Ungureanu Bogdan Silviu, Pirici Daniel, Dima Simona Olimpia, Popescu Irinel, Hundorfean Gheorghe, Surlin Valeriu, Saftoiu Adrian
Gastroenterology Department, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania.
Histology Department, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania.
Diagnostics (Basel). 2020 Nov 10;10(11):923. doi: 10.3390/diagnostics10110923.
Ex-vivo freshly surgical removed pancreatic ductal adenocarcinoma (PDAC) specimens were assessed using pCLE and then processed for paraffin embeding and histopathological diagnostic in an endeavour to find putative image analysis algorithms that might recognise adenocarcinoma.
Twelve patients diagnosed with PDAC on endoscopic ultrasound and FNA confirmation underwent surgery. Removed samples were sprayed with acriflavine as contrast agent, underwent pCLE with an experimental probe and compared with previous recordings of normal pancreatic tissue. Subsequently, all samples were subjected to cross-sectional histopathology, including surgical resection margins for controls. pCLE records, as well as corespondant cytokeratin-targeted immunohistochemistry images were processed using the same morphological classifiers in the Image ProPlus AMS image analysis software. Specific morphometric classifiers were automatically generated on all images: Area, Hole Area (HA), Perimeter, Roundness, Integrated Optical Density (IOD), Fractal Dimension (FD), Ferret max (Fmax), Ferret mean (Fmean), Heterogeneity and Clumpiness.
After histopathological confirmation of adenocarcinoma areas, we have found that the same morphological classifiers could clearly differentiate between tumor and non-tumor areas on both pathology and correspondand pCLE (area, roundness, IOD, ferret and heterogeneity ( < 0.001), perimeter and hole area ( < 0.05).
This pilot study proves that classical morphometrical classifiers can clearly differentiate adenocarcimoma on pCLE data, and the implementation in a live image-analysis algorithm might help in improving the specificity of pCLE in vivo diagnostic.
使用pCLE对新鲜手术切除的离体胰腺导管腺癌（PDAC）标本进行评估，然后进行石蜡包埋和组织病理学诊断，以寻找可能识别腺癌的推定图像分析算法。
12例经内镜超声和FNA确诊为PDAC的患者接受手术。切除的样本喷洒吖啶黄作为造影剂，使用实验探头进行pCLE，并与先前正常胰腺组织的记录进行比较。随后，所有样本均进行横断面组织病理学检查，包括手术切缘作为对照。使用Image ProPlus AMS图像分析软件中的相同形态分类器处理pCLE记录以及相应的细胞角蛋白靶向免疫组化图像。在所有图像上自动生成特定的形态计量分类器：面积、孔面积（HA）、周长、圆度、积分光密度（IOD）、分形维数（FD）、雪貂最大值（Fmax）、雪貂平均值（Fmean）、异质性和团块性。
在腺癌区域经组织病理学确认后，我们发现相同的形态分类器在病理学和相应的pCLE上都能清楚地区分肿瘤和非肿瘤区域（面积、圆度、IOD、雪貂和异质性（<0.001），周长和孔面积（<0.05）。
这项初步研究证明，经典的形态计量分类器可以根据pCLE数据清楚地区分腺癌，在实时图像分析算法中的应用可能有助于提高pCLE在体内诊断的特异性。
