Nogueira Mariana A, Abreu Pedro H, Martins Pedro, Machado Penousal, Duarte Hugo, Santos João
CISUC - Department of Informatics Engineering - University of Coimbra, - Pólo II Pinhal de Marrocos, Coimbra, 3030-290, Portugal.
IPO-Porto Research Centre (CI-IPOP), Rua Dr. António Bernardino de Almeida, Porto, 4200-072, Portugal.
BMC Med Imaging. 2017 Feb 13;17(1):13. doi: 10.1186/s12880-017-0181-0.
Positron Emission Tomography - Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored.
In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT.
The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced.
After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response-to-treatment classes.
正电子发射断层扫描-计算机断层扫描（PET/CT）成像为评估多种肿瘤疾病的治疗反应奠定了基础。在实际操作中，此类评估由专家手动进行，过程相当复杂且耗时。虽已提出评估方法，但可靠性存疑。利用病变治疗前后的图像描述符进行治疗反应评估仍是有待探索的领域。
在本项目中，基于从PET/CT提取的图像特征，采用人工神经网络方法自动评估神经内分泌肿瘤和霍奇金淋巴瘤患者的治疗反应。
结果表明，所考虑的特征集能够实现非常高的分类性能，尤其是在数据得到适当平衡时。
在生成合成数据并基于主成分分析将维度降至仅两个成分后，LVQNN在4种治疗反应类别上的分类准确率分别为100%、100%、96.3%和100%。
