European Journal of Radiology Open最新文献

Objective

To build a radiomics signature based on MRI images and evaluate its capability for preoperatively identifying the benign and malignant Soft tissue neoplasms (STTs).

Materials and methods

193 patients (99 malignant STTs and 94 benign STTs) were at random segmented into a training cohort (69 malignant STTs and 65 benign STTs) and a validation cohort (30 malignant STTs and 29 benign STTs) with a portion of 7:3. Radiomics features were extracted from T2 with fat saturation and T1 with fat saturation and gadolinium contrast images. Radiomics signature was developed by the least absolute shrinkage and selection operator (LASSO) logistic regression model. The receiver that operated characteristics curve (ROC) analysis was used to assess radiomics signature's prediction performance. Inner validation was performed on an autonomous cohort that contained 40 patients.

Results

A radiomics was developed by a total of 16 radiomics features (5 original shape features and 11 were wavelet features) achieved favorable predictive efficacy. Malignant STTs showed higher radiomics score than benign STTs in both training cohort and validation cohort. A good prediction performance was shown by the radiomics signature in both training cohorts and validation cohorts. The training cohorts and validation cohorts had an area under curves (AUCs) of 0.885 and 0.841, respectively.

Conclusions

A radiomics signature based on MRI images can be a trustworthy imaging biomarker for identification of the benign and malignant STTs, which could help guide treatment strategies.

Purpose

Metastatic melanoma lymph nodes (MMLns) might be challenging to detect on MR-WBI, as both MMLns and normal lymph nodes (NLns) can show restricted water diffusion. Our purpose is to assess the potential contribution of the DIXON sequence in differentiating MMLns from NLns.

Material and methods

We followed a cohort of 107 patients with stage IIIb/c and IV skin melanoma for 32 months using MR-WBI with DIXON, STIR, and DWI/ADC sequences. We compared signal intensity (SI) values of MMLns and NLns in the four series of the DIXON sequence (in/out-of-phase, fat_only, and water_only series). The fat fraction (SI_{fat_only}/SI_in) and the long:short axis ratio of MMLns were calculated. The fat fraction was also calculated in the fatty hila of NLns.

Results

All MMLns (8 from 7 patients) showed SI_out>SI_in with a mean fat fraction of 10%. In 40 normal fatty hila (25 patients), the proportion of SI_out>SI_in was 100% and mean fat fraction was 89% (p<0.001 for fat fraction, Mann-Whitney U-test). In the cortex of NLns, a SI_out>SI_in pattern was identified in 41/113 cases from 19/40 patients. The median long:short axis ratio in MMLns was 1.13 (range 1.03–1.25).

Conclusion

The combination of three features of MMLns (SI_out>SI_in, low-fat fraction and rounded shape) might hold promise in differentiating NLns from MMLns in patients with skin melanoma. Further research is warranted due to the small number of MMLns in our cohort.

Purpose

The objective of this study was to implement a 5-minute MRI protocol for the shoulder in routine clinical practice consisting of accelerated 2D turbo spin echo (TSE) sequences with deep learning (DL) reconstruction at 1.5 and 3 Tesla, and to compare the image quality and diagnostic performance to that of a standard 2D TSE protocol.

Methods

Patients undergoing shoulder MRI between October 2020 and June 2021 were prospectively enrolled. Each patient underwent two MRI examinations: first a standard, fully sampled TSE (TSE_S) protocol reconstructed with a standard reconstruction followed by a second fast, prospectively undersampled TSE protocol with a conventional parallel imaging undersampling pattern reconstructed with a DL reconstruction (TSE_DL). Image quality and visualization of anatomic structures as well as diagnostic performance with respect to shoulder lesions were assessed using a 5-point Likert-scale (5 = best). Interchangeability analysis, Wilcoxon signed-rank test and kappa statistics were performed to compare the two protocols.

Results

A total of 30 participants was included (mean age 50±15 years; 15 men). Overall image quality was evaluated to be superior in TSE_DL versus TSE_S (p<0.001). Noise and edge sharpness were evaluated to be significantly superior in TSE_DL versus TSE_S (noise: p<0.001, edge sharpness: p<0.05). No difference was found concerning qualitative diagnostic confidence, assessability of anatomical structures (p>0.05), and quantitative diagnostic performance for shoulder lesions when comparing the two sequences.

Conclusions

A fast 5-minute TSE_DL MRI protocol of the shoulder is feasible in routine clinical practice at 1.5 and 3 T, with interchangeable results concerning the diagnostic performance, allowing a reduction in scan time of more than 50% compared to the standard TSE_S protocol.

Objectives

Computed tomography pulmonary angiography (CTPA) is the gold standard diagnostic method for patients with suspected pulmonary embolism (PE), but it has its drawbacks, including exposure to ionizing radiation and iodinated contrast agent. The present study aims to evaluate the diagnostic performance of our in-house developed non-contrast MRI protocol for PE diagnosis in reference to CTPA.

Methods

107 patients were included, all of whom underwent MRI immediately before or within 36 hours after CTPA. Additional cases examined only with MRI and a negative result were added to reach a PE prevalence of approximately 20%. The protocol was a non-contrast 2D steady-state free precession (SSFP) sequence under free-breathing, without respiratory or cardiac gating, and repeated five times to capture the vessels at different breathing/cardiac phases. The MRIs were blinded and read by two radiologists and the results were compared to CTPA.

Results

Of the 243 patients included, 47 were positive for PE. Readers 1 and 2 demonstrated 89% and 87% sensitivity, 100% specificity, 98% accuracy and Cohen’s kappa of 0.88 on patient level. In the per embolus comparison, readers 1 and 2 detected, 60 and 59/61 (98, 97%) proximal, 101 and 94/113 (89, 83%) segmental, and 5 and 2/32 (16, 6%) subsegmental emboli, resulting in 81 and 75% sensitivity respectively.

Conclusion

The repeated 2D SSFP can reliably be used for the diagnosis of acute PE at the proximal and segmental artery levels.

Purpose

This study examines periductal infiltration in intrahepatic mass-forming cholangiocarcinoma (IMCC), focusing on its importance for differentiating hepatic tumors and its influence on post-surgical survival in IMCC patients.

Methods

Eighty-three consecutive patients with IMCC (n = 43) and liver cancer whose preoperative images showed intrahepatic bile duct dilatation adjacent to the tumor for differential diagnosis from hepatocellular carcinoma (HCC) [n = 21], metastatic liver cancer (MLC) [n = 16] and combined hepatocellular-cholangiocarcinoma (cHCC-CC) [n = 3] were enrolled. CT and MRI findings of simple bile duct compression, imaged periductal infiltration, and imaged intrabiliary growth adjacent to the main tumor were reviewed. Clinicopathological and imaging features were compared in each group. The sensitivity, specificity, and odds ratio were calculated for each imaging finding of IMCC versus the other tumor groups. Overall survival was compared between cases of IMCC with and without imaged periductal infiltration.

Results

Simple bile duct compression and imaged intrabiliary growth were more frequently observed in HCC than in the others (p < 0.0001 and 0.040, respectively). Imaged periductal infiltration was observed more often in histopathologically confirmed large-duct type IMCC than in the small-duct type IMCC (p = 0.034). Multivariable analysis demonstrated that only imaged periductal infiltration (odds ratio, 50.67) was independently correlated with IMCC. Patients with IMCC who had imaged periductal infiltration experienced a poorer prognosis than those without imaged periductal infiltration (p = 0.0034).

Conclusion

Imaged periductal infiltration may serve as a significant marker for differentiating IMCC from other liver cancers. It may also have the potential to predict post-surgical outcomes in patients with IMCC.

Background

Pancreatic ductal adenocarcinoma (PDAC) is a common and lethal cancer. From diagnosis to disease staging, response to neoadjuvant therapy assessment and patient surveillance after resection, imaging plays a central role, guiding the multidisciplinary team in decision-planning.

Review aims and findings

This review discusses the most up-to-date imaging recommendations, typical and atypical findings, and issues related to each step of patient management. Example cases for each relevant condition are presented, and a structured report for disease staging is suggested.

Conclusion

Despite current issues in PDAC imaging at different stages of patient management, the radiologist is essential in the multidisciplinary team, as the conveyor of relevant imaging findings crucial for patient care.

Purpose

To develop and validate a radiomics machine learning (Rad-ML) model based on preoperative MRI to predict extrahepatic metastasis (EHM) in hepatocellular carcinoma (HCC) patients receiving transarterial chemoembolization (TACE) treatment.

Methods

A total of 355 HCC patients who received multiple TACE procedures were split at random into a training set and a test set at a 7:3 ratio. Radiomic features were calculated from tumor and peritumor in arterial phase and portal venous phase, and were identified using intraclass correlation coefficient, maximal relevance and minimum redundancy, and least absolute shrinkage and selection operator techniques. Cox regression analysis was employed to determine the clinical model. The best-performing algorithm among eight machine learning methods was used to construct the Rad-ML model. A nomogram combining clinical and Rad-ML parameters was used to develop a combined model. Model performance was evaluated using C-index, decision curve analysis, calibration plot, and survival analysis.

Results

In clinical model, elevated neutrophil to lymphocyte ratio and alpha-fetoprotein were associated with faster EHM. The XGBoost-based Rad-ML model demonstrated the best predictive performance for EHM. When compared to the clinical model, both the Rad-ML model and the combination model performed better (C-indexes of 0.61, 0.85, and 0.86 in the training set, and 0.62, 0.82, and 0.83 in the test set, respectively). However, the combined model's and the Rad-ML model's prediction performance did not differ significantly. The most influential feature was peritumoral waveletHLL_firstorder_Minimum in AP, which exhibited an inverse relationship with EHM risk.

Conclusions

Our study suggests that the preoperative MRI-based Rad-ML model is a valuable tool to predict EHM in HCC patients treated with TACE.

Introduction

MRI is negative in a large percentage of autoimmune encephalitis cases or lacks findings specific to an antibody. Even rarer is literature correlating the evolution of imaging findings with treatment timepoints. We aim to characterize imaging findings in autoimmune encephalitis at presentation and on follow up correlated with treatment timepoints for this rare disease.

Methods

A full-text radiological information system search was performed for “autoimmune encephalitis” between January 2012 and June 2022. Patients with laboratory-identified autoantibodies were included. MRI findings were assessed in correlation to treatment timepoints by two readers in consensus. For statistical analysis, cell-surface vs intracellular antibody groups were assessed for the presence of early limbic, early extralimbic, late limbic, and late extralimbic findings using the χ² test.

Results

Thirty-seven patients (female n = 18, median age 58.8 years; range 25.7 to 82.7 years) with 15 different autoantibodies were included in the study. Twenty-three (62%) patients were MRI-negative at time of presentation; 5 of these developed MRI findings on short-term follow up. Of the 19 patients with early MRI findings, 9 (47%) demonstrated improvement upon treatment initiation (7/9 cell-surface group). There was a significant difference (p = 0.046) between the MRI spectrum of cell-surface vs intracellular antibody syndromes as cell-surface antibody syndromes demonstrated more early classic findings of limbic encephalitis and intracellular antibody syndromes demonstrated more late extralimbic abnormalities.

Conclusion

MRI can be used to help narrow the differential diagnosis in autoimmune encephalitis and can be used as a monitoring tool for certain subtypes of this rare disease.

Objectives

To determine whether contrast-enhanced CT radiomics features can preoperatively predict lymphovascular invasion (LVI) and perineural invasion (PNI) in gastric cancer (GC).

Methods

A total of 148 patients were included in the LVI group, and 143 patients were included in the PNI group. Three predictive models were constructed, including clinical, radiomics, and combined models. A nomogram was developed with clinical risk factors to predict LVI and PNI status. The predictive performance of the three models was mainly evaluated using the mean area under the curve (AUC). The performance of three predictive models was assessed concerning calibration and clinical usefulness.

Results

In the LVI group, the predictive power of the combined model (AUC=0.871, 0.822) outperformed the clinical model (AUC=0.792, 0.728) and the radiomics model (AUC=0.792, 0.728) in both the training and testing cohorts. In the PNI group, the combined model (AUC=0.834, 0.828) also had better predictive power than the clinical model (AUC=0.764, 0.632) and the radiomics model (AUC=0.764, 0.632) in both the training and testing cohorts. The combined models also showed good calibration and clinical usefulness for LVI and PNI prediction.

Conclusion

CECT-based radiomics analysis might serve as a non-invasive method to predict LVI and PNI status in GC.

Pancreatic surgery is nowadays considered one of the most complex surgical approaches and not unscathed from complications. After the surgical procedure, cross-sectional imaging is considered the non-invasive reference standard to detect early and late compilations, and consequently to address patients to the best management possible. Contras-enhanced computed tomography (CECT) should be considered the most important and useful imaging technique to evaluate the surgical site. Thanks to its speed, contrast, and spatial resolution, it can help reach the final diagnosis with high accuracy. On the other hand, magnetic resonance imaging (MRI) should be considered as a second-line imaging approach, especially for the evaluation of biliary findings and late complications. In both cases, the radiologist should be aware of protocols and what to look at, to create a robust dialogue with the surgeon and outline a fitted treatment for each patient.