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Review
. 2022 Dec 6;14(12):e32241.
doi: 10.7759/cureus.32241. eCollection 2022 Dec.

Textural Analysis as a Predictive Biomarker in Rectal Cancer

Mahmoud Alrahawy  1 , Medhet Aker  2 , Mohmed Issa  3 , Omer Ali  4 , Khaled Noureldin  5   6 , Ahmed Gaber  1 , Ahmed Mahgoub  7 , Mohamed Ahmed  8 , Mahmoud Yousif  8 , Ashraf Zeinaldine  1
Affiliations
Review

Textural Analysis as a Predictive Biomarker in Rectal Cancer

Mahmoud Alrahawy et al. Cureus. .

Abstract

Colorectal cancer (CRC) is a common deadly cancer. Early detection and accurate staging of CRC enhance good prognosis and better treatment outcomes. Rectal cancer staging is the cornerstone for selecting the best treatment approach. The standard gold method for rectal cancer staging is pelvic MRI. After staging, combining surgery and chemoradiation is the standard management aiming for a curative outcome. Textural analysis (TA) is a radiomic process that quantifies lesions' heterogenicity by measuring pixel distribution in digital imaging. MRI textural analysis (MRTA) of rectal cancer images is growing in current literature as a future predictor of outcomes of rectal cancer management, such as pathological response to neoadjuvant chemoradiotherapy (NCRT), survival, and tumour recurrence. MRTA techniques could validate alternative approaches in rectal cancer treatment, such as the wait-and-watch (W&W) approach in pathologically complete responders (pCR) following NCRT. We consider this a significant step towards implementing precision management in rectal cancer. In this narrative review, we summarize the current knowledge regarding the potential role of TA in rectal cancer management in predicting the prognosis and clinical outcomes, as well as aim to delineate the challenges which obstruct the implementing of this new modality in clinical practice.

Keywords: : rectal cancer; imaging biomarker; life prognosis; recurrence predictors; textural analysis.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Radiomic/textural analysis process and textural feature extractions
Figure 2
Figure 2. Process of texture analysis of medical images or ROI
ROI: region of interest.
See this image and copyright information in PMC

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