Internet tools to enhance breast cancer care

Abstract

Internet tools have become a great aid in the daily practice of physicians who treat breast cancer patients. In cancer care there are frequent and important intersections where major decisions need to be made; these include (1) whether or not to give chemotherapy; (2) how much toxicity to expect, and (3) the life expectancy of the patient, considering non-breast cancer comorbidities. These decisions can be made more accurately using calculators based on data sets of thousands of patients as opposed to physician intuition. Such tools also help patients and caregivers in optimal decision making, as they estimate the absolute benefits and risks of treatment. In this perspective we describe selected internet sites that are useful across several domains of care, including the potential benefits of different adjuvant regimens for early breast cancer, prognosis after neoadjuvant therapy, prognosis for ductal carcinoma in situ, and toxicity and life expectancy estimates. We review the variables required to use the tools, the results obtained, the methods of validation, and the advantages and disadvantages of each tool.

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PERSPECTIVE OPEN

Internet tools to enhance breast cancer care

Shlomit Strulov Shachar1,2 and Hyman B Muss1

Internet tools have become a great aid in the daily practice of physicians who treat breast cancer patients. In cancer care there are frequent and important intersections where major decisions need to be made; these include (1) whether or not to give chemotherapy; (2) how much toxicity to expect, and (3) the life expectancy of the patient, considering non-breast cancer comorbidities. These decisions can be made more accurately using calculators based on data sets of thousands of patients as opposed to physician intuition. Such tools also help patients and caregivers in optimal decision making, as they estimate the absolute benets and risks of treatment. In this perspective we describe selected internet sites that are useful across several domains of care, including the potential benets of different adjuvant regimens for early breast cancer, prognosis after neoadjuvant therapy, prognosis for ductal carcinoma in situ, and toxicity and life expectancy estimates. We review the variables required to use

the tools, the results obtained, the methods of validation, and the advantages and disadvantages of each tool.

npj Breast Cancer (2016) 2, 16011; doi:http://dx.doi.org/10.1038/npjbcancer.2016.11

Web End =10.1038/npjbcancer.2016.11 ; published online 27 April 2016

INTRODUCTIONIn the past decade there has been increased usage of online tools to determine the value of adjuvant systemic therapies for breast cancerincluding neoadjuvant therapyto estimate life expectancy, to predict outcomes for patients with ductal carcinoma in situ (DCIS) based on the varied treatment options, and to estimate chemotherapy-related toxicity in older patients. Having an accessible online tool that can estimate and personalize the benets of different treatment outcomes for individual patients has become a great help in daily practice. There are currently many resources available; this perspective will focus on several which we feel are most helpful. In addition to describing the strengths and weaknesses of each tools application, we will discuss how they were validated. A detailed list of our preferred sites is shown in Table 1.

Tools for systemic adjuvant therapyAdjuvant!. Adjuvant! (https://www.adjuvantonline.com/) is a groundbreaking program that is probably the most widely used tool for estimating the benets of adjuvant endocrine therapy and chemotherapy.1 The tool assesses individual patient risk of recurrence and death at 10 years. Mortality risk is based on surveillance, epidemiology, and end-results (SEER) data for women aged 3669 years, and estimates of the efcacy of adjuvant therapy from data from the Early Breast Cancer Trialists' Collaborative Group. Entering information on age and selected tumor characteristics (tumor size and grade, number of positive axillary nodes, and hormone receptors status) allows for prediction of the 10-year risk of relapse-free and overall survival. A strength of this tool is that one can add a rough estimate of the effect of comorbidity on survival to the model. This allows the clinician to determine the benets of treatment when patients have major competing causes of mortalityin addition to their breast cancer mortality risk. Adjuvant!s strength also lies in the fact that it provides details on deaths from both breast cancer and non-

breast cancer causes. This latter information is especially important in older patients, for whom 10-year mortality is frequently dominated by non-breast cancer related events.

Despite these strengths, Adjuvant! has several limitations. The relapse estimates include localregional recurrence as well as distant metastases; this is important as the proportions of both may vary greatly depending on stage and tumor phenotype. In addition, data from SEER were not available for HER-2 status, and the benets of adjuvant trastuzumab are not available in this model. Validation also poses a problem, as some studies of the model have not been consistent. Although a Dutch study conrmed the accuracy of the tool in the European population (N = 5380),2 a British study (N = 1065) found that in a high percentage of patients survival was overestimated.2,3 Another validation in an elderly population (N = 2012) showed there was an overestimation of the added value of chemotherapy for older patients and those younger than 40 years.2,4

PREDICT. The PREDICT tool (http://www.predict.nhs.uk/) was developed using cancer registry data from 5,694 patients in the UK.5 Validation of the model was made on 5,000 other patients from the U.K. and 3140 patients from Canada.6 An estimation of therapy and prognosis of HER 2 tumors was later incorporated.7 The PREDICT tool utilizes data on patient age and tumor characteristics (the mode of detection (i.e., screening versus discovery of a palpable mass), size, grade, ER status, and KI67 status) to provide a choice for estimating the value of endocrine therapy alone, or endocrine therapy and second-generation chemotherapy (anthracycline-containing, 44 cycles or equivalent) versus third-generation (taxane-containing chemotherapy regimens).8 The predict model allows one to estimate the effects of adjuvant endocrine and chemotherapy treatment on survival at 5 and 10 years, but there is no estimate of relapse and it does not account for non-breast cancer causes of mortality in the overall survival estimate. However, unlike Adjuvant!, the PREDICT model can estimate the benets of anti-HER2 therapy in patients with HER-2 positive tumors.7 In

1Lineberger Comprehensive Cancer Center, Department of Medicine, University of North Carolina, Chapel Hill, NC, USA and 2Division of Oncology, Rambam Health Care Campus, Haifa, Israel.

Correspondence: HB Muss (mailto:[email protected]

Web End [email protected])

Received 17 February 2016; accepted 18 February 2016

2016 Breast Cancer Research Foundation/Macmillan Publishers Limited

Internet tools for breast cancer care

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Table 1. Web sites for breast cancer care

Name Details URL/Link

Breast cancer predictive websitesAdjuvant! (adjvuvantonline.com) Calculate benets of adjuvant therapy for patients with breast cancer. Can add estimates of comorbidity to calculations.Registration and password needed

https://www.adjuvantonline.com/

Web End =https://www.adjuvantonline.com/

CancerMath Several tools for predicting survival at 15 years, estimating therapy benet.

http://www.lifemath.net/cancer/breastcancer/therapy/

Web End =http://www.lifemath.net/cancer/breastcancer/therapy/

DCIS Recurrence Memorial Sloan Kettering

A tool for patients who had BCS for DCIS to predict the likelihood that their breast cancer will return in the same breast that was originally treated.

http://nomograms.mskcc.org/breast/DuctalCarcinomaInSituRecurrencePage.aspx

Web End =http://nomograms.mskcc.org/breast/ http://nomograms.mskcc.org/breast/DuctalCarcinomaInSituRecurrencePage.aspx

Web End =DuctalCarcinomaInSituRecurrencePage.aspx

PREDICT UK-derived tool which calculates benetsof adjuvant therapy for patients with breast cancer. Does not allow for comorbidity.Can calculate benets for patients with HER-2-positive tumors.

http://www.predict.nhs.uk/

Web End =http://www.predict.nhs.uk/

Oncotype DXs tools Tools to understand how hormonal therapy and pathological and clinical factors can be assessed with the Oncotype DXs Breast Cancer Assay Recurrence Score result. Registration and password needed.

https://online.genomichealth.com/Login.aspx

Web End =https://online.genomichealth.com/Login.aspx

Neoadjuvant Therapy Outcomes Tool MD Anderson Cancer Center

Calculates the anticipated 5-year distant metastasis-free survival and disease-specic survival for breast cancer patients following treatment with neoadjuvant chemotherapy. Pathological response also integrated.

http://www3.mdanderson.org/app/medcalc/index.cfm?pagename=bcnt

Web End =http://www3.mdanderson.org/app/medcalc/index.cfm? http://www3.mdanderson.org/app/medcalc/index.cfm?pagename=bcnt

Web End =pagename = bcnt

Life expectancy prediction and geriatric oncology websitesASCO University A series of online modules that explore different care options for older patients, including those with breast cancer. Also has MOC course on geriatric oncology.

http://university.asco.org/geriatric-oncology

Web End =http://university.asco.org/geriatric-oncology

CARG (Cancer and Aging Research Group)

A group of researchers with major interest in geriatric oncology research. Opportunities for mentoring. Website includes online chemotherapy toxicity tool and geriatric assessment tools.

http://www.mycarg.org/

Web End =http://www.mycarg.org/

ePrognosis A series of tools based on systematic review of literature that allows for estimation of life expectancy in older adults.

http://eprognosis.ucsf.edu/default.php

Web End =http://eprognosis.ucsf.edu/default.php

International Society of Geriatric Oncology (SIOG)

International organization that focuses on geriatric oncology. Website has useful links to geriatric oncology guidelines and other educational materials.

http://www.siog.org/

Web End =http://www.siog.org/

Toxicity prediction websites

Moftt Cancer Center Senior Adult Oncology Program Tools

Online tools for estimating chemotherapy toxicity (CRASH score) and other geriatric tools

http://moffitt.org/cancer-types--treatment/cancers-we-treat/senior-adult-oncology-program-tools

Web End =http://moftt.org/cancer-types--treatment/cancers-we- http://moffitt.org/cancer-types--treatment/cancers-we-treat/senior-adult-oncology-program-tools

Web End =treat/senior-adult-oncology-program-tools

CARG (Cancer and Aging Research Group)

Online chemotherapy toxicity tool and geriatric assessment tools.

http://www.mycarg.org/

Web End =http://www.mycarg.org/

Abbreviations: ASCO, American Society of Clinical Oncology; BCS, breast-conserving surgery; CRASH, chemotherapy risk assessment scale for high-age patients; DCIS, ductal carcinoma in situ; HER2, human epidermal growth factor receptor 2; MOC, Maintenance of Certication; UK, United Kingdom.

addition, a recent study has validated this tools ability to provide accurate estimates of the potential benets of treatment at 5 years for older patients.9 Table 2 provides several scenarios showing the effects of treatment selection on survival using the PREDICT model.

CancerMath. CancerMath (http://www.lifemath.net/cancer/breast cancer/therapy/) utilizes tools that estimate the probability of having positive lymph nodes (based on age and tumor characteristics), breast cancer mortality, and the potential benets of

treatment with endocrine therapy and chemotherapy. Estimates are based on SEER data (N = 362,491) and include HER-2 status, tumor size, nodal involvement, tumor phenotype, and grade.10

Oncotype DX. The Oncotype DX website (https://online.genomic health.com/Login.aspx) provides two diagnostic tools that analyze recurrence scores and take into account the type of endocrine therapy (tamoxifen or an aromatase inhibitor) as well as patient age, tumor size, and tumor grade, to further rene the estimates of endocrine

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Table 2. Survival benet of adjuvant treatment in breast cancer by PREDICT

Patient 1 Patient 2 Patient 3 Patient 4

Patient and tumor characteristicsAge 42 55 72 38Mode of detection Symptomatica Screening Symptomatic Symptomatic Tumor size (mm) 18 15 40 32 Tumor grade 3 2 2 3 Number of positive lymph nodes 3 0 2 1 Estrogen Receptor status Negative Positive Positive Negative HER2 status Positive Negative Negative NegativeKI 67 status Positive (410%) Unknown Unknown Positive (410%)

Generation of chemotherapy regimen Second Third Second Third

5-Year survival results (%)No adjuvant treatment 60 96 76 61 Benet of adjuvant chemotherapy 15 1 3 18 Benet of adjuvant Trastuzumab 6 n/a n/a n/a Benet of adjuvant hormone therapy n/a 1 4 n/a Total survival with adjuvant therapy 81 98 83 79

10-Year survival results (%)No adjuvant treatment 49 90 50 49 Benet of adjuvant chemotherapy 18 1 5 22 Benet of adjuvant Trastuzumab 5 n/a n/a n/a Benet of adjuvant Hormone therapy n/a 2 9 n/a Total survival with adjuvant therapy 72 93 64 71

Abbreviations: HER2, human epidermal growth factor2.

aPresented with palpable mass; data modied from PREDICT.5,8

therapy on 10-year metastases-free survival. This can result in small but potentially important changes in our understanding of metastatic relapse risk, and could help physicians make the decision of whether to offer chemotherapy. This combined score has resulted in classifying fewer patients as intermediate risk (17.8% vs 26.7%, Po0.001) and more patients as lower risk (63.8% vs 54.2%, Po0.001).11

Neoadjuvant chemotherapy outcomes tool. The neoadjuvant chemotherapy outcomes tool (http://www3.mdanderson.org/app/ medcalc/index.cfm?pagename = bcnt) provides estimates of 5-year distant metastases-free and disease-specic survival after neoadjuvant treatment, and incorporates initial clinical stage before treatment, post-neoadjuvant pathological stage, estrogen receptor status, and nuclear grade.12

Tools for treatment outcomes for patients with ductal carcinoma in situ

An online tool, the Breast Cancer Nomogram: Ductal Carcinoma In Situ (DCIS) Recurrence has been developed at the Memorial Sloan Kettering Cancer Center (http://nomograms.mskcc.org/breast/DuctalCarcinomaInSituRecurrencePage.aspx

Web End =http://nomograms.mskcc.org/ http://nomograms.mskcc.org/breast/DuctalCarcinomaInSituRecurrencePage.aspx

Web End =breast/DuctalCarcinomaInSituRecurrencePage.aspx ) to predict in-breast recurrence risk after breast-conserving surgery.13 The

program, takes into account many patient and tumor characteristics, including age, family history, presentation, tumor grade, presence of necrosis, surgical margins, year of surgery, and number of excisions, as well as the potential risk-reducing benets of adjuvant breast irradiation and/or endocrine treatment. It provides both the 5- and 10-year probability of in-breast recurrence. This can be especially helpful to patients and physicians, since mortality with this diagnosis is extremely low and many patients may elect to forego radiation and endocrine treatment after reviewing the potential risks and benets of each modality.

Tools for predicting life expectancyePrognosis. The use of chronological age to predict life expectancy in older patients should be discouraged

(http://eprognosis.ucsf.edu/calculators.php). There is great heterogeneity in the health status of older people that can result in dramatic differences in life expectancy in persons of the same chronological age. Comorbidity, nutritional status, physical and cognitive function, social support, and mental health status all are related to longevity. The average practicing oncologist has generally not been trained in geriatric assessment, and the

Table 3. Four and 10-year survival using combined LeeSchonberg calculator in ePrognosis

Variable Patient 1 Patient 2

Age 7579 Years 6569 Years Gender Female Female BMI 25 25

Patients self-reported health Excellent Poor Chronic lung disease No No Prior cancer No No Congestive heart failure No Yes Diabetes or high blood sugar No Yes Describe cigarette use Neversmoked

Current smoker

Difculty walking a quarter mile without help

No Yes

Overnight hospitalization in past 12 months

No Once

Help in routine daily activities No No Memory problems interfering withmanaging nances

No No

Memory problem interfering with bathing or showering

No No

Difculty pushing or pulling large objects

No Yes

Estimated 45-year survival 96% 77% Estimated 10-year survival 81% 24%

Abbreviation: BMI, body mass index.

Data modied from http://www.eprognosis.org

Web End =www.eprognosis.org .

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ACKNOWLEDGMENTS

We thank Ms Erin Laurie for her help in the preparation of this manuscript. SSS gratefully acknowledges support from the Friends of Rambam Medical Center and The J & G Zukier Medical Fund Donation, Haifa, Israel.

COMPETING INTERESTS

The authors declare no conict of interest.

REFERENCES1. Ravdin, P. M. et al. Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J. Clin. Oncol. 19, 980991 (2001).2. Mook, S. et al. Calibration and discriminatory accuracy of prognosis calculation for breast cancer with the online Adjuvant! program: a hospital-based retrospective cohort study. Lancet Oncol. 10, 10701076 (2009).

3. Campbell, H. E., Taylor, M. A., Harris, A. L. & Gray, A. M. An investigation into the performance of the Adjuvant! Online prognostic programme in early breast cancer for a cohort of patients in the United Kingdom. Br. J. Cancer. 101, 10741084 (2009).

4. de Glas, N. A. et al. Validity of Adjuvant! Online program in older patients with breast cancer: a population-based study. Lancet Oncol. 15, 722729 (2014).

5. Wishart, G. C. et al. PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer. Breast Cancer Res. 12, R1 (2010).6. Wishart, G. C. et al. A population-based validation of the prognostic model PREDICT for early breast cancer. Eur. J. Surg. Oncol. 37, 411417 (2011).

7. Wishart, G. C. et al. PREDICT Plus: development and validation of a prognostic model for early breast cancer that includes HER2. Br. J. Cancer. 107, 800807 (2012).

8. PREDICT. PREDICT Tool: Breast Cancer Survival. Available at: http://www.predict.nhs.uk/predict.html

Web End =http://www.predict. http://www.predict.nhs.uk/predict.html

Web End =nhs.uk/predict.html (accessed on 22 January 2016).

9. de Glas, N. A. et al. Validity of the online PREDICT tool in older patients with breast cancer: a population-based study. Br J Cancer. 114, 395400 (2016).

10. Michaelson, J. S. et al. Improved web-based calculators for predicting breast carcinoma outcomes. Breast Cancer Res. Treat. 128, 827835 (2011).

11. Tang, G. et al. Risk of recurrence and chemotherapy benet for patients with node-negative, estrogen receptor-positive breast cancer: recurrence score alone and integrated with pathologic and clinical factors. J. Clin. Oncol. 29, 43654372 (2011).

12. Jeruss, J. S. et al. Combined use of clinical and pathologic staging variables to dene outcomes for breast cancer patients treated with neoadjuvant therapy.J. Clin. Oncol. 26, 246252 (2008).13. Rudloff, U. et al. Nomogram for predicting the risk of local recurrence after breast-conserving surgery for ductal carcinoma in situ. J. Clin. Oncol. 28, 37623769 (2010).

14. Yourman, L. C., Lee, S. J., Schonberg, M. A., Widera, E. W. & Smith, A. K. Prognostic indices for older adults: a systematic review. JAMA 307, 182192 (2012).

15. Jang, R. W. et al. Simple prognostic model for patients with advanced cancer based on performance status. J. Oncol. Pract 10, e335e341 (2014).

16. Institute NC. Common Terminology Criteria for Adverse Events (CTCAE) Version 4.0; http://evs.nci.nih.gov/ftp1/CTCAE/CTCAE_4.03_2010-06-14_QuickReference_5x7.pdf

Web End =http://evs.nci.nih.gov/ftp1/CTCAE/CTCAE_4.03_2010-06-14_QuickReference_5x7.pdf http://evs.nci.nih.gov/ftp1/CTCAE/CTCAE_4.03_2010-06-14_QuickReference_5x7.pdf

Web End =.

17. Hurria, A. et al. Predicting chemotherapy toxicity in older adults with cancer: a prospective multicenter study. J Clin Oncol. 29, 34573465 (2011).

18. Extermann, M. et al. Predicting the risk of chemotherapy toxicity in older patients: the Chemotherapy Risk Assessment Scale for High-Age Patients (CRASH) score. Cancer 118, 33773386 (2012).

19. Paik, S. et al. Gene expression and benet of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J. Clin. Oncol. 24, 37263734 (2006).

20. Zelnak, A. B. & O'Regan, R. M. Genomic subtypes in choosing adjuvant therapy for breast cancer. Oncology (Williston Park) 27, 204210 (2013).

21. Sgroi, D. C. et al. Prediction of late distant recurrence in patients with oestrogen-receptor-positive breast cancer: a prospective comparison of the breast-cancer index (BCI) assay, 21-gene recurrence score, and IHC4 in the TransATAC study population. Lancet Oncol. 14, 10671076 (2013).

22. Drukker, C. A. et al. A prospective evaluation of a breast cancer prognosis signature in the observational RASTER study. Int. J. Cancer. 133, 929936 (2013).

23. Wallden, B. et al. Development and verication of the PAM50-based Prosigna breast cancer gene signature assay. BMC Med. Genomics 8, 54 (2015).

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Web End =http://creativecommons.org/licenses/ http://creativecommons.org/licenses/by/4.0/

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ePrognosis website provides a series of tools for estimating life expectancy from non-breast cancer causes for older adults living in the community, a nursing home, or who are hospitalized.14 The

average remaining life expectancy of women at different ages and with different levels of comorbidity is shown in Table 3 (patient 1 is older but healthier with better 10-year survival than patient 2 who is younger but sick). Clinical efforts to estimate life expectancy from non-breast cancer causes are valuable, since life expectancy is a key factor in making treatment decisions in older patientsespecially decisions concerning chemotherapy. The site also has an extremely helpful palliative performance scale for outpatients with advanced cancer that takes into consideration ambulatory status, the patients level of daily activity and the need for self-assistance, oral intake, and level of consciousness.15 The

tool has moderate discrimination but can be provide a reasonable estimate of an ill patients median survival in days.

Toxicity-risk calculators for older patientsCARG. The Cancer and Aging Research Group (CARG) (http://

www.mycarg.org) has developed a toxicity-risk calculator based on data from 500 patients with a variety of both early and late stage cancers. The calculator allows for prediction of grades 35 toxicities16 and the model includes standard clinical variables (gender, age, weight, height, serum creatinine, hemoglobin level, cancer type, chemotherapy treatment (dosage), and single agent or combination chemotherapy) as well as six variables attained via a short geriatric assessment (hearing status, number of falls, hearing problems, ability to take medications, ability to walk one block, and social activities limitations due to health or emotional problems). From these entries one can calculate a risk score that not only can reasonably predicts severe toxicity, but also is superior to performance status, a poor predictor.17

CRASH (Chemotherapy-risk assessment scale for high-age patients). CRASH (http://moffitt.org/cancer-types--treatment/cancers-we-treat/senior-adult-oncology-program-tools

Web End =http://moftt.org/cancer-types--treatment/cancers-we- http://moffitt.org/cancer-types--treatment/cancers-we-treat/senior-adult-oncology-program-tools

Web End =treat/senior-adult-oncology-program-tools ) is a user-friendly tool to estimate the risk of severe chemotherapy toxicity based on the specic chemotherapy regimen, diastolic blood pressure, instrumental activities of daily living, lactate dehydrogenase, performance status, mini-mental status, and a mini-nutritional assessment. This tool was developed and validated on a cohort of cancer patients 70 years and older (N = 512).18

ConclusionsThe tools we have discussed are readily available for use in daily practice and ofce staff can be trained to use these models and provide information to busy clinicians. Most of them have a user-friendly interface and can be used without registration and a password, which is a great advantage on a busy day. The tools used for assessing the benets of adjuvant systemic therapy and the management of DCIS are frequently used in patients who have had tumor tissues sent for newer genetic-based assays. Such assays may provide more detailed information, especially in patients with node-negative, hormone receptor-positive, and HER-2-negative breast cancers,1923 and in many patients the estimates from genetic-based assays are more appropriate for decision making.

The internet has given us the ability to rapidly use key clinical information at the point of patient contact to help make treatment decisions. The tools discussed in this review give physicians the opportunity to obtain relatively precise, up to date estimates of treatment effect, longevity, and toxicity that are likely to be more accurate than decisions made on intuition and experience alone. It is important, however, that those who have developed these tools and who will develop new tools in the future continually re-validate each tool as new data become available and make appropriate modications as needed.

npj Breast Cancer (2016) 16011 2016 Breast Cancer Research Foundation/Macmillan Publishers Limited