Insights into Imaging最新文献

Objectives: To evaluate the performance of semi-quantitative analysis versus quantitative pharmacokinetic analysis applied to ultrafast dynamic contrast-enhanced (UF-DCE) MRI in: (1) differentiating benign from malignant breast lesions, (2) molecular subtyping, and (3) predicting pathologic complete response (pCR) following neoadjuvant chemotherapy.

Materials and methods: This prospective noninferiority study enrolled 339 consecutive participants with suspected breast lesions between September 2022 and February 2024. All underwent breast DCE MRI with a temporal resolution of 4.5 s, totaling 100 phases. Using the initial 30 phases (early-phase UF-DCE), five semi-quantitative parameters were calculated: wash-in slope (WIS), time-to-peak, bolus arrival time, peak enhancement intensity (PEI), and initial area under the curve in 60 s. Furthermore, three quantitative parameters: volume transfer constant, rate constant (k_ep), and extravascular extracellular space volume, were derived from the 100-phase dataset (full time-course UF-DCE). Diagnostic performance was assessed using areas under the curve (AUC) and the DeLong test, with Pearson analysis evaluating the correlation between semi-quantitative and quantitative parameters.

Results: All semi-quantitative and quantitative parameters showed differences between benign and malignant breast lesions (p < 0.001). Semi-quantitative WIS demonstrated noninferior diagnostic performance to quantitative k_ep in differentiating benign from malignant lesions (AUC: 0.93 vs 0.92; ∆AUC = 0.02, p = 0.35). However, neither approach effectively distinguished molecular subtypes or predicted pCR (p > 0.05). Strong correlations were observed in PEI and K^trans (r = 0.75, p < 0.001).

Conclusion: Semi-quantitative analysis of early-phase UF-DCE exhibits noninferior performance to quantitative analysis of full time-course UF-DCE MRI for distinguishing benign from malignant breast lesions. Both analytical approaches showed limited utility in molecular subtyping and pCR prediction.

Critical relevance statement: Early-phase UF-DCE MRI provides a cost-effective alternative to full time-course UF-DCE MRI for differentiating benign and malignant breast lesions, demonstrating noninferior diagnostic performance with reduced scan time and no need for pharmacokinetic modeling.

Key points: Systematic comparison of early-phase UF-DCE and full time-course UF-DCE MRI for diagnosis, subtyping, and response prediction in a single breast cancer cohort remains limited. Early-phase UF-DCE MRI demonstrated noninferior diagnostic performance to full time-course UF-DCE MRI in differentiating benign and malignant breast lesions. Early-phase UF-DCE MRI is a time-efficient alternative to full time-course UF-DCE MRI for clinical implementation.

Objectives: To evaluate the technical parameters and clinical outcomes of contrast-enhanced mammography (CEM)-guided biopsy for diagnosing breast cancer in patients with suspicious lesions showing post-contrast enhancement on CEM but undetectable on standard digital mammography (DM) or ultrasound (US).

Materials and methods: A prospective study was conducted on 36 patients referred for CEM-guided biopsy based on suspicious (BI-RADS 4-5) enhancing-only lesions detected during previous CEM examinations and/or contrast-enhanced breast MRI (CE-MRI). Procedures were performed on a dedicated prone table using a vacuum-assisted biopsy device. Effectiveness parameters included success rate (lesion enhancement, diagnostic material collection, and correct clip positioning), procedure time, average glandular dose (AGD), compression force, and complication rate.

Results: From January to November 2024, 36 patients underwent CEM-guided biopsy, with a success rate of 97.2% (35/36). The median procedure time was 29 min. The AGD was 0.88 mGy (range 0.5-1.4 mGy, SD ± 0.22). The average compression force was 4.94 kg (range 2-7 kg, SD ± 1.1). Of the 35 lesions biopsied, 20 (57.1%) were masses and 15 (42.9%) non-mass enhancements, with a mean lesion size of 13.2 mm. Breast lesions were classified as BIRADS 4a (10/35), BIRADS 4b (5/35), BIRADS 4c (8/35), and BIRADS 5 (12/35). Histopathological findings showed 57.1% (20/35) of lesions were malignant. Lesion classification included 5.7% (B1), 34.3% (B2), 2.9% (B3), 31.4% (B5a), and 25.7% (B5b).

Conclusion: CEM-guided biopsy is an effective and accessible technique for targeting enhancing-only breast lesions, offering advantages over MRI-guided biopsy in terms of time, cost, and patient comfort.

Critical relevance statement: Contrast-enhanced mammography-guided biopsy is an effective and accessible technique for targeting enhancing-only breast lesions, offering advantages over MRI-guided biopsy in terms of time, cost, and patient comfort.

Key points: Contrast-enhanced mammography (CEM)-guided biopsy has recently gained traction as a reliable alternative to MRI for targeting enhancing-only lesions. This study explores the clinical implementation of CEM-guided biopsy in a prone position in our university hospital, assessing its efficacy, safety, and diagnostic accuracy. CEM-guided biopsy is a promising technique for the precise targeting of breast lesions, with advantages over MRI-guided biopsy.

Renal cell carcinoma is a prevalent malignancy affecting the urinary system and poses significant challenges in precision diagnosis and treatment. Although medical imaging technologies have been widely applied in renal cell carcinoma screening, traditional imaging diagnostics have limitations due to their high degree of subjectivity, relying primarily on the doctor's experiential judgment. The advent of radiomics presents a groundbreaking method for tackling this issue-by extracting high-throughput, deep-level information from conventional medical images to achieve a quantitative assessment of tumor characteristics. Furthermore, the fusion of radiomics and genomics has led to radiogenomics, which combines imaging features with molecular data, enabling the non-invasive evaluation of tumor biological behavior, molecular heterogeneity, and microenvironmental features, thereby providing a more detailed, accurate, and personalized assessment. In this review, we summarize the role radiomics and radiogenomics play in the diagnosis, prediction, and adjuvant treatment of renal cell carcinoma. Radiomics has demonstrated potential in classifying renal cell carcinoma subtypes, predicting patient prognosis, and forecasting disease progression. Radiogenomics further links imaging features to gene mutations and the tumor microenvironment, enabling non-invasive assessment of renal cell carcinoma biology and providing new approaches to diagnosis and treatment. CRITICAL RELEVANCE STATEMENT: By reviewing existing research, we summarize how radiomics and radiogenomics address key clinical challenges in the diagnosis and treatment of renal cell carcinoma, providing non-invasive solutions to overcome tumor heterogeneity and guide precision oncology. KEY POINTS: Renal cell carcinoma lacks reliable non-invasive biomarkers for precision diagnosis and characterization. Radiogenomics bridges imaging and molecular biology for precise predictions. Radiogenomics lacks full multi-omics integration despite data growth.

The increasing integration of artificial intelligence as medical devices (AIaMDs) within diagnostic imaging necessitates a robust understanding of associated regulatory frameworks among clinical practitioners. Despite the growing commercial availability and adoption of AIaMD, a significant awareness gap persists among radiologists regarding pertinent European Union regulations, including the Medical Device Regulation (MDR) and the novel EU AI Act, both of which lack explicit provisions tailored to AI components. This regulatory ambiguity underscores a critical need for clarified guidelines concerning "high-risk" AI classification and best practices for safe deployment within the radiological workflow. Legal responsibility for AIaMD Post-Market Surveillance (PMS) primarily rests with software providers, yet radiologists are expected to contribute to the ongoing monitoring of safety and performance. Recognizing the need to raise awareness and provide practical guidance, the European Society of Radiology (ESR) eHealth and Informatics Subcommittee, supported by the ESR AI Working Group, conducted a modified Delphi procedure involving 16 domain experts (of which 14 acted as panelists) to establish a set of shared recommendations. These aim to establish essential practices for AIaMD PMS and post-market clinical feedback (PMCF), as stipulated by the MDR and partially updated by the AI Act. This paper also provides an overview of relevant regulations to enhance awareness among all stakeholders, particularly deployers (e.g., radiologists) and providers (e.g., vendors). These recommendations represent a foundational step towards improving consistency in AIaMD deployment, providing a critical reference standard for physicians navigating the unique challenges posed by these novel technologies. CRITICAL RELEVANCE STATEMENT: Radiologists need to familiarize themselves with AIaMD EU regulations due to shared PMS responsibilities and current ambiguities. ESR recommendations aim to bridge this awareness gap, standardizing safe AI deployment and enhancing clinical feedback within medical imaging. KEY POINTS: Radiologists need a clear understanding of EU regulations for AIaMDs, as current laws lack imaging-specific guidance. There is a shared responsibility for AIaMD safety, with radiologists contributing to PMS and clinical feedback systems. The ESR provides crucial recommendations to standardize AI deployment and improve clinical feedback in imaging.

Breast screening reduces cancer-specific mortality but can also precipitate avoidable harms through over-detection of benign abnormalities and subsequent over-surveillance. Across mammography and digital breast tomosynthesis (DBT), ultrasound and magnetic resonance imaging (MRI), gains in sensitivity are often offset by reduced specificity, driving false-positive recalls, benign-biopsy burden and resource strain. Within breast imaging reporting and data system (BI-RADS)-guided decision-making, Category 3 and Category 4A trigger short-interval follow-up or biopsy despite low event rates, amplifying anxiety and cost. Artificial intelligence (AI) offers a practical route to mitigate these drawbacks. Prospective and real-world studies indicate that AI-assisted reading can maintain or improve cancer detection while lowering recall rates and workload. AI models also support finer risk stratification-particularly for BI-RADS 4 lesions-thereby reducing unnecessary interventions. This review synthesises evidence on the performance and limitations of mainstream screening technologies, delineates the multidimensional impact of over-detection, and evaluates the capacity of AI to rebalance sensitivity and specificity, optimise follow-up intervals and support risk-adapted workflows. A patient-centred, evidence-driven strategy that integrates validated AI with clearly defined decision thresholds and effective patient-provider communication can maximise benefit while minimising harm. CRITICAL RELEVANCE STATEMENT: This review critically evaluates the causes and consequences of over-detection and over-surveillance in breast cancer screening and highlights how AI can advance radiologic decision-making through improved lesion stratification and more efficient, personalised follow-up strategies. KEY POINTS: BI-RADS thresholds largely drive over-detection; refining downgrade rules for 3 and tightening biopsy in 4A may reduce unnecessary interventions without compromising cancer detection. Over-detection imposes burdens: unnecessary imaging and biopsies, psychosocial distress, economic costs, and environmental impact; its reduction enhances efficiency and patient safety. AI-assisted screening maintains or improves cancer detection while reducing recall rates and workload; it also enables risk-adapted management of BI-RADS 4A lesions, avoiding low-value procedures.

Objectives: To investigate the impact of deep learning reconstruction (DLR) on the image quality of diffusion-weighted imaging (DWI) for liver and its ability to differentiate benign from malignant focal liver lesions (FLLs).

Materials and methods: Consecutive patients with suspected liver disease who underwent liver MRI between January and May 2025 were included. All patients received conventional DWI (DWI_C) and an accelerated reconstructed DWI (DWI_DLR) in which acquisition time was prospectively halved by reducing signal averages. Image quality was compared qualitatively using Likert scores (e.g., lesion conspicuity, overall quality) and quantitatively by measuring signal-to-noise ratio of the liver (SNR_Liver) and lesion (SNR_Lesion), contrast-to-noise ratio (CNR), and edge rise distance (ERD). Apparent diffusion coefficient (ADC) values and diagnostic performance for differentiating benign from malignant FLLs were assessed.

Results: A total of 193 patients (128 males, 65 females; age range, 23-81 years) were included. For quantitative assessment, DWI_DLR demonstrated higher SNR_Liver, SNR_Lesion, CNR, and a shorter ERD (all p < 0.05). For qualitative assessment, DWI_DLR showed improved lesion conspicuity, liver edge sharpness, and overall image quality (all p < 0.01), with no significant difference in artifacts (p = 0.08). ADC values were lower with DWI_DLR for both benign and malignant FLLs (p < 0.001). In differentiating benign from malignant lesions, DWI_DLR achieved better diagnostic performance (AUC: 0.921 vs. 0.904, p < 0.05).

Conclusion: Deep learning-enhanced DWI enables a 50% reduction in acquisition time while simultaneously improving liver MRI image quality and diagnostic performance in differentiating benign from malignant FLLs.

Critical relevance statement: This study demonstrates that deep learning-based reconstruction enables faster, higher-quality liver MRI with improved diagnostic accuracy for focal liver lesions, supporting its integration into routine radiological practice.

Key points: Diffusion-weighted liver MRI commonly suffers from limited image quality and efficiency. Deep learning reconstruction substantially improves liver MRI quality while enabling significantly shorter acquisition times. Improved lesion differentiation enables more accurate clinical diagnosis of liver lesions.

Objectives: To investigate the prognostic value of lipomatous metaplasia (LM) in patients after myocardial infarction (MI) and to explore potential influencing factors of LM.

Materials and methods: A total of 1702 (mean age 59.3 ± 10.27 years, 86.08% men) patients with a history of MI who underwent coronary CT angiography (CCTA) examinations were retrospectively enrolled. The clinical endpoints were major adverse cardiovascular events (MACE). A subgroup of 240 patients who underwent CCTA and cardiac magnetic resonance (CMR) examinations within a 14-day interval was analyzed to compare the prognostic values of LM and CMR parameters and to explore influencing factors of LM.

Results: MACE occurred in 395 (23.21%) patients during a median follow-up of 45.5 months. In the entire cohort, the prevalence of LM was 46.71% on CCTA; in the subgroup, it was 51.25% on CCTA and 21.67% on CMR. LM remained a significant outcome predictor (hazard ratio (HR) 1.39, 95% confidence interval (CI) 1.12-1.73; p = 0.002) in the multivariable model. In subgroup analysis, LM on CCTA (HR 1.83, 95% CI 1.09-3.08; p = 0.023) was a stronger outcome predictor than all CMR parameters. Revascularization history (odds ratio (OR) 2.833, p = 0.006), number of diseased coronary arteries (CA) (OR 0.556, p = 0.006) and infarct size (OR 1.094, p = 0.003) remained associated with LM in the multivariable model.

Conclusion: LM was a significant outcome predictor in patients after MI and was stronger than CMR functional parameters and infarct size. Revascularization, infarct size and fewer diseased CA may be associated with LM development.

Critical relevance statement: Lipomatous metaplasia (LM) was a common complication following myocardial infarction (MI) that increased with infarct age, identifying LM and integration of LM assessment into risk stratification models for post-MI patients may be important for clinical strategy decisions.

Key points: Lipomatous metaplasia was a common complication that increased with infarct age. Lipomatous metaplasia was a significant outcome predictor in patients after myocardial infarction, stronger than CMR functional parameters and infarct size. Revascularization procedure, infarct size and fewer number of diseased coronary arteries were associated with the presence of lipomatous metaplasia.

Introduction: RADUCATION - a digital platform for radiological postgraduate training by the German Young Radiology Forum - provides learning content structured according to the German Training Curriculum, including original board exam questions. This is the first study to evaluate user statistics, experience, and preferences.

Materials and methods: Data were collected through webpage analytics and surveys. User statistics were analyzed for four periods: initial usage (05/2022-05/2023), post-introduction of theoretical board exam questions (05-11/2023), post-introduction of image-based board exam questions (12/2023-05/2024), and ongoing usage (03-12/2024). User perception surveys were conducted before (05/2023-01/2024) and after (06-08/2024) implementation of image-based board exam questions. Analyses included descriptive statistics and multivariable logistic regressions.

Results: User numbers increased steadily, with board exam questions becoming the most accessed feature. Mean monthly active user numbers increased from 372 between 05/2022-05/2023 (total users 4468), to 613 between 03/2024-12/2024 (total 15,828). Survey respondents (n = 243) consistently rated RADUCATION as valuable for board exam preparation (88.6%), for night/weekend shifts (75.6%), and for clinical routine (73.4%). Board exam preparation was more beneficial for 4th/5th-year residents (odds ratio (OR) 1.35 (95% confidence interval (95% CI): 1.15-1.59)), while shift preparation was less critical for senior than for junior residents (OR 0.77 (95% CI: 0.59-0.99)). Video- and image-based learning content were preferred, with users rating the platform highly user-friendly (86.0%) and clearly structured (88.0%).

Conclusions: RADUCATION is a valuable, widely used digital learning tool for radiology residents. Board exam questions substantially increased engagement. Its structured, peer-developed design offers a scalable model for digital postgraduate medical education across specialties and countries.

Critical relevance statement: This is the first comprehensive evaluation of RADUCATION, a peer-developed, competency-based digital learning platform for the radiology residency. Integrating original board exam questions significantly increases engagement and perceived educational value, offering a scalable model for modern postgraduate medical education.

Key points: RADUCATION, a peer-developed, competency-based learning platform for radiology residents, is a highly valued, scalable model for postgraduate medical training. Users prefer video- and image-based content, while engagement and perceived educational value increase when integrating original board exam questions. RADUCATION is perceived as user-friendly and well-structured, and is considered particularly valuable for exam preparation by senior residents.

Objectives: This study aims to establish a radiological model derived from preoperative computed tomography (CT) to predict the likelihood of papillary renal cell carcinoma (PRCC) recurrence after surgical intervention.

Materials and methods: A retrospective multicenter study initially enrolled 384 patients, with 266 eligible for analysis from four centers following partial nephrectomy or radical resection for PRCC. Twelve distinct categories of CT features were evaluated. To assess reproducibility, interobserver variability in radiological assessment was evaluated. A Cox proportional hazards model was employed to identify significant radiological predictors and construct a risk score system. The model's performance was evaluated through Harrell's Concordance Index (C-index), and its effectiveness was compared with that of several histopathologic prognostic systems.

Results: A total of 266 patients were included, comprising a training dataset from one center (n = 152) and an external validation dataset from three other centers (n = 114). Inter-reader agreement was moderate to excellent for the radiological parameters (k = 0.43-0.94). Tumor margin regularity and regional lymph node size on CT scans were found to be independently associated with tumor recurrence (subdistribution hazard ratios ranging from 5.34 to 28.11; p-values ranging from < 0.001 to 0.028) and were incorporated into the predictive model. The model demonstrated superior predictive accuracy for tumor recurrence in the validation set compared to existing prognostic systems (C-index: 0.95 vs. 0.74-0.92; p-values ranging from < 0.001 to 0.08).

Conclusion: A radiological score that combines tumor margin regularity and regional lymph node size predicts PRCC recurrence, demonstrating superior performance compared to existing prognostic systems.

Critical relevance statement: This CT-based scoring system outperforms existing models in prognostic accuracy, aiding clinicians in personalized risk stratification and optimizing treatment decisions for patients.

Key points: The preoperative CT features are associated with the prognosis of papillary renal cell carcinoma (PRCC). Tumor irregularity and lymph node size on CT scans independently predict the postoperative recurrence of PRCC. A CT scoring system that incorporates these two features demonstrates superior prognostic accuracy compared to existing models.

Objectives: This study aimed to explore a multiple-instance learning (MIL) framework incorporating radiomics features and deep learning representations to predict the invasiveness of ground-glass nodules (GGNs) in lung adenocarcinoma (LUAD) using preoperative CT.

Materials and methods: We retrospectively analyzed 1247 GGNs from 1182 LUAD patients across six hospitals, and divided them into training, validation and three test sets. According to postoperative pathological findings, the data were further classified into invasive and non-invasive subgroups. Five kinds of predictive models were developed: radiomics models, 3D deep learning models, 2.5D deep learning models, deep learning-based MIL (MIL-DL) models, and deep learning and radiomics-based MIL (MIL-DL-Rad) models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).

Results: The MIL-DL-Rad model with the ExtraTrees classifier exhibited superior and consistent performance across all sets, achieving AUCs of 0.936, 0.881, 0.868, 0.926, and 0.918 in training, validation and external test sets. In contrast, the AUC performance of MIL-DL and radiomics models was relatively unstable. The calibration curve and DCA indicated that the integrated model achieved favorable predictive efficiency and clinical predictive benefits.

Conclusions: The MIL-DL-Rad model showed better overall performance for invasiveness prediction of GGNs in LUAD patients, providing a novel perspective on feature fusion that can contribute to more accurate preoperative predictions in clinical practice.

Critical relevance statement: Multi-instance learning integrating deep learning and radiomics enhances the prediction of ground-glass nodule (GGN) invasiveness and is expected to provide optimal preoperative clinical decision-making for lung adenocarcinoma patients.

Key points: Ground-glass nodules invasiveness directly influences surgical strategies and prognosis. Multiple-instance learning framework integrates radiomics and deep learning features. Integrated model achieves superior accuracy and consistency in invasiveness prediction.