Abdominal Radiology最新文献

Purpose

The histological grade of hepatocellular carcinoma (HCC) is an important factor associated with early tumor recurrence and prognosis after surgery. Developing a valuable tool to assess this grade is essential for treatment. This study aimed to evaluate the feasibility and efficacy of a deep learning-based three-dimensional super-resolution (SR) magnetic resonance imaging radiomics model for predicting the pathological grade of HCC.

Methods

A total of 197 HCC patients were included and divided into a training cohort (n = 157) and a testing cohort (n = 40). Three-dimensional SR technology based on deep learning was used to obtain SR hepatobiliary phase (HBP) images from normal-resolution (NR) HBP images. High-dimensional quantitative features were extracted from manually segmented volumes of interest in NRHBP and SRHBP images. The gradient boosting, light gradient boosting machine, and support vector machine were used to develop three-class (well-differentiated vs. moderately differentiated vs. poorly differentiated) and binary radiomics (well-differentiated vs. moderately and poorly differentiated) models, and the predictive performance of these models was evaluated using several measures.

Results

All the three-class models using SRHBP images had higher area under the curve (AUC) values than those using NRHBP images. The binary classification models developed with SRHBP images also outperformed those with NRHBP images in distinguishing moderately and poorly differentiated HCC from well-differentiated HCC (AUC = 0.849, sensitivity = 77.8%, specificity = 76.9%, accuracy = 77.5% vs. AUC = 0.603, sensitivity = 48.1%, specificity = 76.9%, accuracy = 57.5%; p = 0.039). Decision curve analysis revealed the clinical value of the models.

Conclusions

Deep learning-based three-dimensional SR technology may improve the performance of radiomics models using HBP images for predicting the preoperative pathological grade of HCC.

Background

Post-pancreatectomy acute pancreatitis (PPAP) has been increasingly recognized as an independent complication since its formal definition by the International Study Group for Pancreatic Surgery (ISGPS) in 2022. This study aimed to evaluate the diagnostic accuracy of multiparameter assessment of pancreatic parenchyma using dual-layer spectral-detector CT (DLCT) in predicting PPAP after pancreaticoduodenectomy (PD).

Methods

Consecutive patients who underwent PD and preoperative DLCT between January 2020 and June 2022 were retrospectively analyzed. Iodine concentration (IC) and Hounsfield unit (HU) values of the pancreatic parenchyma were measured in the non-enhanced (N), arterial (A), portal venous (P), and equilibrium (E) phases. CT enhancement patterns were quantified by the A/E ratios of IC and HU values. The extracellular volume (ECV) fraction was calculated based on iodine concentration (ECV. IC) and HU values (ECV. HU) of the equilibrium phase.

Results

A total of 550 patients were included in the analysis. Of these, 101 (18.3%) patients developed PPAP after PD and had significantly lower IC in the P and E phases compared to those without PPAP (both p < 0.001). Among the composite parameters, the ECV. IC fraction demonstrated the highest accuracy (AUC = 0.784, 95%CI, 0.739–0.829) for predicting PPAP compared to the ECV. HU fraction, A/E HU ratio, (A-N)/(E-N) HU ratio, and A/E IC ratio (AUCs of 0.726, 0.741, 0.743, and 0.751, respectively). On multivariate analysis, an ECV.IC fraction < 30.6% was independently associated with the occurrence of PPAP (OR 7.44, 95% CI: 4.23–13.11, p < 0.001), with sensitivity of 76.2% and specificity of 69.5%.

Conclusions

Multiparameter assessment of pancreatic parenchyma derived from DLCT showed excellent accuracy for preoperatively predicting PPAP after PD.

Objective

This study aimed to identify distinctive computed tomography (CT) features associated with human epidermal growth factor receptor 2 (HER2) status in gallbladder cancer (GBC) that could serve as noninvasive imaging biomarkers.

Materials and methods

This study included 213 patients with pathologically confirmed GBCs with availability of HER2 status (171 HER2-negative, 42 HER2-positive). Pre-treatment contrast-enhanced CT scans were evaluated by two radiologists blinded to HER2 status. Multivariate analysis was performed using logistic regression with L2 regularization. Model discrimination was assessed using receiver operating characteristic (ROC) analysis, and internal validation was performed using bootstrap resampling (1,000 iterations) to correct for optimism.

Results

HER2-positive tumors exhibited larger lymph nodes (1.93 ± 0.79 cm vs. 1.61 ± 0.61 cm, p = 0.015), less frequent gallstones (14.3% vs. 35.7%, p = 0.013), arterial phase hyperenhancement (20.0% vs. 44.1%, p = 0.026), mass-like morphology (35.7% vs. 55.6%, p = 0.033), and more frequent biliary compression by lymph nodes (19.0% vs. 4.1%, p = 0.002). Multivariate analysis identified biliary compression by lymph nodes as the strongest positive predictor of HER2 positivity [odds ratio (OR) 2.99, 95% CI: 1.25–7.04], while arterial phase hyperenhancement (OR 0.40, 95% CI: 0.19–0.75), gallstone presence (OR 0.40, 95% CI: 0.18–0.75), and mass-like morphology (OR 0.54, 95% CI: 0.29–0.95) were significant negative predictors. The model demonstrated good discrimination (area under the ROC curve 0.782) with sensitivity 75%, specificity 79.4%, and negative predictive value 96.2%.

Conclusion

HER2-positive GBCs display characteristic CT findings that can be utilized for noninvasive diagnosis with robust predictive performance.

Objectives

This study aimed to analyze preoperative multimodal magnetic resonance images of patients with rectal cancer using habitat-based, intratumoral, peritumoral, and combined radiomics models for non-invasive prediction of perineural invasion (PNI) status.

Methods

Data were collected from 385 pathologically confirmed rectal cancer cases across two centers. Patients from Center 1 were randomly assigned to training and internal validation groups at an 8:2 ratio; the external validation group comprised patients from Center 2. Tumors were divided into three subregions via K-means clustering. Radiomics features were isolated from intratumoral and peritumoral (3 mm beyond the tumor) regions, as well as subregions, to form a combined dataset based on T2-weighted imaging and diffusion-weighted imaging. The support vector machine algorithm was used to construct seven predictive models. intratumoral, peritumoral, and subregion features were integrated to generate an additional model, referred to as the Total model. For each radiomics feature, its contribution to prediction outcomes was quantified using Shapley values, providing interpretable evidence to support clinical decision-making.

Results

The Total combined model outperformed other predictive models in the training, internal validation, and external validation sets (area under the curve values: 0.912, 0.882, and 0.880, respectively).

Conclusions

The integration of intratumoral, peritumoral, and subregion features represents an effective approach for predicting PNI in rectal cancer, providing valuable guidance for rectal cancer treatment, along with enhanced clinical decision-making precision and reliability.

Objective

Poor magnetic resonance imaging (MRI) quality can lead to missed prostate cancer diagnoses. Our institution had variability in pre-MRI patient preparation, limited collaboration between radiologists and technologists, and no formal, routine image quality auditing process. The Prostate Imaging Quality system version 1 (PI-QUAL v1) is an established framework for assessing prostate MRI quality. Our quality improvement team aimed to increase the percentage of exams that received a PI-QUALv1 score of ≥ 4 and the percentage with at least one Diffusion Weighted Imaging (DWI) sequence rated optimal.

Methods

In partnership with the American College of Radiology (ACR) Learning Network, we introduced numerous interventions to improve our baseline workflow. During our Intervention Phase (May– September 2023), our team standardized pre-MRI patient instructions and documentation regarding nil by mouth (NPO) status and bladder emptying. MRI technologists received image quality education and were involved in the image scoring process. Our team also used weekly PI-QUALv1 scoring criteria which was later embedded into the reporting system. Interventions were maintained during a Sustainment Phase (October 2023– January 2024).

Results

1206 MRI prostate exams were audited. From the Pre-intervention to Sustainment Phase, the percentage of exams with PI-QUAL score ≥ 4 increased from 90% (95% CI: 87–94%) to 97% (95% CI: 95–99%). The percentage of exams meeting DWI criteria increased from 77% (95% CI: 74–79%) to 83% (95% CI: 78–88%). Team members reported that integrating scoring into the reporting system saved time. Patients and staff reported that standardized instructions led to a better understanding of how patient preparation improves image quality.

Conclusions

Consistent patient preparation, team collaboration, and standardized quality scoring led to promising quality trends without using enema. PI-QUAL scores and DWI quality reached the upper limits of performance previously described by the ACR Learning Network Prostate MR QI Collaborative.

Graphical abstract

Objectives

We aimed to compare Bosniak III and IV cystic renal masses (CRM) using Bosniak classification version 2005 (BC-v2005) versus BC-v2019 and analyze radiological findings and patterns of benignity and malignancy.

Methods

We retrospectively reviewed all Bosniak III-IV CRMs using BC-v2005 at our center with four-phase CT confirmed pathology, between January 2014 and June 2019. Two radiologists independently re-evaluated each lesion using both BCs, including findings of benignity and malignancy. Bosniak III-IV CRMs were classified in four radiological patterns: inflammatory (CRM-IP), cystic nephroma (CRM-CNP), papillary (CRM-PP), and clear cell (CRM-CCP). Characteristics of patterns were compared.

Results

Out of 97 patients, 57 Bosniak III-IV CRMs met the inclusion criteria. Twenty-four (42.10%) lesions were reclassified as solid tumours using BC-v2019. Any lesion was downgraded to IIF or lower. Nine (15.78%) lesions were upgraded to Bosniak III-IV CRMs by BC-v2019, despite benign lesions. The presence of acute-angle nodules was the only statistically significant sign of malignancy (p < 0.01). All benign lesions, not considered solid, were included in the two patterns of benignity, characterized by thickened septa and/or wall and without acute-angle nodules. All malignant CRMs presented two patterns of malignancy, proposed as acute-angle nodules. All clear cell carcinomas presented with hyperenhancing acute-angle nodules in the arterial phase. Pattern classification was better for BC-v2005 and BC-v2029, differentiating benign and malignant CRM.

Conclusion

The proposed four radiological patterns improved performance than BC-v2019 in classifying benign lesions (CRM-IP and CRM-CNP) and malignant ones (CRM-PP and CRM-CCP), with wall and septa thickening and acute-angle nodules as relevant biomarkers.

Purpose

This meta-analysis systematically evaluated the diagnostic performance of artificial intelligence (AI) based on contrast-enhanced computed tomography (CECT) in detecting pancreatic ductal adenocarcinoma (PDAC).

Methods

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy (PRISMA-DTA) guidelines, a comprehensive literature search was conducted across PubMed, Embase, and Web of Science from inception to March 2025. Bivariate random-effects models pooled sensitivity, specificity, and area under the curve (AUC). Heterogeneity was quantified via I² statistics, with subgroup analyses examining sources of variability, including AI methodologies, model architectures, sample sizes, geographic distributions, control groups and tumor stages.

Results

Nineteen studies involving 5,986 patients in internal validation cohorts and 2,069 patients in external validation cohorts were included. AI models demonstrated robust diagnostic accuracy in internal validation, with pooled sensitivity of 0.94 (95% CI 0.89–0.96), specificity of 0.93 (95% CI 0.90–0.96), and AUC of 0.98 (95% CI 0.96–0.99). External validation revealed moderately reduced sensitivity (0.84; 95% CI 0.78–0.89) and AUC (0.94; 95% CI 0.92–0.96), while specificity remained comparable (0.93; 95% CI 0.87–0.96). Substantial heterogeneity (I² > 85%) was observed, predominantly attributed to methodological variations in AI architectures and disparities in cohort sizes.

Conclusions

AI demonstrates excellent diagnostic performance for PDAC on CECT, achieving high sensitivity and specificity across validation scenarios. However, its efficacy varies significantly with clinical context and tumor stage. Therefore, prospective multicenter trials that utilize standardized protocols and diverse cohorts, including early-stage tumors and complex benign conditions, are essential to validate the clinical utility of AI.

Purpose

To establish a reduced field-of-view (rFOV) DWI protocol on a 3.0T high-field MRI platform, explore the correlation between ADC histogram parameters derived from rFOV VOI and rectal cancer T/N staging, lymphatic vessel invasion, peripheral nerve invasion, histological differentiation, and Ki-67.

Methods

Prospectively included 47 pathologically confirmed rectal adenocarcinoma patients (March 2023-December 2024) for analyzing rFOV VOI histogram parameters. FireVoxel extracted ADC histogram parameters from tumor VOIs. Spearman correlation assessed relationships between parameters and T/N staging, lymphatic vessel / peripheral nerve invasion, histological differentiation, and Ki-67. ROC curves evaluated diagnostic efficacy.

Results

T-stage linked to Max, Min, Perc.1%, Perc.5%, Perc.10%, Perc.25% (P < 0.05); N-stage to Max, Min, Variance, Skewness, Kurtosis, Perc.99%; lymphatic vessel invasion to Max, Kurtosis, P99% (highest AUC: 0.773, combined diagnosis); peripheral nerve invasion to Max, Variance, Skewness, Kurtosis, Perc.95%, Perc.99% (highest AUC: 0.921, combined diagnosis); histological differentiation to Max, Variance, Skewness, Kurtosis, Perc.90%, Perc.95%, Perc.99% (highest AUC: 0.824, combined diagnosis); Ki-67 to Variance, Skewness, Perc.90%, Perc.95%, Perc.99% (highest AUC: 0.814, combined diagnosis). All P < 0.05.

Conclusions

ADC histogram analysis based on rFOV VOI reveals correlations between histogram parameters and pathological features of rectal cancer, highlighting their potential value as imaging biomarkers for staging and prognosis.

Study objective

The aim of this study was to assess the impact of endometriosis surgery in the retrocervical area and/or USL on the thickness of the posterior uterine isthmus, and subsequently the potential risk of posterior uterine rupture during labor.

Design

Retrospective observational study.

Setting

Improving the management of patients with deep infiltrating endometriosis, keeping in mind potential obstetric consequences.

Patients

All endometriosis patients treated surgically at the Croix-Rousse University Hospital during the study period for the resection of a lesion on one or both USL and/or in the retrocervical region were included if preoperative and postoperative MRI images were available in their medical record.

Interventions

Evaluating the variation of posterior uterine isthmus thickness on MRI images after retrocervical surgery for deep infiltrating endometriosis. Searching for predictive markers of posterior uterine isthmus thinning.

Measurements and mean results

Forty-two patients were included between 2016 and 2020. We observed a significant decrease in the sagittal thickness of the median posterior uterine isthmus after surgery (-2.25 mm, 95% CI -3.18 to -1.32, p<.01). A significant association was also found between the thinning of the median posterior uterine isthmus on the sagittal section and preoperative urinary symptoms (p =.04), a history of digestive resection with ileostomy (p =.02) and the presence of post-voiding residue ≥ 100 cc postoperatively (p<.01). We observed 6 pregnancies in 4 patients (9.5%). Four pregnancies were carried to term with cesarean delivery.

Conclusion

The resection of retrocervical endometriosis is associated with postoperative thinning of the posterior uterine isthmus, which seems positively associated with the complexity of the rest of the surgery.