European Radiology最新文献

Objectives: To provide up-to-date consensus recommendations on the acquisition, interpretation and reporting of MRI for the primary staging of rectal cancer.

Materials and methods: A panel of twenty-six abdominal imaging experts from the European Society of Gastrointestinal and Abdominal Radiology (ESGAR) engaged in an online consensus process, led by three non-voting chairs. The process adhered to an adapted version of the RAND-UCLA appropriateness method. A total of 126 items were scored (22 general, 55 on primary staging, 49 on restaging after neoadjuvant treatment) and classified using ≥ 80% as the cut-off to establish consensus.

Results: Consensus was reached for 121 items (96%). The current manuscript addresses the resulting general recommendations and those focused on baseline staging. Key updates compared to the previous guideline editions include more detailed recommendations for image acquisition, adoption of the sigmoid take-off as a landmark to discern rectal from sigmoid cancer, updated definition of mesorectal fascia involvement by a distance of ≤ 1 mm, including involvement by irregular nodes and extramural vascular invasion; a transition to a patient-level approach for cN-category assessment with updated criteria for lateral nodes including a ≥ 7 mm size threshold, and recommendations on the limited use of DWI for primary staging.

Conclusions: These updated expert consensus recommendations serve as clinical guidelines for the primary staging of rectal cancer using MRI. Recommendations for restaging and response evaluation after neoadjuvant treatment are addressed in a separate publication.

Key points: Question Since the last ESGAR rectal imaging guideline update, the rectal cancer treatment landscape has further evolved, necessitating updates to the existing guidelines. Findings An online consensus process involving 26 panellists led to 96% consensus across 121 items discussed, including 22 general items and 55 related to primary staging. Clinical relevance Key updates related to primary staging include more detailed recommendations for image acquisition, adoption of the sigmoid take-off, refined criteria for MRF involvement, a new patient-level approach for cN-assessment, and recommendations on the limited use of DWI.

Objectives: Adrenal tumors can be functional or malignant, yet they are often overlooked in abdominal CT. This study aimed to develop and validate a fully automated deep learning model for adrenal mass detection and measurement in contrast-enhanced abdominal CT.

Materials and methods: This retrospective study included 415 CT scans with (n = 155) and without (n = 260) adrenal masses for model development (median age, 40 years; 206 men). Adrenal gland masks were automatically generated using a pretrained segmentation model, and adrenal mass masks were manually refined to train a U-Net-based segmentation network. Two secondary test sets were used for validating mass detection (external test set, n = 995) and size measurement (internal test set 2, n = 50). The external test set reflects a real-world incidence of adrenal masses (4.8%) as determined by radiologic evaluation without pathologic confirmation. The model performance was evaluated using the area under the receiver operating characteristic curve (AUC) for adrenal mass detection and the intraclass correlation coefficient (ICC) for adrenal mass measurement.

Results: In the internal test set 1 (n = 42), the AUC for classifying adrenal mass was 0.99 (95% confidence interval [CI]: 0.92-1.0), with an average Dice score of 0.812. The model achieved an AUC of 0.94 (95% CI: 0.93-0.96) with sensitivity, specificity, accuracy, and positive predictive value of 89.6%, 96.9%, 96.6%, and 59.7% (95% CI: 77.3-96.5%, 95.6-97.9%, 95.3-97.5%, and 50.6-68.2%, respectively), respectively, in the external test set. The model identified 44 of 50 adrenal masses in internal test 2. The ICCs of the predicted diameter were 0.848 (95% CI: 0.723-0.917) and 0.855 (95% CI: 0.735-0.921) for the CT-measured and pathologically measured diameters, respectively.

Conclusion: The proposed deep learning model accurately detected and measured adrenal masses in abdominal CT. The model has the potential to improve detection rates of adrenal lesions and facilitate their early management.

Clinical relevance statement: The proposed deep learning model can detect adrenal masses often missed by radiologists and accurately estimate their size, potentially improving patient management in screening, follow-up, and preoperative settings.

Key points: Adrenal masses, which may be functional or malignant, are frequently missed on abdominal CT, leading to delays in appropriate management. A deep learning model can accurately detect the adrenal masses (accuracy 96.6%) and predict the size similar to the sizes measured in CT and surgical specimens (ICC 0.848-0.855). The proposed DL algorithm may help clinicians in various clinical settings, including screening, follow-up, and preoperative evaluation of adrenal masses.

Objectives: This study aimed to evaluate the diagnostic accuracy of MRI-based Node Reporting and Data System (Node-RADS) in diagnosing lymph node metastasis (LNM) and to investigate its prognostic significance in rectal cancer (RC) patients.

Materials and methods: Patients with RC who underwent radical rectal resection (including LN dissection) without any prior anti-tumour therapy between May 2019 and April 2023 were retrospectively included. Two radiologists independently scored lymph nodes using the MRI-based Node-RADS. The diagnostic performance of Node-RADS was estimated using the area under receiver operating characteristic (ROC) curves (AUC) and compared with size criteria and MRI reports conducted by experienced radiologists. Intra- and inter-observer agreement were both assessed. Disease-free survival (DFS), which served as a key postoperative prognostic indicator, was evaluated and compared between patients with low (1-3) and high (4-5) scores.

Results: Overall, 163 patients with RC were enrolled, including 53 with LNM. There were 98 men and 65 women with a mean age of 62.6 ± 10.1 years. Node-RADS showed a larger AUC (0.912) with higher sensitivity (81%) and specificity (97.3%) compared to size criteria (75.5% and 71.8%) and MRI reports (91.0% and 41.8%). Node-RADS scores were also evaluated and correlated with the prognosis in patients who had undergone radical rectal resection. A multivariable Cox model combining Node-RADS and extramural venous invasion (EMVI) showed good predictive performance for DFS (C-index: 0.718).

Conclusion: The Node-RADS scoring system, based on MRI, enhanced both sensitivity and specificity in detecting LNM in RC patients who directly received radical rectal resection and showed potential prognostic significance for RCs.

Key points: Question What is the clinical utility of the MRI-based Node-RADS for treatment-naive RC? Findings The MRI-based Node-RADS demonstrated better diagnostic accuracy (AUC 0.912) for LNM and provided significant prognostic value for DFS in treatment-naive RC patients. Clinical relevance The MRI-based Node-RADS is a reliable diagnostic method for lymph node assessment in treatment-naive RC with higher sensitivity and specificity. It is also useful in predicting postoperative outcomes.

Objectives: Preclinical declines in body mass index (BMI) are linked to accelerated Alzheimer's disease (AD) neurodegeneration and mortality, yet the temporal relationship between premorbid BMI trajectories and AD neuropathology remains unclear. This study aims to characterize stage-specific BMI dynamics preceding mild cognitive impairment (MCI)/AD diagnosis and evaluate their bidirectional associations with core AD pathologies.

Materials and methods: This longitudinal cohort study analyzed 1570 participants (mean age 73.2 ± 6.9 years; 53% male) from the Alzheimer's Disease Neuroimaging Initiative, applied linear mixed-effect models to construct BMI trajectories, and used partial correlation analysis and cross-lagged panel model to assess bidirectional associations between BMI changes and pathological progression, including β-amyloid (Aβ), tau, and neurodegeneration.

Results: Frontotemporal Aβ deposition preceded and predicted preclinical BMI decline (β = -5.74, p = 0.003), which subsequently correlated with accelerated neurodegeneration during MCI transition, including hypometabolism (r = 0.42, p < 0.001) and gray matter atrophy (r = 0.24, p = 0.01). Post-MCI diagnosis, BMI trajectories stabilized, yet lower BMI was associated with elevated cerebrospinal fluid tau levels, regardless of AD conversion. Importantly, lower premorbid BMI at MCI diagnosis was linked to faster temporo-occipital tau accumulation (r = -0.53, p = 0.01) and temporal hypometabolism (r = 0.23, p = 0.002) during MCI-to-AD progression.

Conclusions: This study suggests a temporal relationship between BMI trajectories and AD pathology: early Aβ deposition predicts preclinical BMI decline, which exacerbates tauopathy and neurodegeneration. These findings reveal a self-reinforcing cycle wherein BMI decline reflects incipient pathology and amplifies disease progression through stage-specific mechanisms.

Key points: Question What is the association between changes in body mass index (BMI) and the pathological progression of Alzheimer's disease? Findings Early frontotemporal β-amyloid deposition predicts preclinical BMI decline, which in turn is associated with accelerated tau accumulation and neurodegeneration during symptomatic progression. Clinical relevance Monitoring BMI trajectories provides a low-cost approach to identifying individuals at high risk for Alzheimer's disease and tracking its pathological progression, highlighting the potential value of metabolic interventions during preclinical stages.

Mismatch imaging has become a key concept in neuroradiology, offering valuable insights into the pathophysiology of cerebrovascular and oncological conditions. By highlighting discrepancies between neuroimaging parameters, mismatch-based algorithms have revolutionized diagnosis, treatment planning, and patient prognosis. In stroke-related clinical scenarios, the mismatch concept is now essential in identifying candidates for thrombolysis or estimating the stroke onset time. However, the increasing use of mismatched terminology can lead to confusion, particularly when the exact mismatch target or required imaging modalities are unclear. Concerning stroke evaluation, there is a wide range of computed tomography perfusion (CT) maps and MRI sequences that are currently used for describing and determining mismatch concepts. Apart from the well-known penumbra-core mismatch related to CTP, the combination of features of different MRI sequences has provided a wide range of mismatch scenarios, such as perfusion-weighted imaging (PWI)/diffusion-weighted imaging (DWI) mismatch, magnetic resonance angiography (MRA)/DWI mismatch, susceptibility-weighted imaging (SWI)/DWI mismatch, or the DWI/FLAIR mismatch. Each one of these mismatches has its own clinical and physiopathological meaning, ranging from time-to-onset stroke estimation to selection of endovascular procedures. This article explores the different mismatch concepts used for stroke evaluation, including other related, less-used ones, focusing on their underlying physiopathology, clinical relevance, and supporting scientific evidence, all from a practical and educational perspective. KEY POINTS: Question Mismatch imaging offers transformative diagnostic insights in stroke, yet consistent definitions and standardized methodologies are essential to fully realize its clinical potential. Findings Our study reviews mismatch paradigms, outlines their pathophysiological basis, and compares clinical implications, highlighting critical limitations and the need for standardized imaging methodologies. Clinical relevance Standardizing mismatch imaging enhances diagnostic accuracy, optimizes patient selection for therapies, and improves prognostic assessment, ultimately enabling more consistent and reliable clinical decision-making in stroke.