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Cancers
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Artificial Intelligence and Machine Learning in Precision Oncology
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Artificial Intelligence and Machine Learning in Precision Oncology

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 19173

Special Issue Editors

Dr. Carmen Belli

E-Mail Website
Guest Editor
Division of Early Drug Development for Innovative Therapies, European Institute of Oncology IRCCS, Milan, Italy
Interests: cancer; clinical trials; drug development; drug discovery; artificial intelligence; machine learning
Dr. Santosh Anand

E-Mail Website
Guest Editor
Department of Biology, University of North Carolina Greensboro, Greensboro, NC, USA
Interests: bioinformatics; data science; machine learning; artificial intelligence; cancer; clinical trials; drug discovery; biomarkers

Special Issue Information

Dear Colleagues,

We live in an era where artificial intelligence (AI) is touching and radically transforming every aspect of our lives and the fabric of our society, whether it is self-driving cars, smart digital assistants, clever chatbots, metaverse, or incredible digital art created by OpenAI’s DALL·E system.

The goal of AI is to create intelligent systems capable of emulating human intellect and intelligence. Machine learning (ML), a subset of AI, is any approach that allows machines to learn patterns and gain knowledge from previously provided data without explicitly programming, as well as make predictions on new data.

AI is causing a paradigm shift in scientific research. In precision oncology, AI is reshaping the existing scenario, aiming to integrate and interpret large amounts of patients’ data with current advances in high-performance computing and groundbreaking deep-learning strategies for better treatment decisions.

This special issue of Cancers is focused on the applications of AI/ML in precision oncology and cancer research.  This include, but is not limited to, new AI/ML approaches for cancer detection, screening, diagnosis, and classification, as well as the characterization of the cancer genome, the analysis of tumor microenvironment, the evaluation of biomarkers for prognostic and predictive purposes, strategies for patient follow-up, and drug designing and development. In addition, new modes of interaction between patients and doctors using AI and avatars and the application of artificial intelligence in communication skills training are also welcome.

We invite the research community to submit their latest and most significant research in the above-mentioned areas as original research articles, reviews, or short communications. Both traditional, as well as emerging ML approaches like artificial neural networks and deep learning methods, are welcome. Code availability and reproducibility of results are strongly encouraged.

Dr. Carmen Belli
Dr. Santosh Anand
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 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

  • precision oncology
  • cancer
  • artificial intelligence
  • AI
  • machine learning
  • deep learning
  • drug discovery
  • cancer screening
  • biomarkers

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Published Papers (6 papers)

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Research

Jump to: Review

14 pages, 1300 KiB  
Article
Generation of a Realistic Synthetic Laryngeal Cancer Cohort for AI Applications
by Mika Katalinic, Martin Schenk, Stefan Franke, Alexander Katalinic, Thomas Neumuth, Andreas Dietz, Matthaeus Stoehr and Jan Gaebel
Cancers 2024, 16(3), 639; https://doi.org/10.3390/cancers16030639 - 1 Feb 2024
Cited by 2 | Viewed by 1275
Abstract
Background: Obtaining large amounts of real patient data involves great efforts and expenses, and processing this data is fraught with data protection concerns. Consequently, data sharing might not always be possible, particularly when large, open science datasets are needed, as for AI development. [...] Read more.
Background: Obtaining large amounts of real patient data involves great efforts and expenses, and processing this data is fraught with data protection concerns. Consequently, data sharing might not always be possible, particularly when large, open science datasets are needed, as for AI development. For such purposes, the generation of realistic synthetic data may be the solution. Our project aimed to generate realistic cancer data with the use case of laryngeal cancer. Methods: We used the open-source software Synthea and programmed an additional module for development, treatment and follow-up for laryngeal cancer by using external, real-world (RW) evidence from guidelines and cancer registries from Germany. To generate an incidence-based cohort view, we randomly drew laryngeal cancer cases from the simulated population and deceased persons, stratified by the real-world age and sex distributions at diagnosis. Results: A module with age- and stage-specific treatment and prognosis for laryngeal cancer was successfully implemented. The synthesized population reflects RW prevalence well, extracting a cohort of 50,000 laryngeal cancer patients. Descriptive data on stage-specific and 5-year overall survival were in accordance with published data. Conclusions: We developed a large cohort of realistic synthetic laryngeal cancer cases with Synthea. Such data can be shared and published open source without data protection issues. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Precision Oncology)
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17 pages, 3374 KiB  
Article
Impact of H&E Stain Normalization on Deep Learning Models in Cancer Image Classification: Performance, Complexity, and Trade-Offs
by Nuwan Madusanka, Pramudini Jayalath, Dileepa Fernando, Lasith Yasakethu and Byeong-Il Lee
Cancers 2023, 15(16), 4144; https://doi.org/10.3390/cancers15164144 - 17 Aug 2023
Cited by 5 | Viewed by 2140
Abstract
Accurate classification of cancer images plays a crucial role in diagnosis and treatment planning. Deep learning (DL) models have shown promise in achieving high accuracy, but their performance can be influenced by variations in Hematoxylin and Eosin (H&E) staining techniques. In this study, [...] Read more.
Accurate classification of cancer images plays a crucial role in diagnosis and treatment planning. Deep learning (DL) models have shown promise in achieving high accuracy, but their performance can be influenced by variations in Hematoxylin and Eosin (H&E) staining techniques. In this study, we investigate the impact of H&E stain normalization on the performance of DL models in cancer image classification. We evaluate the performance of VGG19, VGG16, ResNet50, MobileNet, Xception, and InceptionV3 on a dataset of H&E-stained cancer images. Our findings reveal that while VGG16 exhibits strong performance, VGG19 and ResNet50 demonstrate limitations in this context. Notably, stain normalization techniques significantly improve the performance of less complex models such as MobileNet and Xception. These models emerge as competitive alternatives with lower computational complexity and resource requirements and high computational efficiency. The results highlight the importance of optimizing less complex models through stain normalization to achieve accurate and reliable cancer image classification. This research holds tremendous potential for advancing the development of computationally efficient cancer classification systems, ultimately benefiting cancer diagnosis and treatment. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Precision Oncology)
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15 pages, 1562 KiB  
Article
Assessing the Performance of a Novel Stool-Based Microbiome Test That Predicts Response to First Line Immune Checkpoint Inhibitors in Multiple Cancer Types
by Irina Robinson, Maximilian Johannes Hochmair, Manuela Schmidinger, Gudrun Absenger, Martin Pichler, Van Anh Nguyen, Erika Richtig, Barbara Margaretha Rainer, Leyla Ay, Christian Jansen, Cátia Pacífico, Alexander Knabl, Barbara Sladek, Nikolaus Gasche and Arschang Valipour
Cancers 2023, 15(13), 3268; https://doi.org/10.3390/cancers15133268 - 21 Jun 2023
Cited by 3 | Viewed by 3284
Abstract
The intestinal microbiome is by now an undebatable key player in the clinical outcome of ICI therapies. However, no microbiome profiling method to aid therapy decision is yet validated. We conducted a multi-centric study in patients with stage III/IV melanoma, NSCLC, or RCC [...] Read more.
The intestinal microbiome is by now an undebatable key player in the clinical outcome of ICI therapies. However, no microbiome profiling method to aid therapy decision is yet validated. We conducted a multi-centric study in patients with stage III/IV melanoma, NSCLC, or RCC receiving ICI treatment. The stool microbiome profile of 63 patients was analyzed with BiomeOne®, a microbiome-based algorithm that anticipates whether a patient will achieve clinical benefit with ICIs prior to therapy initiation. Classification of patient samples as Rs and NRs was achieved with a sensitivity of 81% and a specificity of 50% in this validation cohort. An ICI-favorable response was characterized by an intestinal microbiome rich in bacteria such as Oscillospira sp., Clostridia UCG-014, Lachnospiraceae UCG-010 sp., Prevotella copri, and a decrease in Sutterella sp., Lactobacillales, and Streptococcus sp. Patients who developed immune-related adverse events (irAEs) had an overall increased microbial diversity and richness, and a stool microbiome depleted in Agathobacter. When compared with the programmed death-ligand 1 (PD-L1) expression test in the subcohort of NSCLC patients (n = 38), BiomeOne® exhibited a numerically higher sensitivity (78.6%) in identifying responders when compared with the PD-L1 test (67.9%). This study provides an evaluation of BiomeOne®, the first microbiome-based test for prediction of ICI response, to achieve market authorization. Validation with further indications and expansion to other microbiome-based interventions will be essential to bring microbiome-based diagnostics into standard clinical practice. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Precision Oncology)
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13 pages, 2768 KiB  
Article
Hybrid Quantum Neural Network for Drug Response Prediction
by Asel Sagingalieva, Mohammad Kordzanganeh, Nurbolat Kenbayev, Daria Kosichkina, Tatiana Tomashuk and Alexey Melnikov
Cancers 2023, 15(10), 2705; https://doi.org/10.3390/cancers15102705 - 10 May 2023
Cited by 33 | Viewed by 5778
Abstract
Cancer is one of the leading causes of death worldwide. It is caused by various genetic mutations, which makes every instance of the disease unique. Since chemotherapy can have extremely severe side effects, each patient requires a personalized treatment plan. Finding the dosages [...] Read more.
Cancer is one of the leading causes of death worldwide. It is caused by various genetic mutations, which makes every instance of the disease unique. Since chemotherapy can have extremely severe side effects, each patient requires a personalized treatment plan. Finding the dosages that maximize the beneficial effects of the drugs and minimize their adverse side effects is vital. Deep neural networks automate and improve drug selection. However, they require a lot of data to be trained on. Therefore, there is a need for machine-learning approaches that require less data. Hybrid quantum neural networks were shown to provide a potential advantage in problems where training data availability is limited. We propose a novel hybrid quantum neural network for drug response prediction based on a combination of convolutional, graph convolutional, and deep quantum neural layers of 8 qubits with 363 layers. We test our model on the reduced Genomics of Drug Sensitivity in Cancer dataset and show that the hybrid quantum model outperforms its classical analog by 15% in predicting IC50 drug effectiveness values. The proposed hybrid quantum machine learning model is a step towards deep quantum data-efficient algorithms with thousands of quantum gates for solving problems in personalized medicine, where data collection is a challenge. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Precision Oncology)
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20 pages, 6737 KiB  
Article
Persistent Homology-Based Machine Learning Method for Filtering and Classifying Mammographic Microcalcification Images in Early Cancer Detection
by Aminah Abdul Malek, Mohd Almie Alias, Fatimah Abdul Razak, Mohd Salmi Md Noorani, Rozi Mahmud and Nur Fariha Syaqina Zulkepli
Cancers 2023, 15(9), 2606; https://doi.org/10.3390/cancers15092606 - 4 May 2023
Cited by 1 | Viewed by 2260
Abstract
Microcalcifications in mammogram images are primary indicators for detecting the early stages of breast cancer. However, dense tissues and noise in the images make it challenging to classify the microcalcifications. Currently, preprocessing procedures such as noise removal techniques are applied directly on the [...] Read more.
Microcalcifications in mammogram images are primary indicators for detecting the early stages of breast cancer. However, dense tissues and noise in the images make it challenging to classify the microcalcifications. Currently, preprocessing procedures such as noise removal techniques are applied directly on the images, which may produce a blurry effect and loss of image details. Further, most of the features used in classification models focus on local information of the images and are often burdened with details, resulting in data complexity. This research proposed a filtering and feature extraction technique using persistent homology (PH), a powerful mathematical tool used to study the structure of complex datasets and patterns. The filtering process is not performed directly on the image matrix but through the diagrams arising from PH. These diagrams will enable us to distinguish prominent characteristics of the image from noise. The filtered diagrams are then vectorised using PH features. Supervised machine learning models are trained on the MIAS and DDSM datasets to evaluate the extracted features’ efficacy in discriminating between benign and malignant classes and to obtain the optimal filtering level. This study reveals that appropriate PH filtering levels and features can improve classification accuracy in early cancer detection. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Precision Oncology)
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Review

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15 pages, 343 KiB  
Review
Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward?
by Antonio Z. Gimeno-García, Anjara Hernández-Pérez, David Nicolás-Pérez and Manuel Hernández-Guerra
Cancers 2023, 15(8), 2193; https://doi.org/10.3390/cancers15082193 - 7 Apr 2023
Cited by 11 | Viewed by 2790
Abstract
Growing evidence indicates that artificial intelligence (AI) applied to medicine is here to stay. In gastroenterology, AI computer vision applications have been stated as a research priority. The two main AI system categories are computer-aided polyp detection (CADe) and computer-assisted diagnosis (CADx). However, [...] Read more.
Growing evidence indicates that artificial intelligence (AI) applied to medicine is here to stay. In gastroenterology, AI computer vision applications have been stated as a research priority. The two main AI system categories are computer-aided polyp detection (CADe) and computer-assisted diagnosis (CADx). However, other fields of expansion are those related to colonoscopy quality, such as methods to objectively assess colon cleansing during the colonoscopy, as well as devices to automatically predict and improve bowel cleansing before the examination, predict deep submucosal invasion, obtain a reliable measurement of colorectal polyps and accurately locate colorectal lesions in the colon. Although growing evidence indicates that AI systems could improve some of these quality metrics, there are concerns regarding cost-effectiveness, and large and multicentric randomized studies with strong outcomes, such as post-colonoscopy colorectal cancer incidence and mortality, are lacking. The integration of all these tasks into one quality-improvement device could facilitate the incorporation of AI systems in clinical practice. In this manuscript, the current status of the role of AI in colonoscopy is reviewed, as well as its current applications, drawbacks and areas for improvement. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Precision Oncology)
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