## 4 Discussion
showed that the SH (cut-off: 189) performed equally well as \(SR\) (cut-off: 1.44) in differentiating malignant from benign breast lesions, but the modality could not improve diagnostics when compared with radiological Breast Imaging-Reporting and Data System in a limited material [7]. The median value of the histogram represents the median hardness of the lesion. [PERSON] et al. suggested using the median histogram value of the reference tissue divided by the median value of the lesion histogram to create a histogram ratio. In a study of pancreatic lesions, this did not perform better than \(SR\) with a ROC-AUC of 0.843 and a cut-off selection yielding a specificity of 98%, a sensitivity of only 50%, and an accuracy of 69% [19]. Unfortunately, we did not record the strain histograms in our studies of surgical specimens and pancreatic lesions as this function was not available at the time. Visual evaluation of the elastogram is direct and intuitive and requires no post-processing. Several scoring systems for categorical scoring of strain images have been proposed. For breast imaging, the five-point Tsukuba scale was proposed by [PERSON] et al. [20]. In two meta-analyses, \(SR\) measurements were found to differentiate malignant from benign breast lesions better than using the visual scoring range of one to five [21; 22]. Amended versions of this visual score have been proposed for other organs, such as for the pancreas and lymph nodes by EUS elastography. Both the continuous visual analog scale (VAS) score and categorical visual scores for endoscopic assessment of rectal tumors has shown comparable results with \(SR\) measurements [23]. In a comparative study of \(SR\) and the five-point visual elastography breast scale, \(SR\) was more accurate. However, the direct visual impression of an experienced examiner using elastography imaging may contain more information than a five-step visual score can comprehend, which is useful in many cases for lesions in doubt. With training, different aspects of the elastogram, such as strain concentration and artefacts caused by veins and natural sliding surfaces, can be recognized. These findings can hardly be recognized by \(SR\), histograms, or other formal quantification methods. One group investigated the use of automated pattern and color recognition of elastography of focal pancreatic lesions in 258 patients. They used an artificial neural network image analysis and information about the histological diagnosis from endoscopic elastography as the input. This improved the accuracy, with a ROC-AUC of 0.94, significantly better compared to using only lesion strain histograms that had a ROC-AUC of 0.85. This evaluation was performed using collected data in a multicenter study [24] but required substantial post-processing. In the future, automated tissue recognition or a material of standardized hardness, serving as a reference within the field-of-view, may be ways to improve \(SR\) in endoscopic applications. Also, averaging strain values over several frames or filtering noisy strain areas in the elastograms may improve \(SR\) measurements. We may possibly also soon see shear-wave elastography for flexible endoscopes.