A novel risk model for predicting peritoneal metastasis in colorectal cancer based on the SEER database

Yao, Li; Shao, Huan; Zhang, Xinyi; Huang, Xuan

doi:10.1007/s00432-023-05368-9

Cited by 2 publications

(2 citation statements)

References 42 publications

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“…Accurate evaluation of patients’ lymph node status holds considerable significance in managing and post-surgery monitoring of esophageal cancer. The LODDS staging system, known for its effective prognostic prediction in various cancers ( 15 , 20 , 21 ), is a relatively new approach to lymph node staging. However, research on its prognostic effectiveness for advanced esophageal squamous cell carcinoma is limited.…”

Section: Discussionmentioning

confidence: 99%

Prognostic prediction and comparison of three staging programs for patients with advanced (T2-T4) esophageal squamous carcinoma after radical resection

Wang,

Li,

Zhu

et al. 2024

Front. Oncol.

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PurposeLymph node-based staging protocols are frequently employed to evaluate the prognosis of esophageal cancer, yet their accuracy remains contentious. The present study was conducted to assess the prognostic significance of three lymph node staging systems, namely N stage, lymph node rate (LNR), and log odds of positive lymph nodes (LODDS), in patients diagnosed with advanced (T2-T4) esophageal squamous cell carcinoma (ESCC).MethodsThis cohort comprised 319 eligible patients, with an additional 409 individuals retrieved from the Surveillance, Epidemiology, and End Results (SEER) database, forming the validation cohort. Differences in overall survival (OS) of patients between groups were assessed using the log-rank test. Prognostic independent risk variables were identified, and lymph nodes (LN) prognostic models were built using multivariate Cox regression analysis. Besides, the predictive accuracy of each model was evaluated utilizing the (-2) log-likelihood ratio (-2LLR), the likelihood ratio χ2 score (LRχ2), the Akaike information criterion (AIC), and Harrell’s concordance index (C-index). To further evaluate the potential superiority of the model, a nomogram was constructed for comparison with the conventional Tumor Node Metastasis (TNM) staging approach.ResultsIndependent prognostic factors for advanced ESCC include the N stage, LNR, and LODDS. Herein, LODDS presented higher values for C-index and LRχ2, and lower values for AIC and -2LLR in OS compared to the others. Consequently, a nomogram was constructed based on LODDS. Calibration curves exhibited strong agreement, and assessment through C-index, receiver operating characteristic (ROC) curves, and clinical decision curve analysis (DCA) demonstrated promising clinical applicability.ConclusionLODDS emerges as a promising future prognostic indicator. After surgery, the proposed model holds the potential to provide valuable treatment recommendations for patients with advanced ESCC.

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Section: Discussionmentioning

confidence: 99%

Prognostic prediction and comparison of three staging programs for patients with advanced (T2-T4) esophageal squamous carcinoma after radical resection

Wang,

Li,

Zhu

et al. 2024

Front. Oncol.

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“…Noninvasive methods are commonly used to predict PM, among which serum tumor markers such as carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) play an important role, which reflects to varying degrees of tumor invasion, proliferation, and invasion of peritoneal mesothelial cells, but not all patients exhibit elevated tumor markers, making them supplementary diagnostic tools [ 8 , 9 ]. Computed tomography’s (CT) sensitivity to peritoneal lesions smaller than 0.5 cm is limited to 11–48% [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

A radiomics-boosted deep-learning for risk assessment of synchronous peritoneal metastasis in colorectal cancer

Zhang,

Zheng,

et al. 2024

Insights Imaging

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Objectives Synchronous colorectal cancer peritoneal metastasis (CRPM) has a poor prognosis. This study aimed to create a radiomics-boosted deep learning model by PET/CT image for risk assessment of synchronous CRPM. Methods A total of 220 colorectal cancer (CRC) cases were enrolled in this study. We mapped the feature maps (Radiomic feature maps (RFMs)) of radiomic features across CT and PET image patches by a 2D sliding kernel. Based on ResNet50, a radiomics-boosted deep learning model was trained using PET/CT image patches and RFMs. Besides that, we explored whether the peritumoral region contributes to the assessment of CRPM. In this study, the performance of each model was evaluated by the area under the curves (AUC). Results The AUCs of the radiomics-boosted deep learning model in the training, internal, external, and all validation datasets were 0.926 (95% confidence interval (CI): 0.874–0.978), 0.897 (95% CI: 0.801–0.994), 0.885 (95% CI: 0.795–0.975), and 0.889 (95% CI: 0.823–0.954), respectively. This model exhibited consistency in the calibration curve, the Delong test and IDI identified it as the most predictive model. Conclusions The radiomics-boosted deep learning model showed superior estimated performance in preoperative prediction of synchronous CRPM from pre-treatment PET/CT, offering potential assistance in the development of more personalized treatment methods and follow-up plans. Critical relevance statement The onset of synchronous colorectal CRPM is insidious, and using a radiomics-boosted deep learning model to assess the risk of CRPM before treatment can help make personalized clinical treatment decisions or choose more sensitive follow-up plans. Key Points Prognosis for patients with CRPM is bleak, and early detection poses challenges. The synergy between radiomics and deep learning proves advantageous in evaluating CRPM. The radiomics-boosted deep-learning model proves valuable in tailoring treatment approaches for CRC patients. Graphical Abstract

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A novel risk model for predicting peritoneal metastasis in colorectal cancer based on the SEER database

Cited by 2 publications

References 42 publications

Prognostic prediction and comparison of three staging programs for patients with advanced (T2-T4) esophageal squamous carcinoma after radical resection

Prognostic prediction and comparison of three staging programs for patients with advanced (T2-T4) esophageal squamous carcinoma after radical resection

A radiomics-boosted deep-learning for risk assessment of synchronous peritoneal metastasis in colorectal cancer

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