Related Experiment Video
Updated: May 21, 2025
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
Harnessing unsupervised machine learning with [18F]FDG PET/CT to develop a composite model for predicting overall
Jinyu Shi1, Lian Wang2, Min Zhou3
1Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, China.
Background And Purpose:
This study sought to develop an advanced composite model to enhance the prognostic accuracy for cervical cancer patients undergoing concurrent chemoradiotherapy (CCRT). The model integrated imaging features from [18F]FDG PET/CT scans with inflammatory markers using a novel unsupervised two-way clustering approach.
Methods:
In this retrospective study, 154 patients diagnosed with primary cervical cancer and treated with CCRT were evaluated using [18F]FDG PET/CT scans. A total of 1,702 radiomic features were extracted from the imaging data. These features underwent rigorous selection based on reproducibility and non-redundancy. The unsupervised two-way clustering method was then employed to simultaneously stratify patients and reduce the dimensionality of features, resulting in the generation of meta-features that were subsequently used to predict overall survival.
Results:
Kaplan-Meier survival analysis demonstrated that the two-way clustering method successfully stratified patients into distinct risk groups with significant survival differences (P<0.001), outperforming traditional K-means clustering. Predictive models constructed using meta-features derived from two-way clustering showed superior performance compared to those using principal component analysis (PCA), particularly when more than four features were included. The highest C-index values for the COX, COX_Lasso, and RSF models were observed with nine meta-features, yielding results of 0.691 ± 0.026, 0.634 ± 0.018, and 0.684 ± 0.020, respectively. In contrast, models based solely on clinical variables exhibited lower predictive performance, with C-index values of 0.645 ± 0.041, 0.567 ± 0.016, and 0.561 ± 0.033. The combination of clinical data, inflammatory markers, and radiomic features achieved the highest predictive accuracy, with a mean AUC of 0.88 ± 0.07.
Conclusion:
Integrating radiomic data with inflammatory markers using unsupervised two-way clustering offered a robust approach for predicting survival outcomes in cervical cancer patients. This methodology presented a promising avenue for personalized patient management, potentially leading to more informed treatment decisions and improved outcomes.
