Chen Lei, Yang Funing, Qi Zhaoyan, Tai Jiandong
Colorectal and Anal Surgery, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, China.
Pediatric Outpatient Clinic, First Hospital of Jilin University, Changchun, Jilin, China.
Front Med (Lausanne). 2022 Sep 15;9:991785. doi: 10.3389/fmed.2022.991785. eCollection 2022.
Tumor budding (TB), a powerful, independent predictor of colorectal cancer (CRC), is important for making appropriate treatment decisions. Currently, TB is assessed only using the tumor bud count (TBC). In this study, we aimed to develop a novel prediction model, which includes different TB features, for lymph node metastasis (LNM) and local recurrence in patients with pT1 CRC. Enrolled patients ( = 354) were stratified into training and validation cohorts. Independent predictors of LNM and recurrence were identified to generate predictive nomograms that were assessed using the area under the receiver operating characteristic (AUROC) and decision curve analysis (DCA). Seven LNM predictors [gross type, histological grade, lymphovascular invasion (LVI), stroma type, TBC, TB mitosis, and TB CDX2 expression] were identified in the training cohort. LNM, histology grade, LVI, TBC, stroma type, and TB mitosis were independent predictors of recurrence. We constructed an LNM predictive nomogram with a high clinical application value using the DCA. Additionally, a nomogram predicting recurrence-free survival (RFS) was constructed. It presented an AUROC value of 0.944 for the training cohort. These models may assist surgeons in making treatment decisions. In the high-risk group, radical surgery with a postoperative adjuvant chemotherapy was associated with RFS. Postoperative chemotherapy can be better for high-risk patients with pT1 CRC. We showed that TB features besides TBC play important roles in CRC pathogenesis, and our study provides prognostic information to guide the clinical management of patients with early stage CRC.
肿瘤芽生（TB）是结直肠癌（CRC）一个强大的独立预测指标，对于做出恰当的治疗决策很重要。目前，仅通过肿瘤芽计数（TBC）来评估TB。在本研究中，我们旨在开发一种包含不同TB特征的新型预测模型，用于预测pT1期CRC患者的淋巴结转移（LNM）和局部复发。入组患者（n = 354）被分层为训练队列和验证队列。确定LNM和复发的独立预测因素，以生成预测列线图，并使用受试者操作特征曲线下面积（AUROC）和决策曲线分析（DCA）进行评估。在训练队列中确定了7个LNM预测因素[大体类型、组织学分级、淋巴管浸润（LVI）、间质类型、TBC、TB有丝分裂和TB CDX2表达]。LNM、组织学分级、LVI、TBC、间质类型和TB有丝分裂是复发的独立预测因素。我们使用DCA构建了具有高临床应用价值的LNM预测列线图。此外，构建了预测无复发生存期（RFS）的列线图。其在训练队列中的AUROC值为0.944。这些模型可能有助于外科医生做出治疗决策。在高危组中，根治性手术联合术后辅助化疗与RFS相关。术后化疗对pT1期CRC高危患者可能更好。我们表明，除TBC外，TB特征在CRC发病机制中起重要作用，并且我们的研究提供了预后信息以指导早期CRC患者的临床管理。
