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

2019 ◽  
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.

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

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.

scholarly journals Role of Omentin in Obesity Paradox in Lung Cancer

Cancers ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 275
Author(s):  
Sheetal Parida ◽  
Sumit Siddharth ◽  
Dipali Sharma
Keyword(s):  
Lung Cancer ◽  
Obesity Paradox ◽  
Health Concern ◽  
Case Control Studies ◽  
Aberrant Expression ◽  
Lung Cancers ◽  
Lung Cancer Patients ◽  
Significant Enrichment ◽  
Normal Lungs ◽  
Related Mortality

Lung cancer remains the second-most-common cancer worldwide and is associated with the highest number of cancer-related mortality. While tobacco smoking is the most important risk factor for lung cancer, many other lifestyles and occupational factors significantly contribute. Obesity is a growing global health concern and contributes to ~30% cancer-related mortality, but unlike other lifestyle diseases, lung cancer is negatively associated with obesity. We meta-analyzed multiple case-control studies confirming increased survival and better outcomes in overweight and obese lung cancer patients. Tumor heterogeneity analysis showed significant enrichment of adipocytes and preadipocytes in normal lungs compared to lung cancers. Interestingly, one of the understudied adipokine, omentin, was significantly and consistently lower in lung neoplasms compared to normal lungs. Omentin has been examined in relation to osteoarthritis, inflammatory bowel disease, cardiovascular diseases, diabetes, chronic liver disease, psoriasis and some other cancers. Aberrant expression of omentin has been reported in solid tumors; however, little is known about its role in lung cancer. We found omentin to be consistently downregulated in lung cancers, and it exhibited a negative correlation with important transcription factors FOXA1, EN1, FOXC1 and ELK4. We, therefore, suggest that omentin may serve as a prognostic factor in lung cancer and explain the “obesity paradox” in lung cancer.

scholarly journals Soluble c-Met Is a Reliable and Sensitive Marker to Detect c-Met Expression Level in Lung Cancer

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Huilai Lv ◽  
Baoen Shan ◽  
Ziqiang Tian ◽  
Yong Li ◽  
Yuefeng Zhang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Tissue Sample ◽  
Sample Collection ◽  
Lung Cancers ◽  
Lung Cancer Patients ◽  
Lung Cancer Therapy ◽  
Reliable Marker ◽  
Soluble C ◽  
Sensitive Marker

c-Met has been demonstrated as an attractive target in lung cancer therapy. Current studies showed that detection of c-Met status in tumor is critical in Met-targeted therapy. However not all patients are suitable for tissue sample collection. It is important to discover novel surrogate markers to detect c-Met status. In the study, soluble c-Met (s-Met) in plasma from 146 Chinese lung cancer patients and 40 disease-free volunteers was measured by enzyme-linked immunosorbent. In parallel, expression of c-Met in those tumors was also assessed by immunohistochemistry. Results showed that, in 146 lung cancer patients, 93 were c-Met expression positive and 74 of 93 were overexpressed. In c-Met-overexpressed patients, plasma s-Met was significantly increased. And further studies showed that plasma s-Met linearly correlated with c-Met expression in tumor. After tumor was removed in Met-overexpressed patients via resection, plasma s-Met significantly decreased to basal level. In addition, plasma s-Met showed to be poorly correlated with tumor size in Met-overexpressed patients. These results demonstrated that plasma s-Met is a sensitive and reliable marker to detect c-Met overexpression in lung cancers, and it is independent of tumor volume.

scholarly journals Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Atsushi Teramoto ◽  
Tetsuya Tsukamoto ◽  
Yuka Kiriyama ◽  
Hiroshi Fujita
Keyword(s):  
Lung Cancer ◽  
Cell Carcinoma ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Automated Classification ◽  
Deep Convolutional Neural Networks ◽  
Lung Cancers ◽  
Microscopic Images ◽  
Cancer Types

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.

Comprehensive cancer genomics-based screening of new therapeutic targets and biomarkers for lung cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e21031-e21031
Author(s):  
Yataro Daigo ◽  
Atsushi Takano ◽  
Yusuke Nakamura
Keyword(s):  
Lung Cancer ◽  
Cancer Cells ◽  
Cancer Patients ◽  
Therapeutic Target ◽  
Cancer Genomics ◽  
Cancer Biomarkers ◽  
Lung Cancer Cells ◽  
Lung Cancers ◽  
Lung Cancer Patients ◽  
Target Molecules

e21031 Background: Since the clinical outcome of advanced lung cancer patients is still poor after standard therapies, development of new anti-cancer drugs with minimum risk of adverse effects and cancer biomarkers for precision medicine is urgently required. Methods: We have been screening new therapeutic target molecules and molecular biomarkers for lung cancers as follows; i) To identify overexpressed genes in lung cancers by the gene expression profile analysis, ii) To verify the target genes for their scarce expression in normal tissues, iii) To validate the clinicopathologic importance of their protein expression by tissue microarray covering 263 lung cancers, and iv) To confirm their function for the growth and/or invasive ability of the lung cancer cells by siRNAs and gene transfection assays. Results: We identified dozens of candidate target molecules and selected a gene encoding protein with a GAP domain, LAPG1 (lung cancer-associated protein with Gap domain 1). Immunohistochemical analysis showed that LAPG1 expression was observed in 69.9% of lung cancers. Moreover positivity of LAPG1 expression was associated with poor prognosis of lung cancer patients. Knockdown of LAPG1 expression by siRNAs suppressed growth of lung cancer cells. Introduction of LAPG1 increased the invasive activity of mammalian cells, indicating that LAPG1 could be a prognostic biomarker and therapeutic target for lung cancers. Conclusions: Comprehensive cancer genomics-based screening could be useful for selection of new cancer biomarkers and molecular targets for developing small molecules, antibodies, nucleic acid drugs, and immunotherapies.

A New Multiparametric Classification in Lung Cancer Patients - S.M.I.G.

1983 ◽  
Vol 69 (5) ◽  
pp. 437-443 ◽  
Author(s):  
Claudio Modini ◽  
Mario Albertucci ◽  
Franco Cicconetti ◽  
Donatella Tirindelli Danesi ◽  
Renzo Cristiani ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Growth Rate ◽  
Mortality Rate ◽  
Cancer Patients ◽  
Cell Type ◽  
Tnm System ◽  
Lung Cancer Patients ◽  
The Difference

The classification of bronchogenic carcinoma as a function of the prognosis is still an open field. The evaluation of stage, by use of the TNM system, and histologic cell type is not sufficient to guarantee a correct prognosis. The growth rate of the neoplasm is another important parameter. We propose a classification that takes into account the stage (S), histologic cell type (M), immune status (I) and the growth rate of the primary tumor (G): S.M.I.G. We studied 90 lung cancer patients according to the S.M.I.G. classification and we observed that their prognoses were directly correlated with their S.M.I.G. scores (the higher the score, the higher the 10-month mortality rate). The mortality rates within the first 10 months of follow-up were respectively 0%, 0%, 36.36%, 68%, 90.9% for the 5 groups obtained by S.M.I.G. The difference is statistically significant (P < 0.0075) and there is a linear correlation between the mortality rate and the score assigned to each group (R = 0.943; P < 0.05). The S.M.I.G. classification can predict the prognosis more efficiently than the usual classification (TNM) and histological cell type.

Histological classification of regional lymph nodes in relation to postoperative survival in primary lung cancer

1981 ◽  
Vol 12 (11) ◽  
pp. 1000-1005 ◽  
Author(s):  
Masanori Kitaichi ◽  
Hitoshi Asamoto ◽  
Takateru Izumi ◽  
Mutsuhiro Furuta
Keyword(s):  
Lung Cancer ◽  
Lymph Nodes ◽  
Primary Lung Cancer ◽  
Postoperative Survival ◽  
Histological Classification ◽  
Regional Lymph Nodes
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