Identification of a Novel Non-Invasive Biomarker for Predicting the Clinical Outcomes of Advanced Lung Cancer Patients Administered Immunotherapy : A Multicenter Cohort Study

2021 ◽  
Author(s):  
Xin Wang ◽  
Xinlei Hu ◽  
Jun Zhang ◽  
Hua Bai ◽  
Jianchun Duan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cohort Study ◽  
Cancer Patients ◽  
Clinical Outcomes ◽  
Advanced Lung Cancer ◽  
Multicenter Cohort Study ◽  
Lung Cancer Patients ◽  
Non Invasive ◽  
Multicenter Cohort

scholarly journals Deep Learning Analysis of CT Images Reveals High-Grade Pathological Features to Predict Survival in Lung Adenocarcinoma

Cancers ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 4077
Author(s):  
Yeonu Choi ◽  
Jaehong Aum ◽  
Se-Hoon Lee ◽  
Hong-Kwan Kim ◽  
Jhingook Kim ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Patients ◽  
Clinical Outcomes ◽  
High Probability ◽  
Advanced Lung Cancer ◽  
High Grade ◽  
Lung Cancer Patients ◽  
Risk Of Death ◽  
3D Network

We aimed to develop a deep learning (DL) model for predicting high-grade patterns in lung adenocarcinomas (ADC) and to assess the prognostic performance of model in advanced lung cancer patients who underwent neoadjuvant or definitive concurrent chemoradiation therapy (CCRT). We included 275 patients with 290 early lung ADCs from an ongoing prospective clinical trial in the training dataset, which we split into internal–training and internal–validation datasets. We constructed a diagnostic DL model of high-grade patterns of lung ADC considering both morphologic view of the tumor and context view of the area surrounding the tumor (MC3DN; morphologic-view context-view 3D network). Validation was performed on an independent dataset of 417 patients with advanced non-small cell lung cancer who underwent neoadjuvant or definitive CCRT. The area under the curve value of the DL model was 0.8 for the prediction of high-grade histologic patterns such as micropapillary and solid patterns (MPSol). When our model was applied to the validation set, a high probability of MPSol was associated with worse overall survival (probability of MPSol >0.5 vs. <0.5; 5-year OS rate 56.1% vs. 70.7%), indicating that our model could predict the clinical outcomes of advanced lung cancer patients. The subgroup with a high probability of MPSol estimated by the DL model showed a 1.76-fold higher risk of death (HR 1.76, 95% CI 1.16–2.68). Our DL model can be useful in estimating high-grade histologic patterns in lung ADCs and predicting clinical outcomes of patients with advanced lung cancer who underwent neoadjuvant or definitive CCRT.

TP53 Exon 8 Mutations are Associated with Shorter Survival in Advanced Lung Cancer Patients

2018 ◽  
Author(s):  
Yutao Liu ◽  
Fang Xu ◽  
Yubo Wang ◽  
Qingchen Wu ◽  
Buhai Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Advanced Lung Cancer ◽  
Lung Cancer Patients
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