AIMC Topic: SEER Program
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Using Machine Learning Approaches to Predict Short-Term Risk of Cardiotoxicity Among Patients with Colorectal Cancer After Starting Fluoropyrimidine-Based Chemotherapy.
Cardiovascular toxicology
Nov 18, 2021
Cardiotoxicity is a severe side effect for colorectal cancer (CRC) patients undergoing fluoropyrimidine-based chemotherapy. To develop and compare machine learning algorithms to predict cardiotoxicity risk among nationally representative CRC patients...
Phenotype Discovery and Geographic Disparities of Late-Stage Breast Cancer Diagnosis across U.S. Counties: A Machine Learning Approach.
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
Oct 25, 2021
BACKGROUND: Disparities in the stage at diagnosis for breast cancer have been independently associated with various contextual characteristics. Understanding which combinations of these characteristics indicate highest risk, and where they are locate...
Estimating the conditional probability of developing human papilloma virus related oropharyngeal cancer by combining machine learning and inverse Bayesian modelling.
PLoS computational biology
Aug 20, 2021
The epidemic increase in the incidence of Human Papilloma Virus (HPV) related Oropharyngeal Squamous Cell Carcinomas (OPSCCs) in several countries worldwide represents a significant public health concern. Although gender neutral HPV vaccination progr...
Machine learning for the prediction of bone metastasis in patients with newly diagnosed thyroid cancer.
Cancer medicine
Mar 12, 2021
OBJECTIVES: This study aimed to establish a machine learning prediction model that can be used to predict bone metastasis (BM) in patients with newly diagnosed thyroid cancer (TC).
Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm.
Scientific reports
Mar 10, 2021
Osteosarcoma is the most common bone malignancy, with the highest incidence in children and adolescents. Survival rate prediction is important for improving prognosis and planning therapy. However, there is still no prediction model with a high accur...
Development of machine learning model algorithm for prediction of 5-year soft tissue myxoid liposarcoma survival.
Journal of surgical oncology
Mar 8, 2021
BACKGROUND: Predicting survival in myxoid liposarcoma (MLS) patients is very challenging given its propensity to metastasize and the controversial role of adjuvant therapy. The purpose of this study was to develop a machine-learning algorithm for the...
Deep learning enabled prediction of 5-year survival in pediatric genitourinary rhabdomyosarcoma.
Surgical oncology
Nov 20, 2020
BACKGROUND: Genitourinary rhabdomyosarcoma (GU-RMS) is a rare, pediatric malignancy originating from embryonic mesenchyme. Current approaches to prognostication rely upon conventional statistical methods such as Cox proportional hazards (CPH) models ...
Using machine learning to create prognostic systems for endometrial cancer.
Gynecologic oncology
Oct 2, 2020
OBJECTIVE: We used a novel machine learning algorithm to develop a precision prognostication system for endometrial cancer.
Development and validation of an artificial neural network prognostic model after gastrectomy for gastric carcinoma: An international multicenter cohort study.
Cancer medicine
Jul 15, 2020
BACKGROUND: Recently, artificial neural network (ANN) methods have also been adopted to deal with the complex multidimensional nonlinear relationship between clinicopathologic variables and survival for patients with gastric cancer. Using a multinati...
Development and Validation of a Deep Learning Model for Non-Small Cell Lung Cancer Survival.
JAMA network open
Jun 1, 2020
IMPORTANCE: There is a lack of studies exploring the performance of a deep learning survival neural network in non-small cell lung cancer (NSCLC).
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