School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Med Phys. 2021 May;48(5):2374-2385. doi: 10.1002/mp.14767. Epub 2021 Mar 30.
The present study assessed the predictive value of peritumoral regions on three tumor tasks, and further explored the influence of peritumors with different sizes.
We retrospectively collected 333 samples of gastrointestinal stromal tumors from the Second Affiliated Hospital of Zhejiang University School of Medicine, and 183 samples of gastrointestinal stromal tumors from Tianjin Medical University Cancer Hospital. We also collected 211 samples of laryngeal carcinoma and 233 samples of nasopharyngeal carcinoma from the First Affiliated Hospital of Jinan University. The tasks of three tumor datasets were risk assessment (gastrointestinal stromal tumor), T3/T4 staging prediction (laryngeal carcinoma), and distant metastasis prediction (nasopharyngeal carcinoma), respectively. First, deep learning and radiomics were respectively used to construct peritumoral models, to study whether the peritumor had predictive value on three tumor datasets. Furthermore, we defined different sizes peritumors including fixed size (not considering tumor size) and adaptive size (according to average tumor radius) to explore the influence of peritumor of different sizes and types of tumors. Finally, we visualized the deep learning and radiomic models to observe the influence of the peritumor in three datasets.
The performance of intra-peritumors are better than intratumors alone in three datasets. Specifically, the comparisons of area under receiver operating characteristic curve in the testing set between intra-peritumoral and intratumoral models are: 0.908 vs 0.873 (P value: 0.037) in gastrointestinal stromal tumor datasets, 0.796 vs 0.756 (P value: 0.188) in laryngeal carcinoma datasets and 0.660 vs 0.579 (P value: 0.431) in nasopharyngeal carcinoma datasets. Furthermore, for gastrointestinal stromal tumor datasets, deep learning is more stable to learn peritumors with both fixed and adaptive size than radiomics. For laryngeal carcinoma datasets, the intra-peritumoral radiomic model could make model performance more balanced. For nasopharyngeal carcinoma datasets, radiomics is also more suitable for modeling peritumors than deep learning. The size of the peritumor is critical in this task, and only the performance of 1.5 mm-4.5 mm peritumors is stable.
Our results indicate that peritumors have additional predictive value in three tumor datasets through deep learning or radiomics. The definitions of the peritumoral region and artificial intelligence method also have great influence on the performance of the peritumor.
本研究评估了肿瘤周围区域在三个肿瘤任务中的预测价值，并进一步探讨了不同大小肿瘤周围区域的影响。
我们回顾性地收集了浙江大学医学院第二附属医院的 333 例胃肠道间质瘤样本和天津医科大学肿瘤医院的 183 例胃肠道间质瘤样本。我们还收集了暨南大学第一附属医院的 211 例喉癌样本和 233 例鼻咽癌样本。三个肿瘤数据集的任务分别是风险评估（胃肠道间质瘤）、T3/T4 分期预测（喉癌）和远处转移预测（鼻咽癌）。首先，分别使用深度学习和放射组学构建肿瘤周围模型，以研究肿瘤周围区域是否对三个肿瘤数据集具有预测价值。此外，我们定义了不同大小的肿瘤周围区域，包括固定大小（不考虑肿瘤大小）和自适应大小（根据平均肿瘤半径），以探讨不同大小和类型肿瘤的肿瘤周围区域的影响。最后，我们可视化了深度学习和放射组学模型，以观察肿瘤周围区域在三个数据集的影响。
在三个数据集，肿瘤内部的性能优于肿瘤本身。具体而言，在测试集中，肿瘤内部和肿瘤周围模型的受试者工作特征曲线下面积的比较结果为：胃肠道间质瘤数据集 0.908 比 0.873（P 值：0.037），喉癌数据集 0.796 比 0.756（P 值：0.188），鼻咽癌数据集 0.660 比 0.579（P 值：0.431）。此外，对于胃肠道间质瘤数据集，深度学习比放射组学更稳定地学习固定和自适应大小的肿瘤周围区域。对于喉癌数据集，肿瘤内部的放射组学模型可以使模型性能更加平衡。对于鼻咽癌数据集，放射组学比深度学习更适合建模肿瘤周围区域。肿瘤周围区域的大小在这个任务中是至关重要的，只有 1.5-4.5mm 的肿瘤周围区域的性能是稳定的。
我们的结果表明，通过深度学习或放射组学，肿瘤周围区域在三个肿瘤数据集中具有额外的预测价值。肿瘤周围区域的定义和人工智能方法对肿瘤周围区域的性能也有很大的影响。
