Literature DB >> 27763897

The Tumor Burden Score: A New "Metro-ticket" Prognostic Tool For Colorectal Liver Metastases Based on Tumor Size and Number of Tumors.

Kazunari Sasaki1, Daisuke Morioka2, Simone Conci3, Georgios A Margonis1, Yu Sawada2, Andrea Ruzzenente3, Takafumi Kumamoto2, Calogero Iacono3, Nikolaos Andreatos1, Alfredo Guglielmi3, Itaru Endo2, Timothy M Pawlik1.   

Abstract

OBJECTIVE: To apply the principles of the Metro-ticket paradigm to develop a prognostic model for patients undergoing hepatic resection of colorectal liver metastasis (CRLM).
BACKGROUND: Whereas the hepatocellular "Metro-ticket" prognostic tool utilizes a continuum of tumor size and number, a similar concept of a CRLM Metro-ticket paradigm has not been investigated.
METHODS: Tumor Burden Score (TBS) was defined using distance from the origin on a Cartesian plane incorporating maximum tumor size (x-axis) and number of lesions (y-axis). The discriminatory power [area under the curve (AUC)] and goodness-of-fit (Akaike information criteria) of the TBS model versus standard tumor morphology categorization were assessed. The TBS model was validated using 2 external cohorts from Asia and Europe.
RESULTS: TBS (AUC 0.669) out-performed both maximum tumor size (AUC 0.619) and number of tumors (AUC 0.595) in predicting overall survival (OS) (P < 0.05). As TBS increased, survival incrementally worsened (5-year OS: zone 1, zone 2, and zone 3-68.9%, 49.4%, and 25.5%; P < 0.05). The stratification of survival based on traditional tumor size and number cut-off criteria was poor. Specifically, 5-year survival for patients in category 1, category 2, and category 3 was 58.3%, 45.5%, and 50.6%, respectively (P > 0.05). The corrected Akaike score information criteria value of the TBS model (2865) was lower than the traditional tumor morphologic categorization model (2905). Survival analysis revealed excellent prognostic discrimination for the TBS model among patients in both external cohorts (P< 0.05).
CONCLUSIONS: An externally validated "Metro-ticket" TBS model had excellent prognostic discriminatory power. TBS may be an accurate tool to account for the impact of tumor morphology on long-term survival among patients undergoing resection of CRLM.

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Year:  2018        PMID: 27763897     DOI: 10.1097/SLA.0000000000002064

Source DB:  PubMed          Journal:  Ann Surg        ISSN: 0003-4932            Impact factor:   12.969


  56 in total

1.  Preoperative Controlling Nutritional Status plus Tumor Burden Score for the Assessment of Prognosis after Curative Liver Resection for Hepatocellular Carcinoma.

Authors:  Yasuyuki Fukami; Takuya Saito; Takaaki Osawa; Takashi Arikawa; Tatsuki Matsumura; Shintaro Kurahashi; Shunichiro Komatsu; Kenitiro Kaneko; Tsuyoshi Sano
Journal:  Med Princ Pract       Date:  2020-12-24       Impact factor: 1.927

2.  Association between the lymph node ratio and hepatic tumor burden: importance for resectable colorectal liver metastases?

Authors:  Kristoffer Watten Brudvik; Kjetil Søreide
Journal:  Hepatobiliary Surg Nutr       Date:  2018-06       Impact factor: 7.293

3.  Predicting Overall Survival in Patients with Metastatic Rectal Cancer: a Machine Learning Approach.

Authors:  Beiqun Zhao; Rodney A Gabriel; Florin Vaida; Nicole E Lopez; Samuel Eisenstein; Bryan M Clary
Journal:  J Gastrointest Surg       Date:  2019-08-29       Impact factor: 3.452

4.  Utility of Tumor Burden Score to Stratify Prognosis of Patients with Hepatocellular Cancer: Results of 4759 Cases from ITA.LI.CA Study Group.

Authors:  Alessandro Vitale; Quirino Lai; Fabio Farinati; Laura Bucci; Edoardo G Giannini; Lucia Napoli; Francesca Ciccarese; Gian Lodovico Rapaccini; Maria Di Marco; Eugenio Caturelli; Marco Zoli; Franco Borzio; Rodolfo Sacco; Giuseppe Cabibbo; Roberto Virdone; Fabio Marra; Martina Felder; Filomena Morisco; Luisa Benvegnù; Antonio Gasbarrini; Gianluca Svegliati-Baroni; Francesco Giuseppe Foschi; Gabriele Missale; Alberto Masotto; Gerardo Nardone; Antonio Colecchia; Mauro Bernardi; Franco Trevisani; Timothy M Pawlik
Journal:  J Gastrointest Surg       Date:  2018-01-19       Impact factor: 3.452

5.  A novel score system for predicting conversion to no evidence of Disease (C-NED) in initially unresectable colorectal cancer liver metastases.

Authors:  Weihao Li; Jian Zhou; Tianqi Zhang; Yi Tai; Yanbo Xu; Yanfang Bai; Yu Jiang; Zhenhai Lu; Liren Li; Jinhua Huang; Zhizhong Pan; Xiaojun Wu; Jianhong Peng; Junzhong Lin
Journal:  Am J Cancer Res       Date:  2022-04-15       Impact factor: 6.166

6.  Comprehensive Evaluation of Relapse Risk (CERR) Score for Colorectal Liver Metastases: Development and Validation.

Authors:  Yijiao Chen; Wenju Chang; Li Ren; Jingwen Chen; Wentao Tang; Tianyu Liu; Mi Jian; Yu Liu; Ye Wei; Jianmin Xu
Journal:  Oncologist       Date:  2020-03-17

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

Authors:  G A Margonis; K Sasaki; S Gholami; Y Kim; N Andreatos; N Rezaee; A Deshwar; S Buettner; P J Allen; T P Kingham; T M Pawlik; J He; J L Cameron; W R Jarnagin; C L Wolfgang; M I D'Angelica; M J Weiss
Journal:  Br J Surg       Date:  2018-04-25       Impact factor: 6.939

Review 8.  Hepatic metastasis from colorectal cancer.

Authors:  Alfred Wei Chieh Kow
Journal:  J Gastrointest Oncol       Date:  2019-12

9.  Using machine learning to construct nomograms for patients with metastatic colon cancer.

Authors:  B Zhao; R A Gabriel; F Vaida; S Eisenstein; G T Schnickel; J K Sicklick; B M Clary
Journal:  Colorectal Dis       Date:  2020-02-16       Impact factor: 3.788

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

Authors:  Y Kawaguchi; S Kopetz; H S Tran Cao; E Panettieri; M De Bellis; Y Nishioka; H Hwang; X Wang; C-W D Tzeng; Y S Chun; T A Aloia; K Hasegawa; A Guglielmi; F Giuliante; J-N Vauthey
Journal:  Br J Surg       Date:  2021-08-19       Impact factor: 6.939

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