Insights into Imaging最新文献

Objective: To assess the image quality of synthetic double inversion recovery (SyDIR) imaging and enhance the value of T2-weighted imaging (T2WI) in evaluating T stage for rectal cancer patients.

Methods: A total of 112 pathologically confirmed rectal cancer patients were retrospectively selected after undergoing MRI, including synthetic MRI. The image quality of T2WI and SyDIR imaging was compared based on signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), overall picture quality, presence of motion artifacts, lesion edge sharpness, and conspicuity. The concordance between MRI and pathological staging results, using T2WI alone and the combination of T2WI and SyDIR for junior and senior radiologists, was assessed using the Kappa test. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic efficacy of extramural infiltration in rectal cancer patients.

Results: No significant differences in imaging quality were observed between conventional T2WI and SyDIR (p = 0.07-0.53). The combination of T2WI and SyDIR notably improved the staging concordance between MRI and pathology for both junior (kappa value from 0.547 to 0.780) and senior radiologists (kappa value from 0.738 to 0.834). In addition, the integration of T2WI and SyDIR increased the AUC for diagnosing extramural infiltration for both junior (from 0.842 to 0.918) and senior radiologists (from 0.917 to 0.938).

Conclusion: The combination of T2WI and SyDIR increased the consistency of T staging between MRI and pathology, as well as the diagnostic performance of extramural infiltration, which would benefit treatment selection.

Critical relevance statement: SyDIR sequence provides additional diagnostic value for T2WI in the T staging of rectal cancer, improving the agreement of T staging between MRI and pathology, as well as the diagnostic performance of extramural infiltration.

Key points: Synthetic double inversion recovery (SyDIR) and T2WI have comparable image quality. SyDIR provides rectal cancer anatomical features for extramural infiltration detections. The combination of T2WI and SyDIR improves the accuracy of T staging in rectal cancer.

Over the last few years, radiation therapy (RT) techniques have evolved very rapidly, with the aim of conforming high-dose volume tightly to a target. Although to date CT is still considered the imaging modality for target delineation, it has some known limited capabilities in properly identifying pathologic processes occurring, for instance, in soft tissues. This limitation, along with other advantages such as dose reduction, can be overcome using magnetic resonance imaging (MRI), which is increasingly being recognized as a useful tool in RT clinical practice. This review has a two-fold aim of providing a basic introduction to the physics of MRI in a narrative way and illustrating the current knowledge on its application "from head to toe" (i.e., different body sites), in order to highlight the numerous advantages in using MRI to ensure the best therapeutic response. We provided a basic introduction for residents and non-radiologist on the physics of MR and reported evidence of the advantages and future improvements of MRI in planning a tailored radiotherapy treatment "from head to toe". CRITICAL RELEVANCE STATEMENT: This review aims to help understand how MRI has become indispensable, not only to better characterize and evaluate lesions, but also to predict the evolution of the disease and, consequently, to ensure the best therapeutic response. KEY POINTS: MRI is increasingly gaining interest and applications in RT planning. MRI provides high soft tissue contrast resolution and accurate delineation of the target volume. MRI will increasingly become indispensable for characterizing and evaluating lesions, and to predict the evolution of disease.

Purpose: Steatotic liver disease (SLD) has become the most common cause of chronic liver disease. Nevertheless, the non-invasive quantitative diagnosis of steatosis is still lacking in clinical practice. This study aimed to evaluate the reproducibility of the new parameter for steatosis quantification named ultrasound-derived fat fraction (UDFF).

Materials and methods: The UDFF values were independently executed by two operators in two periods. In the process, repeated measurements of the same patient were performed by the same operator under different conditions (liver segments, respiration, positions, and dietary). Finally, the results of some subjects (28) were compared with the MRI-derived proton density fat fraction (PDFF). The concordance analysis was mainly achieved by the intraclass correlation coefficient (ICC) and Bland-Altman.

Results: One hundred-five participants were included in the study. UDFF had good reliability in measuring the adult liver (ICC_{intra-observer} = 0.96, ICC_{inter-observer} = 0.94). Meanwhile, the ICC of the two operators increased over time. The variable measurement states did not influence the UDFF values on the surface, but they affected the coefficient of variation (Cov) of the results. Segment 8 (S8), end-expiratory, supine, and fasting images had the most minor variability. On the other hand, the UDFF value of S8 displayed satisfied consistency with PDFF (mean difference, -0.24 ± 1.44), and the results of both S5 (mean difference: -0.56 ± 3.95) and S8 (mean difference: 0.73 ± 1.87) agreed well with the whole-liver PDFF.

Conclusion: UDFF measurements had good reproducibility. Furthermore, the state of S8, end-expiration, supine, and fasting might be the more stable measurement approach.

Critical relevance statement: UDFF is the quantitative ultrasound parameter of hepatic steatosis and has good reproducibility. It can show more robust performance under specific measurement conditions (S8, end-expiratory, supine, and fasting).

Trial registration: The research protocol was registered at the Chinese Clinical Trial Registry on October 9, 2023 ( http://www.chictr.org.cn/ ). The registration number is ChiCTR 2300076457.

Key points: There is a lack of non-invasive quantitative measurement options for hepatic steatosis. UDFF demonstrated excellent reproducibility in measuring hepatic steatosis. S8, end-expiratory, supine, and fasting may be the more stable measuring condition. Training could improve the operators' measurement stability. Variable measurement state affects the repeatability of the UDFF values (Cov).

The Strengthening the Screening of Lung Cancer in Europe (SOLACE) initiative, supported by Europe's Beating Cancer Plan, is dedicated to advancing lung cancer screening. This initiative brings together the most extensive pan-European network of respiratory and radiology experts, involving 37 partners from 15 countries. SOLACE aims to enhance equitable access to lung cancer screening by developing targeted recruitment strategies for underrepresented and high-risk populations. Through comprehensive work packages, SOLACE integrates scientific research, pilot studies, and sustainability efforts to bolster regional and national screening efforts across EU member states. CRITICAL RELEVANCE STATEMENT: The SOLACE project aims to facilitate the optimization and implementation of equitable lung cancer screening programs across the heterogeneous healthcare landscape in EU member states. KEY POINTS: The effectiveness of lung cancer screening is supported by both scientific evidence and now increasing legislative support. SOLACE aims to develop, test, and disseminate tools to facilitate the realization of lung cancer screening at both a national and regional level. Previously underrepresented populations in lung cancer screening will be targeted by tailored recruitment strategies. SOLACE forms the first pan-European network of experts poised to drive real-world implementation of lung cancer screening.

Objectives: To develop and validate nomograms combining radiomics and semantic features to identify the invasiveness and histopathological risk stratification of thymic epithelial tumors (TET) using contrast-enhanced CT.

Methods: This retrospective multi-center study included 224 consecutive cases. For each case, 6764 intratumor and peritumor radiomics features and 31 semantic features were collected. Multi-feature selections and decision tree models were performed on radiomics features and semantic features separately to select the most important features for Masaoka-Koga staging and WHO classification. The selected features were then combined to create nomograms for the two systems. The performance of the radiomics model, semantic model, and combined model was evaluated using the area under the receiver operating characteristic curves (AUCs).

Results: One hundred eighty-seven cases (56.5 years ± 12.3, 101 men) were included, with 62 cases as the external test set. For Masaoka-Koga staging, the combined model, which incorporated five peritumor radiomics features and four semantic features, showed an AUC of 0.958 (95% CI: 0.912-1.000) in distinguishing between early-stage (stage I/II) and advanced-stage (III/IV) TET in the external test set. For WHO classification, the combined model incorporating five peritumor radiomics features and two semantic features showed an AUC of 0.857 (0.760-0.955) in differentiating low-risk (type A/AB/B1) and high-risk (B2/B3/C) TET. The combined models showed the most effective predictive performance, while the semantic models exhibited comparable performance to the radiomics models in both systems (p > 0.05).

Conclusion: The nomograms combining peritumor radiomics features and semantic features could help in increasing the accuracy of grading invasiveness and risk stratification of TET.

Critical relevance statement: Peripheral invasion and histopathological type are major determinants of treatment and prognosis of TET. The integration of peritumoral radiomics features and semantic features into nomograms may enhance the accuracy of grading invasiveness and risk stratification of TET.

Key points: Peritumor region of TET may suggest histopathological and invasive risk. Peritumor radiomic and semantic features allow classification by Masaoka-Koga staging (AUC: 0.958). Peritumor radiomic and semantic features enable the classification of histopathological risk (AUC: 0.857).

Objectives: To develop and evaluate a multi-task deep-learning (DL) model for automated segment-level coronary artery calcium (CAC) scoring on non-contrast computed tomography (CT) for precise localization and quantification of calcifications in the coronary artery tree.

Methods: This study included 1514 patients (mean age, 60.0 ± 10.2 years; 56.0% female) with stable chest pain from 26 centers participating in the multicenter DISCHARGE trial (NCT02400229). The patients were randomly assigned to a training/validation set (1059) and a test set (455). We developed a multi-task neural network for performing the segmentation of calcifications on the segment level as the main task and the segmentation of coronary artery segment regions with weak annotations as an auxiliary task. Model performance was evaluated using (micro-average) sensitivity, specificity, F1-score, and weighted Cohen's κ for segment-level agreement based on the Agatston score and performing interobserver variability analysis.

Results: In the test set of 455 patients with 1797 calcifications, the model assigned 73.2% (1316/1797) to the correct coronary artery segment. The model achieved a micro-average sensitivity of 0.732 (95% CI: 0.710-0.754), a micro-average specificity of 0.978 (95% CI: 0.976-0.980), and a micro-average F1-score of 0.717 (95% CI: 0.695-0.739). The segment-level agreement was good with a weighted Cohen's κ of 0.808 (95% CI: 0.790-0.824), which was only slightly lower than the agreement between the first and second observer (0.809 (95% CI: 0.798-0.845)).

Conclusion: Automated segment-level CAC scoring using a multi-task neural network approach showed good agreement on the segment level, indicating that DL has the potential for automated coronary artery calcification classification.

Critical relevance statement: Multi-task deep learning can perform automated coronary calcium scoring on the segment level with good agreement and may contribute to the development of new and improved calcium scoring methods.

Key points: Segment-level coronary artery calcium scoring is a tedious and error-prone task. The proposed multi-task model achieved good agreement with a human observer on the segment level. Deep learning can contribute to the automation of segment-level coronary artery calcium scoring.

Objectives: To quantitatively investigate the anatomy of the anterior talofibular ligament (ATFL) and calcaneofibular ligament (CFL) for surgical reconstruction procedures in chronic ankle instability (CAI).

Methods: 3D MRI was performed on five fresh-frozen cadaveric ankles using six different spatial resolutions (0.3 × 0.3 × 0.3 mm³, 0.45 × 0.45 × 0.45 mm³, 0.6 × 0.6 × 0.6 mm³, 0.75 × 0.75 × 0.75 mm³, 0.9 × 0.9 × 0.9 mm³, 1.05 × 1.05 × 1.05 mm³). After comparing the MRI results with cadaver dissection, a resolution of 0.45 × 0.45 × 0.45 mm³ was selected for bilateral ankles MRI on 24 volunteers. Classification of the ATFL and four distances of surgically relevant bony landmarkers were analyzed (distance 1 and 3, the fibular origin of the ATFL and CFL to the tip of fibula, respectively; distance 2, the talar insertion of the ATFL to the bare zone of talus; distance 4, the calcaneal insertion of the CFL to the peroneal tubercle).

Results: In subjective evaluation, the interobserver ICC was 0.95 (95% confidence interval (CI): 0.94-0.97) between two readers. The spatial resolution of 0.3 × 0.3 × 0.3 mm³ and 0.45 × 0.45 × 0.45 mm³ received highest subjective score on average and demonstrated highest consistency with autopsy measurements in objective evaluation. Measurements on the 48 volunteer ankles, distance 1 in type I and II were 12.65 ± 2.08 mm, 13.43 ± 2.06 mm (superior-banded in Type II) and 7.69 ± 2.56 mm (inferior-banded in Type II) (means ± SD), respectively. Distance 2 in type I and II were 10.90 ± 2.24 mm, 11.07 ± 2.66 mm (superior-banded in Type II), and 18.44 ± 3.28 mm (inferior-banded in Type II), respectively. Distance 3 and 4 were 4.71 ± 1.04 mm and 14.35 ± 2.22 mm, respectively.

Conclusion: We demonstrated the feasibility of quantifying the distances between bony landmarkers for surgical reconstruction surgery in CAI using high-resolution 3D MRI.

Critical relevance statement: High-resolution 3D MRI examination may have a guiding effect on the preoperative evaluation of chronic ankle instability patients.

Key points: Spatial resolutions of 0.3 × 0.3 × 0.3 mm³ and 0.45 × 0.45 × 0.45 mm³ demonstrated highest consistency with autopsy measurements. The spatial resolution of 0.45 × 0.45 × 0.45 mm³ was conformed more to clinical needs. 3D MRI can assist surgeons in developing preoperative plans for chronic ankle instability.

Purpose: To investigate whether the body composition parameters can be employed as potential biomarkers for predicting the progression risk of chronic kidney disease (CKD).

Materials and methods: Four hundred sixteen patients diagnosed with CKD were included in this retrospective study. Patients with a greater than 50% decline in estimated glomerular filtration rate or progression to end-stage kidney disease were in the high-risk group, otherwise, they were in a low-risk group. Body composition area, the index, and radiodensities in the Hounsfield unit (HU), which reflect the degree of X-ray absorption, were measured on abdominal CT images. Risk factors in body composition and clinical parameters of CKD were identified by Cox regression and utilized to construct the nomogram. The performance of the nomogram was assessed using time receiver operating characteristics curves, calibration curves, and decision curve analysis.

Results: There were 254 patients in low-risk group and 162 in high-risk group (268 males, 148 females, mean age: 55.89 years). Urea, diabetes, 24 h-urinary protein, mean arterial pressure, and subcutaneous adipose tissue radiodensity (SATd) were valuable indicators for predicting the high-risk group. The area under curve values for the nomogram of training/validation set at 1 year, 2 years, and 3 years were 0.805/0.753, 0.784/0.783, and 0.846/0.754, respectively. For diabetic CKD patients, extra attention needs to be paid to visceral to subcutaneous fat ratio and renal sinus fat radiodensity.

Conclusion: SATd was the most valuable noninvasive indicator of all body composition parameters for predicting high-risk populations with CKD. The nomogram we constructed has generalization with easily obtainable indicators, good performance, differentiation, and clinical practicability.

Critical relevance statement: Radiodensity rather than an area of adipose tissue can be used as a new biomarker of prognosis for CKD patients, providing new insights into risk assessment, stratified management, and treatment for CKD patients.

Key points: Obesity is an independent risk factor for the development and prognosis of CKD. Adipose tissue radiodensity is more valuable than fat area in prognosticating for kidney disease. Parameters that prognosticate in diabetic CKD patients are different from those in other CKD patients.

Various healthcare domains have witnessed successful preliminary implementation of artificial intelligence (AI) solutions, including radiology, though limited generalizability hinders their widespread adoption. Currently, most research groups and industry have limited access to the data needed for external validation studies. The creation and accessibility of benchmark datasets to validate such solutions represents a critical step towards generalizability, for which an array of aspects ranging from preprocessing to regulatory issues and biostatistical principles come into play. In this article, the authors provide recommendations for the creation of benchmark datasets in radiology, explain current limitations in this realm, and explore potential new approaches. CLINICAL RELEVANCE STATEMENT: Benchmark datasets, facilitating validation of AI software performance can contribute to the adoption of AI in clinical practice. KEY POINTS: Benchmark datasets are essential for the validation of AI software performance. Factors like image quality and representativeness of cases should be considered. Benchmark datasets can help adoption by increasing the trustworthiness and robustness of AI.