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Cancers
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Novel Approaches to Machine Learning and Artificial Intelligence in Cancer...
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Novel Approaches to Machine Learning and Artificial Intelligence in Cancer Research and Care

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 3115

Special Issue Editors

Dr. Caroline Chung

E-Mail Website
Guest Editor
Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Interests: brain tumors; CNS neoplasms; image-guided therapy; MRI biomarkers; clinical trials
Special Issues, Collections and Topics in MDPI journals
Dr. Christopher Gibbons

E-Mail Website
Guest Editor
1. Division of Internal Medicine, Section of Patient Centered Analytics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
2. MD Anderson Center for INSPiRED Cancer Care (Integrated Systems for Patient-Reported Data), The University of Texas MD Anderson Cancer Center, Houston, TX, USA
Interests: machine learning; statistical analysis; quantitative data analysis; research methodology; clinical trials

Special Issue Information

Dear Colleagues,

This Special Issue of Cancers will highlight cutting-edge research addressing new strategies to improve the performance and safe clinical translation of machine learning (ML) and artificial intelligence (AI) approaches to oncology research and care. Specifically, we aim to address four key elements of AI/ML development and implementation that will help cancer discovery to drive clinical impact: data, models, evaluation, and systems. Topics of interest in data may include new standards for data quality and provenance, and opportunities around the inclusion of metadata in model development and implementation; Topics of interest in models may include efficient and interpretable algorithms suitable for deployment in health systems; systems for deploying and monitoring algorithms to enable accelerated safe deployment across health systems. Regarding model evaluation, we welcome papers presenting new strategies for evaluating ML and AI through clinical trials or ensures the validity of text-generation tools. From a systems perspective, systems for deploying and monitoring algorithms in practice as well as approaches and standards for flagging model behaviors that may lead to unexpected outcomes are of interest.

Dr. Caroline Chung
Dr. Christopher Gibbons
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. 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 2900 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
  • deep learning
  • artificial intelligence
  • interpretable AI
  • data quality
  • metadata
  • data quality
  • cancer
  • MLOps
  • data science
  • real world data

Published Papers (3 papers)

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Research

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13 pages, 4993 KiB  
Article
Predicting Immunotherapy Outcomes in Glioblastoma Patients through Machine Learning
by Guillaume Mestrallet
Cancers 2024, 16(2), 408; https://doi.org/10.3390/cancers16020408 - 18 Jan 2024
Viewed by 979
Abstract
Glioblastoma is a highly aggressive cancer associated with a dismal prognosis, with a mere 5% of patients surviving beyond five years post diagnosis. Current therapeutic modalities encompass surgical intervention, radiotherapy, chemotherapy, and immune checkpoint inhibitors (ICBs). However, the efficacy of ICBs remains limited [...] Read more.
Glioblastoma is a highly aggressive cancer associated with a dismal prognosis, with a mere 5% of patients surviving beyond five years post diagnosis. Current therapeutic modalities encompass surgical intervention, radiotherapy, chemotherapy, and immune checkpoint inhibitors (ICBs). However, the efficacy of ICBs remains limited in glioblastoma patients, necessitating a proactive approach to anticipate treatment response and resistance. In this comprehensive study, we conducted a rigorous analysis involving two distinct glioblastoma patient cohorts subjected to PD-1 blockade treatments. Our investigation revealed that a significant portion (60%) of patients exhibit persistent disease progression despite ICB intervention. To elucidate the underpinnings of resistance, we characterized the immune profiles of glioblastoma patients with continued cancer progression following anti-PD1 therapy. These profiles revealed multifaceted defects, encompassing compromised macrophage, monocyte, and T follicular helper responses, impaired antigen presentation, aberrant regulatory T cell (Tregs) responses, and heightened expression of immunosuppressive molecules (TGFB, IL2RA, and CD276). Building upon these resistance profiles, we leveraged cutting-edge machine learning algorithms to develop predictive models and accompanying software. This innovative computational tool achieved remarkable success, accurately forecasting the progression status of 82.82% of the glioblastoma patients in our study following ICBs, based on their unique immune characteristics. In conclusion, our pioneering approach advocates for the personalization of immunotherapy in glioblastoma patients. By harnessing patient-specific attributes and computational predictions, we offer a promising avenue for the enhancement of clinical outcomes in the realm of immunotherapy. This paradigm shift towards tailored therapies underscores the potential to revolutionize the management of glioblastoma, opening new horizons for improved patient care. Full article
(This article belongs to the Special Issue Novel Approaches to Machine Learning and Artificial Intelligence in Cancer Research and Care)
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18 pages, 6583 KiB  
Article
Automated Laryngeal Cancer Detection and Classification Using Dwarf Mongoose Optimization Algorithm with Deep Learning
by Nuzaiha Mohamed, Reem Lafi Almutairi, Sayda Abdelrahim, Randa Alharbi, Fahad Mohammed Alhomayani, Bushra M. Elamin Elnaim, Azhari A. Elhag and Rajendra Dhakal
Cancers 2024, 16(1), 181; https://doi.org/10.3390/cancers16010181 - 29 Dec 2023
Viewed by 898
Abstract
Laryngeal cancer (LCA) is a serious disease with a concerning global rise in incidence. Accurate treatment for LCA is particularly challenging in later stages, due to its complex nature as a head and neck malignancy. To address this challenge, researchers have been actively [...] Read more.
Laryngeal cancer (LCA) is a serious disease with a concerning global rise in incidence. Accurate treatment for LCA is particularly challenging in later stages, due to its complex nature as a head and neck malignancy. To address this challenge, researchers have been actively developing various analysis methods and tools to assist medical professionals in efficient LCA identification. However, existing tools and methods often suffer from various limitations, including low accuracy in early-stage LCA detection, high computational complexity, and lengthy patient screening times. With this motivation, this study presents an Automated Laryngeal Cancer Detection and Classification using a Dwarf Mongoose Optimization Algorithm with Deep Learning (ALCAD-DMODL) technique. The main objective of the ALCAD-DMODL method is to recognize the existence of LCA using the DL model. In the presented ALCAD-DMODL technique, a median filtering (MF)-based noise removal process takes place to get rid of the noise. Additionally, the ALCAD-DMODL technique involves the EfficientNet-B0 model for deriving feature vectors from the pre-processed images. For optimal hyperparameter tuning of the EfficientNet-B0 model, the DMO algorithm can be applied to select the parameters. Finally, the multi-head bidirectional gated recurrent unit (MBGRU) model is applied for the recognition and classification of LCA. The simulation result analysis of the ALCAD-DMODL technique is carried out on the throat region image dataset. The comparison study stated the supremacy of the ALCAD-DMODL technique in terms of distinct measures. Full article
(This article belongs to the Special Issue Novel Approaches to Machine Learning and Artificial Intelligence in Cancer Research and Care)
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Review

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19 pages, 744 KiB  
Review
Artificial Intelligence-Based Management of Adult Chronic Myeloid Leukemia: Where Are We and Where Are We Going?
by Simona Bernardi, Mauro Vallati and Roberto Gatta
Cancers 2024, 16(5), 848; https://doi.org/10.3390/cancers16050848 - 20 Feb 2024
Viewed by 838
Abstract
Artificial intelligence (AI) is emerging as a discipline capable of providing significant added value in Medicine, in particular in radiomic, imaging analysis, big dataset analysis, and also for generating virtual cohort of patients. However, in coping with chronic myeloid leukemia (CML), considered an [...] Read more.
Artificial intelligence (AI) is emerging as a discipline capable of providing significant added value in Medicine, in particular in radiomic, imaging analysis, big dataset analysis, and also for generating virtual cohort of patients. However, in coping with chronic myeloid leukemia (CML), considered an easily managed malignancy after the introduction of TKIs which strongly improved the life expectancy of patients, AI is still in its infancy. Noteworthy, the findings of initial trials are intriguing and encouraging, both in terms of performance and adaptability to different contexts in which AI can be applied. Indeed, the improvement of diagnosis and prognosis by leveraging biochemical, biomolecular, imaging, and clinical data can be crucial for the implementation of the personalized medicine paradigm or the streamlining of procedures and services. In this review, we present the state of the art of AI applications in the field of CML, describing the techniques and objectives, and with a general focus that goes beyond Machine Learning (ML), but instead embraces the wider AI field. The present scooping review spans on publications reported in Pubmed from 2003 to 2023, and resulting by searching “chronic myeloid leukemia” and “artificial intelligence”. The time frame reflects the real literature production and was not restricted. We also take the opportunity for discussing the main pitfalls and key points to which AI must respond, especially considering the critical role of the ‘human’ factor, which remains key in this domain. Full article
(This article belongs to the Special Issue Novel Approaches to Machine Learning and Artificial Intelligence in Cancer Research and Care)
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