Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization
Open accessAbstract
Over the last decade there has been an extensive evolution in the Artificial Intelligence (AI) field. Modern radiation oncology is based on the exploitation of advanced computational methods aiming to personalization and high diagnostic and therapeutic precision. The quantity of the available imaging data and the increased developments of Machine Learning (ML), particularly Deep Learning (DL), triggered the research on uncovering "hidden" biomarkers and quantitative features from anatomical and functional medical images. Deep Neural Networks (DNN) have achieved outstanding performance and broad implementation in image processing tasks. Lately, DNNs have been considered for radiomics and their potentials for explainable AI (XAI) may help classification and prediction in clinical practice. However, most of them are using limited datasets and lack generalized applicability. In this study we review the basics of radiomics feature extraction, DNNs in image analysis, and major interpretability methods that help enable explainable AI. Furthermore, we discuss the crucial requirement of multicenter recruitment of large datasets, increasing the biomarkers variability, so as to establish the potential clinical value of radiomics and the development of robust explainable AI models.
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Clinical adoption of multimodal XAI in oncology is constrained by limited interpretability and fragmented use of RWD.
“Multimodal explainable artificial intelligence (XAI) is transforming oncology, yet its clinical adoption is constrained by limited interpretability and fragmented use of real-world data (RWD), including electronic medical records, geospatial data, and social determinants of health.”
From X-CART: a multimodal real-world data pipeline for explainable AI-driven patient navigation in radiation oncology (2026) · cites this paper as [1]
Section 1.3 (page unavailable) says interpretability is a challenge that must be addressed to facilitate clinical implementation of deep-learning models in oncological radiomics. Section 1.1 calls for integrating clinical, imaging and other data, but the paper does not establish fragmented use of real-world data—including geospatial data and social determinants of health—as the claimed constraint on adoption. (LLM, conf=0.87)
Experimental — accuracy not yet measuredInformation fusion and multimodal systems can have significant performance over systems working with a single type of data.
“The information fusion and multimodal system can have a significant performance over the systems working with a single type of data [36,100].”
From Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks (2023) · cites this paper as [36]
Challenges remain in data harmonization and generalizability of AI models in Radiation Oncology, such as small dataset size and varying feature extraction sensitivity.
“Although promising, challenges remain in data harmonization and the generalizability of AI models in this area, such as small dataset size and varying feature extraction sensitivity.1”
From X-CART: a multimodal real-world data pipeline for explainable AI-driven patient navigation in radiation oncology (2026) · cites this paper as [1]
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- Authors
- Papadimitroulas, P., Brocki, L., Christopher Chung, N., Marchadour, W., Vermet, F., Gaubert, L., Eleftheriadis, V., Plachouris, D., Visvikis, D., Kagadis, G. C., & Hatt, M.
- Affiliations
- University of Patras (GR)
- University of Warsaw (PL)
- Inserm (FR)
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Kagadis, George C.
- ORCID
- Venue
- Physica Medica
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 83
- Pages
- 108-121
- ISSN
- 1120-1797 (ISSN-L)
- OpenAlex
- W3138961261
- Cited by
- 226
- References
- 162
- Citations by year
- 2026: 29 · 2025: 64 · 2024: 42 · 2023: 40 · 2022: 40 · 2021: 11
- FWCI
- 5.51
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Physica Medica (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Funders
- European Commission (MIS- 5033021, 872735)
- Agence Nationale de la Recherche (ANR-11-LABX-0020)
- State Scholarships Foundation (MIS‐5033021, 5033021)
- European Social Fund (MIS - 5033021, 5033021)
- H2020 Marie Skłodowska-Curie Actions (872735)
- CHIST-ERA (CHIST-ERA-19-XAI-007)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- radiomics, deep learning, explainable AI, interpretability, data harmonization, radiation oncology, deep neural network, radiomic feature extraction, multi-center dataset, biomarkers, image analysis
- MeSH terms
- Machine Learning, Deep Learning, Artificial Intelligence, Image Processing, Computer-Assisted, Multicenter Studies as Topic, Neural Networks, Computer
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