scholarly journals Stage-Specific PET Radiomic Prediction Model for the Histological Subtype Classification of Non-Small-Cell Lung Cancer

2021 ◽  
Vol Volume 13 ◽  
pp. 307-317
Author(s):  
Yanlei Ji ◽  
Qingtao Qiu ◽  
Jing Fu ◽  
Kai Cui ◽  
Xia Chen ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Prediction Model ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Histological Subtype ◽  
Small Cell Lung ◽  
Subtype Classification

Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer

2018 ◽  
Vol 28 (7) ◽  
pp. 2772-2778 ◽  
Author(s):  
Xinzhong Zhu ◽  
Di Dong ◽  
Zhendong Chen ◽  
Mengjie Fang ◽  
Liwen Zhang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Histologic Subtype ◽  
Subtype Classification

Machine-learning-based classification of the histological subtype of non-small-cell lung cancer using MRI texture analysis

2021 ◽  
Vol 66 ◽  
pp. 102446
Author(s):  
Ewelina Bębas ◽  
Marta Borowska ◽  
Marcin Derlatka ◽  
Edward Oczeretko ◽  
Marcin Hładuński ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Texture Analysis ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Histological Subtype ◽  
Small Cell Lung

scholarly journals Classification of Non-Small Cell Lung Cancer Based on Copy Number Alterations

PLoS ONE ◽  
2014 ◽  
Vol 9 (2) ◽  
pp. e88300 ◽  
Author(s):  
Bi-Qing Li ◽  
Jin You ◽  
Tao Huang ◽  
Yu-Dong Cai
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Copy Number ◽  
Small Cell ◽  
Copy Number Alterations ◽  
Small Cell Lung

scholarly journals A prediction model for early death in non-small cell lung cancer patients following curative-intent chemoradiotherapy

2017 ◽  
Vol 57 (2) ◽  
pp. 226-230 ◽  
Author(s):  
Arthur Jochems ◽  
Issam El-Naqa ◽  
Marc Kessler ◽  
Charles S. Mayo ◽  
Shruti Jolly ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Prediction Model ◽  
Small Cell Lung Cancer ◽  
Cancer Patients ◽  
Cell Lung Cancer ◽  
Early Death ◽  
Small Cell ◽  
Small Cell Lung ◽  
Curative Intent ◽  
Lung Cancer Patients

Prognostic factors for survival in advanced non-small-cell lung cancer: univariate and multivariate analyses including recursive partitioning and amalgamation algorithms in 1,052 patients. The European Lung Cancer Working Party.

1995 ◽  
Vol 13 (5) ◽  
pp. 1221-1230 ◽  
Author(s):  
M Paesmans ◽  
J P Sculier ◽  
P Libert ◽  
G Bureau ◽  
G Dabouis ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Prognostic Factors ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Cox Regression ◽  
Recursive Partitioning ◽  
Working Party ◽  
Small Cell ◽  
Small Cell Lung

PURPOSE This study attempted to determine the prognostic value for survival of various pretreatment characteristics in patients with nonresectable non-small-cell lung cancer in the context of more than 10 years of experience of a European Cooperative Group. PATIENTS AND METHODS We included in the analysis all eligible patients (N = 1,052) with advanced non-small-cell lung cancer registered onto one of seven trials conducted by the European Lung Cancer Working Party (ELCWP) during one decade. The patients were treated by chemotherapy regimens based on platinum derivatives. We prospectively collected 23 variables and analyzed them by univariate and multivariate methods. RESULTS The global estimated median survival time was 29 weeks, with a 95% confidence interval of 27 to 30 weeks. After univariate analysis, we applied two multivariate statistical techniques. In a Cox regression model, the selected explanatory variables were disease extent, Karnofsky performance status, WBC and neutrophil counts, metastatic involvement of skin, serum calcium level, age, and sex. These results were confirmed by application of recursive partitioning and amalgamation algorithms (RECPAM), which led to classification of the patients into four homogeneous subgroups. CONCLUSION We confirmed by our analysis the role of well-known independent prognostic factors for survival, but also identified the effect of the neutrophil count, rarely studied, with the use of two methods: a classical Cox regression model and a RECPAM analysis. The classification of patients into the four subgroups we obtained needs to be validated in other series.

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