背景:淋巴结转移(LNM)是一种预后生物标志物,影响结直肠癌(CRC)的治疗选择。当前的评估方法不足以估计CRC中的LNM。H&E图像包含大量的病理信息,胶原也影响肿瘤细胞的生物学行为。因此,本研究的目的是研究肿瘤微环境中完全定量的病理组学-胶原标记(PCS)是否可用于预测LNM.
方法:将组织学证实的I-III期CRC患者接受根治性手术纳入训练队列(n=329),内部验证队列(n=329),和外部验证队列(n=315)。从标本的数字H&E图像和多光子图像中提取完全定量的病理组学特征和胶原蛋白特征,分别。利用LASSO回归来开发PCS。然后,构建PCS列线图,纳入PCS和临床病理预测因子,用于评估训练队列中的LNM.通过校准评估PCS列线图的性能,歧视,和临床有用性。此外,在内部和外部验证队列中测试了PCS列线图.
结果:通过LASSO回归,PCS是基于11种病理组学和9种胶原蛋白特征开发的。在三个队列中,PCS和LNM之间存在显着关联(P<0.001)。然后,基于PCS的PCS列线图,术前CEA水平,CT淋巴结肿大,静脉栓塞和/或淋巴管浸润和/或神经周浸润(VELIPI),在三个队列中,pT分期的AUROC分别为0.939、0.895和0.893。校准曲线确定了列线图预测结果与实际结果之间的良好一致性。决策曲线分析表明,PCS列线图在临床上有用。此外,PCS仍然是站号的LNM的独立预测因子。1、2和3。PCS列线图显示训练队列的AUROC为0.849-0.939,内部验证队列为0.837-0.902,三个节点站的外部验证队列为0.851-0.895。
结论:这项研究表明,PCS整合病理组学和胶原特征与LNM显著相关,PCS列线图有可能成为预测CRC患者个体LNM的有用工具。
Lymph node metastasis (LNM) is a prognostic biomarker and affects therapeutic selection in colorectal cancer (CRC). Current evaluation methods are not adequate for estimating LNM in CRC. H&E images contain much pathological information, and collagen also affects the biological behavior of tumor cells. Hence, the objective of the
study is to investigate whether a fully quantitative
pathomics-collagen signature (PCS) in the tumor microenvironment can be used to predict LNM.
Patients with histologically confirmed stage I-III CRC who underwent radical surgery were included in the training cohort (n = 329), the internal validation cohort (n = 329), and the external validation cohort (n = 315). Fully quantitative
pathomics features and collagen features were extracted from digital H&E images and multiphoton images of specimens, respectively. LASSO regression was utilized to develop the PCS. Then, a PCS-nomogram was constructed incorporating the PCS and clinicopathological predictors for estimating LNM in the training cohort. The performance of the PCS-nomogram was evaluated via calibration, discrimination, and clinical usefulness. Furthermore, the PCS-nomogram was tested in internal and external validation cohorts.
By LASSO regression, the PCS was developed based on 11
pathomics and 9 collagen features. A significant association was found between the PCS and LNM in the three cohorts (P < 0.001). Then, the PCS-nomogram based on PCS, preoperative CEA level, lymphadenectasis on CT, venous emboli and/or lymphatic invasion and/or perineural invasion (VELIPI), and pT stage achieved AUROCs of 0.939, 0.895, and 0.893 in the three cohorts. The calibration curves identified good agreement between the nomogram-predicted and actual outcomes. Decision curve analysis indicated that the PCS-nomogram was clinically useful. Moreover, the PCS was still an independent predictor of LNM at station Nos. 1, 2, and 3. The PCS nomogram displayed AUROCs of 0.849-0.939 for the training cohort, 0.837-0.902 for the internal validation cohort, and 0.851-0.895 for the external validation cohorts in the three nodal stations.
This
study proposed that PCS integrating
pathomics and collagen features was significantly associated with LNM, and the PCS-nomogram has the potential to be a useful tool for predicting individual LNM in CRC patients.