European Journal of Radiology最新文献

Objectives

To evaluate the performance of artificial intelligence (AI) in the preoperative detection of lung metastases on CT.

Materials and methods

Patients who underwent lung metastasectomy in our institution between 2016 and 2020 were enrolled, their preoperative CT reports having been performed before an AI solution (Veye Lung Nodules, version 3.9.2, Aidence) became available as a second reader in our department.

All CT scans were retrospectively processed by AI. The sensitivities of unassisted radiologists (original CT radiology reports), AI reports alone and both combined were compared. Ground truth was established by a consensus reading of two radiologists, who analyzed whether the nodules mentioned in the pathology report were retrospectively visible on CT. Multivariate analysis was performed to identify nodule characteristics associated with detectability.

Results

A total of 167 patients (men: 62.9 %; median age, 59 years [47–68]) with 475 resected nodules were included. AI detected an average of 4 nodules (0–17) per CT, of which 97 % were true nodules. The combination of radiologist plus AI (92.4 %) had significantly higher sensitivity than unassisted radiologists (80.4 %) (p < 0.001). In 27/57 (47.4 %) patients who had multiple preoperative CT scans, AI detected lung nodules earlier than the radiologist. Vascular contact was associated with non-detection by radiologists (OR:0.32[0.19, 0.54], p < 0.001), whilst the presence of cavitation (OR:0.26[0.13, 0.54], p < 0.001) or pleural contact (OR:0.10[0.04, 0.22], p < 0.001) was associated with non-detection by AI.

Conclusion

AI significantly increases the sensitivity of preoperative detection of lung metastases and enables earlier detection, with a significant potential benefit for patient management.

Purpose

To investigate whether longitudinal changes in multiparametric MRI can predict early response to neoadjuvant chemotherapy (NAC) for HER2-positive breast cancer (BC) and to further establish quantitative models based on these features.

Methods

A total of 164 HER2-positive BC patients from three centers were included. MRI was performed at baseline and after two cycles of NAC (early post-NAC). Clinicopathological characteristics were enrolled. MRI features were evaluated at baseline and early post-NAC, as well as longitudinal changes in multiparametric MRI, including changes in the largest diameter (LD) of the tumor (ΔLD), apparent diffusion coefficient (ADC) values (ΔADC), and time–signal intensity curve (TIC) (ΔTIC). The patients were divided into a training set (n = 95), an internal validation set (n = 31), and an independent external validation set (n = 38). Univariate and multivariate logistic regression analyses were used to identify the independent indicators of pCR, which were then used to establish the clinicopathologic model and combined model. The AUC was used to evaluate the predictive power of the different models and calibration curves were used to evaluate the consistency of the prediction of pCR in different models. Additionally, decision curve analysis (DCA) was employed to determine the clinical usefulness of the different models.

Results

Two models were enrolled in this study, including the clinicopathologic model and the combined model. The LD at early post-NAC (OR=0.913, 95 % CI=0.953–0.994 p = 0.026), ΔADC (OR=1.005, 95 % CI=1.005–1.008, p = 0.007), and ΔTIC (OR=3.974, 95 % CI=1.276–12.358, p = 0.017) were identified as the best predictors of NAC response. The combined model constructed by the combination of LD at early post-NAC, ΔADC, and ΔTIC showed good predictive performance in the training set (AUC=0.87), internal validation set (AUC=0.78), and external validation set (AUC=0.79), which performed better than the clinicopathologic model in all sets.

Conclusions

The changes in multiparametric MRI can predict early treatment response for HER2-positive BC and may be helpful for individualized treatment planning.

Purpose

Calculation of extracellular volume fraction (ECV), a marker of myocardial fibrosis in cardiac magnetic resonance imaging (CMR), requires hematocrit (Hct). We aimed to correlate Hct levels with native blood T1 times, to derive a formula for estimating synthetic Hct (Hct_syn) and synthetic ECV (ECV_syn), to assess accuracy of ECV_syn and to compare our model with published formulas.

Method

In this retrospective study, a cohort of 250 CMR scans with T1 mapping (3T, MOLLI 5(3)3, endsystolic aquisition), was divided into a derivation and validation cohort. Native T1 times of the left ventricular blood pool (T1_native,midLV) were correlated with Hct levels from blood sampling within 24 h (Hct_24h) and a formula for calculation of Hct_syn was derived by linear regression.

Results

In the derivation cohort (n = 167), Hct_24h showed a good association with T1_native,midLV (r = −0.711, p < 0.001). The resulting regression equation was Hctsyn = 1/T1_native,midLV * 1355.52–0.310. In the validation cohort (n = 83), Hct_syn and Hct_24h showed good correlation (r = 0.726, p < 0.001), while ECV_syn, and ECV_24h demonstrated excellent correlation (r = 0.940, p < 0.001). ECV_syn had a minimal bias of 0.28 % and the misclassification rate (8.8 %) was comparable to the variability introduced by repeated Hct measurements (misclassification in 7.5 %). Applying published formulas in our cohort resulted in incorrect classification in up to 60 %.

Conclusion

We provide a formula for estimating Hct_syn from native blood T1 on a 3T scanner. The measurement error of ECV_syn is low and comparable to the error due to retest variability of conventional Hct. Scanner- and sequence-specific formulas should be used.

Purpose

The tumor microenvironment (TME) plays a crucial role in tumor progression and treatment response. Radiomics offers a non-invasive approach to studying the TME by extracting quantitative features from medical images. In this study, we present a novel approach to assess the stability and discriminative ability of radiomics features in the TME of vestibular schwannoma (VS).

Methods

Magnetic Resonance Imaging (MRI) data from 242 VS patients were analyzed, including contrast-enhanced T1-weighted (ceT1) and high-resolution T2-weighted (hrT2) sequences. Radiomics features were extracted from concentric peri-tumoral regions of varying sizes. The intraclass correlation coefficient (ICC) was used to assess feature stability and discriminative ability, establishing quantile thresholds for ICCmin and ICCmax.

Results

The identified thresholds for ICCmin and ICCmax were 0.45 and 0.72, respectively. Features were classified into four categories: stable and discriminative (S-D), stable and non-discriminative (S-ND), unstable and discriminative (US-D), and unstable and non-discriminative (US-ND). Different feature groups exhibited varying proportions of S-D features across ceT1 and hrT2 sequences. The similarity of S-D features between ceT1 and hrT2 sequences was evaluated using Jaccard’s index, with a value of 0.78 for all feature groups which is ranging from 0.68 (intensity features) to 1.00 (Neighbouring Gray Tone Difference Matrix (NGTDM) features).

Conclusions

This study provides a framework for identifying stable and discriminative radiomics features in the TME, which could serve as potential biomarkers or predictors of patient outcomes, ultimately improving the management of VS patients.

Objectives

This study aimed to assess the predictive performance of radiomics derived from computed tomography (CT) images of thrombus regions in predicting the risk of intracranial hemorrhage (ICH) following endovascular thrombectomy (EVT).

Materials and Methods

This retrospective multicenter study included 336 patients who underwent admission CT and EVT for acute anterior-circulation large vessel occlusion between December 2018 and December 2023. Follow-up imaging was performed 24 h post-procedure to evaluate the occurrence of ICH. 230 patients from centers A and B were randomly allocated into training and test groups in a 7:3 ratio, while the remaining 106 patients from center C comprised the validation cohort. Radiologists manually segmenting the thrombus on CT images, and the perithrombus region was defined by expanding the initial region of interest (ROI). A total of 428 radiomics features were extracted from both intrathrombus and perithrombus regions on CT images. The Mann–Whitney U test was used for feature selection, and least absolute shrinkage and selection operator (LASSO) regression was employed for model development, followed by validation using a 5-fold cross-validation approach. Model performance was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC).

Results

Among the eligible patients, 128 (38.1 %) experienced ICH after EVT. The combined model exhibited superior performance in the training cohort (AUC: 0.913, 95 % CI: 0.861–0.965), test cohort (AUC: 0.868, 95 % CI: 0.775–0.962), and validation cohort (AUC: 0.850, 95 % CI: 0.768–0.912). Notably, in the validation group, both the perithrombus and combined models demonstrated higher predictive accuracy compared to the intrathrombus model (0.837 vs. 0.684, p = 0.02; AUC: 0.850 vs. 0.684, p = 0.01).

Conclusions

Radiomics features derived from the perithrombus region significantly enhance the prediction of ICH after EVT, providing valuable insights for optimizing post-procedural clinical decisions.

Clinical relevance statement

This study highlights the importance of radiomics extracted from intrathrombus and perithrombus region in predicting intracranial hemorrhage following endovascular thrombectomy, which can aid in improving patient outcomes.

Purpose

To investigate the feasibility of deep learning (DL) based on conventional MRI to differentiate tuberculous spondylitis (TS) from brucellar spondylitis (BS).

Methods

A total of 383 patients with TS (n = 182) or BS (n = 201) were enrolled from April 2013 to May 2023 and randomly divided into training (n = 307) and validation (n = 76) sets. Sagittal T1WI, T2WI, and fat-suppressed (FS) T2WI images were used to construct single-sequence DL models and combined models based on VGG19, VGG16, ResNet18, and DenseNet121 network. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. The AUC of DL models was compared with that of two radiologists with different levels of experience.

Results

The AUCs based on VGG19, ResNet18, VGG16, and DenseNet121 ranged from 0.885 to 0.973, 0.873 to 0.944, 0.882 to 0.929, and 0.801 to 0.933, respectively, and VGG19 models performed better. The diagnostic efficiency of combined models outperformed single-sequence DL models. The combined model of T1WI, T2WI, and FS T2WI based on VGG19 achieved optimal performance, with an AUC of 0.973. In addition, the performance of all combined models based on T1WI, T2WI, and FS T2WI was better than that of two radiologists (P<0.05).

Conclusion

The DL models have potential guiding value in the diagnosis of TS and BS based on conventional MRI and provide a certain reference for clinical work.

Objectives

We conducted a systematic review and meta-analysis of current publications on the potential role of non-contrast-enhanced computed tomography (NCCT) radiomics as a prognostic indicator in patients with intracerebral hemorrhage (ICH).

Methods

We systematically searched PubMed, EMBASE, and the Web of Science from inception until January 8, 2024. Studies with NCCT-based radiomics features for predicting the prognostic outcomes of ICH patients were included. We calculated the pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under curve (AUC) values. The radiomics quality score (RQS), METhodological RadiomICs Score (METRICS), and the quality assessment of diagnostic accuracy studies (QUADAS-2) were used for quality assessment.

Results

Twenty-two studies were included. The pooled sensitivity, specificity, DOR, and AUC of radiomics models were 0.73, 0.78, 10.03, and 0.83, respectively, while on the combined radiomics models with other non-radiomics features were 0.80, 0.80, 16.28, and 0.86. Subgroup analysis showed that studies with the following covariates have a higher accuracy: single center, modified Rankin Scale (mRS) criteria for the ICH outcomes assessment, following patients for evaluation of ICH outcomes for more than a month, automatic segmentation, capturing the radiomics feature from the only intra-hematomal region, using PyRadiomic tool for features extraction, and using non-logistic regression for modeling. The quality of literature using QUADAS-2 and METRICS tools was good and was under-average using the RQS tool. No publication bias was detected.

Conclusions

Radiomics features showed moderate to high accuracy for predicting ICH prognostic outcomes. Although the QUADAS-2 and METRICS assessments indicated good quality, the radiomics pipeline quality was under-average.

Clinical Relevance

NCCT-based radiomics features can provide information about the prognostic outcomes of ICH patients after patient admission. This study exploits the value of current evidence on NCCT-based radiomics methodology in the prediction of ICH prognosis.