Frontiers in radiology最新文献

Background: Chest x-ray (CXR) is widely applied for the detection and diagnosis of children's lung diseases. Lung field segmentation in digital CXR images is a key section of many computer-aided diagnosis systems.

Objective: In this study, we propose a method based on deep learning to improve the lung segmentation quality and accuracy of children's multi-center CXR images.

Methods: The novelty of the proposed method is the combination of merits of TransUNet and ResUNet. The former can provide a self-attention module improving the feature learning ability of the model, while the latter can avoid the problem of network degradation.

Results: Applied on the test set containing multi-center data, our model achieved a Dice score of 0.9822.

Conclusions: This novel lung segmentation method proposed in this work based on TransResUNet is better than other existing medical image segmentation networks.

Introduction: To date, most mammography-related AI models have been trained using either film or digital mammogram datasets with little overlap. We investigated whether or not combining film and digital mammography during training will help or hinder modern models designed for use on digital mammograms.

Methods: To this end, a total of six binary classifiers were trained for comparison. The first three classifiers were trained using images only from Emory Breast Imaging Dataset (EMBED) using ResNet50, ResNet101, and ResNet152 architectures. The next three classifiers were trained using images from EMBED, Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM), and Digital Database for Screening Mammography (DDSM) datasets. All six models were tested only on digital mammograms from EMBED.

Results: The results showed that performance degradation to the customized ResNet models was statistically significant overall when EMBED dataset was augmented with CBIS-DDSM/DDSM. While the performance degradation was observed in all racial subgroups, some races are subject to more severe performance drop as compared to other races.

Discussion: The degradation may potentially be due to ( 1) a mismatch in features between film-based and digital mammograms ( 2) a mismatch in pathologic and radiological information. In conclusion, use of both film and digital mammography during training may hinder modern models designed for breast cancer screening. Caution is required when combining film-based and digital mammograms or when utilizing pathologic and radiological information simultaneously.

Purpose: The purpose of this study was to investigate the value of a clinical radiomics model based on Positron emission tomography-computed tomography (PET-CT) radiomics features combined with clinical predictors of Lymphovascular invasion (LVI) in predicting preoperative LVI in patients with colorectal cancer (CRC).

Methods: A total of 95 CRC patients who underwent preoperative ¹⁸F-fluorodeoxyglucose (FDG) PET-CT examination were retrospectively enrolled. Univariate and multivariate logistic regression analyses were used to analyse clinical factors and PET metabolic data in the LVI-positive and LVI-negative groups to identify independent predictors of LVI. We constructed four prediction models based on radiomics features and clinical data to predict LVI status. The predictive efficacy of different models was evaluated according to the receiver operating characteristic curve. Then, the nomogram of the best model was constructed, and its performance was evaluated using calibration and clinical decision curves.

Results: Mean standardized uptake value (SUVmean), maximum tumour diameter and lymph node metastasis were independent predictors of LVI in CRC patients (P < 0.05). The clinical radiomics model obtained the best prediction performance, with an Area Under Curve (AUC) of 0.922 (95%CI 0.820-0.977) and 0.918 (95%CI 0.782-0.982) in the training and validation cohorts, respectively. A nomogram based on the clinical radiomics model was constructed, and the calibration curve fitted well (P > 0.05).

Conclusion: The clinical radiomics prediction model constructed in this study has high value in the preoperative individualized prediction of LVI in CRC patients.

Purpose: To describe our experience with the use of a novel iodized Polyvinyl Alcohol Polymer liquid agent (Easyx) in type II endoleak treatment with translumbar approach.

Methods: Our case series is a retrospective review of patients with type II endoleak (T2E) treated with Easyx from December 2017 to December 2020. Indication for treatment was a persistent T2E with an increasing aneurysm sac ≥5 mm on computed tomography angiography (CTA) over a 6-month interval. Technical success was defined as the embolization of the endoleak nidus with reduction or elimination of the T2E on sequent CTA evaluation. Clinical success was defined as an unchanged or decreased aneurysm sac on follow-up CTA. Secondary endpoints included the presence of artifacts in the postprocedural cross-sectional tomographic imaging and post and intraprocedural complications.

Results: Ten patients were included in our retrospective analysis. All T2E were successfully embolized. Clinical success was achieved in 9 out of 10 patients (90%). The mean follow-up was 14 3-20 months. No beam hardening artifact was observed in follow-up CT providing unaltered imaging.

Conclusion: Easyx is a novel liquid embolic agent with lava-like characteristics and unaltered visibility on subsequent CT examinations. In our initial experience, Easyx showed to have all the efficacy requisites to be an embolization agent for type II EL management. Its efficacy, however, should be evaluated in more extensive studies and eventually compared with other agents.

The aim of this systematic review was to evaluate the state of the art of radiomics in testicular imaging by assessing the quality of radiomic workflow using the Radiomics Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). A systematic literature search was performed to find potentially relevant articles on the applications of radiomics in testicular imaging, and 6 final articles were extracted. The mean RQS was 11,33 ± 3,88 resulting in a percentage of 31,48% ± 10,78%. Regarding QUADAS-2 criteria, no relevant biases were found in the included papers in the patient selection, index test, reference standard criteria and flow-and-timing domain. In conclusion, despite the publication of promising studies, radiomic research on testicular imaging is in its very beginning and still hindered by methodological limitations, and the potential applications of radiomics for this field are still largely unexplored.

Background: Cardiac infiltration is the major predictor of poor prognosis in patients with systemic amyloidosis, thus it becomes of great importance to evaluate cardiac involvement.

Purpose: We aimed to evaluate left ventricular myocardial deformation alteration in patients with cardiac amyloidosis (CA) using layer-specific tissue tracking MR.

Material and methods: Thirty-nine patients with CA were enrolled. Thirty-nine normal controls were also recruited. Layer-specific tissue tracking analysis was done based on cine MR images.

Results: Compared with the control group, a significant reduction in LV whole layer strain values (GLS, GCS, and GRS) and layer-specific strain values was found in patients with CA (all P < 0.01). In addition, GRS and GLS, as well as subendocardial and subepicardial GLS, GRS, and GCS, were all diminished in patients with CA and reduced LVEF, when compared to those with preserved or mid-range LVEF (all P < 0.05). GCS showed the largest AUC (0.9952, P = 0.0001) with a sensitivity of 93.1% and specificity of 90% to predict reduced LVEF (<40%). Moreover, GCS was the only independent predictor of LV systolic dysfunction (Odds Ratio: 3.30, 95% CI:1.341-8.12, and P = 0.009).

Conclusion: Layer-specific tissue tracking MR could be a useful method to assess left ventricular myocardial deformation in patients with CA.

Introduction: The aneurysms of the anterior inferior cerebellar artery (AICA) are rare lesions of the posterior circulation and to treat them is challenging. We aim to present anatomical and morphological characteristics of AICA aneurysms in a series of 15 patients.

Method: The DSA and CT angiography images of AICA aneurysms in 15 consecutive patients were analyzed retrospectively. Different anatomical characteristics were quantified, including morphology, location, width, neck width, length, bottleneck factor, and aspect ratio.

Results: Eighty percent of the patients were females. The age was 52.4 ± 9.6 (mean ± SD) years. 11 patients were smokers. Ten patients had a saccular aneurysm and five patients had a fusiform aneurysm. Aneurysm in 10 patients were located in the proximal segment, in three patients in the meatal segment, and in two patients in the distal segment. Ten out of 15 patients presented with a ruptured aneurysm. The size of AICA aneurysms was 14.8 ± 18.9 mm (mean ± SD). The aspect ratio was 0.92 ± 0.47 (mean ± SD) and bottleneck factor was 1.66 ± 1.65 (mean ± SD).

Conclusion: AICA aneurysms are rare lesions of posterior circulation predominantly found in females, present predominantly with subarachnoid hemorrhage, and are mostly large in size.

Introduction: In this study, we aim to build radiomics and multiomics models based on transcriptomics and radiomics to predict the response from patients treated with the PD-L1 inhibitor.

Materials and methods: One hundred and ninety-five patients treated with PD-1/PD-L1 inhibitors were included. For all patients, 342 radiomic features were extracted from pretreatment computed tomography scans. The training set was built with 110 patients treated at the Léon Bérard Cancer Center. An independent validation cohort was built with the 85 patients treated in Dijon. The two sets were dichotomized into two classes, patients with disease control and those considered non-responders, in order to predict the disease control at 3 months. Various models were trained with different feature selection methods, and different classifiers were evaluated to build the models. In a second exploratory step, we used transcriptomics to enrich the database and develop a multiomic signature of response to immunotherapy in a 54-patient subgroup. Finally, we considered the HOT/COLD status. We first trained a radiomic model to predict the HOT/COLD status and then prototyped a hybrid model integrating radiomics and the HOT/COLD status to predict the response to immunotherapy.

Results: Radiomic signature for 3 months' progression-free survival (PFS) classification: The most predictive model had an area under the receiver operating characteristic curve (AUROC) of 0.94 on the training set and 0.65 on the external validation set. This model was obtained with the t-test selection method and with a support vector machine (SVM) classifier. Multiomic signature for PFS classification: The most predictive model had an AUROC of 0.95 on the training set and 0.99 on the validation set. Radiomic model to predict the HOT/COLD status: the most predictive model had an AUROC of 0.93 on the training set and 0.86 on the validation set. HOT/COLD radiomic hybrid model for PFS classification: the most predictive model had an AUROC of 0.93 on the training set and 0.90 on the validation set.

Conclusion: In conclusion, radiomics could be used to predict the response to immunotherapy in non-small-cell lung cancer patients. The use of transcriptomics or the HOT/COLD status, together with radiomics, may improve the working of the prediction models.

Breast cancer is a leading cause of death for women globally. A characteristic of breast cancer includes its ability to metastasize to distant regions of the body, and the disease achieves this through first spreading to the axillary lymph nodes. Traditional diagnosis of axillary lymph node metastasis includes an invasive technique that leads to potential clinical complications for breast cancer patients. The rise of artificial intelligence in the medical imaging field has led to the creation of innovative deep learning models that can predict the metastatic status of axillary lymph nodes noninvasively, which would result in no unnecessary biopsies and dissections for patients. In this review, we discuss the success of various deep learning artificial intelligence models across multiple imaging modalities in their performance of predicting axillary lymph node metastasis.