A Decision-Support Tool for Renal Mass Classification
Open accessAbstract
We investigate the viability of statistical relational machine learning algorithms for the task of identifying malignancy of renal masses using radiomics-based imaging features. Features characterizing the texture, signal intensity, and other relevant metrics of the renal mass were extracted from multiphase contrast-enhanced computed tomography images. The recently developed formalism of relational functional gradient boosting (RFGB) was used to learn human-interpretable models for classification. Experimental results demonstrate that RFGB outperforms many standard machine learning approaches as well as the current diagnostic gold standard of visual qualification by radiologists.
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Kunapuli et al. built a CDSS using Relational Functional Gradient Boosting, a statistical relational learning method which attributes its explainability to the usage of tree models and the provision of explanations in terms of features of interest.
“Kunapuli et al. [128] built a CDSS using Relational Functional Gradient Boosting (RFGB), a statistical relational learning method which attributes its explainability to the usage of tree models and the provision of explanations in terms of features of interest.”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [128]
Kunapuli et al. indicated that XAI could support specific rational reasoning processes, enabling CDSS to support their decisions with understandable interpretations to users with/without ML expertise.
“Kunapuli et al. [128] indicated that XAI could support specific rational reasoning processes, enabling CDSS to support their decisions with understandable interpretations to users with/without ML expertise.”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [128]
XAI could greatly enhance the effectiveness of decision-support and clinician confidence, especially when high-stakes decisions are being made, which is the key factor for the success of the model in the practical use stage.
“XAI therefore could greatly enhance the effectiveness of decision-support and clinician confidence [128] especially when high-stakes decisions are being made [127], which is the key factor for the success of the model in the practical use stage [122,124].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [128]
Most reviewed studies implemented a model-specific ante-hoc technique that provided local explanations.
“Most studies implemented a model-specific ante-hoc technique that provided local explanations [122,124-126,128].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [128]
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References 51 · Cited by 58
Research works printed but not linked: 3
- C. Reddy and C. Aggarwal: Healthcare Data Analytics. Chapman & Hall/CRC Data Mining and Knowledge Discovery Series. CRC Press, 2016.
- D. Page, S. Natarajan, V. Santos Costa, P. Peissig, A. Barnard, and M. Caldwell: Identifying adverse drug events from multi-relational healthcare data. In Proceedings of AAAI Conference on Artificial Intelligence, pages 790-793, 2012.
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.
- Authors
- Kunapuli, G., Varghese, B. A., Ganapathy, P., Desai, B., Cen, S., Aron, M., Gill, I., & Duddalwar, V.
- Affiliations
- UtopiaCompression (United States) (US)
- University of Southern California (US)
- Corresponding author
- Kunapuli, Gautam
- ORCID
- Venue
- Journal of Digital Imaging
- Published
- 2018-07-06
- Volume
- 31
- Issue
- 6
- Pages
- 929-939
- ISSN
- 0897-1889 (ISSN-L)
- 1618-727X
- PMC
- PMC6261185
- OpenAlex
- W2858745025
- Cited by
- 58
- References
- 51
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 5 · 2023: 11 · 2022: 7 · 2021: 12 · 2020: 10 · 2019: 9
- FWCI
- 5.34
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of Imaging Informatics in Medicine (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- L. K. Whittier Foundation
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- renal cell carcinoma, malignancy prediction, radiomics, statistical relational learning, interpretable models, decision support system
- MeSH terms
- Clinical Decision-Making, Machine Learning, Algorithms, Contrast Media, Decision Support Techniques, Humans, Kidney, Kidney Neoplasms, Radiographic Image Enhancement, Retrospective Studies, Tomography, X-Ray Computed, Reproducibility of Results
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