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This study aimed to assess the potential of ¹⁸FFluorodeoxyglucose Positron Emission Tomography/Computed Tomography (¹⁸F-FDG PET/CT) parameters, including advanced texture features, to predict pathological complete response (pCR) after the first course of neoadjuvant chemotherapy (NAC) in breast cancer follow-up patients. This approach evaluated pCR after the first course of NAC by combining information from functional images, anatomical images, and clinical data. A total of 204 breast cancer patients underwent ¹⁸F-FDG PET/CT imaging for NAC assessment. From these delayed PET/CT scans, we extracted both metabolic and radiomic features, combining imaging parameters with the breast cancer molecular subtype information for each patient to improve pCR prediction. Lesion segmentation was automated using the no-new-Net (nnUNet) deep learning model. To predict pCR, we employed machine learning classifiers, including Random Forest, XGBoost, and Support Vector Machine. Among all tested models, the highest prediction performance was achieved when PET/CT features (both baseline and follow-up) were combined with breast cancer subtype information. The analysis was conducted on the entire dataset (Human Epidermal Growth Factor Receptor 2 (HER2), Luminal, and Triple-Negative (TN). Moreover, separate analyses were performed specifically on HER2 tumors (N=76) and TN tumors (N=52). The combined model achieved a mean balanced accuracy of 0.76 ± 0.09, surpassing the individual models for HER2 (0.67 ± 0.08) and TN (0.65 ± 0.06). These findings show the importance of integrating baseline and follow-up PET/CT radiomic features, texture analysis, and clinical information for more accurate pCR prediction after the first course of NAC in breast cancer patients. Overall, the features extracted from baseline data and follow-up data or after the first course of NAC, combined with information of breast cancer subtype, offer strong predictive value for pCR in follow-up patients.Clinical Relevance-By providing a more accurate assessment of treatment response after the first course of NAC, this approach empowers clinicians to make artificial intelligence-driven decisions, customize therapy plans for individual patients, and avoid ineffective treatments. Consequently, this strategy could improve patient outcomes and optimize therapeutic efficacy.