Data Integration–Possibilities of Molecular and Clinical Data Fusion on the Example of Thyroid Cancer Diagnostics

Abstract

(1) Background: The data from independent gene expression sources may be integrated for the purpose of molecular diagnostics of cancer. So far, multiple approaches were described. Here, we investigated the impacts of different data fusion strategies on classification accuracy and feature selection stability, which allow the costs of diagnostic tests to be reduced. (2) Methods: We used molecular features (gene expression) combined with a feature extracted from the independent clinical data describing a patient’s sample. We considered the dependencies between selected features in two data fusion strategies (early fusion and late fusion) compared to classification models based on molecular features only. We compared the best accuracy classification models in terms of the number of features, which is connected to the potential cost reduction of the diagnostic classifier. (3) Results: We show that for thyroid cancer, the extracted clinical feature is correlated with (but not redundant to) the molecular data. The usage of data fusion allows a model to be obtained with similar or even higher classification quality (with a statistically significant accuracy improvement, a p-value below 0.05) and with a reduction in molecular dimensionality of the feature space from 15 to 3–8 (depending on the feature selection method). (4) Conclusions: Both strategies give comparable quality results, but the early fusion method provides better feature selection stability.

Exploring the Challenges of Diagnosing Thyroid Disease with Imbalanced Data and Machine Learning: A Systematic Literature Review

“…It is important to note that the majority of studies in the literature review utilized traditional machine learning methods such as SVM, RF, DT, KNN, and NB, 46,35,36 while fewer studies explored the use of deep learning algorithms such as ANN, MLP, CNN, LSTM, and BPNN 39,16,54,55 . While traditional machine learning algorithms have shown promising results in thyroid disease diagnosis, the potential of deep learning algorithms should not be overlooked, as they have shown success in various other medical applications.…”

“…At least 12 of the 41 studies that attempted to develop a model to diagnose thyroid disease used an SVMbased approach that used the standard technique in healthcare system prediction 34 . For instance, Płuciennik et al has developed a model for thyroid cancer diagnostics, which achieved approximately 95% accuracy 35 . Vairale et al compared SVM to Logistic Regression (LR), K-NN, and NN for identifying people with a hypothyroid disease on the actual case dataset.…”

Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence

“…Thus, we decided to follow an early-fusion approach since (1) all markers are 1-dimensional and reasonably independent from each other (unlike, for example, pixels of an image or DNA sequences used in other studies) and (2) early fusion is particularly suitable for allowing interactions between markers. 48…”

Glioblastoma and Solitary Brain Metastasis: Differentiation by Integrating Demographic‐MRI and Deep‐Learning Radiomics Signatures

“…Deep‐learning radiomics (DLR) features comprise radiomics and DL features obtained using an early fusion method 31 . Subsequently, the feature selection methods were separately used for the radiomics, DL, and DLR features.…”

“…Deep-learning radiomics (DLR) features comprise radiomics and DL features obtained using an early fusion method. 31 Subsequently, the feature selection methods were separately used for the radiomics, DL, and DLR features. Prior to feature selection, all features were normalized using the z-score method, as shown in the following formula:…”