18-FDG PSEUDOTUMORAL LESION WITH QUICK FLOWERING TO A TYPICAL LUNG CT COVID-19.
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ABSTRACT:
SUBMITTER: de Barry O
PROVIDER: S-EPMC7365052 | biostudies-literature | 2020 Oct
REPOSITORIES: biostudies-literature
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18-FDG PSEUDOTUMORAL LESION WITH QUICK FLOWERING TO A TYPICAL LUNG CT COVID-19.
de Barry Olivier O Cabral Dominique D Kahn Jean Emmanuel JE Vidal François F Carlier Robert-Yves RY El Hajjam Mostafa M
Radiology case reports 20200716 10
As coronavirus pandemic continues to spread over the world, physicians have to be aware of atypical features of COVID-19 pneumonia. We report the case of a 78-year-old man presenting with pseudo-tumoral focal lesion of the left lung which switched shortly to severe and diffuse COVID-19 induced pneumonia. Nuclear medicine physicians and radiologists should be careful of some misleading hypermetabolic aspects mostly in asymptomatic patients. ...[more]
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Project description:Background and objectiveCoronavirus disease 2019 (COVID-19) is a highly contagious virus spreading all around the world. Deep learning has been adopted as an effective technique to aid COVID-19 detection and segmentation from computed tomography (CT) images. The major challenge lies in the inadequate public COVID-19 datasets. Recently, transfer learning has become a widely used technique that leverages the knowledge gained while solving one problem and applying it to a different but related problem. However, it remains unclear whether various non-COVID19 lung lesions could contribute to segmenting COVID-19 infection areas and how to better conduct this transfer procedure. This paper provides a way to understand the transferability of non-COVID19 lung lesions and a better strategy to train a robust deep learning model for COVID-19 infection segmentation.MethodsBased on a publicly available COVID-19 CT dataset and three public non-COVID19 datasets, we evaluate four transfer learning methods using 3D U-Net as a standard encoder-decoder method. i) We introduce the multi-task learning method to get a multi-lesion pre-trained model for COVID-19 infection. ii) We propose and compare four transfer learning strategies with various performance gains and training time costs. Our proposed Hybrid-encoder Learning strategy introduces a Dedicated-encoder and an Adapted-encoder to extract COVID-19 infection features and general lung lesion features, respectively. An attention-based Selective Fusion unit is designed for dynamic feature selection and aggregation.ResultsExperiments show that trained with limited data, proposed Hybrid-encoder strategy based on multi-lesion pre-trained model achieves a mean DSC, NSD, Sensitivity, F1-score, Accuracy and MCC of 0.704, 0.735, 0.682, 0.707, 0.994 and 0.716, respectively, with better genetalization and lower over-fitting risks for segmenting COVID-19 infection.ConclusionsThe results reveal the benefits of transferring knowledge from non-COVID19 lung lesions, and learning from multiple lung lesion datasets can extract more general features, leading to accurate and robust pre-trained models. We further show the capability of the encoder to learn feature representations of lung lesions, which improves segmentation accuracy and facilitates training convergence. In addition, our proposed Hybrid-encoder learning method incorporates transferred lung lesion features from non-COVID19 datasets effectively and achieves significant improvement. These findings promote new insights into transfer learning for COVID-19 CT image segmentation, which can also be further generalized to other medical tasks.
| S-EPMC7899930 | biostudies-literature
Project description:PurposeIn the prediction of COVID-19 disease progression, a clear illustration and early determination of an area that will be affected by pneumonia remain great challenges. In this study, we aimed to predict and visualize the progression of lung lesions in COVID-19 patients in the early stage of illness by using chest CT.MethodsCOVID-19 patients who underwent three chest CT scans in the progressive phase were retrospectively enrolled. An extended CT ventilation imaging (CTVI) method was proposed in this work that was adapted to use two chest CT scans acquired on different days, and then lung ventilation maps were generated. The prediction maps were obtained according to the fractional ventilation values, which were related to pulmonary regional function and tissue property changes. The third CT scan was used to validate whether the prediction maps could be used to distinguish healthy regions and potential lesions.ResultsA total of 30 patients (mean age ± SD, 43 ± 10 years, 19 females, and 2-12 days between the second and third CT scans) were included in this study. The predicted lesion locations and sizes were almost the same as the true ones visualized in third CT scan. Quantitatively, the predicted lesion volumes and true lesion volumes showed both a good Pearson correlation (R2 = 0.80; P < 0.001) and good consistency in the Bland-Altman plot (mean bias = 0.04 cm3). Regarding the enlargements of the existing lesions, prediction results also exhibited a good Pearson correlation (R2 = 0.76; P < 0.001) with true lesion enlargements.ConclusionThe present findings demonstrated that the extended CTVI method could accurately predict and visualize the progression of lung lesions in COVID-19 patients in the early stage of illness, which is helpful for physicians to predetermine the severity of COVID-19 pneumonia and make effective treatment plans in advance.
| S-EPMC8210511 | biostudies-literature
Project description:The 2019 novel coronavirus pneumonia (COVID-19) is an ongoing global pandemic with a worldwide death toll of over 416,000 as of June 10, 2020. Although the first documented cases in Wuhan, China were patients with severe respiratory symptoms including cough, fever, fatigue, and shortness of breath, the disease process can also be asymptomatic. In this case report, an asymptomatic 63-year-old male with Lynch syndrome undergoing a routine staging fluorodeoxyglucose positron-emission tomography/computed tomography was found to have typical radiologic features of COVID-19 with marked pulmonary FDG uptake and was subsequently diagnosed via reverse-transcription polymerase chain reaction. Many studies have described the appearance of COVID-19 on chest radiography and CT with the most common imaging features being bilateral, peripheral, and basilar predominant ground glass opacities and consolidation. Although these findings are typically nonspecific for an atypical lung infection, early recognition of COVID-19 in the setting of a global pandemic (even in the asymptomatic patient) is critical in order to limit the spread of disease.
| S-EPMC7357466 | biostudies-literature
Project description:Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020.
| S-EPMC9444848 | biostudies-literature
Project description:Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020.
| S-EPMC8183044 | biostudies-literature
Project description:The aim of this study was to assess the risk of atherosclerosis in patients with lung cancer compared to patients with extrapulmonary malignancies using 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT). We hypothesized that patients with lung cancer would demonstrate increased FDG uptake in the thoracic aorta compared to patients with extrapulmonary cancers. Thirty-four lung cancer patients (21 male, 13 female, 64.1 ± 12.9 yo) were retrospectively compared to seventy-eight patients with extrapulmonary malignancies (46 male, 32 female, 59.6 ± 12.8 yo). Average maximum standardized uptake value (avgSUVmax) and maximum target-to-blood pool ratio (TBRmax) were measured by mapping regions of interest of the ascending aorta, aortic arch, and descending aorta. Two-tailed Student's t-test was used to assess the differences in avgSUVmax and TBRmax between the two groups and between smokers and non-smokers. Age and gender distribution between the groups were not statistically different. AvgSUVmax and TBRmax were statistically significant increase in lung cancer patients compared to extrapulmonary cancer patients in the ascending aorta, aortic arch, and descending aorta, suggesting a lung cancer-associated increased risk of atherosclerosis development. AvgSUVmax was not significantly different between smokers and non-smokers in all sections of the thoracic aorta. Moving forward, large, prospective studies that directly compare PET data between different malignancies of different stages will help determine the role of FDG-PET/CT in assessing paraneoplastic vascular disease.
| S-EPMC7936247 | biostudies-literature
Project description:PURPOSE:CT signs that are evocative of lung COVID-19 infections have been extensively described, whereas 18F-FDG-PET signs have not. Our current study aimed to identify specific COVID-19 18F-FDG-PET signs in patients that were (i) suspected to have a lung infection based on 18F-FDG-PET/CT recorded during the COVID-19 outbreak and (ii) whose COVID-19 diagnosis was definitely established or excluded by appropriate viral testing. METHODS:Twenty-two consecutive patients referred for routine 18F-FDG-PET/CT examinations during the COVID-19 outbreak (March 25th to May 15th 2020) and for whom CT slices were evocative of a lung infection were included in the study. All patients had undergone a SARS-COV-2 diagnostic test to confirm COVID-19 infection (positivity was based on molecular and/or serological tests) or exclude it (negativity of at least the serological test). RESULTS:Eleven patients were confirmed to be affected by COVID-19 (COVID+), whereas the other eleven patients were not (COVID-) and were predominantly suspected of having bacterial pneumonia. CT abnormalities were not significantly different between COVID+ and COVID- groups, although trends toward larger CT abnormalities (p?=?0.16) and lower rates of consolidation patterns (0.09) were observed in the COVID+ group. The maximal standardized uptake values (SUVmax) of lung areas with CT abnormalities were however significantly lower in the COVID+ than the COVID- group (3.7?±?1.9 vs. 6.9?±?4.1, p?=?0.03), with the highest SUVmax consistently not associated with COVID-19. CONCLUSION:Among CT abnormalities evocative of lung infection, those related to COVID-19 are associated with a more limited 18F-FDG uptake. This observation may help improve our ability to detect COVID-19 patients.
| S-EPMC7431215 | biostudies-literature
Project description:IntroductionCompartmental modelling is an established method of quantifying 18F-FDG uptake; however, only recently has it been applied to evaluate pulmonary inflammation. Implementation of compartmental models remains challenging in the lung, partly due to the low signal-to-noise ratio compared to other organs and the lack of standardisation. Good reproducibility is a key requirement of an imaging biomarker which has yet to be demonstrated in pulmonary compartmental models of 18F-FDG; in this paper, we address this unmet need.MethodsRetrospective subject data were obtained from the EVOLVE observational study: Ten COPD patients (age =66±9; 8M/2F), 10 α1ATD patients (age =63±8; 7M/3F) and 10 healthy volunteers (age =68±8; 9M/1F) never smokers. PET and CT images were co-registered, and whole lung regions were extracted from CT using an automated algorithm; the descending aorta was defined using a manually drawn region. Subsequent stages of the compartmental analysis were performed by two independent operators using (i) a MIAKATTM based pipeline and (ii) an in-house developed pipeline. We evaluated the metabolic rate constant of 18F-FDG (Kim) and the fractional blood volume (Vb); Bland-Altman plots were used to compare the results. Further, we adjusted the in-house pipeline to identify the salient features in the analysis which may help improve the standardisation of this technique in the lung.ResultsThe initial agreement on a subject level was poor: Bland-Altman coefficients of reproducibility for Kim and Vb were 0.0031 and 0.047 respectively. However, the effect size between the groups (i.e. COPD, α1ATD and healthy subjects) was similar using either pipeline. We identified the key drivers of this difference using an incremental approach: ROI methodology, modelling of the IDIF and time delay estimation. Adjustment of these factors led to improved Bland-Altman coefficients of reproducibility of 0.0015 and 0.027 for Kim and Vb respectively.ConclusionsDespite similar methodology, differences in implementation can lead to disparate results in the outcome parameters. When reporting the outcomes of lung compartmental modelling, we recommend the inclusion of the details of ROI methodology, input function fitting and time delay estimation to improve reproducibility.
| S-EPMC6915187 | biostudies-literature
Project description:BackgroundAn inflammatory reaction in the airways and lung parenchyma, comprised mainly of neutrophils and alveolar macrophages, is present in some patients with chronic obstructive pulmonary disease (COPD). Thoracic fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) has been proposed as a promising imaging biomarker to assess this inflammation. We sought to introduce a fully quantitative analysis method and compare this with previously published studies based on the Patlak approach using a dataset comprising 18F-FDG PET scans from COPD subjects with elevated circulating inflammatory markers (fibrinogen) and matched healthy volunteers (HV). Dynamic 18F-FDG PET scans were obtained for high-fibrinogen (>2.8 g/l) COPD subjects (N = 10) and never smoking HV (N = 10). Lungs were segmented using co-registered computed tomography images and subregions (upper, middle and lower) were semi-automatically defined. A quantitative analysis approach was developed, which corrects for the presence of air and blood in the lung (qABL method), enabling direct estimation of the metabolic rate of FDG in lung tissue. A normalised Patlak analysis approach was also performed to enable comparison with previously published results. Effect sizes (Hedge's g) were used to compare HV and COPD groups.ResultsThe qABL method detected no difference (Hedge's g = 0.15 [-0.76 1.04]) in the tissue metabolic rate of FDG in the whole lung between HV (μ = 6.0 ± 1.9 × 10-3 ml cm-3 min-1) and COPD (μ = 5.7 ± 1.7 × 10-3 ml cm-3 min-1). However, analysis with the normalised Patlak approach detected a significant difference (Hedge's g = -1.59 [-2.57 -0.48]) in whole lung between HV (μ = 2.9 ± 0.5 × 10-3 ml cm-3 min-1) and COPD (μ = 3.9 ± 0.7 × 10-3 ml cm-3 min-1). The normalised Patlak endpoint was shown to be a composite measure influenced by air volume, blood volume and actual uptake of 18F-FDG in lung tissue.ConclusionsWe have introduced a quantitative analysis method that provides a direct estimate of the metabolic rate of FDG in lung tissue. This work provides further understanding of the underlying origin of the 18F-FDG signal in the lung in disease groups and helps interpreting changes following standard or novel therapies.
| S-EPMC5445063 | biostudies-literature
Project description:BackgroundIdentification of epidermal growth factor receptor (EGFR) mutation types is crucial before tyrosine kinase inhibitors (TKIs) treatment. Radiomics is a new strategy to noninvasively predict the genetic status of cancer. In this study, we aimed to develop a predictive model based on 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) radiomic features to identify the specific EGFR mutation subtypes.MethodsWe retrospectively studied 18F-FDG PET/CT images of 148 patients with isolated lung lesions, which were scanned in two hospitals with different CT scan setting (slice thickness: 3 and 5 mm, respectively). The tumor regions were manually segmented on PET/CT images, and 1,570 radiomic features (1,470 from CT and 100 from PET) were extracted from the tumor regions. Seven hundred and ninety-four radiomic features insensitive to different CT settings were first selected using the Mann white U test, and collinear features were further removed from them by recursively calculating the variation inflation factor. Then, multiple supervised machine learning models were applied to identify prognostic radiomic features through: (I) a multi-variate random forest to select features of high importance in discriminating different EGFR mutation status; (II) a logistic regression model to select features of the highest predictive value of the EGFR subtypes. The EGFR mutation predicting model was constructed from prognostic radiomic features using the popular Xgboost machine-learning algorithm and validated using 3-fold cross-validation. The performance of predicting model was analyzed using the receiver operating characteristic curve (ROC) and measured with the area under the curve (AUC).ResultsTwo sets of prognostic radiomic features were found for specific EGFR mutation subtypes: 5 radiomic features for EGFR exon 19 deletions, and 5 radiomic features for EGFR exon 21 L858R missense. The corresponding radiomic predictors achieved the prediction accuracies of 0.77 and 0.92 in terms of AUC, respectively. Combing these two predictors, the overall model for predicting EGFR mutation positivity was also constructed, and the AUC was 0.87.ConclusionsIn our study, we established predictive models based on radiomic analysis of 18F-FDG PET/CT images. And it achieved a satisfying prediction power in the identification of EGFR mutation status as well as the certain EGFR mutation subtypes in lung cancer.
| S-EPMC7354146 | biostudies-literature