Usefulness of Inflammation-Based Prognostic Score in Patients Undergoing Lung Metastasectomy for Colorectal Carcinoma

Prospective nutrition-inflammation markers for predicting early stoma-related complications in patients with colorectal cancer undergoing enterostomy

Background

Enterostomy is important for radical resection of colorectal cancer (CRC). Nevertheless, the notable occurrence of complications linked to enterostomy results in a reduction in patients' quality of life and impedes adjuvant therapy. This study sought to forecast early stoma-related complications (ESRCs) by leveraging easily accessible nutrition-inflammation markers in CRC patients.

Methods

This study involved 470 individuals with colorectal cancer who underwent intestinal ostomy at Changhai Hospital Affiliated with Naval Medical University as the internal cohort. Between January 2016 and December 2018, the patients were enrolled and randomly allocated into a primary training group and a secondary validation group, with a ratio of 2:1 being upheld. The research encompassed collecting data on each patient's clinical and pathological status, along with preoperative laboratory results. Independent risk factors were identified through Lasso regression and multivariate analysis, leading to the development of clinical models represented by a nomogram. The model's utility was assessed using decision curve analysis, calibration curve, and ROC curve. The final model was validated using an external validation set of 179 individuals from January 2015 to December 2021.

Results

Among the internal cohort, stoma complications were observed in 93 cases. Multivariate regression analysis confirmed that age, stoma site, and elevated markers (Mon, NAR, and GLR) in conjunction with diminished markers (GLB and LMR) independently contributed to an increased risk of ESRCs. The clinical model was established based on these seven factors. The training, internal, and external validation groups exhibited ROC curve areas of 0.839, 0.812, and 0.793, respectively. The calibration curve showed good concordance among the forecasted model with real incidence of ostomy complications. The model displayed outstanding predictive capability and is deemed applicable in clinical settings, as evidenced by Decision Curve Analysis.

Conclusion

This study identified nutrition-inflammation markers (GLB, NAR, and GLR) in combination with demographic data as crucial predictors for forecasting ESRCs in colorectal cancer patients. A novel prognostic model was formulated and validated utilizing these markers.

Inflammation-Related Biomarkers for the Prediction of Prognosis in Colorectal Cancer Patients

Colorectal cancer (CRC) is the leading cause of cancer deaths around the world. It is necessary to identify patients with poor prognosis or with high risk for recurrence so that we can selectively perform intensive treatments such as preoperative and/or postoperative chemotherapy and extended surgery. The clinical usefulness of inflammation-related prognostic biomarkers available from routine blood examination has been reported in many types of cancer, e.g., neutrophil–lymphocyte ratio (NLR), lymphocyte–C-reactive protein ratio (LCR), platelet–lymphocyte ratio (PLR), lymphocyte–monocyte ratio (LMR), and so on. Moreover, some scoring systems based on circulating blood cell counts and albumin concentration have been also reported to predict cancer patients’ prognosis, such as the Glasgow prognostic score (GPS), systemic inflammation score (SIS), and prognostic nutritional index (PNI). The optimal biomarker and optimal cutoff value of the markers can be different depending on the cancer type. In this review, we summarize the prognostic impact of each inflammation-related marker in CRC.

Prognostic value of KRAS mutation in patients undergoing pulmonary metastasectomy for colorectal cancer: A systematic review and meta-analysis

BACKGROUND

The purpose of the study was to evaluate whether KRAS mutation could be an independent prognostic biomarker in patients undergoing pulmonary metastasectomy (PM) for colorectal cancer (CRC).

METHODS

A systemic review was performed by searching online databases to identify studies reporting overall survival (OS) and recurrence free survival (RFS) of CRC patients undergoing PM. Pooled HRs were calculated for OS and RFS.

RESULTS

A total of 15233 patients from 60 studies were included. Pooled analysis showed that KRAS mutation was associated with worse OS (HR: 1.86, 95 % Cl: 1.35-2.57) and RFS (HR: 1.68, 95 % Cl: 1.38-2.04). A significant effect on OS and/or RFS was also shown by other 18 factors.

CONCLUSIONS

This meta-analysis found that KRAS mutation is an important prognostic predictor for OS and RFS in CRC patients undergoing PM, supporting a comprehensive model including clinicopathological and biological factors for optimal patients selection and prognosis for surgical treatment.

Inflammation-Based Prognostic Score Predicts Postoperative Survival of Patients with Interstitial Pneumonia After Undergoing Lung Cancer Resection

OBJECTIVES

Idiopathic interstitial pneumonias (IIPs) are associated with an increased risk of lung cancer. Glasgow prognostic score (GPS), which uses serum C-reactive protein (CRP) and albumin levels to indicate systemic inflammatory response and nutrition level, has been reported to be a predictor of overall survival in patients with various types of cancer. We evaluated the usefulness of GPS for prediction of survival of patients with both lung cancer and IIPs following a lung resection procedure.

METHODS

Patients with IIPs who underwent lung cancer resection from January 2006 through December 2015 were investigated. Routine laboratory measurements, including serum CRP and albumin for determining GPS, were performed before the operation. Univariate and multivariate analyses with a COX proportional hazards regression model were used to identify independent risk factors for overall survival (OS), relapse-free survival (RFS), cancer-specific survival (CSS), and other disease-specific survival (ODSS).

RESULTS

A total of 135 patients underwent lung resection during the study period. Multivariate analysis selected sublobar resection (p = 0.035), UIP pattern (p = 0.025), and GPS of 1-2 (p = 0.042) as predictive factors associated with OS, while GPS of 1-2 (p = 0.039) was shown to be a predictive factor associated with RFS. Multivariate analysis also revealed pTNM (p < 0.001), usual interstitial pneumonia pattern (p = 0.006), and GPS of 2 (p = 0.003) as predictive factors associated with CSS, while univariate analysis indicated pTNM (p = 0.042), GPS of 1 (p = 0.044), and %DLCO (p = 0.038) as predictive factors associated with ODSS.

CONCLUSION

GPS is an independent prognostic factor of OS and RFS in lung cancer patients with IIPs undergoing a lung resection procedure. Furthermore, a GPS of 2 was found to be associated with CSS following lung cancer resection, while a score of 1 was associated with ODSS.