European Journal of Radiology Open最新文献

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.

Purpose

Programmed cell death protein-1 ligand (PD-L1) is an important prognostic predictor for immunotherapy of non-small cell lung cancer (NSCLC). This study aimed to develop a non-invasive deep learning and radiomics model based on positron emission tomography and computed tomography (PET/CT) to predict PD-L1 expression in NSCLC.

Methods

A total of 136 patients with NSCLC between January 2021 and September 2022 were enrolled in this study. The patients were randomly divided into the training dataset and the validation dataset in a ratio of 7:3. Radiomics feature and deep learning feature were extracted from their PET/CT images. The Mann-whitney U-test, Least Absolute Shrinkage and Selection Operator algorithm and Spearman correlation analysis were used to select the top significant features. Then we developed a radiomics model, a deep learning model, and a fusion model based on the selected features. The performance of three models were compared by the area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value.

Results

Of the patients, 42 patients were PD-L1 negative and 94 patients were PD-L1 positive. A total of 2446 radiomics features and 4096 deep learning features were extracted per patient. In the training dataset, the fusion model achieved a highest AUC (0.954, 95% confident internal [CI]: 0.890–0.986) compared with the radiomics model (0.829, 95%CI: 0.738–0.898) and the deep learning model (0.935, 95%CI: 0.865–0.975). In the validation dataset, the AUC of the fusion model (0.910, 95% CI: 0.779–0.977) was also higher than that of the radiomics model (0.785, 95% CI: 0.628–0.897) and the deep learning model (0.867, 95% CI: 0.724–0.952).

Conclusion

The PET/CT-based deep learning radiomics model can predict the PD-L1 expression accurately in NSCLC patients, and provides a non-invasive tool for clinicians to select positive PD-L1 patients.

Background

Kirsten rat sarcoma virus (KRAS) has evolved from a genotype with predictive value to a therapeutic target recently. The study aimed to establish non-invasive radiomics models based on MRI to discriminate KRAS from epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) mutations in lung cancer patients with brain metastases (BM), then further explore the optimal sequence for prediction.

Methods

This retrospective study involved 317 patients (218 patients in training cohort and 99 patients in testing cohort) who had confirmed of KRAS, EGFR or ALK mutations. Radiomics features were separately extracted from T2WI, T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (T1-CE) sequences. The maximal information coefficient and recursive feature elimination method were used to select informative features. Then we built four radiomics models for differentiating KRAS from EGFR or ALK using random forest classifier. ROC curves were used to validate the capability of the models.

Results

The four radiomics models for discriminating KRAS from EGFR all worked well, especially DWI and T2WI models (AUCs: 0.942, 0.942 in training cohort, 0.949, 0.954 in testing cohort). When KRAS compared to ALK, DWI and T2-FLAIR models showed excellent performance in two cohorts (AUCs: 0.947, 0.917 in training cohort, 0.850, 0.824 in testing cohort).

Conclusions

Radiomics classifiers integrating MRI have potential to discriminate KRAS from EGFR or ALK, which are helpful to guide treatment and facilitate the discovery of new approaches capable of achieving this long-sought goal of cure in lung cancer patients with KRAS.

Purpose

To evaluate artificial intelligence-based computer-aided diagnosis (AI-CAD) for screening mammography, we analyzed the diagnostic performance of radiologists by providing and withholding AI-CAD results alternatively every month.

Methods

This retrospective study was approved by the institutional review board with a waiver for informed consent. Between August 2020 and May 2022, 1819 consecutive women (mean age 50.8 ± 9.4 years) with 2061 screening mammography and ultrasound performed on the same day in a single institution were included. Radiologists interpreted screening mammography in clinical practice with AI-CAD results being provided or withheld alternatively by month. The AI-CAD results were retrospectively obtained for analysis even when withheld from radiologists. The diagnostic performances of radiologists and stand-alone AI-CAD were compared and the performances of radiologists with and without AI-CAD assistance were also compared by cancer detection rate, recall rate, sensitivity, specificity, accuracy and area under the receiver-operating-characteristics curve (AUC).

Results

Twenty-nine breast cancer patients and 1790 women without cancers were included. Diagnostic performances of the radiologists did not significantly differ with and without AI-CAD assistance. Radiologists with AI-CAD assistance showed the same sensitivity (76.5%) and similar specificity (92.3% vs 93.8%), AUC (0.844 vs 0.851), and recall rates (8.8% vs. 7.4%) compared to standalone AI-CAD. Radiologists without AI-CAD assistance showed lower specificity (91.9% vs 94.6%) and accuracy (91.5% vs 94.1%) and higher recall rates (8.6% vs 5.9%, all p < 0.05) compared to stand-alone AI-CAD.

Conclusion

Radiologists showed no significant difference in diagnostic performance when both screening mammography and ultrasound were performed with or without AI-CAD assistance for mammography. However, without AI-CAD assistance, radiologists showed lower specificity and accuracy and higher recall rates compared to stand-alone AI-CAD.