AIMC Topic: Adenocarcinoma

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An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning.

Nature communications Feb 19, 2021
Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole-slide images (WSIs). Most studies have employed patch-based methods, which often require detailed annotation of image patches. This typically involves l...
Humans Adenocarcinoma Deep Learning ROC Curve Neural Networks, Computer Image Processing, Computer-Assisted Algorithms Lung Neoplasms Carcinoma, Squamous Cell
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Predicting benign, preinvasive, and invasive lung nodules on computed tomography scans using machine learning.

The Journal of thoracic and cardiovascular surgery Feb 16, 2021
OBJECTIVE: The study objective was to investigate if machine learning algorithms can predict whether a lung nodule is benign, adenocarcinoma, or its preinvasive subtype from computed tomography images alone.
Algorithms Retrospective Studies Tomography, X-Ray Computed Female Humans Machine Learning Adenocarcinoma Adenoma Diagnosis, Differential Male Diagnosis, Computer-Assisted Lung Neoplasms
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Deep learning-based differentiation of invasive adenocarcinomas from preinvasive or minimally invasive lesions among pulmonary subsolid nodules.

European radiology Feb 8, 2021
OBJECTIVES: To evaluate a deep learning-based model using model-generated segmentation masks to differentiate invasive pulmonary adenocarcinoma (IPA) from preinvasive lesions or minimally invasive adenocarcinoma (MIA) on CT, making comparisons with r...
Neoplasm Invasiveness Diagnosis, Differential Humans Retrospective Studies Lung Neoplasms Adenocarcinoma Tomography, X-Ray Computed Deep Learning
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Use of a Commercially Available Deep Learning Algorithm to Measure the Solid Portions of Lung Cancer Manifesting as Subsolid Lesions at CT: Comparisons with Radiologists and Invasive Component Size at Pathologic Examination.

Radiology Feb 2, 2021
Background The solid portion size of lung cancer lesions manifesting as subsolid lesions is key in their management, but the automatic measurement of such lesions by means of a deep learning (DL) algorithm needs evaluation. Purpose To evaluate the pe...
Software Retrospective Studies Tomography, X-Ray Computed Female Humans Lung Neoplasms Adenocarcinoma Radiologists Image Interpretation, Computer-Assisted Male Middle Aged Deep Learning
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3D deep learning based classification of pulmonary ground glass opacity nodules with automatic segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Dec 11, 2020
Classifying ground-glass lung nodules (GGNs) into atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) on diagnostic CT images is important to evaluate the th...
Neoplasm Invasiveness Humans Retrospective Studies Lung Neoplasms Adenocarcinoma Tomography, X-Ray Computed Deep Learning
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Risk factors and socio-economic burden in pancreatic ductal adenocarcinoma operation: a machine learning based analysis.

BMC cancer Nov 27, 2020
BACKGROUND: Surgical resection is the major way to cure pancreatic ductal adenocarcinoma (PDAC). However, this operation is complex, and the peri-operative risk is high, making patients more likely to be admitted to the intensive care unit (ICU). The...
Machine Learning Retrospective Studies Humans Middle Aged Socioeconomic Factors Female Adenocarcinoma Risk Factors Carcinoma, Pancreatic Ductal Male
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Radiomic Detection of EGFR Mutations in NSCLC.

Cancer research Nov 4, 2020
Radiomics is defined as the use of automated or semi-automated post-processing and analysis of multiple features derived from imaging exams. Extracted features might generate models able to predict the molecular profile of solid tumors. The aim of th...
ErbB Receptors Carcinoma, Non-Small-Cell Lung Area Under Curve Mutation ROC Curve Lung Neoplasms Adenocarcinoma Humans Machine Learning Algorithms Reproducibility of Results Male Tomography, X-Ray Computed Female
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Automated detection of cribriform growth patterns in prostate histology images.

Scientific reports Sep 10, 2020
Cribriform growth patterns in prostate carcinoma are associated with poor prognosis. We aimed to introduce a deep learning method to detect such patterns automatically. To do so, convolutional neural network was trained to detect cribriform growth pa...
Neoplasm Grading Male Observer Variation Humans Biopsy, Needle Image Processing, Computer-Assisted ROC Curve Neural Networks, Computer Adenocarcinoma Prostatic Neoplasms
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Cone-beam computed tomography-based radiomics in prostate cancer: a mono-institutional study.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] Sep 1, 2020
PURPOSE: The purpose of the reported study was to investigate the value of cone-beam computed tomography (CBCT)-based radiomics for risk stratification and prediction of biochemical relapse in prostate cancer.
Prostate-Specific Antigen Neoplasm Grading Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Adenocarcinoma Area Under Curve Humans Prostatic Neoplasms Neoplasm Staging Image Processing, Computer-Assisted Male Computational Biology Middle Aged Logistic Models ROC Curve Machine Learning Cone-Beam Computed Tomography Aged Aged, 80 and over
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Fully-Connected Neural Networks with Reduced Parameterization for Predicting Histological Types of Lung Cancer from Somatic Mutations.

Biomolecules Aug 28, 2020
Several challenges appear in the application of deep learning to genomic data. First, the dimensionality of input can be orders of magnitude greater than the number of samples, forcing the model to be prone to overfitting the training dataset. Second...
Adenocarcinoma Diagnosis, Differential Reproducibility of Results Mutation Humans Diagnosis, Computer-Assisted Neural Networks, Computer Lung Neoplasms Carcinoma, Squamous Cell
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