Development of Methodology for Transforming CT Images Indicating Location and Size of Lung Cancer Nodule

2017 ◽  
pp. 293-301
Author(s):  
Rajani R. Mhetre ◽  
Pooja P. Kawathekar ◽  
Sneha S. Kadam ◽  
Megha B. Gore
Keyword(s):  
Lung Cancer ◽  
Ct Images

Prevalence of sarcopenia in patients with gastroesophageal and lung malignancies undergoing surgical resection and its impact on immediate postoperative outcomes: A prospective observational study.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18617-e18617
Author(s):  
Sudhakar Gunasekar ◽  
SVS Deo ◽  
Sunil Kumar ◽  
Ekta Dhamija ◽  
Sandeep Kumar Bhoriwal
Keyword(s):  
Lung Cancer ◽  
Weight Loss ◽  
Grip Strength ◽  
Handgrip Strength ◽  
Ct Images ◽  
Control Group ◽  
Gastroesophageal Cancer ◽  
Combined Method ◽  
Skeletal Muscle Index ◽  
Strength Based

e18617 Background: The study evaluated the prevalence & impact of sarcopenia in gastroesophageal cancer (GC) & lung cancer (LC) patients undergoing resection. Methods: An observational prospective study was conducted in department of surgical oncology, AIIMS, New Delhi. All patients aged under 65 years with resectable GC & LC were included. Skeletal muscle index (SMI) using cross-sectional CT images at the level of L3 & Hand grip strength (dynamometer) were used to assess sarcopenia. Random benign patients with CT images were used as control group. Patients were categorized into sarcopenic and non-sarcopenic and outcome parameters were compared. Results: In the study population (n = 66), GC & LC constituted 44 (66.67%) & 22(33.33%) respectively. Mean age was 53.4 years. The prevalence of sarcopenia based on the combined method (CT imaging & handgrip strength) was 57.58%, CT based sarcopenia was 33.34% & handgrip strength-based sarcopenia was 43.93%. Mild and moderate sarcopenia was seen in 37.88% (n = 25) & 19.7 % (n = 13) respectively. Among patients with GC, prevalence of sarcopenia was 59.09% by combined method, 36.36% and 43.18% by CT based method alone & handgrip strength-based method alone respectively. Among LC prevalence of sarcopenia was 54.54% by combined method, 27.27% and 45.45% by CT based & handgrip-based method. The concordance between CT muscle mass & grip strength was 62.12%. Most female patients had weak handgrip strength despite having normal SMI. In control group (n = 44) mean age was 54.5 years, the prevalence of CT based sarcopenia was 34.09%. Weight loss history & BMI correlated with the degree of sarcopenia. Out of 66 patients 13% (n = 9) patients were unresectable. Moderate sarcopenia group had more statistically significant (P -0.02) unresectable disease compared to mild and non-sarcopenic groups. In postoperative period, sarcopenic group (64.51% vs 38.36 %) had more grade 2 complications though statistically insignificant. There was no difference in hospital stay between the two groups. In patients with GC , postoperative respiratory complication occurred in 11.53% (n = 3) of sarcopenic patients and 5.5%(n = 1) of non-sarcopenic patients, anastomotic leak occurred in 7.69% (n = 2) of sarcopenic patients and 5.5% (n = 1) of nonsarcopenic patients. Conclusions: The prevalence of sarcopenia is higher in patients with gastroesophageal cancer compared to lung cancer. The important factors that affect the sarcopenia include age and body mass index and weight loss history. The study has showed a trend towards increased post-operative complications and increased unresectable cases in patients with mild to moderate sarcopenia. Further larger studies are required to validate if sarcopenia can be used as an adjunct to predict resectability and post-operative outcomes.

Automatic nodule detection for lung cancer in CT images: A review

2018 ◽  
Vol 103 ◽  
pp. 287-300 ◽  
Author(s):  
Guobin Zhang ◽  
Shan Jiang ◽  
Zhiyong Yang ◽  
Li Gong ◽  
Xiaodong Ma ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Ct Images ◽  
Nodule Detection

scholarly journals Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PET/CT

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Margarita Kirienko ◽  
Martina Sollini ◽  
Giorgia Silvestri ◽  
Serena Mognetti ◽  
Emanuele Voulaz ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Fdg Pet ◽  
Area Under The Curve ◽  
Ct Images ◽  
Tnm System ◽  
Positron Emission ◽  
Pet Ct ◽  
Test Sets ◽  
Fdg Pet Ct

Aim. To develop an algorithm, based on convolutional neural network (CNN), for the classification of lung cancer lesions as T1-T2 or T3-T4 on staging fluorodeoxyglucose positron emission tomography (FDG-PET)/CT images. Methods. We retrospectively selected a cohort of 472 patients (divided in the training, validation, and test sets) submitted to staging FDG-PET/CT within 60 days before biopsy or surgery. TNM system seventh edition was used as reference. Postprocessing was performed to generate an adequate dataset. The input of CNNs was a bounding box on both PET and CT images, cropped around the lesion centre. The results were classified as Correct (concordance between reference and prediction) and Incorrect (discordance between reference and prediction). Accuracy (Correct/[Correct + Incorrect]), recall (Correctly predicted T3-T4/[all T3-T4]), and specificity (Correctly predicted T1-T2/[all T1-T2]), as commonly defined in deep learning models, were used to evaluate CNN performance. The area under the curve (AUC) was calculated for the final model. Results. The algorithm, composed of two networks (a “feature extractor” and a “classifier”), developed and tested achieved an accuracy, recall, specificity, and AUC of 87%, 69%, 69%, and 0.83; 86%, 77%, 70%, and 0.73; and 90%, 47%, 67%, and 0.68 in the training, validation, and test sets, respectively. Conclusion. We obtained proof of concept that CNNs can be used as a tool to assist in the staging of patients affected by lung cancer.

Lung cancer classification using neural networks for CT images

2014 ◽  
Vol 113 (1) ◽  
pp. 202-209 ◽  
Author(s):  
Jinsa Kuruvilla ◽  
K. Gunavathi
Keyword(s):  
Lung Cancer ◽  
Neural Networks ◽  
Ct Images ◽  
Cancer Classification

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 Combining radiomics and mathematical modeling to elucidate mechanisms of resistance to immune checkpoint blockade in non-small cell lung cancer

2017 ◽  
Author(s):  
Daryoush Saeed-Vafa ◽  
Rafael Bravo ◽  
Jamie A. Dean ◽  
Asmaa El-Kenawi ◽  
Nathaniel Mon Père ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Checkpoint Inhibitor ◽  
Cancer Recurrence ◽  
Initial Response ◽  
Resistance Mechanisms ◽  
Ct Images ◽  
Immune Checkpoint Blockade ◽  
Imaging Features ◽  
Mechanisms Of Resistance ◽  
Number Of Patients

AbstractImmune therapies have shown promise in a number of cancers, and clinical trials using the anti-PD-L1/PD-1 checkpoint inhibitor in lung cancer have been successful for a number of patients. However, some patients either do not respond to the treatment or have cancer recurrence after an initial response. It is not clear which patients might fall into these categories or what mechanisms are responsible for treatment failure. To explore the different underlying biological mechanisms of resistance, we created a spatially explicit mathematical model with a modular framework. This construction enables different potential mechanisms to be turned on and off in order to adjust specific tumor and tissue interactions to match a specific patient's disease. In parallel, we developed a software suite to identify significant computed tomography (CT) imaging features correlated with outcome using data from an anti-PDL-1 checkpoint inhibitor clinical trial for lung cancer and a tool that extracts these features from both patient CT images and “virtual CT” images created from the cellular density profile of the model. The combination of our two toolkits provides a framework that feeds patient data through an iterative pipeline to identify predictive imaging features associated with outcome, whilst at the same time proposing hypotheses about the underlying resistance mechanisms.

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