Body composition radiomic features as a predictor of survival in patients with non-small cellular lung carcinoma: A multicenter retrospective study
M Rozynek, Z Tabor, S Kłęk, W Wojciechowski - Nutrition, 2024 - Elsevier
Objectives This study combined two novel approaches in oncology patient outcome
predictions—body composition and radiomic features analysis. The aim of this study was to …
predictions—body composition and radiomic features analysis. The aim of this study was to …
Body Composition and Radiomics From 18F-FDG PET/CT Together Help Predict Prognosis for Patients With Stage IV Non–Small Cell Lung Cancer
Y Zhang, W Tan, Z Zheng, J Wang… - Journal of computer …, 2023 - journals.lww.com
Purpose To determine whether integration of data on body composition and radiomic
features obtained using baseline 18 F-FDG positron emission tomography/computed …
features obtained using baseline 18 F-FDG positron emission tomography/computed …
[HTML][HTML] A deep learning model based on the attention mechanism for automatic segmentation of abdominal muscle and fat for body composition assessment
H Shen, P He, Y Ren, Z Huang, S Li… - … Imaging in Medicine …, 2023 - ncbi.nlm.nih.gov
Background Quantitative muscle and fat data obtained through body composition analysis
are expected to be a new stable biomarker for the early and accurate prediction of treatment …
are expected to be a new stable biomarker for the early and accurate prediction of treatment …
Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer
Introduction The aim of the study was to extract anthropometric measures from CT by deep
learning and to evaluate their prognostic value in patients with non-small-cell lung cancer …
learning and to evaluate their prognostic value in patients with non-small-cell lung cancer …
Radiomics based deep fully connected neural network (R-DNN) for prognostication of lung cancer
T Upadhaya, M Hadzic, F Legot, M Hatt, D Visvikis… - 2018 - Soc Nuclear Med
329 Objectives: Baseline positron emission tomography with fluorodeoxyglucose (FDG-PET)
based radiomics are of increasing interest for lung cancer prognostic studies. However …
based radiomics are of increasing interest for lung cancer prognostic studies. However …
Organomics: A concept reflecting the importance of PET/CT healthy organ radiomics in non-small cell lung cancer prognosis prediction using machine learning
Purpose: Non-small cell lung cancer (NSCLC) is the most common subtype of lung cancer.
Patient survival prediction using machine learning and radiomics analysis proved to provide …
Patient survival prediction using machine learning and radiomics analysis proved to provide …
CT-derived body composition associated with lung cancer recurrence after surgery
Objectives To evaluate the impact of body composition derived from computed tomography
(CT) scans on postoperative lung cancer recurrence. Methods We created a retrospective …
(CT) scans on postoperative lung cancer recurrence. Methods We created a retrospective …
Identifying radiomics signatures in body composition imaging for the prediction of outcome following pancreatic cancer resection
G van der Kroft, L Wee, SS Rensen… - Frontiers in …, 2023 - frontiersin.org
Background Computerized radiological image analysis (radiomics) enables the
investigation of image-derived phenotypes by extracting large numbers of quantitative …
investigation of image-derived phenotypes by extracting large numbers of quantitative …
[HTML][HTML] Predicting survival time of lung cancer patients using radiomic analysis
Objectives This study investigates the prediction of Non-small cell lung cancer (NSCLC)
patient survival outcomes based on radiomic texture and shape features automatically …
patient survival outcomes based on radiomic texture and shape features automatically …
Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis
W Sun, M Jiang, J Dang, P Chang, FF Yin - Radiation oncology, 2018 - Springer
Background To investigate the effect of machine learning methods on predicting the Overall
Survival (OS) for non-small cell lung cancer based on radiomics features analysis. Methods …
Survival (OS) for non-small cell lung cancer based on radiomics features analysis. Methods …