Risk prediction in intrahepatic cholangiocarcinoma: Direct comparison of the MEGNA score and the 8th edition of the UICC/AJCC Cancer staging system

Hähn, Felix; Müller, Lukáš; Mähringer‐Kunz, Aline; Schotten, Sebastian; Düber, Christoph; Hinrichs, Jan B.; Maschke, Sabine; Galle, Peter R.; Bartsch, Fabian; Lang, Hauke; Weinmann, Arndt; Kloeckner, Roman

doi:10.1371/journal.pone.0228501

Cited by 5 publications

(15 citation statements)

References 26 publications

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“…In the same study, the Wang nomogram reached superior values in estimating prognosis (C-Index 0.72). In two recent evaluations, the MEGNA score was found to be a useful stratification tool but failed in individual risk prediction [ 13 , 29 ]. Thus, none of the scores were implemented in the daily clinical routine.…”

Section: Discussionmentioning

confidence: 99%

“…Most of them were designed primarily for patients undergoing curative resection and use histopathological factors, such as microvascular invasion or tumor grading, which are only available postoperatively [ 9 , 10 , 11 ]. Even though all attempts have initially shown promising results, they have failed external validation and have not entered clinical use [ 12 , 13 ]. The only available score for all patients regardless of subsequent treatment is the Fudan score [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

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Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment

Müller

Mähringer‐Kunz

Gairing

et al. 2021

JCM

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Several scoring systems have been devised to objectively predict survival for patients with intrahepatic cholangiocellular carcinoma (ICC) and support treatment stratification, but they have failed external validation. The aim of the present study was to improve prognostication using an artificial intelligence-based approach. We retrospectively identified 417 patients with ICC who were referred to our tertiary care center between 1997 and 2018. Of these, 293 met the inclusion criteria. Established risk factors served as input nodes for an artificial neural network (ANN). We compared the performance of the trained model to the most widely used conventional scoring system, the Fudan score. Predicting 1-year survival, the ANN reached an area under the ROC curve (AUC) of 0.89 for the training set and 0.80 for the validation set. The AUC of the Fudan score was significantly lower in the validation set (0.77, p < 0.001). In the training set, the Fudan score yielded a lower AUC (0.74) without reaching significance (p = 0.24). Thus, ANNs incorporating a multitude of known risk factors can outperform conventional risk scores, which typically consist of a limited number of parameters. In the future, such artificial intelligence-based approaches have the potential to improve treatment stratification when models trained on large multicenter data are openly available.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment

Müller

Mähringer‐Kunz

Gairing

et al. 2021

JCM

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“…Strengths of this scoring system include external validation and superiority in predicting prognosis and survival to existing models. A comparison of the prognostic separation index between MEGNA and staging systems of the American Joint Committee on Cancer (AJCC) has demonstrated the superiority of MEGNA in patient survival following hepatectomy [ 88 ].…”

Section: Scoring Systemsmentioning

confidence: 99%

“…Current literature highlights substantial heterogeneity in histopathological and genomic characteristics of ICC based on geography [ 87 ]. This heterogeneity is demonstrated in a study by Hahn et al [ 88 ] who evaluated the use of the MEGNA score with a direct comparison of its prognostic value with the AJCC staging system, the most widely used one currently. This study found that the C-index was 0.58 for MEGNA and 0.61 for AJCC, thus demonstrating that the ability of the MEGNA score to predict individual patient prognosis was below the 0.7 threshold of acceptable.…”

Section: Scoring Systemsmentioning

confidence: 99%

Scoring systems for the management of oncological hepato-pancreato-biliary patients

Coombs

Jordan

Hussain

et al. 2022

Ann Hepatobiliary Pancreat Surg

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Oncological scoring systems in surgery are used as evidence-based decision aids to best support management through assessing prognosis, effectiveness and recurrence. Currently, the use of scoring systems in the hepato-pancreato-biliary (HPB) field is limited as concerns over precision and applicability prevent their widespread clinical implementation. The aim of this review was to discuss clinically useful oncological scoring systems for surgical management of HPB patients. A narrative review was conducted to appraise oncological HPB scoring systems. Original research articles of established and novel scoring systems were searched using Google Scholar, PubMed, Cochrane, and Ovid Medline. Selected models were determined by authors. This review discusses nine scoring systems in cancers of the liver (CLIP, BCLC, ALBI Grade, RETREAT, Fong’s score), pancreas (Genç’s score, mGPS), and biliary tract (TMHSS, MEGNA). Eight models used exclusively objective measurements to compute their scores while one used a mixture of both subjective and objective inputs. Seven models evaluated their scoring performance in external populations, with reported discriminatory c-statistic ranging from 0.58 to 0.82. Selection of model variables was most frequently determined using a combination of univariate and multivariate analysis. Calibration, another determinant of model accuracy, was poorly reported amongst nine scoring systems. A diverse range of HPB surgical scoring systems may facilitate evidence-based decisions on patient management and treatment. Future scoring systems need to be developed using heterogenous patient cohorts with improved stratification, with future trends integrating machine learning and genetics to improve outcome prediction.

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“…While proposed as a tool to predict survival following curativeintent liver resection, studies that have externally validated the MEGNA scoring system have been limited due to the small sample size and inclusion of patients from only Western centers. 15,16 Therefore, we sought to assess OS and recurrence-free survival (RFS) utilizing MEGNA in a large cohort of patients undergoing curative-intent resection for ICC using an international multiinstitutional database, comprising of both Eastern and Western countries. In addition, we compared the prognostic ability of MEGNA to other commonly used prognostic systems including the tumor burden score (TBS) and AJCC eighth edition staging system.…”

mentioning

confidence: 99%

Predicting overall and recurrence‐free survival in patients with intrahepatic cholangiocarcinoma using the MEGNA score: A multi‐institutional analysis

Shaikh

Alaimo

Moazzam

et al. 2022

Journal of Surgical Oncology

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Background and Objectives: MEGNA is a prognostic scoring system for intrahepatic cholangiocarcinoma (ICC) based on tumor multifocality, extension, grade, lymph node positivity, and age. We sought to assess its predictive ability for overall survival (OS) and recurrence-free survival (RFS).Methods: Patients who underwent curative-intent liver resection for ICC between 2000 and 2020 were identified using an international multi-institutional database. Multivariate Cox regression was utilized to identify prognostic factors.Results: Among 800 patients with a median age of 58 years (interquartile range[IQR]: 50-68), the majority of patients were male (n = 467, 58.4%). 5-year OS was 40.5%, while 5-year RFS was 27.9%. The prognostic ability of MEGNA score (c-index = 0.60) was similar to AJCC (c-index = 0.58) and TBS (c-index = 0.58).

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Risk prediction in intrahepatic cholangiocarcinoma: Direct comparison of the MEGNA score and the 8th edition of the UICC/AJCC Cancer staging system

Abstract: Background

Cited by 5 publications

References 26 publications

Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment

Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment

Scoring systems for the management of oncological hepato-pancreato-biliary patients

Predicting overall and recurrence‐free survival in patients with intrahepatic cholangiocarcinoma using the MEGNA score: A multi‐institutional analysis

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