# Automated Hybrid Model for Detecting Perineural Invasion in the Histology of Colorectal Cancer
## 4 Discussion  
In this study, a DL-based hybrid model was developed to detect PNI in CRC patients. The proposed framework exhibited excellent performance (accuracy of 0.92, sensitivity of 0.90, AUC of 0.92), with potential for computer-aided diagnosis in PNI screening. Considering the prognostic implications of PNI and the difficulty in detecting PNI in pathology slide images, the automated PNI detector exhibited a high potential for utility, and can potentially save medical resources. In practice, PNI detection is a time-consuming and cumbersome task, with high intra- and inter-observer variation. A review of CRC slides in a study revealed that 46 of 55 PNI-positive cases (from a total of 249 cases) were reported as PNI-negative in the original pathology reports [2]. In another study, [PERSON] et al. also revealed the differences in the PNI-positive rate (24.3%) after review compared to the PNI-positive rate (7.5%) recorded in the initial reports [12]. Furthermore, considerable variation is perceived between observers in defining PNI [11,30]. Some of the variations depend on the evaluation criteria among pathologists in terms of the distance between the cancer source and nerve cells [6]. Uncertainty in defining a nerve is another cause of poor inter-observer PNI detection reproducibility. These difficulties in detecting PNI can be solved by standardizing the evaluation criteria in the algorithms and refining the pixel-wise prediction  
Figure 4: Examples of misclassification with ground truths (**a,c,e**) and the corresponding prediction (**b,d,f**). (**b**) In the false negative (FN) case, both tumor and nerve tissues were missed. (**d**) In the other FN case, nerve tissue surrounded by tumor tissue was predicted as tumor tissue. (**f**) In the FP case, nerve tissue was falsely predicted as a blood vessel. All the inference results from each pipeline are found on the webpage ([[http://pni.ssus.work/](http://pni.ssus.work/)]([http://pni.ssus.work/](http://pni.ssus.work/)), accessed on 13 September 2022). of neural bundles. Thus, expectedly, inter-observer reproducibility would increase, and underestimations could be reduced. Some attempts have been made to use DL-based approaches to extract PNI histologically. [PERSON] et al. used a convolutional neural network to classify PNI in prostate biopsies, and achieved a discrimination AUC of 0.98 [21]. Recently, an international Medical Image Computing and Computer Assisted Intervention Society-Pathology Artificial Intelligence Platform (MICCAI-PAIP) challenge was held to detect PNI in multiple organ cancers ([[https://paip2021.grand-challenge.org](https://paip2021.grand-challenge.org)]([https://paip2021.grand-challenge.org](https://paip2021.grand-challenge.org)), accessed on 13 September 2022). The top-ranked team achieved the best F1 score of 41.55% using a feature pyramid network (FPN) [22]. This result revealed the capacity of the multi-resolution network, FPN, to detect PNI in histological images. However, these algorithms, where the representative images of PNI itself were learned, could not provide sufficient interpretation for false prediction because of the \"black-box\" nature of the DL methods. An interpretable DL-based model was proposed to provide interpretability through semantic classification of the tissue type and the calculation of distances. This process sequence is similar to a diagnosis by a pathologist, enabling us to interpret the predicted results outputted by the proposed DL-based model, increasing model reliability. Thus, the method provided considerable advantages for medical image analysis and applying the models in practice [31; 32]. Numerous challenges exist while using the DL algorithm for clinical applications, including its use in the current model to detect PNI in colon cancer. Therefore, these challenges should be resolved. First, an automated and efficient workflow using digitalized pathology images should be established to effectively use the DL model in practice.