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Review
. 2021 Apr 23;11(5):756.
doi: 10.3390/diagnostics11050756.

Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice

Francesca Coppola  1 , Valentina Giannini  2 , Michela Gabelloni  3 , Jovana Panic  2 , Arianna Defeudis  2 , Silvia Lo Monaco  1 , Arrigo Cattabriga  1 , Maria Adriana Cocozza  1 , Luigi Vincenzo Pastore  1 , Michela Polici  4 , Damiano Caruso  4 , Andrea Laghi  4 , Daniele Regge  2   5 , Emanuele Neri  3 , Rita Golfieri  1 , Lorenzo Faggioni  3
Affiliations
Review

Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice

Francesca Coppola et al. Diagnostics (Basel). .

Abstract

While cross-sectional imaging has seen continuous progress and plays an undiscussed pivotal role in the diagnostic management and treatment planning of patients with rectal cancer, a largely unmet need remains for improved staging accuracy, assessment of treatment response and prediction of individual patient outcome. Moreover, the increasing availability of target therapies has called for developing reliable diagnostic tools for identifying potential responders and optimizing overall treatment strategy on a personalized basis. Radiomics has emerged as a promising, still fully evolving research topic, which could harness the power of modern computer technology to generate quantitative information from imaging datasets based on advanced data-driven biomathematical models, potentially providing an added value to conventional imaging for improved patient management. The present study aimed to illustrate the contribution that current radiomics methods applied to magnetic resonance imaging can offer to managing patients with rectal cancer.

Keywords: deep learning; magnetic resonance imaging; neoadjuvant chemoradiation therapy; personalized medicine; radiomics; rectal cancer; surgery.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flowchart illustrating the basic steps of radiomics workflow.
Figure 2
Figure 2
Manual tumor segmentation from T2-weighted (a,b) and DW (c,d) rectal MR images. Tumor borders are highlighted as dashed lines.
Figure 3
Figure 3
Architecture of a DL algorithm. The upper flowchart shows a human workflow, whereas the lower flowchart shows the steps needed for artificial intelligence (AI) to accomplish the same task.
Figure 4
Figure 4
(a) Pretreatment multiparametric rectal MRI examination in a male patient with RC. Upper row (from left to right): axial MR images from T2-weighted images, DWI b1000, DWI b0, and fusion imaging between T2-weighted and DWI b1000 images. Lower row (from left to right): tumor segmentation performed by an experienced reader used for training, an independent reader, the algorithm output, and the corresponding probability map generated by the algorithm. (b) Performance of CNN-based segmentation. The algorithm correctly identified and segmented the tumor in cases I to IV (small field of view), but it failed with a larger field of view images (V and VI), where parts of the cavernous bodies of the penis were mistakenly included in the segmentation. Adapted from [46].
Figure 5
Figure 5
(a) ROC curves of statistically significant texture feature extracted from axial T2-weighted images for predicting the T-stage of RC. (b) ROC curve of a model for predicting T-stage based on multivariate logistic regression analysis. Adapted from [60].
Figure 6
Figure 6
Performance of three predictive models in predicting downstaging, pathological complete response (PCR) and good response in LARC patients. The red box highlights the performance score associated with the highest AUC. Adapted from [66].
Figure 7
Figure 7
ROC curves corresponding to quantitative texture features derived from T2-weighted MR images for differentiating KRAS mutation status in rectal cancer. Adapted from [76].
See this image and copyright information in PMC

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