Voutouri Chrysovalantis, Englezos Demetris, Zamboglou Constantinos, Strouthos Iosif, Papanastasiou Giorgos, Stylianopoulos Triantafyllos
Cancer Biophysics Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus.
Department of Radiation Oncology, University of Freiburg - Medical Center, Freiburg, Germany.
Commun Med (Lond). 2024 Oct 17;4(1):203. doi: 10.1038/s43856-024-00634-4.
In the era of personalized cancer treatment, understanding the intrinsic heterogeneity of tumors is crucial. Despite some patients responding favorably to a particular treatment, others may not benefit, leading to the varied efficacy observed in standard therapies. This study focuses on the prediction of tumor response to chemo-immunotherapy, exploring the potential of tumor mechanics and medical imaging as predictive biomarkers. We have extensively studied "desmoplastic" tumors, characterized by a dense and very stiff stroma, which presents a substantial challenge for treatment. The increased stiffness of such tumors can be restored through pharmacological intervention with mechanotherapeutics.
We developed a deep learning methodology based on shear wave elastography (SWE) images, which involved a convolutional neural network (CNN) model enhanced with attention modules. The model was developed and evaluated as a predictive biomarker in the setting of detecting responsive, stable, and non-responsive tumors to chemotherapy, immunotherapy, or the combination, following mechanotherapeutics administration. A dataset of 1365 SWE images was obtained from 630 tumors from our previous experiments and used to train and successfully evaluate our methodology. SWE in combination with deep learning models, has demonstrated promising results in disease diagnosis and tumor classification but their potential for predicting tumor response prior to therapy is not yet fully realized.
We present strong evidence that integrating SWE-derived biomarkers with automatic tumor segmentation algorithms enables accurate tumor detection and prediction of therapeutic outcomes.
This approach can enhance personalized cancer treatment by providing non-invasive, reliable predictions of therapeutic outcomes.
在个性化癌症治疗时代，了解肿瘤的内在异质性至关重要。尽管一些患者对特定治疗反应良好，但其他患者可能无获益，这导致在标准疗法中观察到疗效各异。本研究聚焦于肿瘤对化疗免疫疗法反应的预测，探索肿瘤力学和医学成像作为预测生物标志物的潜力。我们广泛研究了“促结缔组织增生性”肿瘤，其特征为致密且非常坚硬的基质，这给治疗带来了巨大挑战。此类肿瘤增加的硬度可通过机械疗法的药物干预得以恢复。
我们基于剪切波弹性成像（SWE）图像开发了一种深度学习方法，其中涉及一个通过注意力模块增强的卷积神经网络（CNN）模型。该模型在机械疗法给药后，针对检测对化疗、免疫疗法或联合疗法有反应、稳定和无反应的肿瘤的背景下，作为预测生物标志物进行开发和评估。从我们之前实验的630个肿瘤中获得了一个包含1365张SWE图像的数据集，并用于训练和成功评估我们的方法。SWE与深度学习模型相结合，在疾病诊断和肿瘤分类方面已显示出有前景的结果，但其在治疗前预测肿瘤反应的潜力尚未完全实现。
我们提供了强有力的证据，表明将源自SWE的生物标志物与自动肿瘤分割算法相结合能够实现准确的肿瘤检测和治疗结果预测。
这种方法可通过提供无创、可靠的治疗结果预测来加强个性化癌症治疗。
