lung nodules
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2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Hui Wang ◽  
Yanying Li ◽  
Shanshan Liu ◽  
Xianwen Yue

At present, the diagnosis and treatment of lung cancer have always been one of the research hotspots in the medical field. Early diagnosis and treatment of this disease are necessary means to improve the survival rate of lung cancer patients and reduce their mortality. The introduction of computer-aided diagnosis technology can easily, quickly, and accurately identify the lung nodule area as an imaging feature of early lung cancer for the clinical diagnosis of lung cancer and is helpful for the quantitative analysis of the characteristics of lung nodules and is useful for distinguishing benign and malignant lung nodules. Growth provides an objective diagnostic reference standard. This paper studies ITK and VTK toolkits and builds a system platform with MFC. By studying the process of doctors diagnosing lung nodules, the whole system is divided into seven modules: suspected lung shadow detection, image display and image annotation, and interaction. The system passes through the entire lung nodule auxiliary diagnosis process and obtains the number of nodules, the number of malignant nodules, and the number of false positives in each set of lung CT images to analyze the performance of the auxiliary diagnosis system. In this paper, a lung region segmentation method is proposed, which makes use of the obvious differences between the lung parenchyma and other human tissues connected with it, as well as the position relationship and shape characteristics of each human tissue in the image. Experiments are carried out to solve the problems of lung boundary, inaccurate segmentation of lung wall, and depression caused by noise and pleural nodule adhesion. Experiments show that there are 2316 CT images in 8 sets of images of different patients, and the number of nodules is 56. A total of 49 nodules were detected by the system, 7 were missed, and the detection rate was 87.5%. A total of 64 false-positive nodules were detected, with an average of 8 per set of images. This shows that the system is effective for CT images of different devices, pixel pitch, and slice pitch and has high sensitivity, which can provide doctors with good advice.


2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Sonali Sethi ◽  
Scott Oh ◽  
Alexander Chen ◽  
Christina Bellinger ◽  
Lori Lofaro ◽  
...  

Abstract Background Incidental and screening-identified lung nodules are common, and a bronchoscopic evaluation is frequently nondiagnostic. The Percepta Genomic Sequencing Classifier (GSC) is a genomic classifier developed in current and former smokers which can be used for further risk stratification in these patients. Percepta GSC has the capability of up-classifying patients with a pre-bronchoscopy risk that is high (> 60%) to “very high risk” with a positive predictive value of 91.5%. This prospective, randomized decision impact survey was designed to test the hypothesis that an up-classification of risk of malignancy from high to very high will increase the rate of referral for surgical or ablative therapy without additional intervening procedures while increasing physician confidence. Methods Data were collected from 37 cases from the Percepta GSC validation cohort in which the pre-bronchoscopy risk of malignancy was high (> 60%), the bronchoscopy was nondiagnostic, and the patient was up-classified to very high risk by Percepta GSC. The cases were randomly presented to U.S pulmonologists in three formats: a pre-post cohort where each case is presented initially without and then with a GSG result, and two independent cohorts where each case is presented either with or without with a GSC result. Physicians were surveyed with respect to subsequent management steps and confidence in that decision. Results One hundred and one survey takers provided a total of 1341 evaluations of the 37 patient cases across the three different cohorts. The rate of recommendation for surgical resection was significantly higher in the independent cohort with a GSC result compared to the independent cohort without a GSC result (45% vs. 17%, p < 0.001) In the pre-post cross-over cohort, the rate increased from 17 to 56% (p < 0.001) following the review of the GSC result. A GSC up-classification from high to very high risk of malignancy increased Pulmonologists’ confidence in decision-making following a nondiagnostic bronchoscopy. Conclusions Use of the Percepta GSC classifier will allow more patients with early lung cancer to proceed more rapidly to potentially curative therapy while decreasing unnecessary intervening diagnostic procedures following a nondiagnostic bronchoscopy.


2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Wenfa Jiang ◽  
Ganhua Zeng ◽  
Shuo Wang ◽  
Xiaofeng Wu ◽  
Chenyang Xu

Lung cancer is one of the malignant tumors with the highest fatality rate and nearest to our lives. It poses a great threat to human health and it mainly occurs in smokers. In our country, with the acceleration of industrialization, environmental pollution, and population aging, the cancer burden of lung cancer is increasing day by day. In the diagnosis of lung cancer, Computed Tomography (CT) images are a fairly common visualization tool. CT images visualize all tissues based on the absorption of X-rays. The diseased parts of the lung are collectively referred to as pulmonary nodules, the shape of nodules is different, and the risk of cancer will vary with the shape of nodules. Computer-aided diagnosis (CAD) is a very suitable method to solve this problem because the computer vision model can quickly scan every part of the CT image of the same quality for analysis and will not be affected by fatigue and emotion. The latest advances in deep learning enable computer vision models to help doctors diagnose various diseases, and in some cases, models have shown greater competitiveness than doctors. Based on the opportunity of technological development, the application of computer vision in medical imaging diagnosis of diseases has important research significance and value. In this paper, we have used a deep learning-based model on CT images of lung cancer and verified its effectiveness in the timely and accurate prediction of lungs disease. The proposed model has three parts: (i) detection of lung nodules, (ii) False Positive Reduction of the detected nodules to filter out “false nodules,” and (iii) classification of benign and malignant lung nodules. Furthermore, different network structures and loss functions were designed and realized at different stages. Additionally, to fine-tune the proposed deep learning-based mode and improve its accuracy in the detection Lung Nodule Detection, Noudule-Net, which is a detection network structure that combines U-Net and RPN, is proposed. Experimental observations have verified that the proposed scheme has exceptionally improved the expected accuracy and precision ratio of the underlined disease.


Author(s):  
Raúl Pedro Aceñero Eixarch ◽  
Raúl Díaz-Usechi Laplaza ◽  
Rafael Berlanga Llavori

This paper presents a study about screening large radiological image streams produced in hospitals for earlier detection of lung nodules. Being one of the most difficult classification tasks in the literature, our objective is to measure how well state-of-the-art classifiers can screen out the images stream to keep as many positive cases as possible in an output stream to be inspected by clinicians. We performed several experiments with different image resolutions and training datasets from different sources, always taking ResNet-152 as the base neural network. Results over existing datasets show that, contrary to other diseases like pneumonia, detecting nodules is a hard task when using only radiographies. Indeed, final diagnosis by clinicians is usually performed with much more precise images like computed tomographies.


2022 ◽  
Vol 10 ◽  
pp. 232470962110633
Author(s):  
Oluyemisi Amoda ◽  
Elmarie Alexander ◽  
Hesham Abowali ◽  
Ese Uwagbale ◽  
Mohammed Zaman

Lung masses are becoming more common, and although most are tumors, benign or malignant, some are not solid masses. Many pathologies can present as lung nodules, including lung cancers, hamartomas, lung abscesses, granulomas, and eosinophilic pneumonia, to name a few. A 40-year-old woman with a long history of smoking presented with cough and left-sided chest pain. After multiple imaging studies, she was thought to have a lung malignancy; however, multiple biopsies proved this was not the case. The histology reports of 3 to 4 biopsies at separate times indicated chronic inflammation ongoing in the lungs without any cancer cells present. She was treated for chronic eosinophilic pneumonia with a resolution of symptoms. The purpose of this case report is to discuss a case that was initially thought to be a lung mass but found to be chronic eosinophilic pneumonia manifesting as a lung mass.


2021 ◽  
Vol 1 (12) ◽  
Author(s):  
Angela M. Barbara ◽  
Hannah Loshak

Evidence of variable quality from 6 diagnostic test accuracy studies indicates that the Pan-Canadian Early Detection of Lung Cancer (PanCan) model may perform better at determining which lung nodules identified by low-dose CT are cancerous compared to the Lung Imaging Reporting and Data System. However, evidence from 3 other studies, also of variable quality, suggests that the risk calculators have similar diagnostic test accuracy. No studies were identified that compared the clinical utility of PanCan versus the Lung Imaging Reporting and Data System. Results from 2 economic evaluations were inconsistent about the cost-effectiveness of the 2 lung cancer risk models. However, each study applied the models to different types of lung nodules. One evidence-based guideline recommended that PanCan be used in the UK for initial risk assessment and for the management of lung nodules.


2021 ◽  
Author(s):  
R. Jenkin Suji ◽  
W.Wilfred Godfrey ◽  
Joydip Dhar
Keyword(s):  

2021 ◽  
Author(s):  
Ju H. Oh ◽  
Hong S. Cho ◽  
Hee S. Hwang ◽  
Wonjun Ji

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Shruthi Panduranga ◽  
Samson Kade ◽  
Swarna Shivakumar ◽  
Harisha V ◽  
Ravindra M. Mehta

Abstract Background Extramedullary plasmacytoma is a rare monoclonal plasma cell neoplasm that originates from tissues other than the bone marrow and constitutes only 3%–5% of all plasma cell neoplasms. Most cases involve the upper respiratory tract. Extramedullary plasmacytoma involving the lung is extremely rare. Primary pulmonary plasmacytoma is a rare type of extramedullary plasmacytoma, usually presenting with a nodule or mass in hilar region. Literature search has shown very few cases of immunohistochemically confirmed cases of pulmonary plasmacytoma. Diffuse pulmonary infiltration is an unusual presentation. Case presentation We report the case of a 56 year old lady with history of cough and breathlessness since one month. Computed Tomography revealed diffusely scattered multiple cavitating nodules and consolidation in both lungs. Computed Tomography guided biopsy of one of the lung nodules was done. Histopathologic examination and immunohistochemistry showed features of pulmonary plasmacytoma .This is an unique case of primary pulmonary plasmacytoma with the rare presentation as diffusely scattered multiple cavitating nodules and consolidation. According to our literature search, primary pulmonary plasmacytoma manifesting as cavitating nodules is being reported for the first time. Conclusions Primary pulmonary plasmacytoma should be also be considered in the differential diagnosis of multiple diffusely scattered cavitating lung nodules.


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