Lung Nodules
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2021 ◽  
pp. 1-13
Author(s):  
Malathi Murugesan ◽  
Kalaiselvi Kaliannan ◽  
Shankarlal Balraj ◽  
Kokila Singaram ◽  
Thenmalar Kaliannan ◽  
...  

Deep learning algorithms will be used to detect lung nodule anomalies at an earlier stage. The primary goal of this effort is to properly identify lung cancer, which is critical in preserving a person’s life. Lung cancer has been a source of concern for people all around the world for decades. Several researchers presented numerous issues and solutions for various stages of a computer-aided system for diagnosing lung cancer in its early stages, as well as information about lung cancer. Computer vision is one of the field of artificial intelligence this is a better way to detect and prevent the lung cancer. This study focuses on the stages involved in detecting lung tumor regions, namely pre-processing, segmentation, and classification models. An adaptive median filter is used in pre-processing to identify the noise. The work’s originality seeks to create a simple yet effective model for the rapid identification and U-net architecture based segmentation of lung nodules. This approach focuses on the identification and segmentation of lung cancer by detecting picture normalcy and abnormalities.


Medicine ◽  
2021 ◽  
Vol 100 (40) ◽  
pp. e27491
Author(s):  
Xiaofang Zhang ◽  
Xiaomin Liu ◽  
Bin Zhang ◽  
Jie Dong ◽  
Bin Zhang ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6655
Author(s):  
Michael Horry ◽  
Subrata Chakraborty ◽  
Biswajeet Pradhan ◽  
Manoranjan Paul ◽  
Douglas Gomes ◽  
...  

Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the “black-box” nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.


Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1830
Author(s):  
Kyungjong Lee ◽  
Mijung Oh ◽  
Kyo-Sun Lee ◽  
Yoon Jin Cha ◽  
Yoon Soo Chang

Background and objective: Methionyl-tRNA synthetase (MARS) and A variant of Aminoacyl-tRNA synthetase interacting multifunctional protein 2 (AIMP2) with an exon 2 deletion (AIMP2-DX2) are known to be overexpressed in lung cancer. However, their role as diagnostic markers in lung cancer has not been well established. Thus, we evaluated their diagnostic performance in brushed cells obtained from nodular lung lesions suspected of lung cancer. Methods: Samples obtained by radial endobronchial ultrasound-guided brushing were processed for cytological examination with Papanicolaou (Pap) staining. Then, double IF staining with MARS and AIMP2-DX2 antibodies was measured in the cytology samples for peripheral lung nodules. The diagnostic performance was compared against biomarkers. Results: MARS IF staining was the only independent staining method used for the prediction of malignant cells. The area under the curve (AUC) of conventional cytology, MARS IF, and MARS IF plus cytology was 0.64, 0.68, and 0.69, respectively. The diagnostic accuracy was increased in MARS IF plus conventional cytology compared with cytology alone (71% vs. 47%). Conclusions: The combination of MARS staining with conventional cytology showed increases in the diagnostic accuracy for diagnosing lung nodules suspected of lung cancer on chest-computed tomography scans.


2021 ◽  
Vol 49 (10) ◽  
pp. 030006052110496
Author(s):  
Yu-Lei Hou ◽  
Jian-Hong Zhang ◽  
Jin-Bao Guo ◽  
Hui Chen

Objective To investigate the clinical significance of serum S100 calcium-binding protein A10 (S100A10) levels in lung cancer. Methods This prospective study enrolled patients with lung cancer, patients with benign lung nodules and healthy control subjects. Serum S100A10 levels and three biomarkers were measured and compared between the groups. Associations between serum S100A10 and clinical characteristics in patients with lung cancer were investigated. The diagnostic efficacy of serum S100A10 and carcinoembryonic antigen for lung cancer was calculated. Results The study enrolled 82 patients with lung cancer, 21 with benign lung nodules and 50 healthy controls. Serum S100A10 levels were significantly higher in patients with lung cancer compared with patients with benign lung nodules and healthy control subjects. Serum S100A10 levels of patients with advanced lung cancer were significantly higher than those with early stage disease. Patients with lymph node metastases had significantly higher serum S100A10 levels than patients without lymph node metastases. The cut-off serum S100A10 value for lung cancer detection was 1.34 ng/ml, which had a sensitivity of 48.2%, a specificity of 76.2% and an area under the curve of 0.63. Conclusion Serum S100A10 was significantly correlated with disease stage and lymph node metastasis. It has the potential to be a tumour biomarker for lung cancer.


CHEST Journal ◽  
2021 ◽  
Vol 160 (4) ◽  
pp. A2518-A2519
Author(s):  
Carla Lamb ◽  
Kimberly Rieger-Christ ◽  
Chakravarthy Reddy ◽  
Jie Ding ◽  
JIanghan Qu ◽  
...  

CHEST Journal ◽  
2021 ◽  
Vol 160 (4) ◽  
pp. A1519
Author(s):  
Fábio Kunita ◽  
Michelle Cailleaux-Cezar ◽  
Barbara Bracarense

CHEST Journal ◽  
2021 ◽  
Vol 160 (4) ◽  
pp. A1737
Author(s):  
Tanya Marshall ◽  
Joseph Parambil

Author(s):  
Taeho Ha ◽  
Wooil Kim ◽  
Jaehyung Cha ◽  
Young Hen Lee ◽  
Hyung Suk Seo ◽  
...  

Author(s):  
Ummu Afeera Binti Zainulabid ◽  
Muhammad Naimmuddin Bin Abdul Azih ◽  
Sasi Kumar A/L Maniyam ◽  
Azliana Binti Abd Fuaa ◽  
Mohd Radhwan Bin Abidin ◽  
...  

Pulmonary phaeohyphomycosis is a rare infection in the lung caused by black fungi containing a cytoplasmic melanin-like pigment. A 42-year-old man with underlying retroviral disease on HAART was investigated for having constitutional symptoms. Despite undetectable viral load and a high CD4 count, he was found to have unexplained significant loss of weight and appetite over a period of 6 months. Clinical examination revealed a cachexic man with multiple inguinal lymphadenopathies. Excisional biopsy of the inguinal lymph node revealed reactive follicular hyperplasia. CT Thorax, Abdomen and Pelvis was arranged to look for occult malignancy or infection and he was found to have multiple non-enhancing subcentimeter lung nodules mainly at the lateral segment of the right middle lobe of his lung. The largest nodule measured about 0.8 x 1.5 x 0.5 (AP x W x CC), with some nodules having an irregular margin with no extension into the adjacent bronchi. Bronchoscopy was done and demonstrated a black patch at the right intermedius, lateral segment of the middle lobe which did not disappear upon bronchial flush or wash. Histopathological examination found focal areas of blackish pigment and the bronchial alveolar lavage sent for fungal culture grew Cladosporium species. The patient was treated with oral Itraconazole with marked clinical improvement. This case highlights an unusual black fungi infection in the lung that stands out not only for its rarity and it's responsiveness to treatment, but also the susceptibility of an RVD positive patient to this infection despite having suppressed viral load and normal CD4 count.International Journal of Human and Health Sciences Supplementary Issue-2: 2021 Page: S17


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