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Artificial Intelligence Applications in Precision Oncology
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Artificial Intelligence Applications in Precision Oncology

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Omics/Informatics".

Deadline for manuscript submissions: closed (25 January 2025) | Viewed by 4984

Special Issue Editors

Prof. Dr. Chi-Jie Lu

E-Mail Website
Guest Editor
Department of Information Management, Fu Jen Catholic University, New Taipei City 24205, Taiwan
Interests: machine learning and its applications; medical/healthcare informatics; time-series data analysis; supply chain management; quality management
Special Issues, Collections and Topics in MDPI journals
Dr. Cheuk-Kay Sun

E-Mail Website
Guest Editor
Division of Hepatology and Gastroenterology, Department of Internal Medicine, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
Interests: hepatobiliary diseases; pancreatic diseases; gastroenterological endoscopy; medical informatics

Special Issue Information

Dear Colleagues,

Precision oncology is revolutionizing cancer care by tailoring treatment strategies to each patient's individual characteristics, including demographic, clinical, pathological, radiological, and genetic variables and molecular profiles, as well as other factors. These strategies have seen rapid advancement in recent years, generating vast numbers of complex data. As the complexity of patient data grows, the need for efficient tools to store, retrieve, and analyze them has become increasingly important. Artificial intelligence (AI) is rapidly evolving, offering techniques like data mining, machine learning, and deep learning that are gaining popularity in clinical applications. AI has unique capabilities for handling complex data, offering valuable benefits such as identifying patterns and making predictions that can aid in clinical decision-making. AI holds significant promise and can bring numerous benefits to the clinical field, particularly in precision oncology, due to its inherent complexity. However, a great deal of untapped potential remains to be explored in this area. This Special Issue of Journal of Personalized Medicine aims to provide a platform to highlight the applications of AI in precision oncology and foster a well-rounded understanding of how AI can be applied in precision oncology.

Prof. Dr. Chi-Jie Lu
Dr. Cheuk-Kay Sun
Guest Editors

Manuscript Submission Information

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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. Journal of Personalized Medicine is an international peer-reviewed open access monthly 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 2600 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

  • personalized medicine
  • artificial intelligence
  • targeted therapy
  • immuno-oncology
  • optimized therapies
  • cancer subtypes
  • cancer immunotherapy
  • precision medicine
  • machine learning
  • medical informatics

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Published Papers (3 papers)

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Research

28 pages, 5098 KiB  
Article
A Methodological Framework for AI-Assisted Diagnosis of Ovarian Masses Using CT and MR Imaging
by Pratik Adusumilli, Nishant Ravikumar, Geoff Hall and Andrew F. Scarsbrook
J. Pers. Med. 2025, 15(2), 76; https://doi.org/10.3390/jpm15020076 - 19 Feb 2025
Viewed by 795
Abstract
Background: Ovarian cancer encompasses a diverse range of neoplasms originating in the ovaries, fallopian tubes, and peritoneum. Despite being one of the commonest gynaecological malignancies, there are no validated screening strategies for early detection. A diagnosis typically relies on imaging, biomarkers, and multidisciplinary [...] Read more.
Background: Ovarian cancer encompasses a diverse range of neoplasms originating in the ovaries, fallopian tubes, and peritoneum. Despite being one of the commonest gynaecological malignancies, there are no validated screening strategies for early detection. A diagnosis typically relies on imaging, biomarkers, and multidisciplinary team discussions. The accurate interpretation of CTs and MRIs may be challenging, especially in borderline cases. This study proposes a methodological pipeline to develop and evaluate deep learning (DL) models that can assist in classifying ovarian masses from CT and MRI data, potentially improving diagnostic confidence and patient outcomes. Methods: A multi-institutional retrospective dataset was compiled, supplemented by external data from the Cancer Genome Atlas. Two classification workflows were examined: (1) whole-volume input and (2) lesion-focused region of interest. Multiple DL architectures, including ResNet, DenseNet, transformer-based UNeST, and Attention Multiple-Instance Learning (MIL), were implemented within the PyTorch-based MONAI framework. The class imbalance was mitigated using focal loss, oversampling, and dynamic class weighting. The hyperparameters were optimised with Optuna, and balanced accuracy was the primary metric. Results: For a preliminary dataset, the proposed framework demonstrated feasibility for the multi-class classification of ovarian masses. The initial experiments highlighted the potential of transformers and MIL for identifying the relevant imaging features. Conclusions: A reproducible methodological pipeline for DL-based ovarian mass classification using CT and MRI scans has been established. Future work will leverage a multi-institutional dataset to refine these models, aiming to enhance clinical workflows and improve patient outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Precision Oncology)
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16 pages, 1029 KiB  
Article
Statistical Analysis of nnU-Net Models for Lung Nodule Segmentation
by Alejandro Jerónimo, Olga Valenzuela and Ignacio Rojas
J. Pers. Med. 2024, 14(10), 1016; https://doi.org/10.3390/jpm14101016 - 24 Sep 2024
Viewed by 1492
Abstract
This paper aims to conduct a statistical analysis of different components of nnU-Net models to build an optimal pipeline for lung nodule segmentation in computed tomography images (CT scan). This study focuses on semantic segmentation of lung nodules, using the UniToChest dataset. Our [...] Read more.
This paper aims to conduct a statistical analysis of different components of nnU-Net models to build an optimal pipeline for lung nodule segmentation in computed tomography images (CT scan). This study focuses on semantic segmentation of lung nodules, using the UniToChest dataset. Our approach is based on the nnU-Net framework and is designed to configure a whole segmentation pipeline, thereby avoiding many complex design choices, such as data properties and architecture configuration. Although these framework results provide a good starting point, many configurations in this problem can be optimized. In this study, we tested two U-Net-based architectures, using different preprocessing techniques, and we modified the existing hyperparameters provided by nnU-Net. To study the impact of different settings on model segmentation accuracy, we conducted an analysis of variance (ANOVA) statistical analysis. The factors studied included the datasets according to nodule diameter size, model, preprocessing, polynomial learning rate scheduler, and number of epochs. The results of the ANOVA analysis revealed significant differences in the datasets, models, and preprocessing. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Precision Oncology)
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24 pages, 2035 KiB  
Article
Cheminformatic Identification of Tyrosyl-DNA Phosphodiesterase 1 (Tdp1) Inhibitors: A Comparative Study of SMILES-Based Supervised Machine Learning Models
by Conan Hong-Lun Lai, Alex Pak Ki Kwok and Kwong-Cheong Wong
J. Pers. Med. 2024, 14(9), 981; https://doi.org/10.3390/jpm14090981 - 15 Sep 2024
Viewed by 1807
Abstract
Background: Tyrosyl-DNA phosphodiesterase 1 (Tdp1) repairs damages in DNA induced by abortive topoisomerase 1 activity; however, maintenance of genetic integrity may sustain cellular division of neoplastic cells. It follows that Tdp1-targeting chemical inhibitors could synergize well with existing chemotherapy drugs to deny cancer [...] Read more.
Background: Tyrosyl-DNA phosphodiesterase 1 (Tdp1) repairs damages in DNA induced by abortive topoisomerase 1 activity; however, maintenance of genetic integrity may sustain cellular division of neoplastic cells. It follows that Tdp1-targeting chemical inhibitors could synergize well with existing chemotherapy drugs to deny cancer growth; therefore, identification of Tdp1 inhibitors may advance precision medicine in oncology. Objective: Current computational research efforts focus primarily on molecular docking simulations, though datasets involving three-dimensional molecular structures are often hard to curate and computationally expensive to store and process. We propose the use of simplified molecular input line entry system (SMILES) chemical representations to train supervised machine learning (ML) models, aiming to predict potential Tdp1 inhibitors. Methods: An open-sourced consensus dataset containing the inhibitory activity of numerous chemicals against Tdp1 was obtained from Kaggle. Various ML algorithms were trained, ranging from simple algorithms to ensemble methods and deep neural networks. For algorithms requiring numerical data, SMILES were converted to chemical descriptors using RDKit, an open-sourced Python cheminformatics library. Results: Out of 13 optimized ML models with rigorously tuned hyperparameters, the random forest model gave the best results, yielding a receiver operating characteristics-area under curve of 0.7421, testing accuracy of 0.6815, sensitivity of 0.6444, specificity of 0.7156, precision of 0.6753, and F1 score of 0.6595. Conclusions: Ensemble methods, especially the bootstrap aggregation mechanism adopted by random forest, outperformed other ML algorithms in classifying Tdp1 inhibitors from non-inhibitors using SMILES. The discovery of Tdp1 inhibitors could unlock more treatment regimens for cancer patients, allowing for therapies tailored to the patient’s condition. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Precision Oncology)
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