Lung nodules assessment in ultra-low-dose CT with iterative reconstruction compared to conventional dose CT

Jin, Shiqi; Zhang, Bo; Zhang, Lina; Shu, Li; Li, Songbai; Li, Peiling

doi:10.21037/qims.2018.06.05

Cited by 29 publications

(21 citation statements)

References 28 publications

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“…The features of NHHNs include hypovascular well-differentiated HCCs, dysplastic nodules, and other benign nodules, and it is difficult to specify their precise nature by imaging (43). In a previous study, 75 ( kjronline.org HCC during a mean follow-up period of 388 days, and similar results have been reported in other studies as well (44)(45)(46). Meanwhile, the presence of an Rim of the tumor on CT or MRI has been reported to be a feature associated with scirrhous and sarcomatous HCCs as well as early tumor recurrence (11,47,48).…”

Section: Discussionsupporting

confidence: 52%

Preoperative Prediction for Early Recurrence Can Be as Accurate as Postoperative Assessment in Single Hepatocellular Carcinoma Patients

Jang

Kim

et al. 2020

Korean J Radiol

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Objective: To evaluate the performance of predicting early recurrence using preoperative factors only in comparison with using both pre-/postoperative factors. Materials and Methods: We retrospectively reviewed 549 patients who had undergone curative resection for single hepatcellular carcinoma (HCC) within Milan criteria. Multivariable analysis was performed to identify pre-/postoperative highrisk factors of early recurrence after hepatic resection for HCC. Two prediction models for early HCC recurrence determined by stepwise variable selection methods based on Akaike information criterion were built, either based on preoperative factors alone or both pre-/postoperative factors. Area under the curve (AUC) for each receiver operating characteristic curve of the two models was calculated, and the two curves were compared for non-inferiority testing. The predictive models of early HCC recurrence were internally validated by bootstrap resampling method. Results: Multivariable analysis on preoperative factors alone identified aspartate aminotransferase/platelet ratio index (OR, 1.632; 95% CI, 1.056-2.522; p = 0.027), tumor size (OR, 1.025; 95% CI, 0.002-1.049; p = 0.031), arterial rim enhancement of the tumor (OR, 2.350; 95% CI, 1.297-4.260; p = 0.005), and presence of nonhypervascular hepatobiliary hypointense nodules (OR, 1.983; 95% CI, 1.049-3.750; p = 0.035) on gadoxetic acid-enhanced magnetic resonance imaging as significant factors. After adding postoperative histopathologic factors, presence of microvascular invasion (OR, 1.868; 95% CI, 1.155-3.022; p = 0.011) became an additional significant factor, while tumor size became insignificant (p = 0.119). Comparison of the AUCs of the two models showed that the prediction model built on preoperative factors alone was not inferior to that including both pre-/postoperative factors {AUC for preoperative factors only, 0.673 (95% confidence interval [CI], 0.623-0.723) vs. AUC after adding postoperative factors, 0.691 (95% CI, 0.639-0.744); p = 0.0013}. Bootstrap resampling method showed that both the models were valid. Conclusion: Risk stratification solely based on preoperative imaging and laboratory factors was not inferior to that based on postoperative histopathologic risk factors in predicting early recurrence after curative resection in within Milan criteria single HCC patients.

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Section: Discussionsupporting

confidence: 52%

Preoperative Prediction for Early Recurrence Can Be as Accurate as Postoperative Assessment in Single Hepatocellular Carcinoma Patients

Jang

Kim

et al. 2020

Korean J Radiol

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“…Majority of the previous research work has focused on the early detection of lungs cancer using the texture‐based interpretation of chest CTs (Reeves & Kostis, ). It includes techniques for nodule detection (Lee, Hara, Fujita, Itoh, & Ishigaki, ), nodule classification (benign or malignant; S. Naqi, Sharif, Yasmin, & Fernandes, ; S. M. Naqi, Sharif, & Jaffar, ), and nodule size and growth rate measurements (Jin et al, ). Some researchers have turned their focus on correct detection and quantification of similar diseases and/or patterns like ground glass opacities (Kauczor et al, ) and Emphysema (Yankelevitz, Reeves, Kostis, Zhao, & Henschke, ).…”

Section: Introductionmentioning

confidence: 99%

Lungs nodule detection framework from computed tomography images using support vector machine

Khan

Nazir

Khan

et al. 2019

Microscopy Res & Technique

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The emergence of cloud infrastructure has the potential to provide significant benefits in a variety of areas in the medical imaging field. The driving force behind the extensive use of cloud infrastructure for medical image processing is the exponential increase in the size of computed tomography (CT) and magnetic resonance imaging (MRI) data. The size of a single CT/MRI image has increased manifold since the inception of these imagery techniques. This demand for the introduction of effective and efficient frameworks for extracting relevant and most suitable information (features) from these sizeable images. As early detection of lungs cancer can significantly increase the chances of survival of a lung scanner patient, an effective and efficient nodule detection system can play a vital role. In this article, we have proposed a novel classification framework for lungs nodule classification with less false positive rates (FPRs), high accuracy, sensitivity rate, less computationally expensive and uses a small set of features while preserving edge and texture information. The proposed framework comprises multiple phases that include image contrast enhancement, segmentation, feature extraction, followed by an employment of these features for training and testing of a selected classifier. Image preprocessing and feature selection being the primary steps—playing their vital role in achieving improved classification accuracy. We have empirically tested the efficacy of our technique by utilizing the well‐known Lungs Image Consortium Database dataset. The results prove that the technique is highly effective for reducing FPRs with an impressive sensitivity rate of 97.45%.

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“…That study used a different anthropomorphic phantom, with different lung nodules sizes and attenuation values (−630 HU and 100 HU), which can partly explain the different results we obtained. Other studies show that low-dose images can reduce the detectability of peripheral lung nodules [ 19 ] and result in differences in nodules volumetry [ 20 ].…”

Section: Discussionmentioning

confidence: 99%

Dosimetry and Comparison between Different CT Protocols (Low Dose, Ultralow Dose, and Conventional CT) for Lung Nodules’ Detection in a Phantom

Leitão

Salvador

Tazoniero

et al. 2021

Radiology Research and Practice

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Background. The effects of dose reduction in lung nodule detection need better understanding. Purpose. To compare the detection rate of simulated lung nodules in a chest phantom using different computed tomography protocols, low dose (LD), ultralow dose (ULD), and conventional (CCT), and to quantify their respective amount of radiation. Materials and Methods. A chest phantom containing 93 simulated lung nodules was scanned using five different protocols: ULD (80 kVp/30 mA), LD A (120 kVp/20 mA), LD B (100 kVp/30 mA), LD C (120 kVp/30 mA), and CCT (120 kVp/automatic mA). Four chest radiologists analyzed a selected image from each protocol and registered in diagrams the nodules they detected. Kruskal–Wallis and McNemar’s tests were performed to determine the difference in nodule detection. Equivalent doses were estimated by placing thermoluminescent dosimeters on the surface and inside the phantom. Results. There was no significant difference in lung nodules’ detection when comparing ULD and LD protocols ( p = 0.208 to p = 1.000 ), but there was a significant difference when comparing each one of those against CCT ( p < 0.001 ). The detection rate of nodules with CT attenuation values lower than −600 HU was also different when comparing all protocols against CCT ( p < 0.001 to p = 0.007 ). There was at least moderate agreement between observers in all protocols (κ-value >0.41). Equivalent dose values ranged from 0.5 to 9 mSv. Conclusion. There is no significant difference in simulated lung nodules’ detection when comparing ULD and LD protocols, but both differ from CCT, especially when considering lower-attenuating nodules.

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Lung nodules assessment in ultra-low-dose CT with iterative reconstruction compared to conventional dose CT

Cited by 29 publications

References 28 publications

Preoperative Prediction for Early Recurrence Can Be as Accurate as Postoperative Assessment in Single Hepatocellular Carcinoma Patients

Preoperative Prediction for Early Recurrence Can Be as Accurate as Postoperative Assessment in Single Hepatocellular Carcinoma Patients

Lungs nodule detection framework from computed tomography images using support vector machine

Dosimetry and Comparison between Different CT Protocols (Low Dose, Ultralow Dose, and Conventional CT) for Lung Nodules’ Detection in a Phantom

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