Insights into Imaging最新文献

Objectives: This study aims to develop a radiomics model based on carotid perivascular adipose tissue (PVAT) from CT angiography to identify histologically confirmed vulnerable plaques in patients with carotid artery stenosis (CAS).

Materials and methods: In this prospective cohort study, we enrolled patients with CAS scheduled for carotid endarterectomy between 2014 and 2023. Histological plaque assessment served as the reference standard for vulnerability. We developed three models: the PVAT attenuation model, the conventional plaque feature model, and the PVAT radiomics model using features extracted from segmented CT images and machine learning. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis across training, validation, and independent testing from three different scanners. Shapley Additive exPlanations (SHAP), a tool that quantifies the contribution of each feature to the model's predictions, was used to enhance model interpretability.

Results: We included 122 patients (mean age 66.45 years, 81.97% male, 63.11% vulnerable). In the training, validation, and testing sets, the PVAT radiomics model predicts an AUC of vulnerability of 0.945, 0.819, and 0.817, respectively, while the plaque score model showed an AUC of 0.688, 0.799, and 0.497, and the PVAT attenuation model showed an AUC of 0.667, 0.708, and 0.493, respectively. The PVAT radiomics model outperforms the PVAT attenuation model (p = 0.01) and plaque score models (p = 0.03) in the test set. SHAP analysis highlighted significant predictors such as logarithm_firstorder_RootMeanSquared.

Conclusions: The PVAT radiomics model is a promising non-invasive tool for identifying vulnerable carotid plaques, offering superior diagnostic efficacy and generalizability across different imaging equipment.

Critical relevance statement: The carotid PVAT radiomics identified histologically vulnerable plaques before surgery through an interpretable and generalizable machine-learning model, beneficial for risk stratification and surgical decision-making.

Key points: Noninvasive and effective identification of histological carotid vulnerable plaques is challenging. The PVAT radiomics outperforms conventional imaging biomarkers in identifying vulnerable plaques. The PVAT radiomic model is generalizable across scanners and interpretable, assisting clinical decision-making.

Objectives: To investigate the prognostic value of artificial intelligence (AI) quantified emphysema and interstitial lung abnormality (ILA) in patients with non-small cell lung cancer (NSCLC).

Materials and methods: This retrospective study used AI to quantify emphysema and ILA in patients diagnosed with NSCLC between January 2015 and December 2017. Associations between AI-quantified emphysema and ILA severity and overall survival (OS) were evaluated using Cox proportional hazards models. The ability of AI-quantified emphysema and ILA severity to predict OS was explored via concordance index (C-index) and area under the time-dependent receiver operating characteristic curve (AUC). Furthermore, exploratory OS analyses were performed on subgroups stratified by chronic obstructive pulmonary disease status, treatment type, and tumor-node-metastasis (TNM) staging.

Results: Of 1675 patients, 830 (49.6%) survived, and 845 (50.4%) died. Whole emphysema (mild: HR, 1.66 [95% CI: 1.26, 2.18]; p < 0.001; more than mild: HR, 2.55 [95% CI: 1.88, 3.48]; p < 0.001) and ILA (equivocal ILA: HR, 1.63 [95% CI: 1.15, 2.32]; p = 0.006; definite ILA: HR, 2.33 [95% CI: 1.61, 3.35]; p < 0.001) severity were independent prognostic factors for NSCLC, while regional emphysema and regional ILA severity were not. The model combining AI-quantified whole emphysema severity and ILA severity outperformed the TNM staging-only model in predicting NSCLC patient outcome (C-index, 0.80 vs. 0.75; AUC, 0.90 vs. 0.85).

Conclusions: Increased AI-quantified whole emphysema and ILA severity were associated with worse OS in NSCLC. The model combining AI-quantified emphysema and ILA showed improved performance for predicting patient survival versus TNM staging alone.

Critical relevance statement: AI-quantified emphysema and ILA severity are associated with NSCLC patient outcome and can provide information complementary to TNM staging for predicting NSCLC patient survival and promoting the development of individualized management strategies.

Key points: The study explores artificial intelligence (AI) quantified emphysema and interstitial lung abnormality (ILA) severity in non-small cell lung cancer (NSCLC) prognosis. The AI-quantified whole emphysema severity and ILA severity were independent prognostic factors for NSCLC patient outcome, while regional emphysema and regional ILA severity were not. AI-quantified emphysema and ILA severity may help predict the survival of NSCLC patients and help clinicians make informed treatment decisions.

Objective: Development of a preoperative mesorectal lymph node metastasis (LNM) prediction model for rectal cancer (RC) based on intratumoral and multiregional peritumoral radiomics features extracted from super-resolution multiparametric MRI.

Materials and methods: This multicenter study included preoperative MRI data from 243 rectal cancer patients (194 from center A, 49 from center B) with SR reconstruction and scoring. Radiomic features were extracted from tumor, peri-3mm and peri-5mm on SR-DWI and SR-T2WI images. The least absolute shrinkage and selection operator (LASSO) and the maximum relevance minimum redundancy (mRMR) were used for feature selection and dimensionality reduction. DWI_T2WI_INTRA, DWI_T2WI_IntraPeri3mm, DWI_T2WI_InterPeri5mm models were developed employing Logistic regression. Independent clinical risk factors identified through univariate and multivariate stepwise regression analyses were used to construct a clinical model. The optimal IntraPeri model integrated with clinical model design the combined model. Predictive performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA).

Results: Qualitative evaluation demonstrated superior scores for SR-T2WI across five metrics compared to original images (all p < 0.001). For DWI, SR images achieved significant improvements in all parameters (p < 0.001), except lesion conspicuity [median (IQR): 3 (1) vs. 3 (1)]. Comparative analysis revealed the DWI_T2WI_IntraPeri3mm model's optimal predictive performance in training, validation, and test cohorts (AUCs: 0.880, 0.735, and 0.714, respectively). The AUC of the combined model, integrating radiomic (DWI_T2WI_IntraPeri3mm) model with clinical risk factors, was 0.933, 0.829, and 0.867 in each cohort, all exceeding those of the clinical and radiomic models.

Conclusion: Using GANs-based 3D-SR of multi-sequence MRI, our multiregional prediction model for preoperative mesorectal LNM in RC demonstrated good diagnostic performance.

Critical relevance statement: The integration of super-resolution-based tumor and peritumoral 3-mm predictive model with clinical risk factors enables performance in predicting mesorectal LNM, potentially aiding clinical therapeutic decision-making.

Key points: How do tumor and peritumoral (3-5 mm) models-based SR images perform in predicting lymph node metastasis (LNM)? The DWI_T2WI_IntraPeri3mm model, when combined with clinical factors, improves diagnostic accuracy. Multiparametric, multiregional super-resolution (SR)-MRI radiomics models exhibit good performance for LNM.

Objectives: The use of AI is gaining relevance in healthcare. There is limited information regarding the views of patients on AI in healthcare. The aim of our study was to assess the views of patients on the use of AI in healthcare with an on-site questionnaire.

Materials and methods: Patients in our tertiary hospital with a diagnostic imaging appointment were invited to complete a paper-based questionnaire between December 2022 and October 2023. We asked about socio-demographic data, experience, knowledge, and their opinion on the use of AI in healthcare, focusing on the fields (1) diagnostics, (2) therapy, and (3) triage.

Results: Out of a total of 198 patients (mean age 49.41 ± 17.6 years, 99 female), 91.5% stated that they expected benefits from the implementation of AI in healthcare, although 73.4% rated their knowledge of AI as moderate to none. The majority of patients were in favour of using AI in diagnostics (87.2%) and therapy (73.1%), while only 28.2% approved its use in patient triage. 84.0% wanted to be informed about the use of AI in at least one of the mentioned areas. Participants with higher education, higher self-assessed knowledge of AI and personal experience with AI showed greater approval for AI in healthcare.

Conclusion: Our interviewed patients have a rather open attitude towards AI in healthcare, with differentiated views depending on the topic; patients are in favour of the use of AI, especially in diagnostics and to a lesser extent also for therapy support, but they reject its use for triage.

Critical relevance statement: Overall, the results emphasise the need for widespread efforts to address patient concerns about AI in healthcare, including enhancing understanding and acceptance while protecting marginalised groups. This will help clinical radiology to adopt AI more effectively.

Key points: There is limited information on patients' views of AI in healthcare, often focused on specific groups, limiting generalizability. Patients are open to AI in healthcare, supporting its use in diagnostics and therapy, but rejecting its use for triage. Overall, patients want to be informed about AI usage and participants with higher education and AI experience showed more approval.

Objectives: To evaluate the stability of radiomic features under different CT acquisition settings and investigate its impact on diagnostic model performance and generalizability.

Materials and methods: 198 patients with 1227 pulmonary nodules underwent chest CT scans using varied settings (three slice thicknesses, two reconstruction matrices, six convolution kernels, two transmission methods). 1394 radiomic features were extracted per nodule. Feature stability was evaluated using the Intraclass Correlation Coefficient (ICC, stable: ICC ≥ 0.8, intermediate stable: 0.4 < ICC < 0.8, unstable: ICC ≤ 0.4). Four diagnostic models (Full-feature, Stable, Unstable, Intermediate stable) were developed using two datasets (lung cancer screening, n = 184; clinical scenarios, n = 1192). In addition, three combination models were constructed for ablation analysis. Model performance and generalizability were assessed via fivefold cross-validation and independent test sets with different CT parameters.

Results: Slice thickness and image transmission methods had the greatest and least impacts on feature stability (7.0% and 83.0% stable features, respectively). In training and validation sets, the Full-feature and Intermediate stable models showed higher AUCs than the Stable and Unstable models (p < 0.05). However, in test sets with varying CT parameters, the Stable model maintained consistent performance (AUC: 0.693-0.728), while the Unstable model exhibited the greatest variability (AUC: 0.523-0.800). Notably, the Full-feature and Intermediate stable models largely predicted nodules as benign, exhibiting limited ability to discriminate malignant cases.

Conclusion: Radiomic feature stability is significantly affected by CT reconstruction parameters, especially slice thickness. Models based on stable features demonstrate better generalizability across varying CT settings, underscoring the importance of assessing feature stability in radiomic-based diagnostics.

Critical relevance statement: Radiomic feature stability is significantly affected by CT acquisition parameters. Stable radiomic features enhance diagnostic model consistency and reliability across diverse CT settings. Therefore, feature stability analysis and selection of stable features are crucial to enhance model generalizability and stability.

Key points: How do CT settings affect radiomic feature stability and model performance? Feature stability varies with CT parameters, but stable features enhance model generalizability. Stable feature models boost diagnostic reliability and clinical applicability.

Diagnostic errors in mammography-particularly missed or delayed breast cancer detection-have a substantial impact on patient outcomes. These misdiagnoses remain a leading cause of malpractice claims in radiology, underscoring their serious clinical and legal implications. Contributing factors to errors in breast imaging include reader-related cognitive biases, lesion characteristics, patient-specific variables, and technical limitations. To address these challenges, a systematic approach is essential. Key strategies include structured error recognition, peer review processes, and robust quality assurance programs. Educational initiatives and system-level interventions-such as structured training, continuous feedback loops, and the integration of AI-driven computer-aided detection (CAD) tools-can significantly reduce diagnostic errors and enhance accuracy in breast imaging interpretation. This article aims to highlight common pitfalls in mammography, analyze root causes, and propose practical strategies for improvement. Real-life cases of missed diagnoses are included to reinforce key learning points and support radiologists in improving diagnostic precision and improving patient care. CRITICAL RELEVANCE STATEMENT: Missed or delayed breast cancer diagnoses stem from multiple factors. A multi-pronged strategy-combining peer review, bias mitigation, education, supportive environments, and AI tools-can improve diagnostic accuracy and enhance interpretive accuracy and advance quality standards in breast imaging practice. KEY POINTS: Missed or delayed breast cancer diagnoses on mammography continue to be a significant source of diagnostic error with serious clinical and medico-legal consequences. Contributing factors to missed or delayed breast cancer diagnoses include cognitive biases, subtle lesion characteristics, patient-specific variables, and technical limitations. Structured peer review, double reading, and robust quality assurance programs can reduce interpretive variability and improve diagnostic performance. Educational initiatives and AI-driven tools, such as computer-aided detection (CAD), support error reduction and enhance accuracy in breast imaging interpretation.

Objectives: To establish a metabolic burden-based clinical-radiological model for predicting postoperative recurrence in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) at Barcelona Clinic Liver Cancer (BCLC) stages 0-A.

Materials and methods: This retrospective multi-center study included HBV-related HCC (BCLC 0-A) undergoing curative surgery. Metabolic burden was defined as the cumulative number of metabolic abnormalities. Trend test assessed dose-dependent relationship. Predictors were identified via univariate and multivariate Cox regression analyses, and a nomogram was developed. The model underwent internal validation (5-fold, 100 times cross) and external validation. Performance was evaluated using C-index, calibration curves, and decision curve analysis.

Results: The internal and external cohorts consisted of 363 patients (55.9 ± 10.7 years, 295 males) and 74 patients (55.5 ± 10.2 years, 55 males). Recurrence risk increased by 1.53 times (p = 0.049) and 1.64 times (p = 0.018) for patients with 2 and 3-4 metabolic abnormalities (ptrend = 0.022). Independent predictors included tumor burden score > 2.4 (HR = 2.40, p = 0.003), metabolic abnormalities ≥ 2 (HR = 1.49, p = 0.023), aspartate transaminase/alanine transaminase ratio > 1 (HR = 1.51, p = 0.012), albumin-bilirubin grade 2 (HR = 1.70, p = 0.020), arterial rim enhancement (HR = 1.87, p = 0.002) and mosaic appearance (HR = 1.55, p = 0.033). C-indices for predicting 2- and 5-year recurrence were 0.728 (95% CI: 0.726-0.729) and 0.674 (95% CI: 0.673-0.675) in training sets, 0.716 (95% CI: 0.711-0.720) and 0.657 (95% CI: 0.653-0.660) in internal validation sets, and 0.710 (95% CI: 0.602-0.855) and 0.683 (95% CI: 0.594-0.798) in external cohort. The model showed higher predictive efficacy (p < 0.001 for all) and better clinical net benefit compared to BCLC and CNLC staging systems in the very early/early-stage of HCCs.

Conclusion: The metabolic burden-based clinical-radiological model effectively predicts postoperative recurrence in HBV-related HCC.

Critical relevance statement: Patients with HBV-related HCC who have two or more coexisting metabolic abnormalities may have a higher risk of postoperative recurrence. The metabolic burden-based clinical-radiological model is valuable in predicting postoperative recurrence KEY POINTS: Metabolic abnormalities were dose-dependently related to the risk of postoperative recurrence. The clinical-radiological model showed well-predictive efficacy in validation cohorts. The clinical-radiological model displayed higher efficacy compared to existing staging systems for the very early/early-stage of HCCs.

Objectives: This study aimed to evaluate the efficacy of two combined ultrafast breast MRI kinetic parameters, combination 1 including time to enhancement [TTE], maximum slope [MS], and initial enhancement phase [IE phase] compared to combination 2 including relative enhancement [RE], maximum enhancement [ME], maximum relative enhancement [MRE], time to peak [TTP], and wash in rate in characterizing benign and malignant breast lesions.

Materials and methods: This prospective study included 264 female patients with 273 breast lesions. The ultrafast protocol was done using the TWIST sequence. The parameters for combination 1 were generated manually; however, the parameters for combination 2 were generated semi-automatically. The overall performance of the ultrafast protocol was compared to the conventional dynamic MRI protocol.

Results: The ultrafast protocol was obtained in 77 s. The mean interpretation time was 5 ± 2.7 and 1 ± 0.5 min for combinations 1 and 2, respectively. Combination 1 showed an AUC of 0.910, a sensitivity of 76.5% and a specificity of 90%, while combination 2 showed an AUC of 0.869, a sensitivity of 76.5%, and a specificity of 85% in differentiating benign from malignant lesions. Upon combining all parameters, the AUC, sensitivity, and specificity in discriminating between the two groups increased to 0.944, 80.4%, and 85%, respectively. Both ultrafast techniques and conventional MRI demonstrated excellent performance in discriminating between benign and malignant lesions (AUC = 0.921 vs 0.940, respectively).

Conclusion: Adding the semiautomatically generated parameters derived from ultrafast breast MRI can improve the performance in characterizing breast lesions.

Critical relevance statement: By studying ultrafast-derived semiautomatic, easily applicable parameters, we aim to reduce the acquisition and interpretation times of breast MRI without compromising performance, when used as a problem-solving modality in indeterminate breast lesions to characterize them as either benign or malignant.

Key points: Adding semiautomatic ultrafast parameters to the MS and TTE improves the overall performance in characterizing breast lesions. The combined ultrafast parameters provide the highest discriminating power between benign and malignant breast lesions. Ultrafast MRI showed comparable performance to conventional dynamic contrast-enhanced MRI in the discrimination between benign and malignant breast lesions.

Objectives: To develop multivariate models for preoperative detection of occult lymph node (LN) metastasis in gastric cancer (GC), by integrating CT-based extracellular volume (ECV) fraction and clinicopathological features, and further evaluate the prognostic value of the combined model.

Materials and methods: This retrospective study included 129 GCs with the N (-) group (n = 49) and the N (+) group (n = 80). The preoperative CT parameters (including ECV fraction), WHO types and differentiation degree based on endoscopic pathological, and 4 hematological indices were assessed. The diagnostic performance of multivariate models was evaluated by receiver operating characteristic curve analysis.

Results: The N (+) group demonstrated significantly higher proportions of poorly cohesive carcinoma and poor differentiation based on endoscope (both p < 0.001). Significantly higher CT-measured tumor area and ECV fraction were seen in the N (+) group (p < 0.001 and p = 0.008, respectively), and a significantly higher proportion of ECV value > 50% in the N (+) group (p = 0.001). The clinicopathological model, CT parameters model, and combined model yielded areas under the curves of 0.768, 0.774, and 0.843, respectively. The combined model with the high-risk group revealed a significantly shorter median recurrence-free survival compared to the low-risk group (p = 0.008).

Conclusion: The proposed preoperative combined model exhibited a promising performance for early predicting occult LN metastasis and stratifying postoperative recurrence risk in GC, by integrating CT-based ECV fraction and clinicopathological features.

Critical relevance statement: The CT-based ECV preoperative model could potentially provide valuable clinical reference for making clinical strategies in GC.

Key points: It is a great challenge for clinicians to evaluate occult lymph node (LN) status in gastric cancer (GC). The N (+) group demonstrated higher CT-based extracellular volume (ECV) fractions and tumor area, and higher proportions of poorly cohesive carcinoma and poor differentiation. This model helped preoperative detection of occult LN metastasis and stratifying postoperative recurrence risk in GC.