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 Quantum and classical machine learning for the classification of non-small-cell lung cancer patients

2020 ◽  
Vol 2 (6) ◽  
Author(s):  
Siddhant Jain ◽  
Jalal Ziauddin ◽  
Paul Leonchyk ◽  
Shashibushan Yenkanchi ◽  
Joseph Geraci
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cancer Patients ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Lung Cancer Patients

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

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.

scholarly journals CT Volumetry and Basic Texture Analysis as Surrogate Markers in Advanced Non–small-cell Lung Cancer

2020 ◽  
Vol 21 (3) ◽  
pp. 225-231
Author(s):  
Benjamin Owen ◽  
David Gandara ◽  
Karen Kelly ◽  
Elizabeth Moore ◽  
David Shelton ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Texture Analysis ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Surrogate Markers ◽  
Small Cell Lung ◽  
Ct Volumetry

scholarly journals Approach and assessment of automated stereotactic radiotherapy planning for early stage non-small-cell lung cancer

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Xue Bai ◽  
Guoping Shan ◽  
Ming Chen ◽  
Binbing Wang
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Automated Planning ◽  
Small Cell ◽  
Radiotherapy Planning ◽  
Plan Quality ◽  
Small Cell Lung ◽  
Planning Time

Abstract Background Intensity-modulated radiotherapy (IMRT) and volumetric-modulated arc therapy (VMAT) are standard physical technologies of stereotactic body radiotherapy (SBRT) that are used for patients with non-small-cell lung cancer (NSCLC). The treatment plan quality depends on the experience of the planner and is limited by planning time. An automated planning process can save time and ensure a high-quality plan. This study aimed to introduce and demonstrate an automated planning procedure for SBRT for patients with NSCLC based on machine-learning algorithms. The automated planning was conducted in two steps: (1) determining patient-specific optimized beam orientations; (2) calculating the organs at risk (OAR) dose achievable for a given patient and setting these dosimetric parameters as optimization objectives. A model was developed using data of historical expertise plans based on support vector regression. The study cohort comprised patients with NSCLC who were treated using SBRT. A training cohort (N = 125) was used to calculate the beam orientations and dosimetric parameters for the lung as functions of the geometrical feature of each case. These plan–geometry relationships were used in a validation cohort (N = 30) to automatically establish the SBRT plan. The automatically generated plans were compared with clinical plans established by an experienced planner. Results All 30 automated plans (100%) fulfilled the dose criteria for OARs and planning target volume (PTV) coverage, and were deemed acceptable according to evaluation by experienced radiation oncologists. An automated plan increased the mean maximum dose for ribs (31.6 ± 19.9 Gy vs. 36.6 ± 18.1 Gy, P < 0.05). The minimum, maximum, and mean dose; homogeneity index; conformation index to PTV; doses to other organs; and the total monitor units showed no significant differences between manual plans established by experts and automated plans (P > 0.05). The hands-on planning time was reduced from 40–60 min to 10–15 min. Conclusion An automated planning method using machine learning was proposed for NSCLC SBRT. Validation results showed that the proposed method decreased planning time without compromising plan quality. Plans generated by this method were acceptable for clinical use.

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