数据采集与数据培育：关于乳腺癌患者基于深度学习的自动勾画中变异与样本量重要性的研究
Data harvesting vs data farming: A study of the importance of variation vs sample size in deep learning-based auto-segmentation for breast cancer patients
AI总结：
本研究比较大型临床、整理后临床及专家专用勾画数据集训练的乳腺癌自动分割模型，发现专用数据质量更优，并提出以大数据预训练、小而精数据微调的方案。
摘要：
本研究旨在考察在三种不同场景下训练模型时输出结果的差异：一个大型临床勾画数据集（训练/测试为700/78名患者，来自丹麦乳腺癌协作组（DBCG）RT Nation研究）、一个经过整理的临床数据集（训练/测试为328/36名患者，来自DBCG RT Nation研究），以及一个由勾画专家创建的规模较小但专用的数据集（训练/测试为123/14名患者，包含勾画专家制定的共识勾画）。模型性能基于Dice相似系数（DSC）、第95百分位Hausdorff距离（HD95）和平均表面距离（MSD）等性能指标进行评估。模型先在各自队列的测试集中接受测试，随后又在专用数据测试集中进行比较。最后，通过测量各模型输出的平均宽度和头尾长度来评估模型输出之间的差异。当在各自测试集中比较临床模型与专用模型的输出时，两个临床模型的性能均差于专用模型，但并非所有指标均具有统计学显著性。在专用数据上测试模型时，专用模型表现出略优的性能，并且分割异常值更少。作为结合两类数据集优势的一种方式，可以选择将大型临床数据集用作基线模型，然后使用规模较小、具有专用勾画的队列进行微调。
英文摘要：
The aim of this study was to investigate the difference in output, when training a model in three different scenarios: a large clinical delineated data set (with 700/78 patients for training/testing, from the Danish Breast Cancer Group (DBCG) RT Nation Study), a clinical but curated dataset (with 328/36 patients for training/testing, from the DBCG RT Nation Study) and a smaller, but dedicated data set created by delineation experts (with 123/14 patients for training/testing, consensus delineations created by delineation experts). The model performance was estimated based on the performance metrics dice similarity coefficient (DSC), Hausdorff 95th percentile (HD95) and mean surface distance (MSD). Models were tested in test sets from their own cohort, and afterwards also compared in the dedicated data test set. The difference between model output was finally estimated by measuring the mean width and cranial caudal length of the model output for the models. When testing the model output between the clinical models and the dedicated models in their own test set, the two clinical models had a poorer performance, than the dedicated models, but not all metrics showed statistically significance. When testing the models in the dedicated data, the dedicated model showed a slightly better performance, along with fewer segmentation outliers. As a way of taking advantage of the strength from both types of data set, it could be an option to use a large clinical data set as a baseline model, and then finetune with smaller sized cohorts with dedicated delineations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
