Polarimetric biomarkers of peri-tumoral stroma can correlate with 5-year survival in patients with left-sided colorectal cancer

Machine learning based local recurrence prediction in colorectal cancer using polarized light imaging

Significance

Current treatment for stage III colorectal cancer (CRC) patients involves surgery that may not be sufficient in many cases, requiring additional adjuvant systemic therapy. Identification of this latter cohort that is likely to recur following surgery is key to better personalized therapy selection, but there is a lack of proper quantitative assessment tools for potential clinical adoption.

Aim

The purpose of this study is to employ Mueller matrix (MM) polarized light microscopy in combination with supervised machine learning (ML) to quantitatively analyze the prognostic value of peri-tumoral collagen in CRC in relation to 5-year local recurrence (LR).

Approach

A simple MM microscope setup was used to image surgical resection samples acquired from stage III CRC patients. Various potential biomarkers of LR were derived from MM elements via decomposition and transformation operations. These were used as features by different supervised ML models to distinguish samples from patients that locally recurred 5 years later from those that did not.

Results

Using the top five most prognostic polarimetric biomarkers ranked by their relevant feature importances, the best-performing XGBoost model achieved a patient-level accuracy of 86%. When the patient pool was further stratified, 96% accuracy was achieved within a tumor-stage-III sub-cohort.

Conclusions

ML-aided polarimetric analysis of collagenous stroma may provide prognostic value toward improving the clinical management of CRC patients.

Mueller matrix polarization parameters correlate with local recurrence in patients with stage III colorectal cancer

The peri-tumoural stroma has been explored as a useful source of prognostic information in colorectal cancer. Using Mueller matrix (MM) polarized light microscopy for quantification of unstained histology slides, the current study assesses the prognostic potential of polarimetric characteristics of peri-tumoural collagenous stroma architecture in 38 human stage III colorectal cancer (CRC) patient samples. Specifically, Mueller matrix transformation and polar decomposition parameters were tested for association with 5-year patient local recurrence outcomes. The results show that some of these polarimetric parameters were significantly different (p value < 0.05) for the recurrence versus the no-recurrence patient cohorts (Mann-Whitney U test). MM parameters may thus be prognostically valuable towards improving clinical management/treatment stratification in CRC patients.

Mueller matrix imaging of pathological slides with plastic coverslips

Mueller matrix microscopy is capable of polarization characterization of pathological samples and polarization imaging based digital pathology. In recent years, hospitals are replacing glass coverslips with plastic coverslips for automatic preparations of dry and clean pathological slides with less slide-sticking and air bubbles. However, plastic coverslips are usually birefringent and introduce polarization artifacts in Mueller matrix imaging. In this study, a spatial frequency based calibration method (SFCM) is used to remove such polarization artifacts. The polarization information of the plastic coverslips and the pathological tissues are separated by the spatial frequency analysis, then the Mueller matrix images of pathological tissues are restored by matrix inversions. By cutting two adjacent lung cancer tissue slides, we prepare paired samples of very similar pathological structures but one with a glass coverslip and the other with a plastic coverslip. Comparisons between Mueller matrix images of the paired samples show that SFCM can effectively remove the artifacts due to plastic coverslip.