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Development and validation of a collagen signature-based nomogram for preoperatively predicting lymph node metastasis and prognosis in colorectal cancer.

比例危险模型 淋巴血管侵犯 接收机工作特性 癌症 危险系数 淋巴结转移
作者
Meiting Fu,Dexin Chen,Fuzheng Luo,Guangxing Wang,Shuoyu Xu,Yadong Wang,Caihong Sun,Xueqin Xu,Aimin Li,Shuangmu Zhuo,Side Liu,Jun Yan
出处
期刊:Annals of Translational Medicine [AME Publishing Company]
卷期号:9 (8): 651-651 被引量:2
标识
DOI:10.21037/atm-20-7565
摘要

Background Current preoperative evaluation approaches cannot provide adequate information for the prediction of lymph node (LN) metastasis in colorectal cancer (CRC). Collagen alterations in the tumor microenvironment affect the progression of tumor cells. To more accurately assess the LN status of CRC preoperatively, we developed and validated a collagen signature-based nomogram for predicting LN metastasis in CRC. Methods In total, 342 consecutive CRC patients were assigned to the training and validation cohorts. A total of 148 fully quantitative collagen features were extracted based on preoperative biopsies using multiphoton imaging, and the least absolute shrinkage and selection operator method was utilized to construct the collagen signature. A collagen signature-based nomogram was developed by multivariable logistic regression in the training cohort. Nomogram performance was evaluated for its discrimination, calibration, and clinical usefulness and then validated in the validation cohort. The prognostic values of the nomogram were also evaluated. Results A seven-feature-based collagen signature was built. We found that the collagen signature showed a significant association with LN metastasis in CRC. Additionally, a nomogram incorporating preoperative tumor differentiation, computed tomography-reported T stage and LN status, carcinoembryonic antigen level, carbohydrate antigen 19-9 level and collagen signature was developed. This nomogram had good discrimination and calibration, with AUROCs of 0.826 and 0.846 in the training and validation cohorts, respectively, and had a sensitivity of 86.5%, a specificity of 68.2%, an accuracy of 76.9%, a negative predictive value of 84.9%, and a positive predictive value of 71.2% for all patients. Compared to the clinicopathological model, which consisted of the clinicopathological risk factors for LN metastasis, the collagen signature-based nomogram demonstrated a significantly improved ability to discriminate LN status. Moreover, a nomogram-predicted high-risk subgroup had remarkably reduced survival compared with that of the low-risk subgroup. Conclusions The collagen signature in the tumor microenvironment of preoperative biopsies is an independent predictor for LN metastasis in CRC, and the collagen signature-based nomogram is helpful for tailored treatment and prognostic predictions in CRC preoperatively.

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