## 4 Discussion
### Characterizing the Attributes of the Needles Discovered Amidst the Haystack  
Based on the applied MIL-approach, we could identify the informative instances or respective image tiles within the analyzed WSI. In conclusion, we are able to find the needles within the haystack, to stick with the analogy. Looking at the histology within these seldom instances or respective needles, there are often image tiles that contain small, isolated tumor formations. These isolated tumor formations are of course well-known within the literature as tumor buds. The phenomenon is known as tumor budding, which is a manifestation of the epithelial-mesenchymal transition [5; 6]. In our data set, it is found mostly in peripheral tumor regions (84.0% of the most informative tiles in regard to nodal status) and only seldom found in central tumor regions (16.0%; compare Section 3.4). Budding itself is considered to be an independent predictor of lymph node metastasis in pT1 and in stage II CRC [6; 8]. It is known to mostly occur at the infiltrating tumor front [6], fitting to our findings. In comparison to the aforementioned peritumoral budding, the occurrence of intra-tumoral budding has been also linked to a lymph node metastasis with inferior scientific evidence [6; 53], also fitting to our findings. In summary, by showing that amongst the most informative image parts tumor budding is present, we could argue for its (albeit well-known) diagnostic power. This is independent of discussions regarding the standardized modus operandi for measuring the tumor budding content [8]. Furthermore, it is also independent of our findings in a previous study regarding the hot spot definition for tumor budding in CRC [13]. In addition, our data set could not show an independence between tumor budding and other histological parameters. Indeed, according to our data, there is a correlation between the tumor budding score assessed by pathologists and the tumor grading. In addition, both parameters correlate comparably to the nodal status (compare Section 3.4 above). The more intriguing inquiry pertains to whether there exists an alternative morphological feature alongside tumor budding, or if tumor budding is the sole factor in question. In the literature, there are works on tumor-infiltrating lymphocytes and their prognostic capabilities. In this context, there are also image-analysis-based approaches [54; 55]. Furthermore, there is evidence that the stromal reaction does contain prognostically relevant information. In this context, there are also publications from the field of digital pathology [56; 57; 58]. Both known, potentially interacting image features, namely tumor-infiltrating lymphocytes and stromal reaction, are not analyzed in our work. Based on our results, we can show by visual inspection of the most informative tiles by a board-certified pathologist that in many tiles tumor budding is present (compare Figure 4). Furthermore, in these tiles, the tumor formations seem to be less differentiated. In addition to these anecdotal points, there are also statistical considerations. The peripheral location of the most informative tiles within tumors, coupled with their comparable statistical properties (in contrast to the outdated expert-based tumor budding scoring discussed in Section 3.4) serves as evidence supporting the argument that the feature identified by the MIL model is indeed tumor budding. However, to verify this hypothesis, more experiments with known tumor budding scores per tile should be conducted. Ideally, these scores should be generated by image analysis tools, such as the one introduced by [PERSON] et al. [59]. Pros and Cons of Automated Prognostic Marker Measurement in General and for Future Routine Applications  
An obvious advantage of the automatic measurement of known (as in the example of tumor budding) or unknown tissue-based markers based on HE-sections for solid tumors in general and CRC in particular is its time efficiency and inter-rater reliability.