Colorectal cancer: Parametric evaluation of morphological, functional and molecular tomographic imaging

Abstract

Colorectal cancer (CRC) represents one of the leading causes of tumor-related deaths worldwide. Among the various tools at physicians’ disposal for the diagnostic management of the disease, tomographic imaging (e.g., CT, MRI, and hybrid PET imaging) is considered essential. The qualitative and subjective evaluation of tomographic images is the main approach used to obtain valuable clinical information, although this strategy suffers from both intrinsic and operator-dependent limitations. More recently, advanced imaging techniques have been developed with the aim of overcoming these issues. Such techniques, such as diffusion-weighted MRI and perfusion imaging, were designed for the “in vivo” evaluation of specific biological tissue features in order to describe them in terms of quantitative parameters, which could answer questions difficult to address with conventional imaging alone (e.g., questions related to tissue characterization and prognosis). Furthermore, it has been observed that a large amount of numerical and statistical information is buried inside tomographic images, resulting in their invisibility during conventional assessment. This information can be extracted and represented in terms of quantitative parameters through different processes (e.g., texture analysis). Numerous researchers have focused their work on the significance of these quantitative imaging parameters for the management of CRC patients. In this review, we aimed to focus on evidence reported in the academic literature regarding the application of parametric imaging to the diagnosis, staging and prognosis of CRC while discussing future perspectives and present limitations. While the transition from purely anatomical to quantitative tomographic imaging appears achievable for CRC diagnostics, some essential milestones, such as scanning and analysis standardization and the definition of robust cut-off values, must be achieved before quantitative tomographic imaging can be incorporated into daily clinical practice.

Association between imaging response and survival following neoadjuvant chemotherapy in patients with resectable colorectal liver metastases: A cohort study

“…It is necessary to move beyond unimodal, clinicopathological, or quantitative/topographic imaging techniques when trying to predict which patients will benefit most from CRLM resection following neoCT. Emerging as promising tools are more advanced hybrid imaging/bioinformatic techniques in the form of structural and metabolic imaging‐based radiomics and radiogenomics 51,52 . These techniques harness all of the available information that can be gathered about a patient and their tumor, combining numerous quantitative images with the mutational signature, gene expression, and proteomic profile of the tumor to generate a statistical model that could reflect the true “biology” of the tumor and aid in better patient selection for CRLM following neoCT 51,52 …”

Characterization of FDG PET Images Using Texture Analysis in Tumors of the Gastro-Intestinal Tract: A Review

“…In-vivo quantitative information, including texture-analysis, of neoplastic processes of the gastro-intestinal tract may also be derived from images obtained using current state-of the-art CT and MRI imaging [ 59 , 60 ]. Their added clinical value to in-vivo quantitative information derived from FDG/PET images warrants exploration.…”

Colorectal Tumor Segmentation of CT Scans Based on a Convolutional Neural Network With an Attention Mechanism

“…CT is superior for detecting calcified metastases. Three-dimensional CT to visualize the vascular anatomy facilitates laparoscopic surgery [6], [7]. CT colonography provides important information for the preoperative assessment and follow up after therapy on CRC patients.…”

“…In clinical routines, segmentation of CT images of colon rectal tumors is usually undertaken by radiologists on a slice-by-slice basis, which is tedious, time consuming and very expensive, and suffers from intra-and interobserver variability [8]. Moreover, the density distributions of the colorectal tumors in the CT scans of different patients may be different, and the size and shape of the tumors also vary considerably across patients [7]. In addition, cleaning and filling the intestinal tract before a CT scan is hardly uniform.…”