Hepatic resection for metastatic colorectal adenocarcinoma: a proposal of a prognostic scoring system

GAME-SCORE predicts pathological and radiological response to chemotherapy in patients with colorectal liver metastases

BACKGROUND

Genetic And Morphological Evaluation (GAME) score is the newest prognostic model for patient with colorectal liver metastases (CRLMs). Pathological and radiological responses to neoadjuvant chemotherapy (NAC) are key factors for prognostic stratification of these patients. The present study aims to evaluate the GAME-score's ability to predict pathological and radiologic responses to NAC.

METHODS

CRLM patients who underwent liver resection after NAC from January 2010 to December 2021 were categorized by GAME scores: low risk (LR, 0-1), moderate risk (MR, 2-3), and high risk (HR, ≥4). Correlations between groups and radiological/pathological features were analyzed. Poor pathological response was defined as Tumor Regression Grade 4-5.

RESULTS

Of 1054 liver resections for CRLMs, 448 were included. GAME scores were LR: 80 (18 %), MR: 228 (51 %), and HR: 140 (31 %). In this cohort, HR-GAME scores were associated with lower pathological response (LR: 67.1 %, MR: 74.9 %, HR: 82.6 %; p = 0.010). Radiologic progression occurred in 10 % of HR patients, significantly more than in LR (3.8 %) and MR (3.5 %) groups (p = 0.011). Multivariable analysis for independent predictors of pathological response confirmed HR-GAME (RR 1.843, p=0.025) along with age higher than 70 years (RR 2.111, p=0.022) and irinotecan-based NAC (RR 3.066, p < 0.001). For radiological progression disease after NAC, the HR-GAME score (RR 2.77, p=0.016) was the only independent predictor. HR-GAME scores were also associated with higher rates of mucinous differentiation (p = 0.021), satellitosis (p = 0.001), vascular invasion (p = 0.011), and perineural invasion (p = 0.010).

CONCLUSIONS

GAME score category should be considered into planning of therapeutic strategy of patients with CRLMs.

Constructing a prognostic model for colorectal cancer with synchronous liver metastases after preoperative chemotherapy: a study based on SEER and an external validation cohort

BACKGROUND

The combination of preoperative chemotherapy and surgical treatment has been shown to significantly enhance the prognosis of colorectal cancer with liver metastases (CRLM) patients. Nevertheless, as a result of variations in clinicopathological parameters, the prognosis of this particular group of patients differs considerably. This study aimed to develop and evaluate Cox proportional risk regression model and competing risk regression model using two patient cohorts. The goal was to provide a more precise and personalized prognostic evaluation system.

METHODS

We collected information on individuals who had a pathological diagnosis of colorectal cancer between 2000 and 2019 from the Surveillance, Epidemiology, and End Results (SEER) Database. We obtained data from patients who underwent pathological diagnosis of colorectal cancer and got comprehensive therapy at the hospital between January 1, 2010, and June 1, 2022. The SEER data collected after screening according to the inclusion and exclusion criteria were separated into two cohorts: a training cohort (training cohort) and an internal validation cohort (internal validation cohort), using a random 1:1 split. Subgroup Kaplan-Meier (K-M) survival analyses were conducted on each of the three groups. The data that received following screening from the hospital were designated as the external validation cohort. The subsequent variables were chosen for additional examination: age, gender, marital status, race, tumor site, pretreatment carcinoembryonic antigen level, tumor size, T stage, N stage, pathological grade, number of tumor deposits, perineural invasion, number of regional lymph nodes examined, and number of positive regional lymph nodes. The primary endpoint was median overall survival (mOS). In the training cohort, we conducted univariate Cox regression analysis and utilized a stepwise regression approach, employing the Akaike information criterion (AIC) to select variables and create Cox proportional risk regression models. We evaluated the accuracy of the model using calibration curve, receiver operating characteristic curve (ROC), and area under curve (AUC). The effectiveness of the models was assessed using decision curve analysis (DCA). To evaluate the non-cancer-related outcomes, we analyzed variables that had significant impacts using subgroup cumulative incidence function (CIF) and Gray's test. These analyses were used to create competing risk regression models. Nomograms of the two models were constructed separately and prognostic predictions were made for the same patients in SEER database.

RESULTS

This study comprised a total of 735 individuals. The mOS of the training cohort, internal validation cohort, and QDU cohort was 55.00 months (95%CI 46.97-63.03), 48.00 months (95%CI 40.65-55.35), and 68.00 months (95%CI 54.91-81.08), respectively. The multivariate Cox regression analysis revealed that age, N stage, presence of perineural infiltration, number of tumor deposits and number of positive regional lymph nodes were identified as independent prognostic risk variables (p < 0.05). In comparison to the conventional TNM staging model, the Cox proportional risk regression model exhibited a higher C-index. After controlling for competing risk events, age, N stage, presence of perineural infiltration, number of tumor deposits, number of regional lymph nodes examined, and number of positive regional lymph nodes were independent predictors of the risk of cancer-specific mortality (p < 0.05).

CONCLUSION

We have developed a prognostic model to predict the survival of patients with synchronous CRLM who undergo preoperative chemotherapy and surgery. This model has been tested internally and externally, confirming its accuracy and reliability.

Long-term survival after hepatic resection for colorectal liver metastases: a single-center study in Iran

BACKGROUND

Surgical resection of colorectal cancer liver metastasis (CRLM) has been associated with improved survival in these patients. The purpose of this study was to investigate the usefulness of liver metastasectomy, also finding independent factors related to survival after liver metastasectomy.

METHODS

In a retrospective study, all patients with CRLM who underwent resection of liver metastases between 2012 and 2022 at Imam Khomeini Hospital Complex in Tehran, Iran, were enrolled. All patients were actively followed based on clinicopathologic and operative data.

RESULTS

A total of 248 patients with a median follow-up time of 46 months (Range, 12 to 122) were studied. Eighty-six patients (35.0%) underwent major hepatectomy, whereas 160 (65.0%) underwent minor hepatectomy. The median overall survival was 43 months (Range, 0 to 122 months), with estimated 1-, 3- and 5-year overall survival rates of 91%, 56%, and 42%, respectively. Multivariate analysis demonstrated that a metastasis size > 6 cm, major hepatectomy, rectum as the primary tumor site, and involved margin (< 1 mm) were independent factors associated with decreased overall survival (OS).

CONCLUSION

Surgical resection is an effective treatment for patients with CRLM that is associated with relatively favorable survival. A negative margin of 1 mm seems to be sufficient for oncological resection.

Clinical Prediction Models for Prognosis of Colorectal Liver Metastases: A Comprehensive Review of Regression-Based and Machine Learning Models

Colorectal liver metastasis (CRLM) is a disease entity that warrants special attention due to its high frequency and potential curability. Identification of "high-risk" patients is increasingly popular for risk stratification and personalization of the management pathway. Traditional regression-based methods have been used to derive prediction models for these patients, and lately, focus has shifted to artificial intelligence-based models, with employment of variable supervised and unsupervised techniques. Multiple endpoints, like overall survival (OS), disease-free survival (DFS) and development or recurrence of postoperative complications have all been used as outcomes in these studies. This review provides an extensive overview of available clinical prediction models focusing on the prognosis of CRLM and highlights the different predictor types incorporated in each model. An overview of the modelling strategies and the outcomes chosen is provided. Specific patient and treatment characteristics included in the models are discussed in detail. Model development and validation methods are presented and critically appraised, and model performance is assessed within a proposed framework.

A New Automated Prognostic Prediction Method Based on Multi-Sequence Magnetic Resonance Imaging for Hepatic Resection of Colorectal Cancer Liver Metastases

Colorectal cancer is a prevalent and life-threatening disease, where colorectal cancer liver metastasis (CRLM) exhibits the highest mortality rate. Currently, surgery stands as the most effective curative option for eligible patients. However, due to the insufficient performance of traditional methods and the lack of multi-modality MRI feature complementarity in existing deep learning methods, the prognosis of CRLM surgical resection has not been fully explored. This paper proposes a new method, multi-modal guided complementary network (MGCNet), which employs multi-sequence MRI to predict 1-year recurrence and recurrence-free survival in patients after CRLM resection. In light of the complexity and redundancy of features in the liver region, we designed the multi-modal guided local feature fusion module to utilize the tumor features to guide the dynamic fusion of prognostically relevant local features within the liver. On the other hand, to solve the loss of spatial information during multi-sequence MRI fusion, the cross-modal complementary external attention module designed an external mask branch to establish inter-layer correlation. The results show that the model has accuracy (ACC) of 0.79, the area under the curve (AUC) of 0.84, C-Index of 0.73, and hazard ratio (HR) of 4.0, which is a significant improvement over state-of-the-art methods. Additionally, MGCNet exhibits good interpretability.

Contemporary Surgical Management of Colorectal Liver Metastases

Colorectal cancer is the third most common cancer in the United States and the second most common cause of cancer-related death. Approximately 20-30% of patients will develop hepatic metastasis in the form of synchronous or metachronous disease. The treatment of colorectal liver metastasis (CRLM) has evolved into a multidisciplinary approach, with chemotherapy and a variety of locoregional treatments, such as ablation and portal vein embolization, playing a crucial role. However, resection remains a core tenet of management, serving as the gold standard for a curative-intent therapy. As such, the input of a dedicated hepatobiliary surgeon is paramount for appropriate patient selection and choice of surgical approach, as significant advances in the field have made management decisions extremely nuanced and complex. We herein aim to review the contemporary surgical management of colorectal liver metastasis with respect to both perioperative and operative considerations.

SBRT for Liver Tumors: What the Interventional Radiologist Needs to Know

This review summarizes the clinical evidence supporting the utilization of stereotactic body radiotherapy (SBRT) for liver tumors, including hepatocellular carcinoma, liver metastases, and cholangiocarcinoma. Emerging prospective evidence has demonstrated the benefit and low rates of toxicity across a broad range of clinical contexts. We provide an introduction for the interventional radiologist, with a discussion of underlying themes such as tumor dose-response, mitigation of liver toxicity, and the technical considerations relevant to performing liver SBRT. Ultimately, we recommend that SBRT should be routinely included in the armamentarium of locoregional therapies for liver malignancies, alongside those liver-directed therapies offered by interventional radiology.

Preoperative IL-8 levels as prognostic indicators of overall survival: an extended follow-up in a prospective cohort with colorectal liver metastases

INTRODUCTION

CRC with liver metastases is a major contributor to cancer-related mortality. Despite advancements in liver resection techniques, patient survival remains a concern due to high recurrence rates. This study seeks to uncover prognostic biomarkers that predict overall survival in patients undergoing curative hepatic resection for CRC liver metastases.

METHODS

Prospectively collected serum samples from a cohort of 49 patients who received curative hepatic resection for CRC liver metastases were studied. The patients are part of a cohort, previously analyzed for perioperative complications (see methods). Various preoperative serum markers, clinical characteristics, and factors were analyzed. Univariate and multivariate Cox regression analyses were conducted to determine associations between these variables and disease-free survival as well as overall survival.

RESULTS

For disease-free survival, univariate analysis highlighted the correlation between poor outcomes and advanced primary tumor stage, high ASA score, and synchronous liver metastases. Multivariate analysis identified nodal-positive primary tumors and synchronous metastases as independent risk factors for disease-free survival. Regarding overall survival, univariate analysis demonstrated significant links between poor survival and high preoperative IL-8 levels, elevated neutrophil-lymphocyte ratio (NLR), and presence of metastases in other organs. Multivariate analysis confirmed preoperative IL-8 and having three or more liver metastases as independent risk factors for overall survival. The impact of IL-8 on survival was particularly noteworthy, surpassing the influence of established clinical factors.

CONCLUSION

This study establishes preoperative IL-8 levels as a potential prognostic biomarker for overall survival in patients undergoing curative liver resection for CRC liver metastases. This study underscores the importance of incorporating IL-8 and other biomarkers into clinical decision-making, facilitating improved patient stratification and tailored treatment approaches. Further research and validation studies are needed to solidify the clinical utility of IL-8 as a prognostic marker.

Neoadjuvant chemotherapy weakens the prognostic value of the pathological tumor burden score for colorectal cancer liver metastases

BACKGROUND

The pathological tumor burden score (TBS) has been proven to be a better risk stratification tool for liver metastasis of colorectal cancer than the traditional clinical risk score (CRS). The aim of this study was to evaluate the prognostic value of the pathological tumor burden score in patients with or without neoadjuvant chemotherapy (NAC).

METHODS

A total of 348 patients with colorectal liver metastases (CRLM) who underwent curative hepatic resection were retrospectively enrolled from September 1999 to December 2016. Univariable and multivariable Cox regression analyses were conducted to identify the independent predictors of prognosis. Kaplan-Meier curves and log-rank tests were used to determine whether TBS has enough discriminatory ability under certain grouping.

RESULTS

Patients who received NAC had a higher median TBS than patients who did not receive NAC (4.07 vs. 2.69, P < 0.001). Among patients who did not receive NAC, those with TBS > 3 showed a significantly worse 3-year RFS (41.1% vs. 63.6%, P < 0.001) and 3-year OS rate (73.3% vs. 84.1%, P = 0.003) than those with TBS ≤ 3. Among the patients who received NAC, those with TBS ≤ 3 or TBS > 3 showed comparable 3-year RFS (33.3% vs. 26.4%, P = 0.400) and 3-year OS rates (76.5% vs. 58.2%, P = 0.064) to those who did not. Regardless of the regimen and response to NAC, there was no significant difference about 3-year RFS and 3-year OS rates between the TBS ≤ 3 and TBS > 3 groups.

CONCLUSION

Pathological TBS can be applied to predict the RFS and OS of patients suffering from CRLM who did not receive NAC. However, pathological TBS might not be regard as prognosis in patients who did receive NAC.

Integrated Clinical-Molecular Classification of Colorectal Liver Metastases: A Biomarker Analysis of the Phase 3 New EPOC Randomized Clinical Trial

Importance

Personalized treatment approaches for patients with oligometastatic colorectal liver metastases are critically needed. We previously defined 3 biologically distinct molecular subtypes of colorectal liver metastases: (1) canonical, (2) immune, and (3) stromal.

Objective

To independently validate these molecular subtypes in the phase 3 New EPOC randomized clinical trial.

Design, Setting, and Participants

This retrospective secondary analysis of the phase 3 New EPOC randomized clinical trial included a bi-institutional discovery cohort and multi-institutional validation cohort. The discovery cohort comprised patients who underwent hepatic resection for limited colorectal liver metastases (98% received perioperative chemotherapy) from May 31, 1994, to August 14, 2012. The validation cohort comprised patients who underwent hepatic resection for liver metastases with perioperative chemotherapy (fluorouracil, oxaliplatin, and irinotecan based) with or without cetuximab from February 26, 2007, to November 1, 2012. Data were analyzed from January 18 to December 10, 2021.

Interventions

Resected metastases underwent RNA sequencing and microRNA (miRNA) profiling in the discovery cohort and messenger RNA and miRNA profiling with microarray in the validation cohort.

Main Outcomes and Measures

A 31-feature (24 messenger RNAs and 7 miRNAs) neural network classifier was trained to predict molecular subtypes in the discovery cohort and applied to the validation cohort. Integrated clinical-molecular risk groups were designated based on molecular subtypes and the clinical risk score. The unique biological phenotype of each molecular subtype was validated using gene set enrichment analyses and immune deconvolution. The primary clinical end points were progression-free survival (PFS) and overall survival (OS).

Results

A total of 240 patients were included (mean [range] age, 63.0 [56.3-68.0] years; 151 [63%] male), with 93 in the discovery cohort and 147 in the validation cohort. In the validation cohort, 73 (50%), 28 (19%), and 46 (31%) patients were classified as having canonical, immune, and stromal metastases, respectively. The biological phenotype of each subtype was concordant with the discovery cohort. The immune subtype (best prognosis) demonstrated 5-year PFS of 43% (95% CI, 25%-60%; hazard ratio [HR], 0.37; 95% CI, 0.20-0.68) and OS of 63% (95% CI, 40%-79%; HR, 0.38; 95% CI, 0.17-0.86), which was statistically significantly higher than the canonical subtype (worst prognosis) at 14% (95% CI, 7%-23%) and 43% (95% CI, 32%-55%), respectively. Adding molecular subtypes to the clinical risk score improved prediction (the Gönen and Heller K for discrimination) from 0.55 (95% CI, 0.49-0.61) to 0.62 (95% CI, 0.57-0.67) for PFS and 0.59 (95% CI, 0.52-0.66) to 0.63 (95% CI, 0.56-0.70) for OS. The low-risk integrated group demonstrated 5-year PFS of 44% (95% CI, 20%-66%; HR, 0.38; 95% CI, 0.19-0.76) and OS of 78% (95% CI, 44%-93%; HR, 0.26; 95% CI, 0.08-0.84), superior to the high-risk group at 16% (95% CI, 10%-24%) and 43% (95% CI, 32%-52%), respectively.

Conclusions and Relevance

In this prognostic study, biologically derived colorectal liver metastasis molecular subtypes and integrated clinical-molecular risk groups were highly prognostic. This novel molecular classification warrants further study as a possible predictive biomarker for personalized systemic treatment for colorectal liver metastases.

Trial Registration

isrctn.org Identifier: ISRCTN22944367.

A machine learning model for colorectal liver metastasis post-hepatectomy prognostications

Background

Currently, surgical resection is the mainstay for colorectal liver metastases (CRLM) management and the only potentially curative treatment modality. Prognostication tools can support patient selection for surgical resection to maximize therapeutic benefit. This study aimed to develop a survival prediction model using machine learning based on a multicenter patient sample in Hong Kong.

Methods

Patients who underwent hepatectomy for CRLM between 1 January 2009 and 31 December 2018 in four hospitals in Hong Kong were included in the study. Survival analysis was performed using Cox proportional hazards (CPH). A stepwise selection on Cox multivariable models with Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to a multiply-imputed dataset to build a prediction model. The model was validated in the validation set, and its performance was compared with that of Fong Clinical Risk Score (CRS) using concordance index.

Results

A total of 572 patients were included with a median follow-up of 3.6 years. The full models for overall survival (OS) and recurrence-free survival (RFS) consist of the same 8 established and novel variables, namely colorectal cancer nodal stage, CRLM neoadjuvant treatment, Charlson Comorbidity Score, pre-hepatectomy bilirubin and carcinoembryonic antigen (CEA) levels, CRLM largest tumor diameter, extrahepatic metastasis detected on positron emission-tomography (PET)-scan as well as KRAS status. Our CRLM Machine-learning Algorithm Prognostication model (CMAP) demonstrated better ability to predict OS (C-index =0.651), compared with the Fong CRS for 1-year (C-index =0.571) and 5-year OS (C-index =0.574). It also achieved a C-index of 0.651 for RFS.

Conclusions

We present a promising machine learning algorithm to individualize prognostications for patients following resection of CRLM with good discriminative ability.

Machine Learning Models for Predicting the Outcomes of Surgical Treatment of Colorectal Liver Metastases

BACKGROUND

Surgical intervention remains the cornerstone of a multidisciplinary approach in the treatment of colorectal liver metastases (CLM). Nevertheless, patient outcomes vary greatly. While predictive tools can assist decision-making and patient counseling, decades of efforts have yet to result in generating a universally adopted tool in clinical practice.

STUDY DESIGN

An international collaborative database of CLM patients who underwent surgical therapy between 2000 and 2018 was used to select 1,004 operations for this study. Two different machine learning methods were applied to construct 2 predictive models for recurrence and death, using 128 clinicopathologic variables: gradient-boosted trees (GBTs) and logistic regression with bootstrapping (LRB) in a leave-one-out cross-validation.

RESULTS

Median survival after resection was 47.2 months, and disease-free survival was 19.0 months, with a median follow-up of 32.0 months in the cohort. Both models had good predictive power, with GBT demonstrating a superior performance in predicting overall survival (area under the receiver operating curve [AUC] 0.773, 95% CI 0.743 to 0.801 vs LRB: AUC 0.648, 95% CI 0.614 to 0.682) and recurrence (AUC 0.635, 95% CI 0.599 to 0.669 vs LRB: AUC 0.570, 95% CI 0.535 to 0.601). Similarly, better performances were observed predicting 3- and 5-year survival, as well as 3- and 5-year recurrence, with GBT methods generating higher AUCs.

CONCLUSIONS

Machine learning provides powerful tools to create predictive models of survival and recurrence after surgery for CLM. The effectiveness of both machine learning models varies, but on most occasions, GBT outperforms LRB. Prospective validation of these models lays the groundwork to adopt them in clinical practice.

Survival analysis and identification of prognostic factors in colorectal liver metastasis after liver resection

INTRODUCTION

Liver resection is the only curative treatment for colorectal liver metastasis. The identification of predictive factors leads to personalize patient management to enhance their long-term outcomes. This population-based study aimed to characterize factors associated with, and survival impact of patients who received hepatectomy for colorectal liver metastasis.

METHODS

A retrospective cohort study of all the hepatectomies for colorectal liver metastasis performed at third-level hospital of Spain (2010-2018) was conducted. The Kaplan-Meier method was used for survival analyses. Multivariable Cox and regression models were used to determine prognostic factors associated with overall survival.

RESULTS

The 5-year overall survival and disease-free survival were 42 and 33%, respectively. Survival analysis showed that metastasis features (number, largest size, distribution, and extrahepatic disease) and postsurgical factors (transfusion, major complications, and positive margin resection), as well as non-mutated KRAS, showed a significant association with survival. Otherwise, on multivariate analysis, only 5 independent risk factors were identified: major size metastasis >4 cm, RAS mutation, positive margin resection, intraoperative transfusion, and major complications.

CONCLUSIONS

According to our findings, major size metastasis >4 cm, intraoperative transfusion, and major postoperative complications continue to be traditional prognostic factors. Meanwhile, the KRAS biomarker has a powerful impact as a survival prognostic factor.

Five-year survival post hepatectomy for colorectal liver metastases in a real-world Chinese cohort: Recurrence patterns and prediction for potential cure

BACKGROUND

Patients with a 5-year recurrence-free survival post liver resection for colorectal cancer liver metastases (CRLM) are considered to be potentially cured. However, there is a deficit of data on long-term follow-up and the recurrence status among these patients in the Chinese population. We analyzed real-world follow-up data of patients with CRLM who underwent hepatectomy, explored the recurrence patterns, and established a prediction model for a potential cure scenario.

METHODS

Patients who underwent radical hepatic resection for CRLM during 2000-2016, with actual follow-up data for at least 5 years, were enrolled. The observed survival rate was calculated and compared among the groups with different recurrence patterns. The predictive factors for 5-year non-recurrence were determined using logistic regression analysis; a recurrence-free survival model was developed to predict long-term survival.

RESULTS

A total of 433 patients were included, of whom 113 patients were found non-recurrence after 5 years follow-up, with a potential cure rate of 26.1%. Patients with late recurrence (>5 months) and lung relapse showed significantly superior survival. Repeated localized treatment significantly improved the long-term survival of patients with intrahepatic or extrahepatic recurrences. Multivariate analysis showed that RAS wild-type CRC, preoperative CEA <10 ng/ml, and liver metastases ≤3 were independent factors for a 5-year disease-free recurrence. A cure model was developed based on the above factors, achieving good performance in predicting long-term survival.

CONCLUSIONS

About one quarter patients with CRLM could achieve potential cure with non-recurrence at 5-year after surgery. The recurrence-free cure model could well distinguish the long-term survival, which would aid clinicians in determining the treatment strategy.

The Impact of Molecular Biology in the Seeding, Treatment Choices and Follow-Up of Colorectal Cancer Liver Metastases-A Narrative Review

There is a clear association between the molecular profile of colorectal cancer liver metastases (CRCLM) and the degree to which aggressive progression of the disease impacts patient survival. However, much of our knowledge of the molecular behaviour of colorectal cancer cells comes from experimental studies with, as yet, limited application in clinical practice. In this article, we review the current advances in the understanding of the molecular behaviour of CRCLM and present possible future therapeutic applications. This review focuses on three important steps in CRCLM development, progression and treatment: (1) the dissemination of malignant cells from primary tumours and the seeding to metastatic sites; (2) the response to modern regimens of chemotherapy; and (3) the possibility of predicting early progression and recurrence patterns by molecular analysis in liquid biopsy.

Prediction of survival in patients with colorectal liver metastases- development and validation of a prognostic score model

BACKGROUND

Metastatic spread of colorectal cancer to the liver impacts prognosis. Advances in chemotherapy have resulted in increased resectability rates and thereby improved survival in patients with colorectal liver metastases (CRLM). However, criteria are needed to ensure that patients selected for hepatic resection benefit from the invasive therapy. The study aimed to construct a predictive model for overall survival (OS) in patients with CRLM, based on preoperatively available information.

METHODS

The retrospective cohort study reviewed all patients with CRLM discussed at multidisciplinary team conference at Karolinska University Hospital, Stockholm, Sweden, 2013-2018. Independent prognostic factors for OS were identified, based on which a score model was generated. The model was validated on patients treated for CRLM at Hôpital Universitaire Paul Brousse, Villejuif, France, 2007-2018. Calibration and discrimination methods were used for internal and external validation.

RESULTS

The Swedish development cohort included 1013 patients, the French validation cohort 391 patients. Poor OS was significantly associated with age>60years (hazard ratio (HR) 3.57 (95%CI 2.18-9.94)), number of CRLM (HR 4.59 (2.83-12.20)), diameter of largest CRLM>5 cm (HR 2.59 (1.74-5.03)), right-sided primary tumour (HR 2.98 (2.00-5.80)), extrahepatic disease (HR 4.14 (2.38-15.87)) and non-resectability (HR 0.77 (0.66-0.90)). The C-statistic for prediction of OS was .74, in the development cohort and 0.69 in the validation cohort.

CONCLUSION

The presented predictive score model can adequately predict OS for patients at the initial diagnosis of CRLM. The prognostic model could be of clinical value in the management of all patients with CRLM, by predicting individualized survival and thereby facilitating treatment recommendations.

Radiomics artificial intelligence modelling for prediction of local control for colorectal liver metastases treated with radiotherapy

•

Computed tomography imaging contains quantifiable information to characterize colorectal liver metastases.

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Shape, texture, and intensity statistical features quantified the computed tomography liver volume.

•

An artificial intelligence model to predict local progression from radiomic features was developed with high accuracy.

•

Maximum dosage and textural coarseness of liver volume were features with highest predictive value.

Background and Purpose

Prognostic assessment of local therapies for colorectal liver metastases (CLM) is essential for guiding management in radiation oncology. Computed tomography (CT) contains liver texture information which may be predictive of metastatic environments. To investigate the feasibility of analyzing CT texture, we sought to build an automated model to predict progression-free survival using CT radiomics and artificial intelligence (AI).

Materials and Methods

Liver CT scans and outcomes for N = 97 CLM patients treated with radiotherapy were retrospectively obtained. A survival model was built by extracting 108 radiomic features from liver and tumor CT volumes for a random survival forest (RSF) to predict local progression. Accuracies were measured by concordance indices (C-index) and integrated Brier scores (IBS) with 4-fold cross-validation. This was repeated with different liver segmentations and radiotherapy clinical variables as inputs to the RSF. Predictive features were identified by perturbation importances.

Results

The AI radiomics model achieved a C-index of 0.68 (CI: 0.62–0.74) and IBS below 0.25 and the most predictive radiomic feature was gray tone difference matrix strength (importance: 1.90 CI: 0.93–2.86) and most predictive treatment feature was maximum dose (importance: 3.83, CI: 1.05–6.62). The clinical data only model achieved a similar C-index of 0.62 (CI: 0.56–0.69), suggesting that predictive signals exist in radiomics and clinical data.

Conclusions

The AI model achieved good prediction accuracy for progression-free survival of CLM, providing support that radiomics or clinical data combined with machine learning may aid prognostic assessment and management.

The Pathologic Complete Response Ratio of Liver Metastases Represents a Valuable Prognostic Indicator

Background and Objectives: The aim of this study was to evaluate the role of the pathologic complete response ratio of liver metastases (PCRRLM) in predicting the prognosis and recurrence of colorectal cancer liver metastases (CRLM).

Methods: A total of 305 CRLM patients who underwent preoperative chemotherapy followed by hepatectomy were included. PCRRLM was defined as the number of liver metastases exhibiting pathologic complete response (PCR) divided by the number of total resected liver metastases. The Kaplan–Meier method was used to calculate survival, and differences were examined by the log-rank test. Univariate and multivariate analyses were performed to identify the predictors of PCRRLM, recurrence-free survival (RFS) and overall survival (OS).

Results: Among the 305 included patients, 44 (14.4%) achieved a PCRRLM ≥0.50 (including PCRRLM = 1), and 261 (85.6%) achieved a PCRRLM <0.50 (including PCRRLM = 0). Patients of an older age (≥55 years old) and those with higher carcinoembryonic antigen (CEA) levels (≥5 ng/ml) were less likely to achieve a PCRRLM ≥0.50. In the multivariate analysis, PCRRLM≥ 0.50 (vs. < 0.50, HR [95% CI]: 0.67 [0.46–0.99], p = 0.043) was associated with better RFS. Positive lymph node status (vs. negative, HR [95% CI]: 1.46 [1.04–2.05], p = 0.028) and TBS ≥5 (vs. < 5, HR [95% CI]: 1.44 [1.02–2.04], p = 0.038) were associated with worse RFS.

Conclusion: PCRRLM was significantly associated with long-term RFS after preoperative chemotherapy and CRLM resection. Thus, it may be a valuable indicator of recurrence in CRLM patients.

Precision surgery for colorectal liver metastases: Current knowledge and future perspectives

Precision surgery for colorectal liver metastases (CRLM) includes optimal selection of both the patient and surgery. Initial attempts of using clinical risk scores to identify patients for whom technically feasible surgery is oncologically futile failed. Since then, patient selection for single-stage hepatectomy followed three distinct approaches, all of which incorporated biomarkers. The BRAF V600E mutation, the G12V KRAS variant, and the triple mutation of RAS, TP53, and SMAD4 appear to be the most promising, but none can be used in isolation to deny surgery in otherwise resectable cases. Combining biomarkers with clinicopathologic factors that predict poor prognosis may be used to select patients for surgery, but external validation and matched analyses with medically treated counterparts are needed. Patient selection for special surgical procedures (two-stage hepatectomy [TSH], Associating Liver Partition and Portal vein Ligation for staged hepatectomy [ALPPS], and liver transplant [LT]) has been recently refined. Specifically, BRAF mutations and right-sided laterality have been proposed as separate contraindications to LT. A similar association of right-sided laterality, particularly when combined with RAS mutations, with very poor outcomes has been observed for ALPPS and has been suggested as a biologic contraindication. Data are scarce for TSH but RAS mutations may portend very poor survival following TSH completion. The selection of the best single-stage hepatectomy (optimal margin and type of resection) based on biomarkers remains debated, although there is some evidence that RAS may play a significant role. Lastly, although there are currently no criteria to select among the three special techniques based on their efficacy or appropriateness in different settings, RAS mutational status may be used to select patients for TSH, while right-sided tumor in conjunction with a RAS mutation may be a contraindication to LT and ALPPS.

The Impact of Postoperative Complications on Survival after Simultaneous Resection of Colorectal Cancer and Liver Metastases

Background: The aim of this study was to investigate the impact of postoperative complications on the long-term outcomes of patients who had undergone simultaneous resection (SR) of colorectal cancer and synchronous liver metastases (SCLMs). Methods: We conducted a single-institution survival cohort study in patients with SR, collecting clinical, pathological, and postoperative complication data. The impact of these variables on overall survival (OS) and disease-free survival (DFS) was compared by log rank test. Multivariate Cox regression analysis identified independent prognostic factors. Results: Out of 243 patients, 122 (50.2%) developed postoperative complications: 54 (22.2%) major complications (Clavien–Dindo grade III–V), 86 (35.3%) septic complications, 59 (24.2%) hepatic complications. Median comprehensive complication index (CCI) was 8.70. Twelve (4.9%) patients died postoperatively. The 3- and 5-year OS and DFS rates were 60.7%, 39.5% and 28%, 21.5%, respectively. Neither overall postoperative complications nor major and septic complications or CCI had a significant impact on OS or DFS. Multivariate analysis identified the N2 stage as an independent prognostic of poor OS, while N2 stage and four or more SCLMs were independent predictors for poor DFS. Conclusion: N2 stage and four or more SCLMs impacted OS and/or DFS, while CCI, presence, type, or grade of postoperative complications had no significant impact on long-term outcomes.

Prognostic factors in colorectal liver metastases patients with various tumor numbers treated by liver resection: a single-center, retrospective study

BACKGROUND

Multiple liver metastases is considered a risk factor for overall survival of colorectal liver metastases patients (CRLM) after curative resection. However, whether the prognostic factors were constant in patients with various liver metastases (LM) numbers has not been adequately investigated. This retrospective study aimed to evaluate the changing of prognostic factors on overall survival (OS) in CRLM patients with various LM after curative resection.

METHODS

Patients who underwent liver resection for CRLM between January 2000 and November 2020 were retrospectively studied. They were divided into three subgroups according to LM numbers by X-tile analysis. Multivariable analysis identified prognostic factors in each subgroup. Nomograms were built using different prognostic factors in three subgroups, respectively. Performance of the nomograms was assessed according to the concordance index (C-index) and calibration plots. The abilities of different scoring systems predicting OS were compared by calculating the area under the time-dependent receiver operating characteristic (ROC) curve (AUC).

RESULTS

A total of 1095 patients were included. Multivariable analysis showed tumor number increasing was an independent risk factor. Patients were subsequently divided into 3 subgroups according to the number of LM by X-tile analysis, namely solitary (n = 375), 2-4 (n = 424), and ≥ 5 (n = 296). The 3-year and 5-year OS rates were 64.1% and 54.0% in solitary LM group, 58.1% and 41.7% in 2-4 LM group, and 50.9% and 32.0% in ≥ 5 LM group, respectively (p < 0.001). In multivariable analysis, RAS mutation was the only constant independent risk factor in all subgroups. The nomograms were built to predict survival based on independent factors in three subgroups. The C-index for OS prediction was 0.707 (95% CI 0.686-0.728) in the solitary LM group, 0.695 (95% CI 0.675-0.715) in the 2-4 LM group, and 0.687 (95% CI 0.664-0.710) in the ≥ 5 LM group. The time-dependent AUC values of nomograms developed using different risk factors after stratifying patients by tumor number were higher than the traditional scoring systems without patient stratification.

CONCLUSIONS

The prognostic factors varied among CRLM patients with different LM numbers. RAS mutation was the only constant risk factor. Building prediction models based on different prognostic factors improve patient stratification.

Efficacy of neoadjuvant chemotherapy for initially resectable colorectal liver metastases: A retrospective cohort study

BACKGROUND

The liver is the most common metastatic site of colorectal cancer. Hepatectomy is the mainstay of treatment for patients with colorectal liver metastases (CRLMs). However, there are cases of early recurrence after upfront hepatectomy alone. In selected high-risk patients, neoadjuvant chemotherapy (NAC) may improve long-term survival.

AIM

To determine the efficacy of NAC for initially resectable CRLMs.

METHODS

Among 644 patients who underwent their first hepatectomy for CRLMs at our institution, 297 resectable cases were stratified into an upfront hepatectomy group (238 patients) and a NAC group (59 patients). Poor prognostic factors for upfront hepatectomy were identified using multivariate logistic regression analysis. Propensity score matching was used to compare clinical outcomes between the upfront hepatectomy and NAC groups, according to the number of poor prognostic factors. Survival curves were estimated using the Kaplan–Meier method and compared using the log-rank test.

RESULTS

Preoperative carcinoembryonic antigen levels (≥ 10 ng/mL) (P = 0.003), primary histological type (other than well/moderately differentiated) (P = 0.04), and primary lymph node metastases (≥ 1) (P = 0.04) were identified as independent poor prognostic factors for overall survival (OS) in the upfront hepatectomy group. High-risk status was defined as the presence of two or more risk factors. After propensity score matching, 50 patients were matched in each group. Among high-risk patients, the 5-year OS rate was significantly higher in the NAC group (13 patients) than in the upfront hepatectomy group (18 patients) (100% vs 34%; P = 0.02).

CONCLUSION

NAC may improve the prognosis of high-risk patients with resectable CRLMs who have two or more risk factors.

The oligometastatic spectrum in the era of improved detection and modern systemic therapy

Metastases remain the leading cause of cancer-related mortality. The oligometastasis hypothesis postulates that a spectrum of metastatic spread exists and that some patients with a limited burden of metastases can be cured with ablative therapy. Over the past decade, substantial advances in systemic therapies have resulted in considerable improvements in the outcomes of patients with metastatic cancers, warranting re-examination of the oligometastatic paradigm and the role of local ablative therapies within the context of the improved therapeutic responses, shifting patterns of disease recurrence and possible synergy with systemic treatments. Herein, we reframe the oligometastatic phenotype as a dynamic state for which locally ablative, metastasis-directed therapy improves clinical outcomes, including by prolonging survival and increasing cure rates. Important risk factors defining the metastatic spectrum are highlighted that inform both staging and therapy. Finally, we synthesize the literature on combining local therapies with modern systemic treatments, identifying general themes to optimally integrate ablative therapies in this context.

Predicting 10-year survival after resection of colorectal liver metastases; an international study including biomarkers and perioperative treatment

BACKGROUND

The aim of this study was to develop a prediction model for 10-year overall survival (OS) after resection of colorectal liver metastasis (CRLM) based on patient, tumour and treatment characteristics.

METHODS

Consecutive patients after complete resection of CRLM were included from two centres (1992-2019). A prediction model providing 10-year OS probabilities was developed using Cox regression analysis, including KRAS, BRAF and histopathological growth patterns. Discrimination and calibration were assessed using cross-validation. A web-based calculator was built to predict individual 10-year OS probabilities.

RESULTS

A total of 4112 patients were included. The estimated 10-year OS was 30% (95% CI 29-32). Fifteen patient, tumour and treatment characteristics were independent prognostic factors for 10-year OS; age, gender, location and nodal status of the primary tumour, disease-free interval, number and diameter of CRLM, preoperative CEA, resection margin, extrahepatic disease, KRAS and BRAF mutation status, histopathological growth patterns, perioperative systemic chemotherapy and hepatic arterial infusion pump chemotherapy. The discrimination at 10-years was 0.73 for both centres. A simplified risk score identified four risk groups with a 10-year OS of 57%, 38%, 24%, and 12%.

CONCLUSIONS

Ten-year OS after resection of CRLM is best predicted with a model including 15 patient, tumour, and treatment characteristics. The web-based calculator can be used to inform patients. This model serves as a benchmark to determine the prognostic value of novel biomarkers.

Association of CT-Based Delta Radiomics Biomarker With Progression-Free Survival in Patients With Colorectal Liver Metastases Undergo Chemotherapy

Predicting the prognosis of patients in advance is conducive to providing personalized treatment for patients. Our aim was to predict the therapeutic efficacy and progression free survival (PFS) of patients with liver metastasis of colorectal cancer according to the changes of computed tomography (CT) radiomics before and after chemotherapy.

Methods

This retrospective study included 139 patients (397 lesions) with colorectal liver metastases who underwent neoadjuvant chemotherapy from April 2015 to April 2020. We divided the lesions into training cohort and testing cohort with a ratio of 7:3. Two - dimensional region of interest (ROI) was obtained by manually delineating the largest layers of each metastasis lesion. The expanded ROI (3 mm and 5 mm) were also included in the study to characterize microenvironment around tumor. For each of the ROI, 1,316 radiomics features were extracted from delineated plain scan, arterial, and venous phase CT images before and after neoadjuvant chemotherapy. Delta radiomics features were constructed by subtracting the radiomics features after treatment from the radiomics features before treatment. Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression were applied in the training cohort to select the valuable features. Based on clinical characteristics and radiomics features, 7 Cox proportional-hazards model were constructed to predict the PFS of patients. C-index value and Kaplan Meier (KM) analysis were used to evaluate the efficacy of predicting PFS of these models. Moreover, the prediction performance of one-year PFS was also evaluated by area under the curve (AUC).

Results

Compared with the PreRad (Radiomics form pre-treatment CT images; C-index [95% confidence interval (CI)] in testing cohort: 0.614(0.552-0.675) and PostRad models (Radiomics form post-treatment CT images; 0.642(0.578-0.707), the delta model has better PFS prediction performance (Delta radiomics; 0.688(0.627-0.749). By incorporating clinical characteristics, CombDeltaRad obtains the best performance in both training cohort [C-index (95% CI): 0.802(0.772-0.832)] and the testing cohort (0.744(0.686-0.803). For 1-year PFS prediction, CombDeltaRad model obtained the best performance with AUC (95% CI) of 0.871(0.828-0.914) and 0.745 (0.651-0.838) in training cohort and testing cohort, respectively.

Conclusion

CT radiomics features have the potential to predict PFS in patients with colorectal cancer and liver metastasis who undergo neoadjuvant chemotherapy. By combining pre-treatment radiomics features, post-treatment radiomics features, and clinical characteristics better prediction results can be achieved.

External Validation of Two Established Clinical Risk Scores Predicting Outcome after Local Treatment of Colorectal Liver Metastases in a Nationwide Cohort

Optimized surgical techniques and systemic therapy have increased the number of patients with colorectal liver metastases (CRLM) eligible for local treatment. To increase postoperative survival, we need to stratify patients to customize therapy. Most clinical risk scores (CRSs) which predict prognosis after CRLM resection were based on the outcome of studies in specialized centers, and this may hamper the generalizability of these CRSs in unselected populations and underrepresented subgroups. We aimed to externally validate two CRSs in a population-based cohort of patients with CRLM. A total of 1105 patients with local treatment of CRLM, diagnosed in 2015/2016, were included from a nationwide population-based database. Survival outcomes were analyzed. The Fong and more recently developed GAME CRS were externally validated, including in pre-specified subgroups (≤70/>70 years and with/without perioperative systemic therapy). The three-year DFS was 22.8%, and the median OS in the GAME risk groups (high/moderate/low) was 32.4, 46.7, and 68.1 months, respectively (p < 0.005). The median OS for patients with versus without perioperative therapy was 47.6 (95%CI [39.8, 56.2]) and 54.9 months (95%CI [48.8, 63.7]), respectively (p = 0.152), and for below/above 70 years, it was 54.9 (95%CI [49.3−64.1]) and 44.2 months (95%CI [37.1−54.3]), respectively (p < 0.005). The discriminative ability for OS of Fong CRS was 0.577 (95%CI [0.554, 0.601]), and for GAME, it was 0.596 (95%CI [0.572, 0.621]), and was comparable in the subgroups. In conclusion, both CRSs showed predictive ability in a population-based cohort and in predefined subgroups. However, the limited discriminative ability of these CRSs results in insufficient preoperative risk stratification for clinical decision-making.

Gene-Mutation-Based Algorithm for Prediction of Treatment Response in Colorectal Cancer Patients

Purpose: Despite the high mortality of metastatic colorectal cancer (mCRC), no new biomarker tools are available for predicting treatment response. We developed gene-mutation-based algorithms as a biomarker classifier to predict treatment response with better precision than the current predictive factors. Methods: Random forest machine learning (ML) was applied to identify the candidate algorithms using the MSK Cohort (n = 471) as a training set and validated in the TCGA Cohort (n = 221). Logistic regression, progression-free survival (PFS), and univariate/multivariate Cox proportional hazard analyses were performed and the performance of the candidate algorithms was compared with the established risk parameters. Results: A novel 7-Gene Algorithm based on mutation profiles of seven KRAS-associated genes was identified. The algorithm was able to distinguish non-progressed (responder) vs. progressed (non-responder) patients with AUC of 0.97 and had predictive power for PFS with a hazard ratio (HR) of 16.9 (p < 0.001) in the MSK cohort. The predictive power of this algorithm for PFS was more pronounced in mCRC (HR = 16.9, p < 0.001, n = 388). Similarly, in the TCGA validation cohort, the algorithm had AUC of 0.98 and a significant predictive power for PFS (p < 0.001). Conclusion: The novel 7-Gene Algorithm can be further developed as a biomarker model for prediction of treatment response in mCRC patients to improve personalized therapies.

Machine Learning Approach to Stratifying Prognosis Relative to Tumor Burden after Resection of Colorectal Liver Metastases: An International Cohort Analysis

BACKGROUND

Assessing overall tumor burden on the basis of tumor number and size may assist in prognostic stratification of patients after resection of colorectal liver metastases (CRLM). We sought to define the prognostic accuracy of tumor burden by using machine learning (ML) algorithms compared with other commonly used prognostic scoring systems.

STUDY DESIGN

Patients who underwent hepatectomy for CRLM between 2001 and 2018 were identified from a multi-institutional database and split into training and validation cohorts. ML was used to define tumor burden (ML-TB) based on CRLM tumor number and size thresholds associated with 5-year overall survival. Prognostic ability of ML-TB was compared with the Fong and Genetic and Morphological Evaluation scores using Cohen's d.

RESULTS

Among 1,344 patients who underwent resection of CRLM, median tumor number (2, interquartile range 1 to 3) and size (3 cm, interquartile range 2.0 to 5.0) were comparable in the training (n = 672) vs validation (n = 672) cohorts; patient age (training 60.8 vs validation 61.0) and preoperative CEA (training 10.2 ng/mL vs validation 8.3 ng/mL) was also similar (p > 0.05). ML empirically derived optimal cutoff thresholds for number of lesions (3) and size of the largest lesion (1.3 cm) in the training cohort, which were then used to categorize patients in the validation cohort into 3 prognostic groups. Patients with low, average, or high ML-TB had markedly different 5-year overall survival (51.6%, 40.9%, and 23.1%, respectively; p < 0.001). ML-TB was more effective at stratifying patients relative to 5-year overall survival (low vs high ML-TB, d = 2.73) vs the Fong clinical (d = 1.61) or Genetic and Morphological Evaluation (d = 0.84) scores.

CONCLUSIONS

Using a large international cohort, ML was able to stratify patients into 3 distinct prognostic categories based on overall tumor burden. ML-TB was noted to be superior to other CRLM prognostic scoring systems.

Surgical Resection of Colorectal Liver Metastases: Attitudes and Practice Patterns in the Deep South

BACKGROUND

Metastatic disease is the leading cause of mortality in colorectal cancer. Resection of colorectal liver metastases, when possible, is associated with improved long-term survival and the possibility of cure. However, nationwide studies suggest that liver resection is under-utilized in the treatment of colorectal liver metastases. This study was undertaken to understand attitudes and practice patterns among medical oncologists in the Deep South.

METHODS

A survey of medical oncologists in the states of Alabama, Mississippi, and the Florida panhandle was performed. Respondents were queried regarding perceptions of resectability and attitudes towards surgical referral.

RESULTS

We received 63 responses (32% response rate). Fifty percent of respondents reported no liver surgeons in their practice area. Commonly perceived contraindications to liver resection included extrahepatic metastatic disease (72%), presence of > 4 metastases (72%), bilobar metastases (61%), and metastases > 5 cm (46%). Bilobar metastatic disease was perceived as a contraindication more frequently by non-academic medical oncologists (70% vs. 33%, p = 0.03).

CONCLUSIONS

Wide variations exist in perceptions of resectability and referral patterns for colorectal liver metastases among surveyed medical oncologists. There is a need for wider dissemination of resectability criteria and more liver surgeon involvement in the management of patients with colorectal liver metastases.

Nontumor related risk score: A new tool to improve prediction of prognosis after hepatectomy for colorectal liver metastases

BACKGROUND

Prognostic stratification of patients with colorectal cancer liver metastasis based solely on tumor-related factors has only moderate discriminatory ability. We hypothesized that the inclusion of nontumor related factors can improve prediction of long-term prognosis of patients with colorectal cancer liver metastasis.

METHODS

Nontumor related laboratory markers were assessed utilizing a training cohort from 2 U.S. institutions (n = 1,205). Factors independently associated with prognosis were used to develop a nontumor related prognostic score. The discriminatory ability, assessed by Harrell's C-statistics (C-index) and net reclassification improvement, was validated and compared with 3 commonly used tumor-related clinical risk scores: Fong clinical risk scores, m-clinical risk scores, and Genetic and Morphological Evaluation (GAME) score in an external validation cohort from 5 Asian (n = 1,307) and 3 European (n = 1,058) institutions. The discriminatory ability of nontumor related prognostic score combined with each of these 3 tumor-related prognostic scores was also estimated.

RESULTS

Alkaline phosphatase (hazard ratio 1.43; 95% confidence interval, 1.11-1.84), albumin (hazard ratio 0.71; 95% confidence interval, 0.57-0.89), and mean corpuscular volume (hazard ratio 19.0, per log unit; 95% confidence interval, 4.79-75.0) were each independently associated with increased risk of death after resection of colorectal cancer liver metastasis (all P < .05). In turn, alkaline phosphatase, albumin, and mean corpuscular volume were combined to form a nontumor related prognostic score (2.942 × mean corpuscular volume + 0.399 × alkaline phosphatase-0.339 × albumin-12) × 10 (median, 16; range, 1-30). The nontumor related prognostic score had good-to-modest discriminatory ability in the external cohort (C-index = 0.58), which was comparable to the 3 established tumor-related prognostic scores (C-index: Fong clinical risk scores, 0.53, m-clinical risk scores, 0.55, GAME, 0.58). The addition of the nontumor related prognostic score to the tumor-related prognostic scores enhanced the discriminatory ability in the entire study cohort (C-index: nontumor related score+Fong, 0.60, nontumor related score+m-clinical risk scores, 0.61, nontumor related score+GAME, 0.64), as well reclassification improvement (42.5, 42.7%, and 21.2%, respectively).

CONCLUSION

Nontumor related prognostic information may help improve the prognostic stratification of patients after resection of colorectal cancer liver metastasis. The nontumor related prognostic score may be combined with tumor-related prognostic tools to enhance prognostic stratification of patients with colorectal cancer liver metastasis.

Is precision medicine for colorectal liver metastases still a utopia? New perspectives by modern biomarkers, radiomics, and artificial intelligence

The management of patients with liver metastases from colorectal cancer is still debated. Several therapeutic options and treatment strategies are available for an extremely heterogeneous clinical scenario. Adequate prediction of patients’ outcomes and of the effectiveness of chemotherapy and loco-regional treatments are crucial to reach a precision medicine approach. This has been an unmet need for a long time, but recent studies have opened new perspectives. New morphological biomarkers have been identified. The dynamic evaluation of the metastases across a time interval, with or without chemotherapy, provided a reliable assessment of the tumor biology. Genetics have been explored and, thanks to their strong association with prognosis, have the potential to drive treatment planning. The liver-tumor interface has been identified as one of the main determinants of tumor progression, and its components, in particular the immune infiltrate, are the focus of major research. Image mining and analyses provided new insights on tumor biology and are expected to have a relevant impact on clinical practice. Artificial intelligence is a further step forward. The present paper depicts the evolution of clinical decision-making for patients affected by colorectal liver metastases, facing modern biomarkers and innovative opportunities that will characterize the evolution of clinical research and practice in the next few years.

Effect of tumour size ratio on liver recurrence-free survival of patients undergoing hepatic resection for colorectal liver metastases

Background

The study aimed to assess the impact of size differences of multiple liver metastases on liver recurrence-free survival (RFS) in patients undergoing hepatic resection for colorectal liver metastases (CRLMs).

Methods

Overall, 147 patients with CRLMs who underwent hepatic resection between January 2010 and December 2016 were retrospectively analysed. Tumour size ratio (TSR) was defined as the maximum diameter of the largest liver lesion over the maximum diameter of the smallest liver lesion. The univariate and multivariate analyses were performed to determine independent prognostic risk factors. The prognostic value of the TSR was further explored in each Tumour Burden Score (TBS) zone. Log-rank survival analyses were used to compare liver RFS in the new clinical score and the Fong clinical score.

Results

Based on the TSR, patients were classified into three groups: TSR < 2, 2 ≤ TSR < 4, and TSR ≥ 4. According to the multivariate analysis, TSR of 2–4 (hazard ratio [HR], 2.580; 95% confidence interval [CI] 1.543–4.312; P < 0.001) and TSR < 2 (HR, 4.435; 95% CI 2.499–7.872; P < 0.001) were associated with worse liver RFS. As TSR decreased, liver RFS worsened. TSR could further stratify patients in zones 1 and 2 into different risk groups according to the TBS criteria (zone 1: median liver RFS, 3.2 and 8.9 months for groups 1 and 2, respectively, P = 0.003; zone 2: median liver RFS, 3.5, 5.0, and 10.9 months for groups 1, 2, and 3, respectively, P < 0.05). The predictive ability of the new clinical score, which includes TSR, was superior to that of the Fong clinical score.

Conclusions

TSR, as a prognostic tool, could accurately assess the effect of size differences across multiple liver metastases on liver RFS in patients undergoing hepatectomy for CRLMs.

Trial registration

Retrospectively registered

Circulating Lipid- and Inflammation-Based Risk (CLIR) Score: A Promising New Model for Predicting Outcomes in Complete Colorectal Liver Metastases Resection

BACKGROUND

Colorectal cancer liver metastasis (CRLM) is a determining factor affecting the survival of colorectal cancer (CRC) patients. This study aims at developing a novel prognostic stratification tool for CRLM resection.

METHODS

In this retrospective study, 666 CRC patients who underwent complete CRLM resection from two Chinese medical institutions between 2001 and 2016 were classified into the training (341 patients) and validation (325 patients) cohorts. The primary endpoint was overall survival (OS). Associations between clinicopathological variables, circulating lipid and inflammation biomarkers, and OS were explored. The five most significant prognostic factors were incorporated into the Circulating Lipid- and Inflammation-based Risk (CLIR) score. The predictive ability of the CLIR score and Fong's Clinical Risk Score (CRS) was compared by time-dependent receiver operating characteristic (ROC) analysis.

RESULTS

Five independent predictors associated with worse OS were identified in the training cohort: number of CRLMs >4, maximum diameter of CRLM >4.4 cm, primary lymph node-positive, serum lactate dehydrogenase (LDH) level >250.5 U/L, and serum low-density lipoprotein-cholesterol (LDL-C)/high-density lipoprotein-cholesterol (HDL-C) ratio >2.9. These predictors were included in the CLIR score and each factor was assigned one point. Median OS for the low (score 0-1)-, intermediate (score 2-3)-, and high (score 4-5)-risk groups was 134.0 months, 39.9 months, and 18.7 months in the pooled cohort. The CLIR score outperformed the Fong score with superior discriminatory capacities for OS and RFS, both in the training and validation cohorts.

CONCLUSIONS

The CLIR score demonstrated a promising ability to predict the long-term survival of CRC patients after complete hepatic resection.

Treatment strategy for resectable colorectal cancer liver metastases from the viewpoint of time to surgical failure

PURPOSE

The efficacy of pre or postoperative chemotherapy for resectable colorectal cancer liver metastases (CRLM) is disputed. This study aimed to examine the risk factors for time to surgical failure (TSF) and analyze the efficacy of pre or postoperative chemotherapy prior to liver resection for CRLM.

METHODS

The clinicopathological factors of 567 patients who underwent initial hepatectomy for CRLM at 7 university hospitals between April 2007 and March 2013 were retrospectively analyzed. The prognostic factors were identified and then stratified into two groups according to the number of preoperative prognostic factors: the high-score group (H-group, score 2-4) and the low-score group (L-group, score 0 or 1).

RESULTS

Patients who experienced unresectable recurrence within 12 months after initial treatment had a significantly shorter prognosis than other patients (p < 0.001). Multivariate analysis identified age ≥ 70 (p = 0.001), pT4 (p = 0.015), pN1 (p < 0.001), carbohydrate antigen 19-9 ≥ 37 U/ml (p = 0.002), Clavien-Dindo grade ≥ IIIa (p = 0.013), and postoperative chemotherapy (p = 0.006) as independent prognostic factors. In the H-group, patients who received chemotherapy had a better prognosis than those who did not (p = 0.001).

CONCLUSION

Postoperative chemotherapy is beneficial in colorectal cancer patients with more than two of the following factors: age ≥ 70, carbohydrate antigen 19-9-positivity, pT4, and lymph node metastasis.

Composite Score: prognostic tool to predict survival in patients undergoing surgery for colorectal liver metastases

BACKGROUND

Several existing scoring systems predict survival of patients with colorectal liver metastases. Many lack validation, rely on old clinical data, and have been found to be less accurate since the introduction of chemotherapy. This study aimed to construct and validate a clinically relevant preoperative prognostic model for patients with colorectal liver metastases.

METHODS

A predictive model with data available before surgery was developed. Survival was analysed by Cox regression analysis, and the quality of the model was assessed using discrimination and calibration. The model was validated using multifold cross-validation.

RESULTS

The model included 1212 consecutive patients who underwent liver resection for colorectal liver metastases between 2005 and 2015. Prognostic factors for survival included advanced age, raised C-reactive protein level, hypoalbuminaemia, extended liver resection, larger number of metastases, and midgut origin of the primary tumour. A Composite Score was developed based on the prognostic variables. Patients were classified into those at low, medium, and high risk. Survival differences between the groups were significant; median overall survival was 87.4 months in the low-risk group, 50.1 months in the medium-risk group, and 22.6 months in the high-risk group. The discriminative performance, assessed by the concordance index, was 0.71, 0.67, and 0.67 respectively at 1, 3, and 5 years. Calibration, assessed graphically, was close to perfect. A multifold cross-validation of the model confirmed its internal validity (C-index 0.63 versus 0.62).

CONCLUSION

The Composite Score categorizes patients into risk strata, and may help identify patients who have a poor prognosis, for whom surgery is questionable.

PRIMARY TUMOR LYMPHOVASCULAR INVASION NEGATIVELY AFFECTS SURVIVAL AFTER COLORECTAL LIVER METASTASIS RESECTION?

BACKGROUND

About 50% of the patients with colorectal adenocarcinoma will present with liver metastasis and 20% are synchronic. Liver resection is associated with improvement in survival in comparison to chemotherapy alone.

AIM

To analyze the overall survival in patients submitted to liver resection of colorectal cancer metastasis and prognostic factors related to the primary and secondary tumors.

METHODS

A retrospective analysis of a prospectively maintained database regarding demographic, primary tumor and liver metastasis characteristics.

RESULTS

There were 84 liver resections due to colorectal cancer metastasis in the period. The 5-year disease-free and overall survivals were 27.5% and 48.8% respectively. The statistically significant factors for survival were tumor grade (p=0.050), lymphovascular invasion (p=0.021), synchronous metastasis (p=0.020), as well as number (p=0.004), bilobar distribution (p=0.019) and diameter of the liver metastasis over 50 mm (p=0.027). Remained as independent negative predictive factors: lymphovascular invasion (HR=2.7; CI 95% 1.106-6.768; p=0.029), synchronous metastasis (HR=2.8; CI 95% 1.069-7.365; p=0.036) and four or more liver metastasis (HR=1.7; CI 95% 1.046-2.967; p=0.033).

CONCLUSION

The resection of liver metastasis of colorectal adenocarcinoma leads to good survival rates. Lymphovascular invasion was the single prognostic factor related to the primary tumor. Synchronous disease and four or more metastasis were the most significant factors related to the secondary tumor.

The prognostic value of C-reactive protein and albumin in patients undergoing resection of colorectal liver metastases. A retrospective cohort study

BACKGROUND

The systemic inflammation-based Glasgow Prognostic Score (GPS) and modified GPS (mGPS), as measured by preoperative C-reactive protein (CRP) and albumin, correlate with poor survival in several cancers. This study evaluates the prognostic value of these scores in patients with colorectal liver metastases (CRLM).

METHODS

This retrospective study assessed the prognostic role of preoperatively measured GPS and mGPS in patients undergoing liver resection because of CRLM. Clinicopathological data were retrieved from local databases. The prognostic value of GPS and mGPS were compared and a Cox regression model was used to find independent predictors of overall survival.

RESULTS

In total, 849 consecutive patients between January 2005 and December 2015 were included. Patients with GPS 0 had a median survival of 70 months compared to 49 months in patients with GPS 1, and 27 months in patients with GPS 2. Multivariable analyses showed that GPS 1 (HR = 1.51, 95%CI [1.14-2.01]) and GPS 2 (HR = 2.78, 95%CI [1.79-4.31]), after correction for age >70 years (HR = 1.75 [1.36-2.26]), and extended resection (HR = 2.53, 95%CI[1.79-3.58]), were associated with poor overall survival.

CONCLUSION

A preoperative GPS is an independent prognostic factor in patients with CRLM, and appears to be a better prognostic tool than mGPS.

Risk scoring system for recurrence after simultaneous resection of colorectal cancer liver metastasis

Background

The simultaneous resection of synchronous colorectal cancer liver metastasis (SCRLM) has been widely applied. It is necessary to establish a risk scoring system to predict post-operative recurrence, especially in patients with neoadjuvant treatment.

Methods

The medical records of 221 patients undergoing simultaneous resection of CRLM were assessed in this study with a further 128 patients allocated to a validation group. All patients in the study group were classified according to their history of neoadjuvant treatment and univariate and multivariate analyses were applied to study independent risk factors. A score model was then generated according to the factors included. Our data set were also applied to validate three other existing scoring models [Fong clinical recurrence score (CRS), Konopke, and Zakaria disease-free survival (DFS) score], and the concordance index was calculated for comparison among these models.

Results

CRLM involving more than three nodes positive for a primary tumor was considered an independent risk factor for progression in patients without neoadjuvant treatment and all score models could discretely stratify patients according to disease free survival. In patients receiving neoadjuvant treatment, CRLM involving more than one node and transfusion invasion were major determinants in patients after treatment. However, only our scoring system and Fong’s CRS score could discretely discriminate patients. In the validation group, patients were significantly classified with the score system.

Conclusions

Existing score models had better values for determining prognosis in patients with SCRLM, especially in those undertaking neoadjuvant treatment. Larger cohorts, along with more detailed clinical features and multicenter validation should be undertaken before utilization.

A Prognostic Scoring System to Predict Survival Outcome of Resectable Colorectal Liver Metastases in this Modern Era

BACKGROUND

An individualized treatment decision is based on the accurate evaluation of clinical risk factors and prognosis for resectable colorectal liver metastases. The current study aimed to develop an effective nomogram to predict progression-free survival (PFS) and to design a treatment schedule preoperatively.

METHODS

The study enrolled a primary cohort of 532 patients with resectable colorectal liver metastases (CRLM) who underwent hepatic resection at two institutions and a validation cohort of 237 patients at two additional institutions with resectable CRLM between 1 January 2008 and 31 December 2018. A nomogram was created based on the independent predictors in the multivariable analysis of progression-free survival in the primary cohort. The predictive accuracy and discriminative ability of the nomogram were determined by the concordance index (C-index) and the calibration curve. The score was compared with the current standard Fong score and validated with an external cohort.

RESULTS

The independent risk factors for CRLM patients identified in the multivariable analysis were tumor larger than 5 cm, more than one tumor, RAS mutation, primary lymph node metastasis, and primary tumor located on the right side. All five factors were considered in the nomogram. The C-index of the nomogram for predicting survival was 0.696. With external validation, the C-index of the nomogram for the prediction of the PFS was 0.682, which demonstrated that this model has a good level of discriminative ability. For high-risk patients (score > 16), neoadjuvant chemotherapy improved PFS and overall survival (OS) after hepatic resection.

CONCLUSION

The current nomogram demonstrated an accurate performance in predicting PFS for resectable CRLM patients with liver-limited disease. Based on the current nomogram, high-risk patients (nomogram score > 16) might benefit from neoadjuvant chemotherapy.

Prospective Non-Randomized Study of Intraoperative Assessment of Surgical Resection Margin of Colo-Rectal Liver Metastases

Introduction: More than 50% of patients with colorectal cancer (CRC) develop liver metastases during the natural course of disease. Surgical resection is currently the most potentially curative method in the treatment of colorectal liver metastases (CRLM). The goal of surgery is to achieve a negative resection margin (RM) of at least 1 mm, which provides the best prognosis for patients. The RM can be assessed by the pathologist of the resected liver specimen (RLS) and by the surgeon intraoperatively. The aim of this research paper is to determine the degree of agreement on intraoperative assessment of the RM by the surgeon and histopathological RM assessment by the pathologist.

Material and methods: This prospective non-randomized double-blind study was approved by the Ethics Committee of the Oncology Institute of Vojvodina and registered on ClinicalTrials.gov #NCT04634526. The study was conducted at the Oncology Institute of Vojvodina, Sremska Kamenica, Serbia. An experienced hepatobiliary surgeon assessed RM for every specimen intra-operatively, immediately after CRLM resection. Resected CRLM lesions were analyzed by two experienced pathologists. These data were compared with pathological RM assessment as a “gold standard”. RM of 1 mm or more was rated as negative RM (RM-). Disease-free survival (DFS) and recurrence rate was calculated by RM status defined by surgeon and by pathologist.

Results: From 01 January 2015 to 31 August 2019, 98 patients were enrolled in the study. There were 219 RLS with 245 CRLM. The surgeon registered positive RM (RM+) of <1mm in 41 (18.7%) RLS. Taking the result of the histopathological assessment (HPA) as the “gold standard”, it was determined that RM was true positive in 32 (14.6%) cases. False positive RM was found in 9 (4.1%) cases. False negative RM was found in 20 (9.1%) cases. True negative RM was found in 158 (72.2%) cases. Sensitivity of surgical assessment (SA) of RM+ was 61.5% (32/52). Specificity of SA of RM+ was 94.6% (158/167). The positive predictive value (PPV) was 78.0% (32/41), while the negative predictive value (NPV) was 88.8% (158/178). The overall accuracy of the RM+ SA was 86.8% (190/219). There was no statistically significant difference in the assessment of RM+ per RLS by surgeon and pathologists (p=0.061), but it was significant when analyses per patients was performed (p=0.017). Recurrence rate for RM+ patients was 48.1% (13/27, p=0.05) for SA and 35.0% (14/40, p=0.17) for HPA. Three year DFS for RM- and RM+ was 66.5% and 27.9% (p=0.04), respectively, by SA, and 64.8% and 42.1% (p=0.106), respectively, by HPA.

Conclusion: Intraoperative assessment of RM- by surgeon of RLS is clinically meaningful. There is not a statistically significant difference in the assessment of RM+ by surgeon and pathologists per RLS, but it was statically significant on a per patient basis. RM determined by surgeon has better prognostic impact on recurrence rate and 1- and 3-year DFS than standard histopathological assessment.

Performance of two prognostic scores that incorporate genetic information to predict long-term outcomes following resection of colorectal cancer liver metastases: An external validation of the MD Anderson and JHH-MSK scores

INTRODUCTION

Two novel clinical risk scores (CRS) that incorporate KRAS mutation status were developed: modified CRS (mCRS) and GAME score. However, they have not been tested in large national and international cohorts. The aim of this study was to validate the prognostic discrimination utility and determine the clinical usefulness of the two novel CRS.

METHODS

Patients undergoing hepatectomy for CRLM (2000-2018) in 10 centers were included. The discriminatory abilities of mCRS, GAME, and Fong CRS were evaluated using Harrell's C-index and Akaike's Information Criterion.

RESULTS

In the entire cohort, the C-index of the GAME score (0.61) was significantly higher than those of Fong score (0.57) and mCRS (0.54), while the C-Index of mCRS was significantly lower than that of Fong score. When we compared the models in the various geographical regions, the C-index of GAME score was significantly higher than that of mCRS in North America, Europe, and South America. The AIC of Fong score, mCRS, and GAME score were 14 405, 14 447, and 14 319, respectively.

CONCLUSION

In conclusion, using the largest and most heterogenous population of CRLM patients with known KRAS status, this independent, external validation demonstrated that the GAME score outperforms both the traditional Fong score and mCRS.

Accuracy of Modern Clinical Risk Score Including RAS Status Changes Based on Whether Patients Received Perioperative Chemotherapy for Colorectal Liver Metastases

BACKGROUND

A modified Fong clinical score (m-Fong CS) that includes the RAS mutation status has recently been proposed and offered an improved survival stratification of patients who undergo surgery and systemic chemotherapy for colorectal liver metastases (CLM). The aim of this study is to assess whether a CS that includes RAS status is influenced by whether patients receive perioperative chemotherapy.

METHODS

We created a new CS using multivariate analysis of data of patients who underwent hepatectomy for CLM for the first time between 2010 and 2016 at a single hospital (n = 341, 79% received perioperative chemotherapy). The resulting CS and m-Fong CS were then validated in the patient cohort at three other hospitals (n = 309). Furthermore, the applicability of the two CS in the total cohort (n = 650) was tested according to whether the patients received perioperative chemotherapy.

RESULTS

The new CS comprised mutant RAS status, ≥4 CLMs, and a CA19-9 level ≥100 U/mL (1 point per factor). Both the new CS and m-Fong CS failed to stratify the survival of the 309 patients in the validation cohort, including those who did not receive perioperative chemotherapy (29%). Both of the CS accurately stratified the survival of patients who underwent perioperative chemotherapy but not of those who underwent surgery alone.

CONCLUSION

A CS that includes the RAS mutation status can stratify the survival of patients who undergo hepatectomy combined with perioperative chemotherapy, but it has limited value for patients who undergo surgery alone.

Contour prognostic model for predicting survival after resection of colorectal liver metastases: development and multicentre validation study using largest diameter and number of metastases with RAS mutation status

BACKGROUND

Most current models for predicting survival after resection of colorectal liver metastasis include largest diameter and number of colorectal liver metastases as dichotomous variables, resulting in underestimation of the extent of risk variation and substantial loss of statistical power. The aim of this study was to develop and validate a new prognostic model for patients undergoing liver resection including largest diameter and number of colorectal liver metastases as continuous variables.

METHODS

A prognostic model was developed using data from patients who underwent liver resection for colorectal liver metastases at MD Anderson Cancer Center and had RAS mutational data. A Cox proportional hazards model analysis was used to develop a model based on largest colorectal liver metastasis diameter and number of metastases as continuous variables. The model results were shown using contour plots, and validated externally in an international multi-institutional cohort.

RESULTS

A total of 810 patients met the inclusion criteria. Largest colorectal liver metastasis diameter (hazard ratio (HR) 1.11, 95 per cent confidence interval 1.06 to 1.16; P < 0.001), number of colorectal liver metastases (HR 1.06, 1.03 to 1.09; P < 0.001), and RAS mutation status (HR 1.76, 1.42 to 2.18; P < 0.001) were significantly associated with overall survival, together with age, primary lymph node metastasis, and prehepatectomy chemotherapy. The model performed well in the external validation cohort, with predicted overall survival values almost lying within 10 per cent of observed values. Wild-type RAS was associated with better overall survival than RAS mutation even when liver resection was performed for larger and/or multiple colorectal liver metastases.

CONCLUSION

The contour prognostic model, based on diameter and number of lesions considered as continuous variables along with RAS mutation, predicts overall survival after resection of colorectal liver metastasis.

Preoperative nomogram to predict survival following colorectal cancer liver metastasis simultaneous resection

Background

Simultaneous resection for patients with synchronous colorectal cancer liver metastases (CRLM) remains an optimal option for the sake of curability. However, few studies so far focus on outcome of this subgroup of patients (who receive simultaneous resection for CRLM). Substantial heterogeneity exists among such patients and more precise categorization is needed preoperatively to identify those who may benefit more from surgery. In this study, we formulated this internally validated scoring system as an option.

Methods

Clinicopathological and follow-up data of 234 eligible CRLM patients undergoing simultaneous resection from January 2010 to March 2019 in our center were included for analysis. Patients were randomized to either a training or validation cohort. We performed multivariable Cox regression analysis to determine preoperative factors with prognostic significance using data in training cohort, and a nomogram scoring system was thus established. Time-dependent receiver operating characteristic (ROC) curve and calibration plot were adopted to evaluate the predictive power of our risk model.

Results

In the multivariable Cox regression analysis, five factors including presence of node-positive primary defined by enhanced CT/MR, preoperative CEA level, primary tumor location, tumor grade and number of liver metastases were identified as independent prognostic indicators of overall survival (OS) and adopted to formulate the nomogram. In the training cohort, calibration plot graphically showed good fitness between estimated and actual 1- and 3-year OS. Time-dependent ROC curve by Kaplan-Meier method showed that our nomogram model was superior to widely used Fong's score in prediction of 1- and 3-year OS (AUC 0.702 vs. 0.591 and 0.848 vs. 0.801 for 1- and 3-year prediction in validation cohort, respectively). Kaplan-Meier curves for patients stratified by the assessment of nomogram showed great discriminability (P<0.001).

Conclusions

In this retrospective analysis we identified several preoperative factors affecting survival of synchronous CRLM patients undergoing simultaneous resection. We also constructed and validated a risk model which showed high accuracy in predicting 1- and 3-year survival after surgery. Our risk model is expected to serve as a predictive tool for CRLM patients receiving simultaneous resection and assist physicians to make treatment decision.

The optimal cut-off values for tumor size, number of lesions, and CEA levels in patients with surgically treated colorectal cancer liver metastases: An international, multi-institutional study

BACKGROUND AND OBJECTIVES

Despite the long-standing consensus on the importance of tumor size, tumor number and carcinoembryonic antigen (CEA) levels as predictors of long-term outcomes among patients with colorectal liver metastases (CRLM), optimal prognostic cut-offs for these variables have not been established.

METHODS

Patients who underwent curative-intent resection of CRLM and had available data on at least one of the three variables of interest above were selected from a multi-institutional dataset of patients with known KRAS mutational status. The resulting cohort was randomly split into training and testing datasets and recursive partitioning analysis was employed to determine optimal cut-offs. The concordance probability estimates (CPEs) for these optimal cut offs were calculated and compared to CPEs for the most widely used cut-offs in the surgical literature.

RESULTS

A total of 1643 patients who met eligibility criteria were identified. Following recursive partitioning analysis in the training dataset, the following cut-offs were identified: 2.95 cm for tumor size, 1.5 for tumor number and 6.15 ng/ml for CEA levels. In the entire dataset, the calculated CPEs for the new tumor size (0.52), tumor number (0.56) and CEA (0.53) cut offs exceeded CPEs for other commonly employed cut-offs.

CONCLUSION

The current study was able to identify optimal cut-offs for the three most commonly employed prognostic factors in CRLM. While the per variable gains in discriminatory power are modest, these novel cut-offs may help produce appreciable increases in prognostic performance when combined in the context of future risk scores.

RAS/TP53 co-Mutation is Associated with Worse Survival after Concurrent Resection of Colorectal Liver Metastases and Extrahepatic Disease

OBJECTIVE

To determine if tumor genetics are associated with overall survival (OS) after concurrent resection of colorectal liver metastases (CLM) and extrahepatic disease (EHD).

SUMMARY BACKGROUND DATA

The prognosis for patients who undergo concurrent resection of CLM/EHD is unclear and the impact of somatic mutations has not been reported.

METHODS

Patients undergoing concurrent resection of CLM and EHD from 2007-2017 were identified from two academic centers. From 1 center, patients were selected from a pre-existing database of patients undergoing cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (HIPEC). The Kaplan-Meier method was used to construct survival curves, compared using the log-rank test. Multivariable Cox analysis for OS was performed.

RESULTS

One hundred nine patients were included. Most common EHD sites included lung (33 patients), peritoneum (32), and portal lymph nodes (14). TP53 mutation was the most common mutation, identified in 75 patients (69%), and RAS/TP53 co-mutation was identified in 31 patients (28%). The median OS was 49 months (IQR, 24-125), and 3- and 5-year OS rates were 66% and 44%, respectively. Compared to patients without RAS/TP53 co-mutation, patients with RAS/TP53 co-mutation had lower median OS: 39 vs. 51 months (P = .02). On multivariable analysis, lung EHD (hazard ratio [HR], 0.7; 95% CI, 0.3-1.4), peritoneal EHD (HR, 2.2; 95% CI, 1.1-4.2) and RAS/TP53 co-mutation (HR, 2.8; 95% CI, 1.1-7.2) were independently associated with OS.

CONCLUSIONS

RAS/TP53 co-mutation is associated with worse OS after concurrent CLM/EHD resection. Mutational status and site of EHD should be included in the evaluation of patients considered for concurrent resection.