scholarly journals Clinical, Conventional CT and Radiomic Feature-Based Machine Learning Models for Predicting ALK Rearrangement Status in Lung Adenocarcinoma Patients

2020 ◽  
Vol 10 ◽  
Author(s):  
Lan Song ◽  
Zhenchen Zhu ◽  
Li Mao ◽  
Xiuli Li ◽  
Wei Han ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Adenocarcinoma ◽  
Learning Models ◽  
Alk Rearrangement ◽  
Feature Based ◽  
Conventional Ct ◽  
Machine Learning Models

scholarly journals Histopathological Images and Multi-Omics Integration Predict Molecular Characteristics and Survival in Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Linyan Chen ◽  
Hao Zeng ◽  
Yu Xiang ◽  
Yeqian Huang ◽  
Yuling Luo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Adenocarcinoma ◽  
Image Features ◽  
Molecular Characteristics ◽  
Learning Models ◽  
Training Set ◽  
Test Set ◽  
Genetic Aberrations ◽  
Histopathological Images ◽  
Machine Learning Models

Histopathological images and omics profiles play important roles in prognosis of cancer patients. Here, we extracted quantitative features from histopathological images to predict molecular characteristics and prognosis, and integrated image features with mutations, transcriptomics, and proteomics data for prognosis prediction in lung adenocarcinoma (LUAD). Patients obtained from The Cancer Genome Atlas (TCGA) were divided into training set (n = 235) and test set (n = 235). We developed machine learning models in training set and estimated their predictive performance in test set. In test set, the machine learning models could predict genetic aberrations: ALK (AUC = 0.879), BRAF (AUC = 0.847), EGFR (AUC = 0.855), ROS1 (AUC = 0.848), and transcriptional subtypes: proximal-inflammatory (AUC = 0.897), proximal-proliferative (AUC = 0.861), and terminal respiratory unit (AUC = 0.894) from histopathological images. Moreover, we obtained tissue microarrays from 316 LUAD patients, including four external validation sets. The prognostic model using image features was predictive of overall survival in test and four validation sets, with 5-year AUCs from 0.717 to 0.825. High-risk and low-risk groups stratified by the model showed different survival in test set (HR = 4.94, p < 0.0001) and three validation sets (HR = 1.64–2.20, p < 0.05). The combination of image features and single omics had greater prognostic power in test set, such as histopathology + transcriptomics model (5-year AUC = 0.840; HR = 7.34, p < 0.0001). Finally, the model integrating image features with multi-omics achieved the best performance (5-year AUC = 0.908; HR = 19.98, p < 0.0001). Our results indicated that the machine learning models based on histopathological image features could predict genetic aberrations, transcriptional subtypes, and survival outcomes of LUAD patients. The integration of histopathological images and multi-omics may provide better survival prediction for LUAD.

scholarly journals PET/CT radiomics and machine learning enable non-invasive survival stratification and histologic tumor risk profiling in patients with lung adenocarcinoma

2021 ◽  
Author(s):  
Meixin Zhao ◽  
Kilian Kluge ◽  
Laszlo Papp ◽  
Marko Grahovac ◽  
Shaomin Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Adenocarcinoma ◽  
Growth Pattern ◽  
Tumor Grade ◽  
Learning Models ◽  
Term Survival ◽  
Positron Emission ◽  
Pet Ct ◽  
Treatment Naïve ◽  
Machine Learning Models

Abstract Purpose Risk stratification in patients with lung adenocarcinoma (LUAD) is mandatory for treatment guiding and outcome prediction. Amongst clinical parameters including histological analyses, imaging procedures provide important information. The present study aimed to investigate the ability of machine learning models trained on clinical and 2-deoxy-2-[¹⁸F]fluoro-D-glucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) derived radiomic data to predict overall survival (OS), tumor grade (TG), and histologic growth pattern risk (GPR) in treatment naïve LUAD patients. Methods 421 treatment naïve patients with histologically diagnosed lung adenocarcinoma and available [18F]FDG PET/CT imaging were retrospectively analyzed. Four patient cohorts were generated based on the available data for predicting 4-year OS (n = 276), 3-year OS (n = 280), TG (n = 298) and GPR (n = 265). [18F]FDG-positive lesions were delineated semiautomatically, from which 2082 radiomic features were extracted and combined with endpoint-specific clinical and demographic parameters. Machine learning models were built for the prediction of 4-year OS (M4OS), 3-year OS (M3OS), tumor grading (MTG) and histologic growth pattern risk (MGPR), respectively. Monte Carlo (MC) cross-validation with 100-folds and 80:20 training to validation split was employed as a performance evaluation for all models. Kaplan-Meier survival analysis was performed to assess the association between the M4OS and M3OS predictions with OS. Results Area under the receiver operator characteristics curve (AUC) was highest for M4OS (AUC 0.88), followed by M3OS (AUC 0.84), while MTG and MGPR performed equally well (AUC 0.76). Predictions of M4OS (HR 0.128, p < 0.000001) and M3OS (HR 0.0942, p < 0.000001) were independently associated with OS. Conclusion In our retrospective cohorts, machine learning models demonstrated the ability to prognosticate long-term survival outcomes in patients with lung adenocarcinoma. Furthermore, tumor lesions could be characterized according to their histologic grade and predominant growth pattern risk with high accuracy.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Sign in / Sign up

Export Citation Format

Share Document