利用PET/MR成像预测晚期黑色素瘤患者免疫治疗的特征反应

Annika Liebgott, S. Gatidis, V. Vu, Tobias Haueise, K. Nikolaou, Bin Yang
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引用次数: 1

摘要

用免疫疗法治疗恶性黑色素瘤是一种治疗晚期疾病的有希望的方法。然而,这种疗法会引起严重的副作用,并不是每个病人都对它有反应。这意味着,关键的时间可能会浪费在无效的治疗上。因此,评估可能的治疗反应是一个重要的研究问题。本研究中提出的研究重点是研究医学成像和机器学习解决这一任务的潜力。为此,我们从多模态图像中提取图像特征,并训练分类器来区分无反应患者和有反应患者。
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Feature-based Response Prediction to Immunotherapy of late-stage Melanoma Patients Using PET/MR Imaging
The treatment of malignant melanoma with immunotherapy is a promising approach to treat advanced stages of the disease. However, the treatment can cause serious side effects and not every patient responds to it. This means, crucial time may be wasted on an ineffective treatment. Assessment of the possible therapy response is hence an important research issue. The research presented in this study focuses on the investigation of the potential of medical imaging and machine learning to solve this task. To this end, we extracted image features from multi-modal images and trained a classifier to differentiate non-responsive patients from responsive ones.
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