Development and validation of a deep learning model for non–small cell lung cancer survival
Importance There is a lack of studies exploring the performance of a deep learning survival
neural network in non–small cell lung cancer (NSCLC). Objectives To compare the …
neural network in non–small cell lung cancer (NSCLC). Objectives To compare the …
Preoperative CT-based deep learning model for predicting disease-free survival in patients with lung adenocarcinomas
Background Deep learning models have the potential for lung cancer prognostication, but
model output as an independent prognostic factor must be validated with clinical risk factors …
model output as an independent prognostic factor must be validated with clinical risk factors …
Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning
Non-small cell lung cancer (NSCLC) is one of the most common lung cancers worldwide.
Accurate prognostic stratification of NSCLC can become an important clinical reference …
Accurate prognostic stratification of NSCLC can become an important clinical reference …
Deep learning for prediction of N2 metastasis and survival for clinical stage I non–small cell lung cancer
Background Preoperative mediastinal staging is crucial for the optimal management of
clinical stage I non–small cell lung cancer (NSCLC). Purpose To develop a deep learning …
clinical stage I non–small cell lung cancer (NSCLC). Purpose To develop a deep learning …
A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets
P Mukherjee, M Zhou, E Lee, A Schicht… - Nature machine …, 2020 - nature.com
Lung cancer is the most common fatal malignancy in adults worldwide, and non-small-cell
lung cancer (NSCLC) accounts for 85% of lung cancer diagnoses. Computed tomography is …
lung cancer (NSCLC) accounts for 85% of lung cancer diagnoses. Computed tomography is …
[HTML][HTML] Predicting lung cancer survival based on clinical data using machine learning: A review
FA Altuhaifa, KT Win, G Su - Computers in Biology and Medicine, 2023 - Elsevier
Abstract Machine learning has gained popularity in predicting survival time in the medical
field. This review examines studies utilizing machine learning and data-mining techniques to …
field. This review examines studies utilizing machine learning and data-mining techniques to …
Lung cancer survival period prediction and understanding: Deep learning approaches
Introduction Survival period prediction through early diagnosis of cancer has many benefits.
It allows both patients and caregivers to plan resources, time and intensity of care to provide …
It allows both patients and caregivers to plan resources, time and intensity of care to provide …
Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study
Background Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical
courses and outcomes, even within the same tumor stage. This study explores deep …
courses and outcomes, even within the same tumor stage. This study explores deep …
A deep learning-based framework for lung cancer survival analysis with biomarker interpretation
Background Lung cancer is the leading cause of cancer-related deaths in both men and
women in the United States, and it has a much lower five-year survival rate than many other …
women in the United States, and it has a much lower five-year survival rate than many other …
Development of artificial intelligence prognostic model for surgically resected non-small cell lung cancer
F Kinoshita, T Takenaka, T Yamashita, K Matsumoto… - Scientific reports, 2023 - nature.com
There are great expectations for artificial intelligence (AI) in medicine. We aimed to develop
an AI prognostic model for surgically resected non-small cell lung cancer (NSCLC). This …
an AI prognostic model for surgically resected non-small cell lung cancer (NSCLC). This …