[HTML][HTML] Deep learning techniques to diagnose lung cancer
L Wang - Cancers, 2022 - mdpi.com
Simple Summary This study investigates the latest achievements, challenges, and future
research directions of deep learning techniques for lung cancer and pulmonary nodule …
research directions of deep learning techniques for lung cancer and pulmonary nodule …
[HTML][HTML] 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 applications for lung cancer diagnosis: a systematic review
Lung cancer has been one of the most prevalent disease in recent years. According to the
research of this field, more than 200,000 cases are identified each year in the US …
research of this field, more than 200,000 cases are identified each year in the US …
Artificial intelligence in lung cancer: bridging the gap between computational power and clinical decision-making
Lung cancer remains the most common cause of cancer death worldwide. Recent advances
in lung cancer screening, radiotherapy, surgical techniques, and systemic therapy have led …
in lung cancer screening, radiotherapy, surgical techniques, and systemic therapy have led …
An explainable AI-driven biomarker discovery framework for Non-Small Cell Lung Cancer classification
Abstract Non-Small Cell Lung Cancer (NSCLC) exhibits intrinsic heterogeneity at the
molecular level that aids in distinguishing between its two prominent subtypes—Lung …
molecular level that aids in distinguishing between its two prominent subtypes—Lung …
[HTML][HTML] Insights into lung cancer immune-based biology, prevention, and treatment
Lung cancer is the number one cause of cancer-related deaths. The malignancy is
characterized by dismal prognosis and poor clinical outcome mostly due to advanced-stage …
characterized by dismal prognosis and poor clinical outcome mostly due to advanced-stage …
[HTML][HTML] DeepLRHE: a deep convolutional neural network framework to evaluate the risk of lung cancer recurrence and metastasis from histopathology images
Z Wu, L Wang, C Li, Y Cai, Y Liang, X Mo, Q Lu… - Frontiers in …, 2020 - frontiersin.org
It is critical for patients who cannot undergo eradicable surgery to predict the risk of lung
cancer recurrence and metastasis; therefore, the physicians can design the appropriate …
cancer recurrence and metastasis; therefore, the physicians can design the appropriate …
Role of artificial intelligence in the care of patients with nonsmall cell lung cancer
M Rabbani, J Kanevsky, K Kafi… - European journal of …, 2018 - Wiley Online Library
Background Lung cancer is the leading cause of cancer death worldwide. In up to 57% of
patients, it is diagnosed at an advanced stage and the 5‐year survival rate ranges between …
patients, it is diagnosed at an advanced stage and the 5‐year survival rate ranges between …
A deep model for lung cancer type identification by densely connected convolutional networks and adaptive boosting
S Pang, Y Zhang, M Ding, X Wang, X Xie - IEEE Access, 2019 - ieeexplore.ieee.org
Timely diagnosis and determination to the type of lung cancer has important clinical
significance. Generally, it requires multiple imaging methods to complement each other to …
significance. Generally, it requires multiple imaging methods to complement each other to …
Imaging biomarker discovery for lung cancer survival prediction
Solid tumors are heterogeneous tissues composed of a mixture of cells and have special
tissue architectures. However, cellular heterogeneity, the differences in cell types are …
tissue architectures. However, cellular heterogeneity, the differences in cell types are …