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

Gurmessa, Daraje kaba; Jimma, Worku

doi:10.1136/bmjhci-2023-100954

Cited by 2 publications

(1 citation statement)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One major challenge is the need for a comprehensive understanding of risk factors within and across cancer types 13 . As machine learning research delves deeper, the utilization of explainable machine learning marks a significant advancement in enhancing the efficacy of cancer prediction models [14][15][16] . The development and application of explainable machine learning not only provide accurate predictions or classifications but also offer insights into how those predictions are made in a transparent and interpretable manner 17 .…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Performance of Explainable Machine Learning Models in Risk Factor Identification for Major Cancers (Preprint)

Huang

2024

Preprint

View full text Add to dashboard Cite

BACKGROUND Cancer is a life-threatening disease and a leading cause of death worldwide, with an estimated 611,000 deaths and over 2 million new cases in the United States in 2024. The rising incidence of major cancers, including among younger individuals, highlights the need for early screening and monitoring of risk factors to manage and decrease cancer risk. OBJECTIVE To identify pivotal factors essential for predicting the risk factors for four major cancer types (breast, colorectal, lung, and prostate) through the utilization of explainable machine learning techniques is imperative due to the increasing burden of cancer patients. METHODS De-identified electronic health record data from MIMIC-III was used to identify patients with four types of cancer who had longitudinal hospital visits prior to receiving a cancer diagnosis. Their records were matched and combined with those of patients without cancer diagnoses using propensity scores based on demographic factors. Three advanced models, penalized Logistic Regression (LR), Random Forest (RF), and Multilayer Perceptron (MLP), were conducted to identify the rank of risk factors for each cancer type, with feature importance analysis for RF and MLP models. The Rank Biased Overlap was adopted to compare the similarity of ranked risk factors across cancer types. RESULTS Our framework evaluated the prediction performance of explainable ML models, in which MLP achieved an AUC of 0.78 for breast cancer, 0.76 for colorectal cancer, 0.84 for lung cancer, and 0.78 for prostate cancer, respectively. In addition to demographic risk factors, the most prominent non-traditional risk factors overlapped across models and cancer types, including hyperlipidemia, diabetes, depressive disorders, heart diseases, and anemia. The similarity analysis indicated the unique risk factor pattern for lung cancer from other cancer types. CONCLUSIONS The study's findings demonstrate the effectiveness of explainable ML models in predicting non-traditional risk factors for major cancers and highlight the importance of considering unique risk profiles for different cancer types. These insights may contribute to efficient cancer screening and tailored cancer prevention strategies, which, in turn, offer fundamental support for clinical decision-making processes.

show abstract

Section: Introductionmentioning

confidence: 99%

Analyzing the Performance of Explainable Machine Learning Models in Risk Factor Identification for Major Cancers (Preprint)

Huang

2024

Preprint

View full text Add to dashboard Cite

show abstract

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

Gurmessa,

Jimma

2024

BMJ Health Care Inform

Self Cite

View full text Add to dashboard Cite

BackgroundBreast 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.MethodsIn 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.ResultsThis 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.ConclusionXAI 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 numberCRD42023458665.

show abstract

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

Cited by 2 publications

References 43 publications

Analyzing the Performance of Explainable Machine Learning Models in Risk Factor Identification for Major Cancers (Preprint)

Analyzing the Performance of Explainable Machine Learning Models in Risk Factor Identification for Major Cancers (Preprint)

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

Contact Info

Product

Resources

About