Insights into Imaging最新文献

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.

Objective: The study sought to develop and validate an MRI-based deep learning radiomics (DLR) nomogram for preoperative prediction of vessels encapsulating tumor clusters (VETC) and recurrence-free survival (RFS) in hepatocellular carcinoma (HCC).

Materials and methods: The dual-center study retrospectively enrolled 625 HCC patients who underwent preoperative Gd-EOB-DTPA-enhanced MRI, including training (n = 296), internal (n = 126), and external (n = 203) test sets. Clinical-radiologic characteristics were selected to develop a clinical-radiologic model. Habitat radiomics and deep learning (DL) features were extracted and selected to develop the habitat radiomics and DL models using the machine learning classifiers. The DLR nomogram model was ultimately constructed by integrating univariate-selected clinical-radiologic characteristics with habitat radiomics and DL scores. Both univariable and multivariable Cox regression analyses were performed to identify independent prognostic factors and develop a prognostic model for RFS.

Results: In the external test set, the DLR nomogram model yielded a higher area under the curve (AUC) than the clinical-radiologic model (0.752 vs 0.678; p = 0.004), while habitat radiomics (0.750) and DL models (0.748) showed comparable performance (both p > 0.05). The DLR nomogram consistently demonstrated the higher F1-scores across all three sets. The prognostic model incorporating AFP (hazard ratio (HR), 1.628 [95% CI: 1.113-2.380]; p = 0.012) and DLR score (1.279 [1.051-1.557]; p = 0.014) achieved C-indexes of 0.679 and 0.642 for RFS in the internal and external test sets.

Conclusion: The DLR nomogram model helps predict VETC in HCC and assess the risk for RFS.

Critical relevance statement: Interpretable deep learning radiomics nomogram model provides clinicians with more precise technical support for preoperative prediction of VETC status and RFS in HCC, potentially aiding in clinical decision-making and follow-up strategies.

Key points: Vessels encapsulating tumor clusters (VETC) is a critical predictor of aggressive hepatocellular carcinoma. The deep learning radiomics (DLR) nomogram model helps predict VETC, and the DLR score serves as an independent prognostic factor for recurrence-free survival. The model demonstrated favorable interpretability through the SHAP method.

Magnetic resonance imaging (MRI) of the neuroaxis is prone to a variety of artifacts. Familiarity with these artifacts and their respective mitigation techniques is essential for accurate neuroradiological interpretation. In this educational review, we focus on artifacts caused by the physiological flow of cerebrospinal fluid (CSF), which are encountered commonly and, depending on the context, may be beneficial or detrimental in diagnostic decision-making. The pictorial examples provided will illustrate key cases with their practical implications. CRITICAL RELEVANCE STATEMENT: This paper highlights common CSF flow artifacts, including phase encoding artifacts, time-of-flight signal loss, entry slice phenomenon, and intravoxel dephasing, emphasizing their impact on diagnosis interpretation and mitigation strategies. KEY POINTS: CSF artifacts stem from flow dynamics, phase differences, or magnetic field interactions. Artifacts obscure or mimic pathologies, degrade image quality, or occasionally aid in diagnostic decision-making. Mitigation strategies are simple and intuitive, including modification of phase directions, employing alternate imaging sequences, and altering MRI parameters.