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Open AccessFeature PaperReview

Radiomics and Machine Learning in Anal Squamous Cell Carcinoma: A New Step for Personalized Medicine?

1
Radiation Oncology Department, Hôpital Haut-Lévêque, CHU Bordeaux, 33600 Pessac, France
2
Institut de Mathématiques de Bordeaux CNRS UMR 5251 & Université de Bordeaux, MOnc Team, INRIA Bordeaux-Sud-Ouest, Talence, 33076 Bordeaux, France
3
Division of Radiation Oncology, Department of Oncology, McGill University, Montreal, QC H4A 3J1, Canada
4
Department of Radiation Therapy, Centre Catalan d’Oncologie, 66000 Perpignan, France
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(6), 1988; https://doi.org/10.3390/app10061988
Received: 24 February 2020 / Revised: 8 March 2020 / Accepted: 10 March 2020 / Published: 14 March 2020
(This article belongs to the Special Issue Artificial Intelligence for Personalised Medicine)
Anal squamous cell carcinoma (ASCC) is an uncommon yet rising cancer worldwide. Definitive chemo-radiation (CRT) remains the best curative treatment option for non-metastatic cases in terms of local control, recurrence-free and progression-free survival. Still, despite overall good results, with 80% five-year survival, a subgroup of ASCC patients displays a high level of locoregional and/or metastatic recurrence rates, up to 35%, and may benefit from a more aggressive strategy. Beyond initial staging, there is no reliable marker to predict recurrence following CRT. Imaging, mostly positron emission tomography-computed tomography (PET-CT) and magnetic resonance imaging (MRI), bears an important role in the diagnosis and follow-up of ASCC. The routine use of radiomics may enhance the quality of information derived from these modalities. It is thought that including data derived from radiomics into the input flow of machine learning algorithms may improve the prediction of recurrence. Although some studies have shown glimmers of hope, more data is needed before offering practitioners tools to identify high-risk patients and enable extensive clinical application, especially regarding the matters of imaging normalization, radiomics process standardization and access to larger patient databases with external validation in order to allow results extrapolation. The aim of this review is to present a critical overview from this data. View Full-Text
Keywords: radiomics; machine learning; anal cancer; prediction medicine; precision medicine radiomics; machine learning; anal cancer; prediction medicine; precision medicine
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MDPI and ACS Style

Giraud, N.; Sargos, P.; Leduc, N.; Saut, O.; Vuong, T.; Vendrely, V. Radiomics and Machine Learning in Anal Squamous Cell Carcinoma: A New Step for Personalized Medicine? Appl. Sci. 2020, 10, 1988. https://doi.org/10.3390/app10061988

AMA Style

Giraud N, Sargos P, Leduc N, Saut O, Vuong T, Vendrely V. Radiomics and Machine Learning in Anal Squamous Cell Carcinoma: A New Step for Personalized Medicine? Applied Sciences. 2020; 10(6):1988. https://doi.org/10.3390/app10061988

Chicago/Turabian Style

Giraud, Nicolas; Sargos, Paul; Leduc, Nicolas; Saut, Olivier; Vuong, Te; Vendrely, Veronique. 2020. "Radiomics and Machine Learning in Anal Squamous Cell Carcinoma: A New Step for Personalized Medicine?" Appl. Sci. 10, no. 6: 1988. https://doi.org/10.3390/app10061988

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