College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou 310027, China; Key Laboratory for Biomedical Engineering, Ministry of Education, China.
Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute, Beijing 100142, China.
Artif Intell Med. 2020 Jul;107:101921. doi: 10.1016/j.artmed.2020.101921. Epub 2020 Jun 30.
Lung cancer is the leading cause of cancer death worldwide. Prognosis of lung cancer plays a crucial role in the clinical decision-making process to optimize the treatment for patients. Most of the existing data-driven prognostic prediction models explore the relations between patient's characteristics and outcomes at a specific time interval. Although valuable, they neglect the relations between long-term and short-term prognoses and thus may limit the prediction performance.
In this study, we present a novel prognostic prediction approach for postoperative NSCLC patients. Specifically, we formulate the learning objective function by exploiting the relations between long-term and short-term prognoses via a long short-term relational regularization. The regularization term is composed of two parts, i.e., the similarities between prognoses measured by patients' outcomes and the L -norms between the corresponding prognoses' weight vectors. Based on this regularization, the proposed method can extract critical risk factors that comprehensively consider the long-term and short-term prognoses to facilitate the estimation of clinical risks.
We evaluate the proposed model on a clinical dataset containing 693 consecutive postoperative NSCLC patients with more than 5-year follow-up from 2006 to 2015. Our best models achieve 0.743, 0.709, and 0.746 AUCs for 1-year, 3-year, and 5-year survival prediction, 0.696, 0.724, and 0.736 AUCs for 1-year, 3-year, and 5-year recurrence prediction, respectively. The experimental results show the efficiency of our proposed model in improving the performances on 1-year prognostic prediction in comparison with benchmark models. By comparing with the model without the long short-term relational regularization, the proposed model extracts more consistent critical risk factors for both long-term and short-term prognoses and contains fewer unreasonable risk factors under the clinician's review.
We conclude that the proposed model can effectively exploit the relations between long-term and short-term prognoses. And the risk factors recognized by the proposed model have the potentials for further prognostic prediction of postoperative non-small cell lung cancer patients.
肺癌是全球癌症死亡的主要原因。肺癌的预后在为患者优化治疗的临床决策过程中起着至关重要的作用。大多数现有的基于数据的预后预测模型都在探索患者特征与特定时间间隔内结果之间的关系。虽然这些模型很有价值，但它们忽略了长期和短期预后之间的关系，因此可能会限制预测性能。
在本研究中，我们提出了一种新的用于术后非小细胞肺癌患者的预后预测方法。具体来说，我们通过利用长期和短期预后之间的关系，通过长短期关系正则化来构建学习目标函数。正则化项由两部分组成，即通过患者的结果来衡量预后的相似性，以及相应预后权重向量的 L-范数之间的差异。基于这种正则化，所提出的方法可以提取综合考虑长期和短期预后的关键风险因素，从而便于估计临床风险。
我们在一个包含 2006 年至 2015 年间 693 名连续接受术后非小细胞肺癌治疗且随访时间超过 5 年的临床数据集上评估了所提出的模型。我们的最佳模型在 1 年、3 年和 5 年生存预测中分别达到了 0.743、0.709 和 0.746 的 AUC，在 1 年、3 年和 5 年复发预测中分别达到了 0.696、0.724 和 0.736 的 AUC。实验结果表明，与基准模型相比，所提出的模型在提高 1 年预后预测的性能方面具有效率。通过与没有长短期关系正则化的模型进行比较，所提出的模型可以为长期和短期预后提取更一致的关键风险因素，并且在临床医生的审查下，所包含的不合理风险因素更少。
我们得出结论，所提出的模型可以有效地利用长期和短期预后之间的关系。并且所提出的模型识别的风险因素具有进一步预测术后非小细胞肺癌患者预后的潜力。
