Role of Machine Learning (ML)-Based Classification Using Conventional
18F-FDG PET Parameters in Predicting Postsurgical Features of Endometrial Cancer Aggressiveness.
Cancers (Basel) 2023;
15:cancers15010325. [PMID:
36612321 PMCID:
PMC9818853 DOI:
10.3390/cancers15010325]
[Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 12/23/2022] [Accepted: 12/26/2022] [Indexed: 01/05/2023] Open
Abstract
PURPOSE
to investigate the preoperative role of ML-based classification using conventional 18F-FDG PET parameters and clinical data in predicting features of EC aggressiveness.
METHODS
retrospective study, including 123 EC patients who underwent 18F-FDG PET (2009-2021) for preoperative staging. Maximum standardized uptake value (SUVmax), SUVmean, metabolic tumour volume (MTV), and total lesion glycolysis (TLG) were computed on the primary tumour. Age and BMI were collected. Histotype, myometrial invasion (MI), risk group, lymph-nodal involvement (LN), and p53 expression were retrieved from histology. The population was split into a train and a validation set (80-20%). The train set was used to select relevant parameters (Mann-Whitney U test; ROC analysis) and implement ML models, while the validation set was used to test prediction abilities.
RESULTS
on the validation set, the best accuracies obtained with individual parameters and ML were: 61% (TLG) and 87% (ML) for MI; 71% (SUVmax) and 79% (ML) for risk groups; 72% (TLG) and 83% (ML) for LN; 45% (SUVmax; SUVmean) and 73% (ML) for p53 expression.
CONCLUSIONS
ML-based classification using conventional 18F-FDG PET parameters and clinical data demonstrated ability to characterize the investigated features of EC aggressiveness, providing a non-invasive way to support preoperative stratification of EC patients.
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