scholarly journals Chest Computerized Tomography Images under Iterative Model Reconstruction Algorithm in Patients with Lung Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jie Li ◽  
Wei Wang ◽  
Shizhi Long ◽  
Xin Liu ◽  
Long Huang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Pulmonary Nodule ◽  
Area Under The Curve ◽  
Pulmonary Nodules ◽  
Reconstruction Algorithm ◽  
Chest Ct ◽  
Iterative Model ◽  
Iterative Model Reconstruction ◽  
Sensitivity Specificity ◽  
Density Resolution

To explore the effect of the full iterative model reconstruction algorithm (IMR) on chest CT image processing and its adoption value in the clinical diagnosis of lung cancer patients, multislice spiral CT (MSCT) scans were performed on 96 patients with pulmonary nodules. Reconstruction was performed by hybrid iterative reconstruction (iDose4) and IMR2 algorithms. Then, the image contrast, spatial resolution, density resolution, image uniformity, and noise of the CT reconstructed image were recorded. The benign and malignant pulmonary nodules of patients were collected and classified into malignant pulmonary nodule group and benign pulmonary nodule group, and the differences in chest CT imaging characteristics between the two groups were compared. The subject’s receiver operating characteristic (ROC) curve was used to analyze the diagnostic sensitivity, specificity, and area under the curve (AUC) of CT for benign and malignant pulmonary nodules. It was found that the spatial resolution, density resolution, image uniformity, and contrast of the CT image reconstructed by the IMR2 algorithm were remarkably greater than those of the iDose4 algorithm, and the noise was considerably less than that of the iDose4 algorithm ( P < 0.05 ). Among 96 patients with pulmonary nodules, 65 were malignant nodules, including 15 squamous cell carcinoma, 31 adenocarcinoma, and 19 small cell carcinomas. There were 31 cases of benign nodules, including 14 cases of hamartoma, 10 cases of tuberculous granuloma, 2 cases of sclerosing hemangioma, and 5 cases of diffuse lymphocyte proliferation. The pulmonary nodule malignant group and the pulmonary nodule benign group had statistical differences in pulmonary nodule size, nodule morphology, burr sign, lobular sign, vascular sign, bronchial sign, and pleural depression sign ( P < 0.05 ). The sensitivity, specificity, and area under the curve (AUC) of IMR2 algorithm processing chest CT images for liver cancer diagnosis were 85.7%, 82.3%, and 0.815, respectively, which were significantly higher than the original CT images ( P < 0.05 ). In short, chest MSCT based on the IMR2 algorithm can greatly improve the diagnosis efficiency of lung cancer and had practical significance for the timely detection of early lung cancer.

scholarly journals Effect of a pulmonary nodule fact sheet on patient anxiety and knowledge: a quality improvement initiative

2018 ◽  
Vol 7 (3) ◽  
pp. e000437 ◽  
Author(s):  
Matthew T Koroscil ◽  
Mitchell H Bowman ◽  
Michael J Morris ◽  
Andrew J Skabelund ◽  
Andrew M Hersh
Keyword(s):  
Lung Cancer ◽  
Quality Improvement ◽  
Pulmonary Nodule ◽  
Early Stage ◽  
Pulmonary Nodules ◽  
Low Risk ◽  
Quality Improvement Initiative ◽  
Patient Anxiety ◽  
Patient Understanding ◽  
Fact Sheet

IntroductionThe utilisation of chest CT for the evaluation of pulmonary disorders, including low-dose CT for lung cancer screening, is increasing in the USA. As a result, the discovery of both screening-detected and incidental pulmonary nodules has become more frequent. Despite an overall low risk of malignancy, pulmonary nodules are a common cause of emotional distress among adult patients.MethodsWe conducted a multi-institutional quality improvement (QI) initiative involving 101 participants to determine the effect of a pulmonary nodule fact sheet on patient knowledge and anxiety. Males and females aged 35 years or older, who had a history of either screening-detected or incidental solid pulmonary nodule(s) sized 3–8 mm, were included. Prior to an internal medicine or pulmonary medicine clinic visit, participants were given a packet containing a pre-fact sheet survey, a pulmonary nodule fact sheet and a post-fact sheet survey.ResultsOf 101 patients, 61 (60.4%) worried about their pulmonary nodule at least once per month with 18 (17.8%) worrying daily. The majority 67/101 (66.3%) selected chemotherapy, chemotherapy and radiation, or radiation as the best method to cure early-stage lung cancer. Despite ongoing radiographic surveillance, 16/101 (15.8%) stated they would not be interested in an intervention if lung cancer was diagnosed. Following review of the pulmonary nodule fact sheet, 84/101 (83.2%) reported improved anxiety and 96/101 (95.0%) reported an improved understanding of their health situation. Patient understanding significantly improved from 4.2/10.0 to 8.1/10.0 (p<0.01).ConclusionThe incorporation of a standardised fact sheet for subcentimeter solid pulmonary nodules improves patient understanding and alleviates anxiety. We plan to implement pulmonary nodule fact sheets into the care of our patients with low-risk subcentimeter pulmonary nodules.

scholarly journals Malignant melanoma without primary, presenting as solitary pulmonary nodule: a case report

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
George Tsaknis ◽  
Muhammad Naeem ◽  
Advitya Singh ◽  
Siddharth Vijayakumar
Keyword(s):  
Lung Cancer ◽  
Malignant Melanoma ◽  
Pulmonary Nodule ◽  
Solitary Pulmonary Nodule ◽  
Past History ◽  
White Male ◽  
Pulmonary Nodules ◽  
Metastatic Malignant Melanoma ◽  
Unknown Primary ◽  
Solitary Pulmonary Nodules

Abstract Background Solitary pulmonary nodules are the most common incidental finding on chest imaging. Their management is very well defined by several guidelines, with risk calculators for lung cancer being the gold standard. Solitary intramuscular metastasis combined with a solitary pulmonary nodule from malignant melanoma without a primary site is rare. Case presentation A 57-year-old white male was referred to our lung cancer service with solitary pulmonary nodule. After positron-emission tomography, we performed an ultrasound-guided core needle biopsy of an intramuscular solitary lesion, not identified on computed tomography scan, and diagnosed metastatic malignant melanoma. The solitary pulmonary nodule was resected and also confirmed metastatic melanoma. There was no primary skin lesion. The patient received oral targeted therapy and is disease-free 5 years later. Conclusions Clinicians dealing with solitary pulmonary nodules must remain vigilant for other extrathoracic malignancies even in the absence of obvious past history. Lung metastasectomy may have a role in metastatic malignant melanoma with unknown primary.

scholarly journals Thin-slice brain CT with iterative model reconstruction algorithm for small lacunar lesions detection

Medicine ◽  
2017 ◽  
Vol 96 (51) ◽  
pp. e9412 ◽  
Author(s):  
Xiaoyi Liu ◽  
Lei Chen ◽  
Weiwei Qi ◽  
Yan Jiang ◽  
Ying Liu ◽  
...  
Keyword(s):  
Reconstruction Algorithm ◽  
Model Reconstruction ◽  
Brain Ct ◽  
Thin Slice ◽  
Iterative Model ◽  
Iterative Model Reconstruction

scholarly journals The combination of CTCs and CEA can help guide the management of patients with SPNs suspected of being lung cancer

2019 ◽  
Author(s):  
Jian Zheng ◽  
Xiong Ye ◽  
Yuxia Zhao ◽  
Mudan He ◽  
Hui Xiao
Keyword(s):  
Lung Cancer ◽  
Sensitivity And Specificity ◽  
Radiographic Finding ◽  
Gene Copy Number ◽  
Area Under The Curve ◽  
Pulmonary Nodules ◽  
Gene Copy ◽  
Solitary Pulmonary Nodules ◽  
Diagnostic Efficacy ◽  
Diagnostic Biomarkers

Abstract Objective: Solitary pulmonary nodules (SPNs) is a common radiographic finding and require further evaluation because of the possibility of lung cancer. This study aimed to determine the sensitivity and specificity of circulating tumour cells (CTCs) as a marker for the diagnosis of SPNs and the integration of CTCs, carcinoembryonic antigen (CEA) and imaging findings to improve the sensitivity and specificity of diagnosis in patients with SPNs suspected of being lung cancer.Method: For the serum biomarker assay, the concentration of CEA was measured by an automated electrochemiluminescence analyzer. CTCs were collected from 6 ml of blood by i-FISH method, which detects the gene copy number in eight chromosomes and the tumour-associated antigen CK18.Results: With a threshold of 6 CTC units, the method showed a sensitivity of 67.1% and a specificity of 56.5% in the diagnosis of NSCLC, especially in the upper lobe, in which the diagnostic strength was the highest (P < 0.01). CTCs, CEA and nodule type had the highest diagnostic efficacy (area under the curve, 0.827; 95% confidence interval, 0.752-0.901) in patients with SPNs being suspected lung cancer. Combining CTCs (cut-off value 12 units) with CEA (1.78 ng/ml), the method showed a sensitivity of 77.8% and a specificity of 90% in the diagnosis of NSCLC, especially in the upper lobe, sub solid nodules and nodules ≥8 mm.Conclusion: Our results demonstrated that CTCs are feasible diagnostic biomarkers in patients with SPNs, especially in the upper lobe. Furthermore, CTCs combined with CEA showed higher diagnostic efficacy in the upper lobe, sub solid nodules and nodules ≥8 mm.

scholarly journals Clinically Applicable Deep Learning Strategy for Pulmonary Nodule Risk Prediction: Insights into HONORS

2020 ◽  
Author(s):  
Wenhui Lv ◽  
Yang Wang ◽  
Changsheng Zhou ◽  
Sheng Huang ◽  
Xiangming Fang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Medical Errors ◽  
Pulmonary Nodule ◽  
Large Scale ◽  
Learning Algorithm ◽  
Pulmonary Nodules ◽  
Youden Index ◽  
List Type ◽  
Clinical Scenarios

AbstractBackground and PurposeLimited optimization was clinically applicable for reducing missed diagnosis, misdiagnosis and inter-reader variability in pulmonary nodule diagnosis. We aimed to propose a deep learning-based algorithm and a practical strategy to better stratify the risk of pulmonary nodules, thus reducing medical errors and optimizing the clinical workflow.Materials and MethodsA total of 2,348 pulmonary nodules (1,215 with lung cancer) containing screened nodules from National Lung Cancer Screening Trial (NLST) and incidentally detected nodules from Jinling Hospital (JLH) were used to train and evaluate a deep learning algorithm, Filter-guided pyramid network (FGP-NET). Internal and external test of FGP-NET were performed on two independent datasets (n=542). The performance of FGP-NET at Youden point which maximizing the Youden index was compared with 126 board-certificated radiologists. We further proposed Hierarchical Ordered Network ORiented Strategy (HONORS), which manipulates the emphasis either on sensitivity or specificity to target risk-stratified clinical scenarios, directly making decisions for some patients.ResultsFGP-NET achieved a high area under the curve (AUC) of 0.969 and 0.855 for internal and external testing, and was comparable or even outperformed the radiologists when considering sensitivity. HONORS-guided FGP-NET identified benign nodules with a high sensitivity (95.5%) in the screening scenario, and demonstrated satisfactory performance for the rest ambiguous nodules with 0.945 of AUC by the Youden point. FGP-NET also detected lung cancer with a high specificity of 94.5% in routine diagnostic scenario; an AUC of 0.809 was achieved for the rest nodules.ConclusionThe combination of HONORS and FGP-NET provides well-organized stratification for pulmonary nodules and also offers the potential for reducing medical errors.HighlightsPulmonary nodules were managed for both screening and diagnostic scenariosProposal of a hierarchical strategy for targeting risk-stratified clinical scenariosA large scale Human-deep learning contest for reliable performance evaluation

scholarly journals Evaluation of the Effectiveness of Artificial Intelligence Chest CT Lung Nodule Detection Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fukui Liang ◽  
Caiqin Li ◽  
Xiaoqin Fu
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Deep Learning ◽  
Medical Imaging ◽  
Lung Nodule ◽  
Pulmonary Nodules ◽  
Chest Ct ◽  
Detection Sensitivity ◽  
Lung Nodule Detection ◽  
Nodule Detection

Lung cancer is one of the most malignant tumors. If it can be detected early and treated actively, it can effectively improve a patient’s survival rate. Therefore, early diagnosis of lung cancer is very important. Early-stage lung cancer usually appears as a solitary lung nodule on medical imaging. It usually appears as a round or nearly round dense shadow in the chest radiograph. It is difficult to distinguish lung nodules and lung soft tissues with the naked eye. Therefore, this article proposes a deep learning-based artificial intelligence chest CT lung nodule detection performance evaluation study, aiming to evaluate the value of chest CT imaging technology in the detection of noncalcified nodules and provide help for the detection and treatment of lung cancer. In this article, the Lung Medical Imaging Database Consortium (LIDC) was selected to obtain 536 usable cases based on inclusion criteria; 80 cases were selected for examination, artificial intelligence software, radiologists, and thoracic imaging specialists. Using 80 pulmonary nodules detection in each case, the pathological type of pulmonary nodules, nonlime tuberculous test results, detection sensitivity, false negative rate, false positive rate, and CT findings were individually analyzed, and the detection efficiency software of artificial intelligence was evaluated. Experiments have proved that the sensitivity of artificial intelligence software to detect noncalcified nodules in the pleural, peripheral, central, and hilar areas is higher than that of radiologists, indicating that the method proposed in this article has achieved good detection results. It has a better nodule detection sensitivity than a radiologist, reducing the complexity of the detection process.

scholarly journals Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Ilker Ozsahin ◽  
Boran Sekeroglu ◽  
Musa Sani Musa ◽  
Mubarak Taiwo Mustapha ◽  
Dilber Uzun Ozsahin
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Neural Networks ◽  
Deep Learning ◽  
Chest Radiography ◽  
Area Under The Curve ◽  
Chest Ct ◽  
Chest Computed Tomography ◽  
Classification Tasks ◽  
Sensitivity Specificity

The COVID-19 diagnostic approach is mainly divided into two broad categories, a laboratory-based and chest radiography approach. The last few months have witnessed a rapid increase in the number of studies use artificial intelligence (AI) techniques to diagnose COVID-19 with chest computed tomography (CT). In this study, we review the diagnosis of COVID-19 by using chest CT toward AI. We searched ArXiv, MedRxiv, and Google Scholar using the terms “deep learning”, “neural networks”, “COVID-19”, and “chest CT”. At the time of writing (August 24, 2020), there have been nearly 100 studies and 30 studies among them were selected for this review. We categorized the studies based on the classification tasks: COVID-19/normal, COVID-19/non-COVID-19, COVID-19/non-COVID-19 pneumonia, and severity. The sensitivity, specificity, precision, accuracy, area under the curve, and F1 score results were reported as high as 100%, 100%, 99.62, 99.87%, 100%, and 99.5%, respectively. However, the presented results should be carefully compared due to the different degrees of difficulty of different classification tasks.

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