Roll Wolfgang, Plagwitz Lucas, Ventura David, Masthoff Max, Backhaus Clemens, Varghese Julian, Rahbar Kambiz, Schindler Philipp
Department of Nuclear Medicine, University Hospital Münster, Münster, Germany.
West German Cancer Centre, Münster, Germany.
Eur J Nucl Med Mol Imaging. 2025 Jun 28. doi: 10.1007/s00259-025-07416-7.
This retrospective study aims to develop a deep learning-based approach to whole-body CT segmentation out of standard PSMA-PET-CT to assess body composition in metastatic castration resistant prostate cancer (mCRPC) patients prior to [Lu]Lu-PSMA radioligand therapy (RLT). Our goal is to go beyond standard PSMA-PET-based pretherapeutic assessment and identify additional body composition metrics out of the CT-component, with potential prognostic value.
We used a deep learning segmentation model to perform fully automated segmentation of different tissue compartments, including visceral- (VAT), subcutaneous- (SAT), intra/intermuscular- adipose tissue (IMAT) from [ Ga]Ga-PSMA-PET-CT scans of n = 86 prostate cancer patients before RLT. The proportions of different adipose tissue compartments to total adipose tissue (TAT) assessed on a 3D CT-volume of the abdomen or on a 2D single slice basis (centered at third lumbal vertebra (L3)) were compared for their prognostic value. First, univariate and multivariate Cox proportional hazards regression analyses were performed. Subsequently, the subjects were dichotomized at the median tissue composition, and these subgroups were evaluated by Kaplan-Meier analysis with the log-rank test.
The automated segmentation model was useful for delineating different adipose tissue compartments and skeletal muscle across different patient anatomies. Analyses revealed significant correlations between lower SAT and higher IMAT ratios and poorer therapeutic outcomes in Cox regression analysis (SAT/TAT: p = 0.038; IMAT/TAT: p < 0.001) in the 3D model. In the single slice approach only IMAT/SAT was significantly associated with survival in Cox regression analysis (p < 0.001; SAT/TAT: p > 0.05). IMAT ratio remained an independent predictor of survival in multivariate analysis when including PSMA-PET and blood-based prognostic factors.
In this proof-of-principle study the implementation of a deep learning-based whole-body analysis provides a robust and detailed CT-based assessment of body composition in mCRPC patients undergoing RLT. Potential prognostic parameters have to be corroborated in larger prospective datasets.
这项回顾性研究旨在开发一种基于深度学习的方法，用于从标准的PSMA-PET-CT中进行全身CT分割，以评估转移性去势抵抗性前列腺癌（mCRPC）患者在[Lu]Lu-PSMA放射性配体治疗（RLT）之前的身体成分。我们的目标是超越基于标准PSMA-PET的治疗前评估，并从CT组件中识别出具有潜在预后价值的其他身体成分指标。
我们使用深度学习分割模型对n = 86例前列腺癌患者在RLT之前的[Ga]Ga-PSMA-PET-CT扫描中的不同组织区域进行全自动分割，包括内脏脂肪组织（VAT）、皮下脂肪组织（SAT）、肌内/肌间脂肪组织（IMAT）。比较了在腹部的3D CT体积或基于二维单一层面（以第三腰椎（L3）为中心）评估的不同脂肪组织区域占总脂肪组织（TAT）的比例的预后价值。首先，进行单变量和多变量Cox比例风险回归分析。随后，将受试者按组织成分中位数进行二分法划分，并通过Kaplan-Meier分析和对数秩检验对这些亚组进行评估。
自动分割模型有助于在不同患者解剖结构中描绘不同的脂肪组织区域和骨骼肌。分析显示，在3D模型的Cox回归分析中，较低的SAT比例和较高的IMAT比例与较差的治疗结果之间存在显著相关性（SAT/TAT：p = 0.038；IMAT/TAT：p < 0.）。在单一层面方法中，只有IMAT/SAT在Cox回归分析中与生存率显著相关（p < 0.001；SAT/TAT：p > 0.05）。当纳入PSMA-PET和基于血液的预后因素时，IMAT比例在多变量分析中仍然是生存的独立预测因子。
在这项原理验证研究中，基于深度学习的全身分析的实施为接受RLT的mCRPC患者提供了一种强大且详细的基于CT的身体成分评估。潜在的预后参数必须在更大的前瞻性数据集中得到证实。
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