从全身 [68Ga]Ga-PSMA-11 PET/CT 图像中的转移性前列腺病变分割中全自动提取预后生物标志物,European Journal of Nuclear Medicine and Molecular Imaging

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从全身 [68Ga]Ga-PSMA-11 PET/CT 图像中的转移性前列腺病变分割中全自动提取预后生物标志物
European Journal of Nuclear Medicine and Molecular Imaging ( IF 8.6 ) Pub Date : 2022-08-17 , DOI: 10.1007/s00259-022-05927-1
Jake Kendrick ₁ , Roslyn J Francis _{2,

3} , Ghulam Mubashar Hassan ₁ , Pejman Rowshanfarzad ₁ , Jeremy S L Ong ₄ , Martin A Ebert _{1,

5,

6}

Affiliation

目的

本研究旨在开发和评估基于深度学习的自动分割框架，用于全身 [ ⁶⁸ Ga]Ga-PSMA-11 PET/CT 图像中的转移性前列腺癌 (mPCa) 病变，以提取患者级别的预后生物标志物.

方法

从一组生化复发性 PCa 患者中检索了三百三十七张 [ ^{68 Ga]Ga-PSMA-11 PET/CT 图像。}提出了一种基于自配置 nnU-Net 框架的全 3D 卷积神经网络 (CNN)，并在这些扫描的子集上进行了训练，并为模型评估保留了独立的测试集。使用骰子相似系数 (DSC)、阳性预测值 (PPV) 和灵敏度评估体素级分割结果。计算灵敏度和 PPV 以评估病变水平检测；患者级别的分类结果通过准确性、PPV 和敏感性进行评估。全身生物标志物总损伤体积（TLV _auto）和总损伤吸收（TLU _auto) 是根据自动分割计算的，Kaplan-Meier 分析用于评估生物标志物与患者总生存期的关系。

结果

在患者层面，准确性、灵敏度和 PPV 均 > 90%，最佳指标是 PPV (97.2%)。病变水平的 PPV 和敏感性分别为 88.2% 和 73.0%。DSC 和 PPV 在测量的观察者间变异性内进行的体素水平测量（DSC，中值 = 50.7% 与第二个观察者 = 32%，p = 0.012；PPV，中值 = 64.9% 与第二个观察者 = 25.7%，p < 0.005）。_{TLV auto}和 TLU _auto的 Kaplan-Meier 分析显示它们与患者总生存期显着相关（均p < 0.005）。

结论

^{使用深度学习对全身 [ 68} Ga]Ga-PSMA-11 PET/CT 图像进行全自动评估显示出重大前景，可产生准确的扫描分类、观察者间变异性内的体素级分割，以及潜在的临床有用的预后生物标志物与患者的总生存期。

试用注册

本研究于 2015 年 6 月 11 日在澳大利亚新西兰临床试验注册中心 (ACTRN12615000608561) 注册。

"点击查看英文标题和摘要"

Fully automatic prognostic biomarker extraction from metastatic prostate lesion segmentations in whole-body [68Ga]Ga-PSMA-11 PET/CT images

Purpose

This study aimed to develop and assess an automated segmentation framework based on deep learning for metastatic prostate cancer (mPCa) lesions in whole-body [⁶⁸Ga]Ga-PSMA-11 PET/CT images for the purpose of extracting patient-level prognostic biomarkers.

Methods

Three hundred thirty-seven [⁶⁸Ga]Ga-PSMA-11 PET/CT images were retrieved from a cohort of biochemically recurrent PCa patients. A fully 3D convolutional neural network (CNN) is proposed which is based on the self-configuring nnU-Net framework, and was trained on a subset of these scans, with an independent test set reserved for model evaluation. Voxel-level segmentation results were assessed using the dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity. Sensitivity and PPV were calculated to assess lesion level detection; patient-level classification results were assessed by the accuracy, PPV, and sensitivity. Whole-body biomarkers total lesional volume (TLV_auto) and total lesional uptake (TLU_auto) were calculated from the automated segmentations, and Kaplan–Meier analysis was used to assess biomarker relationship with patient overall survival.

Results

At the patient level, the accuracy, sensitivity, and PPV were all > 90%, with the best metric being the PPV (97.2%). PPV and sensitivity at the lesion level were 88.2% and 73.0%, respectively. DSC and PPV measured at the voxel level performed within measured inter-observer variability (DSC, median = 50.7% vs. second observer = 32%, p = 0.012; PPV, median = 64.9% vs. second observer = 25.7%, p < 0.005). Kaplan–Meier analysis of TLV_auto and TLU_auto showed they were significantly associated with patient overall survival (both p < 0.005).

Conclusion

The fully automated assessment of whole-body [⁶⁸Ga]Ga-PSMA-11 PET/CT images using deep learning shows significant promise, yielding accurate scan classification, voxel-level segmentations within inter-observer variability, and potentially clinically useful prognostic biomarkers associated with patient overall survival.