Objectives

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.

Methods

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 ${\mathcal{L}}_2$ -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.

Results

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.

Conclusions

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.

中文翻译：

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通过长短期关系正则化预测术后非小细胞肺癌预后。

目标

肺癌是全球癌症死亡的主要原因。肺癌的预后在优化患者治疗的临床决策过程中起着至关重要的作用。大多数现有的数据驱动的预后预测模型探索特定时间间隔内患者特征与结果之间的关系。虽然有价值，但他们忽略了长期和短期预测之间的关系，因此可能会限制预测性能。

方法

在本研究中，我们为术后 NSCLC 患者提出了一种新的预后预测方法。具体来说，我们通过长短期关系正则化利用长期和短期预测之间的关系来制定学习目标函数。正则化项由两部分组成，即由患者结果衡量的预后之间的相似性和${\mathcal{升}}_2$- 相应预测权重向量之间的范数。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，以促进临床风险的估计。

结果

我们在包含 2006 年至 2015 年随访超过 5 年的 693 名连续术后 NSCLC 患者的临床数据集上评估所提出的模型。我们的最佳模型在 1 年、3 年和 0.746 5 年生存预测，1 年、3 年和 5 年复发预测的 AUC 分别为 0.696、0.724 和 0.736。实验结果表明，与基准模型相比，我们提出的模型在提高 1 年预后预测性能方面的效率。通过与没有长短期关系正则化的模型相比，所提出的模型为长期和短期预后提取了更一致的关键风险因素，并且在临床医生的审查下包含较少的不合理风险因素。

结论

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测之间的关系。并且该模型所识别的危险因素具有进一步预测非小细胞肺癌患者术后预后的潜力。