Nomograms for predicting specific distant metastatic sites and overall survival of colorectal cancer patients: A large population-based real-world study

Multifactorial risk prediction analysis of liver metastasis in colorectal cancer: incorporating programmed cell death ligand 1 combined positive score and other factors

BACKGROUND

The occurrence of liver metastasis significantly affects the prognosis of colorectal cancer (CRC). Existing research indicates that primary tumor location, vascular invasion, lymph node metastasis, and abnormal preoperative tumor markers are risk factors for CRC liver metastasis. Positive expression of programmed cell death ligand 1 (PD-L1) may serve as a favorable prognostic marker for nasopharyngeal and gastric cancers, in which combined positive score (CPS) quantifies the level of PD-L1 expression. This study aimed to explore CPS as a potential risk factor for CRC liver metastasis and integrate other independent risk factors to establish a novel predictive model for CRC liver metastasis.

METHODS

A retrospective analysis was conducted on 437 patients with CRC pathologically diagnosed at The Second Xiangya Hospital of Central South University from January 1, 2019, to December 31, 2021. Data were collected, including CPS, age, gender (male and female), primary tumor location, Ki-67 expression, pathologic differentiation, neural invasion, vascular invasion, lymph node metastasis, and preoperative tumor markers. The optimal cutoff point for the continuous variable CPS was determined using the Youden index, and all CPSs were dichotomized into high- and low-risk groups based on this threshold (scores below the threshold were considered high risk, and score above the threshold were considered low risk). Univariate logistic regression analysis was employed to identify risk factors for CRC liver metastasis, followed by multivariate logistic regression analysis to integrate the selected risk factors. The predictive model was validated through the construction of receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). A nomogram was constructed for visualization.

RESULTS

The determined cutoff point for PD-L1 CPS was 4.5, with scores below this threshold indicating a high risk of CRC liver metastasis. In addition, primary tumor origin other than the rectum, presence of pericolonic lymph node metastasis, and abnormal levels of tumor markers carcinoembryonic antigen and cancer antigen 19-9 were identified as independent risk factors for CRC liver metastasis. The constructed clinical prediction model demonstrated good predictive ability and accuracy, with an area under the ROC curve of 0.871 (95% CI, 0.838-0.904).

CONCLUSION

The exploration and validation of CPS as a novel predictor of CRC liver metastasis were performed. Based on these findings, a new clinical prediction model for CRC liver metastasis was developed by integrating other independent risk factors. The DCA, clinical impact curve, and nomogram graph constructed on the basis of this model have significant clinical implications and guide clinical practice.

The landscape of immune checkpoint-related long non-coding RNAs core regulatory circuitry reveals implications for immunoregulation and immunotherapy responses

Long non-coding RNAs (lncRNAs) could modulate expression of immune checkpoints (ICPs) by cooperating with immunity genes in tumor immunization. However, precise functions in immunity and potential for predicting ICP inhibitors (ICI) response have been described for only a few lncRNAs. Here we present an integrated framework that leverages network-based analyses and Bayesian network inference to identify the regulated relationships including lncRNA, ICP and immunity genes as ICP-related LncRNAs mediated Core Regulatory Circuitry Triplets (ICP-LncCRCTs) that can make robust predictions. Hub ICP-related lncRNAs such as MIR155HG and ADAMTS9-AS2 were highlighted to play central roles in immune regulation. Specific ICP-related lncRNAs could distinguish cancer subtypes. Moreover, the ICP-related lncRNAs are likely to significantly correlated with immune cell infiltration, MHC, CYT. Some ICP-LncCRCTs such as CXCL10-MIR155HG-ICOS could better predict one-, three- and five-year prognosis compared to single molecule in melanoma. We also validated that some ICP-LncCRCTs could effectively predict ICI-response using three kinds of machine learning algorithms follow five independent datasets. Specially, combining ICP-LncCRCTs with the tumor mutation burden (TMB) improves the prediction of ICI-treated melanoma patients. Altogether, this study will improve our grasp of lncRNA functions and accelerating discovery of lncRNA-based biomarkers in ICI treatment.

A nomogram based on peripheral lymphocyte for predicting 8-year survival in patients with prostate cancer: a single-center study using LASSO-cox regression

PURPOSE

The purpose of this study was to develop a functional clinical nomogram for predicting 8-year overall survival (OS) of patients with prostate cancer (PCa) primary based on peripheral lymphocyte.

PATIENTS AND METHODS

Using data from a single-institutional registry of 94 patients with PCa in China, this study identified and integrated significant prognostic factors for survival to build a nomogram. The discriminative ability was measured by concordance index (C-index) and ROC curves (Receiver Operating Characteristic Curves). And the predictive accuracy was measured by the calibration curves. Decision curve analyses (DCA) was used to measure the clinical usefulness.

RESULTS

A total of 94 patients were included for analysis. Five independent prognostic factors were identified by LASSO-Cox regression and incorporated into the nomogram: age, the T stage, the absolute counts of peripheral CD3(+)CD4(+) T lymphocytes, CD3(-)CD16(+)CD56(+) NK cells and CD4(+)/CD8(+) ratio. The area under the curve (AUC) values of the predictive model for 5-, 8-, and 10-year overall survival were 0.81, 0.76, and 0.73, respectively. The calibration curves for probability of 5-,8- and 10-year OS showed optimal agreement between nomogram prediction and actual observation. The stratification into different risk groups allowed significant distinction. DCA indicated the good clinical application value of the model.

CONCLUSION

We developed a novel nomogram that enables personalized prediction of OS for patients diagnosed with PCa. This finding revealed a relative in age and survival rate in PCa, and a more favorable prognosis in patients exhibiting higher levels of CD4 + T, CD4+/CD8 + ratio and CD3(-)CD16(+)CD56(+) NK cells specifically. This clinically applicable prognostic model exhibits promising predictive capabilities, offering valuable support to clinicians in informed decision-making process.

A nomogram prediction model based on clinicopathological combined radiological features for metachronous liver metastasis of colorectal cancer

Objective: To establish a nomogram prediction model (based on clinicopathological and radiological features) for the development of metachronous liver metastasis (MLM) in patients with colorectal cancer (CRC). Methods: This retrospective study included patients with CRC who underwent surgery at Changshu No.1 People's Hospital and the Second Affiliated Hospital of Soochow University between January 2016 and December 2018. The clinical, pathological, and radiological features of each patient were investigated. Risk factors for MLM were identified by univariable and multivariable analyses. The predictive nomogram for MLM development was constructed. The predictive performance of the nomogram was estimated by the receiver operating characteristics curve, calibration curve, and decision curve analysis. Results: This study included 161 patients with CRC [median age: 66 (range, 33-87) years]. Fifty-nine developed MLM after a median of 12 (range, 2-52) months after surgery. The multivariable logistic regression analysis showed that age >66 years (OR=3.471, 95% CI: 1.272-9.473, P=0.015), N2 stage (OR=6.534, 95% CI: 1.456-29.317, P=0.014), positive vascular invasion (OR=2.995, 95% CI: 1.132-7.926, P=0.027), positive tumor deposit (OR=4.451, 95% CI: 1.153-17.179, P=0.030), and linear (OR=6.774, 95% CI: 1.306-35.135, P=0.023) and nodal pericolic fat infiltration patterns (OR=8.762, 95% CI: 1.521-50.457, P=0.015) were independently associated with MLM. These five factors were used to create a nomogram. The area under the receiver operating characteristics curve of the nomogram was 0.866 (95% CI: 0.803-0.914), indicating favorable prediction performance. The calibration curve of the nomogram showed a satisfactory agreement between the predicted and actual probabilities. Conclusions: A nomogram prediction model based on five clinicopathological and radiological features might have favorable prediction performance for MLM in patients who underwent surgery for CRC. Hence, the present study proposes a nomogram that can easily be used to predict MLM after CRC surgery based on readily available features.

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

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.

Identifying immune checkpoint-related lncRNA biomarkers for immunotherapy response and prognosis in cancers

Long non-coding RNAs (lncRNAs) could modulate expression of immune checkpoints (ICPs) in tumor-immune. However, precise functions in immunity and potential for predicting ICP inhibitors (ICI) response have been described for only a few lncRNAs. Here, a multiple-step pipeline was developed to identify cancer- and immune-context ICP and lncRNA cooperative regulation pairs (ICPaLncCRPs) across cancers. Immune-related ICPs and lncRNAs were extracted follow immune cell lines and immunologic constant of rejection groups. ICPaLncCRP networks were constructed, which likely to modulate tumor-immune by specific patterns. Common and specific hub ICPaLncs such as MIR155HG, TRG-AS1 and PCED1B-AS1 maybe play central roles in prognosis and circulating. Moreover, these hub ICPaLncs were significantly correlated with immune cell infiltration based on bulk and single-cell RNA sequencing data. Some ICPaLncCRPs such as IDO1-MIR155HG could predict three- and five-year prognosis of melanoma in two independent datasets. We also validated that some ICPaLncCRPs could effectively predict ICI-response follow six independent datasets. Collectively, this study will enhance our understanding of lncRNA functions and accelerate discovery of lncRNA-based biomarkers in ICI treatment.

A machine learning-based model for predicting distant metastasis in patients with rectal cancer

Background

Distant metastasis from rectal cancer usually results in poorer survival and quality of life, so early identification of patients at high risk of distant metastasis from rectal cancer is essential.

Method

The study used eight machine-learning algorithms to construct a machine-learning model for the risk of distant metastasis from rectal cancer. We developed the models using 23867 patients with rectal cancer from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Meanwhile, 1178 rectal cancer patients from Chinese hospitals were selected to validate the model performance and extrapolation. We tuned the hyperparameters by random search and tenfold cross-validation to construct the machine-learning models. We evaluated the models using the area under the receiver operating characteristic curves (AUC), the area under the precision-recall curve (AUPRC), decision curve analysis, calibration curves, and the precision and accuracy of the internal test set and external validation cohorts. In addition, Shapley's Additive explanations (SHAP) were used to interpret the machine-learning models. Finally, the best model was applied to develop a web calculator for predicting the risk of distant metastasis in rectal cancer.

Result

The study included 23,867 rectal cancer patients and 2,840 patients with distant metastasis. Multiple logistic regression analysis showed that age, differentiation grade, T-stage, N-stage, preoperative carcinoembryonic antigen (CEA), tumor deposits, perineural invasion, tumor size, radiation, and chemotherapy were-independent risk factors for distant metastasis in rectal cancer. The mean AUC value of the extreme gradient boosting (XGB) model in ten-fold cross-validation in the training set was 0.859. The XGB model performed best in the internal test set and external validation set. The XGB model in the internal test set had an AUC was 0.855, AUPRC was 0.510, accuracy was 0.900, and precision was 0.880. The metric AUC for the external validation set of the XGB model was 0.814, AUPRC was 0.609, accuracy was 0.800, and precision was 0.810. Finally, we constructed a web calculator using the XGB model for distant metastasis of rectal cancer.

Conclusion

The study developed and validated an XGB model based on clinicopathological information for predicting the risk of distant metastasis in patients with rectal cancer, which may help physicians make clinical decisions. rectal cancer, distant metastasis, web calculator, machine learning algorithm, external validation.

The Management of Oligometastatic Disease in Colorectal Cancer: Present Strategies and Future Perspectives

Oligometastatic disease has been described as an intermediate clinical state between localized cancer and systemically metastasized disease. Recent clinical studies have shown prolonged survival when aggressive locoregional approaches are added to systemic therapies in patients with oligometastases. The aim of this review is to outline the newest options to treat oligometastatic colorectal cancer (CRC), also considering its molecular patterns. We present an overview of the available local treatment strategies, including surgical procedures, stereotactic body radiation therapy (SBRT), thermal ablation, as well as trans-arterial chemoembolization (TACE) and selective internal radiotherapy (SIRT). Moreover, since imaging methods provide crucial information for the early diagnosis and management of oligometastatic CRC, we discuss the role of modern radiologic techniques in selecting patients that are amenable to potentially curative locoregional treatments.

Extramural vascular invasion nomogram before radical resection of rectal cancer based on magnetic resonance imaging

Purpose

This study verified the value of magnetic resonance imaging (MRI) to construct a nomogram to preoperatively predict extramural vascular invasion (EMVI) in rectal cancer using MRI characteristics.

Materials and methods

There were 55 rectal cancer patients with EMVI and 49 without EMVI in the internal training group. The external validation group consisted of 54 rectal cancer patients with EMVI and 55 without EMVI. High-resolution rectal T2WI, pelvic diffusion-weighted imaging (DWI) sequences, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were used. We collected the following data: distance between the lower tumor margin and the anal margin, distance between the lower tumor margin and the anorectal ring, tumor proportion of intestinal wall, mrT stage, maximum tumor diameter, circumferential resection margin, superior rectal vein width, apparent diffusion coefficient (ADC), T2WI EMVI score, DWI and DCE-MRI EMVI scores, demographic information, and preoperative serum tumor marker data. Logistic regression analyses were used to identify independent risk factors of EMVI. A nomogram prediction model was constructed. Receiver operating characteristic curve analysis verified the predictive ability of the nomogram. P < 0.05 was considered significant.

Result

Tumor proportion of intestinal wall, superior rectal vein width, T2WI EMVI score, and carbohydrate antigen 19-9 were significant independent predictors of EMVI in rectal cancer and were used to create the model. The areas under the receiver operating characteristic curve, sensitivities, and specificities of the nomogram were 0.746, 65.45%, and 83.67% for the internal training group, respectively, and 0.780, 77.1%, and 71.3% for the external validation group, respectively.

Data conclusion

A nomogram including MRI characteristics can predict EMVI in rectal cancer preoperatively and provides a valuable reference to formulate individualized treatment plans and predict prognosis.

Real-World Study: A Powerful Tool for Malignant Tumor Research in General Surgery

Real-world study (RWS) is a method to draw conclusions by collecting and analyzing real-world data under a real clinical background. Compared with traditional randomized controlled trials (RCTs), RWSs are favored by clinicians because of their low cost and good extrapolation. In recent years, RWS has made remarkable achievements in the field of general surgery, especially in the drug treatment of advanced malignant tumors. Therefore, to further understand the main contents of the existing RWS and the application prospect of RWS in the future, this paper systematically reviews the clinical application of RWS in malignant tumors in general surgery in the past three years.

Peritumoral tertiary lymphoid structure and tumor stroma percentage predict the prognosis of patients with non-metastatic colorectal cancer

Background

Tertiary lymphoid structures (TLSs) are crucial in promoting and maintaining positive anti-tumor immune responses. The tumor stroma has a powerful immunosuppressive function that could exclude tumor-infiltrating lymphocytes from the tumor beds and lead to a “cold” phenotype. TLSs and tumor stroma percentage (TSP) are significantly associated with the prognosis of patients with certain cancers. However, the exact roles of TLSs and TSP and their intrinsic relationship are still largely unknown in colorectal cancer (CRC).

Methods

TLSs and TSP were assessed using hematoxylin-eosin (H&E) and/or immunohistochemistry (IHC) staining from 114 CRC patients in the training set and 60 CRC patients in the external validation set. The correlation between TILs, TLS and clinicopathological characteristics and their prognostic values were assessed. Finally, we plotted a Nomogram including the TLS, TSP and tumor-node-metastasis (TNM) stage to predict the probability of recurrence-free survival (RFS) at 2- and 5-years in non-metastatic colorectal cancer (nmCRC) patients.

Results

Peritumoral TLS (P-TLS), intratumoral TLS (In-TLS) and high TSP (H-TSP, >50%) were present in 99.1%, 26.3% and 41.2% patients, respectively. H-TSP tumor tends to be associated with lower P-TLS density (P =0.0205). The low P-TLS density (< 0.098/mm2) was significantly associated with reduced RFS (HR=6.597 95% CI: 2.882-15.103, P <0.001) and reduced overall survival (OS) (HR=6.628 95% CI: 2.893-15.183, P < 0.001) of nmCRC patients. In-TLS was not of significance in evaluating the clinical outcomes of nmCRC patients. H-TSP was significantly associated with reduced RFS (HR=0.126 95% CI: 0.048-0.333, P <0.001) and reduced OS (HR=0.125 95% CI: 0.047-0.332, P <0.001) of nmCRC patients. The 5-year RFS of the high P-TLS, low-TLS, H-TSP, and L-TSP groups were 89.7%, 47.2%, 53.2%, and 92.5%, respectively. The P-TLS density, TSP and TNM stage were independent prognosis factors of nmCRC patients. The Nomogram, including the P-TLS density, TSP and TNM stage, outperformed the TNM stage.

Conclusions

High P-TLS density and low TSP (L-TSP) were independent and favorable prognostic factors of nmCRC patients, which might provide new directions for targeted therapy in the CRC tumor microenvironment, especially the tumor immune microenvironment.

A novel epithelial-mesenchymal transition (EMT)-related gene signature of predictive value for the survival outcomes in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a remarkably heterogeneous and aggressive disease with dismal prognosis of patients. The identification of promising prognostic biomarkers might enable effective diagnosis and treatment of LUAD. Aberrant activation of epithelial-mesenchymal transition (EMT) is required for LUAD initiation, progression and metastasis. With the purpose of identifying a robust EMT-related gene signature (E-signature) to monitor the survival outcomes of LUAD patients. In The Cancer Genome Atlas (TCGA) database, least absolute shrinkage and selection operator (LASSO) analysis and cox regression analysis were conducted to acquire prognostic and EMT-related genes. A 4 EMT-related and prognostic gene signature, comprising dickkopf-like protein 1 (DKK1), lysyl oxidase-like 2 (LOXL2), matrix Gla protein (MGP) and slit guidance ligand 3 (SLIT3), was identified. By the usage of datum derived from TCGA database and Western blotting analysis, compared with adjacent tissue samples, DKK1 and LOXL2 protein expression in LUAD tissue samples were significantly higher, whereas the trend of MGP and SLIT3 expression were opposite. Concurrent with upregulation of epithelial markers and downregulation of mesenchymal markers, knockdown of DKK1 and LOXL2 impeded the migration and invasion of LUAD cells. Simultaneously, MGP and SLIT3 silencing promoted metastasis and induce EMT of LUAD cells. In the TCGA-LUAD set, receiver operating characteristic (ROC) analysis indicated that our risk model based on the identified E-signature was superior to those reported in literatures. Additionally, the E-signature carried robust prognostic significance. The validity of prediction in the E-signature was validated by the three independent datasets obtained from Gene Expression Omnibus (GEO) database. The probabilistic nomogram including the E-signature, pathological T stage and N stage was constructed and the nomogram demonstrated satisfactory discrimination and calibration. In LUAD patients, the E-signature risk score was associated with T stage, N stage, M stage and TNM stage. GSEA (gene set enrichment analysis) analysis indicated that the E-signature might be linked to the pathways including GLYCOLYSIS, MYC TARGETS, DNA REPAIR and so on. In conclusion, our study explored an innovative EMT based prognostic signature that might serve as a potential target for personalized and precision medicine.

Primary Tumor Resection Provides Survival Benefits for Patients with Synchronous Brain Metastases from Colorectal Cancer

Background: Brain metastases (BMs), particularly synchronous brain metastases, in colorectal cancer (CRC) patients are uncommon. The survival benefit of primary tumor resection (PTR) in patients with metastatic colorectal cancer is controversial. Whether PTR can bring survival benefits to patients with BMs of CRC has not been reported. Methods: From 2010 to 2016, 581 CRC patients with BMs from the Surveillance, Epidemiology, and End Results (SEER) database were divided into PTR and non-PTR groups. The log-rank test was used to compare the survival distributions. The Kaplan-Meier method was used to estimate survival. By controlling additional prognostic factors, a Cox proportional multivariate regression analysis was used to estimate the survival benefit of PTR. Results: The median overall survival for CRC patients with synchronous BMs was 3 months, with a 1-year survival rate of 27.2% and a 2-year survival rate of 12.8%. The PTR group contained 171 patients (29.4%), whereas the non-PTR group had 410 patients (70.6%). Patients who underwent PTR had a 1-year survival rate of 40.2% compared to 21.7% in those who did not (p < 0.0001). Cox proportional analysis showed that patients ≥60 years (hazard ratio [HR] 1.718, 95% confidence interval [CI] 1.423−2.075, p < 0.0001) had a shorter OS than patients < 60 years of age. OS was better in CEA-negative than in CEA-positive patients (HR 0.652, 95% CI 0.472−0.899, p = 0.009). Patients in whom the primary tumor was removed had considerably improved prognoses (HR 0.654, 95% CI 0.531−0.805, p < 0.0001). Subgroup analysis revealed that the PTR group achieved a survival advantage except for patients with CEA negative. Conclusions: Patients with synchronous BMs from CRC may benefit from primary tumor resection (PTR). Age, CEA level, and PTR were independent prognostic risk factors for CRC patients with synchronous BMs.

Discovering Innate Driver Variants for Risk Assessment of Early Colorectal Cancer Metastasis

Metastasis is the main fatal cause of colorectal cancer (CRC). Although enormous efforts have been made to date to identify biomarkers associated with metastasis, there is still a huge gap to translate these efforts into effective clinical applications due to the poor consistency of biomarkers in dealing with the genetic heterogeneity of CRCs. In this study, a small cohort of eight CRC patients was recruited, from whom we collected cancer, paracancer, and normal tissues simultaneously and performed whole-exome sequencing. Given the exomes, a novel statistical parameter LIP was introduced to quantitatively measure the local invasion power for every somatic and germline mutation, whereby we affirmed that the innate germline mutations instead of somatic mutations might serve as the major driving force in promoting local invasion. Furthermore, via bioinformatic analyses of big data derived from the public zone, we identified ten potential driver variants that likely urged the local invasion of tumor cells into nearby tissue. Of them, six corresponding genes were new to CRC metastasis. In addition, a metastasis resister variant was also identified. Based on these eleven variants, we constructed a logistic regression model for rapid risk assessment of early metastasis, which was also deployed as an online server, AmetaRisk (http://www.bio-add.org/AmetaRisk). In summary, we made a valuable attempt in this study to exome-wide explore the genetic driving force to local invasion, which provides new insights into the mechanistic understanding of metastasis. Furthermore, the risk assessment model can assist in prioritizing therapeutic regimens in clinics and discovering new drug targets, and thus substantially increase the survival rate of CRC patients.

Development and validation of a prognostic scoring system for patients with colorectal cancer hepato-pulmonary metastasis: a retrospective study

Background

Hepato-pulmonary metastasis of colorectal cancer (CRC) is a rare disease with poor prognosis. This study aims to establish a highly efficient nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with colorectal cancer hepato-pulmonary metastasis (CRCHPM).

Methods

We retrospectively analyzed the data of patients with CRCHPM from SEER database and Wuhan Union Hospital Cancer Center (WUHCC). A total of 1250 CRCHPM patients were randomly assigned to the training, internal validation, and external validation cohorts from 2010 to 2016.Univariate and multivariate cox analysis were performed to identify independent clinicopathological predictors of OS and CSS, and a nomogram was constructed to predict OS and CSS in CRCHPM patients.

Results

A nomogram of OS was constructed based on seven independent predictors of age, degree of differentiation, T stage, chemotherapy, number of lsampled lymph nodes, number of positive lymph nodes, and tumor size. Nomogram showed favorable sensitivity in predicting OS at 1, 3 and 5 years, with area under the receiver operating characteristic curve (AUROC) values of 0.802, 0.759 and 0.752 in the training cohort;0.814, 0.769 and 0.716 in the internal validation cohort;0.778, 0.756 and 0.753 in the external validation cohort, respectively. A nomogram of CSS was constructed based on three independent predictors of T stage, chemotherapy, and tumor size. The AUROC values of 1, 3 and 5 years were 0.709,0.588,0.686 in the training cohort; 0.751, 0.648,0.666 in the internal validation cohort;0.781,0.588,0.645 in the external validation cohort, respectively. Calibration curves, Concordance index (C-index), and decision curve analysis (DCA) results revealed that using our model to predict OS and CSS is more efficient than other single clinicopathological characteristics.

Conclusion

A nomogram of OS and CSS based on clinicopathological characteristics can be conveniently used to predict the prognosis of CRCHPM patients.

Nomogram predicting the cancer-specific survival of early-onset colorectal cancer patients with synchronous liver metastasis: a population-based study

PURPOSE

This research aimed to explore prognostic factors for early-onset colorectal cancer (EO-CRC) patients with liver metastasis (LM) and develop nomogram for predicting cancer-specific survival (CSS) probability quantitatively.

METHODS

Our study included 4368 EO-CRC patients with LM registered in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Potential prognostic factors for EO-CRC patients with LM were identified by multivariable Cox regression analysis. Prognostic nomogram was subsequently constructed based on these prognostic factors. The discriminative ability, calibration, and clinical usefulness of the nomogram were assessed by the area under the receiver operating characteristic (ROC) curves (AUC), calibration curves, and decision curve analysis (DCA).

RESULTS

In the training cohort, marital status, primary tumor location, histopathological grade, T stage, number of metastatic organs, carcinoembryonic antigen (CEA), perineural invasion (PI), surgery of primary site, chemotherapy, radiation therapy, and metastatic lymph nodes ratio (LNR) were prognostic factors for cancer-specific mortality of EO-CRC patients with LM. The 1-, 2-, and 3-year AUC values of the prognostic nomogram were 0.777, 0.781, and 0.788, respectively. Calibration curves indicated acceptable agreement between nomogram-predicted survival and actual observed survival at 1, 2, and 3 years. DCA curves exhibited good positive net benefits in the prognostic model in most threshold probabilities at different time points. All of these results were reproducible in the validation cohort.

CONCLUSIONS

This study identified prognostic factors for EO-CRC patients with LM and developed a prognostic nomogram with good performance and clinical usability, which may help clinicians make better treatment decisions.

Predicting Distant Metastasis in Young-Onset Colorectal Cancer After Surgery: A Retrospective Study

Background

Although overall colorectal cancer (CRC) cases have been declining worldwide, there has been an increase in the incidence of the CRC among individuals younger than 50 years old, which is associated with distant metastasis (DM) and poor prognosis.

Methods

Young-onset CRC patients' postoperative data were collected from the Surveillance, Epidemiology, and End Results (SEER) database between January 2010 and December 2015. Data from the SEER database were divided into early stage and advanced stage according to whether chemoradiotherapy was recommended in the guidelines. Independent risk factors for DM were explored by using univariate and multivariate logistic regression separately. A predictive model was established and presented as nomogram in the training set of advanced stage. The model was internally verified in testing set and externally validated in a cohort of 145 patients from Zhongnan Hospital of Wuhan University. The accuracy, reliability, and clinical application value were assessed using the receiver operating characteristic curve (ROC), the area under the curve (AUC), calibration curve, and decision curve analysis (DCA), respectively. Different risk subgroups of DM were classified according to the scores of the nomogram in the training set of advanced stage.

Results

A total of 5,584 patients were eligible and enrolled in our study in which 1,277 were in early stage and 4,307 in advanced stage. Preoperative CEA positive was found to be an independent predictor of DM in early stage. Multivariate logistic regression analysis showed that tumor size, degree of differentiation, T stage, N stage, preoperative CEA, and whether radiation or chemotherapy performed were independent risk factors for DM (all, p < 0.05) in advanced stage. Great accuracies were achieved in our nomogram with AUC of 0.801 in training set, 0.811 in testing set, and 0.791 in the validation cohort, respectively. Calibration curves and DCA in internal validation and external validation both showed good stability and clinical utility values.

Conclusions

Preoperative CEA positive was a significant predictor of DM for young-onset CRC patients. A novel nomogram containing clinical and pathological features was established for predicting DM of advanced CRC in patients younger than 50 years old. This tool may serve as an early alert for clinicians to DM and make better clinical treatment regimens.

Predicting metachronous liver metastasis in patients with colorectal cancer: development and assessment of a new nomogram

Background

We aimed to develop and validate a nomogram model, which could predict metachronous liver metastasis in colorectal cancer within two years after diagnosis.

Methods

A retrospective study was performed on colorectal cancer patients who were admitted to Beijing Shijitan Hospital from January 1, 2016 to June 30, 2019. The least absolute shrinkage and selection operator (LASSO) regression model was used to optimize feature selection for susceptibility to metachronous liver metastasis in colorectal cancer. Multivariable logistic regression analysis was applied to establish a predictive model through incorporating features selected in the LASSO regression model. C-index, receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA) were employed to assess discrimination, distinctiveness, consistency with actual occurrence risk, and clinical utility of candidate predictive model. Internal validation was assessed with bootstrapping method.

Results

Predictors contained in candidate prediction nomogram included age, CEA, vascular invasion, T stage, N stage, family history of cancer, and KRAS mutation. This model displayed good discrimination with a C-index of 0.787 (95% confidence interval: 0.728–0.846) and good calibration, whereas area under the ROC curve (AUC) of 0.786. Internal validation obtained C-index of 0.786, and AUC of validation cohort is 0.784. Based on DCA, with threshold probability range from 1 to 60%; this predictive model might identify colorectal cancer metachronous liver metastasis to achieve a net clinical benefit.

Conclusion

We have developed and validated a prognostic nomogram with good discriminative and high accuracy to predict metachronous liver metastasis in CRC patients.

Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer

Background

Liver is the most common metastatic site of colorectal cancer (CRC) and liver metastasis (LM) determines subsequent treatment as well as prognosis of patients, especially in T1 patients. T1 CRC patients with LM are recommended to adopt surgery and systematic treatments rather than endoscopic therapy alone. Nevertheless, there is still no effective model to predict the risk of LM in T1 CRC patients. Hence, we aim to construct an accurate predictive model and an easy-to-use tool clinically.

Methods

We integrated two independent CRC cohorts from Surveillance Epidemiology and End Results database (SEER, training dataset) and Xijing hospital (testing dataset). Artificial intelligence (AI) and machine learning (ML) methods were adopted to establish the predictive model.

Results

A total of 16,785 and 326 T1 CRC patients from SEER database and Xijing hospital were incorporated respectively into the study. Every single ML model demonstrated great predictive capability, with an area under the curve (AUC) close to 0.95 and a stacking bagging model displaying the best performance (AUC = 0.9631). Expectedly, the stacking model exhibited a favorable discriminative ability and precisely screened out all eight LM cases from 326 T1 patients in the outer validation cohort. In the subgroup analysis, the stacking model also demonstrated a splendid predictive ability for patients with tumor size ranging from one to50mm (AUC = 0.956).

Conclusion

We successfully established an innovative and convenient AI model for predicting LM in T1 CRC patients, which was further verified in the external dataset. Ultimately, we designed a novel and easy-to-use decision tree, which only incorporated four fundamental parameters and could be successfully applied in clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02424-7.

FOLFIRI (folinic acid, fluorouracil, and irinotecan) increases not efficacy but toxicity compared with single-agent irinotecan as a second-line treatment in metastatic colorectal cancer patients: a randomized clinical trial

Background:

FOLFIRI [irinotecan, folinic acid (CF), and fluorouracil] is considered a standard second-line chemotherapy regimen for patients with metastatic colorectal cancer (mCRC) who failed first-line XELOX/FOLFOX regimens. However, it remains unknown whether fluorouracil is still necessary in this case. This trial was designed to test the superiority of FOLFIRI over single-agent irinotecan as a second-line treatment for patients with mCRC.

Methods:

This randomized clinical trial was conducted in five hospitals in China. From 4 November 2016 to 17 January 2020, patients aged 18 years or older with histologically confirmed unresectable mCRC and who had failed first-line XELOX/FOLFOX regimens were screened and enrolled. Patients were randomized to receive either FOLFIRI or irinotecan. The primary endpoint was progression-free survival (PFS). Secondary endpoints included overall survival (OS), objective response rate (ORR), and toxicity. Data were analyzed on an intention-to-treat basis.

Results:

A total of 172 patients with mCRC were randomly treated with FOLFIRI (n = 88) or irinotecan (n = 84). The median PFS was 104 and 112 days (3.5 and 3.7 months) in the FOLFIRI and irinotecan groups, respectively [hazard ratio (HR) = 1.084, 95% confidence interval (CI) = 0.7911–1.485; p = 0.6094], and there was also no significant difference in OS and ORR between the two groups. The incidence of the following adverse events (AEs) was significantly higher in the FOLFIRI group than in the irinotecan group: any grade AEs including leucopenia (73.9% versus 55.4%), neutropenia (72.7% versus 56.6%), thrombocytopenia (31.8% versus 18.1%), jaundice (18.2% versus 7.2%), mucositis (40.9% versus 14.5%), vomiting (37.5% versus 21.7%), and fever (19.3% versus 7.2%) and grade 3–4 neutropenia (47.7% versus 21.7%).

Conclusion:

This is the first head-to-head trial showing that single-agent irinotecan yielded PFS, OS, and ORR similar to FOLFIRI, with a more favorable toxicity profile; therefore, it might be a more favorable standard chemotherapy regimen for mCRC patients who failed first-line XELOX/FOLFOX regimens.

Trial registration:

This study is registered with ClinicalTrials.gov, number NCT02935764, registered 17 October 2016, https://clinicaltrials.gov/ct2/show/NCT02935764.

The natural orifice specimen extraction surgery compared with conventional laparoscopy for colorectal cancer: A meta-analysis of efficacy and long-term oncological outcomes

PURPOSE

Natural orifice specimen extraction surgery (NOSES) has gradually become established in treating colorectal cancer. This meta-analysis assesses NOSES in the treatment of colorectal cancer compared with conventional laparoscopy (CL) and determines the effect of long-term prognosis.

METHODS

Various medical databases were searched up to May 2021. We included retrospective and randomized trials on the treatment of colorectal cancer with NOSES. Pooled weighted/standardized mean differences (WMD/SMD), odds ratios (OR) and hazard ratios (HR) with 95% confidence intervals (CIs) were calculated using either fixed- or random-effects models. STATA was conducted for the meta-analysis.

RESULTS

This meta-analysis included 16 studies comprising 2266 patients. Compared with CL, NOSES had more benefits in terms of overall postoperative complications (OR = 0.47, 95%CI [0.35,0.64]; Z = 4.91, P < 0.001), incision-related complications (OR = 0.15, 95%CI [0.07,0.31]; Z = 4.97, P < 0.001), time to first flatus (SMD = -0.58, 95%CI [-0.68,-0.48]; Z = 11.21, P < 0.001), hospital stay (WMD = -1.03, 95%CI [-1.55,-0.51]; Z = 3.86, P < 0.001), cosmetic scores (WMD = 1.37, 95%CI [0.59,2.14]; Z = 3.47, P = 0.001), the visual analogue scale on postoperative day 1（WMD = -1.46, 95%CI [-2.39,-0.52]; Z = 3.06, P = 0.002), additional analgesics usage (OR = 0.33, 95%CI [0.26, 0.43]; Z = 8.43, P < 0.001), whereas the operative time of NOSES was prolonged (WMD = 13.09, 95%CI [7.07,19.11]; Z = 4.26, P < 0.001). Postoperative anastomotic complications, intra-abdominal infection, pelvic floor function, intraoperative blood loss, number of lymph node dissection, 3-year disease-free and overall survival in the NOSES group were comparable with those in the CL group.

CONCLUSIONS

NOSES is a safe and reliable surgical procedure for the treatment of colorectal cancer and provides good long-term oncological outcomes. Large-scale multicenter studies are required to confirm its clinical benefits.

Incidence and predictors of brain metastasis in colorectal cancer patients

PURPOSE

Brain metastasis (BM) in colorectal cancer patients is rare and is associated with dismal outcomes. Our study aims to evaluate the incidence and predictors of BM in patients with colorectal cancer.

METHODS

We performed a retrospective analysis (2010-2017) of patients with a primary diagnosis of colorectal cancer (CRC). Patients were stratified into two groups (BM vs. No-BM). Outcome measures were the incidence and predictors of BM. Multivariate logistic regression analysis was performed.

RESULTS

A total of 230,806 patients were analyzed. A total of 0.30% (n = 691) of the patients were found to have BM. On multivariate logistics regression, bone (OR: 5.39 [3.36-8.65], p < 0.001), lung (OR: 3.75 [2.67-5.28], < 0.001), and distant node metastasis (OR: 32.75 [20.47-52.41], p < 0.001) were independent predictors of BM.

CONCLUSION

Our study supports the low incidence of brain metastasis in patients with colorectal cancer. A unique set of characteristics is identified to confer an increased risk of brain metastases.

Development and Validation of the Individualized Prognostic Nomograms in Patients With Right- and Left-Sided Colon Cancer

Background

The overall survival (OS) of patients diagnosed with colon cancer (CC) varied greatly, so did the patients with the same tumor stage. We aimed to design a nomogram that is capable of predicting OS in resected left-sided colon cancers (LSCC) and right-sided colon cancers (RSCC), and thus to stratify patients into different risk groups, respectively.

Methods

Records from a retrospective cohort of 577 patients with complete information were used to construct the nomogram. Univariate and multivariate analyses screened risk factors associated with overall survival. The performance of the nomogram was evaluated with concordance index (c-index), calibration plots, and decision curve analyses for discrimination, accuracy, calibration ability, and clinical net benefits, respectively, which was further compared with the American Joint Committee on Cancer (AJCC) 8th tumor-node-metastasis (TNM) classification. Risk stratification based on nomogram scores was performed with recursive partitioning analysis.

Results

The LSCC nomogram incorporated carbohydrate antigen 12-5 (CA12-5), age and log odds of positive lymph nodes (LODDS), and RSCC nomogram enrolled tumor stroma percentage (TSP), age and LODDS. Compared with the TNM classification, the LSCC and RSCC nomograms both had a statistically higher C-index (0.837, 95% CI: 0.827-0.846 and 0.780, 95% CI 0.773-0.787, respectively) and more clinical net benefits, respectively. Calibration plots revealed no deviations from reference lines. All results were reproducible in the validation cohort.

Conclusions

An original predictive nomogram was constructed and validated for OS in patients with CC after surgery, which had facilitated physicians to appraise the individual survival of postoperative patients accurately and to identify high-risk patients who were in need of more aggressive treatment and follow-up strategies.

Combined detection of peripheral blood VEGF and inflammation biomarkers to evaluate the clinical response and prognostic prediction of non-operative ESCC

An association between angiogenesis/inflammation status and tumor has been reported in various types of cancer. This study sought to assess the role of peripheral blood VEGF and some inflammation biomarkers in evaluating clinical response and prognosis in patients with non-operative esophageal squamous cell carcinoma (ESCC). Peripheral blood of 143 patients with non-operative ESCC at our institute was dynamically collected at 5 time points including 1 day before radiotherapy, during radiotherapy (15f), at the end of radiotherapy, 1 month after radiotherapy, and 3 months after radiotherapy. VEGF expression in the peripheral blood was detected and related inflammation biomarkers such as GPS, CAR and CLR were counted. Logistic regression and Cox regression were implemented respectively to analyze the correlation of each predictor with clinical response and prognosis. The performance of combined testing was estimated using AUCs. Based on independent predictors, a nomogram prediction model was established to predict the probabilities of 1- and 2-year PFS of patients. The effectiveness of the nomogram model was characterized by C-index, AUC, calibration curves and DCA. VEGF and CLR levels at the end of radiotherapy were independent predictors of clinical response, while VEGF and GPS levels at 3 months after radiotherapy were independent prognostic predictors. The efficacy of combined detection of VEGF and CLR is superior to the single detection in evaluating clinical response and prognosis. The nomogram showed excellent accuracy in predicting PFS. The combined detection of VEGF and CLR at the end of radiotherapy can be used to evaluate the clinical response of patients with non-operative ESCC, and the combined detection of VEGF and GPS 3 months after radiotherapy can be used to predict the prognosis. Implemented by nomogram model, it is expected to provide practical and reliable method to evaluate the clinical response and prognosis of patients with non-operative ESCC tool.

Regulatory roles of osteopontin in human lung cancer cell epithelial-to-mesenchymal transitions and responses

BACKGROUND

Lung cancer is still the main cause of death in patients with cancer, due to poor understanding of intracellular regulations. Of those, osteopontin (OPN) may induce the epithelial-to-mesenchymal transition (EMT) to promote tumor cell metastasis. The present study aims to evaluate the regulatory mechanism of internal and external OPN in the development of lung cancer.

METHODS

We evaluated genetic variations and different bioinformatics of genes in chromosome 4 among subtypes of lung cancer using global databases. We validated the expression of OPN and EMT-related proteins (e.g., E-cadherin, vimentin) in 208 non-small-cell lung cancer (NSCLC) tumors and the adjacent nontumorous tissues, further to explore the function of OPN in the progression of lung cancer, with a focus on a potential communication between OPN and EMT in the lung cancer.

RESULTS

We found that OPN might act as a target molecule in lung cancer, which is associated with lymph node metastasis, postresection recurrence/metastasis, and prognosis of patients with lung cancer. Biological behaviors and pathological responses of OPN varied among diseases, challenges, and severities. Overexpression of OPN was correlated with the existence of EMT in lung cancer tissues. Internal and external OPN plays the decisive roles in lung cancer cell movement, proliferation, and EMT formation, through the upregulation of OPN-PI3K and OPN-MEK pathways. PI3K and MEK inhibitors downregulated the process of EMT and biological behaviors of lung cancer cells, probably through altering vimentin-associated cytoskeletons.

CONCLUSION

OPN can be a metastasis-associated or specific biomarker for lung cancer and a potential target for antimetastatic treatment.

A Prognostic Model for Patients With Gastric Signet Ring Cell Carcinoma

Background:

The aim of our study was to develop a nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with gastric signet ring cell carcinoma (GSRC).

Methods:

GSRC patients from 2004 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to the training and validation sets. Multivariate Cox regression analyses screened for OS and CSS independent risk factors and nomograms were constructed.

Results:

A total of 7,149 eligible GSRC patients were identified, including 4,766 in the training set and 2,383 in the validation set. Multivariate Cox regression analysis showed that gender, marital status, race, AJCC stage, TNM stage, surgery and chemotherapy were independent risk factors for both OS and CSS. Based on the results of the multivariate Cox regression analysis, prognostic nomograms were constructed for OS and CSS. In the training set, the C-index was 0.754 (95% CI = 0.746-0.762) for the OS nomogram and 0.762 (95% CI: 0.753-0.771) for the CSS nomogram. In the internal validation, the C-index for the OS nomogram was 0.758 (95% CI: 0.746-0.770), while the C-index for the CSS nomogram was 0.762 (95% CI: 0.749-0.775). Compared with TNM stage and SEER stage, the nomogram had better predictive ability. In addition, the calibration curves also showed good consistency between the predicted and actual 3-year and 5-year OS and CSS.

Conclusion:

The nomogram can effectively predict OS and CSS in patients with GSRC, which may help clinicians to personalize prognostic assessments and clinical decisions.

AKP and GGT level can provide an early prediction of first-line treatment efficacy in colorectal cancer patients with hepatic metastases

Aim: It is important to early evaluate or predict the efficacy to avoid ineffective treatment for most colorectal cancer (CRC) patients with liver metastases. Patients & methods: The medical records of 440 patients with histologically confirmed primary CRC admitted to the Fudan University Shanghai Cancer Center were reviewed. Results: High baseline serum alkaline phosphatase (AKP) and γ-glutamyl transferase (GGT) is associated with worse overall survival. In patients with a high serum AKP and GGT a decreased percentage had high objective response rate and better progression-free survival. Conclusion: Measuring the changes of serum AKP or GGT in CRC patients with hepatic metastases before and after the first cycle of treatment is a convenient, fast and economical way to early predict antitumor treatment efficacy.

A Nomogram for Predicting Multiple Metastases in Metastatic Colorectal Cancer Patients: A Large Population-Based Study

OBJECTIVES

The present study aims to discover the risk factors of multiple metastases and develop a functional nomogram to forecast multiple metastases in metastatic colorectal cancer (mCRC) patients.

METHODS

mCRC cases were retrospectively collected from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016. Survival times between multiple metastases and single metastasis were compared using Kaplan-Meier analysis and log-rank tests. Risk factors for multiple metastases were determined by univariate and multivariate logistic regression analyses, and a nomogram was developed to forecast the probability of multiple metastases in mCRC patients. We assessed the nomogram performance in terms of discrimination and calibration, including concordance index (C-index), area under the curve (AUC), and decision curve analysis (DCA). Bootstrap resampling was used as an internal verification method, and at the same time we select external data from Renmin Hospital of Wuhan University as independent validation sets.

RESULTS

A total of 5,302 cases were included in this study as training group, while 120 cases were as validation group. The patients with single metastasis and multiple metastases were 3,531 and 1,771, respectively. The median overall survival (OS) and cancer-specific survival (CSS) for patients with multiple metastases or single metastasis were 19 vs. 31 months, and 20 vs. 33 months, respectively. Based on the univariate and multivariate analyses, clinicopathological characteristics were associated with number of metastasis and were used to establish nomograms to predict the risk of multiple metastases. The C-indexes and AUC for the forecast of multiple metastases were 0.715 (95% confidence interval (CI), 0.707-0.723), which showed the nomogram had good discrimination and calibration curves of the nomogram showed no significant bias from the reference line, indicating a good degree of calibration. In the validation group, the AUC was 0.734 (95% CI, 0.653-0.834), and calibration curve also showed no significant bias, indicating the favorable effects of our nomogram.

CONCLUSIONS

We developed a new nomogram to predict the risk of multiple metastases. The nomogram shows the good prediction effect and can provide assistance for clinical diagnosis and treatment.

Improved Overall Survival of Colorectal Cancer under Multidisciplinary Team: A Meta-Analysis

Purpose

The purpose of the current meta-analysis was to evaluate whether multidisciplinary team improved overall survival of colorectal cancer.

Methods

PubMed, EMBASE, and Cochrane Library database were searched from inception to October 25, 2020. The hazard ratio (HR) and 95% confidence (CI) of overall survival (OS) were calculated.

Results

A total of 11 studies with 30814 patients were included in this meta-analysis. After pooling the HRs, the MDT group was associated with better OS compared with the non-MDT group (HR = 0.81, 95% CI 0.69-0.94, p = 0.005). In subgroup analysis of stage IV colorectal cancer, the MDT group was associated with better OS as well (HR = 0.73, 95% CI 0.59-0.90, p = 0.004). However, in terms of postoperative mortality, no significant difference was found between MDT and non-MDT groups (OR = 0.84, 95% CI 0.44-1.61, p = 0.60).

Conclusion

MDT could improve OS of colorectal cancer patients.

Brain Metastases from Colorectal Cancer: A Systematic Review of the Literature and Meta-Analysis to Establish a Guideline for Daily Treatment

Colorectal cancer (CRC) is the third most common malignancy worldwide. Most patients with metastatic CRC develop liver or lung metastases, while a minority suffer from brain metastases. There is little information available regarding the presentation, treatment, and overall survival of brain metastases (BM) from CRC. This systematic review and meta-analysis includes data collected from three major databases (PubMed, Cochrane, and Embase) based on the key words "brain", "metastas*", "tumor", "colorectal", "cancer", and "malignancy". In total, 1318 articles were identified in the search and 86 studies matched the inclusion criteria. The incidence of BM varied between 0.1% and 11.5%. Most patients developed metastases at other sites prior to developing BM. Lung metastases and KRAS mutations were described as risk factors for additional BM. Patients with BM suffered from various symptoms, but up to 96.8% of BM patients were asymptomatic at the time of BM diagnosis. Median survival time ranged from 2 to 9.6 months, and overall survival (OS) increased up to 41.1 months in patients on a multimodal therapy regimen. Several factors including age, blood levels of carcinoembryonic antigen (CEA), multiple metastases sites, number of brain lesions, and presence of the KRAS mutation were predictors of OS. For BM diagnosis, MRI was considered to be state of the art. Treatment consisted of a combination of surgery, radiation, or systemic treatment.