Preoperative Analysis for Clinical Features of Unsuspected Gallbladder Cancer Based on Random Forest

ObjectiveTo establish a classifier for accurately predicting the overall survival of gallbladder cancer (GBC) patients by analyzing pre-treatment CT images using machine learning technology.MethodsThis retrospective study included 141 patients with pathologically confirmed GBC. After obtaining the pre-treatment CT images, manual segmentation of the tumor lesion was performed and LIFEx package was used to extract the tumor signature. Next, LASSO and Random Forest methods were used to optimize and model. Finally, the clinical information was combined to accurately predict the survival outcomes of GBC patients.ResultsFifteen CT features were selected through LASSO and random forest. On the basis of relative importance GLZLM-HGZE, GLCM-homogeneity and NGLDM-coarseness were included in the final model. The hazard ratio of the CT-based model was 1.462(95% CI: 1.014–2.107). According to the median of risk score, all patients were divided into high and low risk groups, and survival analysis showed that high-risk groups had a poor survival outcome (P = 0.012). After inclusion of clinical factors, we used multivariate COX to classify patients with GBC. The AUC values in the test set and validation set for 3 years reached 0.79 and 0.73, respectively.ConclusionGBC survival outcomes could be predicted by radiomics based on LASSO and Random Forest.

Download Full-text

A Simple Scoring Model Based on Machine Learning Predicts Intravenous Immunoglobulin Resistance in Kawasaki Disease

10.21203/rs.3.rs-1215051/v1 ◽

2022 ◽

Author(s):

Yuto Sunaga ◽

Atsushi Watanabe ◽

Nobuyuki Katsumata ◽

Takako Toda ◽

Masashi Yoshizawa ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Kawasaki Disease ◽

Intravenous Immunoglobulin ◽

Clinical Features ◽

Scoring System ◽

Random Forest Model ◽

Scoring Model ◽

Forest Model ◽

Ivig Resistance

Abstract In Kawasaki disease (KD), accurate prediction of intravenous immunoglobulin (IVIG) resistance is crucial to reduce a risk for developing coronary artery lesions. To establish a simple and accurate scoring model predicting IVIG resistance, we conducted a retrospective cohort study of 996 KD patients that were diagnosed at 11 facilities for 10 years, in which 108 cases (23.5%) were resistant to initial IVIG treatment. We performed machine learning with random forest model using 30 clinical variables at diagnosis in 796 and 200 cases for training and test datasets, respectively. Random forest model accurately predicted IVIG resistance (AUC; 0.75, sensitivity; 0.54, specificity; 0.80). Next, using top five influential features (days of illness at initial therapy, serum levels of C-reactive protein, sodium, total bilirubin, and total cholesterol) in the random forest model, we designed a simple scoring system. In spite of its simplicity, the scoring system predicted IVIG resistance (AUC; 0.73, sensitivity; 0.55, specificity; 0.83) as accurately as the random forest model itself. Moreover, accuracy of our scoring system with five clinical features was almost identical to that of Gunma score with seven clinical features (AUC; 0.73, sensitivity; 0.53, specificity; 0.83), a well-known logistic regression scoring model, and superior to that of two widely used scores (Kurume score; 0.67, 0.46 and 0.76, respectively, and Osaka score; 0.69, 0.33 and 0.84, respectively). Conclusions: Our simple scoring system based on the findings in machine learning, as well as machine learning itself, seems to be useful to accurately predict IVIG resistance in KD patients.

Download Full-text

Random forest model for feature-based Alzheimer’s disease conversion prediction from early mild cognitive impairment subjects

10.1101/2020.12.17.423213 ◽

2020 ◽

Author(s):

Matthew Velazquez ◽

Yugyung Lee ◽

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Random Forest ◽

Clinical Features ◽

Random Forest Model ◽

Clinical Feature ◽

Forest Model ◽

Early Mild Cognitive Impairment

AbstractAlzheimer’s Disease (AD) conversion prediction from the mild cognitive impairment (MCI) stage has been a difficult challenge. This study focuses on providing an individualized MCI to AD conversion prediction using a balanced random forest model that leverages clinical data. In order to do this, 383 Early Mild Cognitive Impairment (EMCI) patients were gathered from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Of these patients, 49 would eventually convert to AD (EMCI_C), whereas the remaining 335 did not convert (EMCI_NC). All of these patients were split into training and testing data sets with 95 patients reserved for testing. Nine clinical features were selected, comprised of a mix of demographic, brain volume, and cognitive testing variables. Oversampling was then performed in order to balance the initially imbalanced classes. Our results showed that a random forest model was effective (93.6% accuracy) at predicting the conversion of EMCI patients to AD based on these clinical features. Additionally, we assessed the importance of each clinical feature at both the individual and model level for interpretation of the prediction itself. Our model could impact the clinical environment as a tool to predict the conversion to AD from a prodromal stage or to identify ideal candidates for clinical trials.

Download Full-text

Death risk and the importance of clinical features in elderly people with COVID-19 using the Random Forest Algorithm

Revista Brasileira de Saúde Materno Infantil ◽

10.1590/1806-9304202100s200007 ◽

2021 ◽

Vol 21 (suppl 2) ◽

pp. 445-451

Author(s):

Tiago Pessoa Ferreira Lima ◽

Gabrielle Ribeiro Sena ◽

Camila Soares Neves ◽

Suely Arruda Vidal ◽

Jurema Telles Oliveira Lima ◽

...

Keyword(s):

Random Forest ◽

Elderly People ◽

Clinical Features ◽

Cross Validation ◽

The Elderly ◽

Death Risk ◽

Risk Of Death ◽

Feature Importance ◽

Rf Performance ◽

Fold Cross Validation

Abstract Objectives: train a Random Forest (RF) classifier to estimate death risk in elderly people (over 60 years old) diagnosed with COVID-19 in Pernambuco. A "feature" of this classifier, called feature importance, was used to identify the attributes (main risk factors) related to the outcome (cure or death) through gaining information. Methods: data from confirmed cases of COVID-19 was obtained between February 13 and June 19, 2020, in Pernambuco, Brazil. The K-fold Cross Validation algorithm (K=10) assessed RF performance and the importance of clinical features. Results: the RF algorithm correctly classified 78.33% of the elderly people, with AUC of 0.839. Advanced age was the factor representing the highest risk of death. The main comorbidity and symptom were cardiovascular disease and oxygen saturation ≤ 95%, respectively. Conclusion: this study applied the RF classifier to predict risk of death and identified the main clinical features related to this outcome in elderly people with COVID-19 in the state of Pernambuco.

Download Full-text

Clinical Features and Prognostic Factors of Elderly Patients with Gallbladder Cancer: A Population-Based Study

10.21203/rs.3.rs-612559/v1 ◽

2021 ◽

Author(s):

Jin Shuai ◽

Li Deyu ◽

Lianyuan Tao ◽

Yu Haibo ◽

Tian Guanjing

Keyword(s):

Prognostic Factors ◽

Lymph Node ◽

Elderly Patients ◽

Gallbladder Cancer ◽

Lymph Node Dissection ◽

Clinical Features ◽

Clinical Characteristics ◽

Young Patients ◽

Node Dissection ◽

Ajcc Stage

Abstract Background: Elderly patients with gallbladder cancer (GBC) may be a special group of individuals. The present study aimed to explore the clinical features and prognostic factors of elderly patients with GBC and establish nomogram to predict their overall survival (OS). Methods: Patients diagnosed with GBC from 2010 to 2015 were identified from the Surveillance Epidemiology and End Results database. Clinical characteristics and prognostic factors in elderly GBC patients were examined. Results: Elderly patients and young patients with GBC differed in many aspects, including race, marital status, AJCC stage, T stage, N stage, M stage, surgery, lymph node dissection, radiation, chemotherapy, and OS (P<0.05). Analysis of prognostic factors showed that chemotherapy and surgery with lymph node dissection (radical surgery), as the main treatment for elderly patients, can significantly improve prognosis. Other factors, including being unmarried, higher grade of histological type and AJCC stage, had a negative effect on OS. Nomogram was developed based on the above prognostic factors. The C-indexes of 1-year survival and 3-year survival nomogram were 0.73 and 0.736 and AUCs at 1 and 3 years were 0.789 and 0.780, respectively. Conclusions: Elderly patients with GBC comprise a distinct group of individuals whose clinical characteristics differ from those of young patients, and the nomogram constructed accurately predicted OS in elderly patients with GBC.

Download Full-text

IDH-Based Radiogenomic Characterization of Glioma Using Local Ternary Pattern Descriptor Integrated with Radiographic Features and Random Forest Classifier

International Journal of Image and Graphics ◽

10.1142/s0219467821400131 ◽

2021 ◽

pp. 2140013

Author(s):

Sonal Gore ◽

Jayant Jagtap

Keyword(s):

Random Forest ◽

Clinical Features ◽

Random Forest Classifier ◽

The Cancer Genome Atlas ◽

Molecular Diagnostic ◽

Low Grade ◽

Wild Type ◽

Radiographic Features ◽

Local Ternary Pattern

Mutations in family of Isocitrate Dehydrogenase (IDH) gene occur early in oncogenesis, especially with glioma brain tumor. Molecular diagnostic of glioma using machine learning has grabbed attention to some extent from last couple of years. The development of molecular-level predictive approach carries great potential in radiogenomic field. But more focused efforts need to be put to develop such approaches. This study aims to develop an integrative genomic diagnostic method to assess the significant utility of textures combined with other radiographic and clinical features for IDH classification of glioma into IDH mutant and IDH wild type. Random forest classifier is used for classification of combined set of clinical features and radiographic features extracted from axial T2-weighted Magnetic Resonance Imaging (MRI) images of low- and high-grade glioma. Such radiogenomic analysis is performed on The Cancer Genome Atlas (TCGA) data of 74 patients of IDH mutant and 104 patients of IDH wild type. Texture features are extracted using uniform, rotation invariant Local Ternary Pattern (LTP) method. Other features such as shape, first-order statistics, image contrast-based, clinical data like age, histologic grade are combined with LTP features for IDH discrimination. Proposed random forest-assisted model achieved an accuracy of 85.89% with multivariate analysis of integrated set of feature descriptors using Glioblastoma and Low-Grade Glioma dataset available with The Cancer Imaging Archive (TCIA). Such an integrated feature analysis using LTP textures and other descriptors can effectively predict molecular class of glioma as IDH mutant and wild type.

Download Full-text

Machine Learning Models with Time-Series Clinical Features to Predict Radiographic Progression in Patients with Ankylosing Spondylitis

10.21203/rs.3.rs-934182/v1 ◽

2021 ◽

Author(s):

Bon San Koo ◽

Miso Jang ◽

Ji Seon Oh ◽

Keewon Shin ◽

Seunghun Lee ◽

...

Keyword(s):

Machine Learning ◽

Time Series ◽

Ankylosing Spondylitis ◽

Random Forest ◽

Clinical Features ◽

Radiographic Progression ◽

Time Series Data ◽

Series Data ◽

Learning Models ◽

Machine Learning Models

Abstract Background: Radiographic progression in patients with ankylosing spondylitis (AS) varies between individuals, and its evaluation requires a long period of time. Previous statistical studies for radiographic progression have limitations in integrating and analyzing multiple variables of various types. The purpose of this study was to establish the application of machine learning models for predicting radiographic progression in patients with AS using time-series data from electronic medical records (EMRs).Methods: EMR data, including baseline characteristics, laboratory finding, drug administration, and modified Stoke Ankylosing Spondylitis Spine Score (mSASSS), were collected from 1,123 AS patients who were followed up for 18 years at a common center at the time of first (T1), second (T2), and third (T3) visits. The radiographic progression of the (n + 1)th visit (Pn+1 = (mSASSSn+1 – mSASSSn) / (Tn+1 – Tn) ≥ 1 unit per year) was predicted using follow-up visit datasets from T1 to Tn. Three machine learning methods (logistic regression with least absolute shrinkage and selection operation, random forest, and extreme gradient boosting algorithms) with three-fold cross validation were used. Results: The random forest model using the T1 EMR dataset showed the highest performance in predicting the radioactive progression P2 among all the machine learning models tested. The mean accuracy and the area under the curves were 73.73% and 0.79, respectively. Among the variables of T1, the most important variables for predicting radiographic progression were in the order of total mSASSS, age, and alkaline phosphatase. Conclusion: Prognosis predictive models using time-series data showed reasonable performance with clinical features of the first visit dataset for predicting radiographic progression. Additional feature data such as spine radiographs or life-log data may improve the performance of these models.

Download Full-text

Random forest model for feature-based Alzheimer’s disease conversion prediction from early mild cognitive impairment subjects

PLoS ONE ◽

10.1371/journal.pone.0244773 ◽

2021 ◽

Vol 16 (4) ◽

pp. e0244773

Author(s):

Matthew Velazquez ◽

Yugyung Lee ◽

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Random Forest ◽

Clinical Features ◽

Random Forest Model ◽

Clinical Feature ◽

Forest Model ◽

Early Mild Cognitive Impairment

Alzheimer’s Disease (AD) conversion prediction from the mild cognitive impairment (MCI) stage has been a difficult challenge. This study focuses on providing an individualized MCI to AD conversion prediction using a balanced random forest model that leverages clinical data. In order to do this, 383 Early Mild Cognitive Impairment (EMCI) patients were gathered from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Of these patients, 49 would eventually convert to AD (EMCI_C), whereas the remaining 334 did not convert (EMCI_NC). All of these patients were split randomly into training and testing data sets with 95 patients reserved for testing. Nine clinical features were selected, comprised of a mix of demographic, brain volume, and cognitive testing variables. Oversampling was then performed in order to balance the initially imbalanced classes prior to training the model with 1000 estimators. Our results showed that a random forest model was effective (93.6% accuracy) at predicting the conversion of EMCI patients to AD based on these clinical features. Additionally, we focus on explainability by assessing the importance of each clinical feature. Our model could impact the clinical environment as a tool to predict the conversion to AD from a prodromal stage or to identify ideal candidates for clinical trials.

Download Full-text