scholarly journals The Effect of Low Carbohydrate Diets on Preventing and Treating Carcinogen-Induced Lung Cancer in Mice

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 323-323
Author(s):  
Ingrid Elisia ◽  
Brandon Cho ◽  
Mariah Hay ◽  
Michelle Yeung ◽  
Sara Kowalski ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Soy Protein ◽  
Lung Tumor ◽  
Lung Nodule ◽  
Lung Tumors ◽  
Western Diet ◽  
Lung Nodules ◽  
Nodule Formation ◽  
Significant Difference ◽  
Low Carbohydrate

Abstract Objectives Since cancer cells typically rely more on glycolysis than normal cells, we hypothesized that lowering carbohydrate intake may reduce cancer risk. We aimed to investigate the efficacy of low-carbohydrate (CHO) diets in preventing and treating a tobacco-specific carcinogen-induced lung cancer in female A/J mice. Methods We evaluated the role of different types of CHO (easily digestible vs resistant), protein (casein vs. soy) and fat (fish vs. coconut vs. a mixture of oils) in modulating 4-(N-methyl-N-nitrosamino)-1-(3- pyridyl)-1-butanone (NNK)-induced lung nodule formation in these mice. To assess the efficacy of these diets in preventing NNK-induced lung nodule formation, we put these mice in the different diets for 2 weeks, intraperitoneally-injected NNK once a week for two weeks to initiate lung nodule formation. After 5 months, the lung nodules in these mice were counted. Results The lowering of easily digestible CHO significantly reduced constitutive blood glucose levels and lung nodule formation in the mice. Interestingly, diets low in easily digestible starch, high in fish oil (FO) and soy protein (15%Amylose/Soy/FO) were the most effective at preventing the formation of NNK-induced lung nodules. To determine if this 15%Amylose/Soy/FO is also effective at slowing tumor progression, we fed NNK-injected A/J mice a Western diet until tumors were established (5 months post NNK) and then either switched them to the 15%Amylose/Soy/FO or kept them on the Western diet for 5 additional months. The 15%Amylose/Soy/FO diet prevented the formation of additional lung tumor nodules and reduced the size of the tumors, although no significant difference was observed in tumor stage.  The reduction in size of the lung tumors on the 15%Amylose/Soy/FO diet was not due to a lower tumor proliferation (Ki67 index) but an increase in apoptosis, as determined by TUNEL assays. Conclusions We conclude that a diet change that lowers glucose intake, incorporates FO and soy protein may be effective not only in preventing lung cancer formation but also in slowing the growth of established lung tumors. Funding Sources Lotte & John Hecht Memorial Foundation.

scholarly journals Evaluation of an AI-Powered Lung Nodule Algorithm for Detection and 3D Segmentation of Primary Lung Tumors

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Thomas Weikert ◽  
Tugba Akinci D’Antonoli ◽  
Jens Bremerich ◽  
Bram Stieltjes ◽  
Gregor Sommer ◽  
...  
Keyword(s):  
Lung Cancer ◽  
False Positive ◽  
Fdg Pet ◽  
Lung Tumor ◽  
Lung Nodule ◽  
Lung Tumors ◽  
Lung Nodules ◽  
3D Segmentation ◽  
Detection Rates ◽  
Primary Lung Tumors

Automated detection and segmentation is a prerequisite for the deployment of image-based secondary analyses, especially for lung tumors. However, currently only applications for lung nodules ≤3 cm exist. Therefore, we tested the performance of a fully automated AI-based lung nodule algorithm for detection and 3D segmentation of primary lung tumors in the context of tumor staging using the CT component of FDG-PET/CT and including all T-categories (T1–T4). FDG-PET/CTs of 320 patients with histologically confirmed lung cancer performed between 01/2010 and 06/2016 were selected. First, the main primary lung tumor within each scan was manually segmented using the CT component of the PET/CTs as reference. Second, the CT series were transferred to a platform with AI-based algorithms trained on chest CTs for detection and segmentation of lung nodules. Detection and segmentation performance were analyzed. Factors influencing detection rates were explored with binominal logistic regression and radiomic analysis. We also processed 94 PET/CTs negative for pulmonary nodules to investigate frequency and reasons of false-positive findings. The ratio of detected tumors was best in the T1-category (90.4%) and decreased continuously: T2 (70.8%), T3 (29.4%), and T4 (8.8%). Tumor contact with the pleura was a strong predictor of misdetection. Segmentation performance was excellent for T1 tumors (r = 0.908, p<0.001) and tumors without pleural contact (r = 0.971, p<0.001). Volumes of larger tumors were systematically underestimated. There were 0.41 false-positive findings per exam. The algorithm tested facilitates a reliable detection and 3D segmentation of T1/T2 lung tumors on FDG-PET/CTs. The detection and segmentation of more advanced lung tumors is currently imprecise due to the conception of the algorithm for lung nodules <3 cm. Future efforts should therefore focus on this collective to facilitate segmentation of all tumor types and sizes to bridge the gap between CAD applications for screening and staging of lung cancer.

A Hybrid deep learning model for effective segmentation and classification of lung nodules from CT images

2021 ◽  
pp. 1-13
Author(s):  
Malathi Murugesan ◽  
Kalaiselvi Kaliannan ◽  
Shankarlal Balraj ◽  
Kokila Singaram ◽  
Thenmalar Kaliannan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Lung Tumor ◽  
Median Filter ◽  
Lung Nodule ◽  
Rapid Identification ◽  
Lung Nodules ◽  
Classification Models ◽  
Deep Learning Model

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.

Low carbohydrate diets containing soy protein and fish oil slow the growth of established NNK-induced lung tumors

2020 ◽  
Vol 41 (8) ◽  
pp. 1083-1093 ◽  
Author(s):  
Ingrid Elisia ◽  
Mariah Hay ◽  
Brandon Cho ◽  
Michelle Yeung ◽  
Sara Kowalski ◽  
...  
Keyword(s):  
Fatty Acid ◽  
Tumor Growth ◽  
Fish Oil ◽  
Soy Protein ◽  
Fatty Acid Synthase ◽  
Lung Nodule ◽  
Western Diet ◽  
Terminal Deoxynucleotidyl Transferase ◽  
Lung Nodules ◽  
Metabolic Switch

Abstract We recently found that a diet composed of 15% of total calories as carbohydrate (CHO), primarily as amylose, 35% soy protein and 50% fat, primarily as fish oil (FO) (15%Amylose/Soy/FO) was highly effective at preventing lung nodule formation in a nicotine-derived nitrosamine ketone (NNK)-induced lung cancer model. We asked herein whether adopting such a diet once cancers are established might also be beneficial. To test this, NNK-induced lung nodules were established in mice on a Western diet and the mice were then either kept on a Western diet or switched to various low CHO diets. Since we previously found that sedentary mice develop more lung nodules than active mice, we also compared the effect of exercise in this cancer progression model. We found that switching to a 15%Amylose/Soy/FO diet reduced lung nodules and slowed tumor growth with both ‘active’ and ‘sedentary’ mice. Ki67, cleaved caspase 3 and Terminal Deoxynucleotidyl Transferase-Mediated dUTP Nick End Labeling assays suggested that the efficacy of the 15%Amylose/Soy/FO in lowering tumor nodule count and size was not due to a reduction in tumor cell proliferation, but to an increase in apoptosis. The 15%Amylose/Soy/FO diet also significantly lowered liver fatty acid synthase and 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 expression, pointing to a global metabolic switch from glycolysis to fatty acid oxidation. Mice fed the 15%Amylose/Soy/FO diet also had significantly reduced plasma levels of interleukin (IL)-1β, IL-6 and tumor necrosis factor α. These results suggest that the 15%Amylose/Soy/FO diet may slow tumor growth by suppressing proinflammatory cytokines, inducing a metabolic switch away from glycolysis and inducing apoptosis in tumors.

scholarly journals The Effect of Diet on the Microbiome Composition and Incidence of NNK-Induced Lung Cancer

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 1557-1557
Author(s):  
Ingrid Elisia ◽  
Mariah Hay ◽  
Michael Li ◽  
Brandon Cho ◽  
Vivian Lam ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Gut Microbiome ◽  
Lung Nodule ◽  
Lung Nodules ◽  
Nodule Formation ◽  
Fecal Microbiome ◽  
Microbiome Composition ◽  
Funding Sources ◽  
16S Rna ◽  
Different Types

Abstract Objectives Since lowering carbohydrate (CHO) intake has been hypothesized to reduce cancer risk, we investigated whether low-CHO diets could prevent lung cancer in A/J mice, induced by the tobacco-specific carcinogen, 4-(N-methyl-N-nitrosamino)-1-(3- pyridyl)-1-butanone (NNK), and, if so if this corresponded to changes in gut microbiome composition. Methods We compared the effect of different quantities and types of CHO (easily digestible vs resistant), protein (casein vs. soy) and fat (fish vs. coconut vs. a mixture of oils) in modulating lung nodule formation and gut microbiome composition in A/J mice. Mice were fed either the Western diet or the low CHO diets, composed of different types of CHO, protein and fat for two weeks before injections with NNK to initiate lung formation. After 20 weeks, their feces were collected for amplification and sequencing of the bacterial 16S RNA gene V4 regions and the lung nodules were counted. Results Diets low in easily digestible starch, high in fish oil and soy protein were the most effective at preventing the formation of NNK-induced lung nodules. Changing protein, CHO and fat type in the diets all resulted in significant differences in the fecal microbiome composition of the NNK-injected mice. We also found a reduced abundance of the Streptococacceae and Clostridiaceae families in mice with low lung nodule numbers. Conclusions We suggest that it is possible that the diets reduced lung nodule formation, at least in part, via alterations in the microbiomes of the mice. Funding Sources Lotte & John Hecht Memorial Foundation.

scholarly journals An Efficient DA-Net Architecture for Lung Nodule Segmentation

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1457
Author(s):  
Muazzam Maqsood ◽  
Sadaf Yasmin ◽  
Irfan Mehmood ◽  
Maryam Bukhari ◽  
Mucheol Kim
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Ct Scan ◽  
Cancer Diagnosis ◽  
Lung Nodule ◽  
Search Space ◽  
Lung Nodules ◽  
Suggested Model ◽  
Lung Cancer Diagnosis ◽  
Segmentation Framework

A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.

scholarly journals Application of Deep Learning in Lung Cancer Imaging Diagnosis

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Wenfa Jiang ◽  
Ganhua Zeng ◽  
Shuo Wang ◽  
Xiaofeng Wu ◽  
Chenyang Xu
Keyword(s):  
Lung Cancer ◽  
Computer Vision ◽  
Deep Learning ◽  
Malignant Tumors ◽  
Lung Nodule ◽  
Ct Images ◽  
Lung Nodules ◽  
X Rays ◽  
Imaging Diagnosis ◽  
Risk Of Cancer

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.

scholarly journals Dosimetry and Comparison between Different CT Protocols (Low Dose, Ultralow Dose, and Conventional CT) for Lung Nodules’ Detection in a Phantom

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Cleverson Alex Leitão ◽  
Gabriel Lucca de Oliveira Salvador ◽  
Priscilla Tazoniero ◽  
Danny Warszawiak ◽  
Cristian Saievicz ◽  
...  
Keyword(s):  
Detection Rate ◽  
Low Dose ◽  
Lung Nodule ◽  
Lung Nodules ◽  
Ultralow Dose ◽  
Thermoluminescent Dosimeters ◽  
Significant Difference ◽  
Nodule Detection ◽  
The Difference ◽  
Conventional Ct

Background. The effects of dose reduction in lung nodule detection need better understanding. Purpose. To compare the detection rate of simulated lung nodules in a chest phantom using different computed tomography protocols, low dose (LD), ultralow dose (ULD), and conventional (CCT), and to quantify their respective amount of radiation. Materials and Methods. A chest phantom containing 93 simulated lung nodules was scanned using five different protocols: ULD (80 kVp/30 mA), LD A (120 kVp/20 mA), LD B (100 kVp/30 mA), LD C (120 kVp/30 mA), and CCT (120 kVp/automatic mA). Four chest radiologists analyzed a selected image from each protocol and registered in diagrams the nodules they detected. Kruskal–Wallis and McNemar’s tests were performed to determine the difference in nodule detection. Equivalent doses were estimated by placing thermoluminescent dosimeters on the surface and inside the phantom. Results. There was no significant difference in lung nodules’ detection when comparing ULD and LD protocols ( p = 0.208 to p = 1.000 ), but there was a significant difference when comparing each one of those against CCT ( p < 0.001 ). The detection rate of nodules with CT attenuation values lower than −600 HU was also different when comparing all protocols against CCT ( p < 0.001 to p = 0.007 ). There was at least moderate agreement between observers in all protocols (κ-value >0.41). Equivalent dose values ranged from 0.5 to 9 mSv. Conclusion. There is no significant difference in simulated lung nodules’ detection when comparing ULD and LD protocols, but both differ from CCT, especially when considering lower-attenuating nodules.

scholarly journals The ERBB network facilitates KRAS-driven lung tumorigenesis

2018 ◽  
Author(s):  
Björn Kruspig ◽  
Tiziana Monteverde ◽  
Sarah Neidler ◽  
Andreas Hock ◽  
Emma Kerr ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Lung Tumor ◽  
Tumor Development ◽  
Treatment Strategies ◽  
Invasive Disease ◽  
Therapeutic Benefit ◽  
Lung Tumors ◽  
Network Activity ◽  
Lung Tumorigenesis

AbstractKRAS is the most frequently mutated driver oncogene in human adenocarcinoma of the lung. There are presently no clinically proven strategies for treatment of KRAS-driven lung cancer. Activating mutations in KRAS are thought to confer independence from upstream signaling, however recent data suggest that this independence may not be absolute. Here we show that initiation and progression of KRAS-driven lung tumors requires input from ERBB family RTKs: Multiple ERBB RTKs are expressed and active from the earliest stages of KRAS driven lung tumor development, and treatment with a multi-ERBB inhibitor suppresses formation of KRasG12D-driven lung tumors. We present evidence that ERBB activity amplifies signaling through the core RAS pathway, supporting proliferation of KRAS mutant tumor cells in culture and progression to invasive disease in vivo. Importantly, brief pharmacological inhibition of the ERBB network significantly enhances the therapeutic benefit of MEK inhibition in an autochthonous tumor setting. Our data suggest that lung cancer patients with KRAS-driven disease may benefit from inclusion of multi-ERBB inhibitors in rationally designed treatment strategies.One Sentence SummaryG12 Mutant KRAS requires tonic ERBB network activity for initiation and maintenance of lung cancer

scholarly journals Research on Lung Nodule Detection Based on Improved Target Detection Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Ye Li ◽  
Qian Wu ◽  
Hongwei Sun ◽  
Xuewei Wang
Keyword(s):  
Lung Cancer ◽  
Target Detection ◽  
Block Structure ◽  
Lung Nodule ◽  
Lung Nodules ◽  
Early Symptom ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Public Datasets ◽  
Convolution Method

Lung nodules are an early symptom of lung cancer. The earlier they are found, the more beneficial it is for treatment. However, in practice, Chinese doctors are likely to cause misdiagnosis. Therefore, deep learning is introduced, an improved target detection network is used, and public datasets are used to diagnose and identify lung nodules. This paper selects the Mask-RCNN network and uses the dense block structure of Densenet and the channel shuffle convolution method to improve the Mask-RCNN network. The experimental results prove that proposed algorithm is extremely effective.

Computer-Aided Detection of Human Lung Nodules on Computer Tomography Images via Novel Optimized Techniques

2021 ◽  
Vol 17 ◽  
Author(s):  
Lim J. Seelan ◽  
Padma Suresh L. ◽  
Abhilash K.S. ◽  
Vivek P.K.
Keyword(s):  
Lung Cancer ◽  
Lung Nodule ◽  
Curvelet Transform ◽  
Lung Nodules ◽  
Primary Role ◽  
Simple Task ◽  
Cad Systems ◽  
Computer Aided ◽  
Percent Accuracy ◽  
Imaging Algorithms

Background: Globally, the most general reason for huge number of passings is Lung disease. The lung malignancy is the most shocking amongst the tumor types and it plays a significant role for the increase of death rate. It is assessed that nearly 1.2 million persons are determined to have this illness and about 1.1 million individuals are losing their lives due to this sickness in every year. The survival rate is superior if the growth is recognized at earlier periods. The premature identification of lung malignant growth isn't a simple task. Various imaging algorithms are available for detecting the lung cancer. Aim: Computer aided diagnosis scheme is more useful for radiologist in detecting and identifying irregularities in advance and more rapidly. The CAD systems usually focus on identifying and detecting the lung nodules. Staging the lung cancer at its detection need to be focused as the treatment is based on the stage of the cancer. The major drawbacks of existing CAD systems are less accuracy in segmenting the nodule and staging the lung cancer. Objective: The most important intention of this work is to divide the lung nodule from CT image and classify as tumorous cells in order to identify the cancer's position with greater sensitivity, precision, and accuracy than other strategies. Methods: The primary role is defined as follows (i) for de-noising and edge sharpening of lung image, the curvelet transform is used. (ii) The Fuzzy thresholding technique is used to perform lung image binarization and lung boundary corrections. (iii) Segmentation is performed by using K-means algorithm. (iv) By using convolutional neural network (CNN), different stages of lung nodules such as benign and malignant are identified. Results: The proposed classifier achieves a 97.3 percent accuracy. The proposed approach is helpful in detecting lung cancer in its early stages. The proposed classifier achieved a sensitivity of 98.6 percent and a specificity of 96.1 percent. Conclusion: The results demonstrated that the established algorithms can be used to assist a radiologist in classifying lung images into various stages, thus supporting the radiologist in decision making.

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