Cost-effectiveness of using artificial intelligence versus polygenic risk score to guide breast cancer screening

Implementing artificial intelligence in breast cancer screening: Women's preferences

BACKGROUND

Artificial intelligence (AI) could improve accuracy and efficiency of breast cancer screening. However, many women distrust AI in health care, potentially jeopardizing breast cancer screening participation rates. The aim was to quantify community preferences for models of AI implementation within breast cancer screening.

METHODS

An online discrete choice experiment survey of people eligible for breast cancer screening aged 40 to 74 years in Australia. Respondents answered 10 questions where they chose between two screening options created by an experimental design. Each screening option described the role of AI (supplementing current practice, replacing one radiologist, replacing both radiologists, or triaging), and the AI accuracy, ownership, representativeness, privacy, and waiting time. Analysis included conditional and latent class models, willingness-to-pay, and predicted screening uptake.

RESULTS

The 802 participants preferred screening where AI was more accurate, Australian owned, more representative and had shorter waiting time for results (all p < .001). There were strong preferences (p < .001) against AI alone or as triage. Three patterns of preferences emerged: positive about AI if accuracy improves (40% of sample), strongly against AI (42%), and concerned about AI (18%). Participants were willing to accept AI replacing one human reader if their results were available 10 days faster than current practice but would need results 21 days faster for AI as triage. Implementing AI inconsistent with community preferences could reduce participation by up to 22%.

Economic evaluation of personalised versus conventional risk assessment for women who have undergone testing for hereditary breast and ovarian cancer genes: a modelling study

BACKGROUND

The management of women with germline pathogenic variants (GPVs) in breast (BC) and ovarian cancer (OC) susceptibility genes is focused on surveillance and risk-reducing surgery/medication. Most women are assigned an average range of risk and treated accordingly, but it is possible to personalise this. Here, we explore the economic impact of risk personalisation.

METHOD

We compared two strategies for risk stratification for female participants: conventional risk assessment (CRA), which only involves information from genetic testing and personalised risk assessment (PRA), using genetic and non-genetic risk modifiers. Three different versions of PRA were compared, which were combinations of polygenic risk score and questionnaire-based factors. A patient-level Markov model was designed to estimate the overall National Health Service cost and quality-adjusted life years (QALYs) after risk assessment. Results were given for 20 different groups of women based on their GPV status and family history.

RESULTS

Across the 20 scenarios, the results showed that PRA was cost-effective compared with CRA using a £20 000 per QALY threshold in women with a GPV in PALB2 who have OC or BC+OC family history, and women with a GPV in ATM, CHEK2, RAD51C or RAD51D. For women with a GPV in BRCA1 or BRCA2, women with no pathogenic variant and women with a GPV in PALB2 who have unknown family history or BC family history, CRA was more cost-effective. PRA was cost-effective compared with CRA in specific situations predominantly associated with moderate-risk BC GPVs (RAD51C/RAD51D/CHEK2/ATM), while CRA was cost-effective compared with PRA predominantly with high-risk BC GPVs (BRCA1/BRCA2/PALB2).

CONCLUSION

PRA was cost-effective in specific situations compared with CRA in the UK for assessment of women with or without GPVs in BC and OC susceptibility genes.

The Cost Effectiveness of Genomic Medicine in Cancer Control: A Systematic Literature Review

BACKGROUND AND OBJECTIVE

Genomic medicine offers an unprecedented opportunity to improve cancer outcomes through prevention, early detection and precision therapy. Health policy makers worldwide are developing strategies to embed genomic medicine in routine cancer care. Successful translation of genomic medicine, however, remains slow. This systematic review aims to identify and synthesise published evidence on the cost effectiveness of genomic medicine in cancer control. The insights could support efforts to accelerate access to cost-effective applications of human genomics.

METHODS

The study protocol was registered with PROSPERO (CRD42024480842), and the review was conducted in line with Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) Guidelines. The search was run in four databases: MEDLINE, Embase, CINAHL and EconLit. Full economic evaluations of genomic technologies at any stage of cancer care, and published after 2018 and in English, were included for data extraction.

RESULTS

The review identified 137 articles that met the inclusion criteria. Most economic evaluations focused on the prevention and early detection stage (n = 44; 32%), the treatment stage (n = 36; 26%), and managing relapsed, refractory or progressive disease (n = 51, 37%). Convergent cost-effectiveness evidence was identified for the prevention and early detection of breast and ovarian cancer, and for colorectal and endometrial cancers. For cancer treatment, the use of genomic testing for guiding therapy was highly likely to be cost effective for breast and blood cancers. Studies reported that genomic medicine was cost effective for advanced and metastatic non-small cell lung cancer. There was insufficient or mixed evidence regarding the cost effectiveness of genomic medicine in the management of other cancers.

CONCLUSIONS

This review mapped out the cost-effectiveness evidence of genomic medicine across the cancer care continuum. Gaps in the literature mean that potentially cost-effective uses of genomic medicine in cancer control, for example rare cancers or cancers of unknown primary, may be being overlooked. Evidence on the value of information and budget impact are critical, and advancements in methods to include distributional effects, system capacity and consumer preferences will be valuable. Expanding the current cost-effectiveness evidence base is essential to enable the sustainable and equitable translation of genomic medicine.

Harnessing Artificial Intelligence to Enhance Global Breast Cancer Care: A Scoping Review of Applications, Outcomes, and Challenges

BACKGROUND

In recent years, Artificial Intelligence (AI) has shown transformative potential in advancing breast cancer care globally. This scoping review seeks to provide a comprehensive overview of AI applications in breast cancer care, examining how they could reshape diagnosis, treatment, and management on a worldwide scale and discussing both the benefits and challenges associated with their adoption.

METHODS

In accordance with PRISMA-ScR and ensuing guidelines on scoping reviews, PubMed, Web of Science, Cochrane Library, and Embase were systematically searched from inception to end of May 2024. Keywords included "Artificial Intelligence" and "Breast Cancer". Original studies were included based on their focus on AI applications in breast cancer care and narrative synthesis was employed for data extraction and interpretation, with the findings organized into coherent themes.

RESULTS

Finally, 84 articles were included. The majority were conducted in developed countries (n = 54). The majority of publications were in the last 10 years (n = 83). The six main themes for AI applications were AI for breast cancer screening (n = 32), AI for image detection of nodal status (n = 7), AI-assisted histopathology (n = 8), AI in assessing post-neoadjuvant chemotherapy (NACT) response (n = 23), AI in breast cancer margin assessment (n = 5), and AI as a clinical decision support tool (n = 9). AI has been used as clinical decision support tools to augment treatment decisions for breast cancer and in multidisciplinary tumor board settings. Overall, AI applications demonstrated improved accuracy and efficiency; however, most articles did not report patient-centric clinical outcomes.

CONCLUSIONS

AI applications in breast cancer care show promise in enhancing diagnostic accuracy and treatment planning. However, persistent challenges in AI adoption, such as data quality, algorithm transparency, and resource disparities, must be addressed to advance the field.

Artificial intelligence for breast cancer detection and its health technology assessment: A scoping review

BACKGROUND

Recent healthcare advancements highlight the potential of Artificial Intelligence (AI) - and especially, among its subfields, Machine Learning (ML) - in enhancing Breast Cancer (BC) clinical care, leading to improved patient outcomes and increased radiologists' efficiency. While medical imaging techniques have significantly contributed to BC detection and diagnosis, their synergy with AI algorithms has consistently demonstrated superior diagnostic accuracy, reduced False Positives (FPs), and enabled personalized treatment strategies. Despite the burgeoning enthusiasm for leveraging AI for early and effective BC clinical care, its widespread integration into clinical practice is yet to be realized, and the evaluation of AI-based health technologies in terms of health and economic outcomes remains an ongoing endeavor.

OBJECTIVES

This scoping review aims to investigate AI (and especially ML) applications that have been implemented and evaluated across diverse clinical tasks or decisions in breast imaging and to explore the current state of evidence concerning the assessment of AI-based technologies for BC clinical care within the context of Health Technology Assessment (HTA).

METHODS

We conducted a systematic literature search following the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P) checklist in PubMed and Scopus to identify relevant studies on AI (and particularly ML) applications in BC detection and diagnosis. We limited our search to studies published from January 2015 to October 2023. The Minimum Information about CLinical Artificial Intelligence Modeling (MI-CLAIM) checklist was used to assess the quality of AI algorithms development, evaluation, and reporting quality in the reviewed articles. The HTA Core Model® was also used to analyze the comprehensiveness, robustness, and reliability of the reported results and evidence in AI-systems' evaluations to ensure rigorous assessment of AI systems' utility and cost-effectiveness in clinical practice.

RESULTS

Of the 1652 initially identified articles, 104 were deemed eligible for inclusion in the review. Most studies examined the clinical effectiveness of AI-based systems (78.84%, n= 82), with one study focusing on safety in clinical settings, and 13.46% (n=14) focusing on patients' benefits. Of the studies, 31.73% (n=33) were ethically approved to be carried out in clinical practice, whereas 25% (n=26) evaluated AI systems legally approved for clinical use. Notably, none of the studies addressed the organizational implications of AI systems in clinical practice. Of the 104 studies, only two of them focused on cost-effectiveness analysis, and were analyzed separately. The average percentage scores for the first 102 AI-based studies' quality assessment based on the MI-CLAIM checklist criteria were 84.12%, 83.92%, 83.98%, 74.51%, and 14.7% for study design, data and optimization, model performance, model examination, and reproducibility, respectively. Notably, 20.59% (n=21) of these studies relied on large-scale representative real-world breast screening datasets, with only 10.78% (n =11) studies demonstrating the robustness and generalizability of the evaluated AI systems.

CONCLUSION

In bridging the gap between cutting-edge developments and seamless integration of AI systems into clinical workflows, persistent challenges encompass data quality and availability, ethical and legal considerations, robustness and trustworthiness, scalability, and alignment with existing radiologists' workflow. These hurdles impede the synthesis of comprehensive, robust, and reliable evidence to substantiate these systems' clinical utility, relevance, and cost-effectiveness in real-world clinical workflows. Consequently, evaluating AI-based health technologies through established HTA methodologies becomes complicated. We also highlight potential significant influences on AI systems' effectiveness of various factors, such as operational dynamics, organizational structure, the application context of AI systems, and practices in breast screening or examination reading of AI support tools in radiology. Furthermore, we emphasize substantial reciprocal influences on decision-making processes between AI systems and radiologists. Thus, we advocate for an adapted assessment framework specifically designed to address these potential influences on AI systems' effectiveness, mainly addressing system-level transformative implications for AI systems rather than focusing solely on technical performance and task-level evaluations.

Genetic Basis of Breast and Ovarian Cancer: Approaches and Lessons Learnt from Three Decades of Inherited Predisposition Testing

Germline variants occurring in BRCA1 and BRCA2 give rise to hereditary breast and ovarian cancer (HBOC) syndrome, predisposing to breast, ovarian, fallopian tube, and peritoneal cancers marked by elevated incidences of genomic aberrations that correspond to poor prognoses. These genes are in fact involved in genetic integrity, particularly in the process of homologous recombination (HR) DNA repair, a high-fidelity repair system for mending DNA double-strand breaks. In addition to its implication in HBOC pathogenesis, the impairment of HR has become a prime target for therapeutic intervention utilizing poly (ADP-ribose) polymerase (PARP) inhibitors. In the present review, we introduce the molecular roles of HR orchestrated by BRCA1 and BRCA2 within the framework of sensitivity to PARP inhibitors. We examine the genetic architecture underneath breast and ovarian cancer ranging from high- and mid- to low-penetrant predisposing genes and taking into account both germline and somatic variations. Finally, we consider higher levels of complexity of the genomic landscape such as polygenic risk scores and other approaches aiming to optimize therapeutic and preventive strategies for breast and ovarian cancer.

Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review

BACKGROUND

Breast cancer is the most common disease in women. Recently, explainable artificial intelligence (XAI) approaches have been dedicated to investigate breast cancer. An overwhelming study has been done on XAI for breast cancer. Therefore, this study aims to review an XAI for breast cancer diagnosis from mammography and ultrasound (US) images. We investigated how XAI methods for breast cancer diagnosis have been evaluated, the existing ethical challenges, research gaps, the XAI used and the relation between the accuracy and explainability of algorithms.

METHODS

In this work, Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist and diagram were used. Peer-reviewed articles and conference proceedings from PubMed, IEEE Explore, ScienceDirect, Scopus and Google Scholar databases were searched. There is no stated date limit to filter the papers. The papers were searched on 19 September 2023, using various combinations of the search terms 'breast cancer', 'explainable', 'interpretable', 'machine learning', 'artificial intelligence' and 'XAI'. Rayyan online platform detected duplicates, inclusion and exclusion of papers.

RESULTS

This study identified 14 primary studies employing XAI for breast cancer diagnosis from mammography and US images. Out of the selected 14 studies, only 1 research evaluated humans' confidence in using the XAI system-additionally, 92.86% of identified papers identified dataset and dataset-related issues as research gaps and future direction. The result showed that further research and evaluation are needed to determine the most effective XAI method for breast cancer.

CONCLUSION

XAI is not conceded to increase users' and doctors' trust in the system. For the real-world application, effective and systematic evaluation of its trustworthiness in this scenario is lacking.

PROSPERO REGISTRATION NUMBER

CRD42023458665.

Economic evaluations of artificial intelligence-based healthcare interventions: a systematic literature review of best practices in their conduct and reporting

Objectives: Health economic evaluations (HEEs) help healthcare decision makers understand the value of new technologies. Artificial intelligence (AI) is increasingly being used in healthcare interventions. We sought to review the conduct and reporting of published HEEs for AI-based health interventions. Methods: We conducted a systematic literature review with a 15-month search window (April 2021 to June 2022) on 17th June 2022 to identify HEEs of AI health interventions and update a previous review. Records were identified from 3 databases (Medline, Embase, and Cochrane Central). Two reviewers screened papers against predefined study selection criteria. Data were extracted from included studies using prespecified data extraction tables. Included studies were quality assessed using the National Institute for Health and Care Excellence (NICE) checklist. Results were synthesized narratively. Results: A total of 21 studies were included. The most common type of AI intervention was automated image analysis (9/21, 43%) mainly used for screening or diagnosis in general medicine and oncology. Nearly all were cost-utility (10/21, 48%) or cost-effectiveness analyses (8/21, 38%) that took a healthcare system or payer perspective. Decision-analytic models were used in 16/21 (76%) studies, mostly Markov models and decision trees. Three (3/16, 19%) used a short-term decision tree followed by a longer-term Markov component. Thirteen studies (13/21, 62%) reported the AI intervention to be cost effective or dominant. Limitations tended to result from the input data, authorship conflicts of interest, and a lack of transparent reporting, especially regarding the AI nature of the intervention. Conclusion: Published HEEs of AI-based health interventions are rapidly increasing in number. Despite the potentially innovative nature of AI, most have used traditional methods like Markov models or decision trees. Most attempted to assess the impact on quality of life to present the cost per QALY gained. However, studies have not been comprehensively reported. Specific reporting standards for the economic evaluation of AI interventions would help improve transparency and promote their usefulness for decision making. This is fundamental for reimbursement decisions, which in turn will generate the necessary data to develop flexible models better suited to capturing the potentially dynamic nature of AI interventions.

Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers

Cancer significantly contributes to global mortality, with 9.3 million annual deaths. To alleviate this burden, the utilization of artificial intelligence (AI) applications has been proposed in various domains of oncology. However, the potential applications of AI and the barriers to its widespread adoption remain unclear. This study aimed to address this gap by conducting a cross-sectional, global, web-based survey of over 1000 AI and cancer researchers. The results indicated that most respondents believed AI would positively impact cancer grading and classification, follow-up services, and diagnostic accuracy. Despite these benefits, several limitations were identified, including difficulties incorporating AI into clinical practice and the lack of standardization in cancer health data. These limitations pose significant challenges, particularly regarding testing, validation, certification, and auditing AI algorithms and systems. The results of this study provide valuable insights for informed decision-making for stakeholders involved in AI and cancer research and development, including individual researchers and research funding agencies.

Saliva microRNA signature to diagnose endometriosis: A cost-effectiveness evaluation of the Endotest®

OBJECTIVE

To evaluate a saliva diagnostic test (Endotest®) for endometriosis compared with the conventional algorithm.

DESIGN

A cost-effectiveness analysis with a decision-tree model based on literature data.

SETTING

France.

POPULATION

Women with chronic pelvic pain.

METHODS

Strategy I is the French algorithm, representing the comparator. For strategy II, all patients have an Endotest®. For strategy III, patients undergo ultrasonography to detect endometrioma and patients with no endometrioma detected have an Endotest®. For strategy IV, patients with no endometrioma detected on ultrasonography undergo pelvic magnetic resonance imaging (MRI) to detect endometrioma and/or deep endometriosis. An Endotest® is then performed for patients with a negative result on MRI.

MAIN OUTCOMES MEASURES

Costs and accuracy rates and incremental cost-effectiveness ratios (ICERs). Three analyses were performed with an Endotest® priced at €500, €750, and €1000. Probabilistic sensitivity analysis was conducted with Monte Carlo simulations.

RESULTS

With an Endotest® priced at €750, the cost per correctly diagnosed case was €1542, €990, €919 and €1000, respectively, for strategies I, II, III and IV. Strategy I was dominated by all other strategies. Strategies IV, III and II were, respectively, preferred for a willingness-to-pay threshold below €473, between €473 and €4670, and beyond €4670 per correctly diagnosed case. At a price of €500 per Endotest®, strategy I was dominated by all other strategies. At €1000, the ICERs of strategies II and III were €724 and €387 per correctly diagnosed case, respectively, compared with strategy I.

CONCLUSION

The present study demonstrates the value of the Endotest® from an economic perspective.

Equity within AI systems: What can health leaders expect?

Artificial Intelligence (AI) for health has a great potential; it has already proven to be successful in enhancing patient outcomes, facilitating professional work and benefiting administration. However, AI presents challenges related to health equity defined as an opportunity for people to reach their fullest health potential. This article discusses the opportunities and challenges that AI presents in health and examines ways in which inequities related to AI can be mitigated.

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson's Disease Affected by COVID-19: A Narrative Review

Background and Motivation: Parkinson's disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.