scholarly journals Comparison of Radiomic Models Based on Low-Dose and Standard-Dose CT for Prediction of Adenocarcinomas and Benign Lesions in Solid Pulmonary Nodules

2021 ◽  
Vol 10 ◽  
Author(s):  
Jieke Liu ◽  
Hao Xu ◽  
Haomiao Qing ◽  
Yong Li ◽  
Xi Yang ◽  
...  
Keyword(s):  
Low Dose ◽  
Validation Cohort ◽  
Standard Dose ◽  
Characteristic Curve ◽  
Pulmonary Nodules ◽  
Reconstruction Method ◽  
Solitary Pulmonary Nodules ◽  
Benign Lesions ◽  
Difference Matrix ◽  
Benign Nodules

ObjectivesThis study aimed to develop radiomic models based on low-dose CT (LDCT) and standard-dose CT to distinguish adenocarcinomas from benign lesions in patients with solid solitary pulmonary nodules and compare the performance among these radiomic models and Lung CT Screening Reporting and Data System (Lung-RADS). The reproducibility of radiomic features between LDCT and standard-dose CT were also evaluated.MethodsA total of 141 consecutive pathologically confirmed solid solitary pulmonary nodules were enrolled including 50 adenocarcinomas and 48 benign nodules in primary cohort and 22 adenocarcinomas and 21 benign nodules in validation cohort. LDCT and standard-dose CT scans were conducted using same acquisition parameters and reconstruction method except for radiation dose. All nodules were automatically segmented and 104 original radiomic features were extracted. The concordance correlation coefficient was used to quantify reproducibility of radiomic features between LDCT and standard-dose CT. Radiomic features were selected to build radiomic signature, and clinical characteristics and radiomic signature were combined to develop radiomic nomogram for LDCT and standard-dose CT, respectively. The performance of radiomic models and Lung-RADS was assessed by area under curve (AUC) of receiver operating characteristic curve, sensitivity, and specificity.ResultsShape and first order features, and neighboring gray tone difference matrix features were highly reproducible between LDCT and standard-dose CT. No significant differences of AUCs were found among radiomic signature and nomogram of LDCT and standard-dose CT in both primary and validation cohort (0.915 vs. 0.919 vs. 0.898 vs. 0.909 and 0.976 vs. 0.976 vs. 0.985 vs. 0.987, respectively). These radiomic models had higher specificity than Lung-RADS (all correct P < 0.05), while there were no significant differences of sensitivity between Lung-RADS and radiomic models.ConclusionsThe diagnostic performance of LDCT-based radiomic models to differentiate adenocarcinomas from benign lesions in solid pulmonary nodules were equivalent to that of standard-dose CT. The LDCT-based radiomic model with higher specificity and lower false-positive rate than Lung-RADS might help reduce overdiagnosis and overtreatment of solid pulmonary nodules in lung cancer screening.

Low-dose volume perfusion computed tomography (VPCT) for diagnosis of solitary pulmonary nodules

2016 ◽  
Vol 85 (6) ◽  
pp. 1208-1218 ◽  
Author(s):  
Yibo Sun ◽  
Minjie Yang ◽  
Dingbiao Mao ◽  
Fanzhen Lv ◽  
Yulei Yin ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Low Dose ◽  
Pulmonary Nodules ◽  
Solitary Pulmonary Nodules ◽  
Perfusion Computed Tomography ◽  
Dose Volume

Transbronchial Biopsy Guided by Low-Dose MDCT: A New Approach for Assessment of Solitary Pulmonary Nodules

2006 ◽  
Vol 187 (4) ◽  
pp. 933-939 ◽  
Author(s):  
Christoph M. Heyer ◽  
Thomas Kagel ◽  
Stefan P. Lemburg ◽  
Joerg W. Walter ◽  
Justus de Zeeuw ◽  
...  
Keyword(s):  
Low Dose ◽  
Pulmonary Nodules ◽  
Transbronchial Biopsy ◽  
Solitary Pulmonary Nodules ◽  
New Approach

Visual assessment of 18F-FDG metabolic spatial distribution improves the differential diagnosis of indeterminate pulmonary nodules and masses with high FDG uptake

2020 ◽  
Author(s):  
Jie Lin ◽  
Ling Wang ◽  
Xiaowei Ji ◽  
Xiangwu Zheng ◽  
Kun Tang
Keyword(s):  
Spatial Distribution ◽  
Visual Analysis ◽  
Characteristic Curve ◽  
Pulmonary Nodules ◽  
Distal Region ◽  
Proximal Region ◽  
Benign Lesions ◽  
Fdg Uptake ◽  
Pet Ct ◽  
Malignant Lesions

Abstract Purpose The aim of this study was to evaluate the value of visual analysis of 18 F-fluorodeoxyglucose ( 18 F-FDG) metabolic spatial distribution (V-FMSD) in diagnosis of indeterminate pulmonary nodules and masses with high 18 F-FDG uptake. Methods A total of 301 patients with indeterminate pulmonary nodules or masses who undergone 18 F-FDG positron emission tomography/computed tomography (PET/ CT) imaging were studied retrospectively. The characteristics of 18 F-FDG metabolic spatial distribution (FMSD) of proximal and distal regions of the lesion were visually analyzed using a 5-point scoring system. The sensitivity, specificity, accuracy and area under receiver operating characteristic curve (AUC) were compared between V-FMSD and conventional PET/CT methods for diagnosis of hypermetabolic indeterminate pulmonary nodules and masses. Results The V-FMSD results showed that 180 (92.8%) malignant lesions’ scores were ≥ 3 and 78 (72.9%) benign lesions’ scores were ≤ 2. It indicated that the FMSD in the proximal region of malignant lesions was significantly higher than that of the distal region, and the FMSD in the proximal region of benign lesions was significantly lower than that of the distal region. The specificity of V-FMSD was 72.9%, which was obviously higher than the maximum standard uptake value (SUVmax) (0%, P < 0.001) and retention index (RI) (26.2%, P < 0.001). The AUC of V-FMSD was 0.886, which was significantly larger than SUVmax (0.626, P < 0.001), RI (0.670, P < 0.001) and PET/CT (0.788, P < 0.05). Conclusions The characteristics of FMSD between pulmonary benign and malignant lesions are different. V-FMSD can be taken as a novel auxiliary marker to improve the diagnostic performance for hypermetabolic indeterminate pulmonary nodules and masses.

Prediction of invasive adenocarcinomas manifesting as pure ground-glass nodules based on radiomic signature of low-dose CT in lung cancer screening

2022 ◽  
Author(s):  
Yong Li ◽  
Jieke Liu ◽  
Xi Yang ◽  
Hao Xu ◽  
Haomiao Qing ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Low Dose ◽  
Validation Cohort ◽  
Standard Dose ◽  
Lung Cancer Screening ◽  
Ground Glass ◽  
Semantic Features ◽  
Low Dose Ct ◽  
Ground Glass Nodules

Objectives: To develop a radiomic model based on low-dose CT (LDCT) to distinguish invasive adenocarcinomas (IAs) from adenocarcinoma in situ/minimally invasive adenocarcinomas (AIS/MIAs) manifesting as pure ground-glass nodules (pGGNs) and compare its performance with conventional quantitative and semantic features of LDCT, radiomic model of standard-dose CT, and intraoperative frozen section (FS). Methods: A total of 147 consecutive pathologically confirmed pGGNs were divided into primary cohort (43 IAs and 60 AIS/MIAs) and validation cohort (19 IAs and 25 AIS/MIAs). Logistic regression models were built using conventional quantitative and semantic features, selected radiomic features of LDCT and standard-dose CT, and intraoperative FS diagnosis, respectively. The diagnostic performance was assessed by area under curve (AUC) of receiver operating characteristic curve, sensitivity, and specificity. Results: The AUCs of quantitative-semantic model, radiomic model of LDCT, radiomic model of standard-dose CT, and FS model were 0.879 (95% CI, 0.801–0.935), 0.929 (95% CI, 0.862–0.971), 0.941 (95% CI, 0.876–0.978), and 0.884 (95% CI, 0.805–0.938) in the primary cohort and 0.897 (95% CI, 0.768–0.968), 0.933 (95% CI, 0.815–0.986), 0.901 (95% CI, 0.773–0.970), and 0.828 (95% CI, 0.685–0.925) in the validation cohort. No significant difference of the AUCs was found among these models in both the primary and validation cohorts (all p > 0.05). Conclusions: The LDCT-based quantitative-semantic score and radiomic signature, with good predictive performance, can be preoperative and non-invasive biomarkers for assessing the invasive risk of pGGNs in lung cancer screening. Advances in knowledge: The LDCT-based quantitative-semantic score and radiomic signature, with the equivalent performance to the radiomic model of standard-dose CT, can be preoperative predictors for assessing the invasiveness of pGGNs in lung cancer screening and reducing excess examination and treatment.

Evaluation of an iterative model-based reconstruction of pediatric abdominal CT with regard to image quality and radiation dose

2017 ◽  
Vol 59 (6) ◽  
pp. 740-747
Author(s):  
Marie-Louise Aurumskjöld ◽  
Marcus Söderberg ◽  
Fredrik Stålhammar ◽  
Kristina Vult von Steyern ◽  
Anders Tingberg ◽  
...  
Keyword(s):  
Image Quality ◽  
Radiation Dose ◽  
Iterative Reconstruction ◽  
Low Dose ◽  
Pediatric Patients ◽  
Standard Dose ◽  
Reconstruction Method ◽  
Abdominal Ct ◽  
Model Based ◽  
Iterative Model

Background In pediatric patients, computed tomography (CT) is important in the medical chain of diagnosing and monitoring various diseases. Because children are more radiosensitive than adults, they require minimal radiation exposure. One way to achieve this goal is to implement new technical solutions, like iterative reconstruction. Purpose To evaluate the potential of a new, iterative, model-based method for reconstructing (IMR) pediatric abdominal CT at a low radiation dose and determine whether it maintains or improves image quality, compared to the current reconstruction method. Material and Methods Forty pediatric patients underwent abdominal CT. Twenty patients were examined with the standard dose settings and 20 patients were examined with a 32% lower radiation dose. Images from the standard examination were reconstructed with a hybrid iterative reconstruction method (iDose4), and images from the low-dose examinations were reconstructed with both iDose4 and IMR. Image quality was evaluated subjectively by three observers, according to modified EU image quality criteria, and evaluated objectively based on the noise observed in liver images. Results Visual grading characteristics analyses showed no difference in image quality between the standard dose examination reconstructed with iDose4 and the low dose examination reconstructed with IMR. IMR showed lower image noise in the liver compared to iDose4 images. Inter- and intra-observer variance was low: the intraclass coefficient was 0.66 (95% confidence interval = 0.60–0.71) for the three observers. Conclusion IMR provided image quality equivalent or superior to the standard iDose4 method for evaluating pediatric abdominal CT, even with a 32% dose reduction.

scholarly journals Spectral CT Analysis of Solitary Pulmonary Nodules for Differentiating Malignancy from Benignancy: The Value of Iodine Concentration Spatial Distribution Difference

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Linyu Wu ◽  
Guoquan Cao ◽  
Liang Zhao ◽  
Kun Tang ◽  
Jie Lin ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Sensitivity And Specificity ◽  
Pulmonary Nodules ◽  
Iodine Concentration ◽  
Proximal Region ◽  
Arterial Phase ◽  
Spectral Ct ◽  
Solitary Pulmonary Nodules ◽  
Multiple Parameters ◽  
Benign Nodules

Objective. The objective is to assess the value of spatial distribution difference in iodine concentration between malignant and benign solitary pulmonary nodules (SPNs) by analyzing multiple parameters of spectral CT. Methods. Sixty patients with 39 malignant nodules and 21 benign nodules underwent chest contrast CT scans using spectral imaging mode during pulmonary arterial phase (PP), arterial phase (AP), and venous phase (VP). Iodine concentrations of proximal and distal regions in pulmonary nodules on iodine-based material decomposition images were recorded. Normalized iodine concentration (NIC) and the differences in NIC between the proximal and the distal regions (dNIC) were calculated. The two-sample t-test and Mann–Whitney U-test were performed to compare the multiple parameters generated from spectral CT between malignant and benign nodules. Receiver operating characteristic (ROC) curves were generated to calculate sensitivity and specificity. Results. NIC in the proximal region (NICpro) and NIC in the distal region (NICdis) between malignant and benign nodules at AP (NICpro, P=0.012; NICdis, P=0.024), and VP (NICpro, P=0.005; NICdis, P =0.004) were significantly different. NICpro at PP (P = 0.037) was also found significantly different between malignant and benign nodules; however, no significant differences were found in NICdis at PP (P = 0.093). In addition, the dNIC of malignant nodules was significantly higher than that of benign ones at PP (median and interquartiles (0.31, 0.11, 0.57 versus -0.26, -0.5, -0.1); p≤0.001), AP (mean dNIC, 0.093 ±0.094 versus -0.075±0.060; p≤0.001), and VP (mean dNIC, 0.171±0.137 versus -0.183±0.127; p≤0.001). The sensitivity and specificity (93%, 95%, respectively) of dNIC during VP were higher than other parameters, with a threshold value of -0.07. Conclusions. Spectral CT imaging with multiple parameters such as NICpro, NICdis, and dNIC may be a new method for differentiating malignant SPNs from benign ones.

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