Confounders in developing a machine learning model for colorectal liver metastasis post-hepatectomy prognostications
Ontology highlight
ABSTRACT:
SUBMITTER:
PROVIDER: S-EPMC11007339 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
Similar Datasets
Project description: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.
| S-EPMC10432293 | biostudies-literature
Project description: Not available
| S-EPMC11336538 | biostudies-literature
Project description:IntroductionPost-hepatectomy liver failure (PHLF) is one of the most serious complications and causes of death in patients with hepatocellular carcinoma (HCC) after hepatectomy. This study aimed to develop a novel machine learning (ML) model based on the light gradient boosting machines (LightGBM) algorithm for predicting PHLF.MethodsA total of 875 patients with HCC who underwent hepatectomy were randomized into a training cohort (n=612), a validation cohort (n=88), and a testing cohort (n=175). Shapley additive explanation (SHAP) was performed to determine the importance of individual variables. By combining these independent risk factors, an ML model for predicting PHLF was established. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, and decision curve analyses (DCA) were used to evaluate the accuracy of the ML model and compare it to that of other noninvasive models.ResultsThe AUCs of the ML model for predicting PHLF in the training cohort, validation cohort, and testing cohort were 0.944, 0.870, and 0.822, respectively. The ML model had a higher AUC for predicting PHLF than did other non-invasive models. The ML model for predicting PHLF was found to be more valuable than other noninvasive models.ConclusionA novel ML model for the prediction of PHLF using common clinical parameters was constructed and validated. The novel ML model performed better than did existing noninvasive models for the prediction of PHLF.
| S-EPMC9667038 | biostudies-literature
Project description:Nearly 50 % of colorectal cancer (CRC) patients develop liver metastases with liver resection being the only option to cure patients. Residual micrometastases or circulating tumor cells are considered a cause of tumor relapse. This work investigates the influence of partial hepatectomy (PH) on the growth and molecular composition of CRC liver metastasis in a syngeneic rat model. One million CC531 colorectal tumor cells were implanted via the portal vein in WAG/Rij rats followed by a 30 % PH a day later. Control groups either received tumor cells followed by a sham-operation or were injected with a buffer solution followed by PH. Animals were examined with magnetic resonance imaging (MRI) and liver tissues were processed for immunolabeling and PCR analysis. One-third PH was associated with an almost threefold increase in relative tumor mass (MRI volumetry: 2.8-fold and transcript levels of CD44: 2.3-fold). Expression of molecular markers for invasiveness and aggressiveness (CD49f, CXCR4, Axin2 and c-met) was increased following PH, however with no significant differences when referring to the relative expression levels (relating to tumor mass). Liver metastases demonstrated a significantly higher proliferation rate (Ki67) 2 weeks following PH and cell divisions also increased in the surrounding liver tissue. Following PH, the stimulated growth of metastases clearly exceeded the compensation in liver volume with long-lasting proliferative effects. However, the distinct tumor composition was not influenced by liver regeneration. Future investigations should focus on the inhibition of cell cycle (i.e. systemic therapy strategies, irradiation) to hinder liver regeneration and therefore restrain tumor growth.
| S-EPMC3663204 | biostudies-literature
Project description: Not available
| S-EPMC4203604 | biostudies-literature
Project description:Postoperative recurrence of liver cancer is the main obstacle to improving the survival rate of patients with liver cancer. We established an mRNA-based model to predict the risk of recurrence after hepatectomy for liver cancer and explored the relationship between immune infiltration and the risk of recurrence after hepatectomy for liver cancer. We performed a series of bioinformatics analyses on the gene expression profiles of patients with liver cancer, and selected 18 mRNAs as biomarkers for predicting the risk of recurrence of liver cancer using a machine learning method. At the same time, we evaluated the immune infiltration of the samples and conducted a joint analysis of the recurrence risk of liver cancer and found that B cell, B cell naive, T cell CD4+ memory resting, and T cell CD4+ were significantly correlated with the risk of postoperative recurrence of liver cancer. These results are helpful for early detection, intervention, and the individualized treatment of patients with liver cancer after surgical resection, and help to reveal the potential mechanism of liver cancer recurrence.
| S-EPMC8692778 | biostudies-literature
Project description:The liver is the most common organ for the formation of colorectal cancer metastasis. Non-invasive prognostication of colorectal cancer liver metastasis (CRLM) may better inform clinicians for decision-making. Contrast-enhanced computed tomography images of 180 CRLM cases were included in the final analyses. Radiomics features, including shape, first-order, wavelet, and texture, were extracted with Pyradiomics, followed by feature engineering by penalized Cox regression. Radiomics signatures were constructed for disease-free survival (DFS) by both elastic net (EN) and random survival forest (RSF) algorithms. The prognostic potential of the radiomics signatures was demonstrated by Kaplan-Meier curves and multivariate Cox regression. 11 radiomics features were selected for prognostic modelling for the EN algorithm, with 835 features for the RSF algorithm. Survival heatmap indicates a negative correlation between EN or RSF risk scores and DFS. Radiomics signature by EN algorithm successfully separates DFS of high-risk and low-risk cases in the training dataset (log-rank test: p < 0.01, hazard ratio: 1.45 (1.07-1.96), p < 0.01) and test dataset (hazard ratio: 1.89 (1.17-3.04), p < 0.05). RSF algorithm shows a better prognostic implication potential for DFS in the training dataset (log-rank test: p < 0.001, hazard ratio: 2.54 (1.80-3.61), p < 0.0001) and test dataset (log-rank test: p < 0.05, hazard ratio: 1.84 (1.15-2.96), p < 0.05). Radiomics features have the potential for the prediction of DFS in CRLM cases.
| S-EPMC11231534 | biostudies-literature
Project description:Histopathological images of colorectal liver metastases (CRLM) contain rich morphometric information that may predict patients' outcomes. However, to our knowledge, no study has reported any practical deep learning framework based on the histology images of CRLM, and their direct association with prognosis remains largely unknown. In this study, we developed a deep learning-based framework for fully automated tissue classification and quantification of clinically relevant spatial organization features (SOFs) in H&E-stained images of CRLM. The SOFs based risk-scoring system demonstrated a strong and robust prognostic value that is independent of the current clinical risk score (CRS) system in independent clinical cohorts. Our framework enables fully automated tissue classification of H&E images of CRLM, which could significantly reduce assessment subjectivity and the workload of pathologists. The risk-scoring system provides a time- and cost-efficient tool to assist clinical decision-making for patients with CRLM, which could potentially be implemented in clinical practice.
| S-EPMC10494211 | biostudies-literature
Project description:BackgroundResection of colorectal liver metastasis (CLM) is beneficial when feasible. However, the benefit of second hepatectomy for hepatic recurrence in CLM remains unclear.MethodsThe Colorectal Liver Operative Metastasis International Collaborative retrospectively examined 1004 CLM cases from 2000 to 2018 from a total of 953 patients. Hepatic recurrence after initial hepatectomy was identified in 218 patients. Kaplan-Meier analysis was performed for overall survival (OS) and recurrence-free survival (RFS). Propensity score matching (PSM) was performed to offset selection bias. Cox proportional-hazards regression was performed to identify risk factors associated with OS.ResultsA total of 51 patients underwent second hepatectomy. Unadjusted median OS was 60.1 months in repeat-hepatectomy versus 38.3 months in the single-hepatectomy group (p = 0.015). In the PSM population, median OS remained significantly better in the repeat-hepatectomy group (60.1 vs. 33.1 months; p = 0.0023); median RFS was 12.4 months for the repeat-hepatectomy group, versus 9.8 months in the single-hepatectomy group (p = 0.0050). Repeat hepatectomy was associated with lower risk of death (hazard ratio: 0.283; p = 0.000012). Obesity, tobacco use, and high intraoperative blood loss were associated with significant risk of death (p < 0.05).ConclusionIn CLM with hepatic recurrence, second hepatectomy was beneficial for OS. With PSM, the OS benefit of performing a second hepatectomy remained significant.
| S-EPMC9613625 | biostudies-literature
Project description:Abstract Background Early diagnosis of liver metastasis is of great importance for enhancing the survival of colorectal adenocarcinoma (CAD) patients, and the combined use of a single biomarker in a classier model has shown great improvement in predicting the metastasis of several types of cancers. However, it is little reported for CAD. This study therefore aimed to screen an optimal classier model of CAD with liver metastasis and explore the metastatic mechanisms of genes when applying this classier model. Methods The differentially expressed genes between primary CAD samples and CAD with metastasis samples were screened from the Moffitt Cancer Center (MCC) dataset GSE131418. The classification performances of six selected algorithms, namely, LR, RF, SVM, GBDT, NN, and CatBoost, for classification of CAD with liver metastasis samples were compared using the MCC dataset GSE131418 by detecting their classification test accuracy. In addition, the consortium datasets of GSE131418 and GSE81558 were used as internal and external validation sets to screen the optimal method. Subsequently, functional analyses and a drug‐targeted network construction of the feature genes when applying the optimal method were conducted. Results The optimal CatBoost model with the highest accuracy of 99%, and an area under the curve of 1, was screened, which consisted of 33 feature genes. A functional analysis showed that the feature genes were closely associated with a “steroid metabolic process” and “lipoprotein particle receptor binding” (eg APOB and APOC3). In addition, the feature genes were significantly enriched in the “complement and coagulation cascade” pathways (eg FGA, F2, and F9). In a drug‐target interaction network, F2 and F9 were predicted as targets of menadione. Conclusion The CatBoost model constructed using 33 feature genes showed the optimal classification performance for identifying CAD with liver metastasis. APOB, APOC3, FGA, F2, F9, and NKX2‐3 were potential biomarkers for classification of CAD with liver metastasis. Menadione might be a promising anti‐metastatic drug of CAD cells through functioning its role at sites of F2 and F9. CatBoost model constructed by 33 feature genes showed the optimal classification performance for identifying CAD liver metastasis.
| S-EPMC7520257 | biostudies-literature