Garcia Carina Nogueira, Wies Christoph, Hauser Katja, Brinker Titus J
Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Medical Faculty, University of Heidelberg, Heidelberg, Germany.
JID Innov. 2024 Jul 20;4(6):100303. doi: 10.1016/j.xjidi.2024.100303. eCollection 2024 Nov.
Early cutaneous squamous cell carcinoma (cSCC) diagnosis is essential to initiate adequate targeted treatment. Noninvasive diagnostic technologies could overcome the need of multiple biopsies and reduce tumor recurrence. To assess performance of noninvasive technologies for cSCC diagnostics, 947 relevant records were identified through a systematic literature search. Among the 15 selected studies within this systematic review, 7 were included in the meta-analysis, comprising of 1144 patients, 224 cSCC lesions, and 1729 clinical diagnoses. Overall, the sensitivity values are 92% (95% confidence interval [CI] = 86.6-96.4%) for high-frequency ultrasound, 75% (95% CI = 65.7-86.2%) for optical coherence tomography, and 63% (95% CI = 51.3-69.1%) for reflectance confocal microscopy. The overall specificity values are 88% (95% CI = 82.7-92.5%), 95% (95% CI = 92.7-97.3%), and 96% (95% CI = 94.8-97.4%), respectively. Physician's expertise is key for high diagnostic performance of investigated devices. This can be justified by the provision of additional tissue information, which requires physician interpretation, despite insufficient standardized diagnostic criteria. Furthermore, few deep learning studies were identified. Thus, integration of deep learning into the investigated devices is a potential investigating field in cSCC diagnosis.
早期皮肤鳞状细胞癌(cSCC)的诊断对于启动适当的靶向治疗至关重要。非侵入性诊断技术可以避免多次活检的需求并减少肿瘤复发。为了评估非侵入性技术对cSCC诊断的性能,通过系统的文献检索确定了947条相关记录。在该系统评价中选择的15项研究中,有7项纳入了荟萃分析,包括1144例患者、224个cSCC病变和1729例临床诊断。总体而言,高频超声的敏感性值为92%(95%置信区间[CI]=86.6-96.4%),光学相干断层扫描为75%(95%CI=65.7-86.2%),反射共聚焦显微镜为63%(95%CI=51.3-69.1%)。总体特异性值分别为88%(95%CI=82.7-92.5%)、95%(95%CI=92.7-97.3%)和96%(95%CI=94.8-97.4%)。医生的专业知识是所研究设备实现高诊断性能的关键。尽管标准化诊断标准不足,但提供额外的组织信息需要医生进行解读,这可以解释这一点。此外,确定的深度学习研究很少。因此,将深度学习集成到所研究的设备中是cSCC诊断的一个潜在研究领域。