Abstract
Colorectal cancer (CRC) is markedly heterogeneous and develops progressively toward malignancy through several stages which include stroma (ST), benign hyperplasia (BH), intraepithelial neoplasia (IN) or precursor cancerous lesion, and carcinoma (CA). Identification of the malignancy stage of CRC pathology tissues (PT) allows the most appropriate therapeutic intervention. This study investigates multiscale texture features extracted from CRC pathology sections using 3D wavelet transform (3D-WT) filter. Multiscale features were extracted from digital whole slide images of 39 patients that were segmented in a pre-processing step using an active contour model. The capacity for multiscale texture to compare and classify between PTs was investigated using ANOVA significance test and random forest classifier models, respectively. 12 significant features derived from the multiscale texture (i.e., variance, entropy, and energy) were found to discriminate between CRC grades at a significance value of p < 0.01 after correction. Combining multiscale texture features lead to a better predictive capacity compared to prediction models based on individual scale features with an average (±SD) classification accuracy of 93.33 (±3.52)%, sensitivity of 88.33 (± 4.12)%, and specificity of 96.89 (± 3.88)%. Entropy was found to be the best classifier feature across all the PT grades with an average of the area under the curve (AUC) value of 91.17, 94.21, 97.70, 100% for ST, BH, IN, and CA, respectively. Our results suggest that multiscale texture features based on 3D-WT are sensitive enough to discriminate between CRC grades with the entropy feature, the best predictor of pathology grade.
Highlights
Colorectal cancer (CRC) is the third most common and newly diagnosed cancer and third most common cause of cancer death in both men and women in the United States [1], accounting for 8% of all new cancer cases per year
Areas enriched for carcinoma tissue were most defined by our methodology, where they were highly enriched in LHL, HHL, LLH, LHH, and HHH texture features compared to areas of ST, benign hyperplasia (BH), and intraepithelial neoplasia (IN)
Despite achieving close performance metrics [23], we suggest that the ability for 3D wavelet transform (3D-Wavelet transform (WT)) texture features to integrate information from multi-spectral layers to derive radiomic features may increase confidence compared to 2D wavelet transform (2D-WT) as feature values are not dependent on single images
Summary
Colorectal cancer (CRC) is the third most common and newly diagnosed cancer and third most common cause of cancer death in both men and women in the United States [1], accounting for 8% of all new cancer cases per year. An estimated 26, 270 men and 24,040 women died of colorectal carcinoma in 2014 as reported by American Cancer Society [1]. Radiomics Analysis of PTs used is surgical therapy with curative intent, followed by a pathological assessment of the resected tissue which directs subsequent treatments [2]. Most benign lesions slowly start as polyps, i.e., abnormal growths from the inner lining of the intestine that protrude into the intestinal canal. Some of these polyps exhibit abnormal cellular growth and progress into a stage called intraepithelial neoplasia (dysplasia). Intraepithelial neoplasia is a form of pre-cancerous lesion which is highly likely to progress into full-fledged cancer or carcinoma [4]
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