Radiomics has emerged as a transformative tool in prostate cancer diagnosis, using advanced image analysis to quantify tumour characteristics and improve diagnostic accuracy. For prostate cancer, early diagnosis is crucial, as it enhances treatment outcomes and overall survival rates. Radiomics extracts large volumes of quantitative data, or "features," from medical images, allowing for a comprehensive assessment of tumour properties that may not be visible through traditional imaging methods. These features can inform clinicians about tumour heterogeneity and malignancy potential, thus aiding in the diagnosis and management of clinically significant prostate cancer (csPCa). However, achieving optimal results from radiomics relies on strategic feature selection, as models can be overwhelmed with high-dimensional data that may dilute diagnostic accuracy. A recent study published in Insights into Imaging evaluates various feature selection methods, machine learning (ML) classifiers and MRI sequences to determine the most effective strategies for csPCa diagnosis.
Selecting Radiomic Features for Improved Diagnostic Accuracy
Feature selection plays a critical role in radiomics, helping to refine models by selecting only the most predictive and relevant variables from potentially thousands of extracted features. The study examined three categories of feature selection methods: filter, wrapper and embedded. Filter methods, such as minimum redundancy maximum relevance (mRMRe), use statistical correlations to reduce features based on redundancy and relevance. Wrapper methods, including Boruta and Recursive Feature Elimination (RFE), create subsets of features and apply them to a predictive model to gauge their utility. Lastly, embedded methods like L1-lasso integrate feature selection into the model-building process itself, making them efficient for high-dimensional datasets.
Among these methods, Boruta, RFE and L1-lasso were found to be the top performers, consistently identifying features that enhanced the model's diagnostic power. For instance, Boruta uses random forest algorithms to compare feature importance, while RFE iteratively removes the least informative features to optimise the model. These methods improve diagnostic accuracy and mitigate the risk of overfitting, a common issue in high-dimensional data analysis. The effectiveness of these methods underscores the importance of careful feature selection in radiomics, as the model’s diagnostic ability heavily depends on using informative and non-redundant features. This finding is critical, as it suggests that investing in optimal feature selection can yield substantial improvements in csPCa detection.
Machine Learning Classifiers and Diagnostic Efficiency
While feature selection is essential, the choice of machine learning classifier also influences model performance. This study compared four ML classifiers: Support Vector Machine (SVM), Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Boosted Generalized Linear Model (GLM). Each classifier has different strengths, with SVM excelling in handling complex, nonlinear relationships and RF providing a robust framework less prone to overfitting.
Interestingly, the study found that while there were minor performance variations, no single classifier emerged as significantly superior across all metrics. For instance, Boosted GLM demonstrated a slight advantage in several cases, but the difference was insufficient to suggest it as the optimal choice for all scenarios. This observation implies that in radiomic-based csPCa diagnosis, the classifier's impact on model accuracy is secondary to the effect of feature selection. This finding is significant, as it simplifies the model-building process by allowing researchers and clinicians to focus on fine-tuning feature selection rather than investing heavily in classifier selection. Nonetheless, the Boosted GLM’s performance suggests it might be a promising choice for future studies, especially when paired with robust feature selection methods like Boruta or RFE.
MRI Sequences: Optimising Data Sources for Prostate Cancer Detection
The choice of MRI sequence also plays a pivotal role in developing accurate radiomic models. This study evaluated three imaging approaches: T2-weighted (T2w) images, apparent diffusion coefficient (ADC) maps and a combination of both (biparametric MRI or bpMRI). Each sequence provides unique information about the tumour, with T2w imaging focusing on anatomical detail and ADC maps highlighting diffusion-related features, often associated with cellular density.
The study revealed that ADC-derived features consistently offered higher discriminatory power than T2w-derived features, suggesting that ADC maps might be more informative for csPCa diagnosis. Interestingly, combining T2w and ADC features did not enhance diagnostic performance, indicating that ADC maps alone may be sufficient for creating robust models. This finding aligns with previous research, which suggests that ADC-focused models may outperform those relying on multi-sequence inputs for certain prostate cancer diagnostics. By narrowing the data source to ADC alone, the model-building process becomes simpler and potentially more cost-effective, as fewer imaging sequences are needed without compromising accuracy. This insight could influence future prostate cancer imaging protocols, prioritising ADC maps to optimise radiomics workflows.
The findings indicate that feature selection methods like Boruta, RFE and L1-lasso are critical for enhancing model accuracy, often surpassing the impact of classifier choice. Additionally, ADC-derived features appear to be the most effective data source for csPCa radiomics, potentially simplifying imaging requirements without compromising diagnostic outcomes. These insights lay the groundwork for future research, where continued exploration of feature selection methods and imaging protocols could further refine radiomic models.
Source: Insights into Imaging
Image Credit: iStock
