Clinical application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography radiomics-based machine learning analyses in the field of oncology

Abstract

Machine learning (ML) analyses using 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomics features have been applied in the field of oncology. The current review aimed to summarize the current clinical articles about 18F-FDG PET/CT radiomics-based ML analyses to solve issues in classifying or constructing prediction models for several types of tumors. In these studies, lung and mediastinal tumors were the most commonly evaluated lesions, followed by lymphatic, abdominal, head and neck, breast, gynecological, and other types of tumors. Previous studies have commonly shown that 18F-FDG PET radiomics-based ML analysis has good performance in differentiating benign from malignant tumors, predicting tumor characteristics and stage, therapeutic response, and prognosis by examining significant differences in the area under the receiver operating characteristic curves, accuracies, or concordance indices (> 0.70). However, these studies have reported several ML algorithms. Moreover, different ML models have been applied for the same purpose. Thus, various procedures were used in 18F-FDG PET/CT radiomics-based ML analysis in oncology, and 18F-FDG PET/CT radiomics-based ML models, which are easy and universally applied in clinical practice, would be expected to be established.

Role of 18F-FDG PET/CT in Head and Neck Squamous Cell Carcinoma: Current Evidence and Innovative Applications

“…Radiomics analysis also performs better than clinicopathological factors in predicting cervical lymph node metastases [194]. Several studies have explored the ability of 18 F-FDG PET/CT radiomics-based machine-learning analysis for predicting treatment outcomes in HN cancer [190,[195][196][197][198][199][200][201][202], reporting a good performance of the radiomic features (alone or combined with genomic data and T and N stage) in predicting loco-regional progression, PFS, 3-year OS, or recurrence-free survival [196,203,204], with higher accuracy than SUV and TLG in distinguishing local recurrence from post-treatment inflammation and predicting local failure [196,205,206]. Deep learning applied to PET/CT in HN cancer has demonstrated high diagnostic accuracy, sensitivity, and PPV in differentiating treatment control and failure, better reflecting the disease-free survival rate than T stage, clinical stage, SUV max , SUV mean , MTV, and TLG [37,207].…”