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Carcinoma, Non-Small-Cell Lung

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PET/CT based cross-modal deep learning signature to predict occult nodal metastasis in lung cancer.

Nature communications
Occult nodal metastasis (ONM) plays a significant role in comprehensive treatments of non-small cell lung cancer (NSCLC). This study aims to develop a deep learning signature based on positron emission tomography/computed tomography to predict ONM of...

An integrated model combined intra- and peritumoral regions for predicting chemoradiation response of non small cell lung cancers based on radiomics and deep learning.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
PURPOSE: The purpose of this study was to develop a model for predicting chemoradiation response in non-small cell lung cancer (NSCLC) patients by integrating radiomics and deep-learning features and combined intra- and peritumoral regions with pre-t...

Nomograms integrating CT radiomic and deep learning signatures to predict overall survival and progression-free survival in NSCLC patients treated with chemotherapy.

Cancer imaging : the official publication of the International Cancer Imaging Society
OBJECTIVES: This study aims to establish nomograms to accurately predict the overall survival (OS) and progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) who received chemotherapy alone as the first-line treatment.

Enlightening the path to NSCLC biomarkers: Utilizing the power of XAI-guided deep learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The early diagnosis of Non-small cell lung cancer (NSCLC) is of prime importance to improve the patient's survivability and quality of life. Being a heterogeneous disease at the molecular and cellular level, the biomarkers r...

Deep Learning Reconstruction to Improve the Quality of MR Imaging: Evaluating the Best Sequence for T-category Assessment in Non-small Cell Lung Cancer Patients.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: Deep learning reconstruction (DLR) has been recommended as useful for improving image quality. Moreover, compressed sensing (CS) or DLR has been proposed as useful for improving temporal resolution and image quality on MR sequences in differ...

Deep Learning for Predicting Effect of Neoadjuvant Therapies in Non-Small Cell Lung Carcinomas With Histologic Images.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Neoadjuvant therapies are used for locally advanced non-small cell lung carcinomas, whereby pathologists histologically evaluate the effect using resected specimens. Major pathological response (MPR) has recently been used for treatment evaluation an...

Short-term outcomes of robot-assisted versus video-assisted thoracoscopic surgery for non-small cell lung cancer patients with neoadjuvant immunochemotherapy: a single-center retrospective study.

Frontiers in immunology
BACKGROUND: Neoadjuvant immunochemotherapy has been increasingly applied to treat non-small cell lung cancer (NSCLC). However, the comparison between robotic-assisted thoracoscopic surgery (RATS) and video-assisted thoracoscopic surgery (VATS) in the...

Robot-assisted thoracoscopic right upper lobectomy with displaced B and absence of minor fissure: a case report.

Surgical and radiologic anatomy : SRA
INTRODUCTION: B downward-shifting is a rare bronchial anomaly characterized by abnormal pulmonary arteries associated with downward displacement of B and complete fusion between the right upper and middle lobes.

High accuracy epidermal growth factor receptor mutation prediction via histopathological deep learning.

BMC pulmonary medicine
BACKGROUND: The detection of epidermal growth factor receptor (EGFR) mutations in patients with non-small cell lung cancer is critical for tyrosine kinase inhibitor therapy. EGFR detection requires tissue samples, which are difficult to obtain in som...