Background/Objective: Penile cancer is aggressive and rapidly progressive. Early recognition is paramount for overall survival. However, many men delay presentation due to a lack of awareness and social stigma. This pilot study aims to develop a convolutional neural network (CNN) model to differentiate penile cancer from precancerous and benign penile lesions.Methods:The CNN was developed using 136 penile lesion images sourced from peer-reviewed open access publications. These images included 65 penile squamous cell carcinoma (SCC), 44 precancerous lesions, and 27 benign lesions. The dataset was partitioned using a stratified split into training (64%), validation (16%), and test (20%) sets. The model was evaluated using ten trials of 10-fold internal cross-validation to ensure robust performance assessment.Results:When distinguishing between benign penile lesions and penile SCC, the CNN achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.94, with a sensitivity of 0.82, specificity of 0.87, positive predictive value of 0.95, and negative predictive value of 0.72. The CNN showed reduced discriminative capability in differentiating precancerous lesions from penile SCC, with an AUROC of 0.74, sensitivity of 0.75, specificity of 0.65, PPV of 0.45, and NPV of 0.88.Conclusion:These findings demonstrate the potential of artificial intelligence in identifying penile SCC. Limitations of this study include the small sample size and reliance on photographs from publications. Further refinement and validation of the CNN using real-life data are needed.
背景/目的：阴茎癌具有侵袭性强、进展迅速的特点。早期识别对患者总体生存至关重要。然而，由于认知不足和社会污名化，许多男性患者往往延迟就诊。本项探索性研究旨在开发一种卷积神经网络（CNN）模型，用于区分阴茎癌与癌前病变及良性阴茎病变。 方法：该CNN模型基于136张来自同行评审开放获取文献的阴茎病变图像构建，其中包括65例阴茎鳞状细胞癌（SCC）、44例癌前病变和27例良性病变。数据集通过分层抽样划分为训练集（64%）、验证集（16%）和测试集（20%）。为确保持续稳定的性能评估，模型采用十轮十折内部交叉验证进行测试。 结果：在区分良性阴茎病变与阴茎鳞状细胞癌时，CNN模型受试者工作特征曲线下面积（AUROC）达0.94，灵敏度0.82，特异度0.87，阳性预测值0.95，阴性预测值0.72。而在区分癌前病变与阴茎鳞状细胞癌时，模型判别能力有所下降，AUROC为0.74，灵敏度0.75，特异度0.65，阳性预测值0.45，阴性预测值0.88。 结论：本研究证实了人工智能在识别阴茎鳞状细胞癌方面的应用潜力。研究局限性包括样本量较小且依赖文献中的照片数据。未来需通过真实临床数据对CNN模型进行进一步优化和验证。
肿瘤(癌症)患者之家