Nomogram for predicting occurrence and prognosis of liver metastasis in colorectal cancer: a population-based study

Development and validation of nomogram model predicting overall survival and cancer specific survival in glioblastoma patients

BACKGROUND

Identifying the incidence and risk factors of Glioblastoma (GBM) and establishing effective predictive models will benefit the management of these patients.

METHODS

Using GBM data from the Surveillance, Epidemiology, and End Results (SEER) database, we used Joinpoint software to assess trends in GBM incidence across populations of different age groups. Subsequently, we identified important prognostic factors by stepwise regression and multivariate Cox regression analysis, and established a Nomogram mathematical model. COX regression model combined with restricted cubic splines (RCS) model was used to analyze the relationship between tumor size and prognosis of GBM patients.

RESULTS

The incidence of GBM has been on the rise since 1978, especially in the age group of 65-84 years. 11498 patients with GBM were included in our study. The multivariate Cox analysis revealed that age, tumor size, sex, primary tumor site, laterality, number of primary tumors, surgery, chemotherapy, radiotherapy, systematic therapy, marital status, median household income, first malignant primary indicator were independent prognostic factors of overall survival (OS) for GBMs. For cancer-specific survival (CSS), race is also independent prognostic factors. Additionally, risk of poor prognosis increased significantly with tumor size in patients with tumors smaller than 49 mm. Moreover, our nomogram model showed favorable discriminative ability.

CONCLUSION

At the population level, the incidence of GBM is on the rise. The relationship between tumor size and patient prognosis is still worthy of further study. Moreover, the proposed nomogram with good performance was constructed and verified to predict the OS and CSS of patients with GBM.

Development and validation of a clinical prognosis prediction model for malignant intestinal obstruction: A retrospective cohort study

Malignant bowel obstruction (MBO) is a common and complex condition in clinical practice, which seriously affects the quality of life and prognosis of patients. However, the current lack of effective prognostic models for MBO has greatly limited clinical precision treatment and patient management. Focusing on this issue, this study aims to construct and validate a prognostic model for the overall survival (OS) of MBO patients, providing crucial support for clinical decision - making and improving the prognosis of patients. In this study, 41 items of real - world data from 192 patients in the Affiliated Hospital of Nantong University from January 2022 to January 2024 were collected, including 39 independent variables, survival time, and survival status. Subsequently, the patients were randomly divided into groups at a ratio of 7:3. Predictor variables were screened using the Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression, and then a Cox model was constructed. The model was validated using the Concordance index (C - index), time - dependent Receiver Operating Characteristic (ROC) curve, and Decision Curve Analysis (DCA). Finally, a nomogram of the model was created. The study found that significant risk factors affecting patient mortality included chemoradiotherapy (β = - 1.24; HR = 0.29;95%CI, 0.14-0.59), conservative treatment (β = 1.34; HR = 3.81; 95%CI, 1.69-8.55), new cases (β = - 0.96; HR = 0.38; 95%CI, 0.19-0.77), AJCC T stage 4 (β = 2.16; HR = 8.64; 95%CI, 1.47-50.76), red blood cell count (RBC, β = - 0.63; HR = 0.53; ; 95%CI, 0.38-0.80), prothrombin time (PT, β = 0.37; HR = 1.45; ; 95%CI, 1.07-1.97), aspartate aminotransferase (AST, β = 0.01; HR = 1.01; 95%CI, 1.00-1.02), and intestinal necrosis (β = 1.73; HR = 5.62; 95%CI, 1.11-28.27). In the development set, the AUC and C - index values of the prognostic models for 30 - day, 90 - day, and 180 - day are 0.87, 0.94, and 0.92 respectively. In the validation set, the corresponding values are 0.83, 0.96, and 0.89. The results of DCA analysis indicated that the model was reliable and could effectively predict the 30 - day, 90 - day, and 180 - day survival periods of MBO patients. This study successfully constructed and validated a prognostic model for the overall survival of MBO patients. This model identified multiple key prognostic factors and exhibited good predictive performance. It provides important reference for clinicians to predict the survival period of MBO patients and develop personalized treatment plans, and is expected to improve the clinical outcomes of MBO patients.

CircSATB1 Promotes Colorectal Cancer Liver Metastasis through Facilitating FKBP8 Degradation via RNF25-Mediated Ubiquitination

Colorectal cancer (CRC) is one of the most common cancers worldwide and liver metastasis is the leading reason for its mortality. Circular RNAs (circRNAs) are conclusively associated with the progression of various cancers, rendering the exploration of its specific mechanisms in colorectal cancer liver metastasis(CRLM) highly valuable. Combined with GEO (Gene Expression Omnibus) databases and clinical data in our center, we found that high expression of circSATB1 is closely related to the progression of CRLM. Functionally, circSATB1 could significantly promote the metastatic ability of CRC cells in vitro and in vivo. Mechanistically, circSATB1 facilitated the RNF25-mediated ubiquitylation and degradation of FKBP8, releasing its inhibitory effects on mTOR signaling. In this process, circSATB1 acted as a scaffold for RNF25-FKBP8 complexes. Additionally, circSATB1 could be packaged in exosomes and secreted from the CRC primary tumors into plasma. In conclusion, this study uncovered a new circSATB1 that acts as a potent promoter of CRLM and offers novel insights into the precision therapeutic strategies for CRLM.

Nomogram based on burn characteristics and the National Early Warning Score to predict survival in severely burned patients

BACKGROUND

Extensive burns are associated with a high mortality rate. Early prediction and action can reduce mortality. The National Early Warning Score (NEWS) is considered the best early warning score for predicting mortality. However, there has been no assessment conducted on the clinical prognostic significance of NEWS in individuals suffering from severe burns. The objective of this research was to establish a nomogram based on burn characteristics and the NEWS to predict survival in severely burned patients.

METHODS

A retrospective analysis was performed on 335 patients diagnosed with extensive burns from 2005 to 2021 in the Department of Burn Surgery of Changhai Hospital, the First Affiliated Hospital of Naval Medical University. Univariate and multivariate analyses were used to determine independent prognostic factors. A nomogram was developed using these prognostic factors and its internal validity was assessed through bootstrap resampling.

RESULTS

The results of multivariate analysis showed that the independent factors affecting the prognosis of severe burn patients were age, full-thickness burn, creatinine, inhalation tracheotomy, and the NEWS, all of which were identified to create the nomogram. The Akaike Information Criterion and Bayesian Information Criterion values of the nomogram demonstrated superior goodness-of-fit in predicting severe burns compared to NEWS, with lower scores (195.21 vs. 201.24; 221.91 vs. 224.12, respectively). The bootstrap-adjusted concordance index (C-index) of the nomogram yielded a higher value of 0.923(95 % CI 0.892-0.953), compared to NEWS which had a C-index of 0.699 (95 % CI 0.628-0.770). The calibration curves demonstrated excellent agreement between predicted probabilities and observed outcomes in the nomogram analysis. Furthermore, decision curve analysis indicated promising clinical utility for the proposed nomogram model. By applying an appropriate cutoff value derived from receiver operating characteristics curve analysis, it was observed that the high-risk group identified by the nomogram exhibited a significantly higher mortality rate than the low-risk group.

CONCLUSION

This study introduces an innovative nomogram that predicts the survival rate of individuals with severe burn injuries by combining clinical attributes and laboratory examinations, demonstrating superior efficacy compared to conventional NEWS systems.

5-hydroxymethylcytosine features of portal venous blood predict metachronous liver metastases of colorectal cancer and reveal phosphodiesterase 4 as a therapeutic target

Metachronous liver metastases (MLM) are characterised by high incidence and high mortality in clinical colorectal cancer treatment. Currently traditional clinical methods cannot effectively predict and prevent the occurrence of metachronous liver metastasis in colorectal cancer. Based on 5hmC-Seal analysis of blood and tissue samples, this study found that portal venous blood was more relevant to tumour gDNA than peripheral blood. We performed a novel epigenetic liquid biopsy strategy using the 10 5hmC epigenetic alterations, to accurately distinguish MLM patients from patients without metastases. Among these epigenetic alterations, phosphodiesterase 4 (PDE4D) was highly increased in MLM patients and correlated with poor survival. Moreover, our studies demonstrated that PDE4D was a key metastasis-driven target for drug development. Interfering with the function of PDE4D significantly repressed liver metastases. Similarly, roflumilast, a PDE4 inhibitor for chronic obstructive pulmonary disease (COPD) therapy, also inhibits liver metastases. Further studies indicate that blocking the function of PDE4D can affect CRC invasion through the HIF-1α-CCN2 pathway. To develop a more efficient PDE4 inhibitor and reduce the occurrence of adverse events, we also designed several new compounds based on 2-arylbenzofurans and discovered lead L11 with potent affinity for PDE4D and significant suppression of liver metastases. In this work, our study provides a promising strategy for predicting metachronous liver metastasis and discovers L11 as a potential repurposed drug for inhibiting liver metastasis, which have the potential to benefit patients with CRC in the future. KEY POINTS: 5hmC epigenetic markers derived from portal venous blood could accurately predict metachronous metastasis of colorectal cancer. PDE4D was a key metastasis-driven target that promoted metachronous metastasis via the HIF-1α-CCN2 pathway. The newly synthesised compound L11 could specifically inhibit PDE4D and abolish metachronous metastasis of colorectal cancer without obvious toxic side effects.

Combined inflammation-related biomarkers and clinicopathological features for the prognosis of stage II/III colorectal cancer by machine learning

BACKGROUND

Inflammation-related biomarkers, such as systemic inflammation score (SIS) and neutrophil-lymphocyte ratio (NLR), are associated with colorectal cancer prognosis. However, the combined role of SIS, NLR, and clinicopathological factors in stage II/III colorectal cancer remains unclear. This study developed a nomogram to predict long-term prognosis for these patients.

METHODS

This retrospective study included 1540 patients (training set) from the First Affiliated Hospital of Kunming Medical University and 152 patients (testing set) from The Honghe Third People's Hospital. Cox regression identified independent prognostic factors, and machine learning established predictive models. Model performance was evaluated by the C-index, area under the curve (AUC), and decision curve analysis (DCA).

RESULTS

In the training set, a total of 1540 patients with stage II/III colorectal cancer were included. More than 70 years old (HR = 1.830, p = 0.000); SIS = 2 (HR = 1.693, p = 0.002); Preoperative CEA more than 5 ng/mL (HR = 1.614, p = 0.000); and Moderately differentiated (HR = 1.438, p = 0.011); or Low/undifferentiated (HR = 2.126, p = 0.000); The pN1 (HR = 2.040, p = 0.000) and pN2 (HR = 3.297, p = 0.000) stages were considered independent prognostic risk factors of stage II/III colorectal cancer. Negative perineural invasion (HR = 0.733, p = 0.014) and NLR less than 4 (HR = 0.696, p = 0.022) were considered independent prognostic protective factors of stage II/III colorectal cancer. A nomogram was established based on SIS, NLR, and the clinicopathological results for predicting and validating the overall survival in the training and testing sets. The C-index of the training set was 0.746, and the C-index of the testing set was 0.708, indicating the high prediction efficiency of the nomogram.

CONCLUSIONS

A nomogram combining SIS, NLR, and clinicopathological factors provides an effective, cost-efficient tool for predicting the prognosis of stage II/III colorectal cancer. Future studies will validate its long-term predictive performance in larger, multicenter cohorts.

Development and validation of a nomogram for predicting survival in gastric signet ring cell carcinoma patients treated with radiotherapy

There is no effective clinical prediction model to predict the prognosis of gastric signet ring cell carcinoma (GSRC) patients treated with radiotherapy. This study retrospectively analyzed the clinical data of 20-80-year-old patients diagnosed with GSRC between 2004 and 2019 from the Surveillance, Epidemiology, and End Results (SEER) database. Using Cox regression analyses revealed independent prognostic factors, and a nomogram was constructed. The C-index, net reclassification index (NRI) and integrated discrimination improvement (IDI) of the nomogram were greater than those of the TNM staging system for predicting OS, indicating that the nomogram predicted prognosis with greater accuracy. The area under the curve (AUC) values were 0.725, 0.753 and 0.745 for the training group; 0.725, 0.763 and 0.752 for the internal validation group; and 0.795, 0.764 and 0.765 for the external validation group, respectively. Calibration plots demonstrated high agreement between the nomogram's prediction and the actual observations. The risk stratification system was able to accurately stratify patients who underwent radiotherapy for GSRC into high- and low-risk subgroups, with significant differences in prognosis. The Kaplan‒Meier survival analysis according to different treatments indicated that surgery combined with chemoradiotherapy is a more effective treatment strategy for improving OS in for GSRC patients. The nomogram is sufficiently accurate to predict the prognostic factors of GSRC receiving radiotherapy, allowing for clinicians to predict the 1-, 3-, and 5-year OS.

Establishment and validation of a nomogram model for early diagnosis of gastric cancer: a large-scale cohort study

Purpose

Identifying high-risk populations and diagnosing gastric cancer (GC) early remains challenging. This study aimed to establish and verify a nomogram model for the early diagnosis of GC based on conventional laboratory indicators.

Methods

We performed a retrospective analysis of the clinical data of 2,770 individuals with first diagnosis of GC and 1,513 patients with benign gastric disease from January 2018 to December 2022. The cases were divided into the training set and validation set randomly, with a ratio of 7:3. Variable screening was performed by least absolute shrinkage and selection operator (LASSO) and logistic regression analysis. A nomogram was constructed in the training set to assist in the early diagnosis of GC.

Results

There were 4283 patients included in the study, with 2998 patients assigned in the training set and 1285 patients in the validation set. Through LASSO regression and logistic regression analysis, independent variables associated with GC were identified, including CEA, CA199, LYM, HGB, MCH, MCHC, PLT, ALB, TG, HDL, and AFR. The nomogram model was constructed using the above 11 independent indicators. The AUC was 0.803 for the training set and 0.797 for the validation set, indicating that the model showed high clinical diagnostic efficacy. The calibration curves and decision curve analysis (DCA) of the nomogram presented good calibration and clinical application ability.

Conclusion

Based on the analysis of large sample size, we constructed a nomogram model with 11 routine laboratory indicators, which showed good discrimination ability and calibration.

CT assessment of liver fat fraction and abdominal fat composition can predict postoperative liver metastasis of colorectal cancer

OBJECTIVE

The aim of this study is to investigate the clinical value of liver fat fraction assessed by CT(CT-LFF) and abdominal fat components. We focus on predicting liver metastasis (LM) after colorectal cancer (CRC) surgery.

METHODS

Clinical and imaging data from 79 patients who underwent radical CRC surgery between January 2019 and December 2021 were retrospectively collected. Semi-automatic software was used to quantify the area of different body tissues at the level of the third lumbar vertebra, and liver fat fraction was calculated based on the CT values. Patients were grouped according to BMI, tumor grade, T stage, N stage, vascular invasion (VI), perineural invasion (PNI), and preoperative levels of CEA and CA199. A multivariate logistic regression model was used to identify independent risk factors for early LM after surgery. The diagnostic performance was assessed using the receiver operating characteristic analysis with 5-fold cross-validation. The Kaplan-Meier method was used to draw survival curves, and Log-Rank test was used for survival analysis.

RESULTS

The study found that the occurrence of LM after CRC surgery was significantly associated with CA199 positivity, VI, PNI, N1-2 stage, CT-LFF, VAT index (VATI). Multivariate logistic regression analysis showed that CA199 positivity (OR = 7.659), N1-2 stage (OR = 6.394), CT-LFF (OR = 1.271), VATI (OR = 1.043) were independent risk factors for predicting LM after CRC surgery. The multivariate logistic regression model, constructed using these independent risk factors, demonstrated robust predictive performance across 5-fold cross-validations, with an average AUC of 0.898 (95 % CI: 0.828-0.969). Survival analysis showed a significant difference in liver metastasis-free survival rates between the high-risk and low-risk groups (P < 0.001).

CONCLUSION

CT-LFF and VATI assessed by CT are independent risk factors for predicting LM after CRC surgery. The multivariate prediction model combining CA199 and N stage shows high predictive performance.

Assessment by a multidisciplinary team conference affects treatment strategy and overall survival in patients with synchronous colorectal liver metastases

BACKGROUND

The aim of this retrospective observational study was to investigate the geographical or sex differences in patients with synchronous colorectal liver metastases (sCRLM) in terms of assessment by a multidisciplinary team conference (MDT), curative treatment, and overall survival.

METHOD

All sCRLM patients in the South-East Health Care Region of Sweden from 2009 to 2015 were included (n = 615). Data were derived from the Swedish Colorectal Cancer Registry, Swedish Registry of Liver and Bile Surgery and medical records.

RESULTS

Patients who had a hepatobiliary unit (HBU) at the nearest hospital were more likely to undergo liver surgery (HBU+, 37% (n = 106), compared to HBU-, 22% (n = 60); p = 0.001) and had a better median survival (p < 0.001). No sex differences were observed. In multivariate Cox regression analyses of overall survival, assessment by an MDT that included a liver surgeon was independently linked to better survival (HR 0.574, 0.433-0.760).

CONCLUSION

There were no sex differences in access to liver surgery or overall survival, however, there were geographical inequalities, where residency near a hospital with HBU was associated with increased overall survival and the possibility to receive liver surgery. Assessment at MDT with liver surgeon present was associated with greater survival, indicating its important role for treatment.

Multifunctional Nanoprobe Au@Gd-SiO2-HA-Lyp-1/DOX with Dual-Targeting Functions Derived from HA and LyP-1: Diagnostic and Therapeutic Potential for Tumor Lymphatic Metastasis

To address lymphatic metastasis in lung cancer, we developed the Au@Gd-SiO2-HA-LyP-1 nanoprobe, assessing its diagnostic and therapeutic capabilities. This nanoprobe integrates a Au core with a Gd-SiO2 shell and dual-targeting HA-LyP-1 molecules. We evaluated its size, shape, and functional properties using various characterization techniques, alongside in vivo and in vitro toxicity tests. The spherical nanoprobes have a 50 nm diameter and contain 1.37% Gd. They specifically target lymphatic metastasis sites and tumor cells, showing enhanced MRI contrast and effective, targeted DOX delivery with reduced normal tissue toxicity. The Au@Gd-SiO2-HA-LyP-1 nanoprobe is a promising tool for diagnosing and treating lung cancer lymphatic metastasis, featuring dual-targeting and superior imaging capabilities.

Identifying risk and prognostic factors for synchronous liver metastasis in small bowel adenocarcinoma: a predictive analysis using the SEER database

Background

Small bowel adenocarcinoma (SBA) is a rare gastrointestinal malignancy with an increasing incidence and a high propensity for liver metastasis (LM). This study aimed to investigate the risk factors for synchronous LM and prognostic factors in patients with LM.

Methods

Utilizing the Surveillance, Epidemiology, and End Results (SEER) database, this study analyzed data from 2,064 patients diagnosed with SBA between 2010 and 2020. Logistic regression was used to determine risk factors for synchronous LM. A nomogram was developed to predict the risk of LM in SBA patients, and its predictive performance was assessed through receiver operating characteristic (ROC) curves and calibration curves. Kaplan-Meier and Cox regression analyses were conducted to evaluate survival outcomes for SBA patients with LM.

Results

Synchronous LM was present in 13.4% of SBA patients (n = 276). Six independent predictive factors for LM were identified, including tumor location, T stage, N stage, surgical intervention, retrieval of regional lymph nodes (RORLN), and chemotherapy. The nomogram demonstrated good discriminative ability, with an area under the curve (AUC) of 83.8%. Patients with LM had significantly lower survival rates than those without LM (P < 0.001). Survival analysis revealed that advanced age, tumor location in the duodenum, surgery, RORLN and chemotherapy were associated with cancer-specific survival (CSS) in patients with LM originating from SBA.

Conclusions

This study highlights the significant impact of LM on the survival of SBA patients and identifies key risk factors for its occurrence. The developed nomogram aids in targeted screening and personalized treatment planning.

Two spurge species, Euphorbia resinifera O. Berg and Euphorbia officinarum subsp. echinus (Hook.f. & Coss.) Vindt inhibit colon cancer

BACKGROUND

Colon cancer, a prominent contributor to global cancer-related deaths, prompts the need for innovative treatment strategies. Euphorbia resinifera O. Berg (E. resinifera) and Euphorbia officinarum subsp. echinus Hook. f. & Coss Vindt (E. echinus) and their bee-derived products have been integral to traditional Moroccan medicine due to their potential health benefits. These plants have historical use in addressing various health issues, including cancer. However, their effects against colon cancer remain unclear, and the specific mechanisms underlying their anti-cancer effects lack comprehensive investigation.

METHODS

The study aimed to assess the potential anti-cancer effects of Euphorbia extract on colon cancer cell lines (DLD-1) through various techniques. The apoptosis, migration, and proliferation of DLD-1 cells were measured in DLD-1 cells. In addition, we conducted High-Performance Liquid Chromatography (HPLC) analysis to identify the profile of phenolic compounds present in the studied extracts.

RESULTS

The extracts demonstrated inhibition of colon cancer cell migration. E. resinifera flower and E. echinus stem extracts show significant anti-migratory effects. Regarding anti-proliferative activity, E. resinifera flower extract hindered proliferation, whereas E. echinus flower extract exhibited dose-dependent inhibition. Apoptosis assays revealed E. resinifera flower extract inducing early-stage apoptosis and E. echinus flower extract promoting late-stage apoptosis. While apoptotic protein expression indicated, E. resinifera stem and propolis extracts had minimal impact on apoptosis.

CONCLUSION

The findings provide evidence supporting the beneficial effects of E resinifera and E. echinus extracts on colon cancer and exerting anti-cancer properties.

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.

Basement membrane-related lncRNA signature for the prognosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the main type of primary liver cancer. This study aimed to develop a basement membrane (BM) related lncRNAs risk signature to evaluate the prognosis of HCC patients. We screened differentially expressed BM-related lncRNAs (DE-BMRlncRNAs) for risk evaluation, and identified six DE-BMRlncRNAs (AC072054.1, NUP50-DT, AC026412.3, AC109322.2, POLH-AS1 and LINC00595) for prognostic risk signature. HCC patients were divided to high or low risk according to median risk score. Our prognostic model predicted that patients with higher risk score had worse prognosis. We also created a nomogram to assist clinical decision-making according to risk score and clinicopathological features. Meanwhile, we confirmed the expression of six lncRNAs in HCC tissue and cells. POLH-AS1 knockdown inhibited the migration and invasion of HCC cells. In conclusion, we established a predictive model based on BMRlncRNAs to predict the prognosis of HCC. Our findings offer a rationale to further explore BM-related biomarkers for HCC.

Establishment of a neutrophil extracellular trap-related prognostic signature for colorectal cancer liver metastasis and expression validation of CYP4F3

Liver metastasis stands as the primary contributor to mortality among patients diagnosed with colorectal cancer (CRC). Neutrophil extracellular traps (NETs) emerge as pivotal players in the progression and metastasis of cancer, showcasing promise as prognostic biomarkers. Our objective is to formulate a predictive model grounded in genes associated with neutrophil extracellular traps and identify novel therapeutic targets for combating CRLM. We sourced gene expression profiles from the Gene Expression Omnibus (GEO) database. Neutrophil extracellular trap-related gene set was obtained from relevant literature and cross-referenced with the GEO datasets. Differentially expressed genes (DEGs) were identified through screening via the least absolute shrinkage and selection operator regression and random forest modeling, leading to the establishment of a nomogram and subtype analysis. Subsequently, a thorough analysis of the characteristic gene CYP4F3 was undertaken, and our findings were corroborated through immunohistochemical staining. We identified seven DEGs (ATG7, CTSG, CYP4F3, F3, IL1B, PDE4B, and TNF) and established nomograms for the occurrence and prognosis of CRLM. CYP4F3 is highly expressed in CRC and colorectal liver metastasis (CRLM), exhibiting a negative correlation with CRLM prognosis. It may serve as a potential therapeutic target for CRLM. A novel prognostic signature related to NETs has been developed, with CYP4F3 identified as a risk factor and potential target for CRLM.

Mortality prediction and influencing factors for intensive care unit patients with acute tubular necrosis: random survival forest and cox regression analysis

Background: Patients with acute tubular necrosis (ATN) not only have severe renal failure, but also have many comorbidities, which can be life-threatening and require timely treatment. Identifying the influencing factors of ATN and taking appropriate interventions can effectively shorten the duration of the disease to reduce mortality and improve patient prognosis. Methods: Mortality prediction models were constructed by using the random survival forest (RSF) algorithm and the Cox regression. Next, the performance of both models was assessed by the out-of-bag (OOB) error rate, the integrated brier score, the prediction error curve, and area under the curve (AUC) at 30, 60 and 90 days. Finally, the optimal prediction model was selected and the decision curve analysis and nomogram were established. Results: RSF model was constructed under the optimal combination of parameters (mtry = 10, nodesize = 88). Vasopressors, international normalized ratio (INR)_min, chloride_max, base excess_min, bicarbonate_max, anion gap_min, and metastatic solid tumor were identified as risk factors that had strong influence on mortality in ATN patients. Uni-variate and multivariate regression analyses were used to establish the Cox regression model. Nor-epinephrine, vasopressors, INR_min, severe liver disease, and metastatic solid tumor were identified as important risk factors. The discrimination and calibration ability of both predictive models were demonstrated by the OOB error rate and the integrated brier score. However, the prediction error curve of Cox regression model was consistently lower than that of RSF model, indicating that Cox regression model was more stable and reliable. Then, Cox regression model was also more accurate in predicting mortality of ATN patients based on the AUC at different time points (30, 60 and 90 days). The analysis of decision curve analysis shows that the net benefit range of Cox regression model at different time points is large, indicating that the model has good clinical effectiveness. Finally, a nomogram predicting the risk of death was created based on Cox model. Conclusion: The Cox regression model is superior to the RSF algorithm model in predicting mortality of patients with ATN. Moreover, the model has certain clinical utility, which can provide clinicians with some reference basis in the treatment of ATN and contribute to improve patient prognosis.

Cell competition promotes metastatic intestinal cancer through a multistage process

Cell competition plays an instrumental role in quality control during tissue development and homeostasis. Nevertheless, cancer cells can exploit this process for their own proliferative advantage. In our study, we generated mixed murine organoids and microtissues to explore the impact of cell competition on liver metastasis. Unlike competition at the primary site, the initial effect on liver progenitor cells does not involve the induction of apoptosis. Instead, metastatic competition manifests as a multistage process. Initially, liver progenitors undergo compaction, which is followed by cell-cycle arrest, ultimately forcing differentiation. Subsequently, the newly differentiated liver cells exhibit reduced cellular fitness, rendering them more susceptible to outcompetition by intestinal cancer cells. Notably, cancer cells leverage different interactions with different epithelial populations in the liver, using them as scaffolds to facilitate their growth. Consequently, tissue-specific mechanisms of cell competition are fundamental in driving metastatic intestinal cancer.

Role of sex in liver tumor occurrence and clinical outcomes: A comprehensive review

Clinical research on sex-based differences in the manifestations, pathophysiology, and prevalence of several diseases, including those affecting the liver, has expanded considerably in recent years. Increasing evidence suggests that liver diseases develop, progress, and respond to treatment differently depending on the sex. These observations support the concept that the liver is a sexually dimorphic organ in which estrogen and androgen receptors are present, which results in disparities between men and women in liver gene expression patterns, immune responses, and the progression of liver damage, including the propensity to develop liver malignancies. Sex hormones play protective or deleterious roles depending on the patient's sex, the severity of the underlying disease, and the nature of precipitating factors. Moreover, obesity, alcohol consumption, and active smoking, as well as social determinants of liver diseases leading to sex-related inequalities, may interact strongly with hormone-related mechanisms of liver damage. Drug-induced liver injury, viral hepatitis, and metabolic liver diseases are influenced by the status of sex hormones. Available data on the roles of sex hormones and gender differences in liver tumor occurrence and clinical outcomes are conflicting. Here, we critically review the main gender-based differences in the molecular mechanisms associated with liver carcinogenesis and the prevalence, prognosis, and treatment of primary and metastatic liver tumors.

An inflammatory gene-related prognostic risk score model for prognosis and immune infiltration in glioblastoma

This study aimed to screen for key genes related to the prognosis of patients with glioblastoma (GBM). First, bioinformatics analysis was performed based on databases such as TCGA and MSigDB. Inflammatory-related genes were obtained from the MSigDB database. The TCGA-tumor samples were divided into cluster A and B groups based on consensus clustering. Multivariate Cox regression was applied to construct the risk score model of inflammatory-related genes based on the TCGA database. Second, to understand the effects of model characteristic genes on GBM cells, U-87 MG cells were used for knockdown experiments, which are important means for studying gene function. PLAUR is an unfavorable prognostic biomarker for patients with glioma. Therefore, the model characteristic gene PLAUR was selected for knockdown experiments. The prognosis of cluster A was significantly better than that of cluster B. The verification results also demonstrate that the risk score could predict overall survival. Although the immune cells in cluster B and high-risk groups increased, no matching survival advantage was observed. It may be that stromal activation inhibits the antitumor effect of immune cells. PLAUR knockdown inhibits tumor cell proliferation, migration, and invasion, and promoted tumor cell apoptosis. In conclusion, a prognostic prediction model for GBM composed of inflammatory-related genes was successfully constructed. Increased immune cell expression may be linked to a poor prognosis for GBM, as stromal activation decreased the antitumor activity of immune cells in cluster B and high-risk groups. PLAUR may play an important role in tumor cell proliferation, migration, invasion, and apoptosis.

Nomogram for predicting occurrence and prognosis of liver metastasis in elderly colorectal cancer patients: a population-based study

Introduction

This study aimed to explore independent risk and prognostic factors in elderly patients with colorectal cancer liver metastasis (ECRLM) and generate nomograms for predicting the occurrence and overall survival (OS) rates of such patients.

Method

Elderly colorectal cancer patients (ECRC) from 2010 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. External validation relied on Chinese patients from the China-Japan Union Hospital of Jilin University. Univariate and multivariate logistic regression analyses were employed to identify liver metastasis (LM) risk variables, which were used to create a nomogram to estimate LM probabilities in patients with ECRC. Univariate and multivariable Cox analyses were performed to identify prognostic variables and further derive nomograms that could predict the OS of patients with ERCLM. Differences in lifespan were assessed using the Kaplan-Meier analysis. Finally, the quality of the nomograms was verified using decision curve analysis (DCA), calibration curves, and receiver operating characteristic curves (ROC).

Result

In the SEER cohort, 32,330 patients were selected, of those, 3,012 (9.32%) were diagnosed with LM. A total of 188 ECRLM cases from a Chinese medical center were assigned for external validation. LM occurrence can be affected by 13 factors, including age at diagnosis, marital status, race, bone metastases, lung metastases, CEA level, tumor size, Grade, histology, primary site, T stage, N stage and sex. Furthermore, in ECRLM patients, 10 variables, including age at diagnosis, CEA level, tumor size, lung metastasis, bone metastasis, chemotherapy, surgery, N stage, grade, and race, have been shown to be independent prognostic predictors. The results from both internal and external validation revealed a high level of accuracy in predicting outcomes, as well as significant clinical utility, for the two nomograms.

Conclusion

We created two nomograms to predict the occurrence and prognosis of LM in patients with ECRC, which would contribute significantly to the improvement in disease detection accuracy and the formulation of personalized cures for that particular demographic.

A predictive model for early death in elderly colorectal cancer patients: a population-based study

Purpose

The purpose of this study is to determine what variables contribute to the early death of elderly colorectal cancer patients (ECRC) and to generate predictive nomograms for this population.

Methods

This retrospective cohort analysis included elderly individuals (≥75 years old) diagnosed with colorectal cancer (CRC) from 2010-2015 in the Surveillance, Epidemiology, and End Result databases (SEER) databases. The external validation was conducted using a sample of the Chinese population obtained from the China-Japan Union Hospital of Jilin University. Logistic regression analyses were used to ascertain variables associated with early death and to develop nomograms. The nomograms were internally and externally validated with the help of the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

Results

The SEER cohort consisted of 28,111 individuals, while the Chinese cohort contained 315 cases. Logistic regression analyses shown that race, marital status, tumor size, Grade, T stage, N stage, M stage, brain metastasis, liver metastasis, bone metastasis, surgery, chemotherapy, and radiotherapy were independent prognostic factors for all-cause and cancer-specific early death in ECRC patients; The variable of sex was only related to an increased risk of all-cause early death, whereas the factor of insurance status was solely associated with an increased risk of cancer-specific early death. Subsequently, two nomograms were devised to estimate the likelihood of all-cause and cancer-specific early death among individuals with ECRC. The nomograms exhibited robust predictive accuracy for predicting early death of ECRC patients, as evidenced by both internal and external validation.

Conclusion

We developed two easy-to-use nomograms to predicting the likelihood of early death in ECRC patients, which would contribute significantly to the improvement of clinical decision-making and the formulation of personalized treatment approaches for this particular population.

Nomograms for predicting overall survival in colorectal cancer patients with metastasis to the liver, lung, bone, and brain

BACKGROUND

The aim of this study was to identify the heterogeneous and homogeneous prognostic factors associated with distant metastasis to the liver, lung, bone, and brain in colorectal cancer (CRC) patients and then construct nomograms to predict the prognosis.

METHODS

CRC patients registered in the surveillance, epidemiology, and end results database between 2010 and 2017 were included. A Cox regression model was used to analyse homogeneous and heterogeneous prognostic factors, and Kaplan‒Meier analysis was performed to estimate overall survival (OS). Predictive nomograms were constructed, and their performance was evaluated with C-indexes, calibration curves and the area under the receiver operating characteristic (ROC) curve (AUC).

RESULTS

A total of 37,641 patients with distant metastasis to the liver, lung, bone, and brain were included. The median survival times of patients with liver metastasis, lung metastasis, bone metastasis, and brain metastasis were 12.00 months (95% CI 11.73-12.27 months), 10.00 months (95% CI 9.60-10.41 months), 5.00 months (95% CI 4.52-5.48 months), and 3.00 months (95% CI 2.28-3.72 months), respectively. An older age, higher N stage, elevated carcinoembryonic antigen level, no surgery at the primary site and no/unknown treatment with chemotherapy were identified as homogeneous prognostic factors for the four types of metastases. The calibration curves, C-indexes and AUCs exhibited good performance for predicting the OS of patients with distant metastases to the liver, lung, bone, and brain.

CONCLUSIONS

CRC patients with distant metastasis to the liver, lung, bone, and brain exhibited homogeneous and heterogeneous prognostic factors, all of which were associated with shorter survival. The nomograms showed good accuracy and may be used as tools for clinicians to predict the prognosis of CRC patients with distant metastasis.

Comprehensive analysis of HDAC7 expression and its prognostic value in diffuse large B cell lymphoma: A review

HDAC7 loss or dysregulation may lead to B cell-based hematological malignancies. This study aimed to explore the prognostic value of HDAC7 in patients with diffuse large B cell lymphoma (DLBCL). RNA sequencing data and clinical information for HDAC7 in DLBCL were collected from the cancer genome atlas database and analyzed using R software. Paired t and Mann-Whitney U tests were used to detect differences between DLBCL and adjacent normal tissues, and the pROC software package was used to generate receiver operator characteristic curves to detect cutoff values for HDAC7. Data from paraffin-embedded specimens from the 2 groups were used for validation of external immunohistochemical staining. The tumor immunity estimation resource and integrated repository portal for tumor immune system interactions databases were used to analyze the correlation between HDAC7 and DLBCL immune cell infiltration. Survival analysis of HDAC7 in patients with DLBCL was performed using the PrognoScan database. Compared with that in normal tissues, HDAC7 mRNA was overexpressed in DLBCL. The HDAC7 immunohistochemical staining scores of stage III and IV DLBCL patients were significantly lower than those of stage I and II DLBCL patients, which was associated with shorter overall survival and disease-specific survival. In addition, the higher expression of HDAC7 may play a role in the lower level of immune infiltration in DLBCL. Downregulation of HDAC7 expression was correlated with poor prognosis and immune infiltration in DLBCL patients.

Prognostic nomogram for colorectal cancer patients with multi-organ metastases: a Surveillance, Epidemiology, and End Results program database analysis

BACKGROUND

A nomogram that integrates risk models and clinical characteristics can accurately predict the prognosis of individual patients. We aimed to identify the prognostic factors and establish nomograms for predicting overall survival (OS) and cause-specific survival (CSS) in patients with multi-organ metastatic colorectal cancer (CRC).

METHODS

Demographic and clinical information on multi-organ metastases from 2010 to 2019 were extracted from the Surveillance, Epidemiology, and End Results (SEER) Program. Univariate and multivariate Cox analyses were used to identify independent prognostic factors that were used to develop nomograms to predict CSS and OS, and to assess the concordance index (C-index), area under the curve (AUC), and calibration curve.

RESULTS

The patients were randomly assigned to the training and validation groups at a 7:3 ratio. A Cox proportional hazards model was conducted for CRC patients to identify independent prognostic factors, including age, sex, tumor size, metastases, degree of differentiation, stage T, stage N, primary and metastasis surgery. The competing risk models employed by Fine and Gray were used to identify the risk factors for CRC. Death from other causes was treated as a competing event, and Cox models were used to identify the factors for death to identify the independent factors of CSS. By incorporating the corresponding independent prognostic factors, we established prognostic nomograms for OS and CSS. Finally, we used the C-index, ROC curve, and calibration plots to assess the utility of the nomogram.

CONCLUSIONS

Using the SEER database, we constructed a predictive model for CRC patients with multi-organ metastases. Nomograms provide clinicians with 1-, 3-, and 5-year OS and CSS predictions for CRC, allowing them to formulate appropriate treatment plans.

Development and validation of a network calculator model for safety and efficacy after pancreaticoduodenectomy in the elderly patients with pancreatic head cancer

BACKGROUND

Benefiting from increased life expectancy and improved perioperative management, more elderly patients with pancreatic head cancer (PHC) underwent pancreaticoduodenectomy (PD). However, individualized predictive models for the safety and efficacy of PD is still lacking. this study aimed to developed three safety- and efficacy-related risk calculators for elderly (> = 65 years) PHC patients.

METHODS

This study was designed with two research cohorts, namely, the training cohort and the validation cohort, and comprises four general steps: (1) Risk factors were analyzed for the incidence of postoperative complications, cancer-specific survival (CSS), and overall survival (OS) in the training cohort (N = 271) using logistic and Cox-regression analysis. (2) Nomograms were then plotted based on the above results. (3) The accuracy of the developed nomogram models was then verified with the validation cohort (N = 134) data using consistency index (C-index) and calibration curves. (4) We then evaluated the efficacy of these nomograms using decision curve analysis (DCA) in both the training and validation cohorts, and ultimately constructed three online calculators based on these nomograms.

RESULTS

We identified ASA, diabetes, smoking, and lymph node invasion as predisposing risk factors for postoperative complications, and the predictive factors that affected both OS and CSS were ASA, diabetes, BMI, CA19-9 level, and tumor diameter. By integrating the above risk factors, we constructed three nomograms on postoperative complication, CSS, and OS. The C-index for complication, CSS, and OS were 0.824, 0.784, and 0.801 in the training cohort and 0.746, 0.718, and 0.708 in the validation cohort. Moreover, the validation curves and DCA demonstrated good calibration and robust compliance in both training and validation cohorts. We then developed three web calculators (https://caiming.shinyapps.io/CMCD/, https://caiming.shinyapps.io/CMCSS/, and https://caiming.shinyapps.io/CMOS/) to facilitate the use of the nomograms.

CONCLUSIONS

The calculators demonstrated promising performance as an tool for predicting the safety and efficacy of PD in elderly PHC patients.

Development and validation of prediction models for the prognosis of colon cancer with lung metastases: a population-based cohort study

Background

Current studies on the establishment of prognostic models for colon cancer with lung metastasis (CCLM) were lacking. This study aimed to construct and validate prediction models of overall survival (OS) and cancer-specific survival (CSS) probability in CCLM patients.

Method

Data on 1,284 patients with CCLM were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly assigned with 7:3 (stratified by survival time) to a development set and a validation set on the basis of computer-calculated random numbers. After screening the predictors by the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression, the suitable predictors were entered into Cox proportional hazard models to build prediction models. Calibration curves, concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) were used to perform the validation of models. Based on model-predicted risk scores, patients were divided into low-risk and high-risk groups. The Kaplan-Meier (K-M) plots and log-rank test were applied to perform survival analysis between the two groups.

Results

Building upon the LASSO and multivariate Cox regression, six variables were significantly associated with OS and CSS (i.e., tumor grade, AJCC T stage, AJCC N stage, chemotherapy, CEA, liver metastasis). In development, validation, and expanded testing sets, AUCs and C-indexes of the OS and CSS prediction models were all greater than or near 0.7, which indicated excellent predictability of models. On the whole, the calibration curves coincided with the diagonal in two models. DCA indicated that the models had higher clinical benefit than any single risk factor. Survival analysis results showed that the prognosis was worse in the high-risk group than in the low-risk group, which suggested that the models had significant discrimination for patients with different prognoses.

Conclusion

After verification, our prediction models of CCLM are reliable and can predict the OS and CSS of CCLM patients in the next 1, 3, and 5 years, providing valuable guidance for clinical prognosis estimation and individualized administration of patients with CCLM.

Endoscopic Treatment of T1 Colorectal Cancer

Commonly accepted criteria for curative resection of T1 colorectal cancer include R0 resection with horizontal and vertical clear margins (R0), absence of lympho-vascular or vessel infiltration (L0, V0), a low to moderate histological grading (G1/2), low tumor cell budding, and limited (<1000 µm) infiltration into the submucosa. However, submucosal infiltration depth in the absence of other high-risk features has recently been questioned as a high-risk situation for lymph-node metastasis. Consequently, endoscopic resection techniques should focus on the acquisition of qualitatively and quantitively sufficient submucosal tissue. Here, we summarize the current literature on lymph-node metastasis risk after endoscopic resection of T1 colorectal cancer. Moreover, we discuss different endoscopic resection techniques with respect to the quality of the resected specimen.

Involvement in treatment decision-making and self-reported efficacy among patients with advanced colorectal cancer: a nationwide multi-center cross-sectional study

Introduction

This cross-sectional study evaluated the involvement of patients with advanced colorectal cancer (CRC) in treatment decision-making, assessed the treatment efficacy according to their self-reports, and investigated the influencing factors.

Methods

Patients with advanced CRC were recruited from 19 hospitals from March 2020 to March 2021 by a multi-stage multi-level sampling method. A self-designed questionnaire was used to collect demographic and clinical characteristics, involvement of CRC patients in treatment decision-making, treatment methods, and self-reported efficacy. Univariate and unordered multinomial logistic regression analyses were used to evaluate the factors affecting the involvement in treatment decision-making and self-reported efficacy.

Results

We enrolled 4533 patients with advanced CRC. The average age at diagnosis was 58.7 ± 11.8 years. For the treatment method, 32.4% of patients received surgery combined with chemotherapy, 13.1% of patients underwent surgery combined with chemotherapy and targeted therapy, and 9.7% of patients were treated with surgery alone. For treatment decision-making, 7.0% of patients were solely responsible for decision-making, 47.0% of patients shared treatment decision-making with family members, 19.0% of patients had family members solely responsible for treatment decision-making, and 27.0% of patients had their physicians solely responsible for treatment decision-making. Gender, age, education level, family income, marital status, treatment cost, hospital type, and treatment method were significantly associated with the involvement of patients in treatment decision-making. A total of 3824 patients submitted self-reported efficacy evaluations during treatment. The percentage of patients with good self-reported efficacy was 76.5% (for patients treated for the first time), 61.7% (for patients treated for the second time), and 43.2% (for patients treated after recurrence and metastasis), respectively. Occupation, education level, average annual family income, place of residence, time since cancer diagnosis, hospital type, clinical stage, targeted therapy, and involvement in treatment decision-making were the main influencing factors of self-reported efficacy of treatment.

Discussion

Conclusively, CRC patients are not highly dominant in treatment decision-making and more likely to make treatment decisions with their family and doctors. Timely and effective communication between doctors and patients can bolster patient involvement in treatment decision-making.

Role of Serum CYFRA 21-1 in Diagnosis and Prognostic in Colorectal Liver Metastases

Purpose

In current studies, the role of serum Cytokeratin-19 fragments (CYFRA 21-1) in colorectal cancer (CRC) remains unclear. This study aimed to clarify the diagnostic and prognostic value of CYFRA 21-1 in CRC.

Patients and Methods

Data were collected for 196 stage I-III CRC patients and 50 colorectal liver metastases (CRLM) patients between January 2018 and December 2019. The serum CYFRA 21-1 levels were measured using the chemiluminescent particle immunoassay (CMIA) kit in all objects and common biomarkers such as CA19-9, CEA, HSP90α, and AFP were measured in all colorectal cancer patients. We investigated the association between CYFRA 21-1 level and clinicopathological features. In addition, we evaluated the ability of serum CRFRA21-1 to differentiate CRLM from CRC. To assess the potential prognostic value, we used Cox proportional hazard model for univariate or multivariate analyses.

Results

Serum CYFRA 21-1 was significantly elevated in CRLM patients compared to stage I-III CRC patients (5.85 ng/mL vs 2.29 ng/mL, p < 0.001). For all CRC patients cohort, stage I-III CRC patients cohort and CRLM patients cohort, the optimal cutoff levels of CYFRA 21-1 for overall survival (OS) were 3.47 ng/mL, 2.14 ng/mL and 7.63 ng/mL, respectively, and the optimal cutoff levels for progression-free survival (PFS) were 3.47 ng/mL, 2.56 ng/mL and 7.63 ng/mL, respectively. For CRLM patients, Kaplan-Meier analysis showed that patients with high CYFRA 21-1 level had poor OS. Multivariate analysis indicated that the CYFRA 21-1 level was an independent prognostic factor for PFS in stage I-III patients. And CYFRA 21-1 levels and age were independent prognostic factors for OS and PFS in CRLM patients.

Conclusion

CYFRA 21-1 can better differentiate CRLM patients from the whole CRC patients and has unique prognostic value for CRLM patients.

Prognostic value of extrahepatic metastasis on colon cancer with liver metastasis: a retrospective cohort study

INTRODUCTION

The occurrence of metastasis is a threat to patients with colon cancer (CC), and the liver is the most common metastasis organ. However, the role of the extrahepatic organs in patients with liver metastasis (LM) has not been distinctly demonstrated. Therefore, this research aimed to explore the prognostic value of extrahepatic metastases (EHMs).

METHODS

In this retrospective study, a total of 13,662 colon patients with LM between 2010 and 2015 were selected from the Surveillance, Epidemiology, and End Results database (SEER). Fine and Gray's analysis and K-M survival analysis were utilized to explore the impacts of the number of sites of EHMs and different sites of EHMs on prognosis. Finally, a prognostic nomogram model based on the number of sites of EHMs was constructed, and a string of validation methods was conducted, including concordance index (C-index), receiver operating characteristic curves (ROC), and decision curve analysis (DCA).

RESULTS

Patients without EHMs had better prognoses in cancer-specific survival (CSS) and overall survival (OS) than patients with EHMs (p < 0.001). Varied EHM sites of patients had different characteristics of primary location site, grade, and histology. Cumulative incidence rates for CSS surpassed that for other causes in patients with 0, 1, 2, ≥ 3 EHMs, and the patients with more numbers of sites of EHMs revealed worse prognosis in CSS (p < 0.001). However, patients with different EHM sites had a minor difference in cumulative incidence rates for CSS (p = 0.106). Finally, a nomogram was constructed to predict the survival probability of patients with EHMs, which is based on the number of sites of EHMs and has been proven an excellent predictive ability.

CONCLUSION

The number of sites of EHMs was a significant prognostic factor of CC patients with LM. However, the sites of EHMs showed limited impact on survival. Furthermore, a nomogram based on the number of sites of EHMs was constructed to predict the OS of patients with EHMs accurately.

Construction and validation of a nomogram of risk factors and cancer-specific survival prognosis for combined lymphatic metastases in patients with early-onset colorectal cancer

PURPOSE

This study aimed to investigate the risk and prognostic factors of lymph node metastasis (LNM) in early-onset colorectal cancer (EO-CRC) and to develop nomograms for quantitatively predicting LNM and the cancer-specific survival (CSS).

METHODS

A total of 22,405 EO-CRC patients were included in this study using the SEER database from 2010 to 2017. Logistic and Cox regression were used to identify risk and the potential prognostic factors, respectively, for EO-CRC with LNM. Subsequently, nomograms regarding the risk of LNM in EO-CRC patients and its corresponding CSS were constructed based on these factors. The discriminative ability, calibration and clinical usefulness of the nomograms were assessed by the area under the receiver operating characteristic (ROC) curves (AUC), calibration curves, and decision curve analysis (DCA).

RESULTS

T-stage and pathological grade were the most represented factors in the predicted LNM nomogram, while histological type and combined distant metastases were the most represented in the nomogram for CSS in EO-CRC patients with LNM (all P < 0.05). The nomogram constructed based on the prognostic factors screened by Cox regression had good performance with C-index of 0.807 and 0.802 for the training and validation cohorts, respectively. The calibration curve showed that the nomograms' predictions were in line with actual observations. Additionally, the ROC curves indicated good discrimination, and the DCA curves implied significant clinical utility of the nomograms.

CONCLUSION

The nomograms we constructed have significant performance in predicting the incidence and prognosis of LNM in EO-CRC patients, which may help clinicians to make better treatment decision making.

Development and validation of a clinical survival model for young-onset colorectal cancer with synchronous liver-only metastases: a SEER population-based study and external validation

Background

The morbidity and mortality of young-onset colorectal cancer (YO-CRC) patients have been increasing in recent years. Moreover, YO-CRC patients with synchronous liver-only metastases (YO-CRCSLM) have various survival outcomes. Therefore, the purpose of this study was to construct and validate a prognostic nomogram for patients with YO-CRCSLM.

Methods

The YO-CRCSLM patients were rigorously screened from the Surveillance, Epidemiology, and End Results (SEER) database in January 2010 and December 2018 and then assigned to a training and validation cohort randomly (1488 and 639 patients, respectively). Moreover, the 122 YO-CRCSLM patients who were enrolled in The First Affiliated Hospital of Nanchang University were served as a testing cohort. The variables were selected using the multivariable Cox model based on the training cohort and then developed a nomogram. The validation and testing cohort were used to validate the model's predictive accuracy. The calibration plots were used to determine the Nomogram's discriminative capabilities and precision, and the decision analysis (DCA) was performed to evaluate the Nomogram's net benefit. Finally, the Kaplan-Meier survival analyses were performed for the stratified patients based on total nomogram scores classified by the X-tile software.

Results

The Nomogram was constructed including ten variables: marital status, primary site, grade, metastatic lymph nodes ratio (LNR), T stage, N stage, carcinoembryonic antigen (CEA), Surgery, and chemotherapy. The Nomogram performed admirably in the validation and testing group according to the calibration curves. The DCA analyses showed good clinical utility values. Low-risk patients (score<234) had significantly better survival outcomes than middle-risk (234-318) and high-risk (>318) patients (P < 0.001).

Conclusion

A nomogram predicting the survival outcomes for patients with YO-CRCSLM was developed. In addition to facilitating personalized survival prediction, this nomogram may assist in developing clinical treatment strategies for patients with YO-CRCSLM who are undergoing treatment.

Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases

Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of algorithms in the cancer setting. A growing body of recent studies have evaluated machine learning (ML) and deep learning (DL) algorithms for pre-screening, diagnosis and management of liver cancer patients through diagnostic image analysis, biomarker discovery and predicting personalized clinical outcomes. Despite the promise of these early AI tools, there is a significant need to explain the 'black box' of AI and work towards deployment to enable ultimate clinical translatability. Certain emerging fields such as RNA nanomedicine for targeted liver cancer therapy may also benefit from application of AI, specifically in nano-formulation research and development given that they are still largely reliant on lengthy trial-and-error experiments. In this paper, we put forward the current landscape of AI in liver cancers along with the challenges of AI in liver cancer diagnosis and management. Finally, we have discussed the future perspectives of AI application in liver cancer and how a multidisciplinary approach using AI in nanomedicine could accelerate the transition of personalized liver cancer medicine from bench side to the clinic.

Effect of abdominal aortic calcification on the prognosis and recurrence of colorectal cancer stages II-III: A retrospective cohort study

PURPOSE

Abdominal aortic calcification (AAC) is a well-known risk marker for cardiovascular disease. However, its clinical effect on patients who underwent radical surgery for colorectal cancer (CRC) stages II-III is unclear. This study aimed to analyze the associations between AAC and prognosis of patients with stage II-III CRC.

METHODS

To evaluate the effect of AAC on clinical outcomes, prognosis, and metastatic patterns of CRC, we analyzed 362 patients who underwent radical surgery for stage II-III CRC between 2010 and 2018.

RESULTS

The high AAC group had significantly worse overall survival (OS), cancer-specific survival (CSS), and recurrence-free survival (RFS) after propensity score matching to adjust for differences in baseline characteristics of patients and tumors. In the multivariate Cox regression analyses, a high AAC was an independent risk factor for poor OS (hazard ratio [HR], 2.38; 95% confidence interval [CI], 1.23-4.59; p = 0.01), poor CSS (HR, 5.22; 95% CI, 1.74-15.6; p < 0.01), and poor RFS (HR, 1.83; 95% CI, 1.19-2.83; p < 0.01). A high AAC was not associated with a risk of lung metastasis or local or peritoneal recurrence, but a risk for liver metastasis of CRC.

CONCLUSION

A high AAC showed a strong relationship with poor OS, CSS, and RFS after curative resection for stage II-III CRC. A high AAC was also associated with a risk for liver metastasis, which may worsen the prognosis in stage II-III CRC. AAC could be a new clinical tool for predicting the prognosis for patients in stage II-III CRC.

Application of machine learning techniques in real-world research to predict the risk of liver metastasis in rectal cancer

Background

The liver is the most common site of distant metastasis in rectal cancer, and liver metastasis dramatically affects the treatment strategy of patients. This study aimed to develop and validate a clinical prediction model based on machine learning algorithms to predict the risk of liver metastasis in patients with rectal cancer.

Methods

We integrated two rectal cancer cohorts from Surveillance, Epidemiology, and End Results (SEER) and Chinese multicenter hospitals from 2010-2017. We also built and validated liver metastasis prediction models for rectal cancer using six machine learning algorithms, including random forest (RF), light gradient boosting (LGBM), extreme gradient boosting (XGB), multilayer perceptron (MLP), logistic regression (LR), and K-nearest neighbor (KNN). The models were evaluated by combining several metrics, such as the area under the curve (AUC), accuracy score, sensitivity, specificity and F1 score. Finally, we created a network calculator using the best model.

Results

The study cohort consisted of 19,958 patients from the SEER database and 924 patients from two hospitals in China. The AUC values of the six prediction models ranged from 0.70 to 0.95. The XGB model showed the best predictive power, with the following metrics assessed in the internal test set: AUC (0.918), accuracy (0.884), sensitivity (0.721), and specificity (0.787). The XGB model was assessed in the outer test set with the following metrics: AUC (0.926), accuracy (0.919), sensitivity (0.740), and specificity (0.765). The XGB algorithm also shows a good fit on the calibration decision curves for both the internal test set and the external validation set. Finally, we constructed an online web calculator using the XGB model to help generalize the model and to assist physicians in their decision-making better.

Conclusion

We successfully developed an XGB-based machine learning model to predict liver metastasis from rectal cancer, which was also validated with a real-world dataset. Finally, we developed a web-based predictor to guide clinical diagnosis and treatment strategies better.

The Effect of Complications on Oncological Outcomes of Colorectal Cancer Patients After Primary Surgery: A Propensity Score Matching Analysis

Purpose

The purpose of this study is to explore the oncologic outcomes of complications on colorectal cancer (CRC) patients who underwent primary surgery using a propensity score matching (PSM) analysis.

Methods

A retrospective study was conducted from Jan 2011 to Jan 2020 in a clinical center. The overall survival (OS) and disease-free survival (DFS) were compared among the no complications group, the major complications group and the minor complications group.

Results

A total of 4250 CRC patients who underwent radical primary surgery were included in the current study. Among them, 927 (21.8%) patients suffered complications. After 1:1 ratio PSM, there were 98 patients in the major complications group and in the minor complications group, and 911 patients in the overall complications group and in the no complications group. There was no significant difference in terms of baseline information after PSM (p>0.05). Complications were independent predictors of OS (p=0.000, HR=1.693, 95% CI=1.476-1.941) and DFS (p=0.000, HR=1.555, 95% CI=1.367-1.768). In terms of specific tumor stage, the no complications group had better OS on all stages (p=0.006) and stage III (p=0.003) CRC than the complications group after PSM. Furthermore, the no complications group had better DFS on all stages (p=0.005) and stage III (p=0.021) CRC than the complications group after PSM. However, there was no significant difference between the minor complications group and the major complications group in different tumor stages (p>0.05).

Conclusion

Complications were associated with poor prognosis of CRC and surgeons should be cautious of the adverse events.

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.

Homogeneous and heterogeneous risk and prognostic factors for lung metastasis in colorectal cancer patients

Background

The lung is one of the most frequent distant metastasis sites in colorectal cancer (CRC) patients; however, lung metastasis risk and prognostic factors have not been comprehensively elucidated. This study aimed to identify the homogeneous and heterogeneous lung metastasis risk and prognostic factors in CRC patients using the Surveillance, Epidemiology, and End Results (SEER) database.

Methods

CRC patients registered in the SEER database between 2010 and 2016 were included to analyse risk factors for developing lung metastasis by using univariable and multivariable logistic regression. Patients diagnosed between 2010 and 2015 were selected to investigate prognostic factors for lung metastasis by conducting Cox regression. Kaplan–Meier analysis was used to estimate overall survival outcomes.

Results

A total of 10,598 (5.2%) patients with synchronous lung metastasis were diagnosed among 203,138 patients with CRC. The median survival time of patients with lung metastasis was 10.0 months (95% CI 9.6–10.5 months). Older age, unmarried status, uninsured status, poor histological differentiation, more lymphatic metastasis, CEA positivity, liver metastasis, bone metastasis and brain metastasis were lung metastasis risk and prognostic factors. Black patients and those with left colon, rectum, and stage T4 disease were more likely to develop lung metastasis, while patients with right colon cancer and no surgical treatment of the primary tumour had poor survival outcomes.

Conclusion

The incidence of lung metastasis in CRC patients was 5.2%. CRC patients with lung metastasis exhibited homogeneous and heterogeneous risk and prognostic factors. These results are helpful for clinical evaluation and individual treatment decision making.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-022-02270-5.

Development and External Validation of a Nomogram for Predicting Acute Kidney Injury in Cardiogenic Shock Patients in Intensive Care Unit

Background

The aim of this study was to construct and external validate a nomogram for predicting cardiogenic shock acute kidney injury (CS-AKI) in patients in intensive care unit (ICU).

Methods

All patients diagnosed with CS from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD) were included in this study. Least absolute shrinkage and selection operator (LASSO) regression and recursive feature elimination for support vector machine (SVM-RFE) were used to determine the overlapping clinical features associated with CS-AKI. The predictive nomogram was established based on the significant clinical parameters and externally verified in this study.

Results

LASSO regression and SVM-RFE demonstrated that Charlson Comorbidity Index (CCI), usage of mechanical ventilation, SOFA score, white blood cell, albumin, eGFR, anion gap, and positive fluid balance were closely associated with CS-AKI in the training cohort. The predictive nomogram based on the eight parameters showed good predictive performance as calculated by C-index were 0.823 (95% confidence index, 95% CI 0.798-0.849), 0.819 (95% CI 0.769-0.849), and 0.733 (95% CI 0.704-0.763) in the training set, in the internal validation set and in the external validation sets, respectively. Moreover, the nomogram exhibited not only encouraging calibration ability but also great clinical utility in the training set and in the validation sets.

Conclusion

CCI, usage of mechanical ventilation, SOFA score, white blood cell, albumin, eGFR, anion gap, and positive fluid balance were closely associated with CS-AKI. The predictive nomogram for CS-AKI manifested well-predictive ability for the identification of ICU patients with CS-AKI.

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.

Nomogram for predicting occurrence of synchronous liver metastasis in colorectal cancer: a single-center retrospective study based on pathological factors

Purpose

The purpose of this study was to explore the risk factors for synchronous liver metastasis (LM) of colorectal cancer (CRC) and to construct a nomogram for predicting the occurrence of synchronous LM based on baseline and pathological information.

Methods

The baseline and pathological information of 3190 CRC patients were enrolled in the study from the Department of Colorectal Surgery, the Second Affiliated Hospital of Harbin Medical University between 2012 and 2020. All patients were divided into development and validation cohorts with the 1:1 ratio. The characters of LM and none-LM patients in newly diagnosed colorectal cancer were utilized to explore the risk factors for synchronous LM with the univariate and multivariate logistic regression analyses. A predictive nomogram was constructed by using an R tool. In addition, receiver operating characteristic (ROC) curves was calculated to describe the discriminability of the nomogram. A calibration curve was plotted to compare the predicted and observed results of the nomogram. Decision-making curve analysis (DCA) was used to evaluate the clinical effect of nomogram.

Results

The nomogram consisted of six features including tumor site, vascular invasion (VI), T stage, N stage, preoperative CEA, and CA-199 level. ROC curves for the LM nomogram indicated good discrimination in the development (AUC = 0.885, 95% CI 0.854–0.916) and validation cohort (AUC = 0.857, 95% CI 0.821–0.893). The calibration curve showed that the prediction results of the nomogram were in good agreement with the actual observation results. Moreover, the DCA curves determined the clinical application value of predictive nomogram.

Conclusions

The pathologic-based nomogram could help clinicians to predict the occurrence of synchronous LM in postoperative CRC patients and provide a reference to perform appropriate metastatic screening plans and rational therapeutic options for the special population.

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.

A Population-Based Study: How to Identify High-Risk T1-2 Esophageal Cancer Patients?

Background

Due to individualized conditions of lymph node metastasis (LNM) and distant metastasis (DM), the following therapeutic strategy and diagnosis of T1-2 esophageal cancer (ESCA) patients are varied. A prediction model for identifying risk factors for LNM, DM, and overall survival (OS) of high-risk T1-2 ESCA patients is of great significance to clinical practice.

Methods

A total of 1,747 T1-2 ESCA patients screened from the surveillance, epidemiology, and end results (SEER) database were retrospectively analyzed for their clinical data. Univariate and multivariate logistic regression models were established to screen out risk factors for LNM and DM of T1-2 ESCA patients, while those of OS were screened out using the Cox regression analysis. The identified risk factors for LNM, DM, and OS were then subjected to the establishment of three nomograms, respectively. The accuracy of the nomograms was evaluated by depicting the calibration curve, and the predictive value and clinical utility were evaluated by depicting the clinical impact curve (CIC) and decision curve analysis (DCA), respectively.

Results

The age, race, tumor grade, tumor size, and T-stage were significant factors for predicting LNM of T1-2 ESCA patients (p < 0.05). The age, T-stage, tumor grade, and tumor size were significant factors for predicting DM of T1-2 ESCA patients (p < 0.05). The age, race, sex, histology, primary tumor site, tumor size, N-stage, M-stage, and surgery were significant factors for predicting OS of T1-2 ESCA patients (p < 0.05). The C-indexes of the three nomograms constructed by these factors were 0.737, 0.764, and 0.740, respectively, suggesting that they were clinically effective.

Conclusions

The newly constructed nomograms can objectively and accurately predict the LNM, DM, and OS of T1-2 ESCA patients, which contribute to the individualized decision making before clinical management.

New nomograms to predict overall and cancer-specific survival of angiosarcoma

Objective

This study was designed to establish and validate promising and reliable nomograms for predicting the survival of angiosarcoma (AS) patients.

Methods

The Surveillance, Epidemiology, and End Results database was queried to collect the clinical information of 785 AS patients between 2004 and 2015. Data were split into a training cohort (n = 549) and a validation cohort (n = 236) without any preference. Univariate Cox and multivariate Cox regression analyses were performed to analyze the clinical parameters. Independent prognostic factors were then identified. Two nomograms were constructed to predict overall survival (OS) and cancer‐specific survival (CSS) at 3 and 5 years. Finally, the models were evaluated using concordance indices (C‐indices), calibration plots, and decision curve analysis (DCA).

Results

Based on the inclusion and exclusion criteria, 785 individuals were included in this analysis. Univariate and multivariate Cox regression analyses revealed that age, tumor size, and stage were prognostic factors independently associated with the OS of AS. Tumor site, tumor size, and stage were associated with the CSS of AS. Based on the statistical results and clinical significance of variables, nomograms were built. The nomograms for OS and CSS had C‐indices of 0.666 and 0.654, respectively. The calibration curves showed good agreement between the predictive values and the actual values. DCA also indicated that the nomograms were clinically useful.

Conclusion

We established nomograms with good predictive ability that could provide clinicians with better predictions about the clinical outcomes of AS patients.

Robotic surgery for colorectal liver metastases resection: A systematic review

BACKGROUND

The role of robotic surgery for colorectal cancer liver metastases (CRCLMs) has never been investigated in large series.

METHODS

A systematic literature review was carried out on PubMed and Cochrane libraries.

RESULTS

We selected nine studies between 2008 and 2021. Two hundred sixty-two patients were included. One hundred thirty-one patients underwent simultaneous resections. The mean blood loss was 309.4 ml (range, 200-450 ml), the mean operative time was 250.5 min (range, 198.5-449.0 min). The mean length of hospital stay was 7.98 days (range, 4.5 to 12 days). The overall postoperative mortality was 0.4%. The overall morbidity rate was 37.0%, Clavien-Dindo grade III-IV complications were 8.4%. The mean 3-year overall survival was 55.25% (range, 44.4-66.1%), the mean 3-year disease free survival was 37% (range, 33.3-41.9%) CONCLUSION: We can conclude that robotic-assisted surgery might be considered as a technical upgrade option for minimally invasive approach to CRCLM resections even for simultaneous operations and challenging cases.