AIMC Topic: Lung Neoplasms
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Performance and reading time of lung nodule identification on multidetector CT with or without an artificial intelligence-powered computer-aided detection system.
Clinical radiology
May 19, 2021
AIM: To compare the performance and reading time of different readers using automatic artificial intelligence (AI)-powered computer-aided detection (CAD) to detect lung nodules in different reading modes.
Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT.
Radiology
May 18, 2021
Background Accurate estimation of the malignancy risk of pulmonary nodules at chest CT is crucial for optimizing management in lung cancer screening. Purpose To develop and validate a deep learning (DL) algorithm for malignancy risk estimation of pul...
Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics.
Scientific reports
May 18, 2021
Few studies have addressed radiomics based differentiation of Glioblastoma (GBM) and intracranial metastatic disease (IMD). However, the effect of different tumor masks, comparison of single versus multiparametric MRI (mp-MRI) or select combination o...
Dual energy CT image prediction on primary tumor of lung cancer for nodal metastasis using deep learning.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
May 15, 2021
Lymph node metastasis (LNM) identification is the most clinically important tasks related to survival and recurrence from lung cancer. However, the preoperative prediction of nodal metastasis remains a challenge to determine surgical plans and pretre...
Pulmonary anatomy, pathology and treatment: What will be the artificial intelligence and deep-learning role on them?
Lung cancer (Amsterdam, Netherlands)
May 11, 2021
Augmenting lung cancer diagnosis on chest radiographs: positioning artificial intelligence to improve radiologist performance.
Clinical radiology
May 11, 2021
AIM: To evaluate the role that artificial intelligence (AI) could play in assisting radiologists as the first reader of chest radiographs (CXRs), to increase the accuracy and efficiency of lung cancer diagnosis by flagging positive cases before passi...
Deep learning-based segmentation of the lung in MR-images acquired by a stack-of-spirals trajectory at ultra-short echo-times.
BMC medical imaging
May 8, 2021
BACKGROUND: Functional lung MRI techniques are usually associated with time-consuming post-processing, where manual lung segmentation represents the most cumbersome part. The aim of this study was to investigate whether deep learning-based segmentati...
A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications.
Scientific reports
May 7, 2021
Lung cancer is a leading cause of cancer death in both men and women worldwide. The high mortality rate in lung cancer is in part due to late-stage diagnostics as well as spread of cancer-cells to organs and tissues by metastasis. Automated lung canc...
Proposing a deep learning-based method for improving the diagnostic certainty of pulmonary nodules in CT scan of chest.
European radiology
May 6, 2021
OBJECTIVE: To compare the performance of a deep learning (DL)-based method for diagnosing pulmonary nodules compared with radiologists' diagnostic approach in computed tomography (CT) of the chest.
Machine learning applied to near-infrared spectra for clinical pleural effusion classification.
Scientific reports
May 3, 2021
Lung cancer patients with malignant pleural effusions (MPE) have a particular poor prognosis. It is crucial to distinguish MPE from benign pleural effusion (BPE). The present study aims to develop a rapid, convenient and economical diagnostic method ...
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