Details

Autor(en) / Beteiligte

• Dou, Xinyu
• Xi, Jiaona
• Zheng, Gaozan
• Ren, Guangming
• Tian, Ye
• Dan, Hanjun
• Xie, Zhenyu
• Niu, Liaoran
• Duan, Lili
• Li, Ruikai
• Wu, Hongze
• Feng, Fan
• Zheng, Jianyong

Titel

A nomogram was developed using clinicopathological features to predict postoperative liver metastasis in patients with colorectal cancer

Ist Teil von

• Journal of cancer research and clinical oncology, 2023-11, Vol.149 (15), p.14045-14056

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2023

Quelle

Springer journals

Beschreibungen/Notizen

• Purpose The objective of this study is to examine the risk factors that contribute to the development of liver metastasis (LM) in patients who have suffered radical resection for colorectal cancer (CRC), and to establish a nomogram model that can be used to predict the occurrence of the LM. Methods The present study enrolled 1377 patients diagnosed with CRC between January 2010 and July 2021. The datasets were allocated to training ( n  = 965) and validation ( n  = 412) sets in a randomly stratified manner. The study utilized univariate and multivariate logistic regression analyses to establish a nomogram for predicting LM in patients with CRC. Results Multivariate analysis revealed that T stage, N stage, number of harvested lymph nodes (LNH), mismatch repair (MMR) status, neutrophil count, monocyte count, postoperative carcinoembryonic antigen (CEA) levels, postoperative cancer antigen 125 (CA125) levels, and postoperative carbohydrate antigen 19–9 (CA19-9) levels were independent predictive factors for LM after radical resection. These factors were then utilized to construct a comprehensive nomogram for predicting LM. The nomogram demonstrated great discrimination, with an area under the curve (AUC) of 0.782 for the training set and 0.768 for the validation set. Additionally, the nomogram exhibited excellent calibration and significant clinical benefit as confirmed by the calibration curves and the decision curve analysis, respectively. Conclusion This nomogram has the potential to support clinicians in identifying high-risk patients who may develop LM post-surgery. Clinicians can devise personalized treatment and follow-up plans, ultimately leading to an improved prognosis for patients.