Frontiers in Radiology
EISSN : 2673-8740
Published by: Frontiers Media SA (10.3389)
Total articles ≅ 29
Latest articles in this journal
Frontiers in Radiology, Volume 2; https://doi.org/10.3389/fradi.2022.810731
Malignant tumors is a serious public health threat. Among them, lung cancer, which has the highest fatality rate globally, has significantly endangered human health. With the development of artificial intelligence (AI) and its integration with medicine, AI research in malignant lung tumors has become critical. This article reviews the value of CAD, computer neural network deep learning, radiomics, molecular biomarkers, and digital pathology for the diagnosis, treatment, and prognosis of malignant lung tumors.
Frontiers in Radiology, Volume 2; https://doi.org/10.3389/fradi.2022.858963
A high proportion of massive patients with hepatocellular carcinoma (HCC) are not amenable for surgical resection at initial diagnosis, owing to insufficient future liver remnant (FLR) or an inadequate surgical margin. For such patients, portal vein embolization (PVE) is an essential approach to allow liver hypertrophy and prepare for subsequent surgery. However, the conversion resection rate of PVE only is unsatisfactory because of tumor progression while awaiting liver hypertrophy. We report here a successfully treated case of primary massive HCC, where surgical resection was completed after PVE and multimodality therapy, comprising hepatic artery infusion chemotherapy (HAIC), Lenvatinib plus Sintilimab. A pathologic complete response was achieved. This case demonstrates for the first time that combined PVE with multimodality therapy appears to be safe and effective for massive, potentially resectable HCC and can produce deep pathological remission in a primary tumor.
Frontiers in Radiology, Volume 2; https://doi.org/10.3389/fradi.2022.809373
In the follow-up treatment of high-grade gliomas (HGGs), differentiating true tumor progression from treatment-related effects, such as pseudoprogression and radiation necrosis, presents an ongoing clinical challenge. Conventional MRI with and without intravenous contrast serves as the clinical benchmark for the posttreatment surveillance imaging of HGG. However, many advanced imaging techniques have shown promise in helping better delineate the findings in indeterminate scenarios, as posttreatment effects can often mimic true tumor progression on conventional imaging. These challenges are further confounded by the histologic admixture that can commonly occur between tumor growth and treatment-related effects within the posttreatment bed. This review discusses the current practices in the surveillance imaging of HGG and the role of advanced imaging techniques, including perfusion MRI and metabolic MRI.
Frontiers in Radiology, Volume 2; https://doi.org/10.3389/fradi.2022.781536
Objective: The disease COVID-19 has caused a widespread global pandemic with ~3. 93 million deaths worldwide. In this work, we present three models—radiomics (MRM), clinical (MCM), and combined clinical–radiomics (MRCM) nomogram to predict COVID-19-positive patients who will end up needing invasive mechanical ventilation from the baseline CT scans.Methods: We performed a retrospective multicohort study of individuals with COVID-19-positive findings for a total of 897 patients from two different institutions (Renmin Hospital of Wuhan University, D1 = 787, and University Hospitals, US D2 = 110). The patients from institution-1 were divided into 60% training, (N = 473), and 40% test set (N = 314). The patients from institution-2 were used for an independent validation test set (N = 110). A U-Net-based neural network (CNN) was trained to automatically segment out the COVID consolidation regions on the CT scans. The segmented regions from the CT scans were used for extracting first- and higher-order radiomic textural features. The top radiomic and clinical features were selected using the least absolute shrinkage and selection operator (LASSO) with an optimal binomial regression model within .Results: The three out of the top five features identified using were higher-order textural features (GLCM, GLRLM, GLSZM), whereas the last two features included the total absolute infection size on the CT scan and the total intensity of the COVID consolidations. The radiomics model (MRM) was constructed using the radiomic score built using the coefficients obtained from the LASSO logistic model used within the linear regression (LR) classifier. The MRM yielded an area under the receiver operating characteristic curve (AUC) of 0.754 (0.709–0.799) on , 0.836 on , and 0.748 . The top prognostic clinical factors identified in the analysis were dehydrogenase (LDH), age, and albumin (ALB). The clinical model had an AUC of 0.784 (0.743–0.825) on , 0.813 on , and 0.688 on . Finally, the combined model, MRCM integrating radiomic score, age, LDH and ALB, yielded an AUC of 0.814 (0.774–0.853) on , 0.847 on , and 0.771 on . The MRCM had an overall improvement in the performance of ~5.85% (: p = 0.0031; p = 0.0165; : p = 0.0369) over MCM.Conclusion: The novel integrated imaging and clinical model (MRCM) outperformed both models (MRM) and (MCM). Our results across multiple sites suggest that the integrated nomogram could help identify COVID-19 patients with more severe disease phenotype and potentially require mechanical ventilation.
Frontiers in Radiology, Volume 2; https://doi.org/10.3389/fradi.2022.835834
Traumatic bowel and mesenteric injuries (TBMI) have significant morbidity and mortality. The physical examination is often limited and sometimes not feasible in the trauma patient. Multidetector CT (MDCT) detection of TBMI is challenging and can be life-saving. Dual-energy CT (DECT) utilizes iodine overlay, monoenergetic imaging, and metal artifact reduction to enhance the conspicuity of TBMI. DECT may improve the conspicuity of TBMI leading to increased diagnostic accuracy and confidence. The aim of the article is to review the state of the art and applications of DECT in bowel trauma.
Frontiers in Radiology, Volume 2; https://doi.org/10.3389/fradi.2022.856460
Hepatocellular carcinoma (HCC) is a primary liver cancer that produces a high mortality rate. It is one of the most common malignancies worldwide, especially in Asia, Africa, and southern Europe. Although surgical resection is an effective treatment, patients with HCC are at risk of recurrence after surgery. Preoperative early recurrence prediction for patients with liver cancer can help physicians develop treatment plans and will enable physicians to guide patients in postoperative follow-up. However, the conventional clinical data based methods ignore the imaging information of patients. Certain studies have used radiomic models for early recurrence prediction in HCC patients with good results, and the medical images of patients have been shown to be effective in predicting the recurrence of HCC. In recent years, deep learning models have demonstrated the potential to outperform the radiomics-based models. In this paper, we propose a prediction model based on deep learning that contains intra-phase attention and inter-phase attention. Intra-phase attention focuses on important information of different channels and space in the same phase, whereas inter-phase attention focuses on important information between different phases. We also propose a fusion model to combine the image features with clinical data. Our experiment results prove that our fusion model has superior performance over the models that use clinical data only or the CT image only. Our model achieved a prediction accuracy of 81.2%, and the area under the curve was 0.869.
Frontiers in Radiology, Volume 2; https://doi.org/10.3389/fradi.2022.782864
Alzheimer's disease (AD) is the leading cause of dementia; however, men and women face differential AD prevalence, presentation, and progression risks. Characterizing metabolomic profiles during AD progression is fundamental to understand the metabolic disruptions and the biological pathways involved. However, outstanding questions remain of whether peripheral metabolic changes occur equally in men and women with AD. Here, we evaluated differential effects of metabolomic and brain volume associations between sexes. We used three cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI), evaluated 1,368 participants, two metabolomic platforms with 380 metabolites in total, and six brain segment volumes. Using dimension reduction techniques, we took advantage of the correlation structure of the brain volume phenotypes and the metabolite concentration values to reduce the number of tests while aggregating relevant biological structures. Using WGCNA, we aggregated modules of highly co-expressed metabolites. On the other hand, we used partial least squares regression-discriminant analysis (PLS-DA) to extract components of brain volumes that maximally co-vary with AD diagnosis as phenotypes. We tested for differences in effect sizes between sexes in the association between single metabolite and metabolite modules with the brain volume components. We found five metabolite modules and 125 single metabolites with significant differences between sexes. These results highlight a differential lipid disruption in AD progression between sexes. Men showed a greater negative association of phosphatidylcholines and sphingomyelins and a positive association of VLDL and large LDL with AD progression. In contrast, women showed a positive association of triglycerides in VLDL and small and medium LDL with AD progression. Explicitly identifying sex differences in metabolomics during AD progression can highlight particular metabolic disruptions in each sex. Our research study and strategy can lead to better-tailored studies and better-suited treatments that take sex differences into account.
Frontiers in Radiology, Volume 2; https://doi.org/10.3389/fradi.2022.837191
Objective: The Koos grading scale is a frequently used classification system for vestibular schwannoma (VS) that accounts for extrameatal tumor dimension and compression of the brain stem. We propose an artificial intelligence (AI) pipeline to fully automate the segmentation and Koos classification of VS from MRI to improve clinical workflow and facilitate patient management.Methods: We propose a method for Koos classification that does not only rely on available images but also on automatically generated segmentations. Artificial neural networks were trained and tested based on manual tumor segmentations and ground truth Koos grades of contrast-enhanced T1-weighted (ceT1) and high-resolution T2-weighted (hrT2) MR images from subjects with a single sporadic VS, acquired on a single scanner and with a standardized protocol. The first stage of the pipeline comprises a convolutional neural network (CNN) which can segment the VS and 7 adjacent structures. For the second stage, we propose two complementary approaches that are combined in an ensemble. The first approach applies a second CNN to the segmentation output to predict the Koos grade, the other approach extracts handcrafted features which are passed to a Random Forest classifier. The pipeline results were compared to those achieved by two neurosurgeons.Results: Eligible patients (n = 308) were pseudo-randomly split into 5 groups to evaluate the model performance with 5-fold cross-validation. The weighted macro-averaged mean absolute error (MA-MAE), weighted macro-averaged F1 score (F1), and accuracy score of the ensemble model were assessed on the testing sets as follows: MA-MAE = 0.11 ± 0.05, F1 = 89.3 ± 3.0%, accuracy = 89.3 ± 2.9%, which was comparable to the average performance of two neurosurgeons: MA-MAE = 0.11 ± 0.08, F1 = 89.1 ± 5.2, accuracy = 88.6 ± 5.8%. Inter-rater reliability was assessed by calculating Fleiss' generalized kappa (k = 0.68) based on all 308 cases, and intra-rater reliabilities of annotator 1 (k = 0.95) and annotator 2 (k = 0.82) were calculated according to the weighted kappa metric with quadratic (Fleiss-Cohen) weights based on 15 randomly selected cases.Conclusions: We developed the first AI framework to automatically classify VS according to the Koos scale. The excellent results show that the accuracy of the framework is comparable to that of neurosurgeons and may therefore facilitate management of patients with VS. The models, code, and ground truth Koos grades for a subset of publicly available images (n = 188) will be released upon publication.
Frontiers in Radiology, Volume 2; https://doi.org/10.3389/fradi.2022.850911
Purpose: To identify common findings visualized on CT following damage control laparotomy, including post-surgical changes and additional injuries, and to determine change in frequency of post-laparotomy CT at our institution over time. Methods: Single institution, IRB-Exempt, retrospective review of the University of Kentucky trauma registry from 1/2006 to 2/2019 for all trauma patients undergoing exploratory laparotomy initially and subsequently undergoing CT of the abdomen and pelvis within 24 hours. Operative findings from surgical operation notes and findings reported on post-laparotomy CT were recorded, including vascular and solid organ injuries, operative changes, free intraperitoneal fluid/air, and retroperitoneal findings. Next steps in management were also recorded. Results: In total 1,047 patients underwent exploratory laparotomy initially at our institution between 1/2006-2/2019. Of those, only 96 had a diagnostic CT of the abdomen and pelvis within 24 h after initial surgery, first occurring in 2010. Among these 96, there were 71 blunt and 25 penetrating injuries. Most common injuries recognized during exploratory laparotomy were bowel/mesentery (55), spleen (34), and liver (26). Regarding CT findings, all patients (96/96, 100%) had residual pneumoperitoneum, 84/96 (87.5%) had residual hemoperitoneum, 36/96 (37.5%) noted post-surgical changes or additional injuries to the spleen, 36/96 (37.5%) to the bowel/mesentery, and 32/96 (33.3%) to the liver, and 34/96 (35.4%) were noted to have pelvic fractures. After CT, 31/96 (32.3%) went back to the OR for relook laparotomy and additional surgical intervention and 7/96 (7.3%) went to IR for embolization. Most common procedures during relaparotomy involved the bowel (8) and solid organs (6). Conclusions: CT examination within 24 h post damage control laparotomy was exceedingly rare at our institution prior to 2012 but has steadily increased. Frequency now averages 20.5% yearly. Damage control laparotomy is an uncommon clinical scenario; however, knowledge of frequent injuries and common post-operative changes will aid in radiologist detection of additional injuries helping shape next step management and provide adequate therapy.
Frontiers in Radiology, Volume 1; https://doi.org/10.3389/fradi.2021.790456
The treatment of recurrent high-grade gliomas remains a major challenge of daily neuro-oncology practice, and imaging findings of new therapies may be challenging. Regorafenib is a multi-kinase inhibitor that has recently been introduced into clinical practice to treat recurrent glioblastoma, bringing with it a novel panel of MRI imaging findings. On the basis of the few data in the literature and on our personal experience, we have identified the main MRI changes during regorafenib therapy, and then, we defined two different patterns, trying to create a simple summary line of the main changes of pathological tissue during therapy. We named these patterns, respectively, pattern A (less frequent, similar to classical progression disease) and pattern B (more frequent, with decreased diffusivity and decrease contrast-enhancement). We have also reported MR changes concerning signal intensity on T1-weighted and T2-weighted images, SWI, and perfusion imaging, derived from the literature (small series or case reports) and from our clinical experience. The clinical implication of these imaging modifications remains to be defined, taking into account that we are still at the dawn in the evaluation of such imaging modifications.