The IASLC lung cancer staging project proposal for the classification of lung cancers with multiple pulmonary sites of involvement: the first step toward finding optimal treatment

Lung cancer is a leading cause of death worldwide. Currently, in differential diagnosis of lung cancer, accurate classification of cancer types (adenocarcinoma, squamous cell carcinoma, and small cell carcinoma) is required. However, improving the accuracy and stability of diagnosis is challenging. In this study, we developed an automated classification scheme for lung cancers presented in microscopic images using a deep convolutional neural network (DCNN), which is a major deep learning technique. The DCNN used for classification consists of three convolutional layers, three pooling layers, and two fully connected layers. In evaluation experiments conducted, the DCNN was trained using our original database with a graphics processing unit. Microscopic images were first cropped and resampled to obtain images with resolution of 256 × 256 pixels and, to prevent overfitting, collected images were augmented via rotation, flipping, and filtering. The probabilities of three types of cancers were estimated using the developed scheme and its classification accuracy was evaluated using threefold cross validation. In the results obtained, approximately 71% of the images were classified correctly, which is on par with the accuracy of cytotechnologists and pathologists. Thus, the developed scheme is useful for classification of lung cancers from microscopic images.

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Lung Cancer: Staging

Chest Imaging ◽

10.1093/med/9780199858064.003.0049 ◽

2019 ◽

pp. 281-287

Author(s):

Ryo E. C. Benson

Keyword(s):

Lung Cancer ◽

Lymph Node ◽

Mediastinal Lymph Node ◽

Tnm Classification ◽

Cancer Staging ◽

Tnm Staging ◽

Small Cell ◽

Small Cell Lung ◽

Lung Cancers ◽

Resected Tumor

Lung cancer staging is a process used to assess the extent of spread of lung cancer, determine the most appropriate treatment and predict the patient’s prognosis. Clinical staging is performed prior to surgical resection, while surgical-pathologic staging is based on histologic analysis of the resected tumor and lymph nodes. Restaging is performed following treatment. Staging is based on the TNM classification system. T refers to the primary tumor, N to thoracic lymph node involvement and M to metastatic disease. Recent changes to T and M descriptors were made to better reflect actual survival. For the majority of non-small cell lung cancers, the presence or absence of mediastinal lymph node spread is the most important outcome predictor. Although no changes were made to the N descriptor, the actual intrathoracic lymph node stations were recently clarified. Although the majority of small cell lung cancers are metastatic at the time of presentation, the presence of limited versus extensive spread of disease determines treatment options. However, the overall prognosis and survival for affected patients is poor. TNM staging is now recommended for carcinoid tumors as well as small cell lung cancer.

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Automated communication system for detection of lung cancer using catastrophe features

Informatologia ◽

10.32914/i.53.3-4.5 ◽

2020 ◽

Vol 53 (3-4) ◽

pp. 184-190

Author(s):

Ramaiah Arun ◽

Shanmugasundaram Singaravelan

Keyword(s):

Lung Cancer ◽

Support Vector ◽

Color Spaces ◽

Lung Cancers ◽

Clustering Techniques ◽

Early Stages ◽

Fuzzy C Means Clustering ◽

Glandular Cells ◽

Medical Advances

One of the biggest challenges the world face today is the mortality due to Cancer. One in four of all diagnosed cancers involve the lung cancer, where the mortality rate is high, even after so much of technical and medical advances. Most lung cancer cases are diagnosed either in the third or fourth stage, when the disease is not treatable. The main reason for the highest mortality, due to lung cancer is because of non availability of prescreening system which can analyze the cancer cells at early stages. So it is necessary to develop a prescreening system which helps doctors to find and detect lung cancer at early stages. Out of all various types of lung cancers, adenocarcinoma is increasing at an alarming rate. The reason is mainly attributed to the increased rate of smoking - both active and passive. In the present work, a system for the classification of lung glandular cells for early detection of Cancer using multiple color spaces is developed. For segmentation, various clustering techniques like K-Means clustering and Fuzzy C-Means clustering on various Color spaces such as HSV, CIELAB, CIEXYy and CIELUV are used. Features are Extracted and classified using Support Vector Machine (SVM).

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Towards the 9th Edition of the Tumour, Node and Metastasis Classification of Lung Cancer. A Historical Appraisal and Future Perspectives

Advances in Thoracic Diseases ◽

10.15342/atd.v1ir.293 ◽

2019 ◽

Vol 1 (2) ◽

Author(s):

Ramón Rami-Porta

Keyword(s):

Lung Cancer ◽

International Association ◽

Tumour Size ◽

Primary Tumour ◽

Cancer Control ◽

Regional Lymph Nodes ◽

Lung Cancers ◽

Lung Cancer Patients ◽

Pathologic Staging

Since 1966 the classification of anatomic extent of lung cancer, based on the primary tumour (T), the loco-regional lymph nodes (N) and the metastases (M) has been used in the management of lung cancer patients. Developed by Pierre Denoix, it was adopted by the Union for International Cancer Control and the American Joint Committee on Cancer. Clifton Mountain revised the second through the sixth editions based on a North American database of more than 5000 patients. For the seventh and the eighth editions, the International Association for the Study of Lung Cancer (IASLC) collected international databases of around 100,000 patients worldwide that allowed the introduction of innovations in both editions, namely the subdivision of the T and M categories based on tumour size and on the location and number of metastases, respectively. The revisions also showed the prognostic relevance of the quantification of nodal disease, and proposed recommendations on how to measure tumour size for solid lung cancers, part-solid adenocarcinomas, and for lung cancers removed after induction therapy. Despite the innovations, prognosis based on the anatomic extent is limited, because prognosis depends on factors related to the tumour, the patient and the environment. For the 9th edition, these factors, especially genetic biomarkers, will be combined in prognostic groups to refine prognosis at clinical and pathologic staging. To achieve this challenging objective, international cooperation is essential, and the IASLC Staging and Prognostic Factors Committee counts on it for the development of the 9th edition due to be published in 2024.

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