MLW-gcForest: a multi-weighted gcForest model towards the staging of lung adenocarcinoma based on multi-modal genetic data

Abstract

BackgroundLung cancer is one of the most common types of cancer, among which lung adenocarcinoma accounts for the largest proportion. Currently, accurate staging is a prerequisite for effective diagnosis and treatment of lung adenocarcinoma. Previous research has used mainly single-modal data, such as gene expression data, for classification and prediction. Integrating multi-modal genetic data (gene expression RNA-seq, methylation data and copy number variation) from the same patient provides the possibility of using multi-modal genetic data for cancer prediction. A new machine learning method called gcForest has recently been proposed. This method has been proven to be suitable for classification in some fields. However, the model may face challenges when applied to small samples and high-dimensional genetic data.ResultsIn this paper, we propose a multi-weighted gcForest algorithm (MLW-gcForest) to construct a lung adenocarcinoma staging model using multi-modal genetic data. The new algorithm is based on the standard gcForest algorithm. First, different weights are assigned to different random forests according to the classification performance of these forests in the standard gcForest model. Second, because the feature vectors generated under different scanning granularities have a diverse influence on the final classification result, the feature vectors are given weights according to the proposed sorting optimization algorithm. Then, we train three MLW-gcForest models based on three single-modal datasets (gene expression RNA-seq, methylation data, and copy number variation) and then perform decision fusion to stage lung adenocarcinoma. Experimental results suggest that the MLW-gcForest model is superior to the standard gcForest model in constructing a staging model of lung adenocarcinoma and is better than the traditional classification methods. The accuracy, precision, recall, and AUC reached 0.908, 0.896, 0.882, and 0.96, respectively.ConclusionsThe MLW-gcForest model has great potential in lung adenocarcinoma staging, which is helpful for the diagnosis and personalized treatment of lung adenocarcinoma. The results suggest that the MLW-gcForest algorithm is effective on multi-modal genetic data, which consist of small samples and are high dimensional.

Computational detection of a genome instability‐derived lncRNA signature for predicting the clinical outcome of lung adenocarcinoma

“…Many attempts have been made to investigate the incidence, growth, diagnosis and treatment of LUAD. 41 , 42 , 43 , 44 Common histopathological features of stage, tumor size and Karnofsky Performance Status (KPS) are now still considered to be the most important prognostic factors in LUAD. Based on their pathological characteristics, LUAD patients are grouped into different groups to accept the corresponding care regimens.…”

A narrative review of artificial intelligence-assisted histopathologic diagnosis and decision-making for non-small cell lung cancer: achievements and limitations

“…Predicting NSCLC subtypes by identifying specific genotypes is a potentially powerful method that can be recommended as a special marker for IHC-stained slides. Therefore, several references determined the sensitivity and specificity of genetic markers to discriminate the subtypes of NSCLCs, as shown in Table 2 (31)(32)(33)(34)(35)(36)(37). A retrospective study selected the genetic features from 77 ADCs and 73 SCCs to construct the support vector machine (SVM) and random forest (RF) classifier (32).…”

“…Furthermore, with the RNA-seq, methylation data, and copy number variation (CNV) of 369 ADCs from TCGA, Dong et al (36) developed the MLW-gcForest model, a machine learning-based ensemble algorithm, which achieved better classification performance in ADC staging (accuracy, 0.908; precision, 0.896; recall, 0.882; F1, 0.889) incorporating multi-modal data compared with single-modal data. Dong et al (36) indicated that MLW-gcForest integrating multimodal genetic data effectively improved the accuracy of ADC staging, which was significantly superior to the traditional machine learning algorithms. Generally, gene mutations play a critical role in tumor progression and tumor phenotypes.…”

Deep learning in systems medicine

“…It has been shown that gcForest has much fewer parameters in comparison to DNN and can work well even when there are only small-scale data available. A multi-weighted gcForest has been proposed and developed as a staging model of lung adenocarcinoma based on multi-modal genetic data which could be used for the diagnosis and personalized treatment of lung cancer [ 163 ].…”