A machine learning based survival prediction model for advanced parotid gland mucoepidermoid carcinoma

Oral Oncology ◽  
2022 ◽  
Vol 124 ◽  
pp. 105664
Author(s):  
Tae Hoon Kim ◽  
Young-Gyu Eun ◽  
Young Chan Lee
Keyword(s):  
Machine Learning ◽  
Parotid Gland ◽  
Prediction Model ◽  
Mucoepidermoid Carcinoma ◽  
Survival Prediction

Machine learning‐based individualized survival prediction model for total knee replacement in osteoarthritis: Data from the Osteoarthritis Initiative

2021 ◽  
Author(s):  
Afshin Jamshidi ◽  
Jean‐Pierre Pelletier ◽  
Aurelie Labbe ◽  
François Abram ◽  
Johanne Martel‐Pelletier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Total Knee Replacement ◽  
Prediction Model ◽  
Knee Replacement ◽  
Survival Prediction ◽  
Osteoarthritis Initiative ◽  
Total Knee

scholarly journals A machine learning‐based survival prediction model of high grade glioma by integration of clinical and dose‐volume histogram parameters

2021 ◽  
Vol 10 (8) ◽  
pp. 2774-2786
Author(s):  
Haiyan Chen ◽  
Chao Li ◽  
Lin Zheng ◽  
Wei Lu ◽  
Yanlin Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
High Grade Glioma ◽  
Survival Prediction ◽  
High Grade ◽  
Dose Volume Histogram ◽  
Dose Volume

scholarly journals NI-13 The effectiveness and limitation of survival prediction in primary glioblastoma using machine learning-based texture analysis

2020 ◽  
Vol 2 (Supplement_3) ◽  
pp. ii14-ii14
Author(s):  
Toru Umehara ◽  
Manabu Kinoshita ◽  
Takahiro Sasaki ◽  
Hideyuki Arita ◽  
Ema Yoshioka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Texture Analysis ◽  
External Validation ◽  
The Cancer Genome Atlas ◽  
Validation Dataset ◽  
Rank Test ◽  
Survival Prediction ◽  
Primary Glioblastoma ◽  
Significant Difference

Abstract Introduction: Clinical application of survival prediction of primary glioblastoma (pGBM) using preoperative images remains challenging due to a lack of robustness and standardization of the method. This research focused on validating a machine learning-based texture analysis model for this purpose using internal and external cohorts. Method: We included all cases of IDH wild-type pGBM available of preoperative MRI (T1WI, T2WI, and Gd-T1WI) from the databases of Kansai Molecular Diagnosis Network for CNS tumors (KN) and The Cancer Genome Atlas (TCGA). Of 242 cases from KN, we assigned 137 cases as a training dataset (D1), and the remaining 105 cases as an internal validation dataset (D2). Furthermore, we extracted 96 cases from TCGA as an external validation dataset (D3). Preoperative MRI scans were semi-quantitatively analyzed, leading to the acquisition of 489 texture features as explanatory variables. Dichotomous overall survival (OS) with a 16.6 months cutoff was regarded as the response variable (short/long OS). We employed Lasso regression for feature selection, and a survival prediction model constructed for D1 via cross-validation (M1) was applied to D2 and D3 to ensure the model robustness. Results: The population of predicted short OS by M1 significantly showed poorer prognosis in D2 (median OS 11.1 vs. 19.4 months; log-rank test, p=0.03), while there was no significant difference in D3 (median OS 14.2 vs. 11.9 months; p=0.61). In the comparative analysis using t-SNE, there was little variation in the feature distribution among three datasets. Conclusion: We were able to validate the prediction model in the internal but not in the external cohort. The presented result supports the use of machine learning-based texture analysis for survival prediction of pGBM in a localized population or country. However, further consideration is required to achieve a universal prediction model for pGBM, irrespective of regional difference.

PD-0735 Time-dependent machine learning survival prediction model of brain metastases with MRI radiomics

2021 ◽  
Vol 161 ◽  
pp. S565-S566
Author(s):  
S.J.C. Crête ◽  
N.B. Campbell ◽  
R. Hu ◽  
J. Peoples ◽  
M. Yan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Metastases ◽  
Prediction Model ◽  
Time Dependent ◽  
Survival Prediction

scholarly journals Machine learning-based prediction of survival prognosis in cervical cancer

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dongyan Ding ◽  
Tingyuan Lang ◽  
Dongling Zou ◽  
Jiawei Tan ◽  
Jia Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cervical Cancer ◽  
Survival Rate ◽  
Prediction Model ◽  
Clustering Algorithm ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
High Survival ◽  
Survival Prognosis ◽  
Cancer Management

Abstract Background Accurately forecasting the prognosis could improve cervical cancer management, however, the currently used clinical features are difficult to provide enough information. The aim of this study is to improve forecasting capability by developing a miRNAs-based machine learning survival prediction model. Results The expression characteristics of miRNAs were chosen as features for model development. The cervical cancer miRNA expression data was obtained from The Cancer Genome Atlas database. Preprocessing, including unquantified data removal, missing value imputation, samples normalization, log transformation, and feature scaling, was performed. In total, 42 survival-related miRNAs were identified by Cox Proportional-Hazards analysis. The patients were optimally clustered into four groups with three different 5-years survival outcome (≥ 90%, ≈ 65%, ≤ 40%) by K-means clustering algorithm base on top 10 survival-related miRNAs. According to the K-means clustering result, a prediction model with high performance was established. The pathways analysis indicated that the miRNAs used play roles involved in the regulation of cancer stem cells. Conclusion A miRNAs-based machine learning cervical cancer survival prediction model was developed that robustly stratifies cervical cancer patients into high survival rate (5-years survival rate ≥ 90%), moderate survival rate (5-years survival rate ≈ 65%), and low survival rate (5-years survival rate ≤ 40%).

Prediction Model for Diabetes Mellitus Using Machine Learning Techniques

2018 ◽  
Vol 6 (3) ◽  
pp. 292-296
Author(s):  
N.A. Farooqui ◽  
◽  
Ritika . ◽  
A. Tyagi ◽  
◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Prediction Model ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1263
Author(s):  
Samy Ammari ◽  
Raoul Sallé de Chou ◽  
Tarek Assi ◽  
Mehdi Touat ◽  
Emilie Chouzenoux ◽  
...  
Keyword(s):  
Machine Learning ◽  
Therapeutic Option ◽  
Binary Classification ◽  
Progression Free Survival ◽  
Recurrent Glioblastoma ◽  
Machine Learning Algorithms ◽  
Survival Prediction ◽  
Classification Models ◽  
Angiogenic Therapy ◽  
Recurrent Gbm

Anti-angiogenic therapy with bevacizumab is a widely used therapeutic option for recurrent glioblastoma (GBM). Nevertheless, the therapeutic response remains highly heterogeneous among GBM patients with discordant outcomes. Recent data have shown that radiomics, an advanced recent imaging analysis method, can help to predict both prognosis and therapy in a multitude of solid tumours. The objective of this study was to identify novel biomarkers, extracted from MRI and clinical data, which could predict overall survival (OS) and progression-free survival (PFS) in GBM patients treated with bevacizumab using machine-learning algorithms. In a cohort of 194 recurrent GBM patients (age range 18–80), radiomics data from pre-treatment T2 FLAIR and gadolinium-injected MRI images along with clinical features were analysed. Binary classification models for OS at 9, 12, and 15 months were evaluated. Our classification models successfully stratified the OS. The AUCs were equal to 0.78, 0.85, and 0.76 on the test sets (0.79, 0.82, and 0.87 on the training sets) for the 9-, 12-, and 15-month endpoints, respectively. Regressions yielded a C-index of 0.64 (0.74) for OS and 0.57 (0.69) for PFS. These results suggest that radiomics could assist in the elaboration of a predictive model for treatment selection in recurrent GBM patients.

scholarly journals Tailored Prediction Model of Survival after Liver Transplantation for Hepatocellular Carcinoma

2021 ◽  
Vol 10 (13) ◽  
pp. 2869
Author(s):  
Indah Jamtani ◽  
Kwang-Woong Lee ◽  
Yun-Hee Choi ◽  
Young-Rok Choi ◽  
Jeong-Moo Lee ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Transplantation ◽  
Prediction Model ◽  
Risk Group ◽  
Significant Risk ◽  
Risk Scores ◽  
Accurate Information ◽  
Survival Prediction ◽  
Good Prediction ◽  
Recurrence Rates

This study aimed to create a tailored prediction model of hepatocellular carcinoma (HCC)-specific survival after transplantation based on pre-transplant parameters. Data collected from June 2006 to July 2018 were used as a derivation dataset and analyzed to create an HCC-specific survival prediction model by combining significant risk factors. Separate data were collected from January 2014 to June 2018 for validation. The prediction model was validated internally and externally. The data were divided into three groups based on risk scores derived from the hazard ratio. A combination of patient demographic, laboratory, radiological data, and tumor-specific characteristics that showed a good prediction of HCC-specific death at a specific time (t) were chosen. Internal and external validations with Uno’s C-index were 0.79 and 0.75 (95% confidence interval (CI) 0.65–0.86), respectively. The predicted survival after liver transplantation for HCC (SALT) at a time “t” was calculated using the formula: [1 − (HCC-specific death(t’))] × 100. The 5-year HCC-specific death and recurrence rates in the low-risk group were 2% and 5%; the intermediate-risk group was 12% and 14%, and in the high-risk group were 71% and 82%. Our HCC-specific survival predictor named “SALT calculator” could provide accurate information about expected survival tailored for patients undergoing transplantation for HCC.

A Machine Learning Distracted Driving Prediction Model

2019 ◽  
Author(s):  
Samira Ahangari ◽  
Mansoureh Jeihani ◽  
Abdollah Dehzangi
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Distracted Driving
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