Wang Kexin, Luo Ning, Sun Zhaonan, Zhao Xiangpeng, She Lilan, Xing Zhangli, Chen Yuntian, He Chunlei, Wu Pengsheng, Wang Xiangpeng, Kong ZiXuan
School of Basic Medical Sciences, Capital Medical University, Beijing, 100069, China.
Department of Radiology, the Second Affiliated Hospital of Dalian Medical University, Dalian, 116023, China.
Insights Imaging. 2025 Jan 15;16(1):20. doi: 10.1186/s13244-024-01865-8.
To evaluate the feasibility of utilizing artificial intelligence (AI)-predicted biparametric MRI (bpMRI) image features for predicting the aggressiveness of prostate cancer (PCa).
A total of 878 PCa patients from 4 hospitals were retrospectively collected, all of whom had pathological results after radical prostatectomy (RP). A pre-trained AI algorithm was used to select suspected PCa lesions and extract lesion features for model development. The study evaluated five prediction methods, including (1) A clinical-imaging model of clinical features and image features of suspected PCa lesions selected by AI algorithm, (2) the PIRADS category, (3) a conventional radiomics model, (4) a deep-learning bases radiomics model, and (5) biopsy pathology.
In the externally validated dataset, the deep learning-based radiomics model showed the highest area under the curve (AUC 0.700 to 0.791). It exceeded the clinical-imaging model (AUC 0.597 to 0.718), conventional radiomic model (AUC 0.566 to 0.632), PIRADS score (AUC 0.554 to 0.613), and biopsy pathology (AUC 0.537 to 0.578). The AUC predicted by the model did not show a statistically significant difference among the three externally verified hospitals (p > 0.05).
Deep-learning radiomics models utilizing AI-extracted image features from bpMRI images can potentially be used to predict PCa aggressiveness, demonstrating a generalized ability for external validation.
Predicting the aggressiveness of prostate cancer (PCa) is important for formulating the best treatment plan for patients. The radiomic model based on deep learning is expected to provide an objective and non-invasive method for evaluating the aggressiveness of PCa.
Predicting the aggressiveness of PCa is important for patients to obtain the best treatment options. The deep learning-based radiomics model can predict the aggressiveness of PCa with high accuracy. The model has good universality when tested on multiple external datasets.
评估利用人工智能（AI）预测的双参数磁共振成像（bpMRI）图像特征预测前列腺癌（PCa）侵袭性的可行性。
回顾性收集了来自4家医院的878例PCa患者，所有患者均在前列腺癌根治术（RP）后有病理结果。使用预训练的AI算法选择疑似PCa病变并提取病变特征以进行模型开发。该研究评估了五种预测方法，包括：（1）由AI算法选择的疑似PCa病变的临床特征和图像特征的临床成像模型；（2）前列腺影像报告和数据系统（PIRADS）类别；（3）传统的放射组学模型；（4）基于深度学习的放射组学模型；（5）活检病理。
在外部验证数据集中，基于深度学习的放射组学模型显示出最高的曲线下面积（AUC为0.700至0.791）。它超过了临床成像模型（AUC为0.597至0.718）、传统放射组学模型（AUC为0.566至0.632）、PIRADS评分（AUC为0.554至0.613）和活检病理（AUC为0.537至0.578）。该模型预测的AUC在三家外部验证医院之间没有显示出统计学上的显著差异（p>0.05）。
利用AI从bpMRI图像中提取的图像特征的深度学习放射组学模型可能可用于预测PCa的侵袭性，显示出外部验证的泛化能力。
预测前列腺癌（PCa）的侵袭性对于为患者制定最佳治疗方案很重要。基于深度学习的放射组学模型有望为评估PCa的侵袭性提供一种客观且非侵入性的方法。
预测PCa的侵袭性对于患者获得最佳治疗选择很重要。基于深度学习的放射组学模型可以高精度预测PCa的侵袭性。该模型在多个外部数据集上进行测试时具有良好的通用性。
