Use of machine learning models for the prognostication of liver transplantation: A systematic review

BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.

DEMAND-PULLED AIRCRAFT SPARE PART PROGNOSTICATION AND CONTROL STRATEGY

This paper presents a control strategy of demand pulled spare parts inventory. It establishes a spare part demand prognosticating model based on reliability analysis. Through parts reliability data the model gets the reliable life function of spare parts and determines parts demand time, depending on part life at given reliabilities. Moreover, a case study is taken to illuminate the demand prognostication and inventory control of on condition maintenance rotables.

PROGNOSTICATION AND INVESTIGATION OF EL NIO EVENT PROBABILITY

Based on the El Nino event data sequence from 1854 to 1993, the nature of sequences was de-termined by using statistical normal and independent tests, etc. With the Markov random process and first order auto-regression predictive model, we set up the prognostication mode and give the time limit of the occurrence of next El Nino event, which probably occurs around 2002.The occurring probability for 2001 is 44 %, and it is 61 % for 2002.

Clinical perspective on C-reactive protein in prognostication of major adverse cardiac events in the elderly with established coronary heart disease

The systemic response to tissue injury, regardless of cause is characterized by a cytokine-mediated alteration in the hepatic synthesis of a number of different plasma proteins,known collectively as 'acute phase reactants'. These proteins include C-reactive protein, serum amyloid A protein, alphal glycoprotein, ceruloplasmin, alpha macroglobulins, complement components (C1-C4, factor B, C9, C11), alpha1antitrypsin, alpha1 antichymotrypsin, fibrinogen, prothrombin,factor Ⅷ, plasminogen, haptoglobin, ferritin, immunoglobulins and lipoproteins. The initiation of the acute phase response is linked to the production of hormone-like polypeptide mediators now called cytokines, namedly, interleukin 1(IL-1),tumor necrosis factor, interferon gamma, interleukin 6 (IL-6),leukemia inhibitory factor, ciliary neurotropic factor, oncostatin M, and interleukin 11 (IL- 11).……

Prognostication algorithm for non-cirrhotic non-B non-C hepatocellular carcinoma-a multicenter study under the aegis of the French Association of Hepato-Biliary Surgery and liver Transplantation

Background:Liver resection and local ablation are the only curative treatment for non-cirrhotic hepatocellular carcinoma(HCC).Few data exist concerning the prognosis of patients resected for non-cirrhotic HCC.The objectives of this study were to determine the prognostic factors of recurrence-free survival(RFS)and overall survival(OS)and to develop a prognostication algorithm for non-cirrhotic HCC.Methods:French multicenter retrospective study including HCC patients with non-cirrhotic liver without underlying viral hepatitis:F0,F1 or F2 fibrosis.Results:A total of 467 patients were included in 11 centers from 2010 to 2018.Non-cirrhotic liver had a fibrosis score of F0(n=237,50.7%),F1(n=127,27.2%)or F2(n=103,22.1%).OS and RFS at 5 years were 59.2%and 34.5%,respectively.In multivariate analysis,microvascular invasion and HCC differentiation were prognostic factors of OS and RFS and the number and size were prognostic factors of RFS(P<0.005).Stratification based on RFS provided an algorithm based on size(P=0.013)and number(P<0.001):2 HCC with the largest nodule≤10 cm(n=271,Group 1);2 HCC with a nodule>10 cm(n=176,Group 2);>2 HCC regardless of size Conclusions:We developed a prognostication algorithm based on the number(≤or>2)and size(≤or>10 cm),which could be used as a treatment decision support concerning the need for perioperative therapy.In case of bifocal HCC,surgery should not be a contraindication.

Temporal dynamics of neonatal hypoxic-ischemic encephalopathy injuries on magnetic resonance imaging

Moderate to severe perinatal hypoxic-ischemic encephalopathy occurs in~1 to 3/1000 live births in high-income countries and is associated with a significant risk of death or neurodevelopmental disability.Detailed assessment is important to help identify highrisk infants,to help families,and to support appropriate interventions.A wide range of monitoring tools is available to assess changes over time,including urine and blood biomarkers,neurological examination,and electroencephalography.At present,magnetic resonance imaging is unique as although it is expensive and not suited to monitoring the early evolution of hypoxic-ischemic encephalopathy by a week of life it can provide direct insight into the anatomical changes in the brain after hypoxic-ischemic encephalopathy and so offers strong prognostic information on the long-term outcome after hypoxic-ischemic encephalopathy.This review investigated the temporal dynamics of neonatal hypoxic-ischemic encephalopathy injuries,with a particular emphasis on exploring the correlation between the prognostic implications of magnetic resonance imaging scans in the first week of life and their relationship to long-term outcome prediction,particularly for infants treated with therapeutic hypothermia.A comprehensive literature search,from 2016 to 2024,identified 20 pertinent articles.This review highlights that while the optimal timing of magnetic resonance imaging scans is not clear,overall,it suggests that magnetic resonance imaging within the first week of life provides strong prognostic accuracy.Many challenges limit the timing consistency,particularly the need for intensive care and clinical monitoring.Conversely,although most reports examined the prognostic value of scans taken between 4 and 10 days after birth,there is evidence from small numbers of cases that,at times,brain injury may continue to evolve for weeks after birth.This suggests that in the future it will be important to explore a wider range of times after hypoxic-ischemic encephalopathy to fully understand the optimal timing for predicting long-term outcomes.

Carcinosarcoma of the breast:Facing the challenge of a rare nosologic entity

Carcinosarcoma(CS),also known as metaplastic breast carcinoma with mesenchymal differentiation,is one of the five distinct subtypes of metaplastic breast cancer.It is considered as a mixed,biphasic neoplasm consisting of a carcinomatous component combined with a malignant nonepithelial element of mesenchymal origin without an intermediate transition zone.Although cellular origin of this neoplasm remains controversial,most researchers declare that neoplastic cells derive from a cellular structure with potential biphasic differentiation.Despite recent research on the therapeutic strategies against CS neoplastic disorders,surgical resection appears the only potentially curative approach.Since CS metastasize by the lymphatic route,axillary assessment with sentinel lymph node biopsy and/or axillary lymph node dissection is always implemented.Nevertheless,the tumor also presents a hematogenous metastatic pattern including pleural,pulmonary,liver,brain and less commonly bone metastases.Thus,surgical removal of breast CS does not necessarily ensure patient’s long-term recovery.Moreover,alternative therapies,such as radio-and chemotherapy proved insufficient and 5-year survival rate is limited.Nevertheless,there is evidence that following surgery,the combination of radio and chemotherapy is associated with a better prognosis than either treatment alone.The aim of this review is to evaluate the results of surgical treatment for breast CS with special reference to the extent of its histological spread.Clinical features,histogenesis,morphological and immunochemical findings are discussed,while the role of current diagnostic and therapeutic management of this aggressive neoplasm is emphasized.

Improving postoperative outcomes in patients with pancreatic cancer:Inflammatory and nutritional biomarkers

This editorial assesses the prognostic value of preoperative inflammatory and nutritional biomarkers in patients undergoing surgical resection for pancreatic cancer.Lu et al evaluated the ability of seven biomarkers to predict postoperative recovery and long-term outcomes.These biomarkers were albumin-to-globulin ratio,prognostic nutritional index(PNI),systemic immune-inflammation index,neutrophil-to-lymphocyte ratio,platelet-to-lymphocyte ratio,nutritional risk index,and geriatric nutritional risk index.The PNI was found to be a strong predictor of both overall and recurrence-free survival,underscoring its clinical relevance in managing patients with pancreatic 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.

Importance of risk assessment,endoscopic hemostasis,and recent advancements in the management of acute non-variceal upper gastrointestinal bleeding

Acute non-variceal upper gastrointestinal bleeding(ANVUGIB)is a common medical emergency in clinical practice.While the incidence has significantly reduced,the mortality rates have not undergone a similar reduction in the last few decades,thus presenting a significant challenge.This editorial outlines the key causes and risk factors of ANVUGIB and explores the current standards and recent updates in risk assessment scoring systems for predicting mortality and endoscopic treatments for achieving hemostasis.Since ANUVGIB predominantly affects the elderly population,the impact of comorbidities may be responsible for the poor outcomes.A thorough drug history is important due to the increasing use of antiplatelet agents and anticoagulants in the elderly.Early risk stratification plays a crucial role in deciding the line of management and predicting mortality.Emerging scoring systems such as the ABC(age,blood tests,co-morbidities)score show promise in predicting mortality and guiding clinical decisions.While conventional endoscopic therapies remain cornerstone approaches,novel techniques like hemostatic powders and over-the-scope clips offer promising alternatives,particularly in cases refractory to traditional modalities.By integrating validated scoring systems and leveraging novel therapeutic modalities,clinicians can enhance patient care and mitigate the substantial morbidity and mortality associated with ANVUGIB.

Predictive model using four ferroptosis-related genes accurately predicts gastric cancer prognosis

BACKGROUND Gastric cancer(GC)is a common malignancy of the digestive system.According to global 2018 cancer data,GC has the fifth-highest incidence and the thirdhighest fatality rate among malignant tumors.More than 60%of GC are linked to infection with Helicobacter pylori(H.pylori),a gram-negative,active,microaerophilic,and helical bacterium.This parasite induces GC by producing toxic factors,such as cytotoxin-related gene A,vacuolar cytotoxin A,and outer membrane proteins.Ferroptosis,or iron-dependent programmed cell death,has been linked to GC,although there has been little research on the link between H.pylori infection-related GC and ferroptosis.AIM To identify coregulated differentially expressed genes among ferroptosis-related genes(FRGs)in GC patients and develop a ferroptosis-related prognostic model with discrimination ability.METHODS Gene expression profiles of GC patients and those with H.pylori-associated GC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus(GEO)databases.The FRGs were acquired from the FerrDb database.A ferroptosis-related gene prognostic index(FRGPI)was created using least absolute shrinkage and selection operator–Cox regression.The predictive ability of the FRGPI was validated in the GEO cohort.Finally,we verified the expression of the hub genes and the activity of the ferroptosis inducer FIN56 in GC cell lines and tissues.RESULTS Four hub genes were identified(NOX4,MTCH1,GABARAPL2,and SLC2A3)and shown to accurately predict GC and H.pylori-associated GC.The FRGPI based on the hub genes could independently predict GC patient survival;GC patients in the high-risk group had considerably worse overall survival than did those in the low-risk group.The FRGPI was a significant predictor of GC prognosis and was strongly correlated with disease progression.Moreover,the gene expression levels of common immune checkpoint proteins dramatically increased in the highrisk subgroup of the FRGPI cohort.The hub genes were also confirmed to be highly overexpressed in GC cell lines and tissues and were found to be primarily localized at the cell membrane.The ferroptosis inducer FIN56 inhibited GC cell proliferation in a dose-dependent manner.CONCLUSION In this study,we developed a predictive model based on four FRGs that can accurately predict the prognosis of GC patients and the efficacy of immunotherapy in this population.

Construction and validation of a pancreatic cancer prognostic model based on genes related to the hypoxic tumor microenvironment

BACKGROUND Pancreatic cancer is one of the most lethal malignancies,characterized by poor prognosis and low survival rates.Traditional prognostic factors for pancreatic cancer offer inadequate predictive accuracy,often failing to capture the complexity of the disease.The hypoxic tumor microenvironment has been recognized as a significant factor influencing cancer progression and resistance to treatment.This study aims to develop a prognostic model based on key hypoxia-related molecules to enhance prediction accuracy for patient outcomes and to guide more effective treatment strategies in pancreatic cancer.AIM To develop and validate a prognostic model for predicting outcomes in patients with pancreatic cancer using key hypoxia-related molecules.METHODS This pancreatic cancer prognostic model was developed based on the expression levels of the hypoxia-associated genes CAPN2,PLAU,and CCNA2.The results were validated in an independent dataset.This study also examined the correlations between the model risk score and various clinical features,components of the immune microenvironment,chemotherapeutic drug sensitivity,and metabolism-related pathways.Real-time quantitative PCR verification was conducted to confirm the differential expression of the target genes in hypoxic and normal pancreatic cancer cell lines.RESULTS The prognostic model demonstrated significant predictive value,with the risk score showing a strong correlation with clinical features:It was significantly associated with tumor grade(G)(bP<0.01),moderately associated with tumor stage(T)(aP<0.05),and significantly correlated with residual tumor(R)status(bP<0.01).There was also a significant negative correlation between the risk score and the half-maximal inhibitory concentration of some chemotherapeutic drugs.Furthermore,the risk score was linked to the enrichment of metabolism-related pathways in pancreatic cancer.CONCLUSION The prognostic model based on hypoxia-related genes effectively predicts pancreatic cancer outcomes with improved accuracy over traditional factors and can guide treatment selection based on risk assessment.

Interaction between inflammatory bowel disease,physical activity,and myokines:Assessment of serum irisin levels

Inflammatory bowel disease(IBD),including Crohn’s disease and ulcerative colitis,showed a wide spectrum of intestinal and extra-intestinal manifestations,which rendered the patients physically inactive and impaired their quality of life.It has been found that physical activity is a non-pharmacological intervention that improves the quality of life for those patients.Irisin is one member of the myokines secreted by muscle contraction during exercise and could be used as an antiinflammatory biomarker in assessing the physical activity of IBD patients.In addition,experimental studies showed that exogenous irisin significantly decreased the inflammatory markers and the histological changes of the intestinal mucosa observed in experimental colitis.Furthermore,irisin produces changes in the diversity of the microbiota.Therefore,endogenous or exogenous irisin,via its anti-inflammatory effects,will improve the health of IBD patients and will limit the barriers to physical activity in patients with IBD.

Lipid metabolism-related long noncoding RNA RP11-817I4.1 promotes fatty acid synthesis and tumor progression in hepatocellular carcinoma

BACKGROUND Hepatocellular carcinoma(HCC)is one of the most common types of tumors.The influence of lipid metabolism disruption on the development of HCC has been demonstrated in published studies.AIM To establish an HCC prognostic model for lipid metabolism-related long non-coding RNAs(LMR-lncRNAs)and conduct in-depth research on the specific role of novel LMR-lncRNAs in HCC.METHODS Correlation and differential expression analyses of The Cancer Genome Atlas data were used to identify differentially expressed LMR-lncRNAs.Quantitative real-time polymerase chain reaction analysis was used to evaluate the expression of LMR-lncRNAs.Nile red staining was employed to observe intracellular lipid levels.The interaction between RP11-817I4.1,miR-3120-3p,and ATP citrate lyase(ACLY)was validated through the performance of dual-luciferase reporter gene and RIP assays.RESULTS Three LMR-lncRNAs(negative regulator of antiviral response,RNA transmembrane and coiled-coil domain family 1 antisense RNA 1,and RP11-817I4.1)were identified as predictive markers for HCC patients and were utilized in the construction of risk models.Additionally,proliferation,migration,and invasion were reduced by RP11-817I4.1 knockdown.An increase in lipid levels in HCC cells was significantly induced by RP11-817I4.1 through the miR-3120-3p/ACLY axis.CONCLUSION LMR-lncRNAs have the capacity to predict the clinical characteristics and prognoses of HCC patients,and the discovery of a novel LMR-lncRNAs,RP11-817I4.1,revealed its role in promoting lipid accumulation,thereby accelerating the onset and progression of HCC.

Evaluating the influence of sarcopenia and myosteatosis on clinical outcomes in gastric cancer patients undergoing immune checkpoint inhibitor

BACKGROUND The development and progression of gastric cancer(GC)are closely linked to the nutritional status of patients.Although immunotherapy has been demonstrated to be clinically effective,the relationships of sarcopenia and myosteatosis with the use of immune checkpoint inhibitors(ICIs)in patients with gastric cancer remain to be characterized.METHODS We performed a retrospective study of patients who were undergoing immuno-therapy for GC.For the evaluation of sarcopenia,the optimal cut-off value for the skeletal muscle index was established using receiver operating characteristic analysis of data obtained from pre-treatment computed tomography images at the L3 vertebral level.Myosteatosis was defined using the mean skeletal muscle density(SMD),with a threshold value of<41 Hounsfield units(HU)for patients with a body mass index(BMI)<25 kg/m^(2)and<33 HU for those with a BMI≥25 kg/m^(2).The log-rank test was used to compare progression-free survival(PFS)and overall survival(OS),and a Cox proportional hazard model was used to identify prognostic factors.Nomograms were developed to predict the PFS and OS of patients on the basis of the results of multivariate analyses.RESULTS We studied 115 patients who were undergoing ICI therapy for GC,of whom 27.4%had sarcopenia and 29.8%had myosteatosis.Patients with sarcopenia or myosteatosis had significantly shorter PFS and OS than those without these conditions.Furthermore,both sarcopenia and myosteatosis were found to be independent predictors of PFS and OS in patients with GC administering an ICI.The prediction models created for PFS and OS were associated with C-indexes of 0.758 and 0.781,respectively.CONCLUSION The presence of sarcopenia or myosteatosis is a reliable predictor of the clinical outcomes of patients with GC who are undergoing treatment with an ICI.

Identification and validation of a pyroptosis-related prognostic model for colorectal cancer based on bulk and single-cell RNA sequencing data

BACKGROUND Pyroptosis impacts the development of malignant tumors,yet its role in colorectal cancer(CRC)prognosis remains uncertain.AIM To assess the prognostic significance of pyroptosis-related genes and their association with CRC immune infiltration.METHODS Gene expression data were obtained from The Cancer Genome Atlas(TCGA)and single-cell RNA sequencing dataset GSE178341 from the Gene Expression Omnibus(GEO).Pyroptosis-related gene expression in cell clusters was analyzed,and enrichment analysis was conducted.A pyroptosis-related risk model was developed using the LASSO regression algorithm,with prediction accuracy assessed through K-M and receiver operating characteristic analyses.A nomo-gram predicting survival was created,and the correlation between the risk model and immune infiltration was analyzed using CIBERSORTx calculations.Finally,the differential expression of the 8 prognostic genes between CRC and normal samples was verified by analyzing TCGA-COADREAD data from the UCSC database.RESULTS An effective pyroptosis-related risk model was constructed using 8 genes-CHMP2B,SDHB,BST2,UBE2D2,GJA1,AIM2,PDCD6IP,and SEZ6L2(P<0.05).Seven of these genes exhibited differential expression between CRC and normal samples based on TCGA database analysis(P<0.05).Patients with higher risk scores demonstrated increased death risk and reduced overall survival(P<0.05).Significant differences in immune infiltration were observed between low-and high-risk groups,correlating with pyroptosis-related gene expression.CONCLUSION We developed a pyroptosis-related prognostic model for CRC,affirming its correlation with immune infiltration.This model may prove useful for CRC prognostic evaluation.

Transient receptor potential channels as predictive marker and potential indicator of chemoresistance in colon cancer

Transient receptor potential(TRP)channels are strongly associated with colon cancer development and progression.This study leveraged a multivariate Cox regression model on publicly available datasets to construct a TRP channels-associated gene signature,with further validation of signature in real world samples from our hospital treated patient samples.Kaplan-Meier(K-M)survival analysis and receiver operating characteristic(ROC)curves were employed to evaluate this gene signature’s predictive accuracy and robustness in both training and testing cohorts,respectively.Additionally,the study utilized the CIBERSORT algorithm and single-sample gene set enrichment analysis to explore the signature’s immune infiltration landscape and underlying functional implications.The support vector machine algorithm was applied to evaluate the signature’s potential in predicting chemotherapy outcomes.The findings unveiled a novel three TRP channels-related gene signature(MCOLN1,TRPM5,and TRPV4)in colon adenocarcinoma(COAD).The ROC and K-M survival curves in the training dataset(AUC=0.761;p=1.58e-05)and testing dataset(AUC=0.699;p=0.004)showed the signature’s robust predictive capability for the overall survival of COAD patients.Analysis of the immune infiltration landscape associated with the signature revealed higher immune infiltration,especially an increased presence of M2 macrophages,in high-risk group patients compared to their low-risk counterparts.High-risk score patients also exhibited potential responsiveness to immune checkpoint inhibitor therapy,evident through increased CD86 and PD-1 expression profiles.Moreover,the TRPM5 gene within the signature was highly expressed in the chemoresistance group(p=0.00095)and associated with poor prognosis(p=0.036)in COAD patients,highlighting its role as a hub gene of chemoresistance.Ultimately,this signature emerged as an independent prognosis factor for COAD patients(p=6.48e-06)and expression of model gene are validated by public data and real-world patients.Overall,this bioinformatics study provides valuable insights into the prognostic implications and potential chemotherapy resistance mechanisms associated with TRPs-related genes in colon cancer.

Preoperative controlling nutritional status as an optimal prognostic nutritional index to predict the outcome for colorectal cancer

BACKGROUND The controlling nutritional status(CONUT)score effectively reflects a patient’s nutritional status,which is closely related to cancer prognosis.This study invest-igated the relationship between the CONUT score and prognosis after radical surgery for colorectal cancer,and compared the predictive ability of the CONUT score with other indexes.AIM To analyze the predictive performance of the CONUT score for the survival rate of colorectal cancer patients who underwent potentially curative resection.METHODS This retrospective analysis included 217 patients with newly diagnosed colorectal.The CONUT score was calculated based on the serum albumin level,total lymphocyte count,and total cholesterol level.The cutoff value of the CONUT score for predicting prognosis was 4 according to the Youden Index by the receiver operating characteristic curve.The associations between the CONUT score and the prognosis were performed using Kaplan-Meier curves and Cox regression analysis.RESULTS Using the cutoff value of the CONUT score,patients were stratified into CONUT low(n=189)and CONUT high groups(n=28).The CONUT high group had worse overall survival(OS)(P=0.013)and relapse-free survival(RFS)(P=0.015).The predictive performance of CONUT was superior to the modified Glasgow prognostic score,the prognostic nutritional index,and the neutrophil-to-lymphocyte ratio.Meanwhile,the predictive performances of CONUT+tumor node metastasis(TNM)stage for 3-year OS[area under the receiver operating characteristics curve(AUC)=0.803]and 3-year RFS(AUC=0.752)were no less than skeletal muscle mass index(SMI)+TNM stage.The CONUT score was negatively correlated with SMI(P<0.01).CONCLUSION As a nutritional indicator,the CONUT score could predict long-term outcomes after radical surgery for colorectal cancer,and its predictive ability was superior to other indexes.The correlation between the CONUT score and skeletal muscle may be one of the factors that play a predictive role.

Unraveling the enigma:A comprehensive review of solid pseudopapillary tumor of the pancreas

Solid pseudopapillary tumor of the pancreas(SPTP)is a rare neoplasm predom-inantly observed in young females.Pathologically,CTNNB1 mutations,β-catenin nuclear accumulation,and subsequent Wnt-signaling pathway activation are the leading molecular features.Accurate preoperative diagnosis often relies on imaging techniques and endoscopic biopsies.Surgical resection remains the mainstay treatment.Risk models,such as the Fudan Prognostic Index,show promise as predictive tools for assessing the prognosis of SPTP.Establishing three types of metachronous liver metastasis can be beneficial in tailoring individu-alized treatment and follow-up strategies.Despite advancements,challenges persist in understanding its etiology,establishing standardized treatments for unresectable or metastatic diseases,and developing a widely recognized grading system.This comprehensive review aims to elucidate the enigma by consolidating current knowledge on the epidemiology,clinical presentation,pathology,molecular characteristics,diagnostic methods,treatment options,and prognostic factors.

Establishment and evaluation of a prognostic model for patients with unresectable gastric cancer liver metastases

BACKGROUND Liver metastases(LM)is the primary factor contributing to unfavorable outcomes in patients diagnosed with gastric cancer(GC).The objective of this study is to analyze significant prognostic risk factors for patients with GCLM and develop a reliable nomogram model that can accurately predict individualized prognosis,thereby enhancing the ability to evaluate patient outcomes.AIM To analyze prognostic risk factors for GCLM and develop a reliable nomogram model to accurately predict individualized prognosis,thereby enhancing patient outcome assessment.METHODS Retrospective analysis was conducted on clinical data pertaining to GCLM(type III),admitted to the Department of General Surgery across multiple centers of the Chinese PLA General Hospital from January 2010 to January 2018.The dataset was divided into a development cohort and validation cohort in a ratio of 2:1.In the development cohort,we utilized univariate and multivariate Cox regression analyses to identify independent risk factors associated with overall survival in GCLM patients.Subsequently,we established a prediction model based on these findings and evaluated its performance using receiver operator characteristic curve analysis,calibration curves,and clinical decision curves.A nomogram was created to visually represent the prediction model,which was then externally validated using the validation cohort.RESULTS A total of 372 patients were included in this study,comprising 248 individuals in the development cohort and 124 individuals in the validation cohort.Based on Cox analysis results,our final prediction model incorporated five independent risk factors including albumin levels,primary tumor size,presence of extrahepatic metastases,surgical treatment status,and chemotherapy administration.The 1-,3-,and 5-years Area Under the Curve values in the development cohort are 0.753,0.859,and 0.909,respectively;whereas in the validation cohort,they are observed to be 0.772,0.848,and 0.923.Furthermore,the calibration curves demonstrated excellent consistency between observed values and actual values.Finally,the decision curve analysis curve indicated substantial net clinical benefit.CONCLUSION Our study identified significant prognostic risk factors for GCLM and developed a reliable nomogram model,demonstrating promising predictive accuracy and potential clinical benefit in evaluating patient outcomes.