Literature DB >> 21327471

Improved web-based calculators for predicting breast carcinoma outcomes.

James S Michaelson1, L Leon Chen, Devon Bush, Allan Fong, Barbara Smith, Jerry Younger.   

Abstract

We describe a set of web-based calculators, available at http://www.CancerMath.net , which estimate the risk of breast carcinoma death, the reduction in life expectancy, and the impact of various adjuvant treatment choices. The published SNAP method of the binary biological model of cancer metastasis uses information on tumor size, nodal status, and other prognostic factors to accurately estimate of breast cancer lethality at 15 years after diagnosis. By combining these 15-year lethality estimates with data on the breast cancer hazard function, breast cancer lethality can be estimated at each of the 15 years after diagnosis. A web-based calculator was then created to visualize the estimated lethality with and without a range of adjuvant therapy options at any of the 15 years after diagnosis, and enable conditional survival calculations. NIH population data was used to estimate non-breast-cancer chance of death. The accuracy of the calculators was tested against two large breast carcinoma datasets: 7,907 patients seen at two academic hospitals and 362,491 patients from the SEER national dataset. The calculators were found to be highly accurate and specific, as seen by their capacity for stratifying patients into groups differing by as little as a 2% risk of death, and accurately accounting for nodal status, histology, grade, age, and hormone receptor status. Our breast carcinoma calculators provide accurate and useful estimates of the risk of death, which can aid in analysis of the various adjuvant therapy options available to each patient.

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Mesh:

Year:  2011        PMID: 21327471     DOI: 10.1007/s10549-011-1366-9

Source DB:  PubMed          Journal:  Breast Cancer Res Treat        ISSN: 0167-6806            Impact factor:   4.872


  24 in total

Review 1.  Incorporation of clinical and biological factors improves prognostication and reflects contemporary clinical practice.

Authors:  Rashmi K Murthy; Juhee Song; Akshara S Raghavendra; Yisheng Li; Limin Hsu; Kenneth R Hess; Carlos H Barcenas; Vicente Valero; Robert W Carlson; Debu Tripathy; Gabriel N Hortobagyi
Journal:  NPJ Breast Cancer       Date:  2020-03-25

2.  Personalized Prognostic Prediction Models for Breast Cancer Recurrence and Survival Incorporating Multidimensional Data.

Authors:  Xifeng Wu; Yuanqing Ye; Carlos H Barcenas; Wong-Ho Chow; Qing H Meng; Mariana Chavez-MacGregor; Michelle A T Hildebrandt; Hua Zhao; Xiangjun Gu; Yang Deng; Elizabeth Wagar; Francisco J Esteva; Debu Tripathy; Gabriel N Hortobagyi
Journal:  J Natl Cancer Inst       Date:  2017-07-01       Impact factor: 13.506

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

Authors:  Aparna Gunda; Mallikarjuna S Eshwaraiah; Kiran Gangappa; Taranjot Kaur; Manjiri M Bakre
Journal:  Breast Cancer Res Treat       Date:  2022-09-10       Impact factor: 4.624

4.  Prediction of lymph node involvement in patients with breast tumors measuring 3-5 cm in a middle-income setting: the role of CancerMath.

Authors:  E N Pijnappel; N Bhoo-Pathy; J Suniza; M H See; G H Tan; C H Yip; M Hartman; N A Taib; H M Verkooijen
Journal:  World J Surg       Date:  2014-12       Impact factor: 3.352

Review 5.  Management of hormone receptor-positive, HER2-negative early breast cancer.

Authors:  Elaine M Walsh; Karen L Smith; Vered Stearns
Journal:  Semin Oncol       Date:  2020-06-03       Impact factor: 4.929

6.  The predictive accuracy of PREDICT: a personalized decision-making tool for Southeast Asian women with breast cancer.

Authors:  Hoong-Seam Wong; Shridevi Subramaniam; Zarifah Alias; Nur Aishah Taib; Gwo-Fuang Ho; Char-Hong Ng; Cheng-Har Yip; Helena M Verkooijen; Mikael Hartman; Nirmala Bhoo-Pathy
Journal:  Medicine (Baltimore)       Date:  2015-02       Impact factor: 1.889

7.  Development of individual survival estimating program for cancer patients' management.

Authors:  Myung-Chul Chang
Journal:  Healthc Inform Res       Date:  2015-04-30

8.  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
Journal:  Br J Cancer       Date:  2015-01-15       Impact factor: 7.640

9.  Prognostic factors for survivals from first relapse in breast cancer patients: analysis of deceased patients.

Authors:  Haeyoung Kim; Doo Ho Choi; Won Park; Seung Jae Huh; Seok Jin Nam; Jeong Eon Lee; Jin Seok Ahn; Young-Hyuck Im
Journal:  Radiat Oncol J       Date:  2013-12-31

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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