A Clinically Applicable Automated Risk Classification Model for Pulmonary Nodules

2019 ◽  
pp. 55-70
Author(s):  
Triparna Poddar ◽  
Jhilam Mukherjee ◽  
Bhaswati Ganguli ◽  
Madhuchanda Kar ◽  
Amlan Chakrabarti
Keyword(s):  
Pulmonary Nodules ◽  
Risk Classification ◽  
Classification Model

The new chronic lithiasic disease management and risk classification model, based on a Kaiser Permanente Pyramid (KPP) design. The 3-year results

2021 ◽  
Vol 79 ◽  
pp. S346-S347
Author(s):  
F. Gómez Palomo ◽  
D.G. Ordaz Jurado ◽  
A. Budía Alba ◽  
D. Vivas-Consuelo ◽  
P. Bahilo Mateu ◽  
...  
Keyword(s):  
Disease Management ◽  
Risk Classification ◽  
Classification Model ◽  
Kaiser Permanente ◽  
Model Based

A Novel Risk Classification Model Predicts Overall Survival and Locoregional Surgery Benefit in Colorectal Patients with Distant Metastasis at the Initial Diagnosis

2020 ◽  
Author(s):  
Mo Chen ◽  
Tian-en Li ◽  
Pei-zhun Du ◽  
Junjie Pan ◽  
Zheng Wang ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Distant Metastasis ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Risk Classification ◽  
Low Risk ◽  
Classification Model ◽  
Intermediate Risk

Abstract Background and aims: In this research, we aimed to construct a risk classification model to predict overall survival (OS) and locoregional surgery benefit in colorectal cancer (CRC) patients with distant metastasis.Methods: We selected a cohort consisting of 12741 CRC patients diagnosed with distant metastasis between 2010 and 2014, from the Surveillance, Epidemiology and End Results (SEER) database. Patients were randomly assigned into training group and validation group at the ratio of 2:1. Univariable and multivariable Cox regression models were applied to screen independent prognostic factors. A nomogram was constructed and assessed by the Harrell’s concordance index (C-index) and calibration plots. A novel risk classification model was further established based on the nomogram.Results: Ultimately 12 independent risk factors including race, age, marriage, tumor site, tumor size, grade, T stage, N stage, bone metastasis, brain metastasis, lung metastasis and liver metastasis were identified and adopted in the nomogram. The C-indexes of training and validation groups were 0.77 (95% confidence interval [CI] 0.73-0.81) and 0.75 (95% CI 0.72-0.78), respectively. The risk classification model stratified patients into three risk groups (low-, intermediate- and high-risk) with divergent median OS (low-risk: 36.0 months, 95% CI 34.1-37.9; intermediate-risk: 18.0 months, 95% CI 17.4-18.6; high-risk: 6.0 months, 95% CI 5.3-6.7). Locoregional therapies including surgery and radiotherapy could prognostically benefit patients in the low-risk group (surgery: hazard ratio [HR] 0.59, 95% CI 0.50-0.71; radiotherapy: HR 0.84, 95% CI 0.72-0.98) and intermediate risk group (surgery: HR 0.61, 95% CI 0.54-0.68; radiotherapy: HR 0.86, 95% CI 0.77-0.95), but not in the high-risk group (surgery: HR 1.03, 95% CI 0.82-1.29; radiotherapy: HR 1.03, 95% CI 0.81-1.31). And all risk groups could benefit from systemic therapy (low-risk: HR 0.68, 95% CI 0.58-0.80; intermediate-risk: HR 0.50, 95% CI 0.47-0.54; high-risk: HR 0.46, 95% CI 0.40-0.53).Conclusion: A novel risk classification model predicting prognosis and locoregional surgery benefit of CRC patients with distant metastasis was established and validated. This predictive model could be further utilized by physicians and be of great significance for medical practice.

scholarly journals A Simple Method to Train the AI Diagnosis Model of Pulmonary Nodules

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Zhehao He ◽  
Wang Lv ◽  
Jian Hu
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Pulmonary Nodules ◽  
Learning System ◽  
Pathological Diagnosis ◽  
Classification Model ◽  
Lung Nodules ◽  
Training Set ◽  
Simple Method ◽  
Test Set

Background. The differential diagnosis of subcentimetre lung nodules with a diameter of less than 1 cm has always been one of the problems of imaging doctors and thoracic surgeons. We plan to create a deep learning model for the diagnosis of pulmonary nodules in a simple method. Methods. Image data and pathological diagnosis of patients come from the First Affiliated Hospital of Zhejiang University School of Medicine from October 1, 2016, to October 1, 2019. After data preprocessing and data augmentation, the training set is used to train the model. The test set is used to evaluate the trained model. At the same time, the clinician will also diagnose the test set. Results. A total of 2,295 images of 496 lung nodules and their corresponding pathological diagnosis were selected as a training set and test set. After data augmentation, the number of training set images reached 12,510 images, including 6,648 malignant nodular images and 5,862 benign nodular images. The area under the P-R curve of the trained model is 0.836 in the classification of malignant and benign nodules. The area under the ROC curve of the trained model is 0.896 (95% CI: 78.96%~100.18%), which is higher than that of three doctors. However, the P value is not less than 0.05. Conclusion. With the help of an automatic machine learning system, clinicians can create a deep learning pulmonary nodule pathology classification model without the help of deep learning experts. The diagnostic efficiency of this model is not inferior to that of the clinician.

scholarly journals  Risk Classification Model for Design and Build Projects

2011 ◽  
Vol 1 (1) ◽  
pp. 46-60 ◽  
Author(s):  
O. E. Ogunsanmi ◽  
◽  
O. A. Salako ◽  
O. M. Ajayi ◽  
◽  
...  
Keyword(s):  
Risk Classification ◽  
Classification Model

392: Novel Risk Classification Model for Recurrencefollowing Radical Prostatectomy (RP)

2007 ◽  
Vol 177 (4S) ◽  
pp. 130-131
Author(s):  
David J. Hernandez ◽  
Matthew E. Nielsen ◽  
Alan W. Partin ◽  
Misop Han
Keyword(s):  
Radical Prostatectomy ◽  
Risk Classification ◽  
Classification Model

scholarly journals Study on Risk Classification of Goaf Based on RS-SVM

2019 ◽  
Vol 295 ◽  
pp. 02001
Author(s):  
Haiping Yuan ◽  
Biaohua Le ◽  
Yubo Wang ◽  
Chenghao Chen ◽  
Hanbing Bian
Keyword(s):  
Eastern China ◽  
Risk Classification ◽  
Classification Model ◽  
Good Effect ◽  
Risk Level ◽  
Support Vector ◽  
Treatment Rate ◽  
Empty Area ◽  
Maximum Exposure

According to the uncertainty and concealment of the risk of goaf, a risk classification model of goaf is constructed based on rough set (RS) knowledge and support vector machine (SVM) theory. In this paper, based on statistical analysis and measured data, nine parameters including mining method, empty area excavation depth, goaf height, maximum exposed area of empty area, maximum exposure height, maximum exposure span, pillar condition, empty volume and treatment rate are selected as the main influencing factors. The RS theory is used to reduce the sample, and SVM is compiled by Matlab. The one-to-one method is used to construct the binary classifier to realize the multi-class classification algorithm of goaf. Finally, a SVM model for evaluating the risk level of the goaf is obtained. The research shows that: based on RS theory, SVM has a good effect on the hazard classification of the goaf iron ore mine, and the difference with the actual situation is 13.3%. The research results have certain theoretical significance and guiding role for the safe mining of an iron mine in Eastern China.

Sign in / Sign up

Export Citation Format

Share Document