Abstract
OBJECTIVE: Breast cancer is the most common cancer and the leading cause of cancer-related deaths in women. Texture analysis provides crucial prognostic information about many types of cancer, including breast cancer. The aim was to examine the relationship between texture features (TFs) of 2-deoxy-2[(18)F] fluoro-D-glucose positron emission tomography (PET)/computed tomography and disease progression in patients with invasive breast cancer. MATERIALS AND METHODS: TFs of the primary malignant lesion were extracted from PET images of 112 patients. TFs that showed significant differences between patients who achieved one-, three-, and five-year progression-free survival (PFS) and those who did not were selected and subjected to the least absolute shrinkage and selection operator regression method to reduce features and prevent overfitting. Machine learning (ML) was used to predict PFS using TFs and selected clinicopathological parameters. RESULTS: In models using only TFs, random forest predicted one-, three-, and five-year PFS with area under the curve (AUC) values of 0.730, 0.758, and 0.797, respectively. Naive Bayes predicted one-, three-, and five-year PFS with AUC values of 0.857, 0.804, and 0.843, respectively. The neural network predicted one-, three-, and five-year PFS with AUC values of 0.782, 0.828, and 0.780, respectively. These findings indicated increased AUC values when the models combined TFs with clinicopathological parameters. The lowest AUC values of the models combining TFs and clinicopathological parameters when predicting one-year, three-year, and five-year PFS were 0.867, 0.898, and 0.867, respectively. CONCLUSION: ML models incorporating PET-derived TFs and clinical parameters may assist in predicting progression during the pre-treatment period in patients with invasive breast carcinoma.