Search Results (538)

Background/Objectives: Preoperative differentiation between oral squamous cell carcinoma (SCC) and minor salivary gland carcinoma (SGC) remains clinically challenging due to overlapping imaging characteristics. This study aimed to develop a diagnostic model based on quantitative dynamic contrast-enhanced MRI (qDCE-MRI) parameters to distinguish SCC from SGC prior to surgery. Methods: Patients with histopathologic confirmed SCC or minor SGC who underwent preoperative 3.0T qDCE-MRI were recruited. Clinical characteristics and pharmacokinetic parameters, including volume transfer constant (K^trans), reverse reflux rate constant (K_ep), volume fraction of extravascular extracellular space (V_e), plasma volume fraction (V_p), time to peak (TTP), maximum concentration (MAXConc), maximal slope (MAXSlope), and area under the concentration-time curve (AUCt), along with the apparent diffusion coefficient (ADC), were extracted. Univariate and multivariable logistic regression analyses were performed to identify independent discriminators. Diagnostic performance was assessed using receiver operating characteristic analysis, and model comparisons were conducted with the DeLong test. Interobserver agreement was evaluated using intraclass correlation coefficients (ICC). Results: All qDCE-MRI parameters demonstrated excellent interobserver agreement (ICC range, 0.82–0.94). Multivariable analysis identified K_ep (OR = 2620.172, p = 0.001), maximal slope (OR = 1.715, p = 0.024), and tumor location (OR = 5.561, p = 0.027) as independent predictors. The qDCE-MRI model achieved superior diagnostic performance compared with the clinical model (AUC: 0.945 vs. 0.747; p = 0.012). Conclusions: A qDCE-MRI–based model incorporating K_ep and MAXSlope was shown to provide excellent accuracy for preoperative differentiation between oral SCC and minor SGC. Full article

Background: The vestibular schwannoma (VS) is the most common cerebellopontine angle tumor in adults, exhibiting a highly variable natural history, from stability to rapid growth. Accurate, the non-invasive prediction of tumor behavior is essential to guide personalized management and avoid overtreatment or delayed intervention. Objective: To systematically review and synthesize the evidence on MRI-based biomarkers for predicting VS growth and treatment responses. Methods: We conducted a PRISMA-compliant search of PubMed, EMBASE, and Cochrane databases for studies published between 1 January 2000 and 1 January 2025, addressing MRI predictors of VS growth. Cohort studies evaluating texture features, signal intensity ratios, perfusion parameters, and apparent diffusion coefficient (ADC) metrics were included. Study quality was assessed using the NOS (Newcastle–Ottawa Scale) score, GRADE (Grading of Recommendations, Assessment, Development and Evaluation), and ROBIS (Risk of Bias in Systematic reviews) tool. Data on diagnostic performance, including the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, and p value, were extracted and descriptively analyzed. Results: Ten cohort studies (five retrospective, five prospective, total n = 525 patients) met the inclusion criteria. Texture analysis metrics, such as kurtosis and gray-level co-occurrence matrix (GLCM) features, yielded AUCs of 0.65–0.99 for predicting volumetric or linear growth thresholds. Signal intensity ratios on gadolinium-enhanced T1-weighted images for tumor/temporalis muscle achieved a 100% sensitivity and 93.75% specificity. Perfusion MRI parameters (K^trans, v_e, ASL, and DSC derived blood-flow metrics) differentiated growing from stable tumors with AUCs up to 0.85. ADC changes post-gamma knife surgery predicted a favorable response, though the baseline ADC had limited value for natural growth prediction. The heterogeneity in growth definitions, MRI protocols, and retrospective designs remains a key limitation. Conclusions: MRI-based biomarkers may provide exploratory signals associated with VS growth and treatment responses. However, substantial heterogeneity in growth definitions and MRI protocols, small single-center cohorts, and the absence of external validation currently limit clinical implementation. Full article

Background/Objectives: Diffusion-weighted imaging (DWI) is a magnetic resonance technique used to map the apparent diffusion coefficient ($ADC$) of water in human tissue. $ADC$ assessment plays a central role in clinical diagnostics, as malignant tissues typically exhibit reduced water mobility and, thus, lower $ADC$ values. Accurately measuring the $ADC$ requires effective fat suppression to prevent contamination from the residual fat signal, which is commonly believed to cause $ADC$ underestimation. This study aimed to demonstrate that $ADC$ overestimation may occur as well. Methods: Our theoretical analysis shows that out-of-phase conditions between fat and water signals lead to $ADC$ overestimations. We performed demonstration experiments on fat–water phantoms and the breasts of 10 healthy female volunteers. In particular, we considered three out-of-phase conditions: First and second, short-time inversion recovery (STIR) fat suppression with incorrect inversion time and incorrect flip angle, respectively. Third, phase differences due to spectral fat saturation. The $ADC$ values were assessed in regions of interest (ROIs) that included both water and residual fat signals. Results: In the phantoms and the volunteer data, ROIs containing both fat and water signals consistently exhibited lower $ADC$ values under in-phase conditions and higher $ADC$ values under out-of-phase conditions. Conclusions: We demonstrated that out-of-phase conditions can result in $ADC$ overestimation in the presence of residual fat signals, potentially resulting in false-negative classifications where malignant lesions are misinterpreted as benign due to an elevated $ADC$. Out-of-phase fat and water signals might also reduce lesion conspicuity in high b-value images, potentially masking clinically relevant findings. Full article

Background/Objectives: The tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) is characterized by an enriched stroma, hampering the effectiveness of therapy. This co-clinical study aimed to (1) provide insight into early post-treatment changes in the TME using multiparametric magnetic resonance imaging (mpMRI)-derived quantitative imaging biomarkers (QIBs) in a preclinical PDAC model treated with radiotherapy and correlate these QIBs with histology; (2) evaluate the feasibility of obtaining these QIBs in patients with PDAC using clinically approved mpMRI data acquisitions. Methods: Athymic mice (n = 12) at pre- and post-treatment as well as patients with PDAC (n = 11) at pre-treatment underwent mpMRI including diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) data acquisition sequences. DW and DCE data were analyzed using monoexponential and extended Tofts models, respectively. DeepLIIF quantified the total percentage (%) of tumor cells in hematoxylin and eosin (H&E)-stained tissues from athymic mice. Spearman correlation and Wilcoxon signed rank tests were performed for statistical analysis. Results: In the preclinical PDAC model, mean pre- and post-treatment ADC and K^trans values differed significantly (p < 0.01), changing by 20.50% and 20.41%, respectively, and the median total tumor cells quantified by DeepLIIF was 24% (range: 15–53%). Post-treatment ADC values and relative change in v_e (rΔv_e) showed a significant negative correlation with total tumor cells (ρ = −0.77, p < 0.014 for ADC and ρ = −0.77, p = 0.009 for rΔv_e). In patients with PDAC, pre-treatment mean ADC and K^trans values were 1.76 × 10⁻³ (mm²/s) and 0.24 (min⁻¹), respectively. Conclusions: QIBs in both preclinical and clinical settings underscore their potential for future co-clinical research to evaluate emerging drug combinations targeting both tumor and stroma. Full article

Background and Objectives: Soft tissue sarcomas account for approximately 7% of all malignant tumors in the pediatric population. Diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) measurements may provide early functional biomarkers of treatment response by reflecting changes in tumor cellularity. This study evaluated whether ADC-derived parameters can serve as quantitative biomarkers of neoadjuvant chemotherapy response in pediatric rhabdomyosarcoma. Materials and Methods: This retrospective single-center study included 14 patients aged ≤18 years with histopathologically confirmed rhabdomyosarcoma who underwent MRI before treatment and after three cycles of chemotherapy. Twenty-five patients were initially identified; eleven were excluded due to imaging artifacts or absence of baseline examination. ADC maps were generated on 1.5T and 3T scanners. Regions of interest were placed over the entire lesion and areas with the lowest ADC signal. Relative ADC (rADC) was calculated by normalizing tumor ADC to adjacent healthy muscle. Paired t-tests were used to compare pre- and post-treatment values. Results: At baseline, 13/14 patients (93%) demonstrated diffusion restriction. Mean ADC increased from 1.11 × 10⁻³ mm²/s (SD ± 0.48) at baseline to 1.63 × 10⁻³ mm²/s (SD ± 0.67) after treatment. The paired t-test for rADC yielded t = −3.089 (p = 0.0086, 95% CI: −0.79 to −0.14), indicating a statistically significant change. There was a significant difference between the ADC values of the entire lesion and the areas with the lowest signal in tumors with a heterogenic structure, t = 2.862, p = 0.013. Conclusions: ADC and rADC increased significantly after neoadjuvant chemotherapy in pediatric rhabdomyosarcoma, suggesting potential utility as early functional biomarkers of treatment response. These preliminary findings require validation in larger multicenter prospective studies with correlation to histopathological response and clinical outcomes before clinical implementation. Full article

The use of deep learning radiomics to predict whether advanced prostate cancer (PCa) will progress within two years after treatment has been validated, yet there remains a lack of research on estimating time to progression. Patients were enrolled from October 2017 to March 2024. One hundred and eighty-two patients with advanced PCa diagnosed through ultrasound-guided systematic prostate biopsy were enrolled. A deep learning-based radiomics model for predicting progression was firstly developed using pretreatment MR apparent diffusion coefficient (ADC) maps, and the performance of manual (ROIref) versus AI-derived (ROIai) tumor segmentations was compared. Then, survival analysis was performed to compare ROIref-based and ROIai-based radiomics-predicted probabilities in the risk stratification. The area under the receiver operating characteristics curve (AUC) was used to estimate the model efficacy. The model achieved high AUC values for progression prediction in test sets (ROIref: 0.840, ROIai: 0.852). No significant difference was observed between ROIai-based and ROIref-based approaches (ΔAUC = 0.012, p = 0.870) in the test set. Both ROIref-predicted and ROIai-predicted probabilities independently predicted progression in multivariate Cox proportional hazard regression models (p < 0.001) and stratified patients into distinct survival groups (log-rank p < 0.001). Decision curve analysis confirmed equivalent clinical utility across thresholds (0.1–0.6), with net benefit exceeding the “treat all” and “treat none” strategies. In conclusion, deep learning-based radiomics models could effectively predict advanced PCa progression, with AI-derived tumor annotations performing equally to manual expert ones. Full article

Background: Magnetic resonance imaging (MRI) plays an increasingly important role in the evaluation of scrotal and penile disorders, complementing ultrasonography in cases where findings are equivocal or complex. With its superior soft-tissue contrast, multiplanar capability, and advanced functional sequences, MRI provides unparalleled anatomic and tissue characterization across a wide range of male genital pathologies. Summary: This review summarizes current clinical applications of MRI in scrotal and penile imaging and discusses its diagnostic value, protocol optimization, and interpretive features. In scrotal pathology, MRI accurately differentiates torsion, trauma, infection, and neoplasms, aiding in the distinction between benign and malignant testicular lesions and supporting testis-sparing management. Quantitative diffusion and perfusion metrics further refine lesion characterization. In andrology, MRI biomarkers such as apparent diffusion coefficient (ADC), magnetization transfer ratio (MTR), and proton spectroscopy serve as promising non-invasive indicators of spermatogenic activity in male infertility. In penile imaging, MRI enables precise local staging of carcinoma, assessment of plaque morphology and activity in Peyronie’s disease, evaluation of tissue viability in priapism, and detection of prosthesis-related complications. Conclusions: MRI has become an essential problem-solving tool in the assessment of scrotal and penile diseases, enhancing diagnostic confidence and surgical planning. Future directions include protocol standardization, quantitative parameter validation, and the integration of radiomics and artificial intelligence to improve reproducibility and clinical impact. Full article

Background/Objectives: This study aimed to evaluate the diagnostic accuracy of Apparent Diffusion Coefficient (ADC) values, derived from Diffusion-Weighted Imaging (DWI), in differentiating benign and malignant ascites. Methods: This retrospective study included 150 patients (85 benign, 65 malignant) who underwent abdominal MRI. All patients were scanned on a DWI sequence (b-values: 0, 500, and 1000 s/mm²). Two experienced radiologists, blinded to clinical and cytological outcomes, measured the mean ADC (ADCmean) from three distinct ROIs and the minimum ADC (ADCmin) from the area of lowest signal intensity on the ADC map. The diagnostic performance of ADC parameters and the Serum-Ascites Albumin Gradient (SAAG) was assessed using Receiver Operating Characteristic (ROC) curve analysis. Results: The mean values of ADCmean (3162 ± 204 × 10⁻⁶ mm²/s) and ADCmin (2885 ± 148 × 10⁻⁶ mm²/s) in the malignant group were significantly lower than those in the benign group (3596 ± 239 and 3322 ± 218 × 10⁻⁶ mm²/s; p = 0.006 and p = 0.0016, respectively). Inter-observer agreement was good for both ADCmean (ICC = 0.844) and ADCmin (ICC = 0.879). In the ROC analysis, ADCmin demonstrated the highest diagnostic performance (AUC: 0.930). An optimal cut-off value for ADCmin of ≤ 2983 × 10⁻⁶ mm²/s yielded 81.5% sensitivity and 85.8% specificity. The diagnostic performance of ADCmin was found to be superior to that of ADCmean (AUC: 0.877) and SAAG (AUC: 0.919). Conclusions: ADC values derived from DWI, particularly ADCmin, represent a highly accurate, non-invasive, and reproducible biomarker for differentiating benign from malignant ascites. The identified ADCmin threshold provides quantitative parameter that can aid in patient triage, especially when cytology is inconclusive, potential surrogate for fluid characterization. Full article

Background/Objectives: This study explored the associations of normal breast tissue characteristics on multiparametric MRI with clinical assessments of breast cancer risk in women with dense breasts. Methods: Women with dense breasts who underwent multiparametric MRI were included. Breast cancer risk was determined based on Tyrer–Cuzick (TC) lifetime risk scores, categorized as high (TC ≥ 20%) or low risk. Qualitative background parenchymal enhancement (BPE) assessment was obtained from imaging reports. Quantitative imaging markers were calculated, including median BPE, median apparent diffusion coefficient, and volume measures of the whole breast, fibroglandular tissue (FGT), blood vessels, and BPE regions. The associations between imaging markers and TC risk groups were evaluated using age-adjusted logistic regression and summarized by area under the receiver operating characteristic curve (AUC). Results: Seventy-seven women were evaluated; a total of 20 (26%) were low risk, and 57 (74%) were high risk. After adjusting for age and multiple testing, BPE:breast ratio (adj. p = 0.037), FGT:breast ratio (adj. p = 0.046), and BPE:vessel ratio (adj. p = 0.037) were positively associated with risk, while qualitative BPE was not (adj. p = 0.11). Overall, risk categorizations based on imaging markers were concordant with TC score in up to 70% of women. Conclusions: In women with dense breasts, quantitative measures from multiparametric MRI (BPE:breast, FGT:breast, and BPE:vessel ratios) moderately discriminated high- and low-risk groups, warranting further investigation of their value to supplement conventional breast cancer risk assessment tools. Full article

To explore the relationship between magnetic resonance imaging (MRI) parameters, including T2-weighted intensity and apparent diffusion coefficient (ADC), and intraoperative tumor characteristics, particularly consistency, in vestibular schwannomas (VSs). The association between tumor consistency, facial nerve (FN) function, and postoperative outcomes was analyzed. A single-center retrospective analysis included newly diagnosed VS cases (2020–2023) with cisternal involvement (Samii T3a; volume ≥ 0.7 cm³). T2 and ADC maps from the perimetral region of interest were normalized, and tumors were categorized into 3 classes by combining qualitative consistency (soft, fibrous, or fibrous/hard), ultrasonic aspirator power, and adherence to neurovascular structures. FN function was assessed using the House–Brackmann scale at the immediate postoperative period and 12-month follow-up. MRIs of 33 VSs (18 solid and 15 cystic) were analyzed. Normalized values of both T2 (N-T2_mean) and ADC (N-ADC_min) maps predicted the classical radiological differentiation. N-ADC_min may have some role in predicting consistency (value 1.361, p = 0.017, accuracy 0.48) and demonstrated a significant association (p = 0.04) with the FN outcome in the immediate postoperative period. An augmented consistency could impair FN function by increasing the intrameatal pressure related to greater transmission of shocks derived from the dissection maneuvers of the cisternal component of the tumor. The possibility of non-invasively exploring VS consistency with a parameter easily calculable on MRI might be beneficial in surgical planning, modifying the timing of the opening of the meatus with respect to what could be the surgical routine in some centers. Full article

This study reports measurements of density, viscosity, and ternary mutual diffusion coefficients (D₁₁, D₁₂, D₂₁, D₂₂) for aqueous solutions containing two antibiotics—sulfamethoxazole (SMX) or trimethoprim (TMP) (component 1)—in the presence of various cyclodextrins (α–CD, β–CD, and γ–CD) (component 2) at 298.15 K. The relative viscosity data were analyzed by fitting to a second-order Jones-Dole equation via a least-squares regression to obtain the viscosity B coefficients. Apparent molar volumes (V_ϕ) were derived from the measured densities (ρ) for SMX and TMP in aqueous media. Furthermore, partial molar volumes of transfer at infinite dilution, ΔV_ϕ⁰, were evaluated to elucidate solute–solvent interactions within the ternary systems investigated. Nonzero ΔV_ϕ⁰ values, positive viscosity B coefficients, and negative cross-diffusion coefficients (D₁₂ and D₂₁), evidencing significant coupled diffusion, collectively indicate strong interactions between the antibiotics and cyclodextrins, consistent with host–guest complex formation. Full article

This paper will present in vivo release profiles of Doxorubicin.HCl from halo-spun drug-loaded rubbery porous mats. For the very first time, Fluorescent Organism Bioimaging (FOBI) was used to follow drug release in a live animal model with induced tumors. A new predictive model based on apparent diffusion coefficients to simulate release profiles will also be presented and could have general applications for release profile predictions. Surprisingly, histological evaluation found that the tissue layer forming next to the drug-eluting mats had unordered morphology and only necrotic cells. This is a stunning contrast to the highly regular collagen structure next to mats without the drug, typical of an adverse foreign body type reaction. The findings suggest that this drug-eluting fiber mat can be used as a local chemotherapy approach coupled with mitigation of capsular contracture, the major complication associated with breast reconstruction following mastectomy. Full article

Objectives: The potential use of electron density (ED) and effective atomic number (Zeff) derived from dual-energy computed tomography (DECT) as novel quantitative imaging biomarkers for differentiating malignant brain tumors was investigated. Methods: Data pertaining to 136 patients with a pathological diagnosis of brain metastasis (BM), glioblastoma, and primary central nervous system lymphoma (PCNSL) were retrospectively reviewed. The 10th percentile, mean and 90th percentile values of conventional 120-kVp CT value (CTconv), ED, Zeff, and relative apparent diffusion coefficient derived from diffusion-weighted magnetic resonance imaging (rADC: ADC of lesion divided by ADC of normal-appearing white matter) within the contrast-enhanced tumor region were compared across the three groups. Furthermore, machine learning (ML)-based diagnostic models were developed to maximize diagnostic performance for each tumor classification using the indices of DECT parameters and rADC. Machine learning models were developed using the AutoGluon-Tabular framework with rigorous patient-level data splitting into training (60%), validation (20%), and independent test sets (20%). Results: The 10th percentile of Zeff was significantly higher in glioblastomas than in BMs (p = 0.02), and it was the only index with a significant difference between BMs and glioblastomas. In the comparisons including PCNSLs, all indices of CTconv, Zeff, and rADC exhibited significant differences (p < 0.001–0.02). DECT-based ML models exhibited high area under the receiver operating characteristic curves (AUC) for all pairwise differentiations (BMs vs. Glioblastomas: AUC = 0.83; BMs vs. PCNSLs: AUC = 0.91; Glioblastomas vs. PCNSLs: AUC = 0.82). Combined models of DECT and rADC demonstrated excellent diagnostic performance between BMs and PCNSLs (AUC = 1) and between Glioblastomas and PCNSLs (AUC = 0.93). Conclusion: This study suggested the potential of DECT-derived ED and Zeff as novel quantitative imaging biomarkers for differentiating malignant brain tumors. Full article

Objectives: This exploratory preclinical study aimed to compare the correlations of apparent diffusion coefficient (ADC) and native T1 mapping histogram features with tumor cell proliferation, microvessel density (MVD), and extracellular matrix composition in neuroblastoma xenografts. Methods: Neuroblastoma xenografts (n = 42) were established by subcutaneously injecting three MYCN-amplified/non-amplified human neuroblastoma cell lines (IMR-32, SK-N-BE(2), and SH-SY5Y; n = 14 per group) into female immunodeficient BALB/c-nude mice. Once tumors reached a diameter within the range of 12–15 mm, native T1 mapping and diffusion-weighted imaging were performed using a 3.0T clinical MRI scanner. Tumor cell proliferation and MVD were assessed via immunohistochemical Ki-67 staining and CD31 staining, respectively. Collagen fibers were visualized using Masson staining to calculate the collagen volume fraction (CVF). Pearson correlation coefficients with false discovery rate (FDR) correction were used to evaluate their associations. Results: Significant negative correlations were observed between Ki-67 expression and multiple ADC values after FDR correction, including ADC_10Percentile (r = −0.397, adjusted p = 0.032), ADC_90Percentile (r = −0.394, adjusted p = 0.032), ADC_maximum (r = −0.362, adjusted p = 0.048), ADC_mean (r = −0.421, adjusted p = 0.032), ADC_median (r = −0.422, adjusted p = 0.032), ADC_minimum (r = −0.390, adjusted p = 0.032), and ADC_{rootmeansquared} (r = −0.419, adjusted p = 0.032). In contrast, multiple T1 mapping features showed significant positive correlations with CVF (adjusted p < 0.05). Conclusions: ADC and T1 mapping provide complementary insights into tumor proliferation and extracellular matrix composition in neuroblastoma. These preclinical findings support further research to validate their potential clinical utility. Full article

Background/Objectives: Crohn’s disease (CD) requires precise, noninvasive monitoring to guide therapy and support treat-to-target management. Magnetic resonance enterography (MRE), particularly diffusion-weighted imaging (DWI), is the preferred cross-sectional technique for assessing small-bowel inflammation. Indices such as the Magnetic Resonance Index of Activity (MaRIA) and its diffusion-weighted variant (DWI MaRIA) are widely used for grading disease activity. This study evaluated whether unsupervised clustering of MRI-derived features can complement these indices by providing more coherent and biologically grounded stratification of disease activity. Materials and Methods: Fifty patients with histologically confirmed CD underwent 1.5 T MRE. Of 349 bowel segments, 84 were pathological and classified using literature-based thresholds (MaRIA, DWI MaRIA) and unsupervised clustering. Differences between inactive, active, and severe disease were analyzed using multivariate analysis of variance (MANOVA), analysis of variance (ANOVA), and t-tests. Mahalanobis distances were calculated to quantify and compare separation between categories. Results: Using MaRIA thresholds, 5, 16, and 63 segments were classified as inactive, active, and severe (Mahalanobis distances 2.60, 4.95, 4.12). Clustering redistributed them into 22, 37, and 25 (9.26, 24.22, 15.27). For DWI MaRIA, 21, 14, and 49 segments were identified under thresholds (3.59, 5.72, 2.85) versus 21, 37, and 26 with clustering (7.40, 16.35, 9.41). Wall thickness dominated cluster-derived separation, supported by diffusion metrics and the apparent diffusion coefficient (ADC). Conclusions: Cluster-derived classification yielded clearer and more biologically consistent separation of disease-activity groups than fixed thresholds, emphasizing its potential to refine boundary definition, enhance MRI-based assessment, and inform future AI-driven diagnostic modeling. Full article