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

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Cleverson Alex Leitão ◽  
Gabriel Lucca de Oliveira Salvador ◽  
Priscilla Tazoniero ◽  
Danny Warszawiak ◽  
Cristian Saievicz ◽  
...  
Keyword(s):  
Detection Rate ◽  
Low Dose ◽  
Lung Nodule ◽  
Lung Nodules ◽  
Ultralow Dose ◽  
Thermoluminescent Dosimeters ◽  
Significant Difference ◽  
Nodule Detection ◽  
The Difference ◽  
Conventional Ct

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.

Lung Nodule Detectability of Artificial Intelligence-assisted CT Image Reading in Lung Cancer Screening

2021 ◽  
Vol 17 ◽  
Author(s):  
Yaping Zhang ◽  
Beibei Jiang ◽  
Lu Zhang ◽  
Marcel J.W. Greuter ◽  
Geertruida H. de Bock ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Detection Rate ◽  
Lung Nodule ◽  
Chest Ct ◽  
Lung Nodules ◽  
Lung Nodule Detection ◽  
Radiology Reports ◽  
Nodule Detection ◽  
Assisted Reading

Background: Artificial intelligence (AI)-based automatic lung nodule detection system improves the detection rate of nodules. It is important to evaluate the clinical value of AI system by comparing AI-assisted nodule detection with actu-al radiology reports. Objective: To compare the detection rate of lung nodules between the actual radiology reports and AI-assisted reading in lung cancer CT screening. Methods: Participants in chest CT screening from November to December 2019 were retrospectively included. In the real-world radiologist observation, 14 residents and 15 radiologists participated to finalize radiology reports. In AI-assisted reading, one resident and one radiologist reevaluated all subjects with the assistance of an AI system to lo-cate and measure the detected lung nodules. A reading panel determined the type and number of detected lung nodules between these two methods. Results: In 860 participants (57±7 years), the reading panel confirmed 250 patients with >1 solid nodule, while radiolo-gists observed 131, lower than 247 by AI-assisted reading (p<0.001). The panel confirmed 111 patients with >1 non-solid nodule, whereas radiologist observation identified 28, lower than 110 by AI-assisted reading (p<0.001). The accuracy and sensitivity of radiologist observation for solid nodules were 86.2% and 52.4%, lower than 99.1% and 98.8% by AI-assisted reading, respectively. These metrics were 90.4% and 25.2% for non-solid nodules, lower than 98.8% and 99.1% by AI-assisted reading, respectively. Conclusion: Comparing with the actual radiology reports, AI-assisted reading greatly improves the accuracy and sensi-tivity of nodule detection in chest CT, which benefits lung nodule detection, especially for non-solid nodules.

Multi-level 3D Densenets for False-positive Reduction in Lung Nodule Detection Based on Chest Computed Tomography

2020 ◽  
Vol 16 (8) ◽  
pp. 1004-1021
Author(s):  
Xiaoqi Lu ◽  
Yu Gu ◽  
Lidong Yang ◽  
Baohua Zhang ◽  
Ying Zhao ◽  
...  
Keyword(s):  
False Positive ◽  
Contextual Information ◽  
Lung Nodule ◽  
Positive Sample ◽  
Lung Nodules ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Level Information ◽  
Multi Level ◽  
Cade System

Objective: False-positive nodule reduction is a crucial part of a computer-aided detection (CADe) system, which assists radiologists in accurate lung nodule detection. In this research, a novel scheme using multi-level 3D DenseNet framework is proposed to implement false-positive nodule reduction task. Methods: Multi-level 3D DenseNet models were extended to differentiate lung nodules from falsepositive nodules. First, different models were fed with 3D cubes with different sizes for encoding multi-level contextual information to meet the challenges of the large variations of lung nodules. In addition, image rotation and flipping were utilized to upsample positive samples which consisted of a positive sample set. Furthermore, the 3D DenseNets were designed to keep low-level information of nodules, as densely connected structures in DenseNet can reuse features of lung nodules and then boost feature propagation. Finally, the optimal weighted linear combination of all model scores obtained the best classification result in this research. Results: The proposed method was evaluated with LUNA16 dataset which contained 888 thin-slice CT scans. The performance was validated via 10-fold cross-validation. Both the Free-response Receiver Operating Characteristic (FROC) curve and the Competition Performance Metric (CPM) score show that the proposed scheme can achieve a satisfactory detection performance in the falsepositive reduction track of the LUNA16 challenge. Conclusion: The result shows that the proposed scheme can be significant for false-positive nodule reduction task.

Overview of Computer Aided Detection and Computer Aided Diagnosis Systems for Lung Nodule Detection in Computed Tomography

2020 ◽  
Vol 16 (1) ◽  
pp. 16-26 ◽  
Author(s):  
Shabana Rasheed Ziyad ◽  
Venkatachalam Radha ◽  
Thavavel Vayyapuri
Keyword(s):  
Computed Tomography ◽  
Review Paper ◽  
Lung Nodule ◽  
Lung Nodules ◽  
Computer Aided Detection ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Computer Aided ◽  
Aided Diagnosis

Background: Lung cancer has become a major cause of cancer-related deaths. Detection of potentially malignant lung nodules is essential for the early diagnosis and clinical management of lung cancer. In clinical practice, the interpretation of Computed Tomography (CT) images is challenging for radiologists due to a large number of cases. There is a high rate of false positives in the manual findings. Computer aided detection system (CAD) and computer aided diagnosis systems (CADx) enhance the radiologists in accurately delineating the lung nodules. Objectives: The objective is to analyze CAD and CADx systems for lung nodule detection. It is necessary to review the various techniques followed in CAD and CADx systems proposed and implemented by various research persons. This study aims at analyzing the recent application of various concepts in computer science to each stage of CAD and CADx. Methods: This review paper is special in its own kind because it analyses the various techniques proposed by different eminent researchers in noise removal, contrast enhancement, thorax removal, lung segmentation, bone suppression, segmentation of trachea, classification of nodule and nonnodule and final classification of benign and malignant nodules. Results: A comparison of the performance of different techniques implemented by various researchers for the classification of nodule and non-nodule has been tabulated in the paper. Conclusion: The findings of this review paper will definitely prove to be useful to the research community working on automation of lung nodule detection.

scholarly journals Low Dose Perioperative Intravenous Tranexamic Acid in Patients Undergoing Total Knee Arthroplasty: A Double-Blind Randomized Placebo Controlled Clinical Trial

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Mahdi Motififard ◽  
Mohammad Ali Tahririan ◽  
Mehdi Saneie ◽  
Sajad Badiei ◽  
Amin Nemati
Keyword(s):  
Tranexamic Acid ◽  
Primary Outcome ◽  
Low Dose ◽  
Study Groups ◽  
Controlled Clinical Trial ◽  
Double Blind ◽  
Significant Difference ◽  
Duration Of Hospitalization ◽  
Slow Infusion ◽  
The Difference

Background and Objectives. The null hypothesis of this study was that TA has no effect on postsurgical bleeding in patients undergoing TKA. Methods. This study was a double-blind randomized trial. In the first group (T) patients received 500 mg of intravenous Tranexamic acid (TA) twice (once preoperatively and once 3 hours postoperatively) and in the second group (P) they received slow infusion of normal saline as placebo. The primary outcome of the study was the level of Hb 48 hours after surgery. Results. Hb levels 48 hours after surgery as the primary outcome were 10.92±0.97 and 10.23±0.98 (g/dL) in groups T and P, respectively, and the difference was statistically significant (P=0.001). Statistically significant differences were also observed in Hb levels 6 and 24 hours after surgery, the drain output 48 hours after surgery, and the number of units of packed cells transfused between study groups (P<0.05). There was no significant difference in duration of hospitalization between the study groups (P = n.s.). Conclusions. The low dose perioperative intravenous TA significantly reduces blood loss, requirement for blood transfusion, and drain output in patients undergoing TKA. However, duration of hospitalization did not change significantly.

Small Hepatocellular Carcinoma

1996 ◽  
Vol 37 (1P1) ◽  
pp. 69-74 ◽  
Author(s):  
C. Bartolozzi ◽  
R. Lencioni ◽  
D. Caramella ◽  
A. Palla ◽  
A. M. Bassi ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Mr Imaging ◽  
Detection Rate ◽  
Preoperative Evaluation ◽  
Small Hepatocellular Carcinoma ◽  
Imaging Studies ◽  
Detection Rates ◽  
Significant Difference ◽  
Nodular Lesions ◽  
The Difference

Twenty-two patients with 37 small (3 cm or less) nodular lesions of hepatocellular carcinoma (HCC) were examined with ultrasonography (US), CT, MR imaging, digital subtraction angiography (DSA), and CT following intraarterial injection of Lipiodol (Lipiodol-CT). All patients subsequently underwent surgery, and the gold standard was provided by intraoperative US. The detection rate was 70% for US, 65% for CT, 62% for MR imaging, 73% for DSA, and 86% for Lipiodol-CT. A significant difference (p<0.05) was observed between the detection rate of Lipiodol-CT and the detection rates of all the other imaging modalities. The difference was even more manifest (p<0.02) when only lesions smaller than or equal to 1 cm were considered. It is concluded that Lipiodol-CT is the single most sensitive examination to detect small nodules of HCC. It should therefore be considered a mandatory step in the preoperative evaluation of patients with HCC considered to be surgical candidates after noninvasive imaging studies.

Lung nodule detection in low-dose and thin-slice computed tomography

2008 ◽  
Vol 38 (4) ◽  
pp. 525-534 ◽  
Author(s):  
A. Retico ◽  
P. Delogu ◽  
M.E. Fantacci ◽  
I. Gori ◽  
A. Preite Martinez
Keyword(s):  
Computed Tomography ◽  
Low Dose ◽  
Lung Nodule ◽  
Thin Slice ◽  
Lung Nodule Detection ◽  
Nodule Detection

Pulmonary nodule detection: low-dose versus conventional CT.

Radiology ◽  
1998 ◽  
Vol 209 (1) ◽  
pp. 243-249 ◽  
Author(s):  
H Rusinek ◽  
D P Naidich ◽  
G McGuinness ◽  
B S Leitman ◽  
D I McCauley ◽  
...  
Keyword(s):  
Pulmonary Nodule ◽  
Low Dose ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection ◽  
Conventional Ct

scholarly journals 3D CNN with Visual Insights for Early Detection of Lung Cancer Using Gradient-Weighted Class Activation

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Eali Stephen Neal Joshua ◽  
Debnath Bhattacharyya ◽  
Midhun Chakkravarthy ◽  
Yung-Cheol Byun
Keyword(s):  
Low Dose ◽  
Binary Classification ◽  
Lung Nodule ◽  
Fine Grained ◽  
Internal Working ◽  
Nodule Detection ◽  
Classification Tasks ◽  
3D Cnn ◽  
Nodule Classification ◽  
Fold Cross Validation

The 3D convolutional neural network is able to make use of the full nonlinear 3D context information of lung nodule detection from the DICOM (Digital Imaging and Communications in Medicine) images, and the Gradient Class Activation has shown to be useful for tailoring classification tasks and localization interpretation for fine-grained features and visual explanation for the internal working. Gradient-weighted class activation plays a crucial role for clinicians and radiologists in terms of trusting and adopting the model. Practitioners not only rely on a model that can provide high precision but also really want to gain the respect of radiologists. So, in this paper, we explored the lung nodule classification using the improvised 3D AlexNet with lightweight architecture. Our network employed the full nature of the multiview network strategy. We have conducted the binary classification (benign and malignant) on computed tomography (CT) images from the LUNA 16 database conglomerate and database image resource initiative. The results obtained are through the 10-fold cross-validation. Experimental results have shown that the proposed lightweight architecture achieved a superior classification accuracy of 97.17% on LUNA 16 dataset when compared with existing classification algorithms and low-dose CT scan images as well.

scholarly journals Ultra-Low-Dose MDCT of the Chest: Influence on Automated Lung Nodule Detection

2008 ◽  
Vol 9 (2) ◽  
pp. 95 ◽  
Author(s):  
Ji Young Lee ◽  
Myung Jin Chung ◽  
Chin A Yi ◽  
Kyung Soo Lee
Keyword(s):  
Low Dose ◽  
Lung Nodule ◽  
Lung Nodule Detection ◽  
Nodule Detection
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