Artificial intelligence-based diagnosis of the depth of laryngopharyngeal cancer
Artificial intelligence-based diagnosis of the depth of laryngopharyngeal cancer
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DOI：
10.1016/j.anl.2023.09.001
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发表时间：
2024-01-17
影响因子：
1.7
通讯作者：
Takeno，Sachio
中科院分区：
文献类型：
--
作者：
Yumii，Kohei;Ueda，Tsutomu;Takeno，Sachio
经口手术（TOS）是一种广泛用于咽喉癌的治疗方法。在TOS中存在一些难以确定切除范围的病例，特别是在确定垂直切缘时。然而，正的垂直边缘需要额外的治疗。此外，应避免过度切除，因为它会增加出血作为术后并发症的风险，并可能导致生活质量下降，如吞咽困难。考虑到这些问题，在TOS中确定切除范围是一个重要的考虑因素。在这项研究中，我们研究了使用放射组学准确诊断喉咽癌深度的可能性，放射组学是一种基于人工智能（AI）的图像分析方法。MethodsWe包括95个病变的食管胃内窥镜图像，这些病变在2009年8月至2020年4月期间被病理诊断为鳞状细胞癌（SCC）并在我们的机构接受经口手术治疗。95个病灶中，54个为原位SCC，41个为SCC。对95例上消化道内镜下NBI图像进行放射组学分析，以评估其对上皮下浸润的诊断性能。手动描绘内镜图像中的病变，并根据使用最小绝对收缩和选择操作员分析获得的特征评价准确性、灵敏度、特异性和曲线下面积（AUC）。此外，结果进行了比较，由熟练的endoscopists.ResultsIn放射组学研究的深度预测，平均交叉验证为0.833。根据受试者工作特征曲线计算的交叉验证平均AUC为0.868。这些结果相当于由一个熟练的内窥镜。结论使用放射组学分析诊断喉咽癌深度有潜在的临床应用价值。我们计划在未来的实际手术中使用它，并前瞻性地研究它是否可以用于诊断。
ObjectiveTransoral surgery (TOS) is a widely used treatment for laryngopharyngeal cancer. There are some difficult cases of setting the extent of resection in TOS, particularly in setting the vertical margins. However, positive vertical margins require additional treatment. Further, excessive resection should be avoided as it increases the risk of bleeding as a postoperative complication and may lead to decreased quality of life, such as dysphagia. Considering these issues, determining the extent of resection in TOS is an important consideration. In this study, we investigated the possibility of accurately diagnosing the depth of laryngopharyngeal cancer using radiomics, an image analysis method based on artificial intelligence (AI).MethodsWe included esophagogastroduodenoscopic images of 95 lesions that were pathologically diagnosed as squamous cell carcinoma (SCC) and treated with transoral surgery at our institution between August 2009 and April 2020. Of the 95 lesions, 54 were SCCin situ, and 41 were SCC. Radiomics analysis was performed on 95 upper gastrointestinal endoscopic NBI images of these lesions to evaluate their diagnostic performance for the presence of subepithelial invasion. The lesions in the endoscopic images were manually delineated, and the accuracy, sensitivity, specificity, and area under the curve (AUC) were evaluated from the features obtained using least absolute shrinkage and selection operator analysis. In addition, the results were compared with the depth predictions made by skilled endoscopists.ResultsIn the Radiomics study, the average cross-validation was 0.833. The mean AUC for cross-validation calculated from the receiver operating characteristic curve was 0.868. These results were equivalent to those of the diagnosis made by a skilled endoscopist.ConclusionThe diagnosis of laryngopharyngeal cancer depth using radiomics analysis has potential clinical applications. We plan to use it in actual surgery in the future and prospectively study whether it can be used for diagnosis.