AIMC Topic: Neoplasm Invasiveness

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Dr. Answer AI for prostate cancer: Clinical outcome prediction model and service.

PloS one Aug 5, 2020
OBJECTIVES: The importance of clinical outcome prediction models using artificial intelligence (AI) is being emphasized owing to the increasing necessity of developing a clinical decision support system (CDSS) employing AI. Therefore, in this study, ...
Aged, 80 and over Treatment Outcome Prostate Humans Adult Lymphatic Metastasis Probability Artificial Intelligence Male Neoplasm Invasiveness Prostatectomy Algorithms Middle Aged Prostatic Neoplasms Seminal Vesicles Prognosis Decision Support Systems, Clinical Prostate-Specific Antigen Aged
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Deep Learning Predicts Molecular Subtype of Muscle-invasive Bladder Cancer from Conventional Histopathological Slides.

European urology Apr 27, 2020
BACKGROUND: Muscle-invasive bladder cancer (MIBC) is the second most common genitourinary malignancy, and is associated with high morbidity and mortality. Recently, molecular subtypes of MIBC have been identified, which have important clinical implic...
Deep Learning Humans Molecular Diagnostic Techniques Forecasting Urinary Bladder Neoplasms Neoplasm Invasiveness
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Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging: a preliminary study in a single institution.

European radiology Apr 26, 2020
OBJECTIVE: To determine the diagnostic performance of a deep learning (DL) model in evaluating myometrial invasion (MI) depth on T2-weighted imaging (T2WI)-based endometrial cancer (EC) MR imaging (ECM).
Deep Learning Humans Magnetic Resonance Imaging Algorithms Neoplasm Invasiveness Retrospective Studies Endometrial Neoplasms Female Myometrium Middle Aged
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Can a machine-learning model improve the prediction of nodal stage after a positive sentinel lymph node biopsy in breast cancer?

Acta oncologica (Stockholm, Sweden) Mar 9, 2020
The current standard for evaluating axillary nodal burden in clinically node negative breast cancer is sentinel lymph node biopsy (SLNB). However, the accuracy of SLNB to detect nodal stage N2-3 remains debatable. Nomograms can help the decision-mak...
Lymph Node Excision Axilla Regression Analysis Breast Tumor Burden Logistic Models Lymph Nodes Nomograms Lymphatic Metastasis Neoplasm Invasiveness Carcinoma, Ductal, Breast Sentinel Lymph Node Biopsy, Fine-Needle Sentinel Lymph Node Biopsy Humans Machine Learning Retrospective Studies Female Aged Aged, 80 and over Adult Breast Neoplasms Middle Aged ROC Curve
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External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules.

Thorax Mar 5, 2020
BACKGROUND: Estimation of the risk of malignancy in pulmonary nodules detected by CT is central in clinical management. The use of artificial intelligence (AI) offers an opportunity to improve risk prediction. Here we compare the performance of an AI...
Neural Networks, Computer Artificial Intelligence Algorithms Databases, Factual Prognosis Humans Lung Neoplasms Multiple Pulmonary Nodules Early Detection of Cancer Neoplasm Invasiveness Predictive Value of Tests Area Under Curve Neoplasm Staging Cell Transformation, Neoplastic Middle Aged Risk Assessment Retrospective Studies Incidence Female Cohort Studies Aged ROC Curve Adult Male
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CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma.

European radiology Feb 28, 2020
PURPOSE: Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognosis of lung adenocarcinoma. The aims of this study are to develop and validate a computed tomography ...
Humans Machine Learning Retrospective Studies Tomography, X-Ray Computed Female Adenocarcinoma of Lung Image Processing, Computer-Assisted Lung Neoplasms Logistic Models Neoplasm Invasiveness Risk Factors Neoplasm Recurrence, Local Sensitivity and Specificity Male Middle Aged ROC Curve
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Feature-shared adaptive-boost deep learning for invasiveness classification of pulmonary subsolid nodules in CT images.

Medical physics Feb 26, 2020
PURPOSE: In clinical practice, invasiveness is an important reference indicator for differentiating the malignant degree of subsolid pulmonary nodules. These nodules can be classified as atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ ...
Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted Lung Neoplasms Neoplasm Invasiveness Humans
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Preoperative prediction of parametrial invasion in early-stage cervical cancer with MRI-based radiomics nomogram.

European radiology Feb 17, 2020
PURPOSE: To develop and identify a MRI-based radiomics nomogram for the preoperative prediction of parametrial invasion (PMI) in patients with early-stage cervical cancer (ECC).
Neoplasm Grading Peritoneum Hysterectomy Informatics Diffusion Magnetic Resonance Imaging Nomograms Support Vector Machine Age Factors Magnetic Resonance Imaging Neoplasm Invasiveness Humans Algorithms Retrospective Studies Female Reproducibility of Results Aged Adult Uterine Cervical Neoplasms Middle Aged
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Application of artificial intelligence using convolutional neural networks in determining the invasion depth of esophageal squamous cell carcinoma.

Esophagus : official journal of the Japan Esophageal Society Jan 24, 2020
OBJECTIVES: In Japan, endoscopic resection (ER) is often used to treat esophageal squamous cell carcinoma (ESCC) when invasion depths are diagnosed as EP-SM1, whereas ESCC cases deeper than SM2 are treated by surgical operation or chemoradiotherapy. ...
Aged Aged, 80 and over Sensitivity and Specificity Male Middle Aged Neural Networks, Computer Artificial Intelligence Retrospective Studies Female Reproducibility of Results Outcome Assessment, Health Care Esophageal Squamous Cell Carcinoma Preoperative Care Endoscopic Mucosal Resection Deep Learning Neoplasm Invasiveness Area Under Curve Esophageal Neoplasms Japan Humans
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Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method.

Thoracic cancer Jan 16, 2020
BACKGROUND: The aim of the study was to develop a deep learning (DL) algorithm to evaluate the pathological complete response (pCR) to neoadjuvant chemotherapy in breast cancer.
Neoplasm Invasiveness Carcinoma, Ductal, Breast Chemotherapy, Adjuvant Carcinoma, Papillary Carcinoma, Lobular Deep Learning ROC Curve Antineoplastic Combined Chemotherapy Protocols Neoadjuvant Therapy Follow-Up Studies Humans Prognosis Retrospective Studies Female Breast Neoplasms Middle Aged
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