AIMC Topic: Neoplasm Invasiveness

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Prediction of visceral pleural invasion of clinical stage I lung adenocarcinoma using thoracoscopic images and deep learning.

Surgery today Oct 20, 2023
PURPOSE: To develop deep learning models using thoracoscopic images to identify visceral pleural invasion (VPI) in patients with clinical stage I lung adenocarcinoma, and to verify if these models can be applied clinically.
Adenocarcinoma of Lung Pleura Neoplasm Staging Thoracoscopy Viscera Male Middle Aged Deep Learning Neoplasm Invasiveness Predictive Value of Tests ROC Curve Lung Neoplasms Female Aged Sensitivity and Specificity Humans Neural Networks, Computer
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Editorial for "Assessment of Lymphovascular Invasion in Breast Cancer Using a Combined MRI Morphological Features, Radiomics, and Deep Learning Approach Based on Dynamic Contrast-Enhanced MRI".

Journal of magnetic resonance imaging : JMRI Oct 19, 2023
Humans Deep Learning Lymphatic Metastasis Contrast Media Magnetic Resonance Imaging Female Breast Neoplasms Radiomics Neoplasm Invasiveness Breast
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Assessment of Lymphovascular Invasion in Breast Cancer Using a Combined MRI Morphological Features, Radiomics, and Deep Learning Approach Based on Dynamic Contrast-Enhanced MRI.

Journal of magnetic resonance imaging : JMRI Oct 19, 2023
BACKGROUND: Assessment of lymphovascular invasion (LVI) in breast cancer (BC) primarily relies on preoperative needle biopsy. There is an urgent need to develop a non-invasive assessment method.
Magnetic Resonance Imaging Neoplasm Invasiveness Breast Adult ROC Curve Breast Neoplasms Lymphatic Metastasis Retrospective Studies Radiomics Contrast Media Female Deep Learning Aged Humans Cross-Sectional Studies Cohort Studies Middle Aged
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Deep learning-based solid component measuring enabled interpretable prediction of tumor invasiveness for lung adenocarcinoma.

Lung cancer (Amsterdam, Netherlands) Oct 5, 2023
BACKGROUND: The nature of the solid component of subsolid nodules (SSNs) can indicate tumor pathological invasiveness. However, preoperative solid component assessment still lacks a reference standard.
Humans Lung Neoplasms Adenocarcinoma Neoplasm Invasiveness Adenocarcinoma of Lung Retrospective Studies Tomography, X-Ray Computed Deep Learning
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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.
Adenocarcinoma of Lung Humans Retrospective Studies Tomography, X-Ray Computed Adenocarcinoma Neoplasm Invasiveness Deep Learning Lung Neoplasms
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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...
Humans Retrospective Studies Tomography, X-Ray Computed Middle Aged Lung Female Male Neoplasm Invasiveness Deep Learning Hyperplasia Precancerous Conditions Lung Neoplasms Adenocarcinoma in Situ Adenocarcinoma
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Contrast-Enhanced Ultrasound with Deep Learning with Attention Mechanisms for Predicting Microvascular Invasion in Single Hepatocellular Carcinoma.

Academic radiology Dec 23, 2022
RATIONALE AND OBJECTIVES: Prediction of microvascular invasion (MVI) status of hepatocellular carcinoma (HCC) holds clinical significance for decision-making regarding the treatment strategy and evaluation of patient prognosis. We developed a deep le...
Retrospective Studies Deep Learning Liver Neoplasms Humans Carcinoma, Hepatocellular Neoplasm Invasiveness
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Deep learning-based image reconstruction improves radiologic evaluation of pituitary axis and cavernous sinus invasion in pituitary adenoma.

European journal of radiology Dec 12, 2022
PURPOSE: To compare performance of 1-mm deep learning reconstruction (DLR) with 3-mm routine MRI imaging for the delineation of pituitary axis and identification of cavernous sinus invasion for pituitary macroadenoma.
Adenoma Pituitary Diseases Cavernous Sinus Magnetic Resonance Imaging Neoplasm Invasiveness Pituitary Neoplasms Retrospective Studies Humans Female Deep Learning Image Processing, Computer-Assisted
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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...
Neoplasm Invasiveness Adenocarcinoma of Lung Humans Algorithms Lung Neoplasms Tomography, X-Ray Computed Retrospective Studies Adenocarcinoma Deep Learning
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Multi-task deep learning based on T2-Weighted Images for predicting Muscular-Invasive Bladder Cancer.

Computers in biology and medicine Oct 25, 2022
BACKGROUND: An accurate preoperative assessment of Non-Muscle-Invasive Bladder Cancer (NMIBC) and Muscle-Invasive Bladder Cancer (MIBC) in Bladder Cancer (BCa) can help the urologist make diagnostic decisions. Considering the absence of multiparametr...
Deep Learning Humans Urinary Bladder Neoplasms Prospective Studies Neoplasm Invasiveness Retrospective Studies
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