Genetic And Morphological Evaluation (GAME) score for patients with colorectal liver metastases

The role of living donor liver transplantation in colorectal cancer liver metastases

PURPOSE OF REVIEW

Despite technical and therapeutic advances, only 20-40% of patients with colorectal liver metastases (CRLM) have resectable disease. Historically, the remaining patients with unresectable, liver-only CRLM would receive palliative chemotherapy, with a median survival of 8 months.

RECENT FINDINGS

Liver transplantation has emerged as a viable option for selected patients with CRLM. This advancement stems from improved understanding of tumour genomics and biology and better patient selection criteria. The results of recent prospective clinical trials have further ignited enthusiasm for liver transplantation as a viable therapeutic option. Living donor liver transplantation (LDLT) offers several advantages over deceased donor liver transplantation (DDLT) for this disease, including reduced wait-time and optimized timing and coordination of oncologic therapy. On-going LDLT clinical trials have demonstrated favourable outcomes as compared with other liver transplantation indications. However, there is no established consensus or standardization in the implementation of LDLT for CRLM, beyond trials and centre-specific protocols.

SUMMARY

LDLT is an excellent therapeutic option in highly selected patients with CRLM. Refining prognostic factors and selection criteria will help to further optimize the utility and broaden the acceptance and implementation of LDLT for patients with CRLM.

Evaluating Combinations of Biological and Clinicopathologic Factors Linked to Poor Outcomes in Resected Colorectal Liver Metastasis: An External Validation Study

BACKGROUND

Recent studies have suggested that certain combinations of KRAS or BRAF biomarkers with clinical factors are associated with poor outcomes and may indicate that surgery could be "biologically" futile in otherwise technically resectable colorectal liver metastasis (CRLM). However, these combinations have yet to be validated through external studies.

PATIENTS AND METHODS

We conducted a systematic search to identify these studies. The overall survival (OS) of patients with these combinations was evaluated in a cohort of patients treated at 11 tertiary centers. Additionally, the study investigated whether using high-risk KRAS point mutations in these combinations could be associated with particularly poor outcomes.

RESULTS

The recommendations of four studies were validated in 1661 patients. The first three studies utilized KRAS, and their validation showed the following median and 5-year OS: (1) 30 months and 16.9%, (2) 24.3 months and 21.6%, and (3) 46.8 months and 44.4%, respectively. When analyzing only patients with high-risk KRAS mutations, median and 5-year OS decreased to: (1) 26.2 months and 0%, (2) 22.3 months and 15.1%, and (3) not reached and 44.9%, respectively. The fourth study utilized BRAF, and its validation showed a median OS of 10.4 months, with no survivors beyond 21 months.

CONCLUSION

The combinations of biomarkers and clinical factors proposed to render surgery for CRLM futile, as presented in studies 1 (KRAS high-risk mutations) and 4, appear justified. In these studies, there were no long-term survivors, and survival was similar to that of historic cohorts with similar mutational profiles that received systemic therapies alone for unresectable disease.

Patterns and Predictors of Recurrence After Curative Resection of Colorectal Liver Metastasis (CRLM)

BACKGROUND

Our study aims to determine the predictors and patterns of relapses after curative colorectal liver metastasis (CRLM) resection.

METHODS

A single-centre, retrospective study of CRLM patients operated between 2010 and 2022 was performed. The site of first recurrence was either hepatic (marginal (≤ 1 cm) or extramarginal), extrahepatic, or both. Factors that predicted relapse patterns and overall survival were determined by multivariable Cox regression analysis with backward elimination of variables.

RESULTS

The study consisted of 258 patients, with a similar proportion of synchronous (144; 56%) and metachronous(114; 43%) metastasis. At a 43-month median follow-up, 156 patients (60.4%) developed recurrences with 33 (21.1%) in the liver, 62(24.03%) extra-hepatic recurrences, and 58 (22.48%) having both. Isolated marginal liver relapses were seen in seven (9.89%) liver recurrence patients. The median overall and relapse-free survivals were 38 months (30-54) and 13 months (11-16), respectively. The 3-year liver-relapse-free survival was 54.4% (44.9-60.6). Size of liver metastases > 5 cm (HR 2.06 (1.34-3.17), involved surgical margins (HR 2.16 (1.27-3.68)), and adjuvant chemotherapy (HR 1.89 (1.07-3.35)) were predictors of hepatic recurrences. Node positivity of primary (HR 1.61 (1.02-2.56)), presence of baseline extra-hepatic metastases (HR 0.30 (0.18-0.51)), size of liver metastases > 5 cm (HR 2.02 (1.37-2.99)), poorly differentiated histology (HR 2.25 (1.28-3.49)), presence of LVI (HR 2.25 (1.28-3.94)), and adjuvant chemotherapy (HR 2.15 (1.28-3.61)) were predictors of extra-hepatic recurrences.

CONCLUSION

The study found majority relapses occurred at extrahepatic sites whilst isolated marginal recurrences were few. The consistent predictors of recurrence were size and inability to deliver adjuvant therapy. A tailored adjuvant therapy might improve outcomes after liver metastasectomy in colorectal cancers.

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.

Patterns of recurrence after curative intent hepatic resection for colorectal liver metastasis

BACKGROUND

Improved surgical techniques and more intensive systemic therapy have increased the number of patients with oligometastatic colorectal cancer eligible for resection, but a significant percentage of these cases will ultimately recur. Furthermore, distinct recurrence patterns have been associated with different outcomes.

METHODS

Data for 195 patients who underwent curative-intent colorectal liver metastasis (CRLM) resection between 2016 and 2022 at Johns Hopkins Hospital were retrospectively collected. Cox regression univariate and multivariate analyses identified features associated with survival outcomes. Association between risk factors and site of recurrences was conducted via logistic regression with initial recurrences grouped into intrahepatic-only, extrahepatic-only, and concurrent intra- and extrahepatic recurrence.

RESULTS

The 1- and 2-year recurrence-free survival (RFS) rates were 46% and 22%, respectively. The 1- and 2-year overall survival (OS) rates were 95% and 88%, respectively. The median OS was 71.7 months. Multivariate analysis identified age <60 years, N2 nodal status, R1 liver margin, and higher preoperative carcinoembryonic antigen as top prognostic factors for worse RFS. Additionally, patients with left-sided primary tumors had a higher risk of intrahepatic-only recurrence, whereas mutant KRAS was associated with a higher risk of extrahepatic recurrence with or without liver recurrence.

CONCLUSION

These results from a recent cohort of patients treated with current standard-of-care therapies identify features associated with elevated risk and specific patterns of recurrence. These insights into CRLM recurrence patterns support a larger prospective study to identify subgroups of patients who may require additional therapies to prevent recurrence.

Estimating Efficacy of Conversion Therapy on Patients with Initially Unresectable Colorectal Cancer Liver Metastases by using MRI: Development of a Predictive Score

RATIONALE AND OBJECTIVES

The conversion success rate (CSR) has crucial implication for clinical outcomes of initially unresectable colorectal liver metastases (CRLM) following conversion therapy. This study aimed to develop a simple predictive scoring model for identifying CSR according to baseline magnetic resonance imaging (MRI) features, and confirm its performance and prognostic significance in a validation cohort.

METHODS

A total of 155 consecutive patients with initially unresectable CRLM were retrospectively reviewed in the study. A simple MRI-based predictive scoring model for identifying CSR was developed in the development cohort (n = 104) by using multivariable logistic regression analyzes. The diagnostic performance was evaluated for the predictive score. Thereafter, patients in the validation cohort (n = 51) were stratified into groups with predicted high CSR or low CSR according to the score. The progression-free survival (PFS) and overall survival (OS) were compared between two groups using the log-rank test.

RESULTS

The predictive score of CSR, named mrNISE, incorporated the number of CRLM ≥ 10, the largest size ≥ 50 mm, poorly defined tumor-liver interface, and peritumoral enhancement. The AUC of the mrNISE score was 0.845 for the development cohort and 0.776 for the validation cohort. According to the score, patients with predicted high CSR had better PFS and OS than those with low CSR in both development and validation cohorts.

CONCLUSION

The predictive score demonstrated great performance for identifying CSR of initially unresectable CRLM. Stratifying patients by the score, personalized treatment goals can be formulated before conversion therapy to improve clinical prognosis and reduce adverse events caused by ineffective treatment.

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.

Preoperative identification of early extrahepatic recurrence after hepatectomy for colorectal liver metastases: A machine learning approach

BACKGROUND

Machine learning (ML) may provide novel insights into data patterns and improve model prediction accuracy. The current study sought to develop and validate an ML model to predict early extra-hepatic recurrence (EEHR) among patients undergoing resection of colorectal liver metastasis (CRLM).

METHODS

Patients with CRLM who underwent curative-intent resection between 2000 and 2020 were identified from an international multi-institutional database. An eXtreme gradient boosting (XGBoost) model was developed to estimate the risk of EEHR, defined as extrahepatic recurrence within 12 months after hepatectomy, using clinicopathological factors. The relative importance of factors was determined using Shapley additive explanations (SHAP) values.

RESULTS

Among 1410 patients undergoing curative-intent resection, 131 (9.3%) patients experienced EEHR. Median OS among patients with and without EEHR was 35.4 months (interquartile range [IQR] 29.9-46.7) versus 120.5 months (IQR 97.2-134.0), respectively (p < 0.001). The ML predictive model had c-index values of 0.77 (95% CI, 0.72-0.81) and 0.77 (95% CI, 0.73-0.80) in the entire dataset and the validation data set with bootstrapping resamples, respectively. The SHAP algorithm demonstrated that T and N primary tumor categories, as well as tumor burden score were the three most important predictors of EEHR. An easy-to-use risk calculator for EEHR was developed and made available online at: https://junkawashima.shinyapps.io/EEHR/.

CONCLUSIONS

An easy-to-use online calculator was developed using ML to help clinicians predict the chance of EEHR after curative-intent resection for CRLM. This tool may help clinicians in decision-making related to treatment strategies for patients with CRLM.

Proteogenomic Characterization of Early Intrahepatic Recurrence after Curative-Intent Treatment of Colorectal Liver Metastases

Clinical and pathological factors are insufficient to accurately identify patients at risk of early recurrence after curative-intent treatment of colorectal liver metastases (CRLM). This study aimed to identify candidate prognostic proteogenomic biomarkers for early intrahepatic recurrence after curative-intent resection of CRLM. Patients diagnosed with intrahepatic recurrence within 6 months of liver resection were categorized as the "early recurrence" group, while those who achieved a recurrence-free status for 10 years were designated as "durable remission". Comprehensive genomic and proteomic profiling of fresh frozen samples from these prognostically distinct groups was performed using the TruSight Oncology 500 assay and label-free data-dependent acquisition liquid chromatography-mass spectrometry. Genetic alterations were identified in 117 of the 523 profiled genes in patients with early recurrence. The most common somatic mutations linked to early recurrence were TP53 (88%), APC (71%), KRAS (38%), and SMAD4 (21%). SMAD4 alterations were absent in samples from patients with a durable remission. Calponin-2, versican core protein, glutathione peroxidase 3, fibulin-5, and amyloid-β precursor protein were upregulated more than 2-fold in early recurrence. Exploratory analysis of these proteogenomic biomarkers suggests that SMAD4, calponin-2, and glutathione peroxidase 3 may have the potential to predict early recurrence, enabling improved prognostication and precision oncology in CRLM.

Association of various RAS codon mutations and prognostic outcomes of patients with colorectal liver metastases after hepatectomy

PURPOSE

The prognostic and predictive value of RAS mutations in patients with colorectal liver metastases (CRLM) who have undergone hepatectomy holds substantial importance. The present study aimed to investigate the impact of different RAS codon mutations on long-term survival in CRLM patients.

METHODS

A retrospective analysis was conducted on clinicopathological data from 399 CRLM patients with RAS mutations who underwent hepatectomy between January 2000 and December 2020. The RAS mutation gene status was assessed in KRAS codons (G12, G13, Q61, and A146) and NRAS codons (G12, G13, and Q61). Survival curves were generated using the Kaplan-Meier plotter and compared using the log-rank test. Univariate and multivariate analyses were performed to analyze the clinicopathological data.

RESULTS

In the entire cohort, patients with KRAS G12 mutations exhibited the most favorable prognosis (p = 0.018). Comparatively, patients harboring KRAS Q61 mutations experienced poorer overall survival (OS) with a median of 15 months versus 33 months (p = 0.011) when compared to those with KRAS G12 mutations. Moreover, patients with NRAS Q61 mutations also showed decreased OS with a median of 26 months versus 33 months (p = 0.020) in comparison to KRAS G12 mutation patients. The results of multivariate analysis showed that both KRAS Q61 mutation (HR 2.130; 95% CI 1.088-4.168; p = 0.027) and NRAS Q61 mutation (HR 2.877; 95% CI 1.398-5.922; p = 0.004) were independent influencing factors of OS. Based on all identified risk factors, patients with RAS mutation were divided into high-risk and low-risk groups. Notably, in the high-risk group, the incorporation of postoperative chemotherapy was associated with longer OS, while it did not improve the survival of patients in the low-risk group.

CONCLUSIONS

KRAS Q61 and NRAS Q61 mutations are promising predictors for OS in CRLM patients after hepatectomy. Postoperative chemotherapy may significantly benefit CRLM patients with RAS mutations, particularly those identified as high-risk.

RAS mutation associated with short surgically controllable period in colorectal liver metastases: a retrospective study

BACKGROUND

The prognostic implications of the RAS status in colorectal cancer liver metastasis (CRLM) remain unclear. This study investigated the prognostic significance of RAS status after curative hepatectomy, focusing on surgical controllability.

METHODS

This retrospective study included liver-only CRLM patients who underwent the first hepatectomy between 2015 and 2022 at the National Cancer Center Hospital. Recurrence-free survival (RFS), surgically controllable period (SCP), and overall survival (OS) were compared between RAS wild-type (RAS-wt) and mutant (RAS-mt) patients. Multivariate analyses were conducted to identify independent prognostic factors for each outcome and independent risk factors for less than 1 year SCP.

RESULTS

A total of 150 patients were evaluated, comprising 63 patients with RAS-mt status. There was no significant difference in RFS between RAS-mt and RAS-wt (7.00 vs. 8.03 months, p = 0.48). RAS-mt patients exhibited worse SCP (11.80 vs.21.13 months, p < 0.001) and OS (44.03 vs. 70.03 months, p < 0.001) compared to RAS-wt. Multivariate analysis identified RAS-mt as an independent prognostic factor for both OS (hazard ratio [HR]: 3.37, p < 0.001) and SCP (HR: 2.20, p < 0.001), and as an independent risk factor for less than 1 year of SCP (odds ratio, 2.31; p = 0.03).

CONCLUSIONS

CRLM with RAS mutations should be considered for strict surgical indications with preoperative chemotherapy and thorough examination, considering the possibility of short SCP.

A CT-based radiomics tumor quality and quantity model to predict early recurrence after radical surgery for colorectal liver metastases

PURPOSE

This study aimed to develop a tumor radiomics quality and quantity model (RQQM) based on preoperative enhanced CT to predict early recurrence after radical surgery for colorectal liver metastases (CRLM).

METHODS

A retrospective analysis was conducted on 282 cases from 3 centers. Clinical risk factors were examined using univariate and multivariate logistic regression (LR) to construct the clinical model. Radiomics features were extracted using the least absolute shrinkage and selection operator (LASSO) for dimensionality reduction. The LR learning algorithm was employed to construct the radiomics model, RQQM (radiomics-TBS), combined model (radiomics-clinical), clinical risk score (CRS) model and tumor burden score (TBS) model. Inter-model comparisons were made using area under the curve (AUC), decision curve analysis (DCA) and calibration curve. Log-rank tests assessed differences in disease-free survival (DFS) and overall survival (OS).

RESULTS

Clinical features screening identified CRS, KRAS/NRAS/BRAF and liver lobe distribution as risk factors. Radiomics model, RQQM, combined model demonstrated higher AUC values compared to CRS and TBS model in training, internal and external validation cohorts (Delong-test P < 0.05). RQQM outperformed the radiomics model, but was slightly inferior to the combined model. Survival curves revealed statistically significant differences in 1-year DFS and 3-year OS for the RQQM (P < 0.001).

CONCLUSIONS

RQQM integrates both "quality" (radiomics) and "quantity" (TBS). The radiomics model is superior to the TBS model and has a greater impact on patient prognosis. In the absence of clinical data, RQQM, relying solely on imaging data, shows an advantage in predicting early recurrence after radical surgery for CRLM.

The role of superior hemorrhoidal vein ectasia in the preoperative staging of rectal cancer

Objective

The prognosis of colorectal cancer has continuously improved in recent years thanks to continuous progress in both the therapeutic and diagnostic fields. The specific objective of this study is to contribute to the diagnostic field through the evaluation of the correlation between superior hemorrhoidal vein (SHV) ectasia detected on computed tomography (CT) and Tumor (T), Node (N), and distant metastasis (M) examination and mesorectal fascia (MRF) invasion in the preoperative staging of rectal cancer.

Methods

Between January 2018 and April 2022, 46 patients with histopathological diagnosis of rectal cancer were retrospectively enrolled, and the diameter of the SHV was evaluated by CT examination. The cutoff value for SHV diameter used is 3.7 mm. The diameter was measured at the level of S2 during portal venous phase after 4× image zoom to reduce the interobserver variability. The parameters evaluated were tumor location, detection of MRF infiltration (defined as the distance < 1 mm between the tumor margins and the fascia), SHV diameter, detection of mesorectal perilesional lymph nodes, and detection of metastasis.

Results

A total of 67.39% (31/46) of patients had SHV ectasia. All patients with MRF infiltration (4/46, 7.14%) presented SHV ectasia (average diameter of 4.4 mm), and SHV was significantly related with the development of liver metastases at the moment of primary staging and during follow-up.

Conclusion

SHV ectasia may be related to metastasis and MRF involvement; therefore, it could become a tool for preoperative staging of rectal cancer.

Prognostic value of total tumor volume in patients with colorectal liver metastases: A secondary analysis of the randomized CAIRO5 trial with external cohort validation

BACKGROUND

This study aimed to assess the prognostic value of total tumor volume (TTV) for early recurrence (within 6 months) and overall survival (OS) in patients with colorectal liver metastases (CRLM), treated with induction systemic therapy followed by complete local treatment.

METHODS

Patients with initially unresectable CRLM from the multicenter randomized phase 3 CAIRO5 trial (NCT02162563) who received induction systemic therapy followed by local treatment were included. Baseline TTV and change in TTV as response to systemic therapy were calculated using the CT scan before and the first after systemic treatment, and were assessed for their added prognostic value. The findings were validated in an external cohort of patients treated at a tertiary center.

RESULTS

In total, 215 CAIRO5 patients were included. Baseline TTV and absolute change in TTV were significantly associated with early recurrence (P = 0.005 and P = 0.040, respectively) and OS in multivariable analyses (P = 0.024 and P = 0.006, respectively), whereas RECIST1.1 was not prognostic for early recurrence (P = 0.88) and OS (P = 0.35). In the validation cohort (n = 85), baseline TTV and absolute change in TTV remained prognostic for early recurrence (P = 0.041 and P = 0.021, respectively) and OS in multivariable analyses (P < 0.0001 and P = 0.012, respectively), and showed added prognostic value over conventional clinicopathological variables (increase C-statistic, 0.06; 95 % CI, 0.02 to 0.14; P = 0.008).

CONCLUSION

Total tumor volume is strongly prognostic for early recurrence and OS in patients who underwent complete local treatment of initially unresectable CRLM, both in the CAIRO5 trial and the validation cohort. In contrast, RECIST1.1 did not show prognostic value for neither early recurrence nor OS.

Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction

PURPOSE

To assess the efficacy of machine learning and radiomics analysis by computed tomography (CT) in presurgical setting, to predict RAS mutational status in colorectal liver metastases.

METHODS

Patient selection in a retrospective study was carried out from January 2018 to May 2021 considering the following inclusion criteria: patients subjected to surgical resection for liver metastases; proven pathological liver metastases; patients subjected to enhanced CT examination in the presurgical setting with a good quality of images; and RAS assessment as standard reference. A total of 851 radiomics features were extracted using the PyRadiomics Python package from the Slicer 3D image computing platform after slice-by-slice segmentation on CT portal phase by two expert radiologists of each individual liver metastasis performed first independently by the individual reader and then in consensus. Balancing technique was performed, and inter- and intraclass correlation coefficients were calculated to assess the between-observer and within-observer reproducibility of features. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers were considered. Moreover, features selection was performed before and after a normalized procedure using two different methods (3-sigma and z-score).

RESULTS

Seventy-seven liver metastases in 28 patients with a mean age of 60 years (range 40-80 years) were analyzed. The best predictors, at univariate analysis for both normalized procedures, were original_shape_Maximum2DDiameter and wavelet_HLL_glcm_InverseVariance that reached an accuracy of 80%, an AUC ≥ 0.75, a sensitivity ≥ 80% and a specificity ≥ 70% (p value <  < 0.01). However, a multivariate analysis significantly increased the accuracy in RAS prediction when a linear regression model (LRM) was used. The best performance was obtained using a LRM combining linearly 12 robust features after a z-score normalization procedure: AUC of 0.953, accuracy 98%, sensitivity 96%, specificity of 100%, PPV 100% and NPV 96% (p value <  < 0.01). No statistically significant increase was obtained considering the tested machine learning both without normalization and with normalization methods.

CONCLUSIONS

Normalized approach in CT radiomics analysis allows to predict RAS mutational status in colorectal liver metastases patients.

Personalized prediction of postoperative complication and survival among Colorectal Liver Metastases Patients Receiving Simultaneous Resection using machine learning approaches: A multi-center study

BACKGROUND

To predict clinical important outcomes for colorectal liver metastases (CRLM) patients receiving colorectal resection with simultaneous liver resection by integrating demographic, clinical, laboratory, and genetic data.

METHODS

Random forest (RF) models were developed to predict postoperative complications and major complications (binary outcomes), as well as progression-free survival (PFS) and overall survival (OS) (time-to-event outcomes) of the CRLM patients based on data from two hospitals. The models were validated on an external dataset from an independent hospital. The clinical utility of the models was assessed via decision curve analyses (DCA).

RESULTS

There were 1067 patients included in survival prediction analyses and 1070 patients included in postoperative complication prediction analyses. The RF models provided an assessment of the model contributions of features for outcomes and suggested KRAS, BRAF, and MMR status were salient for the PFS or OS predictions. RF model of PFS showed that the Brier scores at 1-, 3-, and 5-year PFS were 0.213, 0.202 and 0.188; and the AUCs of 1-, 3- and 5-year PFS were 0.702, 0.720 and 0.743. RF model of OS revealed that Brier scores of 1-,3-, and 5-year OS were 0.040, 0.183 and 0.211; and the AUCs of 1-, 3- and 5-year OS were 0.737, 0.706 and 0.719. RF model for postoperative complication resulted in an AUC of 0.716 and a Brier score of 0.196. DCA curves clearly demonstrated that the RF models for these outcomes exhibited a superior net benefit across a wide range of threshold probabilities, signifying their favorable clinical utility. The RF models consistently exhibited robust performance in both internal cross-validation and external validation. The individualized risk profile predicted by the models closely aligned with the actual survival outcomes observed for the patients. A web-based tool (https://kanli.shinyapps.io/CRLMRF/) was provided to demonstrate the practical use of the prediction models for new patients in the clinical setting.

CONCLUSION

The predictive models and a web-based tool for personalized prediction demonstrated a moderate predictive performance and favorable clinical utilities on several key clinical outcomes of CRLM patients receiving simultaneous resection, which could facilitate the clinical decision-making and inform future interventions for CRLM patients.

Challenges and Opportunities for Precision Surgery for Colorectal Liver Metastases

The incidence of colorectal cancer and colorectal liver metastases (CRLM) is increasing globally due to an interaction of environmental and genetic factors. A minority of patients with CRLM have surgically resectable disease, but for those who have resection as part of multimodal therapy for their disease, long-term survival has been shown. Precision surgery-the idea of careful patient selection and targeting of surgical intervention, such that treatments shown to be proven to benefit on a population level are the optimal treatment for each individual patient-is the new paradigm of care. Key to this is the understanding of tumour molecular biology and clinically relevant mutations, such as KRAS, BRAF, and microsatellite instability (MSI), which can predict poorer overall outcomes and a poorer response to systemic therapy. The emergence of immunotherapy and hepatic artery infusion (HAI) pumps show potential to convert previously unresectable disease to resectable disease, in addition to established systemic and locoregional therapies, but the surgeon must be wary of poor-quality livers and the spectre of post-hepatectomy liver failure (PHLF). Volume modulation, a cornerstone of hepatic surgery for a generation, has been given a shot in the arm with the advent of liver venous depletion (LVD) ensuring significantly more hypertrophy of the future liver remnant (FLR). The optimal timing of liver resection for those patients with synchronous disease is yet to be truly established, but evidence would suggest that those patients requiring complex colorectal surgery and major liver resection are best served with a staged approach. In the operating room, parenchyma-preserving minimally invasive surgery (MIS) can dramatically reduce the surgical insult to the patient and lead to better perioperative outcomes, with quicker return to function.

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.

Exploring tumor heterogeneity in colorectal liver metastases by imaging: Unsupervised machine learning of preoperative CT radiomics features for prognostic stratification

OBJECTIVES

This study aimed to investigate tumor heterogeneity of colorectal liver metastases (CRLM) and stratify the patients into different risk groups of prognoses following liver resection by applying an unsupervised radiomics machine-learning approach to preoperative CT images.

METHODS

This retrospective study retrieved clinical information and CT images of 197 patients with CRLM from The Cancer Imaging Archive (TCIA) database. Radiomics features were extracted from a segmented liver lesion identified at the portal venous phase. Those features which showed high stability, non-redundancy, and indicative information were selected. An unsupervised consensus clustering analysis on these features was adopted to identify subgroups of CRLM patients. Overall survival (OS), disease-free survival (DFS), and liver-specific DFS were compared between the identified subgroups. Cox regression analysis was applied to evaluate prognostic risk factors.

RESULTS

A total of 851 radiomics features were extracted, and 56 robust features were finally selected for unsupervised clustering analysis which identified two distinct subgroups (96 and 101 patients respectively). There were significant differences in the OS, DFS, and liver-specific DFS between the subgroups (all log-rank p < 0.05). The subgroup with worse outcome using the proposed radiomics model was consistently associated with shorter OS, DFS, and liver-specific DFS, with hazard ratios of 1.78 (95 %CI: 1.12-2.83), 1.72 (95 %CI: 1.16-2.54), and 1.59 (95 %CI: 1.10-2.31), respectively. The general performance of this radiomics model outperformed the traditional Clinical Risk Score and Tumor Burden Score in the prognosis prediction after surgery for CRLM.

CONCLUSION

Radiomics features derived from preoperative CT images can reveal the heterogeneity of CRLM and stratify the patients with CRLM into subgroups with significantly different clinical outcomes.

Molecular profiling and patient selection for the multimodal approaches for patients with resectable colorectal liver metastases

Colorectal cancer represents the third most common cancer and about 20% are diagnosed with synchronous metastatic disease. From a historical point of view, surgery remains the mainstream treatment for resectable colorectal liver metastases (CRLM). Furthermore, disease outcomes are improving due significant advances in systemic treatments and diagnostic methods. However, the optimal timing for neoadjuvant chemotherapy or upfront surgery for CRLM has not yet been established and remains an open question. Thus, patient selection combining image workouts, time of recurrence, positive lymph nodes, and molecular biomarkers can improve the decision-making process. Nevertheless, molecular profiling is rising as a promising field to be incorporated in the multimodal approach and guide patient selection and sequencing of treatment. Tumor biomakers, genetic profiling, and circulating tumor DNA have been used to offer as much personalized treatment as possible, based on the precision oncology concept of tailored care rather than a guideline-based therapy. This review article discusses the role of molecular pathology and biomarkers as prognostic and predictor factors in the diagnosis and treatment of resectable CRLM.

Predicting early recurrence after resection of initially unresectable colorectal liver metastases: the role of baseline and pre-surgery clinical, radiological and molecular factors in a real-life multicentre experience

BACKGROUND

Advances in surgical techniques and systemic treatments have increased the likelihood of achieving radical surgery and long-term survival in metastatic colorectal cancer (mCRC) patients with initially unresectable colorectal liver metastases (CRLMs). Nonetheless, roughly half of the patients resected after an upfront systemic therapy experience disease relapse within 6 months from surgery, thus leading to the question whether surgery is actually beneficial for these patients.

MATERIALS AND METHODS

A real-world dataset of mCRC patients with initially unresectable liver-limited disease treated with conversion chemotherapy followed by radical resection of CRLMs at three high-volume Italian institutions was retrospectively assessed with the aim of investigating the association of baseline and pre-surgical clinical, radiological and molecular factors with the risk of relapse within 6 or 12 months from surgery.

RESULTS

Overall, 268 patients were included in the analysis and 207 (77%) experienced recurrence. Ninety-six (46%) of them had disease relapse within 6 months after CRLM resection and in spite of several variables associated with early recurrence at univariate analyses, only primary tumour resection at diagnosis [odds ratio (OR) 0.53, 95% confidence interval (CI) 0.32-0.89, P = 0.02] remained significant in the multivariable model. Among patients with resected primary tumours, pN+ stage was associated with higher risk of disease relapse within 6 months (OR 3.02, 95% CI 1.23-7.41, P = 0.02). One hundred and forty-nine patients (72%) had disease relapse within 12 months after CRLMs resection but none of the analysed variables was independently associated with outcome.

CONCLUSIONS

Clinical, radiological and molecular factors assessed before and after conversion chemotherapy do not reliably predict early recurrence after secondary resection of initially unresectable CRLMs. While novel markers are needed to optimize the cost/efficacy balance of surgical procedures, CRLM resection should be offered as soon as metastases become resectable during first-line chemotherapy to all patients eligible for surgery.

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.

Machine learning-based radiomics analysis in predicting RAS mutational status using magnetic resonance imaging

PURPOSE

To assess the efficacy of radiomics features, obtained by magnetic resonance imaging (MRI) with hepatospecific contrast agent, in pre-surgical setting, to predict RAS mutational status in liver metastases.

METHODS

Patients with MRI in pre-surgical setting were enrolled in a retrospective study. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. The features were extracted considering the agreement with the Imaging Biomarker Standardization Initiative (IBSI). Balancing was performed through synthesis of samples for the underrepresented classes using the self-adaptive synthetic oversampling (SASYNO) approach. Inter- and intraclass correlation coefficients (ICC) were calculated to assess the between-observer and within-observer reproducibility of all radiomics characteristics. For continuous variables, nonparametric Wilcoxon-Mann-Whitney test was utilized. Benjamini and Hochberg's false discovery rate (FDR) adjustment for multiple testing was used. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. Moreover, features selection were performed before and after a normalized procedure using two different methods (3-sigma and z-score). McNemar test was used to assess differences statistically significant between dichotomic tables. All statistical procedures were done using MATLAB R2021b Statistics and Machine Toolbox (MathWorks, Natick, MA, USA).

RESULTS

Seven normalized radiomics features, extracted from arterial phase, 11 normalized radiomics features, from portal phase, 12 normalized radiomics features from hepatobiliary phase and 12 normalized features from T2-W SPACE sequence were robust predictors of RAS mutational status. The multivariate analysis increased significantly the accuracy in RAS prediction when a LRM was used, combining 12 robust normalized features extracted by VIBE hepatobiliary phase reaching an accuracy of 99%, a sensitivity 97%, a specificity of 100%, a PPV of 100% and a NPV of 98%. No statistically significant increase was obtained, considering the tested classifiers DT, KNN and SVM, both without normalization and with normalization methods.

CONCLUSIONS

Normalized approach in MRI radiomics analysis allows to predict RAS mutational status.

Preoperative chemotherapy in upfront resectable colorectal liver metastases: New elements for an old dilemma?

The use of preoperative or "neoadjuvant" chemotherapy (NAC) has long been controversial for resectable colorectal liver metastases (CRLM). The European Society of Medical Oncology (ESMO) 2023 guidelines on metastatic colorectal cancer (CRC) indicate a combination of surgical/technical and oncologic/prognostic criteria as the two determinants for allocating patients to NAC or upfront hepatectomy. However, surgical and technical criteria have evolved, and oncologic prognostic criteria date from the pre-modern chemotherapy era and lack prospective validation. The traditional literature is interpreted as not supporting the use of NAC because several studies fail to demonstrate a benefit in overall survival (OS) compared to upfront surgery; however, OS may not be the most appropriate endpoint to consider. Moreover, the commonly quoted studies against NAC contain many limitations that may explain why NAC failed to demonstrate its value. The query of the recent literature focused primarily on other aspects than OS, such as surgical technique, the impact of side effects of chemotherapy, the histological growth pattern of metastases, or the detection of circulating tumor DNA, shows data that support a more widespread use of NAC. These should prompt a critical reappraisal of the use of NAC, leading to a more precise selection of patients who could benefit from it.

Current landscape of preoperative neoadjuvant therapies for initial resectable colorectal cancer liver metastasis

Colorectal cancer liver metastasis (CRLM) presents a clinical challenge, and optimizing treatment strategies is crucial for improving patient outcomes. Surgical resection, a key element in achieving prolonged survival, is often linked to a heightened risk of recurrence. Acknowledging the potential benefits of preoperative neoadjuvant chemotherapy in managing resectable liver metastases, this approach has gained attention for its role in tumor downsizing, assessing biological behavior, and reducing the risk of postoperative recurrence. However, the use of neoadjuvant chemotherapy in initially resectable CRLM sparks ongoing debates. The balance between tumor reduction and the risk of hepatic injury, coupled with concerns about delaying surgery, necessitates a nuanced approach. This article explores recent research insights and draws upon the practical experiences at our center to address critical issues regarding considerations for initially resectable cases. Examining the criteria for patient selection and the judicious choice of neoadjuvant regimens are pivotal areas of discussion. Striking the right balance between maximizing treatment efficacy and minimizing adverse effects is imperative. The dynamic landscape of precision medicine is also reflected in the evolving role of gene testing, such as RAS/BRAF and PIK3CA, in tailoring neoadjuvant regimens. Furthermore, the review emphasizes the need for a multidisciplinary approach to navigate the complexities of CRLM. Integrating technical expertise and biological insights is crucial in refining neoadjuvant strategies. The management of progression following neoadjuvant chemotherapy requires a tailored approach, acknowledging the diverse biological behaviors that may emerge. In conclusion, this review aims to provide a comprehensive perspective on the considerations, challenges, and advancements in the use of neoadjuvant chemotherapy for initially resectable CRLM. By combining evidence-based insights with practical experiences, we aspire to contribute to the ongoing discourse on refining treatment paradigms for improved outcomes in patients with CRLM.

Neoadjuvant chemotherapy for borderline resectable colorectal cancer liver metastases: a single-institution retrospective study

INTRODUCTION

This study aimed to extract prognostic factors in patients undergoing neoadjuvant chemotherapy (NAC) for borderline resectable colorectal liver metastasis (BR-CRLM) (tumor size ≥5 cm, number of tumors ≥4, or resectable extrahepatic diseases) and assess validity of this strategy.

MATERIALS AND METHODS

Since 2010, patients with BR-CRLM were treated with hepatectomy after six cycles of NAC. Prognostic factors of these patients were evaluated using clinicopathological data.

RESULTS

Of 650 patients who underwent initial hepatectomy for CRLM from 2010 to 2018, 246 BR-CRLM cases underwent hepatectomy after NAC (BR-NAC). The 5-year recurrence-free survival rate was 16.7% and the 5-year overall survival rate (5y-OS) was 52.9%. Number of tumors ≥6, carcinoembryonic antigen (CEA) level ≥25 ng/mL, tumor diameter ≥5 cm, and progressive disease (PD) after NAC were identified as independent poor prognostic factors for OS. Patients were divided into four groups according to the number of risk factors, and prognoses of the four groups were well stratified.

CONCLUSION

In patients with BR-NAC, number of tumors ≥6, CEA ≥25 ng/mL, tumor diameter ≥5 cm, and PD after NAC were independent poor prognostic factors. Patients with three or four risk factors showed poor prognosis and may need to switch chemotherapy.

RAS mutation status in combination with the JSHBPS nomogram may be useful for preoperative identification of colorectal liver metastases with high risk of recurrence and mortality after hepatectomy

PURPOSE

To investigate the prognostic impact of RAS mutations on the Japanese Society of Hepatobiliary and Pancreatic Surgeons (JSHBPS) nomogram score in patients with colorectal cancer liver metastasis (CRLM) following hepatectomy.

METHODS

We included 218 consecutive patients undergoing hepatectomy for CRLM between 2004 and 2020. The JSHBPS nomogram score was calculated using six preoperative clinical factors. The score ranged from 0 to 25, and higher scores indicated greater tumor burden. Associations of RAS mutations with disease-free survival (DFS) and overall survival (OS) by the JSHBPS nomogram score were examined. Multivariable Cox proportional hazard regression models were used to estimate adjusted hazard ratios (HRs) and confidence intervals (CIs).

RESULTS

RAS mutations were detected in 72 (33%) of the 218 patients. Multivariate analyses revealed that RAS mutations were independently associated with poor DFS (HR, 1.93; 95% CI: 1.20-3.10; p = .007) and OS (HR, 2.65; 95% CI: 1.59-4.71; p = .001) compared with wild-type RAS with JSHBPS nomogram scores ≤ 10. However, in patients with scores ≥ 11, the association of RAS mutations with DFS or OS was not statistically significant (p > .08).

CONCLUSION

RAS mutation status in combination with the JSHBPS nomogram may be useful for preoperatively identifying CRLM with high risk of recurrence and mortality after hepatectomy.

Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment

OBJECTIVE

The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated.

METHODS

The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed.

RESULTS

The best predictor to identify tumor growth was the Wavelet_HLH_glcm_MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet_HLH_ngtdm_Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet_LLH_glcm_Imc1 with an accuracy of 88% and to identify mucinous type was wavelet_LLH_glcm_JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence.

CONCLUSIONS

The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy.

Development and validation of a mutation-annotated prognostic score for intrahepatic cholangiocarcinoma after resection: a retrospective cohort study

BACKGROUND

The value of existing prognostic models for intrahepatic cholangiocarcinoma is limited. The inclusion of prognostic gene mutations would enhance the predictive efficacy.

METHODS

In the screening cohorts, univariable Cox regression analysis was applied to investigate the effect of individual mutant genes on overall survival (OS). In the training set, multivariable analysis was performed to evaluate the independent prognostic roles of the clinicopathological and mutational parameters, and a prognostic model was constructed. Internal and external validations were conducted to evaluate the performance of this model.

RESULTS

Among the recurrent mutations, only TP53 and KRASG12 were significantly associated with OS across all three screening cohorts. In the training cohort, TP53 and KRASG12 mutations in combination with seven other clinical parameters (tumor size, tumor number, vascular invasion, lymph node metastasis, adjacent invasion, CA19-9, and CEA), were independent prognostic factors for OS. A mutation-annotated prognostic score (MAPS) was established based on the nine prognosticators. The C-indices of MAPS (0.782 and 0.731 in the internal and external validation cohorts, respectively) were statistically higher than those of other existing models ( P <0.05). Furthermore, the MAPS model also demonstrated significant value in predicting the possible benefits of upfront surgery and adjuvant therapy.

CONCLUSIONS

The MAPS model demonstrated good performance in predicting the OS of intrahepatic cholangiocarcinoma patients. It may also help predict the possible benefits of upfront surgery and adjuvant therapy.

Value of prognostic scoring systems in the era of multimodal therapy for recurrent colorectal liver metastases

BACKGROUND

Various predictive scoring systems have been developed to estimate outcomes of patients undergoing surgery for colorectal liver metastases (CRLM). However, data regarding their effectiveness in recurrent CRLM (recCRLM) are very limited.

METHODS

Patients who underwent repeat hepatectomy for recCRLM at the University Hospital RWTH Aachen, Germany from 2010 to 2021 were included. Nine predictive scoring systems (Fong's, Nordlinger, Nagashima, RAS mutation, Tumor Burden, GAME, CERR, and Glasgow Prognostic score, Basingstoke Index) were evaluated by likelihood ratio (LR) χ2, linear trend (LT) χ2 and Akaike Information Criterion (AIC) for their predictive value regarding overall survival (OS) and recurrence free survival (RFS).

RESULTS

Among 150 patients, median RFS was 9 (2-124) months with a 5-year RFS rate of 10%. Median OS was 39 (4-131) months with a 5-year OS rate of 32%. For RFS and OS, the Nagashima score showed the best prognostic ability (LT χ2 3.00, LR χ2 9.39, AIC 266.66 and LT χ2 2.91, LR χ2 20.91, 290.36).

DISCUSSION

The Nagashima score showed the best prognostic stratification to predict recurrence as well as survival, and therefore might be considered when evaluating patients with recCRLM for repeat hepatectomy.

Using the win ratio to compare laparoscopic versus open liver resection for colorectal cancer liver metastases

Background

We sought to assess the overall benefit of laparoscopic versus open hepatectomy for treatment of colorectal liver metastases (CRLMs) using the win ratio, a novel methodological approach.

Methods

CRLM patients undergoing curative-intent resection in 2001-2018 were identified from an international multi-institutional database. Patients were paired and matched based on age, number and size of lesions, lymph node status and receipt of preoperative chemotherapy. The win ratio was calculated based on margin status, severity of postoperative complications, 90-day mortality, time to recurrence, and time to death.

Results

Among 962 patients, the majority underwent open hepatectomy (n=832, 86.5%), while a minority underwent laparoscopic hepatectomy (n=130, 13.5%). Among matched patient-to-patient pairs, the odds of the patient undergoing laparoscopic resection "winning" were 1.77 [WR: 1.77, 95% confidence interval (CI): 1.42-2.34]. The win ratio favored laparoscopic hepatectomy independent of low (WR: 2.94, 95% CI: 1.20-6.39), medium (WR: 1.56, 95% CI: 1.16-2.10) or high (WR: 7.25, 95% CI: 1.13-32.0) tumor burden, as well as unilobar (WR: 1.71, 95% CI: 1.25-2.31) or bilobar (WR: 4.57, 95% CI: 2.36-8.64) disease. The odds of "winning" were particularly pronounced relative to short-term outcomes (i.e., 90-day mortality and severity of postoperative complications) (WR: 4.06, 95% CI: 2.33-7.78).

Conclusions

Patients undergoing laparoscopic hepatectomy had 77% increased odds of "winning". Laparoscopic liver resection should be strongly considered as a preferred approach to resection in CRLM patients.

Development and validation of a novel risk score to predict overall survival following surgical clearance of bilobar colorectal liver metastases

BACKGROUND

Bilobar liver metastases from colorectal cancer pose a challenge for obtaining a satisfactory oncological outcome with an adequate future liver remnant. This study aimed to assess the clinical and pathological determinants of overall survival and recurrence-free survival among patients undergoing surgical clearance of bilobar liver metastases from colorectal cancer.

METHODS

A retrospective international multicentre study of patients who underwent surgery for bilobar liver metastases from colorectal cancer between January 2012 and December 2018 was conducted. Overall survival and recurrence-free survival at 1, 2, 3 and 5 years after surgery were the primary outcomes evaluated. The secondary outcomes were duration of postoperative hospital stay, and 90-day major morbidity and mortality rates. A prognostic nomogram was developed using covariates selected from a Cox proportional hazards regression model, and internally validated using a 3:1 random partition into derivation and validation cohorts.

RESULTS

A total of 1236 patients were included from 70 centres. The majority (88 per cent) of the patients had synchronous liver metastases. Overall survival at 1, 2, 3 and 5 years was 86.4 per cent, 67.5 per cent, 52.6 per cent and 33.8 per cent, and the recurrence-free survival rates were 48.7 per cent, 26.6 per cent, 19.2 per cent and 10.5 per cent respectively. A total of 25 per cent of patients had recurrent disease within 6 months. Margin positivity and progressive disease at liver resection were poor prognostic factors, while adjuvant chemotherapy in margin-positive resections improved overall survival. The bilobar liver metastases from colorectal cancer-overall survival nomogram was developed from the derivation cohort based on pre- and postoperative factors. The nomogram's ability to forecast overall survival at 1, 2, 3 and 5 years was subsequently validated on the validation cohort and showed high accuracy (overall C-index = 0.742).

CONCLUSION

Despite the high recurrence rates, overall survival of patients undergoing surgical resection for bilobar liver metastases from colorectal cancer is encouraging. The novel bilobar liver metastases from colorectal cancer-overall survival nomogram helps in counselling and informed decision-making of patients planned for treatment of bilobar liver metastases from colorectal cancer.

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.

The role of tumour biological factors in technical anatomical resectability assessment of colorectal liver metastases following induction systemic treatment: An analysis of the Dutch CAIRO5 trial

BACKGROUND

Large inter-surgeon variability exists in technical anatomical resectability assessment of colorectal cancer liver-only metastases (CRLM) following induction systemic therapy. We evaluated the role of tumour biological factors in predicting resectability and (early) recurrence after surgery for initially unresectable CRLM.

METHODS

482 patients with initially unresectable CRLM from the phase 3 CAIRO5 trial were selected, with two-monthly resectability assessments by a liver expert panel. If no consensus existed among panel surgeons (i.e. same vote for (un)resectability of CRLM), conclusion was based on majority. The association of tumour biological (sidedness, synchronous CRLM, carcinoembryonic antigen and RAS/BRAF^V600E mutation status) and technical anatomical factors with consensus among panel surgeons, secondary resectability and early recurrence (<6 months) without curative-intent repeat local treatment was analysed by uni- and pre-specified multivariable logistic regression.

RESULTS

After systemic treatment, 240 (50%) patients received complete local treatment of CRLM of which 75 (31%) patients experienced early recurrence without repeat local treatment. Higher number of CRLM (odds ratio 1.09 [95% confidence interval 1.03-1.15]) and age (odds ratio 1.03 [95% confidence interval 1.00-1.07]) were independently associated with early recurrence without repeat local treatment. In 138 (52%) patients, no consensus among panel surgeons was present prior to local treatment. Postoperative outcomes in patients with and without consensus were comparable.

CONCLUSIONS

Almost a third of patients selected by an expert panel for secondary CRLM surgery following induction systemic treatment experience an early recurrence only amenable to palliative treatment. Number of CRLM and age, but no tumour biological factors are predictive, suggesting that until there are better biomarkers; resectability assessment remains primarily a technical anatomical decision.

Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics

In this narrative review, we reported un up-to-date on the role of radiomics to assess prognostic features, which can impact on the liver metastases patient treatment choice. In the liver metastases patients, the possibility to assess mutational status (RAS or MSI), the tumor growth pattern and the histological subtype (NOS or mucinous) allows a better treatment selection to avoid unnecessary therapies. However, today, the detection of these features require an invasive approach. Recently, radiomics analysis application has improved rapidly, with a consequent growing interest in the oncological field. Radiomics analysis allows the textural characteristics assessment, which are correlated to biological data. This approach is captivating since it should allow to extract biological data from the radiological images, without invasive approach, so that to reduce costs and time, avoiding any risk for the patients. Several studies showed the ability of Radiomics to identify mutational status, tumor growth pattern and histological type in colorectal liver metastases. Although, radiomics analysis in a non-invasive and repeatable way, however features as the poor standardization and generalization of clinical studies results limit the translation of this analysis into clinical practice. Clear limits are data-quality control, reproducibility, repeatability, generalizability of results, and issues related to model overfitting.

Hepatic Resection in Patients with Colo-Rectal Liver Metastases: Surgical Outcomes and Prognostic Factors of Single-Center Experience

INTRODUCTION

Surgical resection has a fundamental role in increasing the chance of survival in patients with colorectal liver metastases. The guidelines have been modified and expanded in time in order to increase the number of patients that can benefit from this treatment. The aim of this study is to analyze the main prognostic factors related to overall and disease-free survival of a series of consecutive patients undergoing liver resection for colorectal liver metastases (CRLM).

MATERIALS AND METHODS

A retrospective review of patients undergoing liver resection for CRLM between April 2018 and September 2021 was performed. Clinical data and laboratory parameters were evaluated using the log-rank test. OS and DFS were estimated using the Kaplan-Meier method.

RESULTS

A retrospective study on 75 patients who underwent liver resection for CRLM was performed. The OS and DFS at 1 and 3 years were 84.3% and 63.8% for OS, 55.6% and 30.7% for DFS, respectively. From the analysis of the data, the most significant results indicate that: patients with a lower CEA value <25 ng/mL had an OS of 93.6% and 80.1% at 1 and 3 years, with an average of 36.7 months (CI 95% 33.1-40.3); moreover, patients with a value equal to or greater than 25 ng/mL had a 1-year survival equal to 57.4%, with an average of 13.8 months (CI 95% 9.4-18.2) (p < 0.001); adjuvant chemotherapy increases by 3 years the overall survival (OS: 68.6% vs. 49.7%) (p = 0.013); localization of the primary tumor affects OS, with a better prognosis for left colon metastases (OS at 42 months: 85.4% vs. 42.2%) (p value = 0.056); patients with stage T1 or T2 cancer have a better 3 years OS (92.9-100% vs. 49.7-56.3%) (p = 0.696), while the N0 stage results in both higher 3 years OS and DFS than the N + stages (OS: 87.5% vs. 68.5% vs. 24.5%); metachronous metastases have a higher 3 years OS than synchronous ones (80% vs. 47.4%) (p = 0.066); parenchymal sparing resections have a better 3 years DFS than anatomical ones (33.7% vs. 0%) (p = 0.067); a patient with a parenchymal R1 resection has a much worse prognosis than an R0 (3 years OS: 0% vs. 68.7%) (p < 0.001).

CONCLUSIONS

CEA value of less than 25 ng/mL, localization of the primary tumor in the left colon, primary tumor in stage T1/2 and N0, metachronous presentation, R0 resection, fewer than four metastases, and use of adjuvant chemotherapy are all parameters that in our analysis have shown a correlation with a better prognosis; moreover, the evaluation of the series is in line with the latest evidence in the literature in defining the non-inferiority of minimally invasive and parenchymal sparing treatment compared to the classic laparotomic approach with anatomic resection.

Colorectal Cancer Liver Metastases: Genomics and Biomarkers with Focus on Local Therapies

Molecular cancer biomarkers help personalize treatment, predict oncologic outcomes, and identify patients who can benefit from specific targeted therapies. Colorectal cancer (CRC) is the third-most common cancer, with the liver being the most frequent visceral metastatic site. KRAS, NRAS, BRAF V600E Mutations, DNA Mismatch Repair Deficiency/Microsatellite Instability Status, HER2 Amplification, and NTRK Fusions are NCCN approved and actionable molecular biomarkers for colorectal cancer. Additional biomarkers are also described and can be helpful in different image-guided hepatic directed therapies specifically for CRLM. For example, tumors maintaining the Ki-67 proliferation marker after thermal ablation was shown to be particularly resilient to ablation. Ablation margin was also shown to be an important factor in predicting local recurrence, with a ≥10 mm minimal ablation margin being required to attain local tumor control, especially for patients with mutant KRAS CRLM.

FDG metabolic parameter-based models for predicting recurrence after upfront surgery in synchronous colorectal cancer liver metastasis

OBJECTIVE

This study aimed to develop and validate post- and preoperative models for predicting recurrence after curative-intent surgery using an FDG PET-CT metabolic parameter to improve the prognosis of patients with synchronous colorectal cancer liver metastasis (SCLM).

METHODS

In this retrospective multicenter study, consecutive patients with resectable SCLM underwent upfront surgery between 2006 and 2015 (development cohort) and between 2006 and 2017 (validation cohort). In the development cohort, we developed and internally validated the post- and preoperative models using multivariable Cox regression with an FDG metabolic parameter (metastasis-to-primary-tumor uptake ratio [M/P ratio]) and clinicopathological variables as predictors. In the validation cohort, the models were externally validated for discrimination, calibration, and clinical usefulness. Model performance was compared with that of Fong's clinical risk score (FCRS).

RESULTS

A total of 374 patients (59.1 ± 10.5 years, 254 men) belonged in the development cohort and 151 (60.3 ± 12.0 years, 94 men) in the validation cohort. The M/P ratio and nine clinicopathological predictors were included in the models. Both postoperative and preoperative models showed significantly higher discrimination than FCRS (p < .05) in the external validation (time-dependent AUC = 0.76 [95% CI 0.68-0.84] and 0.76 [0.68-0.84] vs. 0.65 [0.57-0.74], respectively). Calibration plots and decision curve analysis demonstrated that both models were well calibrated and clinically useful. The developed models are presented as a web-based calculator ( https://cpmodel.shinyapps.io/SCLM/ ) and nomograms.

CONCLUSIONS

FDG metabolic parameter-based prognostic models are well-calibrated recurrence prediction models with good discriminative power. They can be used for accurate risk stratification in patients with SCLM.

KEY POINTS

• In this multicenter study, we developed and validated prediction models for recurrence in patients with resectable synchronous colorectal cancer liver metastasis using a metabolic parameter from FDG PET-CT. • The developed models showed good predictive performance on external validation, significantly exceeding that of a pre-existing model. • The models may be utilized for accurate patient risk stratification, thereby aiding in therapeutic decision-making.

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.

Predicting early extrahepatic recurrence after local treatment of colorectal liver metastases

BACKGROUND

Patients who develop early extrahepatic recurrence (EHR) may not benefit from local treatment of colorectal liver metastases (CRLMs). This study aimed to develop a prediction model for early EHR after local treatment of CRLMs using a national data set.

METHODS

A Cox regression prediction model for EHR was developed and validated internally using data on patients who had local treatment for CRLMs with curative intent. Performance assessment included calibration, discrimination, net benefit, and generalizability by internal-external cross-validation. The prognostic relevance of early EHR (within 6 months) was evaluated by landmark analysis.

RESULTS

During a median follow-up of 35 months, 557 of the 1077 patients had EHR and 249 died. Median overall survival was 19.5 (95 per cent c.i. 15.6 to 23.0) months in patients with early EHR after CRLM treatment, compared with not reached (45.3 months to not reached) in patients without an early EHR. The EHR prediction model included side and stage of the primary tumour, RAS/BRAFV600E mutational status, and number and size of CRLMs. The range of 6-month EHR predictions was 5.9-56.0 (i.q.r. 12.9-22.0) per cent. The model demonstrated good calibration and discrimination. The C-index through 6 and 12 months was 0.663 (95 per cent c.i. 0.624 to 0.702) and 0.661 (0.632 to 0.689) respectively. The observed 6-month EHR risk was 6.5 per cent for patients in the lowest quartile of predicted risk compared with 32.0 per cent in the highest quartile.

CONCLUSION

Early EHR after local treatment of CRLMs can be predicted.

Liver Transplantation for Hepatic Metastases from Colorectal Cancer: Current Knowledge and Open Issues

More than 40% of patients with colorectal cancer present liver metastases (CRLM) during the course of their disease and up to 50% present with unresectable disease. Without surgical interventions, survival for patients treated with systemic therapies alone is dismal. In the past, liver transplantation (LT) for patients with unresectable CRLM failed to show any survival benefit due to poor selection, ineffective chemotherapeutic regimens, unbalanced immunosuppression and high perioperative mortality. Since then and for many years LT for CRLM was abandoned. The turning point occurred in 2013, when the results from the Secondary Cancer (SECA I) pilot study performed at Oslo University were published reporting a 60% 5-year overall survival after LT in patients with unresectable CRLM. These results effectively reignited the interest in LT as a potential therapy for CRLM, and several trials are undergoing. The aims of this article are to give a comprehensive overview of the available evidence on LT for CRLM, discuss the open issues in this rapidly evolving field, and highlight possible ways to address the future of this fascinating therapeutic alternative for selected patients with CRLM.

Histopathological Growth Pattern in Colorectal Liver Metastasis and The Tumor Immune Microenvironment

Almost half of all patients with colorectal cancer present with or eventually develop metastasis, most frequently in the liver. Understanding the histopathological growth patterns and tumor immune microenvironment of colorectal liver metastases may help determine treatment strategies and assess prognosis. A literature search was conducted to gather information on cancer biology, histopathological growth patterns, and the tumor immune microenvironment in colorectal liver metastases, including their mechanisms and their impact on clinical outcomes. A first consensus on histopathological growth patterns emerged in 2017, identifying five growth patterns. Later studies found benefits from a two-tier system, desmoplastic and non-desmoplastic, incorporated into the updated 2022 consensus. Furthermore, the tumor immune microenvironment shows additional characteristic features with relevance to cancer biology. This includes density of T-cells (CD8+), expression of claudin-2, presence of vessel co-option versus angiogenesis, as well as several other factors. The relation between histopathological growth patterns and the tumor immune microenvironment delineates distinct subtypes of cancer biology. The distinct subtypes are found to correlate with risk of metastasis or relapse, and hence to clinical outcome and long-term survival in each patient. In order to optimize personalized and precision therapy for patients with colorectal liver metastases, further investigation into the mechanisms of cancer biology and their translational aspects to novel treatment targets is warranted.

Colorectal Cancer Liver Metastases: Is an R1 Hepatic Resection Accepted?

Metastatic colorectal cancer is associated with a rather dismal 5-year overall survival. The liver is the most commonly affected organ. Improved 5-year survival rates after successful hepatic resections for metastases confined to the liver have been reported. Certainly, a hepatectomy that results in an incomplete tumor resection, in terms of leaving macroscopic residual tumor in the future liver remnant, is not associated with survival benefits. However, the prognostic implications of a microscopically positive surgical margin or a clear margin of less than 1 mm (R1) on pathology are debatable. Although it has been a field of extensive research, the relevant literature often reports contradictory results. The purpose of the present study was to define, assess the risk factors for, and, ultimately, analyze the effect that an R1 hepatic resection for colorectal cancer liver metastases might have on local recurrence rates and long-term prognosis by reviewing the relevant literature. Achieving an R0 hepatic resection, optimally with more than 1 mm of clear margin, should always be the goal. However, in the era of the aggressive multimodality treatment of liver metastatic colorectal cancer, an R1 resection might be the cost of increasing the pool of patients finally eligible for resection. The majority of literature reports have highlighted the detrimental effect of R1 resections on local recurrence and overall survival. However, there are indeed studies that degraded the prognostic handicap as a consequence of an R1 resection in selected patients and highlighted the presence of RAS mutations, the response to chemotherapy, and, in general, factors that reflect the biology of the disease as important, if not the determinant, prognostic factors. In these patients, the aggressive disease biology seems to outperform the resection margin status as a prognostic factor, and the recorded differences between R1 and R0 resections are equalized. Properly and accurately defining this patient group is a future challenge in the field of the surgical treatment of colorectal cancer liver metastases.

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.

Conversion Strategy in Left-Sided RAS/BRAF Wild-Type Metastatic Colorectal Cancer Patients with Unresectable Liver-Limited Disease: A Multicenter Cohort Study

Colorectal cancer (CRC) patients frequently develop liver metastases. Different treatment strategies are available according to the timing of appearance, the burden of metastatic disease, and the performance status of the patient. Systemic treatment (ST) represents the cornerstone of metastatic disease management. However, in select cases, combined ST and surgical resection can lead to remarkable survival outcomes. In the present multicentric cohort study, we explored the efficacy of a conversion strategy in a selected population of left-sided RAS/BRAF wild-type CRC patients with liver-limited metastatic disease. Methods: The primary endpoint was to compare survival outcomes of patients undergoing ST not leading to surgery, liver resection after conversion ST, and hepatic resection with perioperative ST. Furthermore, we explored survival outcomes depending on whether the case was discussed within a multidisciplinary team. Results: Between 2012 and 2020, data from 690 patients respecting the inclusion criteria were collected. Among these, 272 patients were deemed eligible for the analysis. The conversion rate was 24.1% of cases. Fifty-six (20.6%) patients undergoing surgical resection after induction treatment (i.e., ultimately resectable) had a significant survival advantage compared to those receiving systemic treatment not leading to surgery (176 pts, 64.7%) (5-year OS 60.8% and 11.7%, respectively, Log Rank test p < 0.001; HR = 0.273; 95% CI: 0.16−0.46; p < 0.001; 5-year PFS 22.2% and 6.3%, respectively, Log Rank test p < 0.001; HR = 0.447; 95% CI: 0.32−0.63; p < 0.001). There was no difference in survival between ultimately resectable patients and those who had liver resection with perioperative systemic treatment (potentially resectable—40 pts) (5-year OS 71.1%, Log Rank test p = 0.311. HR = 0.671; 95% CI: 0.31−1.46; p = 0.314; 5-year PFS 25.7%, Log Rank test p = 0.305. HR = 0.782; 95% CI: 0.49−1.25; p = 0.306). Conclusions: In our selected population of left-sided RAS/BRAF wild-type colorectal cancer patients with liver-limited disease, a conversion strategy was confirmed to provide a survival benefit. Patients not deemed surgical candidates at the time of diagnosis and patients judged resectable with perioperative systemic treatment have similar survival outcomes.

Impact of Age of Onset on Survival after Hepatectomy for Patients with Colorectal Cancer Liver Metastasis: A Real-World Single-Center Experience

PURPOSE

The incidence of early-onset CRC is increasing. However, the effect of age of onset on the long-term outcome of colorectal cancer liver metastasis (CRLM) remains unclear. This study aimed to evaluate the association between the age of onset and the oncological outcome of CRLM patients and to investigate whether the prognostic role of RAS mutation is altered with age.

METHODS

We retrospectively investigated consecutive patients at our institution who underwent initial liver resection between 2006 and 2020. The inverse probability of treatment weighting (IPTW) method was used to balance the confounders among early- (≤45 years; EOCRLM), intermediate- (46-70 years; IOCRLM), and late-onset (>70 years; LOCRLM) groups. The prognostic role of RAS was assessed based on age group.

RESULTS

A total of 1189 patients were enrolled: 162 in the EOCRLM group, 930 in the IOCRLM group, and 97 in the LOCRLM group. No difference in disease-free survival (DFS) was found between the three groups. However, EOCRLM were more likely to develop extrahepatic and extrapulmonary metastasis and had significantly lower five-year OS rates than IOCRLM. After IPTW, EOCRLM remained a negative prognostic predictor. RAS mutations were significantly associated with worse survival than wild-type RAS in EOCRLM and IOCRLM. However, RAS mutation did not predict the prognosis of patients with LOCRLM.

CONCLUSIONS

Patients with EOCRLM had a significantly lower OS than IOCRLM patients and age influences the prognostic power of RAS status. These findings may be helpful for doctors to guide the clinical treatments and develop follow-up strategies.

Surgery for liver metastases from primary melanoma: a systematic review and meta-analysis

BACKGROUND

Historically , liver metastases due to melanoma have been associated with dismal prognosis. Moreover, the actual survival benefit from the treatment of melanoma liver metastases is still controversial. Hence, this study aims to evaluate the difference in surgical versus non-surgical options for melanoma liver metastases.

METHODS

Four databases (PubMed, EMBASE, Scopus, and Cochrane Library) were searched from inception to July 17, 2022. Studies were included if they compared outcomes between surgical and non-surgical treatment for patients with liver metastases from resectable melanoma. Meta-analyses were performed for the outcomes of 1-year, 2-year, 3-year and 5-year OS. Sensitivity analyses were performed for outcomes with substantial statistical heterogeneity. To account for possible moderators that might contribute to statistical heterogeneity, univariate meta-regression with mixed-effects models and subgroup analyses were conducted for the outcome of 2-year OS.

RESULTS

The search yielded 6610 articles; 13 studies were included in our analysis. Meta-analyses showed that survival outcomes were in favour of patients undergoing surgery as compared to non-surgery: 1-year OS (HR = 0.29, 95%CI 0.19-0.44, p < 0.00001), 2-year OS (HR = 0.19, 95%CI 0.09-0.38, p < 0.00001), 3-year OS (HR = 0.07, 95%CI 0.03-0.19, p < 0.00001) and 5-year OS (HR = 0.07, 95%CI 0.02-0.22, p < 0.00001). All included studies were of high quality. There was moderate-to-high statistical heterogeneity. Findings were robust to sensitivity analyses. Subgroup analyses and univariate meta-regression revealed neoadjuvant therapy and age as statistically significant subgroup and moderator respectively.

CONCLUSIONS

This study suggests that surgical treatment of melanoma liver metastases could offer better OS outcomes compared with non-surgical treatment.