Insights into Imaging最新文献

Objectives: Renal biopsy has certain limitations for diagnosing membranous nephropathy (MN). The aim is to explore the value of MRI for diagnosing MN.

Materials and methods: MN patients were divided into two subgroups based on estimated glomerular filtration rate, including the mild group and moderate to severe group. Quantitative T1 mapping and renal blood flow (RBF) of bilateral kidneys were measured, including renal cortical T1 mapping (cT1) value, medullary T1 mapping (mT1) value, cortical RBF value (cRBF), and medullary RBF (mRBF) value. The Student's t-test, Mann-Whitney U test, chi-square test, and one-way analysis of variance were used.

Results: Forty-seven MN patients and 54 matched healthy controls (HC) were prospectively enrolled. The cT1 and mT1 average values of HC were significantly lower than those of both MN subgroups (all p < 0.001) after adjusting for age and sex. Compared with the mild group and HC group, the moderate to severe group had lower cRBF (all p < 0.050) and mRBF average values (p = 0.012 and p < 0.001, respectively). The combination model of the T1 mapping and RBF values for differentiating MN from HC had a higher area under the curve of 0.87 (95% confidence intervals, 0.80-0.95) than single-parameter models (all p < 0.050), except the mT1 value model.

Conclusions: Multiparametric MRI shows potential as a noninvasive adjunct tool for assessing MN, offering a possibility to guide clinical decision-making.

Critical relevance statement: Multiparametric MRI provides a noninvasive approach to renal structural and perfusion changes in membranous nephropathy and offers an alternative to guide clinical treatment strategies.

Key points: Renal biopsy has certain limitations for diagnosing membranous nephropathy, and there is an urgent need to develop a noninvasive method. Membranous nephropathy patients had higher cortex, medullary T1 mapping values and lower cortex, medullary renal blood flow values than healthy controls. Quantitative MRI parameters show potential as a noninvasive biomarker for assessing membranous nephropathy.

Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors originating from neural crest-derived chromaffin tissue, marked by clinical heterogeneity and substantial genetic underpinnings. With up to 70% of cases linked to germline or somatic mutations, including Succinate DeHydrogenase genetic alterations (SDHx), and Von Hippel-Lindau (VHL), genetic profiling is central to diagnosis, risk stratification, and therapeutic planning. Clinical presentation varies by tumor location and secretory status-from catecholamine-driven crises to mass effect in head and neck paragangliomas (H&N PGLs). The diagnostic workflow begins with biochemical testing, followed by high-resolution anatomical and functional imaging. Computed tomography (CT) and magnetic resonance imaging (MRI) remain essential for localization and staging, while radiopharmaceuticals such as ⁶⁸Ga-DOTA⁰-Tyr³-octreotate (⁶⁸Ga-DOTATATE), ¹⁸F-fluoro-L-dihydroxyphenylalanine (¹⁸F-FDOPA), and ¹³¹I-metaiodobenzylguanidine (¹³¹I-MIBG) refine tumor characterization and guide peptide receptor radiopharmaceutical therapy (RPT) with radiolabeled octreotide derivatives or therapeutic MIBG Imaging features such as size, necrosis, and diffusion restriction correlate with malignancy risk, but novel molecular imaging offer promise for more precise prognostication. Therapeutic options span from curative surgery to systemic therapies, including temozolomide, tyrosine kinase inhibitors, and nuclide therapy. Minimally invasive, image-guided interventions provide palliation for metastatic or inoperable disease. Importantly, artificial intelligence and molecular assays such as the NETest and ¹H-MRS are emerging as pivotal tools in real-time tumor monitoring, early relapse detection, and biomarker discovery. This review underscores the necessity of a multidisciplinary, genomics-informed, and imaging-guided approach to PPGL management. With the integration of advanced imaging and AI-driven analytics, precision oncology for PPGLs is transitioning from potential to practice. CRITICAL RELEVANCE STATEMENT: This article offers an overview of the diverse manifestations of paragangliomas, illustrated with examples from various anatomical locations. It also highlights different patterns of tumor evolution and provides an up-to-date review of current management and therapeutic strategies, with a special focus on emerging AI-guided approaches. KEY POINTS: Review the genetic associations, including Von Hippel-Lindau, Multiple Endocrine Neoplasia, Neurofibromatosis, and Carney Triad. Overview of anatomical imaging features (CT and MRI) of paragangliomas. Improve knowledge about the different Nuclear Medicine and functional imaging techniques in detecting lesions, depending on their location, secretory function and underlying genetic mutation. Discuss the multiple radiopharmaceuticals available for Scintigraphy and PET-CT, according to the paraganglioma site and mutational pattern.

Objectives: To evaluate the prognostic significance of tumour mutation burden (TMB) in pancreatic ductal adenocarcinoma (PDAC) and explore the performance of dual-layer spectral CT (DLCT) for noninvasive TMB evaluation.

Materials and methods: This retrospective analysis enroled patients with histopathologically confirmed PDAC who underwent DLCT between June 2019 and December 2023. Clinical, qualitative radiological, and quantitative conventional CT and DLCT parameters were evaluated. Survival analysis evaluated TMB's association with progression-free survival (PFS) and identified an optimal TMB cutoff. Independent TMB predictors were identified through univariable and LASSO regression. Predictive performance was quantified via receiver operating characteristic and precision-recall curve assessments.

Results: Among 75 patients (mean age 60.4 ± 11.2 years; 41 males, 34 females), median TMB was 2.13 mut/Mb (interquartile range: 1.00-4.26). A 5 mut/Mb cutoff revealed distinct prognostic groups, with high-TMB cases exhibiting better PFS (median PFS: 7 vs 5 months, p = 0.02). Normalised iodine concentration in the pancreatic phase (nICa) was the sole independent TMB predictor (area under the curve [AUC] = 0.901; cutoff = 0.089; accuracy = 89.3% [89.1-89.6%], sensitivity = 81.8% [59.0-100%], specificity = 90.6% [83.5-97.8%]), surpassing conventional CT attenuation metrics (nCTa, AUC = 0.834), peripancreatic tumour infiltration (AUC = 0.679), and their combined model (AUC = 0.864) with significant net reclassification improvement (all p < 0.05). Precision-recall curve validation reinforced nICa's superior predictive capacity. Patients classified by nICa-predicted high TMB status demonstrated better PFS (median PFS: 7 vs 5 months, p = 0.04).

Conclusion: Elevated TMB is a positive biomarker for PFS in PDAC. DLCT-derived nICa facilitates precise, noninvasive TMB prediction, outperforming conventional imaging parameters and supporting its potential role in therapeutic stratification.

Critical relevance statement: Elevated tumour mutational burden (TMB) in PDAC correlated with prolonged PFS. DLCT provided noninvasive, accurate TMB quantification, enabling meaningful survival stratification.

Key points: High TMB in patients with PDAC portends better PFS, particularly those receiving combination immunotherapy. A clinically applicable TMB cutoff of 5 mut/Mb was identified, stratifying patients into biologically distinct low- and high-TMB prognostic groups. DLCT-derived pancreatic phase normalized iodine concentration emerged as a superior noninvasive TMB biomarker compared to conventional imaging parameters.

Objective: To develop and validate a body composition parameters (BCPs)-based nomogram for predicting recurrence in T1-stage clear cell renal cell carcinoma (ccRCC), comparing its performance with established models while exploring potential biological mechanisms.

Materials and methods: 536 patients from three institutions (training cohort: 343, external validation cohort: 193) were included. Univariate and multivariate Cox regression analyses identified independent prognostic factors for recurrence-free survival (RFS), which were incorporated into the nomogram. The model performance was evaluated, and potential biological mechanisms were explored.

Results: The postoperative nomogram included three independent adverse prognostic factors for RFS: high Leibovich score (HR = 2.18, 95% CI: 1.44-3.31), high visceral adipose tissue density (VATD; HR = 2.34, 95% CI: 1.33-4.12), and high intramuscular adipose tissue content (IMAC; HR = 3.60, 95% CI: 1.29-10.07). The nomogram demonstrated superior discrimination, with a C-index of 0.732 (95% CI: 0.655-0.810) in the training cohort and 0.766 (95% CI: 0.677-0.855) in the validation cohort. The area under the curves (AUCs) for predicting 3- and 5-year RFS were 0.761 and 0.709 (training), and 0.844 and 0.765 (validation), outperforming the TNM, Leibovich, and SSIGN models. Through 5-fold cross-validation within the training cohort, the model achieved mean AUCs of 0.761 (3-year) and 0.683 (5-year). Calibration curves showed good consistency. Decision curve analysis indicated favorable clinical utility. Risk stratification (cutoff = 94.18) based on nomogram scores revealed significant RFS differences. Exploratory in silico analyses of transcriptomic data suggested enrichment in distinct cancer-related and metabolic pathways, as well as varying drug sensitivities between cohorts.

Conclusion: The BCPs-based nomogram effectively predicts recurrence of T1 ccRCC and significantly improves upon existing prognostic models.

Critical relevance statement: The nomogram, combining body composition parameters and Leibovich score, outperformed established prognostic models in predicting T1 ccRCC recurrence, enabling personalized risk stratification.

Key points: Body composition parameters correlate with oncological outcomes in RCC, but remain underexplored in the T1 clear cell subtype. Elevated Leibovich score, visceral adipose tissue density, and intramuscular adipose tissue content independently predicted reduced RFS, linked to cancer-related and metabolic pathways enrichment. The body composition parameters-based nomogram could effectively predict postoperative recurrence in T1 ccRCC patients.

Objective: To evaluate the diagnostic utility of MRI-based radiomics in stratifying the risk of tumor deposits (TD) in patients with rectal cancer (RC).

Materials and methods: This study retrospectively analyzed 729 patients with RC from two institutions (January 2018-August 2024). Patients were classified into three groups according to the number of TD: no TD (TD0), 1-2 TD (TD1-2), and ≥ 3 TD (TD3+). Radiomics features were extracted from the tumor and the largest nodule within the rectal mesentery on MRI images. Predictive models were developed with the XGBoost algorithm. Model performance was evaluated using the receiver operating characteristic curve, area under the curve, confusion matrix, precision, accuracy, recall, and F1 score.

Results: Three hundred seventy-six patients were ultimately included and allocated into training, test, and validation sets. The tumor model (developed using tumor features) achieved AUCs of 0.871 (test set) and 0.848 (validation set), with corresponding accuracy, precision, recall, and F1 of 0.745/0.716, 0.764/0.688, 0.764/0.734, and 0.764/0.710, respectively. The nodule model (developed using the largest nodule) yielded AUCs of 0.839/0.804, accuracy of 0.673/0.637, precision of 0.571/0.614, recall of 0.800/0.686, and F1 of 0.667/0.648 in the test and validation sets, respectively. The fusion model, which combined tumor and nodule features, achieved enhanced performance with AUCs of 0.873/0.858, accuracy of 0.800/0.784, precision of 0.804/0.712, recall of 0.745/0.775, and F1 of 0.774/0.742, outperformed both individual models and two radiologists (accuracy 0.676/0.589).

Conclusions: MRI-derived radiomics demonstrates significant potential for risk stratification of TD in RC.

Critical relevance statement: The radiomics model integrating tumor features and maximal short-axis diameter of mesorectal nodules effectively predicts three distinct quantity-based categories of TD in RC, enabling preoperative risk stratification and assisting personalized treatment planning.

Key points: Tumor and nodule features support effective stratification of TD. The fusion model for TD classification outperforms two radiologists. MRI-based radiomics aids TD risk stratification.

Objectives: Mammographically spiculated breast cancer is frequently less aggressive than cancers with alternative appearances. This study aims to investigate whether the degree of spiculations relative to the tumor mass on mammography, termed the spic mass ratio (SMR), is associated with breast cancer characteristics and survival.

Materials and methods: This retrospective exploratory single-center study analyzed mammograms from 161 women with spiculated breast cancer in the Malmö Diet and Cancer Study cohort (2004-2014). Radiologists segmented the tumor mass and the spiculation areas. The SMR was calculated by dividing the combined tumor and spiculation area by the tumor area alone. The subjects were stratified into tertiles with low, medium, and high SMR. The study examined associations between SMR and breast density, mode of detection, age, tumor size, estrogen receptor status, progesterone receptor status, human epidermal growth factor receptor 2 status, Ki67, histological grade, axillary lymph node involvement (ALNI), histological type, and breast cancer-specific survival, utilizing the Chi-squared test, ANOVA, Fisher's exact test, Kaplan-Meier curves, and Cox regression.

Results: The mean age was 68 years (range 55-91). SMR was statistically significantly associated with both age and breast density. No other significant associations were observed. Among the nine women with the highest SMR values, axillary lymph node negativity, estrogen positivity, and an overall low Ki67 index were noted.

Conclusions: The SMR, representing the degree of spiculations relative to tumor mass, was not significantly associated with breast cancer survival or ALNI. Further research is necessary to explore the prognostic implications of extensive spiculations in spiculated breast cancer.

Critical relevance statement: The degree of spiculation relative to the tumor mass is an unexplored mammographic feature that can be measured subjectively, as in this study. Extensive spiculation was associated with higher age and lower breast density. No certain conclusions could be drawn regarding the impact on breast cancer survival.

Key points: The degree of spiculation relative to the tumor mass on mammography is an unexplored mammographic feature. A high ratio of spiculations in relation to tumor mass was associated with higher age and lower breast density. The nine women with a very high spiculation to tumor mass ratio were all axillary lymph node negative.

Objectives: The aim of this study was to develop an artificial intelligence model to automatically differentiate between non-neoplastic and neoplastic gallbladder polyps, while also distinguishing benign from malignant polyps.

Materials and methods: Patients with gallbladder polyps who underwent cholecystectomy from January 2022 to June 2023 were recruited from two hospitals retrospectively. Conventional ultrasound findings and clinical characteristics of patients before cholecystectomy were acquired. Ultrasound image blocks of gallbladder lesions were automatically segmented by the Unet network for diagnosis. A fusion deep learning model based on dual-mode ultrasound (grey-scale ultrasound and colour Doppler flow imaging) was established to diagnose gallbladder polyps and validated in the validation and test set. Finally, we compared the diagnostic efficiency of the model with that of radiologists and guidelines.

Results: A total of 339 patients (mean ages 53.17 ± 15.89, 182 females) were enroled in this study. The Dice coefficient and intersection over union (IoU) value of the automatic segmentation based on the Unet-efficientnet-b4 network were 0.912 and 0.838. In differentiating non-neoplastic from neoplastic polyps, the integrative deep learning (IDL) model showed area under the curves (AUCs) of 0.829 and 0.802 in validation and test sets, respectively. In differentiating benign and malignant polyps, the IDL model showed AUCs of 0.844 and 0.839 in validation and test sets, respectively. In the test set, the diagnostic performance of two junior radiologists was improved with the assistance of the IDL model.

Conclusion: The IDL model based on dual-mode ultrasound could achieve accurate and automatic segmentation of gallbladder lesions, and showed excellent diagnostic performance for diagnosing gallbladder polyps.

Critical relevant statement: We developed a deep learning model based on conventional ultrasound that performs gallbladder segmentation while differentiating neoplastic from non-neoplastic polyps and benign from malignant polyps.

Key points: Diagnosing gallbladder polyps through a deep learning model based on conventional ultrasound presents challenges. IDL model enables automated segmentation of the gallbladder and diagnosis of gallbladder polyps. The IDL model is a reliable tool to assist junior radiologists in diagnosis and has potential for reducing unnecessary cholecystectomies.

Objective: To investigate the feasibility of susceptibility-weighted imaging (SWI) for the diagnosis of different stages of liver fibrosis, and to assess its diagnostic accuracy compared with the serum fibrosis index commonly used in clinical settings.

Materials and methods: This prospective study included 108 patients and 16 healthy volunteers. All patients underwent MRI with SWI and histopathological evaluation. Liver and bilateral erector spinae signal intensities were measured on SWI to calculate liver-to-muscle signal intensity ratios (SIR). Serological biomarkers were collected to calculate the aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis index based on four factors (FIB-4). Histological correlation analysis between the SIR and liver fibrosis/iron deposition was performed using Spearman's rank correlation analysis. The diagnostic accuracies of SIR, APRI, and FIB-4 for staging liver fibrosis were assessed, and their performances were compared using the DeLong test.

Results: Receiver operating characteristic (ROC) curve analysis showed good-to-excellent diagnostic performance of SIR for different stages of liver fibrosis. The areas under the curve (AUC) of SIR for the diagnosis of liver fibrosis stages S0 vs S1-S4, S0-S1 vs S2-S4, S0-S2 vs S3-S4, and S0-S3 vs S4 were 0.851, 0.868, 0.872, and 0.931. Delong's test showed that the SIR outperformed the APRI and FIB-4 in the diagnosis of liver fibrosis S0-S1 vs S2-S4, S0-S2 vs S3-S4, and S0-S3 vs S4 (p = 0.011-0.036).

Conclusion: SWI-based SIR outperforms the serum indicators APRI and FIB-4 in diagnosing liver fibrosis of S0-S1 vs S2-S4, S0-S2 vs S3-S4, and S0-S3 vs S4.

Critical relevance statement: SWI-based SIR offers a new perspective on non-invasive diagnostic methods to guide the clinical diagnosis of liver fibrosis, particularly in cases where biopsy is contraindicated or impractical.

Key points: Searching for a non-invasive method to accurately diagnose stages of liver fibrosis is necessary because of the limitations of histopathological evaluation. SWI offers a dependable and non-invasive diagnostic approach for evaluating different stages of liver fibrosis compared to serological biomarkers. SWI-based SIR provides a highly accurate, non-invasive alternative to serum biomarkers for detecting advanced liver fibrosis.