Neoadjuvant chemotherapy (NAC) has become a standard clinical practice for tumor downsizing in breast cancer with 18F-FDG Positron Emission Tomography (PET). Our work aims to leverage PET imaging for the segmentation of breast lesions. The focus is on developing an automated system that accurately segments primary tumor regions and extracts key biomarkers from these areas to provide insights into the evolution of breast cancer following the first course of NAC. 243 baseline 18F-FDG PET scans (PET_Bl) and 180 follow-up 18F-FDG PET scans (PET_Fu) were acquired before and after the first course of NAC, respectively. Firstly, a deep learning-based breast tumor segmentation method was developed. The optimal baseline model (model trained on baseline exams) was fine-tuned on 15 follow-up exams and adapted using active learning to segment tumor areas in PET_Fu. The pipeline computes biomarkers such as maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) to evaluate tumor evolution between PET_Fu and PET_Bl. Quality control measures were employed to exclude aberrant outliers. The nnUNet deep learning model outperformed in tumor segmentation on PET_Bl, achieved a Dice similarity coefficient (DSC) of 0.89 and a Hausdorff distance (HD) of 3.52 mm. After fine-tuning, the model demonstrated a DSC of 0.78 and a HD of 4.95 mm on PET_Fu exams. Biomarkers analysis revealed very strong correlations whatever the biomarker between manually segmented and automatically predicted regions. The significant average decrease of SUVmax, MTV and TLG were 5.22, 11.79 cm3 and 19.23 cm3, respectively. The presented approach demonstrates an automated system for breast tumor segmentation from 18F-FDG PET. Thanks to the extracted biomarkers, our method enables the automatic assessment of cancer progression.

翻译：新辅助化疗（NAC）已成为利用18F-氟代脱氧葡萄糖正电子发射断层扫描（PET）进行乳腺癌肿瘤缩小的标准临床实践。本研究旨在利用PET成像实现乳腺病灶的分割，重点开发一个自动化系统，能够精确分割原发肿瘤区域，并从这些区域提取关键生物标志物，从而为首次NAC疗程后乳腺癌的演变提供见解。研究分别采集了243例首次NAC疗程前的基线18F-FDG PET扫描（PET_Bl）和180例疗程后的随访18F-FDG PET扫描（PET_Fu）。首先，开发了一种基于深度学习的乳腺肿瘤分割方法。最优基线模型（在基线检查数据上训练的模型）在15例随访检查数据上进行了微调，并采用主动学习策略进行适配，以分割PET_Fu中的肿瘤区域。该流程计算了最大标准化摄取值（SUVmax）、代谢肿瘤体积（MTV）和病灶糖酵解总量（TLG）等生物标志物，以评估PET_Fu与PET_Bl之间的肿瘤演变。研究采用了质量控制措施以排除异常离群值。nnUNet深度学习模型在PET_Bl的肿瘤分割任务中表现最优，其Dice相似系数（DSC）达到0.89，豪斯多夫距离（HD）为3.52毫米。经微调后，该模型在PET_Fu检查中取得了0.78的DSC和4.95毫米的HD。生物标志物分析显示，无论何种生物标志物，手动分割区域与自动预测区域之间均存在极强的相关性。SUVmax、MTV和TLG的平均值均显著下降，降幅分别为5.22、11.79立方厘米和19.23立方厘米。本研究展示了一种从18F-FDG PET图像中自动分割乳腺肿瘤的系统方法。得益于所提取的生物标志物，该方法能够实现癌症进展的自动评估。