scholarly journals Predicting Short-Term Survival after Gross Total or Near Total Resection in Glioblastomas by Machine Learning-Based Radiomic Analysis of Preoperative MRI

Cancers ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 5047
Author(s):  
Santiago Cepeda ◽  
Angel Pérez-Nuñez ◽  
Sergio García-García ◽  
Daniel García-Pérez ◽  
Ignacio Arrese ◽  
...  
Keyword(s):  
Machine Learning ◽  
Test Data ◽  
Tumor Resection ◽  
Area Under The Curve ◽  
Risk Groups ◽  
Feature Reduction ◽  
Short Term ◽  
Data Set ◽  
Term Survival ◽  
Short Term Survival

Radiomics, in combination with artificial intelligence, has emerged as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection? A retrospective study of GBM patients who underwent surgery was conducted in two institutions between January 2019 and January 2020, along with cases from public databases. Cases with gross total or near total tumor resection were included. Preoperative structural multiparametric magnetic resonance imaging (mpMRI) sequences were pre-processed, and a total of 15,720 radiomic features were extracted. After feature reduction, machine learning-based classifiers were used to predict early mortality (<6 months). Additionally, a survival analysis was performed using the random survival forest (RSF) algorithm. A total of 203 patients were enrolled in this study. In the classification task, the naive Bayes classifier obtained the best results in the test data set, with an area under the curve (AUC) of 0.769 and classification accuracy of 80%. The RSF model allowed the stratification of patients into low- and high-risk groups. In the test data set, this model obtained values of C-Index = 0.61, IBS = 0.123 and integrated AUC at six months of 0.761. In this study, we developed a reliable predictive model of short-term survival in GBM by applying open-source and user-friendly computational means. These new tools will assist clinicians in adapting our therapeutic approach considering individual patient characteristics.

scholarly journals Predicting short-term survival after total resection in glioblastomas by machine learning-based radiomic analysis of preoperative MRI

2021 ◽  
Author(s):  
Santiago Cepeda ◽  
Angel Perez-Nuñez ◽  
Sergio Garcia-Garcia ◽  
Daniel Garcia-Perez ◽  
Ignacio Arrese ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tumor Resection ◽  
Area Under The Curve ◽  
Patient Characteristics ◽  
Risk Groups ◽  
Feature Reduction ◽  
Short Term ◽  
Term Survival ◽  
Reduction Machine ◽  
Short Term Survival

Abstract Background Radiomics, in combination with artificial intelligence, has emerged as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection?Methods A retrospective study of GBM patients who underwent surgery was conducted in two institutions between January 2019 and January 2020, along with cases from public databases. Cases with gross total or near-total tumor resection were included. Preoperative structural multiparametric magnetic resonance imaging (mpMRI) sequences were preprocessed, and a total of 15720 radiomic features were extracted. After feature reduction, machine learning-based classifiers were used to predict early mortality (< 6 months). Additionally, a survival analysis was performed using the random survival forest (RSF) algorithm.Results A total of 203 patients were enrolled in this study. In the classification task, the naive Bayes classifier obtained the best results in the testing cohort, with an area under the curve (AUC) of 0.769 and classification accuracy of 80%. The RSF model allowed the stratification of patients into low- and high-risk groups. In the validation set, this model obtained values of C-Index = 0.61, IBS = 0.123 and integrated AUC at six months of 0.761.Conclusion In this study, we developed a reliable predictive model of short-term survival in GBM by applying open-source and user-friendly computational means. These new tools will assist clinicians in adapting our therapeutic approach considering individual patient characteristics.

scholarly journals Predicting short-term survival after total resection in glioblastomas by machine learning-based radiomic analysis of preoperative MRI

2021 ◽  
Author(s):  
Santiago Cepeda ◽  
Angel Perez-Nuñez ◽  
Sergio Garcia-Garcia ◽  
Daniel Garcia-Perez ◽  
Ignacio Arrese ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tumor Resection ◽  
Area Under The Curve ◽  
Patient Characteristics ◽  
Risk Groups ◽  
Feature Reduction ◽  
Short Term ◽  
Term Survival ◽  
The Individual ◽  
Short Term Survival

Abstract Background Radiomics, in combination with artificial intelligence, emerges as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection?Methods A retrospective study of GBM patients who underwent surgery was conducted in two institutions between January 2019 and January 2020, along with cases from public databases. Cases with gross total or near-total tumor resection were included. Preoperative structural multiparametric magnetic resonance imaging (mpMRI) sequences were pre-processed, and a total of 15720 radiomic features were extracted. After feature reduction, machine learning-based classifiers were used to predict early mortality (< 6 months). Also, a survival analysis was performed using the Random Survival Forest (RSF) algorithm. Results A total of 203 patients were enrolled in this study. In the classification task, the Naive Bayes classifier obtained the best results in the testing cohort, with an area under the curve (AUC) of 0.769 and classification accuracy of 80%. RSF model allowed the stratification of patients into low and high-risk groups. In the validation set, this model obtained values of C-Index = 0.61, IBS = 0.123 and integrated AUC at six months of 0.761.Conclusion In this study, we have developed a reliable predictive model of short-term survival in GBM applying open-source and user-friendly computational means. These new tools will assist clinicians in adapting our therapeutic approach considering the individual patient characteristics.

scholarly journals Predicting short-term survival after total resection in glioblastomas by machine learning-based radiomic analysis of preoperative MRI

2021 ◽  
Author(s):  
Santiago Cepeda ◽  
Angel Perez-Nuñez ◽  
Sergio Garcia-Garcia ◽  
Daniel Garcia-Perez ◽  
Ignacio Arrese ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Predictive Models ◽  
Tumor Resection ◽  
Short Term ◽  
Total Resection ◽  
Complete Tumor Resection ◽  
Term Survival ◽  
Complete Tumor ◽  
Short Term Survival

Abstract Background Radiomics, in combination with artificial intelligence, has emerged as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection?

scholarly journals Predicting Short-term Survival after Liver Transplantation using Machine Learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Chien-Liang Liu ◽  
Ruey-Shyang Soong ◽  
Wei-Chen Lee ◽  
Guo-Wei Jiang ◽  
Yun-Chun Lin
Keyword(s):  
Machine Learning ◽  
Liver Transplantation ◽  
Short Term ◽  
Term Survival ◽  
Short Term Survival

Influence of age and shock severity on short-term survival in patients with cardiogenic shock

2021 ◽  
Author(s):  
Jacob C Jentzer ◽  
Benedikt Schrage ◽  
David R Holmes ◽  
Salim Dabboura ◽  
Nandan S Anavekar ◽  
...  
Keyword(s):  
Cardiogenic Shock ◽  
Mayo Clinic ◽  
Older Patients ◽  
Cox Proportional Hazards ◽  
Circulatory Support ◽  
Short Term ◽  
Term Survival ◽  
Risk Of Death ◽  
Coronary Syndrome ◽  
Short Term Survival

Abstract Aims Cardiogenic shock (CS) is associated with poor outcomes in older patients, but it remains unclear if this is due to higher shock severity. We sought to determine the associations between age and shock severity on mortality among patients with CS. Methods and results Patients with a diagnosis of CS from Mayo Clinic (2007–15) and University Clinic Hamburg (2009–17) were subdivided by age. Shock severity was graded using the Society for Cardiovascular Angiography and Intervention (SCAI) shock stages. Predictors of 30-day survival were determined using Cox proportional-hazards analysis. We included 1749 patients (934 from Mayo Clinic and 815 from University Clinic Hamburg), with a mean age of 67.6 ± 14.6 years, including 33.6% females. Acute coronary syndrome was the cause of CS in 54.0%. The distribution of SCAI shock stages was 24.1%; C, 28.0%; D, 33.2%; and E, 14.8%. Older patients had similar overall shock severity, more co-morbidities, worse kidney function, and decreased use of mechanical circulatory support compared to younger patients. Overall 30-day survival was 53.3% and progressively decreased as age or SCAI shock stage increased, with a clear gradient towards lower 30-day survival as a function of increasing age and SCAI shock stage. Progressively older age groups had incrementally lower adjusted 30-day survival than patients aged &lt;50 years. Conclusion Older patients with CS have lower short-term survival, despite similar shock severity, with a high risk of death in older patients with more severe shock. Further research is needed to determine the optimal treatment strategies for older CS patients.

scholarly journals Parenteral Nutrition and Oxidant Load in Neonates

Nutrients ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 2631
Author(s):  
Kandeepan Karthigesu ◽  
Robert F. Bertolo ◽  
Robert J. Brown
Keyword(s):  
Parenteral Nutrition ◽  
Storage Temperature ◽  
Oral Feeding ◽  
Published Data ◽  
Short Term ◽  
Antioxidant Defences ◽  
Term Survival ◽  
Nutrient Sources ◽  
Short Term Survival

Neonates with preterm, gastrointestinal dysfunction and very low birth weights are often intolerant to oral feeding. In such infants, the provision of nutrients via parenteral nutrition (PN) becomes necessary for short-term survival, as well as long-term health. However, the elemental nutrients in PN can be a major source of oxidants due to interactions between nutrients, imbalances of anti- and pro-oxidants, and environmental conditions. Moreover, neonates fed PN are at greater risk of oxidative stress, not only from dietary sources, but also because of immature antioxidant defences. Various interventions can lower the oxidant load in PN, including the supplementation of PN with antioxidant vitamins, glutathione, additional arginine and additional cysteine; reduced levels of pro-oxidant nutrients such as iron; protection from light and oxygen; and proper storage temperature. This narrative review of published data provides insight to oxidant molecules generated in PN, nutrient sources of oxidants, and measures to minimize oxidant levels.

A registry study on radial head arthroplasties in the Netherlands: Indications, types and short-term survival

2021 ◽  
pp. 175857322098784
Author(s):  
Arno A Macken ◽  
Ante Prkić ◽  
Koen LM Koenraadt ◽  
Iris van Oost ◽  
Anneke Spekenbrink-Spooren ◽  
...  
Keyword(s):  
Radial Head ◽  
Register Data ◽  
Implant Survival ◽  
Short Term ◽  
Term Survival ◽  
Arthroplasty Register ◽  
Common Indication ◽  
Radial Head Arthroplasty ◽  
Kaplan Meier ◽  
Short Term Survival

Background This study aims to use the Dutch Arthroplasty Register data to report an overview of the contemporary indications and implant designs, and report the short-term survival of radial head arthroplasty. Methods From the Dutch Arthroplasty Register, data on patient demographics, surgery and revision were extracted for radial head arthroplasties performed from January 2014 to December 2019. Implant survival was calculated using the Kaplan–Meier method. Results Two hundred fifty-eight arthroplasties were included with a median follow-up of 2.2 years. The most common indication was a fracture of the radial head (178, 69%). One hundred thirty-nine (68%) of the prostheses were of bipolar design, and the most commonly used implant type was the Radial Head System (Tornier; 134, 51%). Of the 258 included radial head arthroplasties, 16 were revised at a median of six months after surgery. Reason for revision was predominantly aseptic loosening (9). The overall implant survival was 95.8% after one year, 90.5% after three years and 89.5% after five years. Discussion For radial head arthroplasties, acute trauma is the most common indication and Radial Head System the most commonly used implant. The implant survival is 89.5% after five years.

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