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.

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.

Segmentation of Lung Nodules in CT Scan Data

2015 ◽  
Vol 3 (2) ◽  
pp. 66-77 ◽  
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.

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.

Automatic nodule detection for lung cancer in CT images: A review

2018 ◽  
Vol 103 ◽  
pp. 287-300 ◽  
Author(s):  
Guobin Zhang ◽  
Shan Jiang ◽  
Zhiyong Yang ◽  
Li Gong ◽  
Xiaodong Ma ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Ct Images ◽  
Nodule Detection

scholarly journals Early diagnosis of Lung Cancer with Probability of Malignancy Calculation and Automatic Segmentation of Lung CT scan Images

2020 ◽  
Vol 2 (4) ◽  
pp. 175-186
Author(s):  
Dr. Samuel Manoharan ◽  
Sathish
Keyword(s):  
Detection System ◽  
Automatic Segmentation ◽  
Pulmonary Nodules ◽  
Automated Image Analysis ◽  
Computer Aided Detection ◽  
Response Characteristics ◽  
Proposed Model ◽  
Neural Classifier ◽  
Diagnosis Of Lung Cancer ◽  
Lung Ct

Computer aided detection system was developed to identify the pulmonary nodules to diagnose the cancer cells. Main aim of this research enables an automated image analysis and malignancy calculation through data and CPU infrastructure. Our proposed algorithm has improvement filter to enhance the imported images and for nodule selection and neural classifier for false reduction. The proposed model is experimented in both internal and external nodules and the obtained results are shown as response characteristics curves.

scholarly journals Deep Learning Based Depthwise Separable Model For Effective Diagnosis And Classification of Lung Ct Images

2019 ◽  
Vol 9 (1) ◽  
pp. 1808-1819 ◽  
Keyword(s):  
Lung Cancer ◽  
Feature Extraction ◽  
Deep Learning ◽  
Noise Removal ◽  
Ct Images ◽  
Classification Model ◽  
Diagnosis And Classification ◽  
Effective Diagnosis ◽  
Lung Ct

Lung cancer is a serious illness which leads to increased mortality rate globally. The identification of lung cancer at the beginning stage is the probable method of improving the survival rate of the patients. Generally, Computed Tomography (CT) scan is applied for finding the location of the tumor and determines the stage of cancer. Existing works has presented an effective diagnosis classification model for CT lung images. This paper designs an effective diagnosis and classification model for CT lung images. The presented model involves different stages namely pre-processing, segmentation, feature extraction and classification. The initial stage includes an adaptive histogram based equalization (AHE) model for image enhancement and bilateral filtering (BF) model for noise removal. The pre-processed images are fed into the second stage of watershed segmentation model for effectively segment the images. Then, a deep learning based Xception model is applied for prominent feature extraction and the classification takes place by the use of logistic regression (LR) classifier. A comprehensive simulation is carried out to ensure the effective classification of the lung CT images using a benchmark dataset. The outcome implied the outstanding performance of the presented model on the applied test images.

Circulating Tumor Cell and Metabolites as Novel Biomarkers for Early-stage Lung Cancer Diagnosis

2020 ◽  
Author(s):  
Lingling Wan ◽  
Yutong He ◽  
Qingyi Liu ◽  
Di Liang ◽  
Yongdong Guo ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Early Diagnosis ◽  
Cancer Patients ◽  
Early Stage ◽  
Untargeted Metabolomics ◽  
Early Stage Lung Cancer ◽  
Lung Cancer Patients ◽  
Diagnosis Of Lung Cancer ◽  
Metabolomics Analysis ◽  
Stage Lung Cancer

Abstract Background: Lung cancer is a malignant tumor that has the highest morbidity and mortality rate among all cancers. Early diagnosis of lung cancer is a key factor in reducing mortality and improving prognosis. Methods: In this study, we performed CTC next-generation sequencing (NGS) in early-stage lung cancer patients to identify lung cancer-related gene mutations. Meanwhile, a serum liquid chromatography-tandem mass spectrometry (LC-MS) untargeted metabolomics analysis was performed in the CTC-positive patients, and the early diagnostic value of these assays in lung cancer was analyzed. Results: 62.5% (30/48) of lung cancer patients had ≥ 1 CTC. By CTC NGS, we found that > 50% of patients had 4 commonly mutated genes, namely, NOTCH1, IGF2, EGFR, and PTCH1. 47.37% (9/19) patients had ARIDH1 mutations. Additionally, 30 CTC-positive patients and 30 healthy volunteers were subjected to LC-MS untargeted metabolomics analysis. We found 100 different metabolites, and 10 different metabolites were identified through analysis, which may have potential clinical application value in the diagnosis of CTC-positive early-stage lung cancer (AUC > 0.9). Conclusions: Our results indicate that NGS of CTC and metabolomics may provide new tumor markers for the early diagnosis of lung cancer. This possibility requires more in-depth large-sample research for verification.

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