Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, Fujian Province, China.
World J Gastroenterol. 2023 Oct 21;29(39):5483-5493. doi: 10.3748/wjg.v29.i39.5483.
Based on the clinical data of colorectal cancer (CRC) patients who underwent surgery at our institution, a model for predicting the formation of tumor deposits (TDs) in this patient population was established.
To establish an effective model for predicting TD formation, thus enabling clinicians to identify CRC patients at high risk for TDs and implement personalized treatment strategies.
CRC patients ( = 645) who met the inclusion criteria were randomly divided into training ( = 452) and validation ( = 193) cohorts using a 7:3 ratio in this retrospective analysis. Least absolute shrinkage and selection operator regression was employed to screen potential risk factors, and multivariable logistic regression analysis was used to identify independent risk factors. Subsequently, a predictive model for TD formation in CRC patients was constructed based on the independent risk factors. The discrimination ability of the model, its consistency with actual results, and its clinical applicability were evaluated using receiver-operating characteristic curves, area under the curve (AUC), calibration curves, and decision curve analysis (DCA).
Thirty-four (7.5%) patients with TDs were identified in the training cohort based on postoperative pathological specimens. Multivariate logistic regression analysis identified female sex, preoperative intestinal obstruction, left-sided CRC, and lymph node metastasis as independent risk factors for TD formation. The AUCs of the nomogram models constructed using these variables were 0.839 and 0.853 in the training and validation cohorts, respectively. The calibration curve demonstrated good consistency, and the training cohort DCA yielded a threshold probability of 7%-78%.
This study developed and validated a nomogram with good predictive performance for identifying TDs in CRC patients. Our predictive model can assist surgeons in making optimal treatment decisions.
基于本机构接受手术治疗的结直肠癌（CRC）患者的临床数据，建立了预测该患者人群中肿瘤沉积（TD）形成的模型。
建立一种有效的 TD 形成预测模型，从而使临床医生能够识别出具有 TD 高风险的 CRC 患者，并实施个性化治疗策略。
本回顾性分析采用 7:3 的比例将符合纳入标准的 CRC 患者（n=645）随机分为训练（n=452）和验证（n=193）队列。使用最小绝对收缩和选择算子回归筛选潜在风险因素，使用多变量逻辑回归分析确定独立风险因素。随后，根据独立风险因素构建 CRC 患者 TD 形成的预测模型。使用受试者工作特征曲线、曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）评估模型的判别能力、与实际结果的一致性及其临床适用性。
根据术后病理标本，在训练队列中发现 34 例（7.5%）TD 患者。多变量逻辑回归分析确定女性、术前肠梗阻、左侧 CRC 和淋巴结转移是 TD 形成的独立风险因素。使用这些变量构建的列线图模型在训练和验证队列中的 AUC 分别为 0.839 和 0.853。校准曲线显示出良好的一致性，训练队列 DCA 的阈值概率为 7%-78%。
本研究开发并验证了一种具有良好预测性能的列线图，用于识别 CRC 患者中的 TD。我们的预测模型可以帮助外科医生做出最佳治疗决策。
