Three-dimensional morphological characterization of colorectal pits from label-free microscopy images.
Luciana Ariadna ErbesMaría Fernanda IzaguirreVíctor Hugo CascoJavier AdurPublished in: Microscopy research and technique (2022)
The prognosis of colorectal cancer (CRC), one of the most prevalent pathologies worldwide, is linked to early detection. Kudo's pit pattern classification states morphological pit patterns of the Lieberkühn crypts by analyzing the superficial mucosa, predicting the histology of colorectal lesions. Its use as a highly accurate two-dimensional diagnostic criterion has increased, mostly involving expert endoscopists' judgment. The processing of autofluorescence images could allow the diagnostic, bypassing staining techniques and decreasing the biopsies, resources and times involved in the inspection. That criterion could be extended by data of the pit three-dimensional (3D) morphology. Thus, this work was aimed at obtaining 3D morphological information by quantifying geometrical and shape descriptors through software processing and analysis of widefield autofluorescence microscopy image stacks acquired by fresh colon tissue samples from a murine model of CRC. Statistical analyses included pits from control mice and from the second (2nd), fourth (4th), and eighth (8th) weeks of treatment. Statistically significant differences were found for almost all parameters between the pits from control and from the 4th treated week, stating that the major morphological changes begin after the 2nd week. In particular, pits from control or initial treatment time points were more tubular, straighter and less rough than the ones from later treatment points. Therefore, they may be more associated to normal or non-neoplastic crypt lumens than linked to adenomas or even cancer crypts. These preliminary outcomes could be considered an advance in 3D pit morphology characterization.
Keyphrases
- deep learning
- label free
- high resolution
- optical coherence tomography
- single molecule
- type diabetes
- healthcare
- high throughput
- clinical trial
- squamous cell carcinoma
- machine learning
- randomized controlled trial
- combination therapy
- papillary thyroid
- study protocol
- adipose tissue
- clinical practice
- squamous cell
- mass spectrometry