Insights into Imaging最新文献

Objectives: To address SPECT's radioactivity, complexity, and costliness in measuring renal function, this study employs artificial intelligence (AI) with non-contrast CT to estimate single-kidney glomerular filtration rate (GFR) and split renal function (SRF).

Methods: 245 patients with atrophic kidney or hydronephrosis were included from two centers (Training set: 128 patients from Center I; Test set: 117 patients from Center II). The renal parenchyma and hydronephrosis regions in non-contrast CT were automatically segmented by deep learning. Radiomic features were extracted and combined with clinical characteristics using multivariable linear regression (MLR) to obtain a radiomics-clinical-estimated GFR (rcGFR). The relative contribution of single-kidney rcGFR to overall rcGFR, the percent renal parenchymal volume, and the percent renal hydronephrosis volume were combined by MLR to generate the estimation of SRF (rcphSRF). The Pearson correlation coefficient (r), mean absolute error (MAE), and Lin's concordance coefficient (CCC) were calculated to evaluate the correlations, differences, and agreements between estimations and SPECT-based measurements, respectively.

Results: Compared to manual segmentation, deep learning-based automatic segmentation could reduce the average segmentation time by 434.6 times to 3.4 s. Compared to single-kidney GFR measured by SPECT, the rcGFR had a significant correlation of r = 0.75 (p < 0.001), MAE of 10.66 mL/min/1.73 m², and CCC of 0.70. Compared to SRF measured by SPECT, the rcphSRF had a significant correlation of r = 0.92 (p < 0.001), MAE of 7.87%, and CCC of 0.88.

Conclusions: The non-contrast CT and AI methods are feasible to estimate single-kidney GFR and SRF in patients with atrophic kidney or hydronephrosis.

Critical relevance statement: For patients with an atrophic kidney or hydronephrosis, non-contrast CT and artificial intelligence methods can be used to estimate single-kidney glomerular filtration rate and split renal function, which may minimize the radiation risk, enhance diagnostic efficiency, and reduce costs.

Key points: Renal function can be assessed using non-contrast CT and AI. Estimated renal function significantly correlated with the SPECT-based measurements. The efficiency of renal function estimation can be refined by the proposed method.

Objectives: To compare imaging differences between bleeding and non-bleeding angiomyolipoma with respect to the proportion and attenuation of the angiomyogenic component and the occurrence and size of aneurysms.

Materials and methods: CT scans and angiographies preceding 58 consecutive embolisations at two institutions from 1999 to 2018 were analysed retrospectively. Tumour volume was measured by contouring the angiomyolipoma on CT scans. The partial volume of the angiomyogenic component (blood vessels and smooth muscle relative to fatty tissue) was derived using attenuation threshold values measured in Hounsfield Units.

Results: Bleeding angiomyolipoma exhibited a significantly higher proportion of angiomyogenic component (23%) than non-bleeding angiomyolipoma (8%) (p = 0.042). Angiomyolipoma with 0-5% angiomyogenic component had a lower risk of bleeding compared to those with ≥ 5% angiomyogenic component (13% vs 42%). Mean attenuation values of angiomyogenic components did not differ between bleeders and non-bleeders. Aneurysms were observed in 24% of angiomyolipoma during angiography. No statistically significant association was found between the occurrence of aneurysms and bleeding, neither when all aneurysms were included nor when only aneurysms ≥ 5 mm were considered. Tuberous sclerosis patients had larger tumours (11.4 cm vs 6.0 cm), but no significant difference in bleeding was observed (p = 0.53).

Conclusions: A higher proportion of the angiomyogenic component in bleeding renal angiomyolipoma suggests a possible association with bleeding. Angiomyolipoma with less than 5% angiomyogenic components may represent a subgroup with a reduced risk of bleeding. Our findings do not confirm the widely accepted assumption that aneurysms significantly increase the risk of bleeding.

Critical relevance statement: Measuring the angiomyogenic component in renal angiomyolipoma could help address current knowledge gaps and aid in the more efficient selection of patients for therapeutic interventions.

Key points: Identifying risk factors for bleeding beyond tumour size is important. Very low angiomyogenic component tumours may have reduced bleeding risk. The presence of aneurysms may not significantly increase bleeding risk. Reporting angiomyogenic proportion on CT may aid in treatment decisions.

Objectives: The reliability of image-based recommendations in the prostate cancer pathway is partially dependent on prostate MRI image quality. We evaluated the current compliance with PI-RADSv2.1 technical recommendations and the prostate MRI image quality in the Netherlands. To aid image quality improvement, we identified factors that possibly influence image quality.

Materials and methods: A survey was sent to 68 Dutch medical centres to acquire information on prostate MRI acquisition. The responding medical centres were requested to provide anonymised prostate MRI examinations of biopsy-naive men suspected of prostate cancer. The images were evaluated for quality by three expert prostate radiologists. The compliance with PI-RADSv2.1 technical recommendations and the PI-QUALv2 score was calculated. Relationships between hardware, education of personnel, technical parameters, and/or patient preparation and both compliance and image quality were analysed using Pearson correlation, Mann-Whitney U-test, or Student's t-test where appropriate.

Results: Forty-four medical centres submitted their compliance with PI-RADSv2.1 technical recommendations, and 26 medical centres completed the full survey. Thirteen hospitals provided 252 usable images. The mean compliance with technical recommendations was 79%. Inadequate PI-QUALv2 scores were given in 30.9% and 50.6% of the mp-MRI and bp-MRI examinations, respectively. Multiple factors with a possible relationship with image quality were identified.

Conclusion: In the Netherlands, the average compliance with PI-RADSv2.1 technical recommendations is high. Prostate MRI image quality was inadequate in 30-50% of the provided examinations. Many factors not covered in the PI-RADSv2.1 technical recommendations can influence image quality. Improvement of prostate MRI image quality is needed.

Critical relevance statement: It is essential to improve the image quality of prostate MRIs, which can be achieved by addressing factors not covered in the PI-RADSv2.1 technical recommendations.

Key points: Prostate MRI image quality influences the diagnostic accuracy of image-based decisions. Thirty to fifty percent of Dutch prostate MRI examinations were of inadequate image quality. We identified multiple factors with possible influence on image quality.

Objectives: This study aimed to develop and validate CT-based radiomics deep learning signatures for the non-invasive prediction of metastatic potential in pheochromocytomas and paragangliomas (PPGLs).

Methods: We conducted a retrospective analysis of 249 PPGL patients from three institutions, dividing them into training (n = 138), test1 (n = 71), and test2 (n = 40) sets. Based on the grading system for adrenal pheochromocytoma and paraganglioma (GAPP), patients were classified into low-risk (GAPP < 3) and high-risk (GAPP ≥ 3) groups. Radiomic features were extracted from CT venous phase images and modeled using six machine learning algorithms. The maximum 2D sections and 3D images of each tumor were input into four ResNet models to obtain predictive probabilities. Optimal models were selected based on receiver operating characteristic analysis and integrated with radiological features to develop a combined model, which was evaluated on external datasets, and explored prognostic information.

Results: The support vector machine radiomics and 2D ResNet-50 models demonstrated good performance. By integrating these two models with intratumoral necrosis features, we constructed a combined model that achieved high accuracy, with area under the curve (AUC) values of 0.90 for the training, 0.86 for the test1, and 0.88 for the test2 sets. This model effectively stratified patients based on metastasis-free survival (p = 0.003). Its predictive ability remains robust below the 6 cm threshold, with AUC values exceeding 0.87 across all datasets.

Conclusions: The combined model can predict the metastatic potential of PPGL in the preoperative stage, providing a precise surgical strategy for pheochromocytoma regarding the 6 cm surgical threshold.

Critical relevance statement: The combined model, established based on radiomic and deep learning signatures, shows potential for early preoperative prediction of metastatic potential in PPGL.

Key points: Metastatic potential of PPGL affects surgical approaches and prognosis. CT-based radiomics deep learning signatures can predict the metastatic potential in PPGL.3. The combined model's predictive ability remains robust below the 6-cm threshold. The combined model's predictive ability remains robust below the 6-cm threshold.

Objectives: To determine the prevalence of ischiofemoral impingement (IFI) in young patients evaluated for joint-preserving hip surgery and investigate its associations with osseous deformities and intra-articular pathologies.

Methods: Retrospective study of 256 hips (224 patients, mean age 34 years) that were examined with radiographs and MR arthrography for hip pain. Quadratus femoris muscle edema was used to indicate IFI and measurements of ischiofemoral space were performed. Imaging analysis assessed cam deformity, femoral torsion, neck-shaft angle, ischial angle, acetabular coverage-/ version, and chondro-labral pathology. Prevalence of MRI findings consistent with IFI was calculated and univariate- and multivariate logistic regression identified associations between IFI and hip deformities.

Results: Quadratus femoris muscle edema consistent with IFI was present in 9% (23/256 hips) with narrowing of the ischiofemoral distance (1.7 ± 0.6 cm vs 2.8 ± 0.7 cm in the control group, p < 0.001) and a higher prevalence in females (89% vs 45%, p < 0.001). Multiple regression identified female sex (OR 12.5, 95% CI: 1.6-98.2, p = 0.017), high femoral torsion (OR 3.9, 1.4-10.4, p = 0.008), and ischial angle > 127° (OR 5.9, 1.3-27.1, p = 0.023) as independent predictors of IFI. Labral tears were highly prevalent in both IFI and control groups (87% vs 89%, p = 0.732); cartilage lesions were less common in the IFI group (26% vs 52%, p = 0.027).

Conclusion: IFI was present in 9% of young patients evaluated for joint-preserving surgery, associated with female sex, high femoral torsion and increased ischial angle. The comparable prevalence of labral lesions but lower prevalence of cartilage damage suggests complex relationships between extra- and intra-articular pathologies.

Critical relevance statement: Recognizing IFI and its link to hip deformities and chondrolabral damage is crucial for clinicians, as it represents an important differential diagnosis, directly impacting joint-preserving treatment strategies in young adults with hip pain.

Key points: The prevalence and imaging predictors of IFI in young patients remain unknown. IFI occurred in 9%, with predictors including female sex, high femoral torsion, and an increased ischial angle. IFI is an important differential diagnosis in joint-preserving hip surgery.

Radiomics has widespread applications in the field of brain tumor research. However, radiomic analyses often function as a 'black box' due to their use of complex algorithms, which hinders the translation of brain tumor radiomics into clinical applications. In this review, we will elaborate extensively on the application of radiomics in brain tumors. Additionally, we will address the interpretability of handcrafted-feature radiomics and deep learning-based radiomics by integrating biological domain knowledge of brain tumors with interpretability methods. Furthermore, we will discuss the current challenges and prospects concerning the interpretability of brain tumor radiomics. Enhancing the interpretability of radiomics may make it more understandable for physicians, ultimately facilitating its translation into clinical practice. CRITICAL RELEVANCE STATEMENT: The interpretability of brain tumor radiomics empowers neuro-oncologists to make well-informed decisions from radiomic models. KEY POINTS: Radiomics makes a significant impact on the management of brain tumors in several key clinical areas. Transparent models, habitat analysis, and feature attribute explanations can enhance the interpretability of traditional handcrafted-feature radiomics in brain tumors. Various interpretability methods have been applied to explain deep learning-based models; however, there is a lack of biological mechanisms underlying these models.

Objectives: To develop and validate a contrast-enhanced MRI-based intratumoral heterogeneity (ITH) model for predicting lymph node (LN) metastasis in resectable pancreatic ductal adenocarcinoma (PDAC).

Methods: Lesions were encoded into different habitats based on enhancement ratios at arterial, venous, and delayed phases of contrast-enhanced MRI. Habitat models on enhanced ratio mapping and single sequences, radiomic models, and clinical models were developed for evaluating LN metastasis. The performance of the models was evaluated via different metrics. Additionally, patients were stratified into high-risk and low-risk groups based on an ensembled model to assess prognosis after adjuvant therapy.

Results: We developed an ensembled radiomics-habitat-clinical (RHC) model that integrates radiomics, habitat, and clinical data for precise prediction of LN metastasis in PDAC. The RHC model showed strong predictive performance, with area under the curve (AUC) values of 0.805, 0.779, and 0.615 in the derivation, internal validation, and external validation cohorts, respectively. Using an optimal threshold of 0.46, the model effectively stratified patients, revealing significant differences in recurrence-free survival and overall survival (OS) (p = 0.004 and p < 0.001). Adjuvant therapy improved OS in the high-risk group (p = 0.004), but no significant benefit was observed in the low-risk group (p = 0.069).

Conclusion: We developed an MRI-based ITH model that provides reliable estimates of LN metastasis for resectable PDAC and may offer additional value in guiding clinical decision-making.

Critical relevance statement: This ensemble RHC model facilitates preoperative prediction of LN metastasis in resectable PDAC using contrast-enhanced MRI. This offers a foundation for enhanced prognostic assessment and supports the management of personalized adjuvant treatment strategies.

Key points: MRI-based habitat models can predict LN metastasis in PDAC. Both the radiomics model and clinical characteristics were useful for predicting LN metastasis in PDAC. The RHC models have the potential to enhance predictive accuracy and inform personalized therapeutic decisions.

Objectives: To develop and validate a deep learning radiopathomics model (DLRPM) integrating radiological and pathological imaging data to predict biliary cyclooxygenase-2 (COX-2) expression in children with pancreaticobiliary maljunction (PBM), and to compare its performance with single-modality radiomics, deep learning radiomics (DLR), and pathomics models.

Methods: This retrospective study included 219 PBM patients, divided into a training set (n = 104; median age, 2.8 years, 75.0% females) and internal test set (n = 71; median age, 2.2 years, 83.1% females) from center I, and an external test set (n = 44; median age, 3.4 years, 65.9% females) from center II. Biliary COX-2 expression was detected using immunohistochemistry. Radiomics, DLR, and pathomics features were extracted from portal venous-phase CT images and H&E-stained histopathological slides, respectively, to build individual single-modality models. These were then integrated to develop the DLRPM, combining three predictive signatures. Model performance was evaluated using AUC, net reclassification index (NRI, for assessing improvement in correct classification) and integrated discrimination improvement (IDI).

Results: The DLRPM demonstrated the highest performance, with AUCs of 0.851 (95% CI, 0.759-0.942) in internal test set and 0.841 (95% CI, 0.721-0.960) in external test set. In comparison, AUCs for the radiomics, DLR, and pathomics models were 0.532-0.602, 0.658-0.660, and 0.787-0.805, respectively. The DLRPM significantly outperformed three single-modality models, as demonstrated by the NRI and IDI tests (all p < 0.05).

Conclusion: The multimodal DLRPM could accurately and robustly predict COX-2 expression, facilitating risk stratification and personalized postoperative management in PBM. However, prospective multicenter studies with larger cohorts are needed to further validate its generalizability.

Critical relevance statement: Our proposed deep learning radiopathomics model, integrating CT and histopathological images, provides a novel and cost-effective approach to accurately predict biliary cyclooxygenase-2 expression, potentially advancing individualized risk stratification and improving long-term outcomes for pediatric patients with pancreaticobiliary maljunction.

Key points: Predicting biliary COX-2 expression in pancreaticobiliary maljunction (PBM) is critical but challenging. A deep learning radiopathomics model achieved high predictive accuracy for COX-2. The model supports patient stratification and personalized postoperative management in PBM.