ArticleFrontiers in medicine2023
Analyzing breast cancer invasive disease event classification through explainable artificial intelligence.
Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions.BMC medical informatics and decision making · 2025Pooled it
- Breast cancer risk prediction using machine learning: a systematic review.Frontiers in oncology · 2024Pooled it
- Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review.Cancer innovation · 2024Article
- Leveraging State-of-the-Art AI Algorithms in Personalized Oncology: From Transcriptomics to Treatment.Diagnostics (Basel, Switzerland) · 2024Article
- An explainable machine learning model to solid adnexal masses diagnosis based on clinical data and qualitative ultrasound indicators.Cancer medicine · 2024Article
- Article
- Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form.PloS one · 2024Article
- Developing an ensemble machine learning study: Insights from a multi-center proof-of-concept study.PloS one · 2024Article
- Advancing Colorectal Cancer Diagnosis with AI-Powered Breathomics: Navigating Challenges and Future Directions.Diagnostics (Basel, Switzerland) · 2023Review
- Crosstalk between Metabolite Production and Signaling Activity in Breast Cancer.International journal of molecular sciences · 2023Article
- Automated Error Labeling in Radiation Oncology via Statistical Natural Language Processing.Diagnostics (Basel, Switzerland) · 2023Article
- Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review.Cancers · 2023Review
- An explainable machine learning ensemble model to predict the risk of ovarian cancer in BRCA-mutated patients undergoing risk-reducing salpingo-oophorectomy.Frontiers in oncology · 2023Article
Corrections and comments
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Authors and funding
22 authors.
Funding
Abstract
Introduction: Recently, accurate machine learning and deep learning approaches have been dedicated to the investigation of breast cancer invasive disease events (IDEs), such as recurrence, contralateral and second cancers. However, such approaches are poorly interpretable. Methods: Thus, we designed an Explainable Artificial Intelligence (XAI) framework to investigate IDEs within a cohort of 486 breast cancer patients enrolled at IRCCS Istituto Tumori "Giovanni Paolo II" in Bari, Italy. Using Shapley values, we determined the IDE driving features according to two periods, often adopted in clinical practice, of 5 and 10 years from the first tumor diagnosis. Results: Age, tumor diameter, surgery type, and multiplicity are predominant within the 5-year frame, while therapy-related features, including hormone, chemotherapy schemes and lymphovascular invasion, dominate the 10-year IDE prediction. Estrogen Receptor (ER), proliferation marker Ki67 and metastatic lymph nodes affect both frames. Discussion: Thus, our framework aims at shortening the distance between AI and clinical practice.
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