Machine Learning–Based Computational Models Derived From Large-Scale Radiographic-Radiomic Images Can Help Predict Adverse Histopathological Status of Gastric Cancer

Abstract

INTRODUCTION:Adverse histopathological status (AHS) decreases outcomes of gastric cancer (GC). With the lack of a single factor with great reliability to preoperatively predict AHS, we developed a computational approach by integrating large-scale imaging factors, especially radiomic features at contrast-enhanced computed tomography, to predict AHS and clinical outcomes of patients with GC.METHODS:Five hundred fifty-four patients with GC (370 training and 184 test) undergoing gastrectomy were retrospectively included. Six radiomic scores (R-scores) related to pT stage, pN stage, Lauren & Borrmann (L&B) classification, World Health Organization grade, lymphatic vascular infiltration, and an overall histopathologic score (H-score) were, respectively, built from 7,000+ radiomic features. R-scores and radiographic factors were then integrated into prediction models to assess AHS. The developed AHS-based Cox model was compared with the American Joint Committee on Cancer (AJCC) eighth stage model for predicting survival outcomes.RESULTS:Radiomics related to tumor gray-level intensity, size, and inhomogeneity were top-ranked features for AHS. R-scores constructed from those features reflected significant difference between AHS-absent and AHS-present groups (P < 0.001). Regression analysis identified 5 independent predictors for pT and pN stages, 2 predictors for Lauren & Borrmann classification, World Health Organization grade, and lymphatic vascular infiltration, and 3 predictors for H-score, respectively. Area under the curve of models using those predictors was training/test 0.93/0.94, 0.85/0.83, 0.63/0.59, 0.66/0.63, 0.71/0.69, and 0.84/0.77, respectively. The AHS-based Cox model produced higher area under the curve than the eighth AJCC staging model for predicting survival outcomes. Furthermore, adding AHS-based scores to the eighth AJCC staging model enabled better net benefits for disease outcome stratification.DISCUSSION:The developed computational approach demonstrates good performance for successfully decoding AHS of GC and preoperatively predicting disease clinical outcomes.

Development and validation of a CT-based radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma

“…Li et al . applied machine learning-based computational models to predict adverse histopathological status, including WHO grade, of gastric cancer, with AUCs of 0.65 and 0.63 in the training and validation sets, respectively ( 32 ), while the AUCs of the radiomics nomogram developed in our study were higher (0.752 in the training set and 0.793 in the validation set). Although biopsy outperformed the radiomics model in our study, as a noninvasive method, radiomics still has the potential to aid in diagnosis when the patient cannot tolerate an endoscopic examination due to coagulation disorders or critically ill status or refuses to undergo endoscopy.…”

“…In addition, a number of studies have shown that radiomic feature extraction and comprehensive feature analysis are conductive to individualized management for patients ( 28 - 31 ). Although a previous study used radiomics signatures to predict adverse histopathological status (including WHO grade) of gastric cancer ( 32 ), an optimal prediction model combining radiomics signatures and clinical features has yet to be developed.…”

CT Radiomics and Morphologic Characteristics for Predicting PD-L1 Expression on Tumour Cells and Tumour Infiltrating Lymphocytes in Gastric Cancer

“…Nowadays, CT radiomics has been used widely in tumour assessment [14][15][16], yet only a few of them focused on PD-L1. Our study found that there were abundant radiomic features with statistical signi cance between GCs with different PD-L1 expressions, whereas the diagnostic performance was not e cient enough with AUCs ranging from 0.646 to 0.756.All those features adding signi cant morphologic characteristics were placed into LASSO for dimension reduction, and after using multiple classi ers,the diagnostic e ciency of SVM, NB, and RF increased except DT with AUCs of 0.807, 0.779, and 0.774, respectively.…”

“…Radiomics, as an emerging image analysis tool, allows extracting quantitative features noninvasively from digital medical images that enables mineable high-dimensional data to be applied in oncological practice within histological classi cation, lymph node metastasis, treatment response, and prognosis [14][15][16]. As previous studies revealed, the presented radiomic-based signatures from CT and the positron emission tomography (PET)/CT were able to achieve signi cant and robust individualized estimation of speci c PD-L1 status in non-small cell lung cancer (NSCLC) and advanced lung adenocarcinoma, respectively [17,18].…”

Artificial Intelligence-Based Multiclass Classification of Benign or Malignant Mucosal Lesions of the Stomach

“…Survival analysis is a crucial ingredient which provides important information about a patient’s prognosis status for treatment design and selection. The combination of clinical features as well as clinicopathological features extracted by machine learning methods like Support Vector Machine ( Zhang et al, 2011 ), Random Forest ( Liao et al, 2020 ), Lasso regression ( Li et al, 2019 ) as well as Deep CNN ( Ren et al, 2019 ) has been proved to substantially enhance the accuracy of survival analysis for different kinds of cancers. To expand the clinical usefulness of our CNN system, we undertook survival analyses which combined the features extracted by the CNN with clinical follow-up data for GC.…”