自动分割乳腺癌PET图像，量化化疗前后肿瘤代谢变化。
Automatic quantification of breast cancer biomarkers from multiple 18F-FDG PET image segmentation
- 用深度学习模型自动分割乳腺肿瘤区域，基于nnUNet框架优化。
- 化疗后肿瘤代谢体积和糖酵解量平均减少11.79cm³和19.23cm³。
- 适用于临床肿瘤动态评估，助力精准治疗决策。
新辅助化疗（NAC）已成为乳腺癌患者通过18F-FDG正电子发射断层扫描（PET）实现肿瘤缩小的标准疗法。本研究旨在利用PET影像实现乳腺病变的自动分割，构建可准确识别原发肿瘤区域并提取关键生物标志物的自动化系统，以分析首次NAC治疗后的肿瘤演变情况。共采集243例基线期18F-FDG PET扫描（PET_Bl）和180例随访期扫描（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15例随访扫描上进行微调，并结合主动学习策略适应后续图像分割。该流程计算最大标准化摄取值（SUVmax）、代谢肿瘤体积（MTV）和总病灶糖酵解量（TLG），用于比较随访与基线图像。质量控制排除异常值。nnUNet模型在基线数据上表现最佳，Dice相似系数（DSC）达0.89，豪斯多夫距离（HD）为3.52 mm；微调后在随访数据上取得DSC 0.78、HD 4.95 mm。人工与自动分割区域间所有生物标志物均呈现强相关性。结果显示，SUVmax、MTV、TLG的平均下降值分别为5.22、11.79 cm³、19.23 cm³。
原文摘要 · Abstract (English)
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.
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