Dittberner Andreas, Rodner Erik, Ortmann Wolfgang, Stadler Joachim, Schmidt Carsten, Petersen Iver, Stallmach Andreas, Denzler Joachim, Guntinas-Lichius Orlando
Department of Otorhinolaryngology, Jena University Hospital, Jena, Germany.
Department of Otorhinolaryngology, Head and Neck Surgery, University of Erlangen-Nuremberg, Erlangen, Germany.
Head Neck. 2016 Apr;38 Suppl 1:E1419-26. doi: 10.1002/hed.24253. Epub 2015 Nov 11.
The purpose of this study was to develop an automated image analysis algorithm to discriminate between head and neck cancer and nonneoplastic epithelium in confocal laser endomicroscopy (CLE) images.
CLE was applied to image head and neck cancer epithelium in vivo. Histopathologic diagnosis from biopsies was used to classify the CLE images offline as cancer or noncancer tissue. The classified images were used to train automated software based on distance map histograms. The performance of the final algorithm was confirmed by "leave 2 patients out" cross-validation and area under the curve (AUC)/receiver operating characteristic (ROC) analysis.
Ninety-two CLE videos and 92 biopsies were analyzed from 12 patients. One hundred two frames of classified neoplastic tissue and 52 frames of nonneoplastic tissue were used for cross-validation of the developed algorithm. AUC varied from 0.52 to 0.92.
The proposed software allows an objective classification of CLE images of head and neck cancer and adjacent nonneoplastic epithelium. © 2015 Wiley Periodicals, Inc. Head Neck 38: E1419-E1426, 2016.
本研究的目的是开发一种自动图像分析算法，以在共聚焦激光内镜检查（CLE）图像中区分头颈癌和非肿瘤性上皮。
将CLE应用于体内头颈癌上皮的成像。活检的组织病理学诊断用于在离线状态下将CLE图像分类为癌组织或非癌组织。分类后的图像用于训练基于距离图直方图的自动化软件。最终算法的性能通过“留2例患者”交叉验证和曲线下面积（AUC）/受试者操作特征（ROC）分析来确认。
对12例患者的92个CLE视频和92份活检样本进行了分析。102帧分类的肿瘤组织和52帧非肿瘤组织用于所开发算法的交叉验证。AUC范围为0.52至0.92。
所提出的软件能够对头颈癌和相邻非肿瘤性上皮的CLE图像进行客观分类。©2015威利期刊公司。《头颈》38：E1419 - E1426，2016年。
