Chaurasia Abadh K, Harris Helen C, Toohey Patrick W, Hewitt Alex W
Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia.
Pandani Solutions Pty Ltd, Hobart, TAS, Australia.
Prostate Cancer Prostatic Dis. 2025 Mar 14. doi: 10.1038/s41391-025-00957-w.
Gleason grading remains the gold standard for prostate cancer histological classification and prognosis, yet its subjectivity leads to grade variability between pathologists, potentially impacting clinical decision-making. Herein, we trained and validated a generalised AI-driven system for diagnosing prostate cancer using diverse datasets from tissue microarray (TMA) core and whole slide images (WSIs) with Haematoxylin and Eosin staining.
We analysed eight prostate cancer datasets, which included 12,711 histological images from 3648 patients, incorporating TMA core images and WSIs. The Macenko method was used to normalise colours for consistency across diverse images. Subsequently, we trained a multi-resolution (5x, 10x, 20x, and 40x) binary classifier to identify benign and malignant tissue. We then implemented a multi-class classifier for Gleason patterns (GP) sub-categorisation from malignant tissue. Finally, the models were externally validated on 11,132 histology images from 2176 patients to determine the International Society of Urological Pathology (ISUP) grade. Models were assessed using various classification metrics, and the agreement between the model's predictions and the ground truth was quantified using the quadratic weighted Cohen's Kappa (κ) score.
Our multi-resolution binary classifier demonstrated robust performance in distinguishing malignant from benign tissue with κ scores of 0.967 on internal validation. The model achieved κ scores ranging from 0.876 to 0.995 across four unseen testing datasets. The multi-class classifier also distinguished GP3, GP4, and GPs with an overall κ score of 0.841. This model was further tested across four datasets, obtaining κ scores ranging from 0.774 to 0.888. The models' performance was compared against an independent pathologist's annotation on an external dataset, achieving a κ score of 0.752 for four classes.
The self-supervised ViT-based model effectively diagnoses and grades prostate cancer using histological images, distinguishing benign and malignant tissues and classifying malignancies by aggressiveness. External validation highlights its robustness and clinical applicability in digital pathology.
Gleason分级仍然是前列腺癌组织学分类和预后的金标准，但其主观性导致病理学家之间的分级存在差异，可能影响临床决策。在此，我们使用苏木精和伊红染色的组织微阵列（TMA）核心和全切片图像（WSI）的不同数据集，训练并验证了一种用于诊断前列腺癌的通用人工智能驱动系统。
我们分析了八个前列腺癌数据集，其中包括来自3648例患者的12711张组织学图像，包括TMA核心图像和WSI。采用Macenko方法对颜色进行归一化，以确保不同图像之间的一致性。随后，我们训练了一个多分辨率（5倍、10倍、20倍和40倍）二元分类器来识别良性和恶性组织。然后，我们为来自恶性组织的Gleason模式（GP）子分类实现了一个多类分类器。最后，在来自2176例患者的11132张组织学图像上对模型进行外部验证，以确定国际泌尿病理学会（ISUP）分级。使用各种分类指标对模型进行评估，并使用二次加权Cohen's Kappa（κ）分数对模型预测与真实情况之间的一致性进行量化。
我们的多分辨率二元分类器在区分恶性组织和良性组织方面表现出强大的性能，内部验证的κ分数为0.967。该模型在四个未见测试数据集上的κ分数范围为0.876至0.995。多类分类器还区分了GP3、GP4和GPs，总体κ分数为0.841。该模型在四个数据集上进一步测试，κ分数范围为0.774至0.888。将模型的性能与外部数据集上独立病理学家的注释进行比较，四类的κ分数为0.752。
基于自监督ViT的模型使用组织学图像有效地诊断前列腺癌并进行分级，区分良性和恶性组织，并根据侵袭性对恶性肿瘤进行分类。外部验证突出了其在数字病理学中的稳健性和临床适用性。
