Data Sheet 4_A predictive model to identify optimal candidates for surgery among patients with metastatic colorectal cancer.pdf

PurposeTo improve clinical decision-making, we developed a predictive model to identify metastatic colorectal cancer (mCRC) patients who might benefit from primary tumor resection (PTR).
Patients and MethodsWe extracted clinical data of stage IV CRC patients between 2010 and 2019 from the Surveillance, Epidemiology, and End Results database. Propensity score matching (PSM) was used to balance confounding factors by categorizing patients into surgery and non-surgery groups. To identify independent predictors of cancer-specific survival (CSS), we used multivariate Cox regression analysis. We further sorted patients who underwent surgery into benefit and non-benefit groups based on the median CSS of the non-surgery group; subsequently, we split the groups into training and test sets at a ratio of 6:4. To construct predictive models, we used the Boruta selection method to further filter variables, focusing on whether patients benefited from the surgery, based on key predictive factors.
ResultsWe identified 23,649 mCRC patients, of whom 80.97% (19,148) underwent PTR. After PSM, compared to no surgical intervention, surgical intervention was independently associated with an extended median CSS [median: 22 vs. 12 months; HR: 2.323, P < 0.001]. Among the nine machine learning models, the Categorical Boosting model performed the best but was still slightly inferior to traditional logistic regression. The traditional logistic regression model showed good discriminative ability in both the training (area under the curve [AUC]: 0.727 [0.699-0.756]) and test (AUC: 0.741 [0.706-0.776]) sets.
ConclusionWe achieved a predictive model which could identify optimal candidates for PTR among mCRC patients with high accuracy.

研究目的：为优化临床决策，我们开发了一款预测模型，用于识别可从原发肿瘤切除术（primary tumor resection, PTR）中获益的转移性结直肠癌（metastatic colorectal cancer, mCRC）患者。
患者与方法：我们从监测、流行病学与最终结果（Surveillance, Epidemiology, and End Results, SEER）数据库中提取了2010年至2019年间的IV期结直肠癌患者临床数据。采用倾向得分匹配（propensity score matching, PSM）平衡混杂因素，将患者划分为手术组与非手术组。为明确癌症特异性生存（cancer-specific survival, CSS）的独立预测因子，我们运用多变量Cox回归分析。我们进一步依据非手术组的癌症特异性生存中位值，将接受手术的患者分为获益组与非获益组；随后以6:4的比例将两组划分为训练集与测试集。为构建预测模型，我们采用Boruta筛选法进一步过滤变量，基于关键预测因子聚焦于患者是否可从手术中获益。
研究结果：我们共纳入23649例转移性结直肠癌患者，其中80.97%（19148例）接受了原发肿瘤切除术。经倾向得分匹配后，与未接受手术干预的患者相比，接受手术干预的患者癌症特异性生存中位时间显著延长[中位值：22个月 vs 12个月；风险比（HR）：2.323，P<0.001]。在九种机器学习模型中，分类提升（Categorical Boosting）模型表现最优，但仍略逊于传统逻辑回归模型。传统逻辑回归模型在训练集与测试集中均展现出良好的区分能力[训练集曲线下面积（AUC）：0.727（95%置信区间：0.699-0.756）；测试集AUC：0.741（95%置信区间：0.706-0.776）]。
研究结论：我们开发的预测模型可较为精准地识别转移性结直肠癌患者中适合接受原发肿瘤切除术的最优候选人群。