Breast Cancer Decision Support: Expert Systems vs. Machine Learning – have we thrown the baby out with the bathwater?

Mustafa Khanbhai^1,  Vivek Patkar^2,  Dany Rutt^3, Ashutosh Kothari^4, Hartmut Kristleitel^5, Majid Kazmi⁶

1. AI Centre for Value Based Healthcare, Becket House, 1 Lambeth Place Road, London SE1 7EU
2. Deontics Ltd Orion House 5 Upper St Martin’s Lane, London, WC2H 9EA United Kingdom
3. Guys & St Thomas’ NHS Foundation Trust, Guy’s Cancer Center, London SE1 9RT
4. Guys & St Thomas’ NHS Foundation Trust, Guy’s Cancer Center, London SE1 9RT
5. Guys & St Thomas’ NHS Foundation Trust, Guy’s Cancer Center, London SE1 9RT
6. Guys & St Thomas’ NHS Foundation Trust, Guy’s Cancer Center, London SE1 9RT

OPEN ACCESS

PUBLISHED: 31 December 2024

CITATION: KHANBHAI, Mustafa et al. Breast Cancer Decision Support: Expert Systems vs. Machine Learning – have we thrown the baby out with the bathwater?. Medical Research Archives, [S.l.], v. 12, n. 12, dec. 2024.  Available at: <https://esmed.org/MRA/mra/article/view/5998>. 

COPYRIGHT: © 2025 European Society of Medicine. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ISSN 2375-1924

DOI: https://doi.org/10.18103/mra.v12i12.5998

Abstract

Background and Aim: Machine learning enabled clinical decision support systems offer the potential to enhance the efficacy of decision-making processes in breast cancer multidisciplinary team meetings. We examine whether a traditional rule-based expert system can still be relevant.

Methods: We conducted a retrospective cohort study evaluating the concordance of clinical decision support systems (CDSS) with the recommendations of breast cancer MDTs. The local breast practice MDT breast cancer recommendations were derived from the consensus decisions of two consultant medical oncologists and one consultant breast surgeon with knowledge of the historical MDT outcome and the expert system, and ML enabled CDSS therapeutic options, and these decisions were considered the gold standard. The historical MDTM included a consultant breast medical oncologist, consultant breast radiologist and pathologist, and clinical nurse specialist.

Results: The study encompassed 213 retrospective cases from 2017 to 2021, following adaptation of the ML CDSS to conform to local best practice. The observed concordance between the ML CDSS treatment plan recommendations and the gold standard consensus panel reported in the previous study was 92% (95% CI: 87.8%-95.7%, 127/137), following adaptation of the ML CDSS to conform to local best practice. The observed difference in concordance for the Deontics and ML CDSS when compared with the gold standard plan was 20% (95% CI: 15.9%-24.1%).

Conclusion: A CDSS is defined as a system intended to enhance the decision-making process by providing healthcare professionals with targeted clinical knowledge, patient data, and other health information. Many studies have shown that CDSS can improve patient outcomes, but the integration of AI into clinical decision support systems remains a challenge.

Keywords

• Breast Cancer
• Decision Support Systems
• Machine Learning
• Multidisciplinary Team
• Clinical Decision Making

 

Introduction

Breast cancer remains a significant global health challenge, affecting millions of individuals and their families each year. The complexity of this disease necessitates a multidisciplinary approach, wherein healthcare professionals from diverse specialties collaborate to devise optimal treatment strategies. The multidisciplinary team meetings (MDTM) serve as a pivotal forum for deliberation and decision-making in breast cancer care. The quality of decisions made during these MDTMs profoundly impacts the course of patient care and outcomes. MDTMs have become burdened by increasing workloads with an unmatched, limited increase in resources to support such work. Some clinicians have raised concerns about the way cancer MDTMs are conducted in the UK as frantic business meetings.

The Association of Breast Surgeons published a toolkit with audit tools to assess the performance on Breast MDTM to identify areas for improvement and streamlining. The guidance together with the audit tools have been put forward to standardise the way in which Breast MDTMs run in the UK. Moreover, in January 2020 NHS England and NHS Improvement issued guidance for Cancer Alliances for Streamlining MDTMs. It proposed a process of “introducing Standards of Care as a routine part of the MDT process to stratify patient cases into those which require full multidisciplinary discussion in the MDTM, and those cases which can be listed but not discussed in the MDT, as patient need is met by a Standard of Care (SoC)”.

A SoC is defined as “a point in the pathway of patient management where there is a recognised international, national, regional or local guideline on the intervention(s) that should be made available to a patient”. The guidance states that for a patient to be assigned for ‘no discussion at the MDT’, the SoC must have been reviewed by an appropriate person or triage group. Effectively, only complex patients, requiring true multidisciplinary input, would be discussed at MDTMs. Streamlining has not been widely adopted in the UK, most likely due to the lack of clinical time required to take on the task of reviewing all patients prior to the MDTM and assigning those eligible to a pre-agreed SoC.

To support MDTMs in reaching the challenging goal of evidence-based informed decision-making, information technology and data science can be helpful to manage, register and re-use all relevant data and generate treatment recommendations. Many studies have shown that clinical decision support systems (CDSS) can be effective tools to increase physician concordance with clinical practice guidelines.

Clinical decision support systems can be classified into two broad categories:
(i) knowledge-driven CDSS (previously known as Expert systems), and
(ii) machine learning (ML) or learning algorithm-based data-driven CDSS.

Recently ML-based CDSS have emerged as a transformative tool, offering the potential to enhance the efficiency of decision-making processes within MDTMs. However, while ML-enabled CDSS excel at handling vast amounts of data and complex pattern recognition, expert systems bring to the table unique advantages, such as transparency, interpretability, predictability and reproducibility. Unlike ML, where the underlying logic remains a black box to the user, the knowledge-based CDSS recommendations can be traced back to the underlying evidence source and the logic remains human understandable.

Therefore, it becomes essential to question whether we have perhaps overlooked the capabilities of traditional expert systems in the rush to embrace ML-based solutions. This study aims to explore the circumstances where expert systems can still prove to be invaluable, challenging the notion that we have entirely discarded their potential in favour of ML, and thus, whether we may have, in some instances, ‘thrown the baby out with the bathwater.’

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Methods

DATA COLLECTION
In this retrospective study, we aimed to evaluate the concordance of expert systems and ML Enabled CDSS with Gold Standard Decisions

Breast cancer MDTMs. The local best practice MDT breast cancer treatment decisions, or “consensus panel decisions,” were derived from the consensus decisions of two consultant medical oncologists and two consultant breast surgeons with knowledge of the historical MDTM outcome and the expert system and ML-enabled CDSS therapeutic options, and these decisions were considered the gold standard. The historical MDTM included a consultant breast medical oncologist, consultant breast surgeon, consultant breast radiologist and pathologist, and clinical nurse specialist.

The study encompassed 213 retrospective cases from a previously published study by the co-authors designed to evaluate an ML-enabled CDSS. Cases had been discussed at the Guy’s Cancer Centre MDTM between 2017 and 2018, with patients diagnosed with Stage 1–3 invasive breast cancer. Exclusions were made for patients who could not be analysed due to unavailable treatment options, including those with recurrent breast cancer, bilateral breast cancer, male patients, and specific histological types. Metastatic patients as a part of the first-line therapy were also excluded from this study. The same clinicopathological data was analysed as per the previously published study. This included demographic details, comorbidities, functional status, endocrine status, tumour characteristics such as biology (grade/stage), including receptor status and nodal status.

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Knowledge-Based Clinical Decision Support (Expert System)

We utilised Deontics, a clinical decision support and workflow management system grounded in cognitive models that simulate human decision-making processes. Deontics technology excels in evaluating and synthesising multiple, potentially conflicting arguments — a key requirement in the medical field where uncertainty, incomplete data, and conflicting evidence are common challenges. This approach aligns closely with the principles of epidemiology and evidence-based medicine, as it systematically handles and integrates diverse evidence sources, such as randomized controlled trials (RCTs) and Clinical Practice Guidelines, into transparent recommendations.

These recommendations, accompanied by clinical justifications, are presented to clinicians in a clear and comprehensive manner. The system operates in two distinct modes. In human-guided mode, it functions as a decision-support tool, offering suggestions for clinicians to consider, such as in MDTMs. In autonomous mode, the system not only supports complex knowledge representation and nuanced decision-making but also facilitates subsequent workflow management. This dual capability makes Deontics an ideal tool for streamlining clinical pathways, integrating evidence-based decision-making with efficient management of clinical workflows.

Locally accepted breast cancer guidelines, National Comprehensive Cancer Network (NCCN) and National Institute for Health and Care Excellence (NICE) guidelines were used in the Deontics software to create a CDSS tool. The outcome from Deontics was labelled as treatment plan using the following categories: Radiotherapy, Surgery, Systemic therapy, Targeted therapy, Endocrine therapy alone or in combination. These outcome labels were matched by two independent surgical oncologists to the gold standard consensus panel outcome labels. Based on the evidence-based guidance described above, one of the breast surgical oncologists then devised a triage clinical “decision tree” that assigned patients either to an appropriate SoC and “not for discussion at MDTM,” or to “refer to the MDTM for discussion.” This decision tree was reviewed by the second senior surgical oncologist and a medical oncologist, and a final version was agreed for use as a triage tool.

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Machine Learning-Based Clinical Decision Support

In a prior study, the authors evaluated a CDSS, Watson for Oncology, developed by IBM, that the system used natural language processing and machine learning to generate ranked, evidence-based therapeutic options. It was developed in collaboration with experts at Memorial Sloan Kettering Cancer Centre. To validate the CDSS as a streamlining tool, the authors developed a decision tree utilizing fast-and-frugal trees (FFTs), created with the R package FFTrees, for the purpose of determining the appropriate triage pathway for breast cancer patients — whether to triage them to “not for discussion at MDTM” or “send to MDTM for discussion.”

FFTs represent supervised learning algorithms geared towards binary classification tasks. However, to permit a direct comparison between the two CDSS approaches, in this study the clinical decision tree described above was used to identify patients eligible for triage to a SoC or to the MDTM for discussion, for both the expert system and ML-based CDSS.

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Concordance Assessment

Concordance, which measures the agreement between different decision-making approaches and a consensus panel’s decisions, was a primary focus of this study. To calculate concordance, each approach’s treatment decision (ML-based and expert system CDSS) was compared to the consensus panel’s decision for each patient case.

The concordance rate was determined, representing the proportion of cases with matching decisions:
Concordance Rate = (Number of Cases with Matching Decisions) / (Total Number of Cases)

Concordance is reported with 95% confidence intervals (CIs) approximated using the Wilson interval, both for overall concordance and for concordance only for those cases triaged by the CDSS to “not for discussion at MDTM” using the clinical decision tree.

Service evaluation by the Guy’s Cancer Information Governance team was granted, obviating the need for ethical approval.

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Results

A total of 213 patients were included in the final analysis (Table 1). Case attributes were missing in five patients and therefore concordance was evaluated on 208 patients.

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Table 1. The characteristics of each case of breast cancer included in the study (n = 213)

Table 1 . The characteristics of each case of breast cancer included in the study, n = 213

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Results 

The overall concordance between the ML CDSS treatment plan recommendations and the gold standard consensus panel reported in the previous study was 92% (95% CI: 88–95%, 197/213), following adaptation of the ML CDSS to conform to local best practice. Concordance between the Deontics CDSS treatment plan and the gold standard consensus panel was 98% (95% CI: 97–99%, 204/208) (Table 2).

The reasons why the Deontics tool provided incorrect recommendations in 5 cases were:

• 3 cases of recurrence (guidance did not cover those),

• 1 case was a special histological type (adenosquamous),

• 1 case had a low-grade G1 but T3 (large) cancer, where NCCN recommended chemotherapy while local guidelines did not.

For reference, the concordance between Deontics and historic MDT was 93% (194/208), and concordance between the gold standard panel and historic MDT was 88% (183/208).

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A clinical decision tree was developed based on local recommendations. Appendix 1 shows the clinical decision tree, which was used to triage patients to a SoC, and off to ‘not for discussion at MDTM’. According to the clinical decision tree, a total of 165/208 patients (79%) were eligible to be triaged to the CDSS for ‘not for discussion at MDTM’. The main reasons for referral to the MDTM rather than for ‘not for discussion’ were due to multifocality and upgrade to T4 disease.

Of the patients sent to ‘not for discussion at MDTM’, the concordance between the Deontics CDSS treatment plan recommendations and the gold standard consensus panel was 98.8% (95% CI: 95.6–99.8%, 163/165).

The ML CDSS treatment plan recommendations and gold standard consensus panel was 78.8% (95% CI: 71.7–84.8%, 130/165). This gives an observed difference in concordance for the Deontics and ML CDSS when compared with the gold standard panel of 20% (95% CI: 13.6–26.9%).

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Table 2. Concordance between gold standard of care consensus panel and machine learning clinical decision support system (CDSS) and Deontics CDSS.

SoC: standard of care. Reported as 95% CI.

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Discussion

A CDSS is defined as a system intended to improve healthcare delivery by enhancing medical decisions with targeted clinical knowledge, patient information, and other health information. Many studies have shown that CDSS can be effective tools to increase physician concordance with clinical practice guidelines.

A systematic review focusing on CDSS impact on process outcomes (e.g., percentage change in MDT treatment decision after using CDSS), guideline adherence, and clinical outcomes found that CDSS implementation did significantly improve process outcomes and guideline adherence, but showed no improvement in clinical outcomes.

Machine learning is already proving of enormous value in areas such as diagnosing, early detection, recognising patterns, or predicting outcomes from many unselected variables, leading to a vast and increasing range of clinical and drug discovery AI applications. However, its relative advantages over traditional rules-based expert systems may be less convincing when used in the context of clinical decision support—where predictable outcomes are needed, based on the representation of scientifically valid clinical guidelines derived from RCTs.

Ideally, a CDSS should import relevant (standardised) data from the electronic health record automatically and use this error-free copied source data for decision support. Machine learning-enabled CDSS are expensive and resource-hungry to produce and update as new research becomes available—both in terms of clinical supervision and training, and computational requirements.

They also lack transparency (the so-called ‘black box’ problem), making it difficult to build clinical confidence in their decisions or to identify potential bias or errors in their recommendations.

In contrast, expert system–enabled CDSS can be produced relatively cheaply, and their algorithms require no data on which to train—only knowledge in the form of written clinical guidelines or protocols, which can be input quickly with no programming skills required. This knowledge can also be easily and instantly updated, for example, as new research evidence becomes available.

Most importantly for clinical practice, the outputs from an expert system are clinically explainable, since they can be traced back to the specific guideline recommendation and evidence from which they derive. Expert systems like Deontics also make uncertainties and conflicts in the source evidence transparent. In this context, rule-based CDSS are more intuitive for clinicians to understand compared to systems using machine learning techniques.

Our study showed a surprising and unexpectedly large advantage in performance of the Deontics CDSS over the ML-enabled CDSS when applied to streamlining non-metastatic breast cancer patients in an MDTM pathway. The Deontics CDSS had 98% overall concordance with the gold standard consensus panel, compared to 92% with the ML-enabled CDSS.

Considering the use of AI to streamline the breast cancer MDT pathway, when a clinical decision tree was used to identify those patients eligible for triage to an AI CDSS and “not for discussion at MDTM,” the Deontics CDSS had 98.8% concordance with the treatment recommendations of a gold standard consensus panel. This compared with only 78.8% concordance for the ML-enabled CDSS, which would result in an unacceptable and clinically unsafe error rate in SoC decisions for those patients triaged to “not for discussion at MDTM” if the tool were deployed in an MDT pathway.

On the other hand, a 98.8% agreement between the expert system CDSS and the gold standard consensus panel suggests it may be acceptable for use in the MDT pathway, where the treating clinician acts as the “human in the loop.” If this level of performance was replicated in prospective studies in a routine clinical setting, the potential efficiencies for UK breast cancer diagnostic and treatment pathways from automated streamlining with tools like Deontics would be transformational.

There are several possible reasons for the low performance observed from the ML-enabled CDSS when compared with the Deontics CDSS. It may reflect differences between US and UK oncology practice that were not fully adjusted for (although the original authors did attempt to “localise” the ML-enabled CDSS to UK practices).

The ML-enabled CDSS treatment recommendations were generated in 2020 based on the version of IBM Watson for Oncology available at the time. Whilst the gold standard consensus panel treatment recommendations were initially created in 2020, they were reviewed and updated in 2023, and the Deontics CDSS was presented with NCCN and NICE guidelines, in addition to local Guy’s Cancer Centre protocols current in 2023.

IBM discontinued Watson for Oncology in 2021, and Watson Health was subsequently sold to Merative, which until now has not replaced the product. The studies using WFO did not prospectively evaluate these tools, and there remains a significant opportunity to demonstrate the value of AI-based CDSS systems in oncology.

Since 2020, a new generation of AI technologies have emerged using transformer-based large language models that represent a leap forward in AI as great as that from rules-based expert systems to deep learning. More recently, large multimodal models have been developed that use text, images, video, and audio data. In medicine, these foundation models can be “fine-tuned” on clinical data from electronic patient records, in addition to images from radiology, histopathology, and even multi-omic and video data.

One example is MedGemini, trained and fine-tuned by Google DeepMind from its Gemini foundation model to perform multiple clinical tasks — even tasks for which it was not specifically trained — including providing treatment recommendations.

MedGemini and other ML technologies based on large foundation models are still at an early research stage. Like other “generative AI,” they tend to hallucinate, providing superficially plausible answers to clinical questions that are in fact incorrect, with potentially lethal consequences. Until this problem is solved — which may be some years off — these technologies are not safe for use in routine clinical practice. Even then, there are other challenges related to costs, safe deployment, routine monitoring, transparency, and explainability.

The Deontics CDSS has since been validated at Guy’s Cancer in the prostate MDT pathway as part of an NIHR (National Institute for Health and Care Research)–funded study, where in both retrospective and prospective studies it was able to automatically identify and correctly assign 33% of all prostate cancer patients to “not for discussion at MDTM,” with 96% concordance with the actual MDTM decision (not yet published, data available on request).

On the strength of these findings, and the findings reported here, Guy’s Cancer is now planning to deploy the Deontics CDSS across all prostate and breast MDT pathways — and potentially to all solid tumour MDT pathways in the future.

Conclusion

Our study has demonstrated the feasibility and validity of using a rules-based expert system to automate the streamlining of the majority of non-metastatic breast cancer patients in the MDT pathway to ‘not for discussion at MDTM’.
This could reduce the referral-to-treatment time and free up significant clinician time to discuss more complex patients or treat more patients.

If these findings are replicated in prospective studies, and for other tumour types, cancer centres across the UK should consider deploying such expert systems — and indeed, in all healthcare systems concerned with delivering cost-effective cancer care.

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Conflict of Interest Statement

VP is the Chief Medical Officer for Deontics Ltd.

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Funding Statement

Mustafa Khanbhai was funded by the AI Centre for Value-Based Healthcare, which is funded by public sector grants from UK Research and Innovation (UKRI) and the Department of Health and Social Care (DHSC), Office of Life Sciences, delivered through Innovate UK.

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Acknowledgements

Martha Martin contributed to the populating of the initial dataset.

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Appendix 1

Pre-operative

Invasive cancer AND
ER +ve AND
HER2 -ve AND
Unifocal AND
Axilla normal AND
Suitable for BCS & SLNb

OR

Invasive cancer AND
ER +ve AND
HER2 +ve AND
<2cm AND
Unifocal AND
Axilla normal AND
Suitable for BCS & SLNb

OR

Invasive cancer AND
ER +ve AND
HER2 +ve AND

2cm AND
Unifocal AND
Targeted therapy AND
Axilla normal AND
Suitable for BCS & SLNb

OR

Invasive cancer AND
ER -ve AND
HER2 -ve AND
<1cm AND
Axilla normal AND
Suitable for BCS & SLNb

OR

Invasive cancer AND
ER -ve AND
HER2 -ve AND

1cm AND
Unifocal AND
NACT AND
Axilla normal AND
Suitable for BCS & SLNb

---

Post-operative

ER +ve AND
HER2 -ve AND
WLE/Mx and SLNb AND
<2cm AND
Clear margins AND
Negative SLNb AND
Adjuvant RT and ET

OR

ER +ve AND
HER2 +ve AND
WLE/Mx and SLNb AND
<2cm AND
Clear margins AND
Negative SLNb AND
Adjuvant RT and ET AND
Targeted therapy

OR

ER -ve AND
HER2 -ve AND
WLE/Mx and SLNb AND
Clear margins AND
Adjuvant RT

OR

ER +ve AND
HER2 -ve AND
WLE/Mx and SLNb AND

2cm AND
Clear margins AND
Negative SLNb AND
Postmenopausal AND
Oncotype DX <25

OR

ER +ve AND
HER2 -ve AND

2cm AND
WLE/Mx and SLNb AND
Negative SLNb AND
Premenopausal AND
Oncotype DX <15

OR

Invasive cancer AND
ER +ve AND
HER2 -ve AND
Axilla LNb5 AND
Postmenopausal AND
Suitable for BCS & ANC

OR

Invasive cancer AND
ER +ve AND
HER2 -ve AND
Axilla LNb5 AND
Premenopausal AND
NACT AND
Suitable for BCS & ANC/TAD