Academic Radiology最新文献

Background: Freehand computed tomography (CT)-guided percutaneous needle puncture can be inaccurate, especially for small or difficult thoracoabdominal lesions. Automatic gantry-mounted laser navigation projects the planned trajectory onto the patient, offering a low-cost alternative to electromagnetic (EM) or robotic guidance systems.

Purpose: To determine whether laser guidance improves the accuracy and efficiency of CT-guided punctures compared to the conventional freehand technique.

Materials and methods: In this multicenter randomized trial, 170 adults with thoracic or abdominal lesions ≥10mm were assigned to laser-guided intervention (n = 85) or freehand control (n = 85). The primary endpoint was successful lesion access with ≤2 needle repositionings. Secondary endpoints included needle-tip error, number of CT scans, puncture time, and complications.

Results: Baseline characteristics were comparable between groups. Laser guidance increased the successful puncture rate to 91.4% versus 37.3% with freehand (P<.001) and reduced mean targeting error (2.1±0.9 mm vs 3.5±0.8 mm; P<.001). Fewer confirmatory scans were required (4.1±2.1 vs 4.9±2.4; P = .014). Puncture duration was unchanged (18.3±4.1 vs 19.0±5.2 min; P = .45). Major complication rates were low and similar (∼5% in each group, P = 1.00), consisting of pneumothoraces requiring chest tubes in each arm.

Conclusion: Gantry-mounted laser guidance markedly enhances first-pass success and accuracy of CT-guided thoracoabdominal punctures without adding procedure time or risk, providing an efficient, low-cost alternative to the traditional freehand technique.

Background: Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) is a critical prognostic marker in breast cancer, yet its prediction remains challenging due to tumor heterogeneity and limitations of conventional imaging. While radiomics and deep learning (DL) have shown promise, prior studies often neglect the peritumoral microenvironment, a key determinant of therapeutic response.

Methods: We developed a multimodal model integrating intratumoral radiomics, peritumoral features (9-mm expansion), and DL-derived patterns from pre-NAC MRI. The model was trained and internally validated on a high-quality, multicenter cohort from the I-SPY2 trial (n = 929) and externally validated on an independent cohort (n = 95). We extracted 3190 radiomic and 2048 DL features, selecting optimal subsets via Lasso regression and bidirectional selection. Nine machine learning algorithms were evaluated, with logistic regression (LR) emerging as the top performer.

Results: The final integrated model (Intra-Peri-DL) demonstrated favorable performance, achieving an area under the curve (AUC) of 0.888 (95% CI: 0.841-0.933) in internal validation and 0.890 (95% CI: 0.804-0.958) in external validation. This performance was statistically superior to single-modality models (intratumoral radiomics, peritumoral radiomics, or DL features alone; all P<0.05), although the improvement over the combined DL+Intra model did not reach statistical significance. The model achieved high sensitivity (>0.91) in both cohorts and suggested potential clinical utility in decision curve analysis.

Conclusion: By synergizing radiomics and DL to capture tumor-microenvironment interplay, our model enhances pCR prediction accuracy, offering a potential clinically actionable tool for personalized NAC decision-making. This framework bridges imaging phenotypes with biological insights, paving the way for precision oncology in breast cancer.

Rationale and objectives: We investigated the safety and effectiveness of the WAVE-track from the perspectives of imaging and pathology with a swine thrombectomy model to provide a basis for its clinical application, using the ACE aspiration catheter as the control group.

Materials and methods: In a swine model, various types of thrombi were prepared and placed in the maxillary artery, ascending pharyngeal artery, lingual artery, and renal artery. The WAVE-track group was considered to be the study group, and the ACE aspiration catheter was used as the control group. Thrombectomy with the ADAPT technique or/and repeatedly pushed and withdrawn with aspiration were performed in two groups. The swine were sacrificed on the day of completion of procedure or at 30 ±5 days.

Results: According to the angiographic analysis, although the study group showed a better trend in mTICI distribution, no significant differences were recorded in the recanalization rates between the study group and the control group (mTICI≥2b: WAVE-track group 96.15% vs. ACE group 84.37%). Furthermore, the first-pass effect rates were similar in both groups (WAVE-track group 48.08% vs. ACE group 40.63%). Procedural safety was confirmed in both groups and pathological analysis revealed no clinically significant abnormalities in the two groups. In the subgroup analysis of single-pass and multiple-pass, there were no clinically significant differences found between the two pass types in angiographic and pathology assessment.

Conclusion: Compared to the ACE catheter, the WAVE-track aspiration catheter demonstrated high thrombectomy efficacy and safety in a swine model. Additionally, the WAVE-track aspiration catheter demonstrated a favorable safety profile even after multiple thrombectomy passes, with no clinically significant increase in vascular injury compared to single pass.

Rationale and objectives: Staging gynecological malignancies is a complex process, and radiologists should be familiar with the evolution of FIGO staging criteria. Large Language Models (LLMs) offer potential to support radiologists by automating classification tasks from free-text MRI reports.

Materials and methods: We conducted a retrospective study using two curated datasets of pelvic MRI reports from patients with cervical (n = 261, FIGO 2018) and endometrial cancer (n = 555, FIGO 2023). A general-purpose LLM (Cohere Command-A) was evaluated under three prompting strategies (zero-shot, guided, and chain-of-thought [CoT]), using exact stage accuracy, an ordinal FIGO distance metric, and the rate of severe errors. The Cohere Command-A model was chosen for its long-context reasoning, instruction-following capabilities, reproducible fixed version, and secure handling of sensitive clinical data. While alternative LLMs (eg, GPT-4o, Gemini, Llama-3, DeepSeek) could offer complementary insights, access, resources, and compliance constraints limited broader comparisons.

Results: For cervical cancer, CoT prompting achieved the highest accuracy (80.5%) and the lowest FIGO distance, with 23 severe misclassifications (≥2-stage deviation), outperforming guided and zero-shot prompting. For endometrial cancer, all strategies performed appropriately, with CoT again yielding the best results (accuracy, 90.6%) and the lowest number of severe misclassifications (37 cases), compared with guided and zero-shot prompting. In a small subset of cases with no agreement between any prompting strategy and the reference label, manual review showed that only a minority presented potentially suboptimal annotations, suggesting that CoT-based predictions may also help flag doubtful reports.

Conclusion: The LLMs used demonstrated strong performance in automatically assigning FIGO stages for cervical and endometrial cancers from MRI reports. Their integration could reduce workload and improve consistency in staging. Further validation is needed before clinical implementation.

RATIONALE AND OBJECTIVES: To evaluate correlations between a standardized vascular obstruction score and invasive hemodynamic parameters in chronic thromboembolic pulmonary hypertension (CTEPH), using dual-energy CT (DECT), cone-beam CT (CBCT), and digital subtraction angiography (DSA).

Materials and methods: In this retrospective single-center study, 109 patients with CTEPH underwent DECT, CBCT, and DSA within a 3-month interval. A standardized vascular obstruction score was applied independently to each modality. Linear regression models were constructed to assess associations with mean pulmonary arterial pressure (mPAP), pulmonary vascular resistance (PVR), cardiac output (CO), and cardiac index (CI), quantified by adjusted R². Score distributions were compared using Friedman and Wilcoxon tests, and interobserver agreement was assessed with Cohen's κ.

Results: DSA demonstrated the highest degree of association with mPAP and PVR among the evaluated modalities (adjusted R² = 0.089 and 0.126), followed by DECT (0.075 and 0.098) and CBCT (0.050 and 0.062). DSA also correlated with CO and CI. Mean obstruction scores differed significantly across modalities (p < 0.001), with DECT yielding higher values than CBCT (p < 0.001) and DSA (p < 0.001). Interobserver agreement was highest for CBCT (κ = 0.76) and DECT (κ = 0.74), and lowest for DSA (κ = 0.57). None of the modalities correlated significantly with NYHA class or 6MWD.

Conclusion: A unified morphologic vascular obstruction score applied across DECT, CBCT, and DSA demonstrates reproducible associations with invasive hemodynamic parameters in CTEPH. While not a replacement for right heart catheterization, it provides a standardized framework for multimodality assessment and may support methodological integration across imaging modalities.

Critical relevance statement: This study presents a systematic application of a unified vascular obstruction scoring system across dual-energy CT, cone-beam CT, and digital subtraction angiography in patients with chronic thromboembolic pulmonary hypertension. The results demonstrate significant correlations with invasive hemodynamic parameters, with dual-energy CT and cone-beam CT providing higher reproducibility than angiography. These findings support the use of a standardized scoring framework to enable consistent multimodality assessment, improve reproducibility in structured multimodality imaging assessment, and facilitate cross-institutional comparisons in chronic thromboembolic pulmonary hypertension.

Rationale and objectives: Accurate prediction of axillary lymph node metastasis (ALNM) after neoadjuvant chemotherapy (NAC) remains challenging in breast cancer. This study aimed to develop an interpretable machine learning model integrating MRI-based radiomics, deep learning features, and the Node-RADS score for noninvasive ALNM prediction after NAC.

Materials and methods: In this multicenter retrospective study, 641 patients with pathologically confirmed breast cancer who underwent surgery between April 2017 and December 2024 across three institutions were enrolled. Preoperative dynamic contrast-enhanced MRI and clinicopathologic data were analyzed. Quantitative radiomics and ResNet50-derived deep learning features were extracted. Patients were divided into a training cohort (n = 397), an internal validation cohort (n = 99), and two external validation cohorts (n = 90 and n = 55). Three models-a clinical model, a deep learning-radiomics (DLR) model, and a combined clinical-deep learning-radiomics (CDLR) model-were constructed using five machine learning algorithms. Model performance was evaluated by ROC analysis, AUC, calibration, and decision curve analysis. SHapley Additive exPlanations (SHAP) were used to interpret feature importance.

Results: The CDLR model demonstrated superior predictive performance, with AUCs of 0.879, 0.805, 0.737, and 0.781 in the training, internal, and two external cohorts, respectively, outperforming both the DLR and clinical models. The CDLR model also showed good calibration and the highest net clinical benefit. SHAP analysis identified Node-RADS, lbp_3D_m1_glcm_Correlation, and DL_50 as the most influential predictors.

Conclusion: The interpretable CDLR model enables accurate, noninvasive prediction of ALNM after NAC in breast cancer and may assist in individualized clinical decision-making.