Prediction of the clinical outcome of NMIBC using Artificial Intelligence
E. Frassini (TU Delft - Electrical Engineering, Mathematics and Computer Science)
MB Van Gijzen – Mentor (TU Delft - Numerical Analysis)
Farbod Khoraminia – Graduation committee member (Erasmus MC)
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Abstract
In our research, we have made a significant advancement in predicting the clinical outcome of high-risk non-muscle invasive bladder cancer (HR-NMIBC) by combining clinicopathological data with image-related features. This integrated approach has shown remarkable improvements in the accuracy of artificial intelligence techniques for outcome prediction.
We developed a novel methodology that effectively combines information from cell nuclei per patient, resulting in enhanced classification accuracy. By integrating clinicopathological data with image-related features extracted from medical imaging, we demonstrated the power of AI in more accurately predicting clinical outcomes for HR-NMIBC.
Our study provides a comprehensive view of the disease, taking into account both macroscopic characteristics and microscopic details observed at the cellular level. By aggregating information from thousands of cell nuclei for each patient, we transformed raw data into a format suitable for machine learning algorithms, improving the performance of AI techniques in clinical outcome prediction.
However, it is essential to address the potential biases and imbalanced variables present in the dataset. We noticed gender imbalance, differences in tumor size, and uneven grading levels, which may affect the generalizability of our conclusions.
To enhance our analysis, we retrained a convolutional neural network (CNN) using our image dataset, achieving high accuracy in segmenting hematoxylin and eosin stained images and accurately identifying cell nuclei boundaries. Additionally, we implemented an innovative clustering technique called FlowSOM, enabling us to group and classify millions of cell nuclei based on their characteristics, providing valuable insights into cellular heterogeneity.
Our AI models exhibited high performance metrics, particularly the random forest algorithm, which proved most suitable for the task. We also conducted a variable importance analysis, revealing specific cell clusters with significant impact on predicting clinical outcomes, emphasizing the relevance of cellular size and shape in disease progression and treatment response.