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.

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.

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.

Multi-level 3D Densenets for False-positive Reduction in Lung Nodule Detection Based on Chest Computed Tomography

2020 ◽  
Vol 16 (8) ◽  
pp. 1004-1021
Author(s):  
Xiaoqi Lu ◽  
Yu Gu ◽  
Lidong Yang ◽  
Baohua Zhang ◽  
Ying Zhao ◽  
...  
Keyword(s):  
False Positive ◽  
Contextual Information ◽  
Lung Nodule ◽  
Positive Sample ◽  
Lung Nodules ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Level Information ◽  
Multi Level ◽  
Cade System

Objective: False-positive nodule reduction is a crucial part of a computer-aided detection (CADe) system, which assists radiologists in accurate lung nodule detection. In this research, a novel scheme using multi-level 3D DenseNet framework is proposed to implement false-positive nodule reduction task. Methods: Multi-level 3D DenseNet models were extended to differentiate lung nodules from falsepositive nodules. First, different models were fed with 3D cubes with different sizes for encoding multi-level contextual information to meet the challenges of the large variations of lung nodules. In addition, image rotation and flipping were utilized to upsample positive samples which consisted of a positive sample set. Furthermore, the 3D DenseNets were designed to keep low-level information of nodules, as densely connected structures in DenseNet can reuse features of lung nodules and then boost feature propagation. Finally, the optimal weighted linear combination of all model scores obtained the best classification result in this research. Results: The proposed method was evaluated with LUNA16 dataset which contained 888 thin-slice CT scans. The performance was validated via 10-fold cross-validation. Both the Free-response Receiver Operating Characteristic (FROC) curve and the Competition Performance Metric (CPM) score show that the proposed scheme can achieve a satisfactory detection performance in the falsepositive reduction track of the LUNA16 challenge. Conclusion: The result shows that the proposed scheme can be significant for false-positive nodule reduction task.

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.

Optimisation of volume-doubling time cutoff for fast-growing lung nodules in CT lung cancer screening reduces false-positive referrals

2013 ◽  
Vol 23 (7) ◽  
pp. 1836-1845 ◽  
Author(s):  
Marjolein A. Heuvelmans ◽  
Matthijs Oudkerk ◽  
Geertruida H. de Bock ◽  
Harry J. de Koning ◽  
Xueqian Xie ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
False Positive ◽  
Doubling Time ◽  
Lung Cancer Screening ◽  
Lung Nodules ◽  
Fast Growing ◽  
Volume Doubling Time ◽  
Volume Doubling

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.

Clinicopathologic Significance of False-Positive Lymph Node Status on FDG-PET in Lung Cancer

2020 ◽  
Author(s):  
Hideki Endoh ◽  
Ryohei Yamamoto ◽  
Akihiro Ichikawa ◽  
Satoshi Shiozawa ◽  
Nobuhiro Nishizawa ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Lymph Node ◽  
False Positive ◽  
Fdg Pet ◽  
Positive Lymph Node ◽  
Lymph Node Status ◽  
Node Status ◽  
Positive Lymph Node Status

scholarly journals Prognostic Implication of Predominant Histologic Subtypes of Lymph Node Metastases in Surgically Resected Lung Adenocarcinoma

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Kenichi Suda ◽  
Katsuaki Sato ◽  
Shigeki Shimizu ◽  
Kenji Tomizawa ◽  
Toshiki Takemoto ◽  
...  
Keyword(s):  
Lymph Node ◽  
Lung Adenocarcinoma ◽  
Lymph Node Metastases ◽  
Lung Tumor ◽  
International Association ◽  
Lung Tumors ◽  
Prognostic Implication ◽  
Primary Lung Tumors ◽  
Node Metastases ◽  
Histologic Subtypes

The International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society (IASLC/ATS/ERS) proposed a new classification for lung adenocarcinoma (AD) based on predominant histologic subtypes, such as lepidic, papillary, acinar, solid, and micropapillary; this system reportedly reflects well outcomes of patients with surgically resected lung AD. However, the prognostic implication of predominant histologic subtypes in lymph nodes metastases is unclear so far. In this study, we compared predominant subtypes between primary lung tumors and lymph node metastatic lesions in 24 patients with surgically treated lung adenocarcinoma with lymph node metastases. Additionally, we analyzed prognostic implications of these predominant histologic subtypes. We observed several discordance patterns between predominant subtypes in primary lung tumors and lymph node metastases. Concordance rates were 22%, 64%, and 100%, respectively, in papillary-, acinar-, and solid-predominant primary lung tumors. We observed that the predominant subtype in the primary lung tumor (HR 12.7,P = 0.037), but not that in lymph node metastases (HR 0.18,P = 0.13), determines outcomes in patients with surgically resected lung AD with lymph node metastases.

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