Artificial Intelligence in Colorectal Disease

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 1248

Special Issue Editor


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Guest Editor
Department of Radiology, Netherlands Cancer Institute-Antoni Van Leeuwenhoek, Amsterdam, The Netherlands
Interests: machine learning; deep learning; CNN; medical images; CT; tumor; endoscopy; colorectal cancer

Special Issue Information

Dear Colleagues,

Artificial intelligence is becoming increasingly important in the detection and personalized treatment of cancer. The applications to clinical workflows can include tumour burden annotation as well as treatment response assessment and prediction together with recurrence and metastasis prediction. All of these use cases are relevant for colorectal cancer, which is among the four most common cancers. The long-term survival of colorectal cancer patients has improved considerably for some types, such as locally advanced rectal cancer with the introduction of total mesorectal excision. However, mortality, particularly in elderly populations, remains high. In addition, treatment can have significant impacts on quality of life such as colostomy and long-term comorbidities. This presents significant opportunities for personalised treatment, in which AI models can identify which patients will have a low chance of recurrence, thereby preventing unnecessary comorbidities. With the relatively large datasets provided by colorectal cancer cohorts, the possibilities provided by artificial intelligence can have a real impact to both the survival and quality of life of colorectal cancer patients.

Dr. Sean Benson
Guest Editor

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Published Papers (1 paper)

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Research

15 pages, 4552 KiB  
Article
A Deep Learning Framework with Explainability for the Prediction of Lateral Locoregional Recurrences in Rectal Cancer Patients with Suspicious Lateral Lymph Nodes
by Tania C. Sluckin, Marije Hekhuis, Sabrine Q. Kol, Joost Nederend, Karin Horsthuis, Regina G. H. Beets-Tan, Geerard L. Beets, Jacobus W. A. Burger, Jurriaan B. Tuynman, Harm J. T. Rutten, Miranda Kusters and Sean Benson
Diagnostics 2023, 13(19), 3099; https://doi.org/10.3390/diagnostics13193099 - 29 Sep 2023
Cited by 1 | Viewed by 865
Abstract
Malignant lateral lymph nodes (LLNs) in low, locally advanced rectal cancer can cause (ipsi-lateral) local recurrences ((L)LR). Accurate identification is, therefore, essential. This study explored LLN features to create an artificial intelligence prediction model, estimating the risk of (L)LR. This retrospective multicentre cohort [...] Read more.
Malignant lateral lymph nodes (LLNs) in low, locally advanced rectal cancer can cause (ipsi-lateral) local recurrences ((L)LR). Accurate identification is, therefore, essential. This study explored LLN features to create an artificial intelligence prediction model, estimating the risk of (L)LR. This retrospective multicentre cohort study examined 196 patients diagnosed with rectal cancer between 2008 and 2020 from three tertiary centres in the Netherlands. Primary and restaging T2W magnetic resonance imaging and clinical features were used. Visible LLNs were segmented and used for a multi-channel convolutional neural network. A deep learning model was developed and trained for the prediction of (L)LR according to malignant LLNs. Combined imaging and clinical features resulted in AUCs of 0.78 and 0.80 for LR and LLR, respectively. The sensitivity and specificity were 85.7% and 67.6%, respectively. Class activation map explainability methods were applied and consistently identified the same high-risk regions with structural similarity indices ranging from 0.772–0.930. This model resulted in good predictive value for (L)LR rates and can form the basis of future auto-segmentation programs to assist in the identification of high-risk patients and the development of risk stratification models. Full article
(This article belongs to the Special Issue Artificial Intelligence in Colorectal Disease)
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