A scoping review of interactive and personalized web-based clinical tools to support treatment decision making in breast cancer

Gene expression profiling tests to guide adjuvant chemotherapy decisions in lymph node-positive early breast cancer: a systematic review

PURPOSE

To systematically review the effectiveness of gene expression profiling tests to inform adjuvant chemotherapy decisions in people with hormone receptor-positive (HR+), lymph node-positive (LN+) breast cancer.

METHODS

This systematic review assessed the effectiveness of Oncotype DX, Prosigna, EndoPredict and MammaPrint for guiding adjuvant chemotherapy decisions in HR+ early breast cancer with 1-3 positive nodes, in terms of prognostic ability, prediction of chemotherapy benefit, impact on chemotherapy decisions, quality of life and anxiety. Searches covered MEDLINE, EMBASE and Cochrane databases in April 2023.

RESULTS

Fifty-five articles were included. All four tests were prognostic for distant recurrence in LN+ patients. The RxPONDER trial reported no chemotherapy benefit in post-menopausal LN+ patients with low Oncotype DX (RS 0-25), whilst pre-menopausal patients had statistically significant chemotherapy benefit. An RCT reanalysis of Oncotype DX (SWOG-8814) suggested greater chemotherapy benefit with higher RS in post-menopausal LN+ patients. The MINDACT trial reported that LN+ patients with high clinical risk and low MammaPrint risk had a non-statistically significant chemotherapy benefit, but was not designed assess differential chemotherapy benefit per risk group. Decisions to undergo chemotherapy reduced by 12-75% following Oncotype DX testing in LN+ patients in the UK and Europe. No studies in LN+ populations were identified for prediction of chemotherapy benefit by Prosigna or EndoPredict; or for chemotherapy decisions for Prosigna, EndoPredict or MammaPrint; or for anxiety or quality of life impact for any test.

CONCLUSIONS

All four tests have prognostic ability in LN+ patients. Evidence on predictive benefit is weaker, with equivocal evidence that Oncotype DX may predict chemotherapy benefit in LN+ post-menopausal patients. Use of Oncotype DX leads to fewer patients being recommended chemotherapy.

Bridging the Gap: A Mixed-Methods Study on Factors Influencing Breast Cancer Clinicians' Decisions to Use Clinical Prediction Models

Background. Clinical prediction models provide tailored risk estimates that can help guide decisions in breast cancer care. Despite their potential, few models are widely used in clinical practice. We aimed to identify the factors influencing breast cancer clinicians' decisions to adopt prediction models and assess their relative importance. Methods. We conducted a mixed-methods study, beginning with semi-structured interviews, followed by a nationwide online survey. Thematic analysis was used to qualitatively summarize the interviews and identify key factors. For the survey, we used descriptive analysis to characterize the sample and Mann-Whitney U and Kruskal-Wallis tests to explore differences in score (0 = not important to 10 = very important) distributions. Results. Interviews (N = 16) identified eight key factors influencing model use. Practical/methodological factors included accessibility, cost, understandability, objective accuracy, actionability, and clinical relevance. Perceptual factors included acceptability, subjective accuracy, and risk communication. In the survey (N = 146; 137 model users), clinicians ranked online accessibility (median score = 9 [interquartile range = 8-10]) as most important. Cost was also highly rated, with preferences for freely available models (9 [8-10]) and those with reimbursable tests (8 [8-10]). Formal regulatory approval (7 [5-8]) and direct integration with electronic health records (6 [3-8]) were considered less critical. Subgroup analysis revealed differences in score distributions; for example, clinicians from general hospitals prioritized inclusion of new biomarkers more than those in academic settings. Conclusions. Breast cancer clinicians' decisions to initiate use of prediction models are influenced by practical and perceptual factors, extending beyond technical metrics such as discrimination and calibration. Addressing these factors more holistically through collaborative efforts between model developers, clinicians, and communication and implementation experts, for instance, by developing clinician-friendly online tools that prioritize usability and local adaptability, could increase model uptake.

Highlights

Accessibility, cost, and practical considerations, such as ease of use and clinical utility, were prioritized slightly more than technical validation metrics, such as discrimination and calibration, when deciding to start using a clinical prediction model.Most breast cancer clinicians valued models with clear inputs (e.g., variable definitions, cutoffs) and outputs; few were interested in the exact model specifications.Perceptual or subjective factors, including perceived accuracy and peer acceptability, also influenced model adoption but were secondary to practical considerations.Sociodemographic variables, such as clinical specialization and hospital setting, influenced the importance of factors for model use.

Patient Decision Aids for Breast Cancer Reconstruction: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials

BACKGROUND

Breast cancer has surpassed lung cancer to become the most frequently diagnosed cancer in women. There has been a dramatic increase in the use of breast reconstruction after mastectomy. However, struggle in making decisions regarding breast reconstruction has existed. Thus, a study of decision aids (DAs) needs to be conducted, and further studies are needed to promote better DAs. This review discusses how DAs can be used to help women make decisions about breast reconstruction after mastectomy. In addition, the review was the first to compare different DA formats to determine which one is most effective.

METHODS

The authors searched for relevant studies published before October of 2022 in PubMed and Embase using the medical subject headings "breast reconstruction" and "decision aid." Demographic data and decision, outcomes, and instruments used for assessment were also collected. Risk of bias was measured by the Cochrane Risk of Bias 2 tool.

RESULTS

A network meta-analysis of 14 RCTs with a total of 1401 patients were included. A total of 90.9% participants presented usable results for evaluation of decisional conflict, and web-based DA (-0.3; 95% CI, -0.56 to -0.05) showed significant improvement; 50.3% of participants provided results of decisional regret, and no subgroups showed significant reduction; 60.3% of participants contributed to results for knowledge, and web-based DA (0.61; 95% CI, 0.01 to 1.21) showed the most positive effect. A total of 44.5% of participants were included for evaluation of satisfaction, and web-based DA (0.44; 95% CI, 0.15 to 0.72) revealed significant increase.

CONCLUSION

The review concluded that web-based DAs are the favorable format of DA.

CLINICAL QUESTION/LEVEL OF EVIDENCE

Therapeutic, II.

Decisional conflict and knowledge in women with BRCA1/2 pathogenic variants: An exploratory age group analysis of a randomised controlled decision aid trial

Female BRCA1/2 pathogenic variant (PV) carriers face substantial risks for breast and ovarian cancer. Evidence-based decision aids (DAs) can facilitate these women in their decision-making process on an individually suitable preventive strategy. However, there is a gap in previous literature exploring whether DA effectiveness varies according to women's age. This is an exploratory subanalysis with a descriptive approach from a randomised controlled study assessing the effectiveness of a German decision aid (DA) for women with BRCA1/2 PVs compared to no DA use. From the original sample, women aged 18-40 years and >40 years and the intervention and control groups (IG, CG) within each of the age groups were compared regarding decisional conflict (using the Decisional Conflict Scale DCS) and knowledge at baseline and after DA use three and six months post study inclusion. The subanalysis involved 236 women aged 18-40 and 181 women aged >40 years. At baseline, both age groups differed significantly in all socio-demographic variables, except BRCA1/2 PV distributions. The younger age group displayed higher scores in the DCS subscale informed (p = .002) and higher knowledge (p = .010). Among the 18-40-year-olds, DA use (versus no DA) led to improvements in the DCS subscale informed at three (p = .025) and six months (p = .000). In the >40-year-olds, DA use (versus no DA) led to improvements in the DCS subscales informed (p = .028), values clarity (p = .028) and support (p = .030) and increased knowledge at three months (p = .048). These results indicate that both age groups benefited from DA use, but the older ones did so to a greater extent. This suggests that it might be useful to tailor DAs more closely to age- or life stage-related needs to enable more personalised care and support for women with BRCA1/2 PVs.

The impact of a web-based prognostic calculator on prognostic confidence in outpatient palliative care

PURPOSE

Clinicians are often uncertain about their prognostic estimates, which may impede prognostic communication and clinical decision-making. We assessed the impact of a web-based prognostic calculator on physicians' prognostic confidence.

METHODS

In this prospective study, palliative care physicians estimated the prognosis of patients with advanced cancer in an outpatient clinic using the temporal, surprise, and probabilistic approaches for 6 m, 3 m, 2 m, 1 m, 2 w, 1 w, and 3 d. They then reviewed information from www.predictsurvival.com , which calculated survival estimates from seven validated prognostic scores, including the Palliative Prognostic Score, Palliative Prognostic Index, and Palliative Performance Status, and again provided their prognostic estimates after calculator use. The primary outcome was prognostic confidence in temporal CPS (0-10 numeric rating scale, 0 = not confident, 10 = most confident).

RESULTS

Twenty palliative care physicians estimated prognoses for 217 patients. The mean (standard deviation) prognostic confidence significantly increased from 5.59 (1.68) before to 6.94 (1.39) after calculator use (p < 0.001). A significantly greater proportion of physicians reported feeling confident enough in their prognosis to share it with patients (44% vs. 74%, p < 0.001) and formulate care recommendations (80% vs. 94%, p < 0.001) after calculator use. Prognostic accuracy did not differ significantly before or after calculator use, ranging from 55-100%, 29-98%, and 48-100% for the temporal, surprise, and probabilistic approaches, respectively.

CONCLUSION

This web-based prognostic calculator was associated with increased prognostic confidence and willingness to discuss prognosis. Further research is needed to examine how prognostic tools may augment prognostic discussions and clinical decision-making.

Workflow analysis of breast cancer treatment decision-making: challenges and opportunities for informatics to support patient-centered cancer care

Objective

Decision support can improve shared decision-making for breast cancer treatment, but workflow barriers have hindered widespread use of these tools. The goal of this study was to understand the workflow among breast cancer teams of clinicians, patients, and their family caregivers when making treatment decisions and identify design guidelines for informatics tools to better support treatment decision-making.

Materials and Methods

We conducted observations of breast cancer clinicians during routine clinical care from February to August 2022. Guided by the work system model, a human factors engineering model that describes the elements of work, we recorded all aspects of clinician workflow using a tablet and smart pencil. Observation notes were transcribed and uploaded into Dedoose. Two researchers inductively coded the observations. We identified themes relevant to the design of decision support that we classified into the 4 components of workflow (ie, flow of information, tasks, tools and technologies, and people).

Results

We conducted 20 observations of breast cancer clinicians (total: 79 hours). We identified 10 themes related to workflow that present challenges and opportunities for decision support design. We identified approximately 48 different decisions discussed during breast cancer visits. These decisions were often interdependent and involved collaboration across the large cancer treatment team. Numerous patient-specific factors (eg, work, hobbies, family situation) were discussed when making treatment decisions as well as complex risk and clinical information. Patients were frequently asked to remember and relay information across the large cancer team.

Discussion and Conclusion

Based on these findings, we proposed design guidelines for informatics tools to support the complex workflows involved in breast cancer care. These guidelines should inform the design of informatics solutions to better support breast cancer decision-making and improve patient-centered cancer care.

Assessing the Content and Effect of Web-Based Decision Aids for Postmastectomy Breast Reconstruction: Systematic Review and Meta-Analysis of Randomized Controlled Trials

BACKGROUND

Web-based decision aids have been shown to have a positive effect when used to improve the quality of decision-making for women facing postmastectomy breast reconstruction (PMBR). However, the existing findings regarding these interventions are still incongruent, and the overall effect is unclear.

OBJECTIVE

We aimed to assess the content of web-based decision aids and its impact on decision-related outcomes (ie, decision conflict, decision regret, informed choice, and knowledge), psychological-related outcomes (ie, satisfaction and anxiety), and surgical decision-making in women facing PMBR.

METHODS

This systematic review and meta-analysis followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 6 databases, PubMed, Embase, Cochrane Library, CINAHL, PsycINFO, and Web of Science Core Collection, were searched starting at the time of establishment of the databases to May 2023, and an updated search was conducted on April 1, 2024. MeSH (Medical Subject Headings) terms and text words were used. The Cochrane Risk of Bias Tool for randomized controlled trials was used to assess the risk of bias. The certainty of evidence was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation approach.

RESULTS

In total, 7 studies included 579 women and were published between 2008 and 2023, and the sample size in each study ranged from 26 to 222. The results showed that web-based decision aids used audio and video to present the pros and cons of PMBR versus no PMBR, implants versus flaps, and immediate versus delayed PMBR and the appearance and feel of the PMBR results and the expected recovery time with photographs of actual patients. Web-based decision aids help improve PMBR knowledge, decisional conflict (mean difference [MD]=-5.43, 95% CI -8.87 to -1.99; P=.002), and satisfaction (standardized MD=0.48, 95% CI 0.00 to 0.95; P=.05) but have no effect on informed choice (MD=-2.80, 95% CI -8.54 to 2.94; P=.34), decision regret (MD=-1.55, 95% CI -6.00 to 2.90 P=.49), or anxiety (standardized MD=0.04, 95% CI -0.50 to 0.58; P=.88). The overall Grading of Recommendations, Assessment, Development, and Evaluation quality of the evidence was low.

CONCLUSIONS

The findings suggest that the web-based decision aids provide a modern, low-cost, and high dissemination rate effective method to promote the improved quality of decision-making in women undergoing PMBR.

TRIAL REGISTRATION

PROSPERO CRD42023450496; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=450496.

A scoping review of web-based, interactive, personalized decision-making tools available to support breast cancer treatment and survivorship care

PURPOSE

We reviewed existing personalized, web-based, interactive decision-making tools available to guide breast cancer treatment and survivorship care decisions in clinical settings.

METHODS

The study was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). We searched PubMed and related databases for interactive web-based decision-making tools developed to support breast cancer treatment and survivorship care from 2013 to 2023. Information on each tool's purpose, target population, data sources, individual and contextual characteristics, outcomes, validation, and usability testing were extracted. We completed a quality assessment for each tool using the International Patient Decision Aid Standard (IPDAS) instrument.

RESULTS

We found 54 tools providing personalized breast cancer outcomes (e.g., recurrence) and treatment recommendations (e.g., chemotherapy) based on individual clinical (e.g., stage), genomic (e.g., 21-gene-recurrence score), behavioral (e.g., smoking), and contextual (e.g., insurance) characteristics. Forty-five tools were validated, and nine had undergone usability testing. However, validation and usability testing included mostly White, educated, and/or insured individuals. The average quality assessment score of the tools was 16 (range: 6-46; potential maximum: 63).

CONCLUSIONS

There was wide variation in the characteristics, quality, validity, and usability of the tools. Future studies should consider diverse populations for tool development and testing.

IMPLICATIONS FOR CANCER SURVIVORS

There are tools available to support personalized breast cancer treatment and survivorship care decisions in clinical settings. It is important for both cancer survivors and physicians to carefully consider the quality, validity, and usability of these tools before using them to guide care decisions.

A Scoping Review of Personalized, Interactive, Web-Based Clinical Decision Tools Available for Breast Cancer Prevention and Screening in the United States

Introduction. Personalized web-based clinical decision tools for breast cancer prevention and screening could address knowledge gaps, enhance patient autonomy in shared decision-making, and promote equitable care. The purpose of this review was to present evidence on the availability, usability, feasibility, acceptability, quality, and uptake of breast cancer prevention and screening tools to support their integration into clinical care. Methods. We used the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews Checklist to conduct this review. We searched 6 databases to identify literature on the development, validation, usability, feasibility, acceptability testing, and uptake of the tools into practice settings. Quality assessment for each tool was conducted using the International Patient Decision Aid Standard instrument, with quality scores ranging from 0 to 63 (lowest-highest). Results. We identified 10 tools for breast cancer prevention and 9 tools for screening. The tools included individual (e.g., age), clinical (e.g., genomic risk factors), and health behavior (e.g., alcohol use) characteristics. Fourteen tools included race/ethnicity, but no tool incorporated contextual factors (e.g., insurance, access) associated with breast cancer. All tools were internally or externally validated. Six tools had undergone usability testing in samples including White (median, 71%; range, 9%-96%), insured (99%; 97%-100%) women, with college education or higher (60%; 27%-100%). All of the tools were developed and tested in academic settings. Seven (37%) tools showed potential evidence of uptake in clinical practice. The tools had an average quality assessment score of 21 (range, 9-39). Conclusions. There is limited evidence on testing and uptake of breast cancer prevention and screening tools in diverse clinical settings. The development, testing, and integration of tools in academic and nonacademic settings could potentially improve uptake and equitable access to these tools.

Highlights

There were 19 personalized, interactive, Web-based decision tools for breast cancer prevention and screening.Breast cancer outcomes were personalized based on individual clinical characteristics (e.g., age, medical history), genomic risk factors (e.g., BRCA1/2), race and ethnicity, and health behaviors (e.g., smoking). The tools did not include contextual factors (e.g., insurance status, access to screening facilities) that could potentially contribute to breast cancer outcomes.Validation, usability, acceptability, and feasibility testing were conducted mostly among White and/or insured patients with some college education (or higher) in academic settings. There was limited evidence on testing and uptake of the tools in nonacademic clinical settings.

Breast cancer mortality in 500 000 women with early invasive breast cancer diagnosed in England, 1993-2015: population based observational cohort study

OBJECTIVES

To describe long term breast cancer mortality among women with a diagnosis of breast cancer in the past and estimate absolute breast cancer mortality risks for groups of patients with a recent diagnosis.

DESIGN

Population based observational cohort study.

SETTING

Routinely collected data from the National Cancer Registration and Analysis Service.

PARTICIPANTS

All 512 447 women registered with early invasive breast cancer (involving only breast and possibly axillary nodes) in England during January 1993 to December 2015, with follow-up to December 2020.

MAIN OUTCOME MEASURES

Annual breast cancer mortality rates and cumulative risks by time since diagnosis, calendar period of diagnosis, and nine characteristics of patients and tumours.

RESULTS

For women with a diagnosis made within each of the calendar periods 1993-99, 2000-04, 2005-09, and 2010-15, the crude annual breast cancer mortality rate was highest during the five years after diagnosis and then declined. For any given time since diagnosis, crude annual breast cancer mortality rates and risks decreased with increasing calendar period. Crude five year breast cancer mortality risk was 14.4% (95% confidence interval 14.2% to 14.6%) for women with a diagnosis made during 1993-99 and 4.9% (4.8% to 5.0%) for women with a diagnosis made during 2010-15. Adjusted annual breast cancer mortality rates also decreased with increasing calendar period in nearly every patient group, by a factor of about three in oestrogen receptor positive disease and about two in oestrogen receptor negative disease. Considering just the women with a diagnosis made during 2010-15, cumulative five year breast cancer mortality risk varied substantially between women with different characteristics: it was <3% for 62.8% (96 085/153 006) of women but ≥20% for 4.6% (6962/153 006) of women.

CONCLUSIONS

These five year breast cancer mortality risks for patients with a recent diagnosis may be used to estimate breast cancer mortality risks for patients today. The prognosis for women with early invasive breast cancer has improved substantially since the 1990s. Most can expect to become long term cancer survivors, although for a few the risk remains appreciable.

myBeST-A Web-Based Survival Prognostic Tool for Women with Breast Cancer in Malaysia: Development Process and Preliminary Validation Study

Women with breast cancer are keen to know their predicted survival. We developed a new prognostic model for women with breast cancer in Malaysia. Using the model, this study aimed to design the user interface and develop the contents of a web-based prognostic tool for the care provider to convey survival estimates. We employed an iterative website development process which includes: (1) an initial development stage informed by reviewing existing tools and deliberation among breast surgeons and epidemiologists, (2) content validation and feedback by medical specialists, and (3) face validation and end-user feedback among medical officers. Several iterative prototypes were produced and improved based on the feedback. The experts (n = 8) highly agreed on the website content and predictors for survival with content validity indices ≥ 0.88. Users (n = 20) scored face validity indices of more than 0.90. They expressed favourable responses. The tool, named Malaysian Breast cancer Survival prognostic Tool (myBeST), is accessible online. The tool estimates an individualised five-year survival prediction probability. Accompanying contents were included to explain the tool's aim, target user, and development process. The tool could act as an additional tool to provide evidence-based and personalised breast cancer outcomes.

Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review

BACKGROUND

Clinical prediction models are often not evaluated properly in specific settings or updated, for instance, with information from new markers. These key steps are needed such that models are fit for purpose and remain relevant in the long-term. We aimed to present an overview of methodological guidance for the evaluation (i.e., validation and impact assessment) and updating of clinical prediction models.

METHODS

We systematically searched nine databases from January 2000 to January 2022 for articles in English with methodological recommendations for the post-derivation stages of interest. Qualitative analysis was used to summarize the 70 selected guidance papers.

RESULTS

Key aspects for validation are the assessment of statistical performance using measures for discrimination (e.g., C-statistic) and calibration (e.g., calibration-in-the-large and calibration slope). For assessing impact or usefulness in clinical decision-making, recent papers advise using decision-analytic measures (e.g., the Net Benefit) over simplistic classification measures that ignore clinical consequences (e.g., accuracy, overall Net Reclassification Index). Commonly recommended methods for model updating are recalibration (i.e., adjustment of intercept or baseline hazard and/or slope), revision (i.e., re-estimation of individual predictor effects), and extension (i.e., addition of new markers). Additional methodological guidance is needed for newer types of updating (e.g., meta-model and dynamic updating) and machine learning-based models.

CONCLUSION

Substantial guidance was found for model evaluation and more conventional updating of regression-based models. An important development in model evaluation is the introduction of a decision-analytic framework for assessing clinical usefulness. Consensus is emerging on methods for model updating.

A prognostic survival model for women diagnosed with invasive breast cancer in Queensland, Australia

PURPOSE

Prognostic models can help inform patients on the future course of their cancer and assist the decision making of clinicians and patients in respect to management and treatment of the cancer. In contrast to previous studies considering survival following treatment, this study aimed to develop a prognostic model to quantify breast cancer-specific survival at the time of diagnosis.

METHODS

A large (n = 3323), population-based prospective cohort of women were diagnosed with invasive breast cancer in Queensland, Australia between 2010 and 2013, and followed up to December 2018. Data were collected through a validated semi-structured telephone interview and a self-administered questionnaire, along with data linkage to the Queensland Cancer Register and additional extraction from medical records. Flexible parametric survival models, with multiple imputation to deal with missing data, were used.

RESULTS

Key factors identified as being predictive of poorer survival included more advanced stage at diagnosis, higher tumour grade, "triple negative" breast cancers, and being symptom-detected rather than screen detected. The Harrell's C-statistic for the final predictive model was 0.84 (95% CI 0.82, 0.87), while the area under the ROC curve for 5-year mortality was 0.87. The final model explained about 36% of the variation in survival, with stage at diagnosis alone explaining 26% of the variation.

CONCLUSIONS

In addition to confirming the prognostic importance of stage, grade and clinical subtype, these results highlighted the independent survival benefit of breast cancers diagnosed through screening, although lead and length time bias should be considered. Understanding what additional factors contribute to the substantial unexplained variation in survival outcomes remains an important objective.