A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts Tumor Immune Profiles in Non-Small Cell Lung Cancer: A Retrospective Multicohort Study

Abstract

BackgroundThe tumor immune microenvironment (TIME) phenotypes have been reported to mainly impact the efficacy of immunotherapy. Given the increasing use of immunotherapy in cancers, knowing an individual’s TIME phenotypes could be helpful in screening patients who are more likely to respond to immunotherapy. Our study intended to establish, validate, and apply a machine learning model to predict TIME profiles in non-small cell lung cancer (NSCLC) by using 18F-FDG PET/CT radiomics and clinical characteristics.MethodsThe RNA-seq data of 1145 NSCLC patients from The Cancer Genome Atlas (TCGA) cohort were analyzed. Then, 221 NSCLC patients from Daping Hospital (DPH) cohort received18F-FDG PET/CT scans before treatment and CD8 expression of the tumor samples were tested. The Artificial Intelligence Kit software was used to extract radiomic features of PET/CT images and develop a radiomics signature. The models were established by radiomics, clinical features, and radiomics-clinical combination, respectively, the performance of which was calculated by receiver operating curves (ROCs) and compared by DeLong test. Moreover, based on radiomics score (Rad-score) and clinical features, a nomogram was established. Finally, we applied the combined model to evaluate TIME phenotypes of NSCLC patients in The Cancer Imaging Archive (TCIA) cohort (n = 39).ResultsTCGA data showed CD8 expression could represent the TIME profiles in NSCLC. In DPH cohort, PET/CT radiomics model outperformed CT model (AUC: 0.907 vs. 0.861, P = 0.0314) to predict CD8 expression. Further, PET/CT radiomics-clinical combined model (AUC = 0.932) outperformed PET/CT radiomics model (AUC = 0.907, P = 0.0326) or clinical model (AUC = 0.868, P = 0.0036) to predict CD8 expression. In the TCIA cohort, the predicted CD8-high group had significantly higher immune scores and more activated immune pathways than the predicted CD8-low group (P = 0.0421).ConclusionOur study indicates that 18F-FDG PET/CT radiomics-clinical combined model could be a clinically practical method to non-invasively detect the tumor immune status in NSCLCs.

Prediction of short-term prognosis of patients with hypertensive intracerebral hemorrhage by radiomic-clinical nomogram

“…In addition, in a study about predicting the outcomes of acute ischemic stroke at 6 months after hospital discharge, the AUC of the R-C combined model is 0.868 in the training cohort and 0.890 in the validation cohort, which is significantly higher than that of the clinical or radiomics model (32). Similarly, a machine learning model based on PET/CT radiomics and clinical characteristics predicted the tumor immune microenvironment profiles of nonsmall cell lung cancer, which showed that the R-C combined model has the best performance (33). In our research, the GCS, hematoma volume, midline shift, black hole sign, and Rad-score were employed to build the R-C combined model for predicting the short-term prognosis of HICH patients.…”

The role of artificial intelligence based on PET/CT radiomics in NSCLC: Disease management, opportunities, and challenges

“…The PET/CT-clinical combined model has a significant advantage in predicting the ALK mutation status, with the highest AUC value (0.88) in the test group. In addition, another study has successfully predicted the tumor immune microenvironment phenotype of NSCLC based on the PET/CT-clinical combined model ( 62 ). We summarized the main findings on genotype in Table 3 .…”

MRI histogram analysis of tumor-infiltrating CD8+ T cell levels in patients with glioblastoma

“…Immunotherapy has led to dramatic changes in the traditional treatment strategies for cancer ( Ansell, 2016 ). However, the efficacy of immunotherapy is mainly affected by the TME phenotype, especially the levels of tumor-infiltrating CD8+ T cells, which are positively correlated with the efficacy of immunotherapy and survival ( Tong et al, 2022 ). T1C is a routine scan sequence for brain tumor patients, and it is more widely used in the diagnosis and evaluation of brain tumor patients at all levels of hospitals than the advanced functional MRI methods, such as DWI, PWI, APT, etc., and is more easily accessible than other scan sequences.…”