Non-invasive Imaging Biomarkers Research Articles

Superior performance in classification of breast cancer molecular subtype and histological factors by radiomics based on ultrafast MRI over standard MRI: evidence from a prospective study.

To compare the performance of ultrafast MRI with standard MRI in classifying histological factors and subtypes of invasive breast cancer among radiologists with varying experience. From October 2021 to November 2022, this prospective study enrolled 225 participants with 233 breast cancers before treatment (NCT06104189 at clinicaltrials.gov). Tumor segmentation on MRI was performed independently by two readers (R1, dedicated breast radiologist; R2, radiology resident). We extracted 1618 radiomic features and four kinetic features from ultrafast and standard images, respectively. Logistic regression algorithms were adopted for prediction modeling, following feature selection by the least absolute shrinkage and selection operator. The performance of predicting histological factors and subtypes was evaluated using the area under the receiver-operating characteristic curve (AUC). Performance differences between MRI methods and radiologists were assessed using the DeLong test. Ultrafast MRI outperformed standard MRI in predicting HER2 status (AUCs [95% CI] of ultrafast MRI vs standard MRI; 0.87 [0.83-0.91] vs 0.77 [0.64-0.90] for R1 and 0.88 [0.83-0.91] vs 0.77 [0.69-0.84] for R2) (all P < 0.05). Both ultrafast MRI and standard MRI showed comparable performance in predicting hormone receptors. Ultrafast MRI exhibited superior performance to standard MRI in classifying subtypes. The classification of the luminal subtype for both readers, the HER2-overexpressed subtype for R2, and the triple-negative subtype for R1 was significantly better with ultrafast MRI (P < 0.05). Ultrafast MRI-based radiomics holds promise as a noninvasive imaging biomarker for classifying hormone receptors, HER2 status, and molecular subtypes compared to standard MRI, regardless of radiologist experience.

Non-invasive quantification of bone (re) modeling dynamics in adults with osteogenesis imperfecta treated with Setrusumab using timelapse HR-pQCT.

Timelapse imaging using high-resolution peripheral quantitative computed tomography (HR-pQCT) has emerged as a noninvasive method to quantify bone (re)modelling. However, there is no consensus on how to perform the procedure. As part of the ASTEROID phase-2b multicenter trial, we used 29 same-day repeated scans from adults with osteogenesis imperfecta (OI) to identify a method that minimized measurement error. We evaluated input image type, registration method, segmentation mask, and for grayscale images various values for the voxel density difference considered formed or resorbed, minimum formation/resorption cluster size, and Gaussian smoothing sigma. We verified the accuracy of our method and then used it on longitudinal scans (baseline, 6, 12, 18 and 24month) from 78 participants to assess bone formation and resorption induced by an anabolic (setrusumab) and anti-catabolic (zoledronic acid) treatments as part of the ASTEROID trial. Regardless of image registration method, binary input images resulted in large errors ~13% and ~ 8% for first- and second-generation scanners, respectively. For the grayscale input images, errors were smaller for 3D compared to matched angle registration. For both scanner generations, a density threshold of 200mgHA/cm3 combined with Gaussian noise reduction resulted in errors <1%. We verified the method was accurate by showing that similar regions of bone formation and resorption were identified when comparing each scan from the same-day repeated scans with a scan from another timepoint. Timelapse analysis revealed a dose-dependent increase in bone formation and resorption with setrusumab treatment. Zoledronic acid altered bone changes in favor of formation, although no changes reached statistical significance. This study identifies a timelapse method that minimizes measurement error, which can be used in future studies to improve the uniformity of results. This noninvasive imaging biomarker revealed dose dependent bone (re) modeling outcomes from one year of setrusumab treatment in adults with OI.

Development of Proton Density Fat Fraction Micro-MRI for Non-Invasive Quantitative Assessment of Bone Marrow Changes with Age and Radiation in Mouse Models.

Bone marrow (BM) adipocytes are critical in progressing solid tumor metastases and hematological malignancies across pediatric to aging populations. Single-point biopsies remain the gold standard for monitoring BM diseases, including hematologic malignancies, but are limited in capturing the full complexity of loco-regional and global BM microenvironments. Non-invasive imaging techniques like Magnetic Resonance Imaging (MRI), could offer valuable alternatives for real-time evaluation of BM diseases in both preclinical translational and clinical studies. We developed a preclinical proton density fat fraction (PDFF) MRI technique for quantitative BM composition assessment, focusing on fat fraction (FF) within mouse femurs. We validated this method using aging mice and young mice subjected to 10 Gy X-ray irradiation, compared with young unirradiated mice as controls. Water-fat phantoms (0% to 100% fat content) were used to optimize the imaging sequence, and immunohistochemical (IHC) staining with H&E validates equivalent adipose content in the femur BM regions. Significant differences in FF were observed across age groups (p = 0.001 for histology and p = 0.0002 for PDFF) and between irradiated and control mice (p = 0.005 for histology and p = 0.002 for PDFF). A strong correlation (R 2 ∼ 0.84) between FF values from PDFF and histology validates the accuracy of the technique. These findings demonstrate the potential of PDFF MRI as a non-invasive real-time imaging biomarker for quantifying BM fat fraction in preclinical mice model studies, particularly in evaluating the effects of aging, disease progression, and irradiation therapy in pediatric and translational oncology research.

Dual biomarkers CT-based deep learning model incorporating intrathoracic fat for discriminating benign and malignant pulmonary nodules in multi-center cohorts.

Recent studies in the field of lung cancer have emphasized the important role of body composition, particularly fatty tissue, as a prognostic factor. However, there is still a lack of practice in combining fatty tissue to discriminate benign and malignant pulmonary nodules. This study proposes a deep learning (DL) approach to explore the potential predictive value of dual imaging markers, including intrathoracic fat (ITF), in patients with pulmonary nodules. We enrolled 1321 patients with pulmonary nodules from three centers. Image feature extraction was performed on computed tomography (CT) images of pulmonary nodules and ITF by DL, multimodal information was used to discriminate benign and malignant in patients with pulmonary nodules. Here, the areas under the receiver operating characteristic curve (AUC) of the model for ITF combined with pulmonary nodules were 0.910(95% confidence interval [CI]: 0.870-0.950, P=0.016), 0.922(95% CI: 0.883-0.960, P=0.037) and 0.899(95% CI: 0.849-0.949, P=0.033) in the internal test cohort, external test cohort1 and external test cohort2, respectively, which were significantly better than the model for pulmonary nodules. Intrathoracic fat index (ITFI) emerged as an independent influencing factor for benign and malignant in patients with pulmonary nodules, correlating with a 9.4% decrease in the risk of malignancy for each additional unit. This study demonstrates the potential auxiliary predictive value of ITF as a noninvasive imaging biomarker in assessing pulmonary nodules.

Reliability of radiomics features as imaging biomarkers for evaluating brain aging: A study based on myelin protein and diffusion tensor imaging.

Radiomics has made considerable progress in neurodegenerative diseases. However, previous studies only explored the feasibility of radiomics in clinical applications. Therefore, the objective of this study was to obtain the most relevant radiomics features with the aging changes of myelin proteins and compare their diagnostic performances with the diffusion tensor imaging (DTI) parameters to identify the reliability of these features as imaging biomarkers for assessing brain aging. Thirty middle-aged and thirty old-aged mice were assigned to the training set to explore the most relevant features of myelin proteins and their diagnostic performances. Ten middle-aged and ten old-aged mice were assigned to the testing set to further validate the reproducibility of the features. T2-weighted imaging and DTI were conducted to obtain white matter radiomics features and DTI parameters. Myelin proteins, including proteolipid protein (PLP) and myelin basic protein (MBP), were examined by immunofluorescence staining in the regions of the whole brain, cortex, corpus callosum, striatum, and anterior commissure. The Pearson correlation analysis was used to observe the correlations between radiomics features and myelin proteins. The four most relevant features with the top four correlation coefficients were selected to compare their diagnostic performances with the DTI parameters, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AxD), and radial diffusivity (RD). Wavelet-HLL_glszm_ZoneEntropy, wavelet-HLL_gldm_DependenceEntropy, wavelet-LHL_glszm_ZoneEntropy, and log-sigma-2-0-mm-3D_gldm_DependenceEntropy were the four most relevant features, which had moderately significant correlations with PLP. The area under the receiver operating characteristic curves (AUC) of the four features were 0.940, 0.917, 0.831, and 0.964 in the training set, and 0.880, 0.840, 0.860, and 0.880 in the testing set. The AUCs of FA, MD, AxD, and RD were 0.864, 0.743, 0.673, and 0.778 in the training set, and 0.780, 0.710, 0.670, and 0.730 in the testing set. These results demonstrated that radiomics features of white matter displayed significant correlations with myelin proteins and their performances were comparable or even superior to DTI parameters, which ensured their reliability as non-invasive imaging biomarkers for evaluating brain aging.

Deep learning radiomics on grayscale ultrasound images assists in diagnosing benign and malignant of BI-RADS 4 lesions

This study aimed to explore a deep learning radiomics (DLR) model based on grayscale ultrasound images to assist radiologists in distinguishing between benign breast lesions (BBL) and malignant breast lesions (MBL). A total of 382 patients with breast lesions were included, comprising 183 benign lesions and 199 malignant lesions that were collected and confirmed through clinical pathology or biopsy. The enrolled patients were randomly allocated into two groups: a training cohort and an independent test cohort, maintaining a ratio of 7:3.We created a model called CLDLR that utilizes clinical parameters and DLR to diagnose both BBL and MBL through grayscale ultrasound images. In order to assess the practicality of the CLDLR model, two rounds of evaluations were conducted by radiologists. The CLDLR model demonstrates the highest diagnostic performance in predicting benign and malignant BI-RADS 4 lesions, with areas under the receiver operating characteristic curve (AUC) of 0.988 (95% confidence interval : 0.949, 0.985) in the training cohort and 0.888 (95% confidence interval : 0.829, 0.947) in the testing cohort.The CLDLR model outperformed the diagnoses made by the three radiologists in the initial assessment of the testing cohorts. By utilizing AI scores from the CLDLR model and heatmaps from the DLR model, the diagnostic performance of all radiologists was further enhanced in the testing cohorts. Our study presents a noninvasive imaging biomarker for the prediction of benign and malignant BI-RADS 4 lesions. By comparing the results from two rounds of assessment, our AI-assisted diagnostic tool demonstrates practical value for radiologists with varying levels of experience.

Can quantitative perfusion CT-based biomarkers predict renal cell carcinoma subtypes?

To assess diagnostic accuracy of perfusion CT (pCT) based biomarkers in differentiating clear-cell renal cell carcinoma (ccRCC) from non-ccRCC. This retrospective study comprised 95 patients with RCCs (70 ccRCCs and 25 non-ccRCCs) who had perfusion CT (pCT) before surgery between January 2017 and December 2022. Two readers independently recorded PCT parameters [blood flow (BF), blood volume (BV), mean transit time (MTT), and time to peak (TTP)] by drawing a circular ROI on the tumor. The open-source program "Labelme" was used to create a polygonal bounding box to outline tumor borders. The intraclass correlation coefficient (ICC) was used to determine interreader agreement. The pCT model was evaluated using multivariable logistic regression analysis with the STATA 18 program to determine the importance of each of these characteristics in predicting the type of tumor. Clear cell RCC had significantly greater MIP and lower TTP values than non-clear cell RCC (p < 0.05). RCCs showed considerably higher TTP, MTT, and lower MIP values than the normal renal cortex (p < 0.05). At a threshold of 129 HU, MIP had an AUC of 0.78, sensitivity and specificity of 80% and 70%, respectively, according to ROC analysis. pCT has a high diagnostic accuracy in distinguishing between ccRCC and non-ccRCC tumors; Clinical relevance: A non-invasive, accurate, reliable, and reproducible imaging biomarker for RCC subtype prediction is possible on pCT, which may be significant for evaluating the response to antiangiogenic therapy.

Molecular MR Imaging of T-Cell Immune Response to Cryoablation in Immunologically Hot vs. Cold Hepatocellular Carcinoma.

Background and aimsThe increasing enthusiasm for integrating locoregional therapy in primary liver cancer with systemic immunotherapy underscores the need for non-invasive imaging biomarkers. This study aims to establish advanced molecular magnetic resonance imaging (MRI) tools for monitoring T-cell responses to cryoablation in murine models, distinguishing between immunologically "hot" and "cold" hepatocellular carcinoma (HCC). Materials and methodsImmunocompetent 7-10-weeks-old C57BL/6J and BALB/cJ mice (n = 18 each) received carbon tetrachloride for 12 weeks to induce liver cirrhosis. Intrinsically immunogenic Hepa1-6 (“hot”) and non-immunogenic TiB75 (“cold”) cells were orthotopically implanted in C57BL/6 or BALB/c mice, respectively, to generate focal HCC lesions. After one week, animals were randomly assigned to (A) partial cryoablation (pCryo) (1.2 mm cryoprobe, -40°C) or (B) no treatment groups (n=8 per group and tumor type). Gadolinium 160 (160Gd)-labeled CD8+ antibody was administered intravenously either one week after tumor induction (control) or one-week post (pCryo) (treatment). T1-weighted MRI scans were performed using a 9.4 Tesla MRI scanner. Radiological-pathological correlation included imaging mass cytometry (IMC) and immunohistochemistry. ResultsPCryo-treated Hepa1-6 tumors displayed peritumoral ring enhancement on T1-weighted MRI with 160Gd-CD8, correlating with IMC signal patterns. Untreated Hepa1-6 tumors lacked such enhancement. Radiological-pathological correlation confirmed significantly increased tumor-infiltrating CD8+ T-lymphocytes in pCryo Hepa1-6 tumors compared with untreated tumors (p < 0.001), and a stronger local response compared with systemic lymph nodes (p=0.0415). Increased T-lymphocyte infiltration was not observed in TiB75 tumors, as indicated by MRI and histopathology. ConclusionPCryo induced increased T-cell infiltration in Hepa1-6 tumors compared to TiB75 tumors. T1-weighted MRI, following 160Gd-CD8 antibody administration, reproducibly detected the ablation-induced changes. These findings encourage further investigation of MR-based molecular imaging biomarkers to assess immune responses in local tumor therapies. Impact and implicationsThis study successfully established reliable MR-based molecular imaging tools to visualize CD8+ anti-tumor specific T-cell infiltration following partial cryoablation (pCryo) in murine tumor models. The study's significance lies in advancing our understanding of immune responses within induced cirrhosis and distinguishing between "hot" and "cold" tumor phenotypes. The findings not only build upon previous proof-of-principle data but also extend this technology to include different immune cell types in hepatocellular carcinoma (HCC). The study reveals that pCryo may exert specific effects on the tumor microenvironment, augmenting the anti-tumor immune response in immunogenic tumors while displaying a weaker local effect in non-immunogenic tumors.

CD206+ macrophages are relevant non-invasive imaging biomarkers and therapeutic targets in experimental lung fibrosis

BackgroundInterstitial lung diseases (ILDs) include a large number of diseases associated with progressive pulmonary fibrosis (PPF), including idiopathic pulmonary fibrosis (IPF). Despite the rarity of each of the fibrotic ILDs...

Repeatability, Reproducibility, and Observer Variability of Cortical T1 Mapping for Renal Tissue Characterization.

The global rise in kidney diseases underscores the need for reliable, noninvasive imaging biomarkers. Among these, renal cortical T1 has shown promise but further technical validation is still required. To evaluate the repeatability, reproducibility, and observer variability of kidney cortical T1 mapping in human volunteers without known renal disease. Prospective. Three cohorts without renal disease: 1) 25 volunteers (median age 38 [interquartile range, IQR: 28-42] years, female N = 11) for scan-rescan assessments on GE 1.5 T and Siemens 1.5 T; 2) 29 volunteers (median age 29 [IQR: 24-40] years, female N = 15) for scan-rescan assessments on Siemens 3 T; and 3) 16 volunteers (median age 34 [IQR: 31-42] years, female N = 8) for cross-scanner reproducibility. 1.5 T and 3 T, a modified Look-Locker imaging (MOLLI) sequence with a balanced steady-state free precession (bSSFP) readout. Kidney cortical T1 data was acquired on GE 1.5 T scanner, Siemens 1.5 T and 3 T scanners. Within-scanner repeatability and inter/intra-observer variability: GE 1.5 T and Siemens 1.5 T, and cross-scanner manufacturer reproducibility: Siemens 1.5 T-GE 1.5 T. Bland Altman analysis, coefficient of variation (CoV), intra-class coefficient (ICC), and repeatability coefficient (RC). Renal cortical T1 mapping showed high repeatability and reliability across scanner field strengths and manufacturers (repeatability: CoV 1.9%-2.8%, ICC 0.79-0.88, pooled RC 73 msec; reproducibility: CoV 3.0%, ICC 0.75, RC 90 msec). The method also showed robust observer variability (CoV 0.6%-1.4%, ICC 0.93-0.98, RC 22-48 msec). Kidney cortical T1 mapping is a highly repeatable and reproducible method across MRI manufacturers, field strengths, and observer conditions. 2 TECHNICAL EFFICACY: Stage 2.

Metabolic dysfunction-associated steatotic liver disease and cardiovascular risk: a comprehensive review.

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously termed nonalcoholic fatty liver disease (NAFLD), poses a significant global health challenge due to its increasing prevalence and strong association with cardiovascular disease (CVD). This comprehensive review summarizes the current knowledge on the MASLD-CVD relationship, compares analysis of how different terminologies for fatty liver disease affect cardiovascular (CV) risk assessment using different diagnostic criteria, explores the pathophysiological mechanisms connecting MASLD to CVD, the influence of MASLD on traditional CV risk factors, the role of noninvasive imaging techniques and biomarkers in the assessment of CV risk in patients with MASLD, and the implications for clinical management and prevention strategies. By incorporating current research and clinical guidelines, this review provides a comprehensive overview of the complex interplay between MASLD and cardiovascular health.

An MRI-based radiomics nomogram to predict progression-free survival in patients with endometrial cancer.

Aim: This study aimed to explore the importance of an MRI-based radiomics nomogram in predicting the progression-free survival (PFS) of endometrial cancer.Methods: Based on clinicopathological and radiomic characteristics, we established three models (clinical, radiomics and combined model) and developed a nomogram for the combined model. The Kaplan-Meier method was utilized to evaluate the association between nomogram-based risk scores and PFS.Results: The nomogram had a strong predictive ability in calculating PFS with areas under the curve (ROC) of 0.905 and 0.901 at 1 and 3years, respectively. The high-risk groups identified by the nomogram-based scores had shorter PFS compared with the low-risk groups.Conclusion: The radiomics nomogram has the potential to serve as a noninvasive imaging biomarker for predicting individual PFS of endometrial cancer.

Imaging evaluation focused on microstructural tissue changes using tensor-valued diffusion encoding in breast cancers after neoadjuvant chemotherapy: is it a promising way forward?

Single diffusion encoding is a widely used, noninvasive technique for probing the tissue microstructure in breast tumors. However, it does not provide detailed information about the microenvironmental complexity. This study investigated the clinical utility of tensor-valued diffusion encoding for evaluating microstructural changes in breast cancer after neoadjuvant chemotherapy (NAC). We retrospectively included patients underwent chemotherapy for histologically proven invasive breast cancer between July 2020 and June 2023 and monitored the tumor response with breast magnetic resonance imaging (MRI), including tensor-valued diffusion encoding. We reviewed pre- and post-NAC MRIs regarding chemotherapy in 23 breast cancers. Q-space trajectory imaging (QTI) parameters were estimated at each time-point, and were compared with histopathological parameters. The mean total mean kurtosis (MKT), anisotropic mean kurtosis (MKA), and microscopic fractional anisotropy (µFA) were significantly decreased on post-NAC MRI compared with pre-NAC MRI, with the large effect size (ES) in MKA and µFA (0.81±0.41 vs. 0.99±0.33, ES: 0.48, P=0.03; 0.48±0.30 vs. 0.73±0.27, ES: 0.88, P<0.001; 0.58±0.14 vs. 0.68±0.11, ES: 0.79, P=0.003; respectively). Regarding prognostic factors, tumors with high Ki-67 expression showed significantly lower pre-NAC mean diffusivity (MD) and higher pre-NAC µFA compared to tumors with low Ki-67 expression (0.98±0.09 vs. 1.25±0.20, P=0.002; and 0.72±0.07 vs. 0.57±0.10, P=0.005; respectively). And negative progesterone receptor (PR) group revealed significantly lower MKT, MKA, and isotropic mean kurtosis than positive PR group on the post-NAC MRI (0.60±0.31 vs. 1.03±0.40, P=0.008; 0.36±0.21 vs. 0.61±0.33, P=0.04; and 0.23±0.17 vs. 0.42±0.25, P=0.046; respectively). QTI parameters reflected the microstructural changes in breast cancer treated with NAC and can be used as noninvasive imaging biomarkers correlated with prognostic factors.

Diagnostic accuracy of an uncorrected native T1 mapping sequence for liver fibrosis and inflammation in autoimmune hepatitis: a prospective study using histopathology as reference standard.

There is an unmet clinical need for non-invasive imaging biomarkers that could replace liver biopsy in the management of patients with autoimmune hepatitis (AIH). In this study, we sought to evaluate the diagnostic accuracy of a simple uncorrected, non-contrast T1 mapping for detecting fibrosis and inflammation in AIH patients using histopathology as a reference standard. Over 3years, 33 patients with AIH were prospectively studied using a multiparametric liver MRI protocol which included T1 mapping. Biopsies were performed up to 3months before imaging, and a standardized histopathological score for fibrosis (F0-F4) and inflammatory activity (PPA0-4) was used as a reference. Statistical analysis included independent t test, Mann-Whitney U-test, and ROC (receiver operating characteristic) analysis. T1 mapping values were significantly higher in patients with advanced fibrosis (F0-2 vs. F3-4; p < 0.015), significant fibrosis (F0-1 vs. F2-4; p < 0.005), and significant inflammatory activity (PPA 0-1 vs. PPA 2-4 p = 0.048). Moreover, the technique demonstrated a good diagnostic performance in detecting significant (AUC 0.856) and advanced fibrosis (AUC 0.835), as well as significant inflammatory activity (AUC 0.763). A rapid, simple, uncorrected, non-contrast T1 mapping sequence showed satisfactory diagnostic performance in comparison with histopathology for detecting significant tissue inflammation and fibrosis in AIH patients, being a potential non-invasive imaging biomarker for monitoring disease activity in such individuals.

The Use of Apparent Diffusion Coefficient Values for Differentiating Bevacizumab-Related Cytotoxicity from Tumor Recurrence and Radiation Necrosis in Glioblastoma.

Glioblastomas (GBM) are the most common primary invasive neoplasms of the brain. Distinguishing between lesion recurrence and different types of treatment related changes in patients with GBM remains challenging using conventional MRI imaging techniques. Therefore, accurate and precise differentiation between true progression or pseudoresponse is crucial in deciding on the appropriate course of treatment. This retrospective study investigated the potential of apparent diffusion coefficient (ADC) map values derived from diffusion-weighted imaging (DWI) as a noninvasive method to increase diagnostic accuracy in treatment response. A cohort of 21 glioblastoma patients (mean age: 59.2 ± 11.8, 12 Male, 9 Female) that underwent treatment with bevacizumab were selected. The ADC values were calculated from the DWI images obtained from a standardized brain protocol across 1.5-T and 3-T MRI scanners. Ratios were calculated for rADC values. Lesions were classified as bevacizumab-induced cytotoxicity based on characteristic imaging features (well-defined regions of restricted diffusion with persistent diffusion restriction over the course of weeks without tissue volume loss and absence of contrast enhancement). The rADC value was compared to these values in radiation necrosis and recurrent lesions, which were concluded in our prior study. The nonparametric Wilcoxon signed rank test with p < 0.05 was used for significance. The mean ± SD age of the selected patients was 59.2 ± 11.8. ADC values and corresponding mean rADC values for bevacizumab-induced cytotoxicity were 248.1 ± 67.2 and 0.39 ± 0.10, respectively. These results were compared to the ADC values and corresponding mean rADC values of tumor progression and radiation necrosis. Significant differences between rADC values were observed in all three groups (p < 0.001). Bevacizumab-induced cytotoxicity had statistically significant lower ADC values compared to both tumor recurrence and radiation necrosis. The study demonstrates the potential of ADC values as noninvasive imaging biomarkers for differentiating recurrent glioblastoma from radiation necrosis and bevacizumab-induced cytotoxicity.

Perivascular fat attenuation index value and plaque volume increased in non-target lesions of coronary arteries after stenting.

Progression of non-target lesions (NTLs) after stenting has been reported and is associated with the triggering of an inflammatory response. The perivascular fat attenuation index (FAI) may be used as a novel imaging biomarker for the direct quantification of coronary inflammation. To investigate whether FAI values can help identify changes in inflammation status in patients undergoing stent implantation, especially in NTLs. Patients who underwent pre- and post-stenting coronary computed tomography angiography (CCTA) examination between January 2015 and February 2021 were consecutively enrolled. The pre- and post-stenting FAIs of the full coronary arteries were compared in both the non- and stent-implanted coronary arteries. Moreover, local FAI values were measured and compared between the NTLs and target lesions in the stent implantations. We also compared changes in plaque type and volume in NTLs before and after stenting. A total of 89 patients (mean age 61 years; male 59) were enrolled. The perivascular FAI values in the full coronary arteries decreased after stenting in both the non- and stent-implanted coronary arteries, similar to those in the target lesions. Conversely, the perivascular FAI values in the NTLs increased after stenting (p < 0.05). In addition, the plaque volumes significantly increased in the NTLs after stenting, regardless of whether they were non-calcified, mixed, or calcified (p < 0.05). Perivascular FAI values and plaque volumes increased in the NTLs after stenting. Perivascular FAI can be a promising imaging biomarker for monitoring coronary inflammation after stenting and facilitate long-term monitoring in clinical settings. Perivascular fat attenuation index, a non-invasive imaging biomarker, may help identify coronary arteries with high inflammation in non-target lesions and facilitate long-term monitoring, potentially providing an opportunity for more targeted treatment. • Perivascular fat attenuation index (FAI) values and plaque volumes increased in the non-target lesions (NTLs) after stenting, suggesting potential focal inflammation progression after stenting. However, stenting along with anti-inflammatory treatment ameliorated inflammation in the full coronary arteries. • Perivascular FAI, a non-invasive imaging biomarker, may help identify coronary arteries with high inflammation in NTLs and facilitate long-term monitoring, potentially providing an opportunity for more targeted treatment.

Decision-Tree Models Indicative of Microvascular Invasion on MRI Predict Survival in Patients with Hepatocellular Carcinoma Following Tumor Ablation.

Histological microvascular invasion (MVI) is a risk factor for poor survival and early recurrence in hepatocellular carcinoma (HCC) after surgery. Its prognostic value in the setting of locoregional therapies (LRT), where no tissue samples are obtained, remains unknown. This study aims to establish CT-derived indices indicative of MVI on liver MRI with superior soft tissue contrast and evaluate their association with patient survival after ablation via interstitial brachytherapy (iBT) versus iBT combined with prior conventional transarterial chemoembolization (cTACE). Ninety-five consecutive patients, who underwent ablation via iBT alone (n = 47) or combined with cTACE (n = 48), were retrospectively included between 01/2016 and 12/2017. All patients received contrast-enhanced MRI prior to LRT. Overall (OS), progression-free survival (PFS), and time-to-progression (TTP) were assessed. Decision-tree models to determine Radiogenomic Venous Invasion (RVI) and Two-Trait Predictor of Venous Invasion (TTPVI) on baseline MRI were established, validated on an external test set (TCGA-LIHC), and applied in the study cohorts to investigate their prognostic value for patient survival. Statistics included Fisher's exact and t-test, Kaplan-Meier and cox-regression analysis, area under the receiver operating characteristic curve (AUC-ROC) and Pearson's correlation. OS, PFS, and TTP were similar in both treatment groups. In the external dataset, RVI showed low sensitivity but relatively high specificity (AUC-ROC = 0.53), and TTPVI high sensitivity but only low specificity (AUC-ROC = 0.61) for histological MVI. In patients following iBT alone, positive RVI and TTPVI traits were associated with poorer OS (RVI: p < 0.01; TTPVI: p = 0.08), PFS (p = 0.04; p = 0.04), and TTP (p = 0.14; p = 0.03), respectively. However, when patients with combined cTACE and iBT were stratified by RVI or TTPVI, no differences in OS (p = 0.75; p = 0.55), PFS (p = 0.70; p = 0.43), or TTP (p = 0.33; p = 0.27) were observed. The study underscores the role of non-invasive imaging biomarkers indicative of MVI to identify patients, who would potentially benefit from embolotherapy via cTACE prior to ablation rather than ablation alone.

Protocol and rationale of the Australian multicentre registry for serial cardiac computed tomography angiography (ARISTOCRAT): a prospective observational study of the natural history of pericoronary adipose tissue attenuation and radiomics.

Vascular inflammation plays a crucial role in the development of atherosclerosis and atherosclerotic plaque rupture resulting in acute coronary syndrome (ACS). Pericoronary adipose tissue (PCAT) attenuation quantified from routine coronary computed tomography angiography (CCTA) has emerged as a promising non-invasive imaging biomarker of coronary inflammation. However, a detailed understanding of the natural history of PCAT attenuation is required before it can be used as a surrogate endpoint in trials of novel therapies targeting coronary inflammation. This article aims to explore the natural history of PCAT attenuation and its association with changes in plaque characteristics. The Australian natuRal hISTOry of periCoronary adipose tissue attenuation, RAdiomics and plaque by computed Tomographic angiography (ARISTOCRAT) registry is a multi-centre observational registry enrolling patients undergoing clinically indicated serial CCTA in 9 centres across Australia. CCTA scan parameters will be matched across serial scans. Quantitative analysis of plaque and PCAT will be performed using semiautomated software. The primary endpoint is to explore temporal changes in patient-level and lesion-level PCAT attenuation by CCTA and their associations with changes in plaque characteristics. Secondary endpoints include evaluating: (I) impact of statin therapy on PCAT attenuation and plaque characteristics; and (II) changes in PCAT attenuation and plaque characteristics in specific subgroups according to sex and risk factors. ARISTOCRAT will further our understanding of the natural history of PCAT attenuation and its association with changes in plaque characteristics. This study has been prospectively registered with the Australia and New Zealand Clinical Trials Registry (ACTRN12621001018808).

Dual-source dual-energy CT and deep learning for equivocal lymph nodes on CT images for thyroid cancer.

This study investigated the diagnostic performance of dual-energy computed tomography (CT) and deep learning for the preoperative classification of equivocal lymph nodes (LNs) on CT images in thyroid cancer patients. In this prospective study, from October 2020 to March 2021, 375 patients with thyroid disease underwent thin-section dual-energy thyroid CT at a small field of view (FOV) and thyroid surgery. The data of 183 patients with 281 LNs were analyzed. The targeted LNs were negative or equivocal on small FOV CT images. Six deep-learning models were used to classify the LNs on conventional CT images. The performance of all models was compared with pathology reports. Of the 281 LNs, 65.5% had a short diameter of less than 4 mm. Multiple quantitative dual-energy CT parameters significantly differed between benign and malignant LNs. Multivariable logistic regression analyses showed that the best combination of parameters had an area under the curve (AUC) of 0.857, with excellent consistency and discrimination, and its diagnostic accuracy and sensitivity were 74.4% and 84.2%, respectively (p < 0.001). The visual geometry group 16 (VGG16) based model achieved the best accuracy (86%) and sensitivity (88%) in differentiating between benign and malignant LNs, with an AUC of 0.89. The VGG16 model based on small FOV CT images showed better diagnostic accuracy and sensitivity than the spectral parameter model. Our study presents a noninvasive and convenient imaging biomarker to predict malignant LNs without suspicious CT features in thyroid cancer patients. Our study presents a deep-learning-based model to predict malignant lymph nodes in thyroid cancer without suspicious features on conventional CT images, which shows better diagnostic accuracy and sensitivity than the regression model based on spectral parameters. Many cervical lymph nodes (LNs) do not express suspicious features on conventional computed tomography (CT). Dual-energy CT parameters can distinguish between benign and malignant LNs. Visual geometry group 16 model shows superior diagnostic accuracy and sensitivity for malignant LNs.

Radiomics of pericoronary adipose tissue on computed tomography angiography predicts coronary heart disease in patients with type 2 diabetes mellitus

BackgroundDiabetes is a common chronic metabolic disease. The progression of the disease promotes vascular inflammation and the formation of atherosclerosis, leading to cardiovascular disease. The coronary artery perivascular adipose tissue attenuation index based on CCTA is a new noninvasive imaging biomarker that reflects the spatial changes in perivascular adipose tissue attenuation in CCTA images and the inflammation around the coronary arteries. In this study, a radiomics approach is proposed to extract a large number of image features from CCTA in a high-throughput manner and combined with clinical diagnostic data to explore the predictive ability of vascular perivascular adipose imaging data based on CCTA for coronary heart disease in diabetic patients.MethodsR language was used for statistical analysis to screen the variables with significant differences. A presegmentation model was used for CCTA vessel segmentation, and the pericoronary adipose region was screened out. PyRadiomics was used to calculate the radiomics features of pericoronary adipose tissue, and SVM, DT and RF were used to model and analyze the clinical data and radiomics data. Model performance was evaluated using indicators such as PPV, FPR, AAC, and ROC.ResultsThe results indicate that there are significant differences in age, blood pressure, and some biochemical indicators between diabetes patients with and without coronary heart disease. Among 1037 calculated radiomic parameters, 18.3% showed significant differences in imaging omics features. Three modeling methods were used to analyze different combinations of clinical information, internal vascular radiomics information and pericoronary vascular fat radiomics information. The results showed that the dataset of full data had the highest ACC values under different machine learning models. The support vector machine method showed the best specificity, sensitivity, and accuracy for this dataset.ConclusionsIn this study, the clinical data and pericoronary radiomics data of CCTA were fused to predict the occurrence of coronary heart disease in diabetic patients. This provides information for the early detection of coronary heart disease in patients with diabetes and allows for timely intervention and treatment.