A comprehensive AI model development framework for consistent Gleason grading

BACKGROUND

Artificial Intelligence(AI)-based solutions for Gleason grading hold promise for pathologists, while image quality inconsistency, continuous data integration needs, and limited generalizability hinder their adoption and scalability.

METHODS

We present a comprehensive digital pathology workflow for AI-assisted Gleason grading. It incorporates A!MagQC (image quality control), A!HistoClouds (cloud-based annotation), Pathologist-AI Interaction (PAI) for continuous model improvement, Trained on Akoya-scanned images only, the model utilizes color augmentation and image appearance migration to address scanner variations. We evaluate it on Whole Slide Images (WSI) from another five scanners and conduct validations with pathologists to assess AI efficacy and PAI.

RESULTS

Our model achieves an average F1 score of 0.80 on annotations and 0.71 Quadratic Weighted Kappa on WSIs for Akoya-scanned images. Applying our generalization solution increases the average F1 score for Gleason pattern detection from 0.73 to 0.88 on images from other scanners. The model accelerates Gleason scoring time by 43% while maintaining accuracy. Additionally, PAI improve annotation efficiency by 2.5 times and led to further improvements in model performance.

CONCLUSIONS

This pipeline represents a notable advancement in AI-assisted Gleason grading for improved consistency, accuracy, and efficiency. Unlike previous methods limited by scanner specificity, our model achieves outstanding performance across diverse scanners. This improvement paves the way for its seamless integration into clinical workflows.

Why do errors arise in artificial intelligence diagnostic tools in histopathology and how can we minimize them?

Artificial intelligence (AI)-based diagnostic tools can offer numerous benefits to the field of histopathology, including improved diagnostic accuracy, efficiency and productivity. As a result, such tools are likely to have an increasing role in routine practice. However, all AI tools are prone to errors, and these AI-associated errors have been identified as a major risk in the introduction of AI into healthcare. The errors made by AI tools are different, in terms of both cause and nature, to the errors made by human pathologists. As highlighted by the National Institute for Health and Care Excellence, it is imperative that practising pathologists understand the potential limitations of AI tools, including the errors made. Pathologists are in a unique position to be gatekeepers of AI tool use, maximizing patient benefit while minimizing harm. Furthermore, their pathological knowledge is essential to understanding when, and why, errors have occurred and so to developing safer future algorithms. This paper summarises the literature on errors made by AI diagnostic tools in histopathology. These include erroneous errors, data concerns (data bias, hidden stratification, data imbalances, distributional shift, and lack of generalisability), reinforcement of outdated practices, unsafe failure mode, automation bias, and insensitivity to impact. Methods to reduce errors in both tool design and clinical use are discussed, and the practical roles for pathologists in error minimisation are highlighted. This aims to inform and empower pathologists to move safely through this seismic change in practice and help ensure that novel AI tools are adopted safely.

Neoadjuvant hormonal therapy before radical prostatectomy in high-risk prostate cancer

Patients with high-risk prostate cancer treated with curative intent are at an increased risk of biochemical recurrence, metastatic progression and cancer-related death compared with patients treated for low-risk or intermediate-risk disease. Thus, these patients often need multimodal therapy to achieve complete disease control. Over the past two decades, multiple studies on the use of neoadjuvant treatment have been performed using conventional androgen deprivation therapy, which comprises luteinizing hormone-releasing hormone agonists or antagonists and/or first-line anti-androgens. However, despite results from these studies demonstrating a reduction in positive surgical margins and tumour volume, no benefit has been observed in hard oncological end points, such as cancer-related death. The introduction of potent androgen receptor signalling inhibitors (ARSIs), such as abiraterone, apalutamide, enzalutamide and darolutamide, has led to a renewed interest in using neoadjuvant hormonal treatment in high-risk prostate cancer. The addition of ARSIs to androgen deprivation therapy has demonstrated substantial survival benefits in the metastatic castration-resistant, non-metastatic castration-resistant and metastatic hormone-sensitive settings. Intuitively, a similar survival effect can be expected when applying ARSIs as a neoadjuvant strategy in high-risk prostate cancer. Most studies on neoadjuvant ARSIs use a pathological end point as a surrogate for long-term oncological outcome. However, no consensus yet exists regarding the ideal definition of pathological response following neoadjuvant hormonal therapy and pathologists might encounter difficulties in determining pathological response in hormonally treated prostate specimens. The neoadjuvant setting also provides opportunities to gain insight into resistance mechanisms against neoadjuvant hormonal therapy and, consequently, to guide personalized therapy.