The determination of resection extent traditionally relies on the microscopic invasiveness of frozen sections (FSs) and is crucial for surgery of early lung cancer with preoperatively unknown histology. While previous research has shown the value of optical coherence tomography (OCT) for instant lung cancer diagnosis, tumor grading through OCT remains challenging. Therefore, this study proposes an interactive human–machine interface (HMI) that integrates a mobile OCT system, deep learning algorithms, and attention mechanisms. The system is designed to mark the lesion’s location on the image smartly and perform tumor grading in real time, potentially facilitating clinical decision making. Twelve patients with a preoperatively unknown tumor but a final diagnosis of adenocarcinoma underwent thoracoscopic resection, and the artificial intelligence (AI)-designed system mentioned above was used to measure fresh specimens. Results were compared to FSs benchmarked on permanent pathologic reports. Current results show better differentiating power among minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IA), and normal tissue, with an overall accuracy of 84.9%, compared to 20% for FSs. Additionally, the sensitivity and specificity, the sensitivity and specificity were 89% and 82.7% for MIA and 94% and 80.6% for IA, respectively. The results suggest that this AI system can potentially produce rapid and efficient diagnoses and ultimately improve patient outcomes.
传统上，手术切除范围的确定依赖于术中冰冻切片（FSs）的镜下浸润性评估，这对于术前组织学类型不明的早期肺癌手术至关重要。尽管先前研究已证实光学相干断层扫描（OCT）在肺癌即时诊断中的价值，但通过OCT进行肿瘤分级仍具挑战性。因此，本研究提出一种集成移动OCT系统、深度学习算法与注意力机制的交互式人机界面（HMI）。该系统能够智能标注图像中病灶位置并实时进行肿瘤分级，有望辅助临床决策。研究纳入12例术前肿瘤性质不明但最终确诊为腺癌的患者，在胸腔镜切除术后使用上述人工智能（AI）系统对新鲜标本进行检测，并将结果与以永久病理报告为金标准的冰冻切片结果进行对比。当前结果显示，该系统对微浸润性腺癌（MIA）、浸润性腺癌（IA）与正常组织的鉴别能力优于冰冻切片，总体准确率达84.9%（冰冻切片为20%）。此外，对MIA的敏感性与特异性分别为89%和82.7%，对IA的敏感性与特异性分别为94%和80.6%。结果表明，该AI系统有望实现快速高效的诊断，最终改善患者预后。
Rapid On-Site AI-Assisted Grading for Lung Surgery Based on Optical Coherence Tomography
肿瘤(癌症)患者之家