Guinea-Pérez Javier, Ceresi Alessandro, García Anaida Fernández, Galende Borja Arroyo, Belmonte-Hernández Alberto, Peluso Sara, Alvarez Federico
Universidad Politécnica de Madrid, Avenida Complutense, 30, Madrid, 28040, Spain.
Universidad Politécnica de Madrid, Avenida Complutense, 30, Madrid, 28040, Spain.
Comput Methods Programs Biomed. 2025 Nov;271:109019. doi: 10.1016/j.cmpb.2025.109019. Epub 2025 Aug 19.
F FDG PET/CT imaging is emerging as a tool for Multiple Myeloma (MM) evaluation. The goal of this study is to assess the most informative regions and features in MM patients using a fully automatic imaging-based pipeline by evaluating the performance of survival models with radiomics features for risk stratification.
A dataset of whole-body-F FDG PET/CT images from 227 patients was used. All images were first automatically segmented to find the spine and the remaining skeletal structures. Several masks were then generated using this segmentation, and radiomic features were extracted from both images applying these masks. The features were aggregated to create three datasets per mask: one per imaging type and a dataset combining both. Four survival model families were trained on these datasets to estimate progression-free survival. In total, 128 model × image-origin × mask configurations were tuned to maximize mean c-index via five-fold cross-validation. The best performing model that utilized both imaging modalities was then analyzed to assess the prognostic power of its features.
To achieve the best ranking performance, the best configuration was a DeepSurv model trained solely with features from the PET image extracted using a mask created by using the spine and the region around and inside it. It achieved a mean c-index of 0.657 and IBS of 0.197 using 5-fold cross-validation. Image-texture features were the most relevant for risk prediction, with higher heterogeneity correlating with increased risk of an event. The pipeline is available in the project's GitLab repository.
The best performing models were random survival forests and DeepSurv, which outperformed Cox-based linear models. Models with both CT and PET provided better results than with just one, on average, however the most informative imaging technique was PET, with more features present when combining both images and the overall best performing model. When building the masks, the inclusion of the paramedullary region was important, although adding the whole skeleton did not improve results. The most relevant features for survival analysis were textural features, with image heterogeneity correlating with a higher risk of disease progression.
¹⁸F FDG PET/CT成像正逐渐成为评估多发性骨髓瘤（MM）的一种工具。本研究的目的是通过使用基于影像的全自动流程，评估MM患者中最具信息量的区域和特征，方法是通过评估具有放射组学特征的生存模型用于风险分层的性能。
使用了一个包含227例患者的全身¹⁸F FDG PET/CT图像的数据集。所有图像首先进行自动分割以找到脊柱和其余骨骼结构。然后使用该分割生成多个掩码，并应用这些掩码从两幅图像中提取放射组学特征。将这些特征汇总以针对每个掩码创建三个数据集：每种成像类型一个，以及一个组合两者的数据集。在这些数据集上训练了四个生存模型家族以估计无进展生存期。总共对128种模型×图像来源×掩码配置进行了调整，以通过五折交叉验证最大化平均c指数。然后分析表现最佳的同时利用两种成像方式的模型，以评估其特征的预后能力。
为了获得最佳排名性能，最佳配置是仅使用从PET图像中提取的特征训练的DeepSurv模型，该特征是使用由脊柱及其周围和内部区域创建的掩码提取的。使用五折交叉验证，其平均c指数为0.657，综合Brier分数（IBS）为0.197。图像纹理特征与风险预测最相关，更高的异质性与事件风险增加相关。该流程可在项目的GitLab存储库中获取。
表现最佳的模型是随机生存森林和DeepSurv，它们优于基于Cox的线性模型。平均而言，同时具有CT和PET的模型比仅使用一种的模型提供了更好的结果，然而最具信息量的成像技术是PET，在组合两幅图像时存在更多特征，并且是整体表现最佳的模型。在构建掩码时，包含髓周区域很重要，尽管添加整个骨骼并没有改善结果。生存分析中最相关的特征是纹理特征图像异质性与疾病进展的较高风险相关。
