AIMC Topic: Lung Neoplasms
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Measuring pure ground-glass nodules on computed tomography: assessing agreement between a commercially available deep learning algorithm and radiologists' readings.
Acta radiologica (Stockholm, Sweden : 1987)
Oct 31, 2022
BACKGROUND: Deep learning algorithms (DLAs) could enable automatic measurements of solid portions of mixed ground-glass nodules (mGGNs) in agreement with the invasive component sizes measured during pathologic examinations. However, the measurement o...
Trends in segmentectomy for the treatment of stage 1A non-small cell lung cancers: Does the robot have an impact?
American journal of surgery
Oct 26, 2022
OBJECTIVES: Lobectomy may unnecessarily resect healthy lung parenchyma in Stage 1A non-small cell lung cancers (NSCLC). Segmentectomies may provide a lung-sparing option. VATS segmentectomies can be technically challenging; robotics may have features...
Image quality improvement in low-dose chest CT with deep learning image reconstruction.
Journal of applied clinical medical physics
Oct 9, 2022
OBJECTIVES: To investigate the clinical utility of deep learning image reconstruction (DLIR) for improving image quality in low-dose chest CT in comparison with 40% adaptive statistical iterative reconstruction-Veo (ASiR-V40%) algorithm.
Reducing uncertainty in cancer risk estimation for patients with indeterminate pulmonary nodules using an integrated deep learning model.
Computers in biology and medicine
Sep 29, 2022
OBJECTIVE: Patients with indeterminate pulmonary nodules (IPN) with an intermediate to a high probability of lung cancer generally undergo invasive diagnostic procedures. Chest computed tomography image and clinical data have been in estimating the p...
Development of deep learning-assisted overscan decision algorithm in low-dose chest CT: Application to lung cancer screening in Korean National CT accreditation program.
PloS one
Sep 29, 2022
We propose a deep learning-assisted overscan decision algorithm in chest low-dose computed tomography (LDCT) applicable to the lung cancer screening. The algorithm reflects the radiologists' subjective evaluation criteria according to the Korea insti...
Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model.
European radiology
Sep 28, 2022
OBJECTIVE: To develop and validate a deep learning (DL) signature for predicting lymph node (LN) metastasis in patients with lung adenocarcinoma.
Automatic lung tumor segmentation from CT images using improved 3D densely connected UNet.
Medical & biological engineering & computing
Sep 28, 2022
Accurate lung tumor segmentation has great significance in the treatment planning of lung cancer. However, robust lung tumor segmentation becomes challenging due to the heterogeneity of tumors and the similar visual characteristics between tumors and...
Robotic low anterior resection with liver wedge metastasectomy.
Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland
Sep 23, 2022
Deterministic small-scale undulations of image-based risk predictions from the deep learning of lung tumors in motion.
Medical physics
Sep 23, 2022
INTRODUCTION: Deep learning (DL) models that use medical images to predict clinical outcomes are poised for clinical translation. For tumors that reside in organs that move, however, the impact of motion (i.e., degenerated object appearance or blur) ...
Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer.
BMC cancer
Sep 21, 2022
BACKGROUND: Despite the fact that tumor microenvironment (TME) and gene mutations are the main determinants of progression of the deadliest cancer in the world - lung cancer, their interrelations are not well understood. Digital pathology data provid...
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