scholarly journals Relationship between solitary pulmonary nodule lung cancer and CT image features based on gradual clustering

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 400-404
Author(s):  
Weipeng Zhang
Keyword(s):  
Lung Cancer ◽  
Pulmonary Nodule ◽  
Solitary Pulmonary Nodule ◽  
Ct Images ◽  
Image Features ◽  
Ct Image ◽  
Lung Cancers ◽  
Diagnosis Rate ◽  
Malignant Lesions ◽  
The Relationship

Abstract Background The relationship between the medical characteristics of lung cancers and computer tomography (CT) images are explored so as to improve the early diagnosis rate of lung cancers. Methods This research collected CT images of patients with solitary pulmonary nodule lung cancer, and used gradual clustering methodology to classify them. Preliminary classifications were made, followed by continuous modification and iteration to determine the optimal condensation point, until iteration stability was achieved. Reasonable classification results were obtained. Results the clustering results fell into 3 categories. The first type of patients was mostly female, with ages between 50 and 65 years. CT images of solitary pulmonary nodule lung cancer for this group contain complete lobulation and burr, with pleural indentation; The second type of patients was mostly male with ages between 50 and 80 years. CT images of solitary pulmonary nodule lung cancer for this group contain complete lobulation and burr, but with no pleural indentation; The third type of patients was also mostly male with ages between 50 and 80 years. CT images for this group showed no abnormalities. Conclusions the application of gradual clustering methodology can scientifically classify CT image features of patients with lung cancer in the initial lesion stage. These findings provide the basis for early detection and treatment of malignant lesions in patients with lung cancer.

scholarly journals AUTOMATED CLASSIFICATION OF SOLITARY PULMONARY NODULES USING CONVOLUTIONAL NEURAL NETWORK BASED ON TRANSFER LEARNING STRATEGY

2021 ◽  
pp. 2140002
Author(s):  
SHIWEI LI ◽  
DANDAN LIU
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Transfer Learning ◽  
Pulmonary Nodule ◽  
Learning Strategy ◽  
Solitary Pulmonary Nodule ◽  
Ct Images ◽  
Ct Image ◽  
Nodule Classification ◽  
Lung Ct

This study aimed to propose an effective malignant solitary pulmonary nodule classification method based on improved Faster R-CNN and transfer learning strategy. In practice, the existing solitary pulmonary nodule classification methods divide the lung cancer images into two categories only: normal and cancerous. This study proposed the deep convolution neural network to classify the computed tomography (CT) images of lung cancer into four categories: lung adenocarcinoma, lung squamous cell carcinoma, metastatic lung cancer, and normal types of lung cancer. Some high-resolution lung CT images have unnecessary characters such as a large number of high-density continuity features, small-size lung nodule targets, CT image background complexity, and so forth. In this study, the CT image sub-block preprocessing strategy was used to extract nodule features for enhancement and alleviate the aforementioned problems. The experimental results showed that the proposed system was effective in resolving issues such as high false-positive rate and long classification time cost based on the original Faster R-CNN detection method. Meanwhile, the transfer learning strategy was used to improve the classification efficiency so as to avoid the overfitting problem caused by a few labeled samples of lung cancer datasets. The classification results were integrated using the majority vote algorithm. The classification results of the lung CT imaging showed that the proposed method had an average detection accuracy of 89.7% and reduced the rate of misdiagnosis to meet the clinical needs.

Solitary Pulmonary Nodule Detection on Thoracic CT Images through Object Continuity Analyses

2013 ◽  
Vol 284-287 ◽  
pp. 1681-1685 ◽  
Author(s):  
Seok Lyong Lee ◽  
Du Hyung Cho
Keyword(s):  
Lung Cancer ◽  
Pulmonary Nodule ◽  
Solitary Pulmonary Nodule ◽  
Ct Images ◽  
Experimental Result ◽  
Morphological Operations ◽  
Thoracic Ct ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection ◽  
Prompt Diagnosis

Lung cancer has been a leading cause of death in the world, and it is known that prompt diagnosis and treatment may be the only chance for curing the cancer. Early lung cancer often presents as a solitary pulmonary nodule (SPN) and the timely detection of it is critical to save life from cancer death. In this paper, we present an effective method to detect SPNs on thoracic CT images through object continuity analyses. First, a lung region is segmented from other chest organs using morphological operations and thresholding techniques, and an initial set of candidate SPNs are identified. To represent the SPN, we define the rotation-invariant bounding rectangle (riBR) that tightly encloses an object. The subsequent processing is based on the riBR instead of an object itself to avoid the processing overhead. Next, non-nodule objects are pruned using geometric features and the object continuity analyses on a series of CT slice images. Through the analyses, cylinder-shaped non-nodule objects such as blood vessels and bronchia are eliminated and a final set of candidate SPNs is obtained. An experimental result shows that the proposed method works effectively in detecting SPNs. The application context addressed in this study is the pulmonary nodule detection but other application areas also can benefit.

Enhancing 18F-FDG-PET/CT analysis in lung cancer patients

2015 ◽  
Vol 54 (06) ◽  
pp. 247-254 ◽  
Author(s):  
A. Kapfhammer ◽  
T. Winkens ◽  
T. Lesser ◽  
A. Reissig ◽  
M. Steinert ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Fusion ◽  
Chest Wall ◽  
Fdg Pet ◽  
Ct Image ◽  
Lung Cancers ◽  
Pet Ct ◽  
Peripheral Lung ◽  
Fdg Pet Ct ◽  
Tumour Infiltration

SummaryAim: To retrospectively evaluate the feasibility and value of CT-CT image fusion to assess the shift of peripheral lung cancers with/-out chest wall infiltration, comparing computed tomography acquisitions in shallow-breathing (SB-CT) and deep-inspiration breath-hold (DIBH-CT) in patients undergoing FDG-PET/ CT for lung cancer staging. Methods: Image fusion of SB-CT and DIBH-CT was performed with a multimodal workstation used for nuclear medicine fusion imaging. The distance of intrathoracic landmarks and the positional shift of tumours were measured using semitransparent overlay of both CT series. Statistical analyses were adjusted for confounders of tumour infiltration. Cutoff levels were calculated for prediction of no-/infiltration. Results: Lateral pleural recessus and diaphragm showed the largest respiratory excursions. Infiltrating lung cancers showed more limited respiratory shifts than non-infiltrating tumours. A large respiratory tumour-motility accurately predicted non-infiltration. However, the tumour shifts were limited and variable, limiting the accuracy of prediction. Conclusion: This pilot fusion study proved feasible and allowed a simple analysis of the respiratory shifts of peripheral lung tumours using CT-CT image fusion in a PET/CT setting. The calculated cutoffs were useful in predicting the exclusion of chest wall infiltration but did not accurately predict tumour infiltration. This method can provide additional qualitative information in patients with lung cancers with contact to the chest wall but unclear CT evidence of infiltration undergoing PET/CT without the need of additional investigations. Considering the small sample size investigated, further studies are necessary to verify the obtained results.

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 Tobacco Smoke–Induced Lung Cancer in Animals—A Challenge to Toxicology (?)

2007 ◽  
Vol 26 (4) ◽  
pp. 339-344 ◽  
Author(s):  
Hanspeter Witschi
Keyword(s):  
Lung Cancer ◽  
Tobacco Smoke ◽  
Lung Tumor ◽  
Distant Metastases ◽  
Female Rats ◽  
Chemopreventive Agents ◽  
Lung Cancers ◽  
Malignant Lesions ◽  
Product Modification ◽  
Rats And Mice

Tobacco smoke is a known human carcinogen that primarily produces malignant lesions in the respiratory tract, although it also affects multiple other sites. A reliable and practical animal model of tobacco smoke–induced lung cancer would be helpful for in studies of product modification and chemoprevention. Over the years, many attempts to reproduce lung cancer in experimental animals exposed to tobacco smoke have been made, most often with negative or only marginally positive results. In hamsters, malignant lesions have been produced in the larynx, but not in the deeper lung. Female rats and female B6C3F1 mice, when exposed over lifetime to tobacco smoke, develop tumors in the nasal passages and also in the lung. Contrary to what is seen in human lung cancers, most rodent tumors are located peripherally and only about half of them show frank malignant features. Distant metastases are extremely rare. Male and female strain A mice exposed to 5 months to tobacco smoke and then kept for another 4 months in air respond to tobacco smoke with increased lung tumor multiplicities. However, the increase over background levels is comparatively small, making it difficult to detect significant differences when the effects of chemopreventive agents are evaluated. On the other hand, biomarkers of exposure and of effect as well as evaluation of putative carcinogenic mechanisms in rats and mice exposed to tobacco smoke allow detection of early events and their modification by different smoke types or chemopreventive agents. The challenge will be to make such data broadly acceptable and accepted in lieu of having to do more and more long term studies involving larger and larger number of animals.

scholarly journals A Comparative Study of Auto-contouring Softwares in Delineation of Organs at Risk in Lung Cancer and Rectal Cancer

2021 ◽  
Author(s):  
weijun chen ◽  
Cheng Wang ◽  
Wenming Zhan ◽  
Yongshi Jia ◽  
Fangfang Ruan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
At Risk ◽  
Rectal Cancer ◽  
Organs At Risk ◽  
Similarity Index ◽  
Slight Modification ◽  
Ct Images ◽  
Target Area ◽  
Ct Image ◽  
Significant Difference

Abstract Background:Radiotherapy requires the target area and the organs at risk to be contoured on the CT image of the patient. During the process of organs-at-Risk (OAR) of the chest and abdomen, the doctor needs to contour at each CT image. The delineations of large and varied shapes are time-consuming and laborious.This study aims to evaluate the results of two automatic contouring software on OAR definition of CT images of lung cancer and rectal cancer patients. Methods: The CT images of 15 patients with rectal cancer and 15 patients with lung cancer were selected separately, and the organs at risk were outlined by the same experienced doctor as references, and then the same datasets were automatically contoured based on AiContour®© (Manufactured by Linking MED, China) and Raystation®© (Manufactured by Raysearch, Sweden) respectively. Overlap index (OI), Dice similarity index (DSC) and Volume difference (DV) were evaluated based on the auto-contours, and independent-sample t-test analysis is applied to the results. Results: The results of AiContour®© on OI and DSC were better than that of Raystation®© with statistical difference. There was no significant difference in DV between the results of two software. Conclusions: With AiContour®©, auto-contouring results of most organs in the chest and abdomen are good, and with slight modification, it can meet the clinical requirements for planning. With Raystation®©, auto-contouring results in most OAR is not as good as AiContour®©, and only the auto-contouring results of some organs can be used clinically after modification.

scholarly journals Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I–III

2016 ◽  
Vol 43 (8) ◽  
pp. 1477-1485 ◽  
Author(s):  
Marie-Charlotte Desseroit ◽  
Dimitris Visvikis ◽  
Florent Tixier ◽  
Mohamed Majdoub ◽  
Rémy Perdrisot ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Features ◽  
Small Cell ◽  
Clinical Staging ◽  
Cancer Stage ◽  
Ct Image ◽  
Small Cell Lung ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

Exploring new quantitative CT image features to improve assessment of lung cancer prognosis

2015 ◽  
Author(s):  
Nastaran Emaminejad ◽  
Wei Qian ◽  
Yan Kang ◽  
Yubao Guan ◽  
Fleming Lure ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Features ◽  
Cancer Prognosis ◽  
Ct Image ◽  
Quantitative Ct
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