AIMC Topic: Nomograms

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Identification of a blood-based 12-gene signature that predicts the severity of coronary artery stenosis: An integrative approach based on gene network construction, Support Vector Machine algorithm, and multi-cohort validation.

Atherosclerosis Oct 9, 2019
BACKGROUND AND AIMS: We aimed to identify a blood-based gene expression score (GES) to predict the severity of coronary artery stenosis in patients with known or suspected coronary artery disease (CAD) by integrative use of gene network construction,...
Retrospective Studies Female Reproducibility of Results Risk Factors Aged Male Gene Regulatory Networks Humans Middle Aged Genetic Predisposition to Disease Decision Support Techniques Severity of Illness Index Nomograms Phenotype Gene Expression Profiling Support Vector Machine Predictive Value of Tests Transcriptome Coronary Stenosis China
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Machine learning-based prediction of breast cancer growth rate in vivo.

British journal of cancer Aug 9, 2019
BACKGROUND: Determining the rate of breast cancer (BC) growth in vivo, which can predict prognosis, has remained elusive despite its relevance for treatment, screening recommendations and medicolegal practice. We developed a model that predicts the r...
Adenocarcinoma, Mucinous Carcinoma, Lobular Female Aged Survival Rate Nomograms Breast Neoplasms Middle Aged Follow-Up Studies Humans Early Detection of Cancer Machine Learning Neoplasm Invasiveness Algorithms Mammography Prognosis Image Interpretation, Computer-Assisted Retrospective Studies
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A radiomics approach based on support vector machine using MR images for preoperative lymph node status evaluation in intrahepatic cholangiocarcinoma.

Theranostics Jul 9, 2019
: Accurate lymph node (LN) status evaluation for intrahepatic cholangiocarcinoma (ICC) patients is essential for surgical planning. This study aimed to develop and validate a prediction model for preoperative LN status evaluation in ICC patients. : A...
Calibration Cholangiocarcinoma Preoperative Care Reproducibility of Results Aged Aged, 80 and over Middle Aged Adult Humans ROC Curve Male Female Lymph Nodes Nomograms Support Vector Machine Magnetic Resonance Imaging
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A preoperative nomogram to predict major complications after robot assisted partial nephrectomy (UroCCR-57 study).

Urologic oncology Jun 8, 2019
OBJECTIVE: To generate a nomogram based on preoperative parameters to predict the occurrence of a major complication within 30-days of robotic partial nephrectomy.
Humans Female Aged Male Middle Aged Nomograms Robotics Nephrectomy Preoperative Care
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Machine Learning to Build and Validate a Model for Radiation Pneumonitis Prediction in Patients with Non-Small Cell Lung Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research Apr 16, 2019
PURPOSE: Radiation pneumonitis is an important adverse event in patients with non-small cell lung cancer (NSCLC) receiving thoracic radiotherapy. However, the risk of radiation pneumonitis grade ≥ 2 (RP2) has not been well predicted. This study hypot...
Male Middle Aged ROC Curve Lung Neoplasms Nomograms Carcinoma, Non-Small-Cell Lung Models, Statistical Radiotherapy Radiation Pneumonitis Cytokines Humans Prospective Studies Machine Learning Female Aged Biomarkers, Tumor
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Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma.

Clinical cancer research : an official journal of the American Association for Cancer Research Apr 11, 2019
PURPOSE: We aimed to evaluate the value of deep learning on positron emission tomography with computed tomography (PET/CT)-based radiomics for individual induction chemotherapy (IC) in advanced nasopharyngeal carcinoma (NPC).
Adult Survival Rate Male Middle Aged Deep Learning Adolescent Retrospective Studies Female Risk Factors Aged Young Adult Nasopharyngeal Neoplasms Induction Chemotherapy Child ROC Curve Nomograms Positron Emission Tomography Computed Tomography Nasopharyngeal Carcinoma Humans Prognosis
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Preoperative prediction of cavernous sinus invasion by pituitary adenomas using a radiomics method based on magnetic resonance images.

European radiology Sep 25, 2018
OBJECTIVES: To predict cavernous sinus (CS) invasion by pituitary adenomas (PAs) pre-operatively using a radiomics method based on contrast-enhanced T1 (CE-T1) and T2-weighted magnetic resonance (MR) imaging.
Humans Retrospective Studies Female Risk Factors Aged Adult Male Middle Aged Nomograms Pituitary Neoplasms Support Vector Machine Adenoma Contrast Media Area Under Curve Magnetic Resonance Imaging Cavernous Sinus Neoplasm Invasiveness
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A review of statistical and machine learning methods for modeling cancer risk using structured clinical data.

Artificial intelligence in medicine Jul 14, 2018
Advancements are constantly being made in oncology, improving prevention and treatment of cancers. To help reduce the impact and deadliness of cancers, they must be detected early. Additionally, there is a risk of cancers recurring after potentially ...
Neoplasm Staging Data Mining Recurrence Humans Diagnosis, Computer-Assisted Electronic Health Records Nomograms Machine Learning Clinical Decision-Making Early Detection of Cancer Decision Support Techniques Risk Factors Decision Trees Risk Assessment Data Interpretation, Statistical Neoplasms
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A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme.

Scientific reports Sep 4, 2017
Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall sur...
Adult Male Middle Aged Deep Learning Adolescent Child Female Reproducibility of Results Aged Aged, 80 and over Young Adult Glioblastoma Image Interpretation, Computer-Assisted Kaplan-Meier Estimate Humans ROC Curve Prognosis Image Processing, Computer-Assisted Nomograms Models, Theoretical Magnetic Resonance Imaging
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Sentinel Lymph Node Dissection to Select Clinically Node-negative Prostate Cancer Patients for Pelvic Radiation Therapy: Effect on Biochemical Recurrence and Systemic Progression.

International journal of radiation oncology, biology, physics Oct 19, 2016
PURPOSE: To assess the efficacy of robotic-assisted laparoscopic sentinel lymph node (SLN) dissection (SLND) to select those patients with prostate cancer (PCa) who would benefit from additional pelvic external beam radiation therapy and long-term an...
Robotic Surgical Procedures Radiotherapy, Conformal Pelvis Nomograms Humans Statistics, Nonparametric Androgen Antagonists Sentinel Lymph Node Biopsy Lymphatic Metastasis Aged Neoplasm Recurrence, Local Lymphatic Irradiation Prostatic Neoplasms Male Patient Selection Neoplasm Staging Middle Aged Prostate-Specific Antigen Disease Progression
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