Diagnosis efficacy of structural (CT) and functional (FDG-PET) imaging methods in the thoracic and extrathoracic staging of non-small cell lung cancer

Fernández, Antonio Rodríguez; Rio, Miguel; Elvira, J.M. Llamas; Ramos, A. Sánchez-Palencia; Guardia, M. Bellón; Font, C. Ramos; Poyatos, P Torné; Muriel, Vicente Pedraza

doi:10.1007/s12094-007-0007-6

Cited by 10 publications

(4 citation statements)

References 49 publications

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Section: Histopathological Image Preprocessingmentioning

confidence: 99%

Exploration and Enhancement of Classifiers in the Detection of Lung Cancer from Histopathological Images

Shanmugam,

Rajaguru

2023

Diagnostics

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Lung cancer is a prevalent malignancy that impacts individuals of all genders and is often diagnosed late due to delayed symptoms. To catch it early, researchers are developing algorithms to study lung cancer images. The primary objective of this work is to propose a novel approach for the detection of lung cancer using histopathological images. In this work, the histopathological images underwent preprocessing, followed by segmentation using a modified approach of KFCM-based segmentation and the segmented image intensity values were dimensionally reduced using Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). Algorithms such as KL Divergence and Invasive Weed Optimization (IWO) are used for feature selection. Seven different classifiers such as SVM, KNN, Random Forest, Decision Tree, Softmax Discriminant, Multilayer Perceptron, and BLDC were used to analyze and classify the images as benign or malignant. Results were compared using standard metrics, and kappa analysis assessed classifier agreement. The Decision Tree Classifier with GWO feature extraction achieved good accuracy of 85.01% without feature selection and hyperparameter tuning approaches. Furthermore, we present a methodology to enhance the accuracy of the classifiers by employing hyperparameter tuning algorithms based on Adam and RAdam. By combining features from GWO and IWO, and using the RAdam algorithm, the Decision Tree classifier achieves the commendable accuracy of 91.57%.

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Section: Histopathological Image Preprocessingmentioning

confidence: 99%

Exploration and Enhancement of Classifiers in the Detection of Lung Cancer from Histopathological Images

Shanmugam,

Rajaguru

2023

Diagnostics

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“…In the realm of diagnosing and treating medical conditions, healthcare professionals heavily rely on histopathological images. These images establish a crucial cornerstone for predicting patient survival rates [31]. As per available reports, histopathological images present several challenges: 1) The images exhibit intricate geometric structures and complex textures that arise from the vast diversity in structural morphology [32].…”

Section: Preprocessing and Segmentationmentioning

confidence: 99%

Exploration and Enhancement of Classifiers in the Detection of Lung Cancer from Histopathological Images

Shanmugam,

Rajaguru

2023

Preprint

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Lung cancer is a prevalent malignancy that impacts individuals of all genders and is often diagnosed late due to delayed symptoms. To catch it early, researchers are developing algorithms to study lung cancer images. One such method uses computer aided detection to classify as benign or malignant from the histopathological images. Standard histopathological images were used from a Lung and Colon Cancer Histopathological Image Dataset (LC25000) which contains two classes of benign and malignant of 5000 each. Images were preprocessed and features extracted using Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). Feature selection methods used are KL Divergence and Invasive Weed Optimization (IWO). Seven different classifiers like SVM, KNN, Random Forest, Decision Tree, Softmax Discriminant, Multilayer Perceptron, and BLDC were used to analyze and classify the images as benign or malignant. Results were compared using standard metrics, and kappa analysis assessed classifier agreement. The Decision Tree Classifier with GWO feature extraction achieved good accuracy of 85.01% without Feature selection and Hyperparameter Tuning approaches. Furthermore, we present a methodology to enhance the accuracy of the classifiers by employing hyperparameter tuning algorithms based on Adam and RAdam. By combining features from GWO and IWO, and using the RAdam algorithm, the Decision Tree classifier achieves the commendable accuracy of 91.57%

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“…18 F-fluorodeoxyglucose (FDG) is the only Medicare approved PET/CT tracer for cancer imaging and FDG-PET/CT is the most widely available PET/CT procedure used in daily oncology practice. Studies have shown that FDG-PET/CT improves staging accuracy, with a 20–30% improvement in specificity and sensitivity over CT scanning (Toloza et al, 2003; Rodríguez Fernández et al, 2007; Yi et al, 2008). PET/CT systems offer a unique opportunity of improving target localization and facilitating treatment planning for radiation therapy.…”

Section: Introductionmentioning

confidence: 99%

Application of FDG-PET/CT in Radiation Oncology

Li¹,

Xiao²

2013

Front. Oncol.

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Positron emission tomography (PET)/computed tomography (CT), which combines the advantages of high sensitivity and specificity of PET and high resolution of CT, is a unique tool for cancer management. PET/CT has been widely used in cancer diagnosis and treatment. The article reviews the recent applications of PET/CT in radiation oncology, with a focus on 18F-fluorodeoxyglucose (FDG)-PET/CT, addressing the applications in treatment planning and treatment response assessment of radiation therapy.

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Diagnosis efficacy of structural (CT) and functional (FDG-PET) imaging methods in the thoracic and extrathoracic staging of non-small cell lung cancer

Abstract: Although complementary, the functional method (FDG-PET) is significantly superior to the structural method (CT) for detection of mediastinal tumour disease.

Cited by 10 publications

References 49 publications

Exploration and Enhancement of Classifiers in the Detection of Lung Cancer from Histopathological Images

Exploration and Enhancement of Classifiers in the Detection of Lung Cancer from Histopathological Images

Exploration and Enhancement of Classifiers in the Detection of Lung Cancer from Histopathological Images

Application of FDG-PET/CT in Radiation Oncology

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