PDF(Pubmed)
Abstract：
OBJECTIVE: Neuroendocrine tumors (NETs) are a rare form of cancer that can occur anywhere in the body and commonly metastasizes. The large variance in location and aggressiveness of the tumors makes it a difficult cancer to treat. Assessments of the whole-body tumor burden in a patient image allow for better tracking of disease progression and inform better treatment decisions. Currently, radiologists rely on qualitative assessments of this metric since manual segmentation is unfeasible within a typical busy clinical workflow.
METHODS: We address these challenges by extending the application of the nnU-net pipeline to produce automatic NET segmentation models. We utilize the ideal imaging type of 68Ga-DOTATATE PET/CT to produce segmentation masks from which to calculate total tumor burden metrics. We provide a human-level baseline for the task and perform ablation experiments of model inputs, architectures, and loss functions.
RESULTS: Our dataset is comprised of 915 PET/CT scans and is divided into a held-out test set (87 cases) and 5 training subsets to perform cross-validation. The proposed models achieve test Dice scores of 0.644, on par with our inter-annotator Dice score on a subset 6 patients of 0.682. If we apply our modified Dice score to the predictions, the test performance reaches a score of 0.80.
CONCLUSIONS: In this paper, we demonstrate the ability to automatically generate accurate NET segmentation masks given PET images through supervised learning. We publish the model for extended use and to support the treatment planning of this rare cancer.
METHODS: We address these challenges by extending the application of the nnU-net pipeline to produce automatic NET segmentation models. We utilize the ideal imaging type of 68Ga-DOTATATE PET/CT to produce segmentation masks from which to calculate total tumor burden metrics. We provide a human-level baseline for the task and perform ablation experiments of model inputs, architectures, and loss functions.
RESULTS: Our dataset is comprised of 915 PET/CT scans and is divided into a held-out test set (87 cases) and 5 training subsets to perform cross-validation. The proposed models achieve test Dice scores of 0.644, on par with our inter-annotator Dice score on a subset 6 patients of 0.682. If we apply our modified Dice score to the predictions, the test performance reaches a score of 0.80.
CONCLUSIONS: In this paper, we demonstrate the ability to automatically generate accurate NET segmentation masks given PET images through supervised learning. We publish the model for extended use and to support the treatment planning of this rare cancer.
摘要：
目的：神经内分泌肿瘤（NETs）是一种罕见的癌症形式，可发生在体内任何地方，通常会转移。肿瘤的位置和侵袭性的巨大差异使其成为难以治疗的癌症。对患者图像中的全身肿瘤负荷的评估允许更好地跟踪疾病进展并告知更好的治疗决策。目前,放射科医师依赖于定性评估的这一指标，因为手动分割是不可行的，在一个典型的繁忙的临床工作流程。
方法：我们通过扩展nnU-net管道的应用来产生自动NET分割模型来解决这些挑战。我们利用68Ga-DOTATATEPET/CT的理想成像类型来产生分割掩模，从中计算总肿瘤负荷指标。我们为任务提供人类水平的基线，并执行模型输入的消融实验，架构,和损失函数。
结果：我们的数据集由915次PET/CT扫描组成，并分为保留测试集（87例）和5个训练子集以进行交叉验证。所提出的模型实现了0.644的测试骰子得分，与我们在0.682的6位患者中的注释者间骰子得分相当。如果我们将修改后的骰子得分应用于预测，测试性能达到0.80分。
结论：在本文中，我们展示了通过监督学习自动生成给定PET图像的精确NET分割掩模的能力。我们发布了用于扩展使用的模型，并支持这种罕见癌症的治疗计划。
方法：我们通过扩展nnU-net管道的应用来产生自动NET分割模型来解决这些挑战。我们利用68Ga-DOTATATEPET/CT的理想成像类型来产生分割掩模，从中计算总肿瘤负荷指标。我们为任务提供人类水平的基线，并执行模型输入的消融实验，架构,和损失函数。
结果：我们的数据集由915次PET/CT扫描组成，并分为保留测试集（87例）和5个训练子集以进行交叉验证。所提出的模型实现了0.644的测试骰子得分，与我们在0.682的6位患者中的注释者间骰子得分相当。如果我们将修改后的骰子得分应用于预测，测试性能达到0.80分。
结论：在本文中，我们展示了通过监督学习自动生成给定PET图像的精确NET分割掩模的能力。我们发布了用于扩展使用的模型，并支持这种罕见癌症的治疗计划。
