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Life
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Diagnostic Imaging in Colorectal Cancer
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Diagnostic Imaging in Colorectal Cancer

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Radiobiology and Nuclear Medicine".

Deadline for manuscript submissions: 30 May 2025 | Viewed by 521

Special Issue Editors

Dr. Filippo Crimì

E-Mail Website
Guest Editor
Institute of Radiology, Department of Medicine—DIMED, University of Padua, 35128 Padua, Italy
Interests: MRI; PET; diagnostic imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Chiara Zanon

E-Mail Website
Guest Editor
Institute of Radiology, Department of Medicine—DIMED, University of Padua, 35128 Padua, Italy
Interests: artificial intelligence; radiomics; radioprotection; photon counting CT; oncological imaging; cardiovascular imaging

Special Issue Information

Dear Colleagues,

Imaging plays a crucial role in the complex approach to colorectal cancer, based on screening, diagnosis, staging, restaging and monitoring for recurrence. The push for standardized reporting systems by scientific societies is to refine the detection of small lesions and reduce report variability.

Artificial intelligence (AI), radiomics and PET/MRI are at the forefront of advances in colorectal cancer imaging. In this context, texture analysis is a groundbreaking tool that allows for software to extract additional features that are not visible to the human eye in ultrasound, CT and MRI images. This quantitative approach in colorectal cancer assays can present a new era in diagnostics, potentially augmented by AI for data management.

PET/MRI has emerged as a superior imaging modality, offering a fusion of detailed anatomical views from MRI with functional insights from PET scans. This combination has been proven to improve the accuracy of PET/CT and MRI for staging tumor spread (T and N staging), supporting its use in determining the suitability for less invasive rectal cancer treatments.

This Special Issue aims to collate state-of-the-art studies targeting advancements in colorectal cancer multimodal imaging that can lead to improved diagnostic accuracy and refine colorectal cancer assessment.

Dr. Filippo Crimì
Dr. Chiara Zanon
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Life is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • colorectal cancer
  • artificial intelligence
  • texture analysis
  • ultrasound
  • magnetic resonance imaging
  • PET/MRI

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Published Papers (1 paper)

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Research

14 pages, 1564 KiB  
Article
A Machine Learning Model Based on MRI Radiomics to Predict Response to Chemoradiation Among Patients with Rectal Cancer
by Filippo Crimì, Carlo D’Alessandro, Chiara Zanon, Francesco Celotto, Christian Salvatore, Matteo Interlenghi, Isabella Castiglioni, Emilio Quaia, Salvatore Pucciarelli and Gaya Spolverato
Life 2024, 14(12), 1530; https://doi.org/10.3390/life14121530 - 22 Nov 2024
Viewed by 153
Abstract
Background: With rectum-sparing protocols becoming more common for rectal cancer treatment, this study aimed to predict the pathological complete response (pCR) to preoperative chemoradiotherapy (pCRT) in rectal cancer patients using pre-treatment MRI and a radiomics-based machine learning approach. Methods: We divided MRI-data from [...] Read more.
Background: With rectum-sparing protocols becoming more common for rectal cancer treatment, this study aimed to predict the pathological complete response (pCR) to preoperative chemoradiotherapy (pCRT) in rectal cancer patients using pre-treatment MRI and a radiomics-based machine learning approach. Methods: We divided MRI-data from 102 patients into a training cohort (n = 72) and a validation cohort (n = 30). In the training cohort, 52 patients were classified as non-responders and 20 as pCR based on histological results from total mesorectal excision. Results: We trained various machine learning models using radiomic features to capture disease heterogeneity between responders and non-responders. The best-performing model achieved a receiver operating characteristic area under the curve (ROC-AUC) of 73% and an accuracy of 70%, with a sensitivity of 78% and a positive predictive value (PPV) of 80%. In the validation cohort, the model showed a sensitivity of 81%, specificity of 75%, and accuracy of 80%. Conclusions: These results highlight the potential of radiomics and machine learning in predicting treatment response and support the integration of advanced imaging and computational methods for personalized rectal cancer management. Full article
(This article belongs to the Special Issue Diagnostic Imaging in Colorectal Cancer)
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