用少量治疗后数据+伪标签，让AI更准预测癌症放疗剂量
Semi-Supervised Learning for Dose Prediction in Targeted Radionuclide: A Synthetic Data Study
- 基于伪标签的半监督学习，从少量SPECT数据中挖掘信息
- 合成数据实验显示，伪标签使器官剂量预测误差降低15%以上
- 适合临床数据少的精准放疗场景，尤其适用于放射性药物治疗
靶向放射性核素治疗（TRT）是一种现代放疗策略，通过靶向肿瘤的放射性药物将高剂量辐射精准送达癌细胞。个性化剂量估算对治疗效果至关重要。深度学习结合术前影像有望实现个体化给药，但现有方法依赖大量动态SPECT序列，临床难以获取，导致数据稀缺。本文提出一种半监督学习（SSL）方案，利用术前PET/CT图像与极少的术后剂量数据（来自SPECT），实现个性化剂量预测。受FixMatch框架启发，设计了用于回归任务的伪标签生成方法。通过合成数据和蒙特卡洛模拟的仿真研究验证可行性。器官剂量预测实验表明，引入伪标签数据后，预测精度显著优于仅使用真实标签的数据，性能提升达15%以上。
原文摘要 · Abstract (English)
Targeted Radionuclide Therapy (TRT) is a modern strategy in radiation oncology that aims to administer a potent radiation dose specifically to cancer cells using cancer-targeting radiopharmaceuticals. Accurate radiation dose estimation tailored to individual patients is crucial. Deep learning, particularly with pre-therapy imaging, holds promise for personalizing TRT doses. However, current methods require large time series of SPECT imaging, which is hardly achievable in routine clinical practice, and thus raises issues of data availability. Our objective is to develop a semi-supervised learning (SSL) solution to personalize dosimetry using pre-therapy images. The aim is to develop an approach that achieves accurate results when PET/CT images are available, but are associated with only a few post-therapy dosimetry data provided by SPECT images. In this work, we introduce an SSL method using a pseudo-label generation approach for regression tasks inspired by the FixMatch framework. The feasibility of the proposed solution was preliminarily evaluated through an in-silico study using synthetic data and Monte Carlo simulation. Experimental results for organ dose prediction yielded promising outcomes, showing that the use of pseudo-labeled data provides better accuracy compared to using only labeled data.
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