PD-0448: Are advanced quantitative CT imaging features associated with survival in HNSCC patients?

Objectives COVID-19 and Non-Covid-19 (NC) Pneumonia encountered high CT imaging overlaps during pandemic. The study aims to evaluate the effectiveness of image-based quantitative CT features in discriminating COVID-19 from NC Pneumonia. Materials and methods 145 patients with highly suspected COVID-19 were retrospectively enrolled from four centers in Sichuan Province during January 23 to March 23, 2020. 88 cases were confirmed as COVID-19, and 57 patients were NC. The dataset was randomly divided by 3:2 into training and testing sets. The quantitative CT radiomics features were extracted and screened sequentially by correlation analysis, Mann-Whitney U test, the least absolute shrinkage and selection operator (LASSO) logistic regression (LR) and backward stepwise LR with minimum AIC methods. The selected features were used to construct the LR model for differentiating COVID-19 from NC. Meanwhile, the differentiation performance of traditional quantitative CT features such as lesion volume ratio, ground glass opacity (GGO) or consolidation volume ratio were also considered and compared with Radiomics-based method. The receiver operating characteristic curve (ROC) analysis were conducted to evaluate the predicting performance. Results Compared with traditional CT quantitative features, radiomics features performed best with the highest Area Under Curve (AUC), sensitivity, specificity and accuracy in the training (0.994, 0.942, 1.0 and 0.965) and testing sets (0.977, 0.944, 0.870, 0.915) (Delong test, P < 0.001). Among CT volume-ratio based models using lesion or GGO component ratio, the model combining CT lesion score and component ratio performed better than others, with the AUC, sensitivity, specificity and accuracy of 0.84, 0.692, 0.853, 0.756 in the training set and 0.779, 0.667, 0.826, 0.729 in the testing set. The significant difference of the most selected wavelet transformed radiomics features between COVID-19 and NC might well reflect the CT signs. Conclusions The differentiation between COVID-19 and NC could be well improved by using radiomics features, compared with traditional CT quantitative values.

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Subtyping COPD by Using Visual and Quantitative CT Imaging Features

CHEST Journal ◽

10.1016/j.chest.2019.06.015 ◽

2020 ◽

Vol 157 (1) ◽

pp. 47-60 ◽

Cited By ~ 6

Author(s):

Jinkyeong Park ◽

Brian D. Hobbs ◽

James D. Crapo ◽

Barry J. Make ◽

Elizabeth A. Regan ◽

...

Keyword(s):

Ct Imaging ◽

Imaging Features ◽

Quantitative Ct

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Quantitative CT imaging features correlate with colorectal cancer liver metastases (CLM) mutational status

HPB ◽

10.1016/j.hpb.2018.02.268 ◽

2018 ◽

Vol 20 ◽

pp. S56-S57

Author(s):

R.M. Marcus ◽

D.T. Fuentes ◽

H.A. Lillemoe ◽

A. Qayyum ◽

T.A. Aloia

Keyword(s):

Colorectal Cancer ◽

Liver Metastases ◽

Ct Imaging ◽

Imaging Features ◽

Quantitative Ct ◽

Colorectal Cancer Liver Metastases ◽

Mutational Status

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PEMBELAJARAN SIMULASI PENCITRAAN CT DENGAN MENGGUNAKAN PRINSIP REKONSTRUKSI CITRA DALAM SOFTWARE MATLAB

Jurnal Dinamika Pendidikan ◽

10.33541/jdp.v8i3.127 ◽

2015 ◽

Vol 8 (3) ◽

pp. 161

Author(s):

Samuel Gideon

Keyword(s):

Image Reconstruction ◽

Ct Imaging ◽

The Body ◽

Reconstruction Technique ◽

Imaging Features ◽

Back Projection ◽

Cross Sectional ◽

Low Contrast ◽

Image Reconstruction Technique ◽

Imaging Algorithms

This research was conducted as a learning alternatives for study of CT (computed tomograpghy) imaging using image reconstruction technique which are inversion matrix, back projection and filtered back projection. CT imaging can produce images of objects that do not overlap. Objects more easily distinguishable although given the relatively low contrast. The image is generated on CT imaging is the result of reconstruction of the original object. Matlab allows us to create and write imaging algorithms easily, easy to undersand and gives applied and exciting other imaging features. In this study, an example cross-sectional image recon-struction performed on the body of prostate tumors using. With these methods, medical prac-titioner (such as oncology clinician, radiographer and medical physicist) allows to simulate the reconstruction of CT images which almost resembles the actual CT visualization techniques.Keywords : computed tomography (CT), image reconstruction, Matlab

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CT imaging features of the normal parathyroid gland in the dog

Tierärztliche Praxis Ausgabe K Kleintiere / Heimtiere ◽

10.1055/a-1236-4542 ◽

2020 ◽

Vol 48 (05) ◽

pp. 313-320

Author(s):

Esther Lautscham ◽

Clea von Klopmann ◽

Sebastian Schaub ◽

Christiane Stengel ◽

Antje Hartmann

Keyword(s):

Ct Scan ◽

Parathyroid Gland ◽

Spiral Ct ◽

Ct Imaging ◽

Field Of View ◽

Imaging Features ◽

Hounsfield Units ◽

Klinische Relevanz

Zusammenfassung Gegenstand und Ziel Ziel dieser prospektiven Pilotstudie war zu beurteilen, ob die physiologische Glandula parathyroidea beim Hund computertomografisch dargestellt werden kann, und eine Beschreibung ihres CT-Erscheinungsbildes zu geben. Material und Methoden In die Studie wurden 25 Hunde aufgenommen, bei denen aufgrund von Erkrankungen im Halsbereich ohne Bezug zu Schild- oder Nebenschilddrüse ein CT-Scan erfolgte. Einschlusskriterium waren unauffällige Befunde bei der allgemeinen klinischen Untersuchung und der Blutuntersuchung (Blutbild und blutchemische Analyse). CT-Bilder vor und nach Kontrastmittelapplikation (30–45 Sekunden nach der Kontrastmittelinjektion, frühe venöse Phase) wurden mit einem 16-Schichten-Spiral-CT unter Verwendung eines Field of View von 18 cm, einer Schichtdicke von 1 mm und einer Matrix von 512 × 512 angefertigt. Zwei Radiologen begutachteten die CT-Aufnahmen unabhängig voneinander. Die Sichtbarkeit der Parathyreoidea wurde erfasst und die Interobserver-Reliabilität ermittelt. Bei den darstellbaren Nebenschilddrüsen wurden folgende Parameter bestimmt: Größe, Dichte (in Hounsfield Units [HU], vor und nach Kontrastmittelgabe), Dichte der Schilddrüse, Abgrenzung (exzellent, mäßig, schlecht). Ergebnisse Nur 20 bzw. 25 Nebenschilddrüsen waren durch die beiden Untersucher erkennbar. Die Anzahl differierte zwischen Nativaufnahmen und Bildern nach Kontrastmittelgabe nicht. Die Interobserver-Reliabilität hinsichtlich der Erkennbarkeit war moderat (κ = 0,40). Für Länge, Breite und Höhe der Nebenschilddrüsen (Mittelwert ± Standardabweichung) ergaben sich 4,2 × 2,5 × 2,9 mm ± 1,3 × 0,8 × 1,0 mm. Die Dichte betrug 39,7 ± 20,6 HU vor und 103,1 ± 47,1 HU nach Kontrastmittelgabe. Damit stellten sich die Nebenschilddrüsen im Vergleich zur Schilddrüse (vor und nach Kontrastmittelgabe 166,7 ± 34,3 HU bzw. 234,0 ± 60,1 HU) hypoattenuierend dar. Schlussfolgerung Diese Studie liefert die erste Beschreibung des CT-Erscheinungsbilds der angenommen physiologischen Nebenschilddrüse beim Hund. Die Sichtbarkeit des Organs war jedoch schlecht. Klinische Relevanz Trotz der schlechten Visualisierung der Nebenschilddrüse im CT ist sie gelegentlich wahrnehmbar. Die ermittelten Dimensionen waren teilweise größer als bisher für sonografische Darstellung beschrieben, ohne dass die untersuchten Hunde erkennbare Symptome eines Hyperparathyreodismus aufwiesen. Eine computertomografisch sichtbare Nebenschilddrüse impliziert daher möglicherweise nicht unbedingt eine Erkrankung. Weitere Studien dazu sind notwendig.

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Semiautomated carotid artery plaque composition: are intraplaque CT imaging features associated with cardiovascular risk factors?

Neuroradiology ◽

10.1007/s00234-021-02662-6 ◽

2021 ◽

Author(s):

John C. Benson ◽

Giuseppe Lanzino ◽

Valentina Nardi ◽

Luis Savastano ◽

Amir Lerman ◽

...

Keyword(s):

Risk Factors ◽

Cardiovascular Risk ◽

Carotid Artery ◽

Cardiovascular Risk Factors ◽

Ct Imaging ◽

Imaging Features ◽

Plaque Composition ◽

Carotid Artery Plaque

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Early CT features of COVID-19 pneumonia, association with patients’ age and duration of presenting complaint

Egyptian Journal of Radiology and Nuclear Medicine ◽

10.1186/s43055-021-00539-5 ◽

2021 ◽

Vol 52 (1) ◽

Author(s):

Reem M. EL Kady ◽

Hosam A. Hassan ◽

Tareef S. Daqqaq ◽

Rania Makboul ◽

Hanan Mosleh Ibrahim

Keyword(s):

Pleural Effusion ◽

Ground Glass Opacity ◽

Ct Imaging ◽

Older Age ◽

Ground Glass ◽

Imaging Features ◽

Age Group ◽

Patient Age ◽

Atypical Features ◽

Patient Âgé

Abstract Background Coronavirus disease (COVID-19) is a respiratory syndrome with a variable degree of severity. Imaging is a vital component of disease monitoring and follow-up in coronavirus pulmonary syndromes. The study of temporal changes of CT findings of COVID-19 pneumonia can help in better understanding of disease pathogenesis and prediction of disease prognosis. In this study, we aim to determine the typical and atypical CT imaging features of COVID-19 and discuss the association of typical CT imaging features with the duration of the presenting complaint and patients’ age. Results The lesions showed unilateral distribution in 20% of cases and bilateral distribution in 80% of cases. The lesions involved the lower lung lobes in 30% of cases and showed diffuse involvement in 58.2% of cases. The lesions showed peripheral distribution in 74.5% of cases. The most common pattern was multifocal ground glass opacity found in 72.7% of cases. Atypical features like cavitation and pleural effusion can occur early in the disease course. There was significant association between increased number of the lesions, bilaterality, diffuse pattern of lung involvement and older age group (≥ 50 years old) and increased duration of presenting complaint (≥ 4 days). There was significant association between crazy-paving pattern and increased duration of presenting complaint. No significant association could be detected between any CT pattern and increased patient age. Conclusion The most common CT feature of COVID-19 was multifocal ground glass opacity. Atypical features like cavitation and pleural effusion can occur early in the course of the disease. Our cases showed more extensive lesions with bilateral and diffuse patterns of distribution in the older age group and with increased duration of presenting complaint. There was a significant association between crazy-paving pattern and increased duration of presenting complaint. No significant association could be detected between any CT pattern and increased patient age.

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Predictive CT features for the diagnosis of primary pulmonary mucoepidermoid carcinoma: comparison with squamous cell carcinomas and adenocarcinomas

Cancer Imaging ◽

10.1186/s40644-020-00375-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Xiaohua Ban ◽

Xinping Shen ◽

Huijun Hu ◽

Rong Zhang ◽

Chuanmiao Xie ◽

...

Keyword(s):

Logistic Regression ◽

Roc Curve ◽

Multinomial Logistic Regression ◽

Univariate Analysis ◽

Ct Imaging ◽

Squamous Cell Carcinomas ◽

Curve Analysis ◽

Imaging Features ◽

Roc Curve Analysis ◽

Ct Features

Abstract Background To determine the predictive CT imaging features for diagnosis in patients with primary pulmonary mucoepidermoid carcinomas (PMECs). Materials and methods CT imaging features of 37 patients with primary PMECs, 76 with squamous cell carcinomas (SCCs) and 78 with adenocarcinomas were retrospectively reviewed. The difference of CT features among the PMECs, SCCs and adenocarcinomas was analyzed using univariate analysis, followed by multinomial logistic regression and receiver operating characteristic (ROC) curve analysis. Results CT imaging features including tumor size, location, margin, shape, necrosis and degree of enhancement were significant different among the PMECs, SCCs and adenocarcinomas, as determined by univariate analysis (P < 0.05). Only lesion location, shape, margin and degree of enhancement remained independent factors in multinomial logistic regression analysis. ROC curve analysis showed that the area under curve of the obtained multinomial logistic regression model was 0.805 (95%CI: 0.704–0.906). Conclusion The prediction model derived from location, margin, shape and degree of enhancement can be used for preoperative diagnosis of PMECs.

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