Learning-based Cancer Treatment Outcome Prognosis using Multimodal Biomarkers.
Learning-based Cancer Treatment Outcome Prognosis using Multimodal Biomarkers.
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DOI：
10.1109/trpms.2021.3104297
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发表时间：
2022-03
影响因子：
4.4
通讯作者：
Li, Hua
中科院分区：
文献类型：
--
作者：
Saad, Maliazurina;He, Shenghua;Thorstad, Wade;Gay, Hiram;Barnett, Daniel;Zhao, Yujie;Ruan, Su;Wang, Xiaowei;Li, Hua
关键词：
在治疗早期预测肿瘤是否可能有反应是一项困难但重要的任务，以支持临床决策。研究表明，多模式生物标志物可以提供补充信息，并导致比单峰生物标志物更准确的治疗结果预后。然而，多模态数据的异质性和不完整性可能会影响预测的准确性。小规模和不平衡的数据集也为训练设计的预后模型带来了额外的挑战。在这项研究中，提出了一个采用多模式生物标志物进行癌症治疗结果预测的模块化框架。它包括合成数据生成、深度特征提取、多模态特征融合和分类四个模块，以解决上述挑战。设计的框架的可行性和优势，通过一个例子研究，其中的目标是分层口咽鳞状细胞癌（OPSCC）患者的治疗失败的低风险和高风险的正电子发射断层扫描（PET）图像数据和microRNA（miRNA）的生物标志物。上级预测性能和与其他方法的比较表明，所提出的框架的效率和它的能力，使无缝集成，验证和比较的各种算法在框架的每个模块。最后讨论了本文的局限性和未来的工作。
Predicting early in treatment whether a tumor is likely to be responsive is a difficult yet important task to support clinical decision-making. Studies have shown that multimodal biomarkers could provide complementary information and lead to more accurate treatment outcome prognosis than unimodal biomarkers. However, the prognosis accuracy could be affected by multimodal data heterogeneity and incompleteness. The small-sized and imbalance datasets also bring additional challenges for training a designed prognosis model. In this study, a modular framework employing multimodal biomarkers for cancer treatment outcome prediction was proposed. It includes four modules of synthetic data generation, deep feature extraction, multimodal feature fusion, and classification to address the challenges described above. The feasibility and advantages of the designed framework were demonstrated through an example study, in which the goal was to stratify oropharyngeal squamous cell carcinoma (OPSCC) patients with low- and high-risks of treatment failures by use of positron emission tomography (PET) image data and microRNA (miRNA) biomarkers. The superior prognosis performance and the comparison with other methods demonstrated the efficiency of the proposed framework and its ability of enabling seamless integration, validation and comparison of various algorithms in each module of the framework. The limitation and future work was discussed as well.
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