Li Yanong, He Yixuan, Liu Yawei, Wang Bingchen, Li Bo, Qiu Xiaoguang
Department of Radiation Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Department of Pediatric Department, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
J Med Internet Res. 2025 Jan 30;27:e58760. doi: 10.2196/58760.
Primary intracranial germ cell tumors (iGCTs) are highly malignant brain tumors that predominantly occur in children and adolescents, with an incidence rate ranking third among primary brain tumors in East Asia (8%-15%). Due to their insidious onset and impact on critical functional areas of the brain, these tumors often result in irreversible abnormalities in growth and development, as well as cognitive and motor impairments in affected children. Therefore, early diagnosis through advanced screening techniques is vital for improving patient outcomes and quality of life.
This study aimed to investigate the application of facial recognition technology in the early detection of iGCTs in children and adolescents. Early diagnosis through advanced screening techniques is vital for improving patient outcomes and quality of life.
A multicenter, phased approach was adopted for the development and validation of a deep learning model, GVisageNet, dedicated to the screening of midline brain tumors from normal controls (NCs) and iGCTs from other midline brain tumors. The study comprised the collection and division of datasets into training (n=847, iGCTs=358, NCs=300, other midline brain tumors=189) and testing (n=212, iGCTs=79, NCs=70, other midline brain tumors=63), with an additional independent validation dataset (n=336, iGCTs=130, NCs=100, other midline brain tumors=106) sourced from 4 medical institutions. A regression model using clinically relevant, statistically significant data was developed and combined with GVisageNet outputs to create a hybrid model. This integration sought to assess the incremental value of clinical data. The model's predictive mechanisms were explored through correlation analyses with endocrine indicators and stratified evaluations based on the degree of hypothalamic-pituitary-target axis damage. Performance metrics included area under the curve (AUC), accuracy, sensitivity, and specificity.
On the independent validation dataset, GVisageNet achieved an AUC of 0.938 (P<.01) in distinguishing midline brain tumors from NCs. Further, GVisageNet demonstrated significant diagnostic capability in distinguishing iGCTs from the other midline brain tumors, achieving an AUC of 0.739, which is superior to the regression model alone (AUC=0.632, P<.001) but less than the hybrid model (AUC=0.789, P=.04). Significant correlations were found between the GVisageNet's outputs and 7 endocrine indicators. Performance varied with hypothalamic-pituitary-target axis damage, indicating a further understanding of the working mechanism of GVisageNet.
GVisageNet, capable of high accuracy both independently and with clinical data, shows substantial potential for early iGCTs detection, highlighting the importance of combining deep learning with clinical insights for personalized health care.
原发性颅内生殖细胞肿瘤（iGCTs）是高度恶性的脑肿瘤，主要发生于儿童和青少年，在东亚地区其发病率在原发性脑肿瘤中位列第三（8%-15%）。由于其起病隐匿且影响大脑关键功能区域，这些肿瘤常导致患病儿童生长发育出现不可逆异常以及认知和运动功能障碍。因此，通过先进筛查技术进行早期诊断对于改善患者预后和生活质量至关重要。
本研究旨在探讨面部识别技术在儿童和青少年iGCTs早期检测中的应用。通过先进筛查技术进行早期诊断对于改善患者预后和生活质量至关重要。
采用多中心、分阶段的方法来开发和验证深度学习模型GVisageNet，该模型专门用于从正常对照（NCs）中筛查中线脑肿瘤以及从其他中线脑肿瘤中筛查iGCTs。该研究包括数据集的收集和划分，分为训练集（n = 847，iGCTs = 358，NCs = 300，其他中线脑肿瘤 = 189）和测试集（n = 212，iGCTs = 79，NCs = 70，其他中线脑肿瘤 = 63），另外还有一个来自4家医疗机构的独立验证数据集（n = 336，iGCTs = 130，NCs = 100，其他中线脑肿瘤 = 106）。利用临床相关的、具有统计学意义的数据开发了一个回归模型，并将其与GVisageNet的输出结果相结合，创建了一个混合模型。这种整合旨在评估临床数据的增量价值。通过与内分泌指标的相关性分析以及基于下丘脑 - 垂体 - 靶腺轴损伤程度的分层评估来探索该模型的预测机制。性能指标包括曲线下面积（AUC）、准确性、敏感性和特异性。
在独立验证数据集上，GVisageNet在区分中线脑肿瘤与NCs时的AUC为0.938（P <.01）。此外，GVisageNet在区分iGCTs与其他中线脑肿瘤方面表现出显著的诊断能力，AUC为0.739，优于单独的回归模型（AUC = 0.632，P <.001），但低于混合模型（AUC = 0.789，P =.04）。发现GVisageNet的输出结果与7种内分泌指标之间存在显著相关性。性能随下丘脑 - 垂体 - 靶腺轴损伤情况而有所不同，这表明对GVisageNet的工作机制有了进一步了解。
GVisageNet无论是独立使用还是与临床数据结合都具有高精度，在iGCTs早期检测方面显示出巨大潜力，突出了将深度学习与临床见解相结合以实现个性化医疗保健的重要性。
