Tracking time interval changes of pulmonary nodules on follow-up 3D CT images via image-based risk score of lung cancer

2013 ◽  
Author(s):  
Y. Kawata ◽  
N. Niki ◽  
H. Ohmatsu ◽  
M. Kusumoto ◽  
T. Tsuchida ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Risk Score ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Time Interval ◽  
3D Ct ◽  
Tracking Time

scholarly journals Current Practice in the Management of Pulmonary Nodules Detected on Computed Tomography Chest Scans

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Clarus Leung ◽  
Tawimas Shaipanich
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Surgical Resection ◽  
Pulmonary Nodule ◽  
Current Practice ◽  
Pulmonary Nodules ◽  
Lung Cancer Screening ◽  
Time Interval ◽  
Positive Finding

Lung cancer is associated with high mortality. It can present as one or more pulmonary nodules identified on computed tomography (CT) chest scans. The National Lung Screening Trial has shown that the use of low-dose CT chest screening can reduce deaths due to lung cancer. High adherence to appropriate follow-up of positive results, including imaging or interventional approaches, is an important aspect of pulmonary nodule management. Our study is one of the first to evaluate the current practice in managing pulmonary nodules and to explore potential causes for nonadherence to follow-up. This is a retrospective analysis at St. Paul’s Hospital, a tertiary healthcare center in Vancouver, British Columbia, Canada. We first identified CT chest scans between January 1 to June 30, 2014, that demonstrated one or more pulmonary nodules equal to or greater than 6 mm in diameter. We then looked for evidence of interventional (surgical resection or biopsy, or bronchoscopy for transbronchial biopsy and cytology) and radiological follow-up of the pulmonary nodule by searching on the province-wide CareConnect eHealth Viewer patient database. A total of 1614 CT reports were analyzed and 139 (8.6%) had a positive finding. Out of the 97 patients who received follow-up, 54.6% (N = 53) was referred for a repeat CT chest scan and 36.1% (N = 35) and 9.3% (N = 9) were referred for interventional biopsy and surgical resection, respectively. In our study, 30.2% (N = 42) of the patients with pulmonary nodules were nonadherent to follow-up. Despite the radiologist’s recommendation for follow-up within a certain time interval, only 36% had repeat imaging in a timely manner. Our findings reflect the current practice in the management of pulmonary nodules and suggest that there is a need for improvement at our academic center. Adherence to follow-up is important for the potentially near-future implementation of lung cancer screening.

scholarly journals Reducing False-Positives in Lung Nodules Detection Using Balanced Datasets

2021 ◽  
Vol 9 ◽  
Author(s):  
Jinglun Liang ◽  
Guoliang Ye ◽  
Jianwen Guo ◽  
Qifan Huang ◽  
Shaohui Zhang
Keyword(s):  
Lung Cancer ◽  
Early Stage ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
False Positives ◽  
Classification Systems ◽  
Cad System ◽  
Nodule Detection ◽  
Diagnosis Of Lung Cancer ◽  
Lung Ct

Malignant pulmonary nodules are one of the main manifestations of lung cancer in early CT image screening. Since lung cancer may have no early obvious symptoms, it is important to develop a computer-aided detection (CAD) system to assist doctors to detect the malignant pulmonary nodules in the early stage of lung cancer CT diagnosis. Due to the recent successful applications of deep learning in image processing, more and more researchers have been trying to apply it to the diagnosis of pulmonary nodules. However, due to the ratio of nodules and non-nodules samples used in the training and testing datasets usually being different from the practical ratio of lung cancer, the CAD classification systems may easily produce higher false-positives while using this imbalanced dataset. This work introduces a filtering step to remove the irrelevant images from the dataset, and the results show that the false-positives can be reduced and the accuracy can be above 98%. There are two steps in nodule detection. Firstly, the images with pulmonary nodules are screened from the whole lung CT images of the patients. Secondly, the exact locations of pulmonary nodules will be detected using Faster R-CNN. Final results show that this method can effectively detect the pulmonary nodules in the CT images and hence potentially assist doctors in the early diagnosis of lung cancer.

Artificial intelligence based on deep learning for differential diagnosis between benign and malignant pulmonary nodules: A real-world, multicenter, diagnostic study.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 9037-9037
Author(s):  
Tao Xu ◽  
Chuoji Huang ◽  
Yaoqi Liu ◽  
Jing Gao ◽  
Huan Chang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Real World ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Test Method ◽  
Histopathological Diagnosis ◽  
Diagnostic Study

9037 Background: Lung cancer is the most common cancer worldwide. Artificial intelligence (AI) platform using deep learning algorithms have made a remarkable progress in improving diagnostic accuracy of lung cancer. But AI diagnostic performance in identifying benign and malignant pulmonary nodules still needs improvement. We aimed to validate a Pulmonary Nodules Artificial Intelligence Diagnostic System (PNAIDS) by analyzing computed tomography (CT) imaging data. Methods: This real-world, multicentre, diagnostic study was done in five different tier hospitals in China. The CT images of patients, who were aged over 18 years and never had previous anti-cancer treatments, were retrieved from participating hospitals. 534 eligible patients with 5-30mm diameter pulmonary nodules identified by CT were planning to confirm with histopathological diagnosis. The performance of PNAIDS was also compared with respiratory specialists and radiologists with expert or competent degrees of expertise as well as Mayo Clinic’s model by area under the curve (AUC) and evaluated differences by calculating the 95% CIs using the Z-test method. 11 selected participants were tested circulating genetically abnormal cells (CACs) before surgery with doctors suggested. Results: 611 lung CT images from 534 individuals were used to test PNAIDS. The diagnostic accuracy, valued by AUC, in identifying benign and malignant pulmonary nodules was 0.765 (95%CI [0.729 - 0.798]). The diagnostic sensitivity of PNAIDS is 0.630(0.579 – 0.679), specificity is 0.753 (0.693 – 0.807). PNAIDS achieved diagnostic accuracy similar to that of the expert respiratory specialists (AUC difference: 0.0036 [-0.0426 - 0.0497]; p = 0.8801) and superior when compared with Mayo Clinic’s model (0.120 [0.0649 - 0.176], p < 0·0001), expert radiologists (0.0620 [0.0124 - 0.112], p = 0.0142) and competent radiologists (0.0751 [0.0248 - 0.125], p = 0.0034). 11 selected participants were suggested negative in AI results but positive in respiratory specialists’ result. 8 of them were malignant in histopathological diagnosis with tested more than 3 CACs in their blood. Conclusions: PNAIDS achieved high diagnostic accuracy in differential diagnoses between benign and malignant pulmonary nodules, with diagnostic accuracy similar to that of expert respiratory specialists and was superior to that of Mayo Clinic’s model and radiologists. CACs may be able to assist CT-based AI in improving their effectiveness but it still need more data to be proved. Clinical trial information: ChiCTR1900026233.

Curvature-based internal structure analysis of pulmonary nodules using thoracic 3D CT images

2001 ◽  
Vol 32 (11) ◽  
pp. 9-19 ◽  
Author(s):  
Yoshiki Kawata ◽  
Noboru Niki ◽  
Hironobu Ohmatsu
Keyword(s):  
Internal Structure ◽  
Structure Analysis ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
3D Ct

scholarly journals Screen-detected subsolid pulmonary nodules: long-term follow-up and application of the PanCan lung cancer risk prediction model

2016 ◽  
Vol 89 (1060) ◽  
pp. 20160016 ◽  
Author(s):  
Henry Zhao ◽  
Henry M Marshall ◽  
Ian A Yang ◽  
Rayleen V Bowman ◽  
John Ayres ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Lung Cancer Risk ◽  
Pulmonary Nodules ◽  
Risk Prediction Model ◽  
Long Term Follow Up

Long-term follow-up of non-calcified pulmonary nodules (<10 mm) identified during low-dose CT screening for lung cancer

2012 ◽  
Vol 22 (9) ◽  
pp. 1923-1928 ◽  
Author(s):  
Michael M. Slattery ◽  
Claire Foley ◽  
Dermot Kenny ◽  
Richard W. Costello ◽  
P. Mark Logan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Low Dose ◽  
Pulmonary Nodules ◽  
Ct Screening ◽  
Low Dose Ct ◽  
Long Term Follow Up

Segmentation of Lung Nodules in CT Scan Data

2017 ◽  
pp. 601-613 ◽  
Author(s):  
Shehzad Khalid ◽  
Anwar C. Shaukat ◽  
Amina Jameel ◽  
Imran Fareed
Keyword(s):  
Lung Cancer ◽  
Lung Nodule ◽  
Clinical Importance ◽  
Pulmonary Nodules ◽  
Cad System ◽  
Nodule Detection ◽  
Scan Data ◽  
Effective Domain ◽  
Aided Diagnosis

Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. Several studies have shown the feasibility and robustness of automated matching of corresponding nodule pairs between follow up examinations. Different image pre-processing and segmentation techniques are used in various research sides to segment different tumors or ulcers from different images. This paper aims to make a review on the existing segmentation algorithms used for CT images of pulmonary nodules and presents a study of the existing methods on automated lung nodule detection. It provides a comparison of the performance of the existing approaches in regards to effective domain results.

Imaging Of Lung Disease, Part II: ­Cavities, Cysts, Nodules, And Masses

2018 ◽  
Author(s):  
Gerald W. Staton Jr ◽  
Eugene A Berkowitz ◽  
Adam Bernheim
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Lung Disease ◽  
Interstitial Pneumonia ◽  
Pulmonary Nodules ◽  
Lung Cancer Screening ◽  
Cystic Lung ◽  
Cystic Lung Disease ◽  
Clinical Scenarios

Cavitary lesions may occur in the setting of pulmonary infection, neoplasm, or vasculitis.  Cystic lung disease must be differentiated from emphysema and is seen in lymphangioleiomyomatosis, Langerhans cell histiocytosis (LCH), and lymphoid interstitial pneumonia (LIP).  Pulmonary nodules are routinely encountered on chest imaging and may be due to benign or malignant etiologies.  There are follow-up algorithms that provide recommendations for solid and sub-solid nodules in certain clinical scenarios.  Nodules characteristics (such as size, morphology, and number [solitary versus multiple]) and patient characteristics (including age, oncology history, and cigarette smoking status) are important to consider in formulating a differential diagnosis and follow-up plan.  Lung cancer screening computed tomography (CT) is now a recommended screening test for high-risk patients who meet certain eligibility requirements, and should be reported according to the Lung Imaging Reporting and Data System (Lung-RADS). This review contains 28 figures, 3 tables and 26 references Keywords: Cavitary Lung Disease, Granulomatosis with Polyangiitis, Cystic Lung Disease, Lymphoid Interstitial Pneumonia, Pulmonary Emphysema, Pulmonary Nodules, Pulmonary Granulomatous Disease, Arteriovenous Malformation, Lung Cancer Screening, Pulmonary Fungal Infection

Quantitative analysis for differential diagnosis of lung cancer on blood vessel connectivity using thoracic 3D CT images

2000 ◽  
Author(s):  
Akira Tanaka ◽  
Tetsuya Tozaki ◽  
Yoshiki Kawata ◽  
Noboru Niki ◽  
Hironobu Ohmatsu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Differential Diagnosis ◽  
Quantitative Analysis ◽  
Blood Vessel ◽  
Ct Images ◽  
3D Ct ◽  
Diagnosis Of Lung Cancer

scholarly journals Overuse of Follow-up Chest Computed Tomography in Patients With Incidentally Identified Nodules Suspicious for Lung Cancer

2021 ◽  
Author(s):  
Ran Guo ◽  
Yang Zhang ◽  
Zelin Ma ◽  
Chaoqiang Deng ◽  
Fangqiu Fu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Pulmonary Nodules ◽  
Ground Glass Opacity ◽  
Cost Effective ◽  
Chest Ct ◽  
Ground Glass ◽  
Ct Screening ◽  
Effective Care ◽  
Mixed Ground

Abstract Purpose: Regardless of professional societies agreed that CT screening inconsistent with recommendation leads to radiation-related cancer and unexpected cost, many patients undergo unnecessary chest CT before treatment. The goal of this study was to assess the overuse of Chest CT in different type of patients.Methods: Data on 1853 patients who underwent pulmonary resection from May 2019 to May 2020 were retrospectively analyzed. Data collected include age, sex, follow-up time, density and size of nodules and frequency of undergoing Chest CT. Pearson χ2 test and logistic regression were conducted to compare the receipt of CT screening.Results: Among 1853 patients in the study, 689 (37.2%) had overused Chest CT during follow-up of the lung cancer. This rate was 16.2% among patients with solid nodules, 57.5% among patients with pure ground glass opacity (pGGO), and 41.4% among patients with mixed ground glass opacity (mGGO) (P<.001). 50.7% in the “age ≤40” group, 39.8% in the “41≤age ≤50” group, 38.7% in the “51≤age ≤60” group, 32.3% in the “61≤age ≤70” group, 27.8% in the “>70” group underwent unnecessary CT (P<.001). Female get more unnecessary CT than male (40.6% vs 32.8%, P<.001). Factors associated with a greater likelihood of Chest CT is the density of nodules (odds ratios [ORs] of 0.53 for mGGO; 0.15 for solid nodule, P<.0001, vs patients with pGGO).Conclusion: roughly 37% patients with pulmonary nodules received Chest CT too frequently despite national recommendations against the practice. Closer adherence to clinical guidelines is likely to result in more cost-effective care.

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