AI models are likely to become useful tools to support clinical decision-making especially in high mortality diseases such as lung cancer. However, the black-box nature of these models remains nowadays the main challenge to be addressed to employ AI in the clinic. This work describes the preliminary stages and results obtained by implementing a novel explainable approach in radiomics pipeline for local recurrence prediction of lung cancer and its technical validation relying on the high energy physics domain.

Monteleone, M., Gennai, S., Govoni, P., Paganelli, C. (2023). A novel explainable approach in radiomics pipeline for local recurrence prediction of lung cancer: a feasibility study exploiting high energy physics potential to evaluate the model. In ICBRA '23: Proceedings of the 2023 10th International Conference on Bioinformatics Research and Applications (pp.184-187) [10.1145/3632047.3632074].

A novel explainable approach in radiomics pipeline for local recurrence prediction of lung cancer: a feasibility study exploiting high energy physics potential to evaluate the model

Govoni P.;
2023

Abstract

AI models are likely to become useful tools to support clinical decision-making especially in high mortality diseases such as lung cancer. However, the black-box nature of these models remains nowadays the main challenge to be addressed to employ AI in the clinic. This work describes the preliminary stages and results obtained by implementing a novel explainable approach in radiomics pipeline for local recurrence prediction of lung cancer and its technical validation relying on the high energy physics domain.
paper
Artificial Intelligence; Clinical Decision Support; High Energy Physics; Machine Learning; Non-Small Cell Lung Cancer; Radiomics; Survival Analysis;
English
10th International Conference on Bioinformatics Research and Applications, ICBRA 2023 - 22 September 2023 through 24 September 2023
2023
ICBRA '23: Proceedings of the 2023 10th International Conference on Bioinformatics Research and Applications
9798400708152
2023
184
187
none
Monteleone, M., Gennai, S., Govoni, P., Paganelli, C. (2023). A novel explainable approach in radiomics pipeline for local recurrence prediction of lung cancer: a feasibility study exploiting high energy physics potential to evaluate the model. In ICBRA '23: Proceedings of the 2023 10th International Conference on Bioinformatics Research and Applications (pp.184-187) [10.1145/3632047.3632074].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/476041
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