Employing machine learning and microscopy images of AIB1‐stained biopsy material to assess the 5‐year survival of patients with colorectal cancer

Abstract

Our purpose was to employ microscopy images of amplified in breast cancer 1 (AIB1)‐stained biopsy material of patients with colorectal cancer (CRC) to: (a) find statistically significant differences (SSDs) in the texture and color of the epithelial gland tissue, between 5‐year survivors and non‐survivors after the first diagnosis and (b) employ machine learning (ML) methods for predicting the CRC‐patient 5‐year survival. We collected biopsy material from 54 patients with diagnosed CRC from the archives of the University Hospital of Patras, Greece. Twenty‐six of the patients had survived 5 years after the first diagnosis. We selected regions of interest containing the epithelial gland at different microscope lens magnifications. We computed 69 textural and color features. Furthermore, we identified features with SSDs between the two groups of patients and we designed a supervised ML system for predicting the CRC‐patient 5‐year survival. Additionally, we employed the VGG16 pretrained convolution neural network to extract deep learning (DL) features, the support vector machines classifier, and the bootstrap cross‐validation method for boosting the accuracy of predicting 5‐year survival. Fourteen features sustained SSDs between the two groups of patients. The supervised ML system achieved 87% accuracy in predicting 5‐year survival. In comparison, the DL system, using images from all magnifications, gave 97% classification accuracy. Glandular texture in 5‐year non‐survivors appeared to be of lower contrast, coarseness, roughness, local pixel correlation, and lower AIB1 variation, all indicating loss of textural definition. The supervised ML system revealed useful information regarding features that discriminate between 5‐year survivors and non‐survivors while the DL system displayed superior accuracy by employing DL features.

Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review

“…IHC slides stained for pan-cytokeratin from patients with pT3 and pT4 colon ADC were used to train a DCNN to predict the occurrence of distant metastasis based on tumor architecture [100]. Another study showed that IHC-stained images of the amplified breast cancer 1 (AIB1) protein from CRC patients could operate as a predictive 5-year survival marker [101].…”

“…The most common pre-trained model used for CRC is based on the VGG architectures. Four of the studies, presented by Zhao et al [95,97], Xu et al [76] and Jiao et al [70], employed the VGG-19, while two of the studies employed the VGG-16 [41,101]. Furthermore, two other studies compared different parameters of the general VGG architecture [38,80].…”