AIMC Topic: Lung Neoplasms

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PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study.

European journal of nuclear medicine and molecular imaging Sep 19, 2023
PURPOSE: No consensus on a grading system for invasive lung adenocarcinoma had been built over a long period of time. Until October 2020, a novel grading system was proposed to quantify the whole landscape of histologic subtypes and proportions of pu...
Biological Products Positron Emission Tomography Computed Tomography Adenocarcinoma of Lung Tumor Microenvironment Deep Learning Humans Lung Neoplasms Prospective Studies Retrospective Studies Tomography, X-Ray Computed
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Tailoring pretext tasks to improve self-supervised learning in histopathologic subtype classification of lung adenocarcinomas.

Computers in biology and medicine Sep 16, 2023
Lung adenocarcinoma (LUAD) is a morphologically heterogeneous disease with five predominant histologic subtypes. Fully supervised convolutional neural networks can improve the accuracy and reduce the subjectivity of LUAD histologic subtyping using he...
Humans Neural Networks, Computer Adenocarcinoma of Lung Lung Neoplasms Supervised Machine Learning Image Interpretation, Computer-Assisted
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Predicting successful clinical candidates for fiducial-free lung tumor tracking with a deep learning binary classification model.

Journal of applied clinical medical physics Sep 11, 2023
OBJECTIVES: The CyberKnife system is a robotic radiosurgery platform that allows the delivery of lung SBRT treatments using fiducial-free soft-tissue tracking. However, not all lung cancer patients are eligible for lung tumor tracking. Tumor size, de...
Deep Learning Robotics Humans Lung Neoplasms Computer Simulation Lung
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75% radiation dose reduction using deep learning reconstruction on low-dose chest CT.

BMC medical imaging Sep 11, 2023
OBJECTIVE: Few studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to compare the image quality and lung nodule detectability between chest CT using a quarter of the low do...
Humans Deep Learning Retrospective Studies Tomography, X-Ray Computed Lung Neoplasms Middle Aged Drug Tapering
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Simultaneous object detection and segmentation for patient-specific markerless lung tumor tracking in simulated radiographs with deep learning.

Medical physics Sep 8, 2023
BACKGROUND: Real-time tumor tracking is one motion management method to address motion-induced uncertainty. To date, fiducial markers are often required to reliably track lung tumors with X-ray imaging, which carries risks of complications and leads ...
Image Processing, Computer-Assisted Lung Neoplasms Humans Fiducial Markers Algorithms Lung Deep Learning
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Intra-frame motion deterioration effects and deep-learning-based compensation in MR-guided radiotherapy.

Medical physics Sep 4, 2023
BACKGROUND: Current commercially available hybrid magnetic resonance linear accelerators (MR-Linac) use 2D+t cine MR imaging to provide intra-fractional motion monitoring. However, given the limited temporal resolution of cine MR imaging, target intr...
Magnetic Resonance Imaging Motion Radiotherapy, Image-Guided Humans Deep Learning Lung Neoplasms
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A novel image deep learning-based sub-centimeter pulmonary nodule management algorithm to expedite resection of the malignant and avoid over-diagnosis of the benign.

European radiology Sep 2, 2023
OBJECTIVES: With the popularization of chest computed tomography (CT) screening, there are more sub-centimeter (≤ 1 cm) pulmonary nodules (SCPNs) requiring further diagnostic workup. This area represents an important opportunity to optimize the SCPN ...
Humans Algorithms Multiple Pulmonary Nodules Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Lung Deep Learning Lung Neoplasms Precancerous Conditions Overdiagnosis
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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 Sep 1, 2023
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...
Multidetector Computed Tomography Image Interpretation, Computer-Assisted Signal-To-Noise Ratio Image Processing, Computer-Assisted Lung Neoplasms Carcinoma, Non-Small-Cell Lung Contrast Media Magnetic Resonance Imaging Humans Lung Retrospective Studies Deep Learning Male Female Imaging, Three-Dimensional Middle Aged Aged Aged, 80 and over Adult
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Combining radiomics and deep learning features of intra-tumoral and peri-tumoral regions for the classification of breast cancer lung metastasis and primary lung cancer with low-dose CT.

Journal of cancer research and clinical oncology Aug 29, 2023
PURPOSE: To investigate the performance of deep learning and radiomics features of intra-tumoral region (ITR) and peri-tumoral region (PTR) in the diagnosing of breast cancer lung metastasis (BCLM) and primary lung cancer (PLC) with low-dose CT (LDCT...
Deep Learning Tomography, X-Ray Computed Humans Lung Neoplasms Retrospective Studies Female Breast Neoplasms
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Early detection of lung cancer using artificial intelligence-enhanced optical nanosensing of chromatin alterations in field carcinogenesis.

Scientific reports Aug 22, 2023
Supranucleosomal chromatin structure, including chromatin domain conformation, is involved in the regulation of gene expression and its dysregulation has been associated with carcinogenesis. Prior studies have shown that cells in the buccal mucosa ca...
Chromatin Humans Early Detection of Cancer Carcinogenesis Artificial Intelligence Lung Neoplasms
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