Literature DB >> 19658184

The impact of primary tumor size, lymph node status, and other prognostic factors on the risk of cancer death.

L Leon Chen1, Matthew E Nolan, Melvin J Silverstein, Martin C Mihm, Arthur J Sober, Kenneth K Tanabe, Barbara L Smith, Jerry Younger, James S Michaelson.   

Abstract

BACKGROUND: : Although many prognostic factors are associated with differences in cancer lethality, it may not be obvious whether a factor truly makes an independent contribution to lethality or simply is correlated with tumor size. There is currently no method for integrating tumor size, lymph node status, and other prognostic information from a patient into a single risk of death estimate.
METHODS: : The SizeOnly equation, which captures the relation between tumor size and risk of death, makes it possible to determine whether a prognostic factor truly makes an independent contribution to cancer lethally or merely is associated with tumor size (SizeAssessment method). The magnitude of each factor's lethal contribution can be quantified by a parameter, g, inserted into the SizeOnly equation (PrognosticMeasurement method). A series of linked equations (the Size+Nodes+PrognosticFactors [SNAP] method) combines information on tumor size, lymph node status, and other prognostic factors from a patient into a single estimate of the risk of death.
RESULTS: : Nine prognostic factors were identified that made marked, independent contributions to breast carcinoma lethality: grade; mucinous, medullary, tubular, and scirrhous adenocarcinoma; male sex; inflammatory disease; Paget disease; and lymph node status. In addition, it was determined that lymph node status made an independent contribution to melanoma lethality. The SNAP method was able to accurately estimate the risk of death and to finely stratify patients by risk.
CONCLUSIONS: : The methods described provide a new framework for identifying and quantifying those factors that contribute to cancer lethality and provide a basis for web-based calculators (available at: http://www.CancerMath.net accessed July 29, 2009) that accurately estimate the risk of death for each patient. Cancer 2009. (c) 2009 American Cancer Society.

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Mesh:

Year:  2009        PMID: 19658184     DOI: 10.1002/cncr.24565

Source DB:  PubMed          Journal:  Cancer        ISSN: 0008-543X            Impact factor:   6.860


  17 in total

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2.  A comparative analysis of recurrence risk predictions in ER+/HER2- early breast cancer using NHS Nottingham Prognostic Index, PREDICT, and CanAssist Breast.

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Journal:  J Neurooncol       Date:  2019-09-09       Impact factor: 4.130

4.  ASO Author Reflections: Careful Development and Thoughtful Interpretation are Needed when Developing Online Prognostic Tools.

Authors:  Emily C Zabor
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5.  Variability in Predictions from Online Tools: A Demonstration Using Internet-Based Melanoma Predictors.

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6.  Development and validation of a novel nomogram for predicting distant metastasis-free survival among breast cancer patients.

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Journal:  Ann Transl Med       Date:  2019-10

7.  Are we able to predict survival in ER-positive HER2-negative breast cancer? A comparison of web-based models.

Authors:  E Laas; P Mallon; M Delomenie; V Gardeux; J-Y Pierga; P Cottu; F Lerebours; D Stevens; R Rouzier; F Reyal
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8.  Validation of the CancerMath prognostic tool for breast cancer in Southeast Asia.

Authors:  Hui Miao; Mikael Hartman; Helena M Verkooijen; Nur Aishah Taib; Hoong-Seam Wong; Shridevi Subramaniam; Cheng-Har Yip; Ern-Yu Tan; Patrick Chan; Soo-Chin Lee; Nirmala Bhoo-Pathy
Journal:  BMC Cancer       Date:  2016-10-21       Impact factor: 4.430

9.  MMP11 and CD2 as novel prognostic factors in hormone receptor-negative, HER2-positive breast cancer.

Authors:  Jinil Han; Yoon-La Choi; Haein Kim; Jun Young Choi; Se Kyung Lee; Jeong Eon Lee; Joon-Seok Choi; Sarah Park; Jong-Sun Choi; Young Deug Kim; Seok Jin Nam; Byung-Ho Nam; Mi Jeong Kwon; Young Kee Shin
Journal:  Breast Cancer Res Treat       Date:  2017-04-13       Impact factor: 4.872

10.  Development and External Validation of Prediction Models for 10-Year Survival of Invasive Breast Cancer. Comparison with PREDICT and CancerMath.

Authors:  Solon Karapanagiotis; Paul D P Pharoah; Christopher H Jackson; Paul J Newcombe
Journal:  Clin Cancer Res       Date:  2018-02-14       Impact factor: 12.531

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