Digital pathology may finally be coming of age. Whole slide scanners are becoming ubiquitous to the practise of surgical pathology paving way for new opportunities in computational pathology and digital image analysis.
The advent of open access image analysis software has been paramount to the success of digital pathology. Qupath[13] works to meet the need for a friendly, understandable digital pathology solution. It offers a comprehensive panel for image analysis and here we demonstrate the utility of SIS for calculating PSA and myxoid stroma percentages.
Importantly, PSA is not the same as the tumoral-stromal-ratio (TSR)[9]. The PSA technique includes the entire tumor area is computational, while traditional TSR is calculated only at the invasive tumoral border and performed manually by a pathologist on glass slides, allowing for observer bias.
In colon cancer, TSR has been found to correlate with prognostic outcomes, where high stroma ratios at the invasive tumor front are associated with poor outcomes[9]. However, we found the opposite effect, with low PSA values having worse prognostic outcomes. This may be secondary to the fact that PSA encompasses the entire tumor, not just the invasive front.
If they are just looking at the invasive front in isolation, then this may overestimate the stroma to tumor ratio. DIA may allow for a more holistic assessment of the tumoral microenvironment, which in addition to showing tumor-to-stroma ratios, also quantifies the ratio (MSR) and percentage (ISP) of immature, myxoid stromal differentiation (degeneration). For the PSA, it is possible that more tumor probably means more tumor budding, which correlates with worse prognosis. Whereas for the TSR performed at the tumor invasive front, more stroma could mean more immature SD, also associated with a with worse prognostic outcomes[17].
Regarding DIA, our findings are also consistent with what was found in luminal tumors of the breast[11] and in intrahepatic cholangiocarcinoma[10], where higher proportions of tumor (low PSA) were found to be associated with worse overall survival outcomes.
A key point to consider is the variation in DIA methods between the two mentioned studies and this publication. In the breast publication[11] a single region of interest (ROI) was identified from a WSI and tissue microarrays (TMA) were created from tumor hotpots. In this publication we performed DIA on the entire tumor from a complete WSI, also perfumed on the study of intrahepatic cholangiocarcinoma [10]. WSIs may be the better solution, especially considering the significant tumor heterogeneity present in CRC, we feel this is the more pragmatic approach. Here we choose to use the slide with the largest section of tumor as we felt this most accurately represented the tumoral microenvironment.
Superpixel methods[18] are gaining traction in the field of medicine and recent attempts have been made to combine SIS and DL. In dermatology the combination of superpixels and deep learning models outperformed other competing methods[19]. The advantages for SIS in the deep learning space is that is can represent the structure of an image in adaptive sizes and shapes, with the ability to improve classification performance, especially for noisy classification (corrupted labels), as well as boundary misclassification[20]. Future studies could combine applications in deep learning with superpixel methods in order to further analyze the tumoral microenvironment. This is the road to precision oncology.
Recent advances have been made in DL for CRC by Skrede et al.[21] who applied ten convolutional neural networks on supersized heterogeneous WSI. The DoMore-v.1 assay was able to differentiate prognostic groups stage independently and tested on on large patient populations, suggesting its superiority to other genomic and pathological prognostic markers. The algorithm predicted cancer-specific survival in stage II patients as (HR 2·71, 95% CI 1·25 to 5·86, p = 0·011) and stage III (4·09, 2·77 to 6·03, p < 0·0001) [21].
More practically, the differentiation of the extracellular matrix leads to characteristic immature, myxoid stroma seen on routine histologic evaluation. Understanding the peculiarities of the ECM in colorectal cancer will be necessary for future and novel therapeutic approaches [3]; it is possible that myxoid degeneration of the ECM decreases the physical barrier, improving the delivery of therapeutics, nutrients, and immune cells to solid tumors. On the other hand, mature SD may act as a barrier (FIG 11); this is supported by preclinical studies demonstrating ECM degradation to improve drug uptake and response [3].
In our study we were able to more accurately characterize SD though DIA by calculating the ISP and MSR. Through SIS we were able to predict patient outcomes and clinical profiles better than manual analysis of SD. Importantly, these techniques could be used to tailor the need for adjuvant chemotherapy, although well-designed, robust clinical studies will be needed to determine this.
Interestingly, MSR was not found to be associated with tumor stage, suggesting that it may be able to predict clinical outcomes independent of tumor stage, it may be the key to unraveling the clinical heterogeneity present in CRC. Today, the use of adjuvant treatment in stage II colon cancer has garnered much controversy[22] and recommendations options range from observation, to single agent chemotherapy, to combination regimens. We need to better tailor the need for adjuvant therapy.
For patients with colorectal cancer, a rudimentary assessment of tumoral differentiation and stage may be insufficient. Looking forward, pathologists will have to borrow concepts from biology and develop new, novel approaches to prognosticate and treat patients with CRC. The tumoral microenvironment is a prime candidate for future applications in digital pathology, and the techniques described in this body of work can be performed easily by a surgical pathologist.