PDF(Pubmed)
Abstract：
UNASSIGNED: Lymphovascular invasion (LVI) is an invasive biologic behavior that affects the treatment and prognosis of patients with early-stage lung cancer. This study aimed to identify LVI diagnostic and prognostic biomarkers using deep learning-powered 3D segmentation with artificial intelligence (AI) technology.
UNASSIGNED: Between January 2016 and October 2021, we enrolled patients with clinical T1 stage non-small cell lung cancer (NSCLC). We used commercially available AI software (Dr. Wise system, Deep-wise Corporation, China) to extract quantitative AI features of pulmonary nodules automatically. Dimensionality reduction was achieved through least absolute shrinkage and selection operator regression; subsequently, the AI score was calculated.Then, the univariate and multivariate analysis was further performed on the AI score and patient baseline parameters.
UNASSIGNED: Among 175 enrolled patients, 22 tested positive for LVI at pathology review. Based on the multivariate logistic regression results, we incorporated the AI score, carcinoembryonic antigen, spiculation, and pleural indentation into the nomogram for predicting LVI. The nomogram showed good discrimination (C-index = 0.915 [95% confidence interval: 0.89-0.94]); moreover, calibration of the nomogram revealed good predictive ability (Brier score = 0.072). Kaplan-Meier analysis revealed that relapse-free survival and overall survival were significantly higher among patients with a low-risk AI score and without LVI than those among patients with a high-risk AI score (p = 0.008 and p = 0.002, respectively) and with LVI (p = 0.013 and p = 0.008, respectively).
UNASSIGNED: Our findings indicate that a high-risk AI score is a diagnostic biomarker for LVI in patients with clinical T1 stage NSCLC; accordingly, it can serve as a prognostic biomarker for these patients.
UNASSIGNED: Between January 2016 and October 2021, we enrolled patients with clinical T1 stage non-small cell lung cancer (NSCLC). We used commercially available AI software (Dr. Wise system, Deep-wise Corporation, China) to extract quantitative AI features of pulmonary nodules automatically. Dimensionality reduction was achieved through least absolute shrinkage and selection operator regression; subsequently, the AI score was calculated.Then, the univariate and multivariate analysis was further performed on the AI score and patient baseline parameters.
UNASSIGNED: Among 175 enrolled patients, 22 tested positive for LVI at pathology review. Based on the multivariate logistic regression results, we incorporated the AI score, carcinoembryonic antigen, spiculation, and pleural indentation into the nomogram for predicting LVI. The nomogram showed good discrimination (C-index = 0.915 [95% confidence interval: 0.89-0.94]); moreover, calibration of the nomogram revealed good predictive ability (Brier score = 0.072). Kaplan-Meier analysis revealed that relapse-free survival and overall survival were significantly higher among patients with a low-risk AI score and without LVI than those among patients with a high-risk AI score (p = 0.008 and p = 0.002, respectively) and with LVI (p = 0.013 and p = 0.008, respectively).
UNASSIGNED: Our findings indicate that a high-risk AI score is a diagnostic biomarker for LVI in patients with clinical T1 stage NSCLC; accordingly, it can serve as a prognostic biomarker for these patients.
摘要：
■淋巴管浸润（LVI）是一种侵袭性生物学行为，会影响早期肺癌患者的治疗和预后。本研究旨在通过人工智能（AI）技术，使用深度学习驱动的3D分割来识别LVI诊断和预后生物标志物。
■在2016年1月至2021年10月之间，我们招募了临床T1期非小细胞肺癌（NSCLC）患者。我们使用了市售的人工智能软件(怀斯博士系统，深度智慧公司，中国）自动提取肺结节的定量AI特征。降维是通过最小绝对收缩和选择算子回归实现的；随后，计算AI评分。然后，我们进一步对AI评分和患者基线参数进行了单变量和多变量分析.
■在175名登记患者中，22在病理学检查时LVI检测为阳性。根据多元逻辑回归结果，我们加入了AI评分，癌胚抗原,刺突,和胸膜凹陷进入列线图以预测LVI。列线图显示出良好的辨别力（C指数=0.915[95％置信区间：0.89-0.94]）；此外，列线图的校准显示出良好的预测能力(Brier评分=0.072)。Kaplan-Meier分析显示，低风险AI评分和无LVI的患者的无复发生存率和总生存率明显高于高风险AI评分（分别为p=0.008和p=0.002）和有LVI的患者（分别为p=0.013和p=0.008）。
■我们的研究结果表明，在临床T1期NSCLC患者中，高风险AI评分是LVI的诊断生物标志物；因此，它可以作为这些患者的预后生物标志物。
■在2016年1月至2021年10月之间，我们招募了临床T1期非小细胞肺癌（NSCLC）患者。我们使用了市售的人工智能软件(怀斯博士系统，深度智慧公司，中国）自动提取肺结节的定量AI特征。降维是通过最小绝对收缩和选择算子回归实现的；随后，计算AI评分。然后，我们进一步对AI评分和患者基线参数进行了单变量和多变量分析.
■在175名登记患者中，22在病理学检查时LVI检测为阳性。根据多元逻辑回归结果，我们加入了AI评分，癌胚抗原,刺突,和胸膜凹陷进入列线图以预测LVI。列线图显示出良好的辨别力（C指数=0.915[95％置信区间：0.89-0.94]）；此外，列线图的校准显示出良好的预测能力(Brier评分=0.072)。Kaplan-Meier分析显示，低风险AI评分和无LVI的患者的无复发生存率和总生存率明显高于高风险AI评分（分别为p=0.008和p=0.002）和有LVI的患者（分别为p=0.013和p=0.008）。
■我们的研究结果表明，在临床T1期NSCLC患者中，高风险AI评分是LVI的诊断生物标志物；因此，它可以作为这些患者的预后生物标志物。
