Learning Function Classes with Genetic Programming for Explainable Meta-Learning of Tumor Growth Patterns

Keskeiset käsitteet

This work proposes a novel approach based on Genetic Programming to learn a parameterized function class that can be fit anew for each tumor growth data set, enabling explainable meta-learning of the underlying growth patterns.

Tiivistelmä

The paper presents a novel approach called Function Class GOMEA (FC-GOMEA) for learning function classes from multiple related data sets, using Genetic Programming. The key insights are:

Paragangliomas are rare, slow-growing tumors with unknown underlying growth patterns, making it difficult to determine the best treatment approach.
FC-GOMEA learns a parameterized function class that can be specialized to each individual tumor growth data set, enabling explainable meta-learning of the general growth patterns.
The authors evaluate FC-GOMEA on both synthetic and real-world tumor growth data sets.
On the synthetic data, FC-GOMEA is able to recover the ground-truth function classes (logistic and Gompertz) in many cases, especially when the noise level is low.
On the real-world data, FC-GOMEA identifies three distinct function classes that capture the general growth patterns, including a new class that may better represent the slow-growing nature of paragangliomas.
The authors discuss the potential clinical implications of the found function classes and the computational cost considerations of the proposed method.

Tilastot

The volume of paraganglioma tumors is measured over time for multiple patients.
The synthetic data is generated by fitting logistic and Gompertz function classes to the real-world data, and then sampling from these functions.
Gaussian noise with 0%, 5%, and 15% standard deviation is added to the synthetic data.

Lainaukset

"Paragangliomas are rare, primarily slow-growing tumors for which the underlying growth pattern is unknown. Therefore, determining the best care for a patient is hard."
"Being able to predict the growth accurately could assist in determining whether a patient will need treatment during their lifetime and, if so, the timing of this treatment."
"We argue that function class learning is more interpretable than creating a separate function for each local data set and more effective than creating a single (unparameterized) function for the global data set at once."

Tärkeimmät oivallukset

Function Class Learning with Genetic Programming

by E.M.C. Sijbe... klo arxiv.org 04-10-2024

https://arxiv.org/pdf/2402.12510.pdf

Syvällisempiä Kysymyksiä

How could the found function classes be further validated and tested for clinical applicability in predicting and managing paraganglioma tumor growth

To further validate and test the found function classes for clinical applicability in predicting and managing paraganglioma tumor growth, several steps can be taken:

Clinical Validation Studies: Conducting clinical validation studies where the predicted tumor growth based on the function classes is compared to actual patient data. This would involve analyzing the accuracy of the predictions, assessing any discrepancies, and evaluating the clinical relevance of the predictions in guiding treatment decisions.

Longitudinal Data Analysis: Analyzing longitudinal data from patients with paragangliomas to track the actual tumor growth over time and comparing it with the predicted growth patterns from the function classes. This would provide insights into the effectiveness of the function classes in capturing the real-world tumor growth dynamics.

External Validation: Testing the function classes on an independent dataset of paraganglioma tumor growth data to assess the generalizability and robustness of the models. This would help validate the predictive performance of the function classes across different patient populations.

Clinical Utility Assessment: Evaluating the clinical utility of the function classes by assessing their impact on treatment decisions, patient outcomes, and healthcare resource utilization. This would involve analyzing whether the predictions derived from the function classes lead to improved patient management and outcomes.

Expert Review and Feedback: Seeking feedback from clinical experts in the field of paraganglioma management to validate the clinical relevance of the predicted growth patterns. Expert review can provide valuable insights into the practical applicability of the function classes in a clinical setting.

What other types of tumor growth data could benefit from this function class learning approach, and how would the results differ compared to paragangliomas

The function class learning approach could benefit other types of tumor growth data, such as gliomas, breast cancer, or prostate cancer. The results obtained from applying this approach to different tumor types would likely differ based on the underlying growth patterns specific to each type of cancer. Here's how the results might differ compared to paragangliomas:

Gliomas: Gliomas exhibit different growth patterns compared to paragangliomas, often characterized by infiltrative growth and heterogeneous behavior. The function classes derived for gliomas may need to capture non-linear and spatial growth dynamics, reflecting the unique nature of brain tumors.

Breast Cancer: Breast cancer growth patterns vary based on the subtype and hormone receptor status. The function classes for breast cancer may need to account for factors like tumor size, hormone sensitivity, and lymph node involvement, which could influence the growth trajectory.

Prostate Cancer: Prostate cancer growth is influenced by factors such as Gleason score, PSA levels, and tumor stage. The function classes for prostate cancer may focus on predicting the growth rate and aggressiveness of the tumor, considering the variability in disease progression.

The results of applying function class learning to different tumor types would provide insights into the specific growth dynamics and patterns unique to each cancer, enabling personalized predictions and treatment strategies.

Could the function class learning approach be extended to other domains beyond tumor growth modeling where there are multiple related data sets with potentially shared underlying patterns

The function class learning approach can be extended to other domains beyond tumor growth modeling where multiple related data sets with shared underlying patterns exist. Some potential domains where this approach could be beneficial include:

Financial Data Analysis: Analyzing financial time series data from different markets or companies to identify common underlying patterns in stock price movements, exchange rates, or commodity prices. The function classes could help predict future trends and guide investment decisions.

Climate Modeling: Studying climate data from various regions to understand common trends in temperature, precipitation, or extreme weather events. Function class learning could assist in predicting climate patterns and assessing the impact of climate change.

Healthcare Outcomes Prediction: Utilizing patient data from diverse healthcare settings to predict clinical outcomes, disease progression, or treatment responses. Function classes could help identify common predictors of patient outcomes and support personalized healthcare interventions.

By applying function class learning to these domains, researchers can uncover hidden patterns, improve predictive accuracy, and enhance decision-making based on shared underlying structures in complex datasets.