Invited Editorial: Does Side Really Matter? Survival Analysis Among Patients with Right- Versus Left-Sided Colon Cancer: A Propensity Score-Adjusted Analysis

Machine learning for predicting liver and/or lung metastasis in colorectal cancer: A retrospective study based on the SEER database

OBJECTIVE

This study aims to establish a machine learning (ML) model for predicting the risk of liver and/or lung metastasis in colorectal cancer (CRC).

METHODS

Using the National Institutes of Health (NIH)'s Surveillance, Epidemiology, and End Results (SEER) database, a total of 51265 patients with pathological diagnosis of colorectal cancer from 2010 to 2015 were extracted for model development. On this basis, We have established 7 machine learning algorithm models. Evaluate the model based on accuracy, and AUC of receiver operating characteristics (ROC) and explain the relationship between clinical pathological features and target variables based on the best model. We validated the model among 196 colorectal cancer patients in Beijing Electric Power Hospital of Capital Medical University of China to evaluate its performance and universality. Finally, we have developed a network-based calculator using the best model to predict the risk of liver and/or lung metastasis in colorectal cancer patients.

RESULTS

51265 patients were enrolled in the study, of which 7864 (15.3 %) had distant liver and/or lung metastasis. RF had the best predictive ability, In the internal test set, with an accuracy of 0.895, AUC of 0.956, and AUPR of 0.896. In addition, the RF model was evaluated in the external validation set with an accuracy of 0.913, AUC of 0.912, and AUPR of 0.611.

CONCLUSION

In this study, we constructed an RF algorithm mode to predict the risk of colorectal liver and/or lung metastasis, to assist doctors in making clinical decisions.

Change in abdominal obesity after colon cancer surgery - effects of left-sided and right-sided colonic resection

BACKGROUND

Excess abdominal visceral adipose tissue (VAT) is associated with metabolic diseases and poor survival in colon cancer (CC). We assessed the impact of different types of CC surgery on changes in abdominal fat depots.

MATERIAL AND METHODS

Computed tomography (CT)-scans performed preoperative and 3 years after CC surgery were analyzed at L3-level for VAT, subcutaneous adipose tissue (SAT) and total adipose tissue (TAT) areas. We assessed changes in VAT, SAT, TAT and VAT/SAT ratio after 3 years and compared the changes between patients who had undergone left-sided and right-sided colonic resection in the total population and in men and women separately.

RESULTS

A total of 134 patients with stage I-III CC undergoing cancer surgery were included. Patients who had undergone left-sided colonic resection had after 3 years follow-up a 5% (95% CI: 2-9%, p < 0.01) increase in abdominal VAT, a 4% (95% CI: 2-6%, p < 0.001) increase in SAT and a 5% increase (95% CI: 2-7%, p < 0.01) in TAT. Patients who had undergone right-sided colonic resection had no change in VAT, but a 6% (95% CI: 4-9%, p < 0.001) increase in SAT and a 4% (95% CI: 1-7%, p < 0.01) increase in TAT after 3 years. Stratified by sex, only males undergoing left-sided colonic resection had a significant VAT increase of 6% (95% CI: 2-10%, p < 0.01) after 3 years.

CONCLUSION

After 3 years follow-up survivors of CC accumulated abdominal adipose tissue. Notably, those who underwent left-sided colonic resection had increased VAT and SAT, whereas those who underwent right-sided colonic resection demonstrated solely increased SAT.

Distant Metastasis Pattern and Prognostic Prediction Model of Colorectal Cancer Patients Based on Big Data Mining

Aims

This study aimed to investigate the distant metastasis pattern from newly diagnosed colorectal cancer (CRC) and also construct and validate a prognostic nomogram to predict both overall survival (OS) and cancer-specific survival (CSS) of CRC patients with distant metastases.

Methods

Primary CRC patients who were initially diagnosed from 2010 to 2016 in the SEER database were included in the analysis. The independent risk factors affecting the OS, CSS, all-cause mortality, and CRC-specific mortality of the patients were screened by the Cox regression and Fine–Gray competitive risk model. The nomogram models were constructed to predict the OS and CSS of the patients. The reliability and accuracy of the prediction model were evaluated by consistency index (C-index) and calibration curve. The gene chip GSE41258 was downloaded from the GEO database, and differentially expressed genes (DEGs) were screened by the GEO2R online tool (p < 0.05, |logFC|>1.5). The Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway and Gene Ontology (GO) annotation and String website were used for enrichment analysis and protein–protein interaction (PPI) analysis of DEGs, respectively, and Cytoscape software was used to construct PPI network and screen function modules and hub genes.

Results

A total of 57,835 CRC patients, including 47,823 without distant metastases and 10,012 (17.31%) with metastases, were identified. Older age, unmarried status, poorly differentiated or undifferentiated grade, right colon site, larger tumor size, N2 stage, more metastatic sites, and elevated carcinoembryonic antigen (CEA) might lead to poorer prognosis (all p < 0.01). The independent risk factors of OS and CSS were included to construct a prognosis prediction model for predicting OS and CSS in CRC patients with distant metastasis. C-index and calibration curve of the training group and validation group showed that the models had acceptable predictive performance and high calibration degree. Furthermore, by comparing CRC tissues with and without liver metastasis, 158 DEGs and top 10 hub genes were screened. Hub genes were mainly concentrated in liver function and coagulation function.

Conclusion

The big data in the public database were counted and transformed into a prognostic evaluation tool that could be applied to the clinic, which has certain clinical significance for the formulation of the treatment plan and prognostic evaluation of CRC patients with distant metastasis.

Segmental Distribution of Hepatocellular Carcinoma in Cirrhotic Livers

Background: To evaluate the segmental distribution of hepatocellular carcinoma (HCC) according to Couinaud’s anatomical division in cirrhotic patients. Methods: Between 2020 and 2021, a total of 322 HCC nodules were diagnosed in 217 cirrhotic patients who underwent computed tomography (CT) or magnetic resonance imaging (MRI) for the evaluation of suspicious nodules (>1 cm) detected during ultrasound surveillance. For each patient, the segmental position of the HCC nodule was recorded according to Couinaud’s description. The clinical data and nodule characteristics were collected. Results: A total of 234 (72.7%) HCC nodules were situated in the right lobe whereas 79 (24.5%) were detected in the left lobe (p < 0.0001) and only 9 nodules were in the caudate lobe (2.8%). HCC was most common in segment 8 (n = 88, 27.4%) and least common in segment 1 (n = 9, 2.8%). No significant differences were found in the frequencies of segmental or lobar involvement considering patient demographic and clinical characteristics, nodule dimension, or disease appearance. Conclusions: The intrahepatic distribution of HCC differs among Couinaud’s segments, with segment 8 being the most common location and segment 1 being the least common. The segmental distribution of tumour location was similar to the normal liver volume distribution, supporting a possible correlation between HCC location and the volume of hepatic segments and/or the volumetric distribution of the portal blood flow.