Papillary vs clear cell renal cell carcinoma. Differentiation and grading by iodine concentration using DECT—correlation with microvascular density

Marcon, Julian; Graser, Anno; Horst, David; Casuscelli, Jozefina; Spek, Annabel; Stief, Christian G.; Reiser, Maximilian; Rübenthaler, Johannes; Büchner, Alexander; Staehler, Michael

doi:10.1007/s00330-019-06298-2

Cited by 36 publications

(23 citation statements)

References 24 publications

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“…during the first week of the disease [39]. This renders the outcome predictor "extent of lung involvement" highly susceptible to the time point of imaging during early COVID-19.…”

Section: Plos Onementioning

confidence: 99%

Calcification of the thoracic aorta on low-dose chest CT predicts severe COVID-19

et al. 2020

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Background Cardiovascular comorbidity anticipates poor prognosis of SARS-CoV-2 disease (COVID-19) and correlates with the systemic atherosclerotic transformation of the arterial vessels. The amount of aortic wall calcification (AWC) can be estimated on low-dose chest CT. We suggest quantification of AWC on the low-dose chest CT, which is initially performed for the diagnosis of COVID-19, to screen for patients at risk of severe COVID-19. Methods Seventy consecutive patients (46 in center 1, 24 in center 2) with parallel low-dose chest CT and positive RT-PCR for SARS-CoV-2 were included in our multi-center, multi-vendor study. The outcome was rated moderate (no hospitalization, hospitalization) and severe (ICU, tracheal intubation, death), the latter implying a requirement for intensive care treatment. The amount of AWC was quantified with the CT vendor's software. Results Of 70 included patients, 38 developed a moderate, and 32 a severe COVID-19. The average volume of AWC was significantly higher throughout the subgroup with severe COVID-19, when compared to moderate cases (771.7 mm3 (Q1 = 49.8 mm3, Q3 = 3065.5 mm3) vs. 0 mm3 (Q1 = 0 mm3, Q3 = 57.3 mm3)). Within multivariate regression analysis, including AWC, patient age and sex, as well as a cardiovascular comorbidity score, the volume of AWC was the only significant regressor for severe COVID-19 (p = 0.004). For AWC > 3000 mm3, the logistic regression predicts risk for a severe progression of 0.78. If there are no visually detectable AWC risk for severe progression is 0.13, only. Conclusion AWC seems to be an independent biomarker for the prediction of severe progression and intensive care treatment of COVID-19 already at the time of patient admission to the hospital; verification in a larger multi-center, multi-vendor study is desired.

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“…during the first week of the disease [39]. This renders the outcome predictor "extent of lung involvement" highly susceptible to the time point of imaging during early COVID-19.…”

Section: Plos Onementioning

confidence: 99%

Calcification of the thoracic aorta on low-dose chest CT predicts severe COVID-19

et al. 2020

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“…Dual-energy CT (DECT) emerged as a cutting-edge technique that embraced a material decomposition algorithm (13,14) to separate different materials and obtain quantitative material concentrations, such as iodine uptake (IU, mg/ml). Preliminary studies have reported the use of the IU in different tumors (15)(16)(17). A study in 2015 reported the utility of IU in evaluating the response after NAC in gastric cancer (14).…”

Section: Introductionmentioning

confidence: 99%

The Performance of a Dual-Energy CT Derived Radiomics Model in Differentiating Serosal Invasion for Advanced Gastric Cancer Patients After Neoadjuvant Chemotherapy: Iodine Map Combined With 120-kV Equivalent Mixed Images

et al. 2021

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ObjectivesThe aim was to determine whether the dual-energy CT radiomics model derived from an iodine map (IM) has incremental diagnostic value for the model based on 120-kV equivalent mixed images (120 kVp) in preoperative restaging of serosal invasion with locally advanced gastric cancer (LAGC) after neoadjuvant chemotherapy (NAC).MethodsA total of 155 patients (110 in the training cohort and 45 in the testing cohort) with LAGC who had standard NAC before surgery were retrospectively enrolled. All CT images were analyzed by two radiologists for manual classification. Volumes of interests (VOIs) were delineated semi-automatically, and 1,226 radiomics features were extracted from every segmented lesion in both IM and 120 kVp images, respectively. Spearman’s correlation analysis and the least absolute shrinkage and selection operator (LASSO) penalized logistic regression were implemented for filtering unstable and redundant features and screening out vital features. Two predictive models (120 kVp and IM-120 kVp) based on 120 kVp selected features only and 120 kVp combined with IM selected features were established by multivariate logistic regression analysis. We then build a combination model (ComModel) developed with IM-120 kVp signature and ycT. The performance of these three models and manual classification were evaluated and compared.ResultThree radiomics models showed great predictive accuracy and performance in both the training and testing cohorts (ComModel: AUC: training, 0.953, testing, 0.914; IM-120 kVp: AUC: training, 0.953, testing, 0.879; 120 kVp: AUC: training, 0.940, testing, 0.831). All these models showed higher diagnostic accuracy (ComModel: 88.9%, IM-120 kVp: 84.4%, 120 kVp: 80.0%) than manual classification (68.9%) in the testing group. ComModel and IM-120 kVp model had better performances than manual classification both in the training (both p<0.001) and testing cohorts (p<0.001 and p=0.034, respectively).ConclusionsDual-energy CT-based radiomics models demonstrated convincible diagnostic performance in differentiating serosal invasion in preoperative restaging for LAGC. The radiomics features derived from IM showed great potential for improving the diagnostic capability.

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“…These were common imaging signs observed during the progression of COVID-19, second only to GGO. Crazy paving pattern has a similar pathological basis to reticular pattern, but it is more severe in degree and is also a signal that pneumonia is entering its peak stage [20].…”

Section: Plos Onementioning

confidence: 99%

“…Semiquantitative scoring was positively correlated with clinical severity [26][27][28][29]. Therefore, it may assist healthcare workers to quickly classify CT-positive cases of suspected COVID-19 [30,31] in the aforementioned cases and to choose the appropriate treatment by medical guidelines to save lives and control the outbreak. The comparatively wide range of scores (0-40) used in our study helps to refine the assessment, and thus may help clinicians to understand and identify the severity of the patients' condition.…”

Section: Plos Onementioning

confidence: 99%

Chest computed tomography (CT) findings and semiquantitative scoring of 60 patients with coronavirus disease 2019 (COVID-19): A retrospective imaging analysis combining anatomy and pathology

Zhang¹,

Jiang²,

Liu³

et al. 2020

PLoS ONE

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In this study, we ascertained the chest CT data of 60 patients admitted to 3 hospitals in Chongqing with confirmed COVID-19. We conducted anatomical and pathological analyses to elucidate the possible reasons for the distribution, morphology, and characteristics of COVID-19 in chest CT. We also shared a semiquantitative scoring of affected lung segments, which was recommended by our local medical association. This scoring system was applied to quantify the severity of the disease. The most frequent imaging findings of COVID-19 were subpleural ground glass opacities and consolidation; there was a significant difference in semiquantitative scores between the early, progressive, and severe stages of the disease. We conclude that the chest CT findings of COVID-19 showed certain characteristics because of the anatomical features of the human body and pathological changes caused by the virus. Therefore, chest CT is a valuable tool for facilitating the diagnosis of COVID-19 and semiquantitative scoring of affected lung segments may further elucidate diagnosis and assessment of disease severity. This will assist healthcare workers in diagnosing COVID-19 and assessing disease severity, facilitate the selection of appropriate treatment options, which is important for reducing the spread of the virus, saving lives, and controlling the pandemic.

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Papillary vs clear cell renal cell carcinoma. Differentiation and grading by iodine concentration using DECT—correlation with microvascular density

Cited by 36 publications

References 24 publications

Calcification of the thoracic aorta on low-dose chest CT predicts severe COVID-19

Calcification of the thoracic aorta on low-dose chest CT predicts severe COVID-19

The Performance of a Dual-Energy CT Derived Radiomics Model in Differentiating Serosal Invasion for Advanced Gastric Cancer Patients After Neoadjuvant Chemotherapy: Iodine Map Combined With 120-kV Equivalent Mixed Images

Chest computed tomography (CT) findings and semiquantitative scoring of 60 patients with coronavirus disease 2019 (COVID-19): A retrospective imaging analysis combining anatomy and pathology

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