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Predicting Lapatinib Dose Regimen Using Machine Learning and Deep Learning Techniques Based on a Real-World Study.

Ze Yu¹, Xuan Ye^2,3, Hongyue Liu^2,3

¹Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China.

Frontiers in Oncology

|June 20, 2022

Summary

This summary is machine-generated.

This study developed a deep learning model to predict optimal lapatinib dosage for metastatic HER2(+) breast cancer patients. The model achieved high accuracy, personalizing treatment based on key patient factors.

Keywords:

TabNet breast cancer deep learning individualized medication model lapatinib machine learning real-world study

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Area of Science:

Oncology
Pharmacology
Artificial Intelligence

Background:

Lapatinib is a targeted therapy for metastatic HER2-positive breast cancer.
Accurate dosing is crucial for treatment efficacy and minimizing toxicity.

Purpose of the Study:

To develop and validate a predictive model for optimal lapatinib dosing.
To leverage machine learning and deep learning techniques on real-world data.
To identify key factors influencing lapatinib dosage.

Main Methods:

A real-world study involving 149 breast cancer patients.
Variable selection using sequential forward selection with random forest.
Comparison of 12 machine learning and deep learning algorithms.
Model development using the TabNet algorithm.

Main Results:

TabNet demonstrated the best performance with accuracy = 0.82 and AUC = 0.83.
Key predictors for lapatinib dose included treatment protocols, weight, chemotherapy count, and metastasis count.
Model validation showed good precision and recall for 1250 mg and 1000 mg regimens.

Conclusions:

A deep learning model (TabNet) can effectively predict individualized lapatinib doses.
The model utilizes real-world evidence and identifies significant influencing variables.
This approach supports achieving optimal therapeutic outcomes in metastatic breast cancer patients.