Special Issue "Artificial Intelligence and Machine Learning in Cancer Research"

A special issue of Cancers (ISSN 2072-6694).

Deadline for manuscript submissions: 1 December 2022.

Special Issue Editors

Dr. Jean-Emmanuel Bibault
E-Mail Website
Guest Editor
Radiation Oncology Department, Hôpital Européen Georges Pompidou, Assistance Publique - Hôpitaux de Paris, Université de Paris, F-75014 Paris, France
Interests: machine learning; artificial intelligence; prostate cancer; lung cancer; stereotactic body radiation
Dr. Lei Xing
E-Mail Website
Guest Editor
Department of Radiation Oncology, Stanford University, Stanford, CA, USA
Interests: artificial intelligence; biomedical physics; bioengineering

Special Issue Information

Dear Colleagues,

In the near future, Artificial Intelligence and machine learning are poised to radically transform cancer care. Current research in the field of machine learning applied to oncology includes cancer screening through image analysis with deep learning, automated pathology and diagnosis, prognosis prediction and treatment personalization, drug discovery and automated treatment planning.

In this Special Issue, we invite teams working on applied AI to submit their latest and most significant research in this area. Beyond this, teams exploring interpretability and expert-augmented machine learning are invited to contribute to this new Special Issue. Studies describing new datasets and new methods are welcome. Datasets and code availability are strongly encouraged.

Dr. Jean-Emmanuel Bibault
Dr. Lei Xing
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 papers will be 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. Cancers is an international peer-reviewed open access semimonthly 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 2200 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

  • Machine learning
  • Artificial Intelligence
  • Automation
  • Prediction
  • Drug discovery

Published Papers (1 paper)

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Research

Article
Development and Validation of an Interpretable Artificial Intelligence Model to Predict 10-Year Prostate Cancer Mortality
Cancers 2021, 13(12), 3064; https://doi.org/10.3390/cancers13123064 - 19 Jun 2021
Viewed by 807
Abstract
Prostate cancer treatment strategies are guided by risk-stratification. This stratification can be difficult in some patients with known comorbidities. New models are needed to guide strategies and determine which patients are at risk of prostate cancer mortality. This article presents a gradient-boosting model [...] Read more.
Prostate cancer treatment strategies are guided by risk-stratification. This stratification can be difficult in some patients with known comorbidities. New models are needed to guide strategies and determine which patients are at risk of prostate cancer mortality. This article presents a gradient-boosting model to predict the risk of prostate cancer mortality within 10 years after a cancer diagnosis, and to provide an interpretable prediction. This work uses prospective data from the PLCO Cancer Screening and selected patients who were diagnosed with prostate cancer. During follow-up, 8776 patients were diagnosed with prostate cancer. The dataset was randomly split into a training (n = 7021) and testing (n = 1755) dataset. Accuracy was 0.98 (±0.01), and the area under the receiver operating characteristic was 0.80 (±0.04). This model can be used to support informed decision-making in prostate cancer treatment. AI interpretability provides a novel understanding of the predictions to the users. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Cancer Research)
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