Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans ...for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.
Multiparametric (mp) prostate magnetic resonance imaging (MRI) is playing an increasingly prominent role in the diagnostic work-up of patients with suspected prostate cancer. Performing mpMRI before ...biopsy offers several advantages including biopsy avoidance under certain clinical circumstances and targeting biopsy of suspicious lesions to enable the correct diagnosis. The success of the technique is heavily dependent on high-quality image acquisition, interpretation, and report communication, all areas addressed by previous versions of the Prostate Imaging-Reporting and Data System (PI-RADS) recommendations. Numerous studies have validated the approach, but the widespread adoption of PI-RADS version 2 has also highlighted inconsistencies and limitations, particularly relating to interobserver variability for evaluation of the transition zone. These limitations are addressed in the recently released version 2.1. In this article, we highlight the key changes proposed in PI-RADS v2.1 and explore the background reasoning and evidence for the recommendations.
Chest X-ray plays a key role in diagnosis and management of COVID-19 patients and imaging features associated with clinical elements may assist with the development or validation of automated image ...analysis tools. We aimed to identify associations between clinical and radiographic features as well as to assess the feasibility of deep learning applied to chest X-rays in the setting of an acute COVID-19 outbreak.
A retrospective study of X-rays, clinical, and laboratory data was performed from 48 SARS-CoV-2 RT-PCR positive patients (age 60±17 years, 15 women) between February 22 and March 6, 2020 from a tertiary care hospital in Milan, Italy. Sixty-five chest X-rays were reviewed by two radiologists for alveolar and interstitial opacities and classified by severity on a scale from 0 to 3. Clinical factors (age, symptoms, comorbidities) were investigated for association with opacity severity and also with placement of central line or endotracheal tube. Deep learning models were then trained for two tasks: lung segmentation and opacity detection. Imaging characteristics were compared to clinical datapoints using the unpaired student's t-test or Mann-Whitney U test. Cohen's kappa analysis was used to evaluate the concordance of deep learning to conventional radiologist interpretation.
Fifty-six percent of patients presented with alveolar opacities, 73% had interstitial opacities, and 23% had normal X-rays. The presence of alveolar or interstitial opacities was statistically correlated with age (P = 0.008) and comorbidities (P = 0.005). The extent of alveolar or interstitial opacities on baseline X-ray was significantly associated with the presence of endotracheal tube (P = 0.0008 and P = 0.049) or central line (P = 0.003 and P = 0.007). In comparison to human interpretation, the deep learning model achieved a kappa concordance of 0.51 for alveolar opacities and 0.71 for interstitial opacities.
Chest X-ray analysis in an acute COVID-19 outbreak showed that the severity of opacities was associated with advanced age, comorbidities, as well as acuity of care. Artificial intelligence tools based upon deep learning of COVID-19 chest X-rays are feasible in the acute outbreak setting.
CE-MRC has been in use for the past 15 years and was reported to be a useful method in the evaluation of CSF disorders and hydrocephalus. The use of CE-MRC in conjunction with other MR imaging ...techniques has been shown to be effective in selected cases for the evaluation of several disorders of cerebrospinal system. CE-MRC has certain advantages over other cisternographic studies with fewer side effects if performed properly. Although intrathecal Gd administration is not widely accepted yet, several recent studies have reported the safety of small-dose intrathecal gadolinium injection. In this review, we describe CE-MRC and review recent applications in several clinical conditions.
Prostate cancer is the second most prevalent cancer in men worldwide and its incidence is expected to double by 2030. Multi-parametric magnetic resonance imaging (MRI) incorporating anatomical and ...functional imaging has now been validated as a means of detecting and characterising prostate tumours and can aid in risk stratification and treatment selection. The European Society of Urogenital Radiology (ESUR) in 2012 established the Prostate Imaging—Reporting and Data System (PI-RADS) guidelines aimed at standardising the acquisition, interpretation and reporting of prostate MRI. Subsequent experience and technical developments have highlighted some limitations, and a joint steering committee formed by the American College of Radiology, ESUR, and the AdMeTech Foundation have recently announced an updated version of the proposals. We summarise the main proposals of PI-RADS version 2, explore the evidence behind the recommendations, and highlight key differences for the benefit of those already familiar with the original.
Alveolar soft part sarcoma (ASPS) is a rare, highly vascular tumor, for which no effective standard systemic treatment exists for patients with unresectable disease. Cediranib is a potent, oral ...small-molecule inhibitor of all three vascular endothelial growth factor receptors (VEGFRs).
We conducted a phase II trial of once-daily cediranib (30 mg) given in 28-day cycles for patients with metastatic, unresectable ASPS to determine the objective response rate (ORR). We also compared gene expression profiles in pre- and post-treatment tumor biopsies and evaluated the effect of cediranib on tumor proliferation and angiogenesis using positron emission tomography and dynamic contrast-enhanced magnetic resonance imaging.
Of 46 patients enrolled, 43 were evaluable for response at the time of analysis. The ORR was 35%, with 15 of 43 patients achieving a partial response. Twenty-six patients (60%) had stable disease as the best response, with a disease control rate (partial response + stable disease) at 24 weeks of 84%. Microarray analysis with validation by quantitative real-time polymerase chain reaction on paired tumor biopsies from eight patients demonstrated downregulation of genes related to vasculogenesis.
In this largest prospective trial to date of systemic therapy for metastatic ASPS, we observed that cediranib has substantial single-agent activity, producing an ORR of 35% and a disease control rate of 84% at 24 weeks. On the basis of these results, an open-label, multicenter, randomized phase II registration trial is currently being conducted for patients with metastatic ASPS comparing cediranib with another VEGFR inhibitor, sunitinib.
A better understanding of temporal relationships between chest CT and labs may provide a reference for disease severity over the disease course. Generalized curves of lung opacity volume and density ...over time can be used as standardized references from well before symptoms develop to over a month after recovery, when residual lung opacities remain. 739 patients with COVID-19 underwent CT and RT-PCR in an outbreak setting between January 21st and April 12th, 2020. 29 of 739 patients had serial exams (121 CTs and 279 laboratory measurements) over 50 ± 16 days, with an average of 4.2 sequential CTs each. Sequential volumes of total lung, overall opacity and opacity subtypes (ground glass opacity GGO and consolidation) were extracted using deep learning and manual segmentation. Generalized temporal curves of CT and laboratory measurements were correlated. Lung opacities appeared 3.4 ± 2.2 days prior to symptom onset. Opacity peaked 1 day after symptom onset. GGO onset was earlier and resolved later than consolidation. Lactate dehydrogenase, and C-reactive protein peaked earlier than procalcitonin and leukopenia. The temporal relationships of quantitative CT features and clinical labs have distinctive patterns and peaks in relation to symptom onset, which may inform early clinical course in patients with mild COVID-19 pneumonia, or may shed light upon chronic lung effects or mechanisms of medical countermeasures in clinical trials.
Purpose:
To develop an automated system for mediastinal lymph node detection and station mapping for chest CT.
Methods:
The contextual organs, trachea, lungs, and spine are first automatically ...identified to locate the region of interest (ROI) (mediastinum). The authors employ shape features derived from Hessian analysis, local object scale, and circular transformation that are computed per voxel in the ROI. Eight more anatomical structures are simultaneously segmented by multiatlas label fusion. Spatial priors are defined as the relative multidimensional distance vectors corresponding to each structure. Intensity, shape, and spatial prior features are integrated and parsed by a random forest classifier for lymph node detection. The detected candidates are then segmented by the following curve evolution process. Texture features are computed on the segmented lymph nodes and a support vector machine committee is used for final classification. For lymph node station labeling, based on the segmentation results of the above anatomical structures, the textual definitions of mediastinal lymph node map according to the International Association for the Study of Lung Cancer are converted into patient-specific color-coded CT image, where the lymph node station can be automatically assigned for each detected node.
Results:
The chest CT volumes from 70 patients with 316 enlarged mediastinal lymph nodes are used for validation. For lymph node detection, their system achieves 88% sensitivity at eight false positives per patient. For lymph node station labeling, 84.5% of lymph nodes are correctly assigned to their stations.
Conclusions:
Multiple-channel shape, intensity, and spatial prior features aggregated by a random forest classifier improve mediastinal lymph node detection on chest CT. Using the location information of segmented anatomic structures from the multiatlas formulation enables accurate identification of lymph node stations.
Diagnosis of AS and periaqueductal abnormalities by routine MR imaging sequences is challenging for neuroradiologists. The aim of our study was to evaluate the utility of the 3D-SPACE sequence with ...VFAM in patients with suspected AS.
PC-MRI and 3D-SPACE images were obtained in 21 patients who had hydrocephalus on routine MR imaging scans and had clinical suspicion of AS, as well as in 12 control subjects. Aqueductal patency was visually scored (grade 0, normal; grade 1, partial obstruction; grade 2, complete stenosis) by 2 experienced radiologists on PC-MRI (plus routine T1-weighted and T2-weighted images) and 3D-SPACE images. Two separate scores were statistically compared with each other as well as with the consensus scores obtained from general agreement of both radiologists.
There was an excellent correlation between 3D-SPACE and PC-MRI scores (κ = 0.828). The correlation between 3D-SPACE scorings and consensus-based scorings was higher compared with the correlation between PC-MRI and consensus-based scorings (r = 1, P < .001 and r = 0.966, P < .001, respectively).
3D-SPACE sequence with VFAM alone can be used for adequate and successful evaluation of the aqueductal patency without the need for additional sequences and examinations. Noninvasive evaluation of the whole cranium is possible in a short time with high resolution by using 3D-SPACE.
PDF
