Lung Cancer

Reducing radiation dose in routine CT scans: an AI-driven approach with deep learning-based dual-energy CT reconstruction.

Suitability of DNN-based vessel segmentation for SIRT planning.

PURPOSE: The segmentation of the hepatic arteries (HA) is essential for state-of-the-art pre-interve...

Correlation Between Intranodular Vessels and Tumor Invasiveness of Lung Adenocarcinoma Presenting as Ground-glass Nodules: A Deep Learning 3-Dimensional Reconstruction Algorithm-based Quantitative Analysis on Noncontrast Computed Tomography Images.

PURPOSE: To evaluate the role of quantitative features of intranodular vessels based on deep learnin...

The learning curve on robot-assisted transaxillary thyroidectomy performed by a single endocrine surgeon in a third-level institution in Europe: a cumulative sum (CUSUM) analysis.

Robot-assisted transaxillary thyroidectomy is widely performed in Asian countries, although it is st...

Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy.

: Multiple factors are associated with postoperative functional outcomes, such as acute kidney injur...

Machine-learning and combined analysis of single-cell and bulk-RNA sequencing identified a DC gene signature to predict prognosis and immunotherapy response for patients with lung adenocarcinoma.

BACKGROUND: Innate immune effectors, dendritic cells (DCs), influence cancer prognosis and immunothe...

Lung-PNet: An Automated Deep Learning Model for the Diagnosis of Invasive Adenocarcinoma in Pure Ground-Glass Nodules on Chest CT.

Pure ground-glass nodules (pGGNs) on chest CT representing invasive adenocarcinoma (IAC) warrant lo...

Ultra-low-dose computed tomography with deep learning reconstruction for craniosynostosis at radiation doses comparable to skull radiographs: a pilot study.

BACKGROUND: Craniofacial computed tomography (CT) is the diagnostic investigation of choice for cran...

An AI Predictive Model to Determine Who Benefits from ADT with Radiation: Working Smarter, Not Harder.

Whether you are a surgical, medical, or radiation oncologist, the care goals remain the same, that i...

Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortality.

BACKGROUND: Colorectal cancers are the fourth most diagnosed cancer and the second leading cancer in...

Deep learning-based diagnosis of histopathological patterns for invasive non-mucinous lung adenocarcinoma using semantic segmentation.

OBJECTIVES: The application of artificial intelligence (AI) to the field of pathology has facilitate...

A deep-learning assisted bioluminescence tomography method to enable radiation targeting in rat glioblastoma.

. A novel solution is required for accurate 3D bioluminescence tomography (BLT) based glioblastoma (...

A CT-based Deep Learning Radiomics Nomogram for the Prediction of EGFR Mutation Status in Head and Neck Squamous Cell Carcinoma.

RATIONALE AND OBJECTIVES: Accurately assessing epidermal growth factor receptor (EGFR) mutation stat...

Stapler port position determination using 3-dimensional virtual simulation software in robot-assisted thoracic surgery.

BACKGROUND: In robot-assisted thoracic surgery (RATS) lobectomy using a robotic stapler, stapling is...

Evaluating staging laparoscopy indications for pancreatic cancer based on resectability classification and treatment strategies for patients with positive peritoneal washing cytology.

INTRODUCTION: The prognosis of pancreatic ductal adenocarcinoma (PDAC) in patients with positive per...

A deep learning model using hyperspectral image for EUS-FNA cytology diagnosis in pancreatic ductal adenocarcinoma.

BACKGROUND AND AIMS: Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) is ...

Development of a novel combined nomogram integrating deep-learning-assisted CT texture and clinical-radiological features to predict the invasiveness of clinical stage IA part-solid lung adenocarcinoma: a multicentre study.

AIM: To develop a novel combined nomogram based on deep-learning-assisted computed tomography (CT) t...

Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma.

OBJECTIVES: To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, 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.

BACKGROUND: Neoadjuvant immunochemotherapy has been increasingly applied to treat non-small cell lun...

Open versus robot-assisted partial nephrectomy for highly complex renal masses: a meta-analysis of perioperitive and functional outcomes.

Robot-assisted partial nephrectomy (RAPN) is increasingly being used for the complex surgical manage...

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

INTRODUCTION: B downward-shifting is a rare bronchial anomaly characterized by abnormal pulmonary ar...