Imaging Of Lung Cancer

This issue gives the general radiologist a solid overview of lung cancer imaging techniques. CT screening for lung cancer is discussed, and the evaluation and management of indeterminate pulmonary nodules is reviewed. Revised TNM lung cancer staging, as well as the optimal imaging protocols for lung cancer staging (CT, MR and PET) are thoroughly examined. A multidisciplinary approach to tissue sampling and updated histopathologic classification of lung cancer are discussed. Image-guided ablative therapies for lung cancer are reviewed. Finally, future trends in lung cancer diagnosis and staging and genetics are reviewed, as well as novel biomarkers for lung cancer detection.

While specialists often guide the care to lung cancer patients, it is often a general radiologist who is left to interpret studies that impact patient care and management. Lung Cancer Imaging provides a comprehensive guide to the diagnosis, staging and overview of the management of lung cancer relevant to practicing radiologists so that they can better understand the decision making issues and provide more directed and useful communication to the treating physicians. It Primary Care physicians will also find this book valuable to understand the relevant issues that they face when one of their patients is being treated for lung cancer.

This open access book focuses on diagnostic and interventional imaging of the chest, breast, heart, and vessels. It consists of a remarkable collection of contributions authored by internationally respected experts, featuring the most recent diagnostic developments and technological advances with a highly didactical approach. The chapters are disease-oriented and cover all the relevant imaging modalities, including standard radiography, CT, nuclear medicine with PET, ultrasound and magnetic resonance imaging, as well as imaging-guided interventions. As such, it presents a comprehensive review of current knowledge on imaging of the heart and chest, as well as thoracic interventions and a selection of "hot topics". The book is intended for radiologists, however, it is also of interest to clinicians in oncology, cardiology, and pulmonology.

Lung cancer is one of the most common cancers in both men and women worldwide. Early diagnosis of lung cancer can significantly increase the chances of a patient's survival, yet early detection has historically been difficult. As a result, there has been a great deal of progress in the development of accurate and fast diagnostic tools in recent years. Lung Cancer and Imaging provides an introduction to both the methods currently used in lung cancer diagnosis and the promising new techniques that are emerging. Areas covered include the major trends and challenges in lung cancer detection and diagnosis, classification of cancer types, lung feature extraction in joint PET/CT images, and algorithms in the area of low dosage CT lung cancer images. Part of Series in Physics and Engineering in Medicine and Biology.

Standard radiography of the chest remains one of the most widely used imaging modalities but it can be difficult to interpret. The possibility of producing cross-sectional, reformatted 2D and 3D images with CT makes this technique an ideal tool for reinterpreting standard radiography of the chest. The aim of this book is to provide a comprehensive overview of chest radiography interpretation by means of a side-by-side comparison between chest radiographs and CT images. Introductory chapters address the indications for and difficulties of chest radiography as well as the technical and practical aspects of CT reconstruction and image comparison. Thereafter, the radiographic and CT presentations of both anatomical variants and a wide range of diseases and disorders are illustrated and discussed by renowned experts in thoracic imaging. The book is complemented by online extra material which provides many further educational examples.

This book focuses on major trends and challenges in the detection of lung cancer, presenting work aimed at identifying new techniques and their use in biomedical analysis. This volume covers recent advancements in lung cancer and imaging detection and classification, examining the main applications of Computer aided diagnosis (CAD) relating to lung cancer: lung nodule segmentation, lung nodule classification, and Big Data in lung cancer. Ideal for academics working in lung cancer, data-mining, machine learning, deep learning and reinforcement learning, as well as industry professionals working in the areas of healthcare, lung cancer imaging, machine learning, deep learning and reinforcement learning, this edited collection comprises an essential reference for researchers at the forefront of the field, and provides a high-level entry point for more advanced students. Key Features:  -Unique focus on advance work in detection system and classification systems. -An updated reference for lung cancer detection via imaging. -Focus on progressive deep learning and machine learning applications for more effective detection.