with glioma. However, studies on the grading of pediatric gliomas using radiomics are limited. Meanwhile, existing methods are mainly based on only radiomics features, ignoring intuitive information about tumor morphology on traditional imaging features. This study aims to utilize multiparametric magnetic resonance imaging (MRI) to identify high-grade and low-grade gliomas in children and establish a classification model based on radiomics features and clinical features. A total of 85 children with gliomas underwent tumor resection, and part of the tumor tissue was examined pathologically. Patients were categorized into high-grade and low-grade groups according to World Health Organization guidelines. Preoperative multiparametric MRI data, including contrast-enhanced T1-weighted imaging, T2-weighted imaging, T2-weighted fluid-attenuated inversion recovery, diffusion-weighted images, and apparent diffusion coefficient sequences, were obtained and labeled by two radiologists. The images were preprocessed, and radiomics features were extracted for each MRI sequence. Feature selection methods were used to select radiomics features, and statistically significant clinical features were identified using t-tests. The selected radiomics features and conventional MRI features were used to train the AutoGluon models. The improved model, based on radiomics features and conventional MRI features, achieved a balanced classification accuracy of 66.59%. The cross-validated areas under the receiver operating characteristic curve for the classifier of AutoGluon frame were 0.8071 on the test dataset. The results indicate that the performance of AutoGluon models can be improved by incorporating conventional MRI features, highlighting the importance of the experience of radiologists in accurately grading pediatric gliomas. This method can help predict the grade of pediatric glioma before pathological examination and assist in determining the appropriate treatment plan, including radiotherapy, chemotherapy, drugs, and gene surgery.

Mesenteric polycystic lymphangiomatosis in a pediatric patient. Case report

Introduction: Mesenteric polycystic lymphangiomas are rare cystic tumors, with a 5% incidence compared to their incidence in the neck and axillae (95%), seen more frequently in pediatric patients, rare in patients over 20 years of age.Clinical case: A 3-year-old male patient presents with a clinical picture of one day of evolution, reporting abdominal pain of moderate intensity, temperature rises and vomiting. Physical findings show abdominal distension, with ultrasound showing a polycystic lesion, and contrast-enhanced tomography corroborating a lobulated multi-separated cystic formation. Exploratory laparotomy revealed a polycystic tumor at the level of the ileum mesentery. The anatomopathological study describes a polycystic mesenteric lesion. Exeresis is performed and the postoperative evolution is satisfactory.Conclusions: Polycystic mesenteric lymphangiomatosis is a rare tumor that mainly affects the mesentery of the small intestine and/or colon. In pediatric age, this condition should be considered as a differential diagnosis of acute abdomen. Conventional or laparoscopic surgery is the cornerstone of treatment

The sensitivity of MIPs of 3D contrast-enhanced VIBE T1-weighted imaging for the detection of small brain metastases (≤ 5mm) on 1.5tesla MRI.

To evaluate whether the use of Maximum Intensity Projection (MIP) images derived from contrast-enhanced 3D-T1-weighted volumetric interpolated breath-hold examination (VIBE) would allow more sensitive detection of small (≤5mm) brain metastases (BM) compared with source as well as 2D-T1-weighted spin-echo (SE) images. We performed a single center retrospective study on subjects with BM who underwent 1.5tesla brain magnetic resonance imaging. Two readers counted the number of small BM for each of the seven sets of contrast-enhanced images created: axial 2D-T1-weighted SE, 3D-T1-weighted VIBE, 2.5mm-thick-MIP T1-weighted VIBE, and 5mm-thick-MIP T1-weighted VIBE; sagittal 3D-T1-weighted VIBE, 2.5mm-thick-MIP T1-weighted VIBE, and 5mm-thick-MIP T1-weighted VIBE. Total number of lesions detected on each image type was compared. Sensitivity, the average rates of false negatives and false positives, and the mean discrepancy were evaluated. A total of 403 small BM were identified in 49 patients. Significant differences were found: in the number of true positives and false negatives between the axial 2D-T1-weighted SE sequence and all other imaging techniques; in the number of false positives between the axial 2D-T1-weighted SE and the axial 3D-T1-weighted VIBE sequences. The two image types that combined offered the highest sensitivity were 2D-T1-weighted SE and axial 2.5mm-thick-MIP T1-weighted VIBE. The axial 2D-T1-weighted SE sequence differed significantly in sensitivity from all other sequences. MIP images did not show a significant difference in sensitivity for the detection of small BM compared with native images.

Clinical impact of CEUS on non-characterizable observations and observations with intermediate probability of malignancy on CT/MRI in patients at risk for HCC

BackgroundHepatocellular carcinoma (HCC) is a unique cancer allowing tumor diagnosis with identification of definitive patterns of enhancement on contrast-enhanced imaging, avoiding invasive biopsy. However, it is still unclear to what extent Contrast-Enhanced Ultrasound (CEUS) is a clinically useful additional step when Computed tomography (CT) or Magnetic resonance imaging (MRI) are inconclusive.MethodsA prospective international multicenter validation study for CEUS Liver Imaging Reporting and Data System (LI-RADS) was conducted between January 2018 and August 2021. 646 patients at risk for HCC with focal liver lesions were enrolled. CEUS was performed using an intravenous ultrasound contrast agent within 4 weeks of CT/MRI. Liver nodules were categorized based on LI-RADS (LR) criteria. Histology or one-year follow-up CT/MRI imaging results were used as the reference standard. The diagnostic performance of CEUS was evaluated for inconclusive CT/MRI scan in two scenarios for which the AASLD recommends repeat imaging or imaging follow-up: observations deemed non-characterizable (LR-NC) or with indeterminate probability of malignancy (LR-3).Results75 observations on CT or MRI were categorized as LR-3 (n = 54) or LR-NC (n = 21) CEUS recategorization of such observations into a different LR category (namely, into one among LR-1, LR-2, LR-5, LR-M, or LR-TIV) resulted in management recommendation changes in 33.3% (25/75) and in all but one (96.0%, 24/25) observation, the new management recommendations were correct.ConclusionCEUS LI-RADS resulted in management recommendations change in substantial number of liver observations with initial indeterminate CT/MRI characterization, identifying both non-malignant lesions and HCC, potentially accelerating the diagnostic process and alleviating the need for biopsy or follow-up imaging.ClinicalTrials.gov number, NCT03318380.

LEFB: A new low-light image contrast enhancement algorithm

Low-light images are challenging for both human observation and computer vision algorithms due to low visibility. To address this issue, various image enhancement techniques such as dehazing, histogram equalization, and neural network-based methods have been proposed. However, most existing methods often suffer from the problems of insufficient contrast and over-enhancement while enhancing the brightness, which not only affects the visual quality of images but also adversely impacts their subsequent analysis and processing. To tackle these problems, this paper proposes a low-light image enhancement method called LEFB. Specifically, the low-light image is first transformed into the LAB color space, and the L channel controlling brightness is enhanced using a local contrast enhancement algorithm. Then, the enhanced image is further enhanced using an exposure fusion-based contrast enhancement algorithm, and finally, a bilateral filtering function is applied to reduce image edge blurriness. Experimental evaluations are conducted on real datasets with four comparison algorithms. The results demonstrate that the proposed method has superior performance in enhancing low-light images, effectively addressing problems of insufficient contrast and over-enhancement, while preserving fine details and texture information, resulting in more natural and realistic enhanced images.

Pixel-based small-window parametric ultrasound imaging for liver tumor characterization.

Characterizing liver tumors remains a challenge in clinical practice. Ultrasound parametric imaging based on statistical distribution can enhance image contrast compared with B-mode imaging, requiring scatterers following specific distributions. This study proposes a pixel-based small-window parametric ultrasound imaging method using weighted horizontally normalized Shannon entropy (WhNSE) and fuzzy entropy (FE) to improve detectability liver tumor. Pixel-based parametric imaging requires a sliding window to traverse across the B-mode image with the step of one pixel, while calculating the entropy by the pixel values in the window. The entropy is assigned to the center pixel of the sliding window. The entropy image is obtained after getting the entropy values of all pixels. FE and WhNSE are two novel entropies first applied to parametric imaging. The detection abilities of regions of interest (ROI) and the contrast-to-noise ratio (CNR) were evaluated through simulations and clinical explorations. In simulations, FE imaging showed the highest improvement in detecting hyperechoic ROIs, with a CNR gain up to 457.31% (p < 0.01) in simulations. WhNSE imaging demonstrated the best performance in hyperechoic ROI detection, with a CNR of 1.607 ± 0.816 (p = 0.05), significantly higher than B-mode images. The proposed pixel-based parametric imaging method based on fuzzy entropy and weighted horizontally normalized Shannon entropy both effectively enhance the contrast and detectability of ultrasound images. The imaging enhancement method of the pixel-based fuzzy entropy imaging with proper parameters got better detection performance, due to the consideration of the relationship of neighboring pixels.

Thermal Infrared-Image-Enhancement Algorithm Based on Multi-Scale Guided Filtering

Obtaining thermal infrared images with prominent details, high contrast, and minimal background noise has always been a focal point of infrared technology research. To address issues such as the blurriness of details and low contrast in thermal infrared images, an enhancement algorithm for thermal infrared images based on multi-scale guided filtering is proposed. This algorithm fully leverages the excellent edge-preserving characteristics of guided filtering and the multi-scale nature of the edge details in thermal infrared images. It uses multi-scale guided filtering to decompose each thermal infrared image into multiple scales of detail layers and a base layer. Then, CLAHE is employed to compress the grayscale and enhance the contrast of the base layer image. Then, detail-enhancement processing of the multi-scale detail layers is performed. Finally, the base layer and the multi-scale detail layers are linearly fused to obtain an enhanced thermal infrared image. Our experimental results indicate that, compared to other methods, the proposed method can effectively enhance image contrast and enrich image details, and has higher image quality and stronger scene adaptability.

Detail-preserving image warping by enforcing smooth image sampling

Multi-phase dynamic contrast-enhanced magnetic resonance imaging image registration makes a substantial contribution to medical image analysis. However, existing methods (e.g., VoxelMorph, CycleMorph) often encounter the problem of image information misalignment in deformable registration tasks, posing challenges to the practical application. To address this issue, we propose a novel smooth image sampling method to align full organic information to realize detail-preserving image warping. In this paper, we clarify that the phenomenon about image information mismatch is attributed to imbalanced sampling. Then, a sampling frequency map constructed by sampling frequency estimators is utilized to instruct smooth sampling by reducing the spatial gradient and discrepancy between all-ones matrix and sampling frequency map. In addition, our estimator determines the sampling frequency of a grid voxel in the moving image by aggregating the sum of interpolation weights from warped non-grid sampling points in its vicinity and vectorially constructs sampling frequency map through projection and scatteration. We evaluate the effectiveness of our approach through experiments on two in-house datasets. The results showcase that our method preserves nearly complete details with ideal registration accuracy compared with several state-of-the-art registration methods. Additionally, our method exhibits a statistically significant difference in the regularity of the registration field compared to other methods, at a significance level of p < 0.05. Our code will be released at https://github.com/QingRui-Sha/SFM.

Evaluation of the extracranial “multifocal arcuate sign,” a novel MRI finding for the diagnosis of giant cell arteritis, on STIR and contrast-enhanced T1-weighted images

BackgroundWhile early diagnosis of giant cell arteritis (GCA) based on clinical criteria and contrast-enhanced MRI findings can lead to early treatment and prevention of blindness and cerebrovascular accidents, previously reported diagnostic methods which utilize contrast-enhanced whole head images are cumbersome. Diagnostic delay is common as patients may not be aware of initial symptoms and their significance. To improve current diagnostic capabilities, new MRI-based diagnostic criteria need to be established. This study aimed to evaluate the “multifocal arcuate sign” on short tau inversion recovery (STIR) and contrast-enhanced T1-weighted (CE-T1W) images as a novel extracranial finding for the diagnosis of GCA.MethodsA total of 17 consecutive patients (including five with GCA) who underwent CE-T1W and whole-brain axial STIR imaging simultaneously between June 2010 and April 2020 were enrolled. We retrospectively reviewed their MR images. The “multifocal arcuate sign” was defined as “multiple distant arcuate areas with high signal intensity in extracranial soft tissues such as subcutaneous fat, muscles, and tendons.” Extracranial abnormal high-signal-intensity areas were classified as “None,” when no lesions were detected; “Monofocal,” when lesions were detected only in one place; and “Multifocal,” when lesions were detected in multiple places. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of “Multifocal” areas were calculated using cross tabulation. Fisher’s exact test was used to compare “Multifocal” areas in five patients with GCA and those with other diseases. In addition, mean Cohen’s kappa and Fleiss’ kappa statistics were used to compare inter-reader agreement.ResultsThe sensitivity, specificity, PPV, and NPV of the “multifocal arcuate sign” in patients with GCA were 60%, 92–100%, 75–100%, and 85–86%, respectively. Significantly more patients with GCA had “Multifocal” areas compared to those with other diseases (Fisher’s exact test, p = 0.008–0.027). Mean Cohen’s kappa and Fleiss’ kappa for inter-reader agreement with respect to the five GCA patients were 0.52 and 0.49, respectively, for both STIR and CE-T1W sequences.ConclusionsThe new radiologic finding of “multifocal arcuate sign” on STIR and CE-T1W images may be used as a radiologic criterion for the diagnosis of GCA, which can make plain MRI a promising diagnostic modality.

Metasurface‐Based Mueller Matrix Microscope

AbstractIn conventional optical microscopes, image contrast of objects mainly results from the differences in light intensity and/or color. Muller matrix optical microscopes (MMMs), on the other hand, can provide significantly enhanced image contrast and rich information about objects by analyzing their interactions with polarized light. However, state‐of‐the‐art MMMs are fundamentally limited by bulky and slow polarization state generators and analyzers. Here, the study demonstrates a metasurface‐based MMM, i.e., Meta‐MMM, which is equipped with a chip‐integrated, single‐shot metasurface polarization state analyzer (Meta‐PSA). The Meta‐MMM is featured with high‐speed measurement (≈2s per Muller matrix (MM) image), superior operation stability, dual‐color operation, and high measurement accuracy (measurement error 1–2%) for MM imaging. The Meta‐MMM is applied to nanostructure characterization, surface morphology analysis, and discovering birefringent structures in honeybee wings. The Meta‐MMMs hold the promise to revolutionize various applications from biological imaging, medical diagnosis, and material characterization to industry inspection and space exploration.

Deep Learning-Based Approach for Identifying and Measuring Focal Liver Lesions on Contrast-Enhanced MRI.

The number of focal liver lesions (FLLs) detected by imaging has increased worldwide, highlighting the need to develop a robust, objective system for automatically detecting FLLs. To assess the performance of the deep learning-based artificial intelligence (AI) software in identifying and measuring lesions on contrast-enhanced magnetic resonance imaging (MRI) images in patients with FLLs. Retrospective. 395 patients with 1149 FLLs. The 1.5 T and 3 T scanners, including T1-, T2-, diffusion-weighted imaging, in/out-phase imaging, and dynamic contrast-enhanced imaging. The diagnostic performance of AI, radiologist, and their combination was compared. Using 20 mm as the cut-off value, the lesions were divided into two groups, and then divided into four subgroups: <10, 10-20, 20-40, and ≥40 mm, to evaluate the sensitivity of radiologists and AI in the detection of lesions of different sizes. We compared the pathologic sizes of 122 surgically resected lesions with measurements obtained using AI and those made by radiologists. McNemar test, Bland-Altman analyses, Friedman test, Pearson's chi-squared test, Fisher's exact test, Dice coefficient, and intraclass correlation coefficients. A P-value <0.05 was considered statistically significant. The average Dice coefficient of AI in segmentation of liver lesions was 0.62. The combination of AI and radiologist outperformed the radiologist alone, with a significantly higher detection rate (0.894 vs. 0.825) and sensitivity (0.883 vs. 0.806). The AI showed significantly sensitivity than radiologists in detecting all lesions <20 mm (0.848 vs. 0.788). Both AI and radiologists achieved excellent detection performance for lesions ≥20 mm (0.867 vs. 0.881, P = 0.671). A remarkable agreement existed in the average tumor sizes among the three measurements (P = 0.174). AI software based on deep learning exhibited practical value in automatically identifying and measuring liver lesions. Stage 2.

Contrast-enhanced pelvic magnetic resonance imaging (MRI) for the prediction of treatment response in mucinous rectal cancer.

In mucinous rectal cancer, it can be difficult to differentiate between cellular and acellular mucin. The purpose of this study was to evaluate, in patients with mucinous rectal cancer, the value of static enhancement (enh) and pharmacokinetic parameters of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in predicting pathologic complete response. This retrospective cross-sectional study performed at Memorial Sloan Kettering Cancer Center included 43 patients (24 males and 19 females; mean age, 57 years) with mucinous rectal cancer who underwent MRI at baseline as well as after neoadjuvant chemoradiotherapy but before surgical resection between 2008 and 2019. Two radiologists independently segmented tumors on contrast-enhanced axial 3D T1-weighted images and sagittal DCE magnetic resonance images. On contrast-enhanced axial T1-weighted images, the static parameters enh and relative enhancement (renh) were estimated. On DCE images, the pharmacokinetic parameters Ktrans, kep, relative Ktrans (rKtrans), and relative kep (rkep) were estimated. Associations between all parameters with pathologic complete response were tested using Wilcoxon signed-rank tests. Receiver operating characteristic (ROC) analysis was performed to assess the area under the curve (AUC) for each parameter. Of the 43 patients who were included in the study, 42/43 (98%) had evaluable contrast-enhanced axial T1-weighted images and 35/43 (81%) had evaluable DCE images. Of the patients with evaluable contrast-enhanced axial T1-weighted images, 9/42 (21%) had pathologic complete response and 33/42 (79%) did not have pathologic complete response. For reader 1, enh(pre-neoadjuvant chemotherapy), enh(post-neoadjuvant chemotherapy), and renh were significant predictors of pathologic complete response [P=0.045 (AUC =0.73), 0.039 (AUC =0.74), and 0.0042, respectively]. For reader 2, enh(pre-neoadjuvant chemotherapy) and renh were significant predictors [P=0.021 (AUC =0.77) and 0.002, respectively]. For renh, the AUC was 0.83 for reader 1, and 0.82 for reader 2. Meanwhile, of those patients with evaluable DCE images, 9/35 (26%) had pathologic complete response and 26/35 (74%) did not have pathologic complete response. Ktrans(pre-neoadjuvant chemotherapy), kep(pre-neoadjuvant chemotherapy), and rkep were significant predictors [P=0.016 (AUC =0.73), 0.00057 (AUC =0.81), and 0.0096 (AUC =0.74), respectively]. Static and pharmacokinetic parameters of contrast-enhanced MRI show promise to predict neoadjuvant treatment response. Static enh parameters, which are simpler to assess, showed the strongest prediction.

Subventricular zone-associated classification in isocitrate dehydrogenase-wildtype glioblastomas: improved prognostic value through integration of FLAIR with contrast-enhanced imaging.

Controversy surrounds the prognostic value of contrast-enhanced T1-weighted (T1CE) imaging-based subventricular zone (SVZ) classification in isocitrate dehydrogenase (IDH)-wildtype glioblastomas (GBMs). In this study, the authors aimed to assess the potential of incorporating FLAIR imaging into T1CE imaging-based classification for improving prognostic accuracy. A retrospective analysis was conducted on 281 patients with IDH-wildtype GBM. T1CE imaging-based classification was performed, and T2-weighted/FLAIR imaging was integrated to evaluate its prognostic estimation ability. Based on the relationship between the tumors and SVZ, patients were categorized into SVZ+ and SVZ- cohorts based on T1CE and T2-weighted/FLAIR imaging findings. Kaplan-Meier and Cox proportional hazards regression analyses were used to assess progression-free survival (PFS) and overall survival (OS), respectively. Patients were then categorized into three subgroups based on their combined classifications: group 1 (SVZ+ on T1CE and T2-weighted/FLAIR imaging), group 2 (SVZ- on T1CE but SVZ+ on T2-weighted/FLAIR imaging), and group 3 (SVZ- on T1CE and T2-weighted/FLAIR imaging). Subgroup analysis was used to evaluate differences in clinical and molecular factors as well as in prognoses. The T1CE imaging-based classification failed to stratify OS between SVZ+ and SVZ- cohorts (16.0 vs 20.0 months, p = 0.36). Survival analysis revealed similar prognoses for patients in groups 1 and 2, and patients in group 2 exhibited worse OS compared with those in group 3 (19.0 vs 23.5 months, p = 0.024). Logistic regression identified lower Karnofsky Performance Status (KPS) (p = 0.011), tumor diameter (p = 0.002), and telomerase reverse transcriptase (TERT) promoter mutation (p = 0.003) to be associated with a higher incidence of group 2 GBMs. Additionally, T2-weighted/FLAIR imaging-based classification provided significant prognostic value (17.0 vs 23.5 months p = 0.021) and was found to be an independent prognostic factor in the Cox multivariate analysis (HR 1.79, 95% CI 1.08-2.96; p = 0.024). This study underscores the limitations of T1CE imaging-based SVZ-associated classification in predicting prognosis for IDH-wildtype GBMs. The authors therefore propose an integrated approach that involves T2-weighted/FLAIR imaging that can provide improved prognostic ability. Notably, the presence of TERT promoter mutation was identified as a critical factor in nonenhancing tumor infiltration into the SVZ. Further validation through extensive cohort studies is recommended to confirm these findings.

Contrast-enhanced Imaging in Peripheral Pulmonary Lesions: The Role in US-guided Biopsies.

Purpose To compare the tissue adequacy and diagnostic accuracy of US-guided biopsies of peripheral pulmonary lesions (PPLs) with and without contrast agents. Materials and Methods A retrospective study was conducted at four medical centers in patients with PPLs who underwent US-guided percutaneous transthoracic needle biopsy (PTNB) between January 2017 and October 2022. The patients were divided into contrast-enhanced US (CEUS) and US groups based on whether prebiopsy CEUS evaluation was performed. Tissue adequacy and the diagnostic accuracy of PTNB, stratified by lesion size, were analyzed and compared between groups. A propensity score matching (PSM) analysis was conducted using the nearest-neighbor matching method. Results A total of 1027 lesions were analyzed, with 634 patients (mean age, 59.4 years ± 13.0 [SD]; 413 male) in the US group and 393 patients (mean age, 61.2 years ± 12.5; 270 male) in the CEUS group. The CEUS group produced more acceptable samples than the US group (98.2% vs 95.7%; P = .03) and achieved higher diagnostic accuracy (96.9% vs 94.2%; P = .04), with no evidence of a difference in sensitivity (96.7% vs 94.0%; P = .06). PSM and stratified analyses (n = 358 per group) indicated higher tissue adequacy (99.0% vs 95.7%; P = .04) and diagnostic accuracy (98.5% vs 92.9%; P = .006) in the CEUS group compared with the US group for 2-7-cm PPLs but not for lesions larger than 7 cm. Conclusion PTNB with prebiopsy CEUS evaluation demonstrated significantly better tissue adequacy and diagnostic accuracy compared with US guidance alone for PPLs ranging from 2 to 7 cm, with similar biopsy performance achieved between groups for lesions larger than 7 cm. Keywords: Contrast Material, Thoracic Diseases, Ultrasonography, Image-Guided Biopsy © RSNA, 2024.

Development of a Preoperative Prediction Model Based on Spectral CT to Evaluate Axillary Lymph Node With Macrometastases in Clinical T1/2N0 Invasive Breast Cancer

ObjectivesTo develop a prediction model based on spectral computed tomography (CT) to evaluate axillary lymph node (ALN) with macrometastases in clinical T1/2N0 invasive breast cancer. MethodsA total of 217 clinical T1/2N0 invasive breast cancer patients who underwent spectral CT scans were retrospectively enrolled and categorized into a training cohort (n = 151) and validation cohort (n = 66). These patients were classified into ALN nonmacrometastases (stage pN0 or pN0 [i+] or pN1mi) and ALN macrometastases (stage pN1-3) subgroups. The morphologic criteria and quantitative spectral CT parameters of the most suspicious ALN were measured and compared. Least absolute shrinkage and selection operator (Lasso) was used to screen predictive indicators to build a logistic model. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the models. ResultsThe combined arterial-venous phase spectral CT model yielded the best diagnostic performance in discrimination of ALN nonmacrometastases and ALN macrometastases with the highest AUC (0.963 in the training cohort and 0.945 in validation cohorts). Among single phase spectral CT models, the venous phase spectral CT model showed the best performance (AUC = 0.960 in the training cohort and 0.940 in validation cohorts). There was no significant difference in AUCs among the 3 models (DeLong test, P > .05 for each comparison). ConclusionA Lasso-logistic model that combined morphologic features and quantitative spectral CT parameters based on contrast-enhanced spectral imaging potentially be used as a noninvasive tool for individual preoperative prediction of ALN status in clinical T1/2N0 invasive breast cancers.

Discriminating atypical parotid carcinoma and pleomorphic adenoma utilizing extracellular volume fraction and arterial enhancement fraction derived from contrast-enhanced CT imaging: A multicenter study.

To investigate the added value of extracellular volume fraction (ECV) and arterial enhancement fraction (AEF) derived from enhanced CT to conventional image and clinical features for differentiating between pleomorphic adenoma (PA) and atypical parotid adenocarcinoma (PCA) pre-operation. From January 2010 to October 2023, a total of 187 cases of parotid tumors were recruited, and divided into training cohort (102 PAs and 51 PCAs) and testing cohort (24 PAs and 10 atypical PCAs). Clinical and CT image features of tumor were assessed. Both enhanced CT-derived ECV and AEF were calculated. Univariate analysis identified variables with statistically significant differences between the two subgroups in the training cohort. Multivariate logistic regression analysis with the forward variable selection method was used to build four models (clinical model, clinical model+ECV, clinical model+AEF, and combined model). Diagnostic performances were evaluated using receiver operating characteristic (ROC) curve analyses. Delong's test compared model differences, and calibration curve and decision curve analysis (DCA) assessed calibration and clinical application. Age and boundary were chosen to build clinical model, and to construct its ROC curve. Amalgamating the clinical model, ECV, and AEF to establish a combined model demonstrated superior diagnostic effectiveness compared to the clinical model in both the training and test cohorts (AUC = 0.888, 0.867). There was a significant statistical difference between the combined model and the clinical model in the training cohort (p = 0.0145). ECV and AEF are helpful in differentiating PA and atypical PCA, and integrating clinical and CT image features can further improve the diagnostic performance.

Patient-specific arterial input function for accurate perfusion assessment in intraoperative fluorescence imaging.

The arterial input function (AIF) plays a crucial role in correcting the time-dependent concentration of the contrast agent within the arterial system, accounting for variations in agent injection parameters (speed, timing, etc.) across patients. Understanding the significance of the AIF can enhance the accuracy of tissue vascular perfusion assessment through indocyanine green-based dynamic contrast-enhanced fluorescence imaging (DCE-FI). We evaluate the impact of the AIF on perfusion assessment through DCE-FI. A total of 144 AIFs were acquired from 110 patients using a pulse dye densitometer. Simulation and patient intraoperative imaging were conducted to validate the significance of AIF for perfusion assessment based on kinetic parameters extracted from fluorescence images before and after AIF correction. The kinetic model accuracy was evaluated by assessing the variability of kinetic parameters using individual AIF versus population-based AIF. Individual AIF can reduce the variability in kinetic parameters, and population-based AIF can potentially replace individual AIF for estimating wash-out rate ( ), maximum intensity ( ), ingress slope with lower differences compared with those in estimating blood flow, volume transfer constant ( ), and time to peak. Individual AIF can provide the most accurate perfusion assessment compared with assessment without AIF or based on population-based AIF correction.