LASIGE, Department of Informatics, Faculty of Sciences, University of Lisbon, Lisbon, Portugal.
Champalimaud Foundation, Centre for the Unknown, Lisbon, Portugal.
JCO Clin Cancer Inform. 2024 Sep;8:e2300180. doi: 10.1200/CCI.23.00180.
Emerging evidence suggests that the use of artificial intelligence can assist in the timely detection and optimization of therapeutic approach in patients with prostate cancer. The conventional perspective on radiomics encompassing segmentation and the extraction of radiomic features considers it as an independent and sequential process. However, it is not necessary to adhere to this viewpoint. In this study, we show that besides generating masks from which radiomic features can be extracted, prostate segmentation and reconstruction models provide valuable information in their feature space, which can improve the quality of radiomic signatures models for disease aggressiveness classification.
We perform 2,244 experiments with deep learning features extracted from 13 different models trained using different anatomic zones and characterize how modeling decisions, such as deep feature aggregation and dimensionality reduction, affect performance.
While models using deep features from full gland and radiomic features consistently lead to improved disease aggressiveness prediction performance, others are detrimental. Our results suggest that the use of deep features can be beneficial, but an appropriate and comprehensive assessment is necessary to ensure that their inclusion does not harm predictive performance.
The study findings reveal that incorporating deep features derived from autoencoder models trained to reconstruct the full prostate gland (both zonal models show worse performance than radiomics only models), combined with radiomic features, often lead to a statistically significant increase in model performance for disease aggressiveness classification. Additionally, the results also demonstrate that the choice of feature selection is key to achieving good performance, with principal component analysis (PCA) and PCA + relief being the best approaches and that there is no clear difference between the three proposed latent representation extraction techniques.
新出现的证据表明，人工智能的使用可以帮助及时检测和优化前列腺癌患者的治疗方法。传统的放射组学观点包括分割和提取放射组学特征，认为它是一个独立的、顺序的过程。然而，没有必要坚持这种观点。在这项研究中，我们表明，除了从生成的掩模中提取放射组学特征之外，前列腺分割和重建模型在其特征空间中提供了有价值的信息，这可以提高放射组学特征模型的质量，从而更好地对疾病侵袭性进行分类。
我们进行了 2244 次实验，从 13 种不同的模型中提取深度学习特征，这些模型使用不同的解剖区域进行训练，并对建模决策（如深度特征聚合和降维）如何影响性能进行了特征刻画。
虽然使用全腺体的深度学习特征和放射组学特征的模型始终能提高疾病侵袭性预测的性能，但其他模型则不然。我们的结果表明，使用深度特征可能是有益的，但需要进行适当和全面的评估，以确保它们的加入不会损害预测性能。
研究结果表明，将从自动编码器模型中提取的深度特征（这些模型旨在重建整个前列腺，无论是区域模型还是全腺体模型，其表现均逊于仅使用放射组学特征的模型）与放射组学特征结合使用，通常会导致疾病侵袭性分类模型的性能在统计学上显著提高。此外，研究结果还表明，特征选择的选择是实现良好性能的关键，主成分分析（PCA）和 PCA+relief 是最佳方法，并且这三种提出的潜在表示提取技术之间没有明显区别。
