Ho Wai Lone J, Fetisov Nikolai, Hall Lawrence O, Goldgof Dmitry, Schabath Matthew B
University of South Florida, Morsani College of Medicine, 560 Channelside Dr, Tampa, FL, USA 33602.
Dept. of Computer Science and Engineering, University of South Florida, Tampa, FL, USA 33620.
Proc SPIE Int Soc Opt Eng. 2024 Feb;12926. doi: 10.1117/12.3006091. Epub 2024 Apr 2.
Among patients with early-stage non-small cell lung cancer (NSCLC) undergoing surgical resection, identifying who is at high-risk of recurrence can inform clinical guidelines with respect to more aggressive follow-up and/or adjuvant therapy. While predicting recurrence based on pre-surgical resection data is ideal, clinically important pathological features are only evaluated postoperatively. Therefore, we developed two supervised classification models to assess the importance of pre- and post-surgical features for predicting 5-year recurrence. An integrated dataset was generated by combining clinical covariates and radiomic features calculated from pre-surgical computed tomography images. After removing correlated radiomic features, the SHapley Additive exPlanations (SHAP) method was used to measure feature importance and select relevant features. Binary classification was performed using a Support Vector Machine, followed by a feature ablation study assessing the impact of radiomic and clinical features. We demonstrate that the post-surgical model significantly outperforms the pre-surgical model in predicting lung cancer recurrence, with tumor pathological features and peritumoral radiomic features contributing significantly to the model's performance.
在接受手术切除的早期非小细胞肺癌（NSCLC）患者中，识别出复发高危患者可为更积极的随访和/或辅助治疗的临床指南提供依据。虽然基于术前切除数据预测复发是理想的，但具有临床重要性的病理特征仅在术后进行评估。因此，我们开发了两种监督分类模型，以评估术前和术后特征对预测5年复发的重要性。通过结合临床协变量和从术前计算机断层扫描图像计算出的放射组学特征，生成了一个综合数据集。在去除相关的放射组学特征后，使用SHapley加法解释（SHAP）方法来衡量特征重要性并选择相关特征。使用支持向量机进行二元分类，随后进行特征消融研究，评估放射组学和临床特征的影响。我们证明，术后模型在预测肺癌复发方面明显优于术前模型，肿瘤病理特征和肿瘤周围放射组学特征对模型性能有显著贡献。
