Stroke diagnosis and management have seen remarkable advancements in recent years, largely driven by innovations in medical imaging. Among these, non-contrast computed tomography (NCCT) continues to be a fundamental tool in acute stroke evaluation due to its accessibility, rapid acquisition and cost-effectiveness. Traditional techniques like Net Water Uptake (NWU) have served as benchmarks for estimating ischaemic lesion age, a critical factor in determining appropriate treatment strategies. However, NWU’s limitations, including sensitivity to confounding variables and dependency on manual segmentation, have necessitated more sophisticated approaches. Enter the convolutional neural network-radiomics (CNN-R) model, a machine-learning solution that significantly improves accuracy and efficiency. A recent review published in npj Digital Medicine, explores how CNN-R enhances lesion age estimation, its practical integration into clinical workflows and its broader implications for patient outcomes.
Improving Accuracy in Lesion Age Estimation
Accurate determination of ischaemic lesion age is pivotal in acute stroke care as it directly influences the choice and timing of therapeutic interventions, such as thrombolysis or thrombectomy. NWU, the conventional method, utilises relative tissue hypoattenuation on NCCT to estimate lesion age, which correlates with the progression of water uptake in brain tissue. Although effective, NWU is hindered by its inability to capture within-lesion variability and its susceptibility to confounding factors such as chronic white matter lesions or anatomical asymmetries. Furthermore, its reliance on manual segmentation introduces subjectivity and practical challenges.
The CNN-R model addresses these shortcomings using a deep learning approach that analyses high-dimensional imaging features. These features include subtle patterns of signal heterogeneity and lesion anatomy variations that are often imperceptible to human experts. Unlike NWU, which focuses on a single relative intensity metric, CNN-R combines convolutional neural networks with radiomic features such as lesion texture, shape and depth-weighted metrics to generate more comprehensive assessments. Validation studies have demonstrated that CNN-R is nearly twice as accurate as NWU in estimating both chronometric and biological lesion ages. This increased accuracy is particularly beneficial in clinical scenarios requiring rapid and reliable data to guide treatment decisions.
Integrating Machine Learning into Clinical Practice
The clinical integration of CNN-R represents a paradigm shift in how acute ischaemic lesions are analysed. The model operates in two main stages: automated lesion segmentation and lesion age prediction. In the first stage, NCCT images are processed using convolutional neural networks to segment relevant ischaemic regions. While fully automated segmentation remains challenging due to variability in image quality and lesion characteristics, the CNN-R approach achieves remarkable consistency by incorporating expert adjudication for cases with multiple or ambiguous lesions.
The second stage involves estimating lesion age by combining CNN-derived predictions with radiomic features. This dual-layered approach enables the model to account for factors that traditional methods often overlook, such as focal swelling and lesion signal distribution. Unlike NWU, which frequently requires adjunctive imaging or pre-defined regions of interest, CNN-R functions independently on NCCT, optimising workflows and eliminating additional logistical hurdles. Furthermore, CNN-R’s adaptability across diverse imaging datasets, including slice thickness and scanner types variations, underscores its robustness and potential for widespread clinical adoption.
Despite these strengths, the model’s dependency on expert input for final lesion selection remains a limitation. In approximately 25% of cases, the top-ranked automated segmentation failed to align with clinical presentation, necessitating manual review. However, this semi-automated approach still significantly improves over purely manual or fully automated systems, balancing efficiency with diagnostic precision. Future iterations of CNN-R, potentially incorporating advanced algorithms like transformer networks, may further enhance automation without compromising accuracy.
Impact on Stroke Management and Patient Outcomes
The implications of CNN-R extend well beyond improved diagnostic accuracy. By providing precise and reliable estimates of lesion reversibility and progression, the model empowers clinicians to make more informed treatment decisions. For example, CNN-R’s superior correlation with core-to-penumbra ratios—a key indicator of salvageable brain tissue—enhances its utility in selecting patients for thrombolysis or thrombectomy. Furthermore, its performance is less influenced by the time elapsed since symptom onset, making it a valuable tool in cases where onset time is unknown or ambiguous.
Another critical advantage of CNN-R is its ability to predict infarct growth dynamics. Studies have shown that CNN-R outperforms NWU and chronometric onset-to-scan time in forecasting lesion expansion over 24-48 hours, a critical period for determining long-term functional outcomes. This predictive capability not only aids in acute decision-making but also informs post-acute care strategies, such as rehabilitation planning and secondary prevention measures. Additionally, the model’s compatibility with NCCT ensures that it can be seamlessly integrated into existing imaging protocols without requiring significant infrastructural changes.
While the primary application of CNN-R lies in medium-to-large strokes with visible ischaemic changes, its utility may extend to other neurological conditions in the future. For instance, the underlying radiomic framework could be adapted to assess conditions such as traumatic brain injury or neurodegenerative diseases, broadening its clinical relevance. However, challenges remain, including the need for further validation across diverse populations and healthcare settings to ensure its generalisability.
The advent of CNN-R in ischaemic lesion age estimation represents a transformative milestone in stroke diagnostics. By addressing the limitations of traditional methods like NWU, CNN-R offers a more accurate, efficient and scalable solution for guiding acute stroke management. Its integration into clinical practice can potentially improve patient outcomes through better-informed treatment decisions. With further advancements, such as fully automated pipelines and broader applications, the model could redefine the standard of care for neurological emergencies, paving the way for more personalised and effective interventions.
Source: npj Digital Medicine
Image Credit: iStock
