AIMC Topic: Adenocarcinoma
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Predicting Lymph Node Metastasis From Primary Cervical Squamous Cell Carcinoma Based on Deep Learning in Histopathologic Images.
Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Aug 26, 2023
We developed a deep learning framework to accurately predict the lymph node status of patients with cervical cancer based on hematoxylin and eosin-stained pathological sections of the primary tumor. In total, 1524 hematoxylin and eosin-stained whole ...
Standardized Classification of Lung Adenocarcinoma Subtypes and Improvement of Grading Assessment Through Deep Learning.
The American journal of pathology
Aug 5, 2023
The histopathologic distinction of lung adenocarcinoma (LADC) subtypes is subject to high interobserver variability, which can compromise the optimal assessment of patient prognosis. Therefore, this study developed convolutional neural networks capab...
Editorial Comment: Implementing an End-to-End Deep Learning Model in the Task of Differentiating Pure Ground-Glass Nodules on Chest CT.
AJR. American journal of roentgenology
Aug 2, 2023
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.
Journal of thoracic imaging
Aug 2, 2023
PURPOSE: To evaluate the role of quantitative features of intranodular vessels based on deep learning in distinguishing pulmonary adenocarcinoma invasiveness.
Lung-PNet: An Automated Deep Learning Model for the Diagnosis of Invasive Adenocarcinoma in Pure Ground-Glass Nodules on Chest CT.
AJR. American journal of roentgenology
Jul 26, 2023
Pure ground-glass nodules (pGGNs) on chest CT representing invasive adenocarcinoma (IAC) warrant lobectomy with lymph node resection. For pGGNs representing other entities, close follow-up or sublobar resection without node dissection may be appropr...
Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortality.
EBioMedicine
Jul 25, 2023
BACKGROUND: Colorectal cancers are the fourth most diagnosed cancer and the second leading cancer in number of deaths. Many clinical variables, pathological features, and genomic signatures are associated with patient risk, but reliable patient strat...
Editorial for "Deep Learning Nomogram for the Identification of Deep Stromal Invasion in Patients With Early-Stage Cervical Adenocarcinoma and Adenosquamous Carcinoma: A Multicenter Study".
Journal of magnetic resonance imaging : JMRI
Jul 6, 2023
High accuracy epidermal growth factor receptor mutation prediction via histopathological deep learning.
BMC pulmonary medicine
Jul 5, 2023
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...
Deep Learning Nomogram for the Identification of Deep Stromal Invasion in Patients With Early-Stage Cervical Adenocarcinoma and Adenosquamous Carcinoma: A Multicenter Study.
Journal of magnetic resonance imaging : JMRI
Jul 1, 2023
BACKGROUND: Deep stromal invasion (DSI) is one of the predominant risk factors that determined the types of radical hysterectomy (RH). Thus, the accurate assessment of DSI in cervical adenocarcinoma (AC)/adenosquamous carcinoma (ASC) can facilitate o...
DARMF-UNet: A dual-branch attention-guided refinement network with multi-scale features fusion U-Net for gland segmentation.
Computers in biology and medicine
Jun 26, 2023
Accurate gland segmentation is critical in determining adenocarcinoma. Automatic gland segmentation methods currently suffer from challenges such as less accurate edge segmentation, easy mis-segmentation, and incomplete segmentation. To solve these p...
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