European Radiology最新文献

Objective: Obstructive hydrocephalus is a critical radiographic finding requiring emergent treatment. Its identification on head CT by an AI model could facilitate sooner life-saving interventions, although there are common co-occurring findings, including intracranial hemorrhage, that can confound this interpretation. This external validation assessed the accuracy of an AI model at identifying obstructive hydrocephalus, including in the presence or absence of other findings.

Materials and methods: This retrospective cohort included 177 thin (≤ 1.5 mm) series and 194 thick (> 1.5 and ≤ 5 mm) series from 200 non-contrast head CT cases. These cases were obtained from patients aged ≥ 18 years at 5 hospitals in the United States. Each case was interpreted independently by up to three neuroradiologists. Each series was then interpreted by the AI model.

Results: The AI model performed with an area under the curve of 0.988 (95% confidence interval (CI): 0.971-0.998) on thin series and 0.986 (95% CI: 0.969-0.997) on thick series. These results were broadly maintained in subgroups for the presence or absence of intracranial hemorrhage, parenchymal abnormality, and ventricular drain, and across demographic and scanner manufacturer subgroups.

Conclusions: The AI model accurately identified obstructive hydrocephalus in this dataset. Its performance in subgroup analyses reflected its robustness.

Key points: Question Can an artificial intelligence model accurately identify obstructive hydrocephalus on head computed tomography, including in the presence or absence of common co-occurring imaging findings? Findings This model accurately identified obstructive hydrocephalus on thin and thick series, including in the presence or absence of intracranial hemorrhage, parenchymal abnormality, and ventricular drain. Clinical relevance This model could assist with triaging abnormal cases, enabling earlier identification and management of obstructive hydrocephalus. Its maintained performance with or without co-occurring findings suggests it specifically identifies obstructive hydrocephalus rather than these findings.

Objectives: Microvascular invasion is a strong prognostic factor in hepatocellular carcinomas. The aim of our study was to assess the diagnostic value of mechanical parameters measured with compression MR elastography to detect microvascular invasion in hepatocellular carcinomas.

Materials and methods: In this prospective preoperative MR elastographic study, consecutive patients with hepatocellular carcinomas, scheduled for tumor surgical resection, were included. The tumor parameters assessed with MR elastography were the basal visco-elastic parameters (storage modulus, loss modulus, and phase angle, reflecting elasticity, viscosity and visco-elastic ratio) during expiration and inspiration, and the tumor stiffening slope during compression induced by respiration, reflecting non-linear elasticity. Microvascular invasion was determined with histopathological examination of resected tumors. Diagnostic performance of MR elastography was assessed with area under the receiver operating curve (AUC) analysis.

Results: The final study group consisted of 53 patients with complete surgical resection, MR elastography and histological data, including 31 patients with microvascular invasion. Compression stiffening slope and storage modulus difference between inspiration and expiration were significantly higher in hepatocellular carcinomas without than with microvascular invasion (p < 0.001 and p = 0.03, respectively). Among clinical, morphological and biomechanical imaging features, the MR elastography compression stiffening slope (p = 0.004) and histological WHO differentiation (p = 0.02-0.03) were the only independent determinants of hepatocellular carcinoma microvascular invasion. In contrast to basal biomechanical parameters, the compression stiffening slope had high diagnostic performance for detecting microvascular invasion (AUC_{compression stiffening} = 0.83, p < 0.001).

Conclusion: Our results suggest that the compression stiffening slope at MR elastography is useful to diagnose microvascular invasion in patients with hepatocellular carcinomas.

Key points: Question Because non-invasive imaging markers of hepatocellular microvascular invasion are lacking, the development of new MRI markers is advisable. Findings In our MR elastography study, respiration-induced tumor stiffening, in contrast to basal visco-elastic parameters, had good accuracy for diagnosing hepatocellular carcinoma microvascular invasion. Clinical relevance Our results in patients with hepatocellular carcinomas suggest that the non-invasive measurement of MR elastography tumor compression stiffening slope may assess microvascular invasion.

Objectives: This study developed an automated AI-based method for accurate image reconstruction, stenosis detection and plaque calculation in high-resolution magnetic resonance vessel wall imaging (HR-MRVWI) and compared its performance with radiologists.

Materials and methods: A deep learning algorithm trained on HR-MRVWI was collected retrospectively from three tertiary hospitals. An independent test set was collected prospectively at another hospital. Model performance was evaluated via the Dice similarity coefficient, average centerline distance and average surface distance in centerline extraction and vessel wall segmentation. Two radiologists reviewed the reconstructed images in randomized order to determine whether the quality matched the clinical diagnosis. The stenosis diagnosis and plaque calculation of the algorithm were compared with the ground truth of the consensus by two radiologists. The relationships of the calculated parameters with plaque vulnerability were also analyzed.

Results: 476 patients (mean age 61 years ± 15 [SD], 286 men) were evaluated. The accuracy of image reconstruction in the independent test set was 92.3%. The consistency between the radiologists and the deep learning-assisted algorithm for stenosis detection was 0.89 (95% CI: 85.4, 90.2) in ≥ 50% stenosis. The accuracies of algorithm in normalized wall index, eccentricity and remodeling indices were 0.94, 0.83 and 0.87. The normalized wall index was highly related to plaque vulnerability. The AI-assisted in diagnosis and vessel wall analysis, which reduced the time from 32.0 ± 11.8 to 12.9 ± 4.3 min (p < 0.001).

Conclusion: A deep learning algorithm for HR-MRVWI interpretation could achieve image reconstruction, vessel stenosis and plaque calculation, which has satisfactory diagnostic performance.

Key points: Question Can a deep learning system achieve image reconstruction, stenosis diagnosis and plaque calculation in high-resolution MR vessel wall imaging (HR-MRVWI)? Findings The overall time reduced from 32.0 ± 11.8 to 12.9 ± 4.3 min (p < 0.001) with the aid of the system. Clinical relevance This effective deep learning system has great potential for processing head and neck HR-MRVWI images; it assists radiologists' workloads and saves considerable time in hospitals. Additionally, it provides plaque-related parameters automatically for the evaluation of atherosclerosis patients.

Objectives: To assess the diagnostic accuracy of chest CT for rib fractures in live children investigated for suspected physical abuse (SPA), using initial and follow-up chest radiographs (CXRs) as the reference standard.

Materials and methods: A retrospective 10-year (September 2011-2021) multicentre search was performed for children less than two years of age who received CXRs and chest CT for SPA. Nineteen consultant radiologists independently read the images: Round 1 (initial CXRs only), Round 2 (CTs only) and Round 3 (initial and follow-up CXRs). No reporter performed Round 3 before Round 1 or 2. Radiologists reported the presence of rib fractures, fracture age, fracture location and confidence level. CT diagnostic accuracy (sensitivity, specificity, and accuracy) was calculated per patient, per rib and per specific location along the rib arc.

Results: A total of 64 patients (36 boys) with a median age of 2 months were included and assessed by 19 independent consultant radiologists. Patient level analysis: CT sensitivity = 90.6% (95% confidence interval [CI]: 88.2-92.6), specificity = 74.2% (95% CI: 70.2-78.0). Rib level analysis: CT sensitivity = 85.6% (95% CI: 84.1-87.0), specificity = 94.16% (95% CI: 93.8-94.4). Location level analysis: CT sensitivity = 75.7% (95% CI: 74.0-77.4), specificity = 97.09% (95% CI: 96.9-97.2).

Conclusion: Chest CT confers accurate rib fracture detection in live children with SPA, with the potential to replace the current standard of performing six CXRs as part of initial and follow-up imaging for SPA.

Key points: Question What is the diagnostic performance of chest CT in detecting rib fractures in live children with SPA, using CXR as a reference standard? Findings Chest CT showed 90.6% sensitivity and 74.2% specificity for detecting rib fractures on patient-based analysis, with 79.7% sensitivity for posterior rib fractures. Clinical relevance Chest CT accurately detects rib fractures in children investigated for SPA and may serve as an alternative to initial and follow-up CXR, supporting timely clinical assessment and management.

Objective: To evaluate the safety and efficacy of intravascular lithotripsy (IVL)-assisted endovascular revascularisation in patients with chronic mesenteric ischaemia (CMI) and heavily calcified mesenteric artery stenoses.

Materials and methods: In this single-centre retrospective study (May 2020-June 2025), consecutive patients with symptomatic CMI, ≥ 50% mesenteric artery stenosis, and moderate-to-severe calcification on CT angiography underwent IVL-assisted endovascular revascularisation. Outcomes included technical success (successful IVL with ≤ 30% residual stenosis after any adjunctive therapy), moderate-to-severe adverse events (AEs), symptom recurrence, clinically driven target vessel revascularisation (CD-TVR), patency, and survival. Kaplan-Meier analysis assessed patency and survival at 6 and 12 months.

Results: Fifty-one patients (median age, 71.5 years; 51% women) underwent treatment of 57 arteries (median stenosis, 72.0%; 96.5% moderate-to-severe calcification). IVL was followed by stenting in 53 de-novo lesions (47 bare-metal, 6 covered), and balloon angioplasty in 4 lesions (3 de-novo, 1 in-stent restenosis). Technical success was 93.0%, with predilatation required in 45.6% of vessels. Median residual stenosis was 16.7% (IQR 11.7), and median lumen gain was 3.5 mm (IQR 2.1). Moderate-to-severe AEs occurred in 27.5% of patients. Two patients were lost to follow-up. During a median follow-up of 578.0 days (IQR 529.5), symptom recurrence occurred in 18.4% of patients, and CD-TVR was required in 16.3%. Primary clinical patency was 93.4% at 6 months and 91.0% at 12 months. Survival rates were 91.7% and 89.4% at 6 and 12 months, respectively; mesenteric ischaemia-related mortality was 2.0%.

Conclusion: IVL is a safe and effective vessel preparation strategy for heavily calcified mesenteric arteries, facilitating endovascular revascularisation in CMI.

Key points: Question Can vessel preparation with intravascular lithotripsy reduce the rate of endovascular treatment failure associated with moderate-to-severe calcification in mesenteric artery stenosis without amplifying procedural risks? Findings Calcium modification with intravascular lithotripsy prior to stenting yielded high technical and clinical success with favourable lumen gain, safety profile, and durable patency. Clinical relevance Adjunctive intravascular lithotripsy is a valuable strategy to mitigate the challenges of calcification in mesenteric artery stenosis, achieving high technical and clinical success while preserving procedural safety, thereby broadening treatment feasibility and improving outcomes in complex disease.

Objectives: Prostate cancer (PCa) diagnosis is increasingly guided by imaging, with ultrasound (US) emerging as a cost-effective and widely accessible modality. This study develops a deep learning-based classifier predicting the presence of clinically significant (cs)PCa using quantitative features extracted from 3D multiparametric (mp)US.

Materials and methods: A multicenter prospective cohort of 327 patients with suspicion of PCa underwent transrectal 3D mpUS scanning, including dynamic contrast-enhanced US and shear-wave elastography. Acquisitions were registered to 3D histology from radical prostatectomy, which served as the reference standard for the presence of csPCa. Voxels within lesions with International Society of Urological Pathology (ISUP) Grade Group ≥ 2 were considered malignant, and the rest were benign. A 3D deep learning classifier was trained on quantitative mpUS features to detect csPCa. The classifier was trained and internally evaluated on 250 patients and externally evaluated on 77 patients acquired later. Classifier performance was evaluated per voxel using the area under the receiver operating characteristic curve (ROC AUC).

Results: Using quantitative mpUS features from 327 patients, the classifier achieved a ROC AUC of 0.87 (95% CI: 0.85-0.89) on the internal evaluation set, using 7-fold cross-validation. On the external evaluation cohort, the classifier achieved a ROC AUC of 0.88 (95% CI: 0.87-0.89).

Conclusion: The proposed classifier accurately detects csPCa using quantitative features from 3D mpUS and generalizes well to the external dataset. These results support mpUS as a promising, cost-effective tool for csPCa diagnosis.

Key points: Question: Can quantitative features extracted from 3D multiparametric ultrasound (mpUS) reliably detect clinically significant prostate cancer (csPCa), enabling more accessible and affordable diagnosis?

Findings: Predicting csPCa using quantitative multiparametric ultrasound features achieved an area under the receiver operating characteristic curve of 0.87, increasing to 0.88 when externally evaluated.

Clinical relevance: Our proposed deep learning-based classifier using quantitative 3D mpUS features accurately detects csPCa, as validated on the largest mpUS prostate dataset to date. This opens the door to ultrasound as an accurate, cost-effective method for csPCa detection.

Objectives: To develop and validate a multimodal deep learning model integrating clinical data, contrast-enhanced CT, and laryngoscopic images for differentiating early-stage (I-II) from advanced-stage (III-IV) laryngeal squamous cell carcinoma (LSCC).

Materials and methods: This retrospective multicenter study included 450 patients with pathologically confirmed LSCC from two Chinese medical centers. All patients had contrast-enhanced CT, white-light laryngoscopy, and clinical records. They were divided into training (n = 235), internal validation (n = 101), and external validation (n = 114) cohorts. Three single-modality models (CT-based deep learning [CT-DL], laryngoscopy-based multiple instance learning [L-MIL], and a clinical logistic regression model [CL]) and their combinations were compared. A feature-level fusion strategy was applied, and the final integrated multimodal model (CL + CT + L) was built using a stochastic gradient descent (SGD) classifier. Performance was evaluated by AUC, accuracy, sensitivity, specificity, calibration, and decision curve analysis (DCA), with prognostic value assessed by Kaplan-Meier and concordance index (C-index).

Results: A total of 450 patients were included (median age, 62 years [range, 31-88]; 365 men). The integrated multimodal model achieved AUCs of 0.902 (0.833-0.954) in the internal cohort and 0.888 (0.826-0.944) in the external cohort, outperforming all single- and dual-modality models (p < 0.05). Calibration and DCA confirmed strong consistency and clinical utility. The model categorized patients into distinct risk groups, which exhibited notable differences in progression-free survival (C-index = 0.584, p = 0.036).

Conclusion: The integrated multimodal model showed high accuracy and generalizability for preoperative LSCC staging and may aid individualized treatment planning.

Key points: Question Can a multimodal deep learning model combining clinical, CT, and laryngoscopic data improve preoperative staging accuracy of LSCC? Findings The integrated multimodal model achieved higher diagnostic accuracy and provided reliable prognostic stratification compared with conventional approaches. Clinical relevance This multimodal model offers a non-invasive, accurate, and generalizable tool for LSCC staging, supporting individualized treatment planning and enhancing patient management.