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Computational Approaches for Cancer Research
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Computational Approaches for Cancer Research

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 6627

Special Issue Editors

Dr. Dimitrios Kleftogiannis

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Guest Editor
1. Centre for Cancer Biomarkers (CCBIO), Department of Clinical Science, University of Bergen, Bergen, Norway
2. Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
Interests: bioinformatics; machine learning; cancer genomics; NGS data analysis
Dr. Giovanni Cugliari

E-Mail Website
Guest Editor
Department of Medical Sciences, Genomic Variation in Human Population and Complex Diseases Unit, University of Turin, 10126 Turin, Italy
Interests: cancer; genomics; artificial intelligence; machine learning; deep learning; advanced statistics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yosep Chong

E-Mail Website
Guest Editor
Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul 06591, Republic of Korea
Interests: pathology; hematopathology; cytology; metaanalysis; digital pathology; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Nikolaos Dikaios

E-Mail Website
Guest Editor
Mathematics Research Centre, Academy of Athens, 10679 Athens, Greece
Interests: tomography; inverse problems; mathematical optimisation; cancer informatics; physics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cancer, one of the leading causes of worldwide mortality, is a complex group of diseases associated with abnormal cell growth and metastasis. Nowadays, thanks to the recent advances in biomedical technologies, researchers are able to use different types of information to characterize cancers and identify more effective therapeutic targets. As the drive towards precision cancer medicine has been accelerated, the volume of high-throughput “omics” data has also exploded. Modern cancer research is heavily data-driven, and this poses new challenges for more effective data analysis and integration.

Therefore, the aim of this Special Issue is to present novel ideas and new computational approaches for cancer research. Areas relevant to computational cancer research include but are not limited to, bioinformatics analyses of molecular genomics/transcriptomics/epigenomics data, analyses of clinical data, applications of machine learning, artificial intelligence and deep learning, statistical algorithms, imaging techniques, data visualization, and methods for “big data” integration. This Special Issue will publish high-quality, original research papers on all aspects of computational cancer research including:

  • Cancer genomics and genetics for a better understanding of biological mechanisms underlying somatic evolution and drug resistance.
  • Precision oncology and translational bioinformatics.
  • Next-generation sequencing data analysis, applications, and software tools.
  • Single-cell data analysis and applications.
  • Proteomics and protein-based analyses of cancers.
  • Image processing and analyses with applications in digital pathology, mass cytometry imaging, and spatial transcriptomics.
  • Artificial intelligence, machine learning, deep learning, data mining, and knowledge discovery techniques.
  • Multi-omics data integration.
  • Advanced statistics and data science approaches for “big” omic data.

Dr. Dimitrios Kleftogiannis
Dr. Giovanni Cugliari
Dr. Yosep Chong
Dr. Nikolaos Dikaios
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. Applied Sciences 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 2400 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

  • bioinformatics
  • systems biology
  • genomics
  • single-cell omics
  • precision medicine
  • image analysis
  • machine learning
  • multi-omics data integration

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Published Papers (5 papers)

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19 pages, 2255 KiB  
Article
A Genetic Programming Approach to Radiomic-Based Feature Construction for Survival Prediction in Non-Small Cell Lung Cancer
by Elisa Scalco, Wilfrido Gómez-Flores and Giovanna Rizzo
Appl. Sci. 2024, 14(16), 6923; https://doi.org/10.3390/app14166923 - 7 Aug 2024
Cited by 1 | Viewed by 898
Abstract
Machine learning (ML) is commonly used to develop survival-predictive radiomic models in non-small cell lung cancer (NSCLC) patients, which helps assist treatment decision making. Radiomic features derived from computer tomography (CT) lung images aim to capture quantitative tumor characteristics. However, these features are [...] Read more.
Machine learning (ML) is commonly used to develop survival-predictive radiomic models in non-small cell lung cancer (NSCLC) patients, which helps assist treatment decision making. Radiomic features derived from computer tomography (CT) lung images aim to capture quantitative tumor characteristics. However, these features are determined by humans, which poses a risk of including irrelevant or redundant variables, thus reducing the model’s generalization. To address this issue, we propose using genetic programming (GP) to automatically construct new features with higher discriminant power than the original radiomic features. To achieve this goal, we introduce a fitness function that measures the classification performance ratio of output to input. The constructed features are then input for various classifiers to predict the two-year survival of NSCLC patients from two public CT datasets. Our approach is compared against two popular feature selection methods in radiomics to choose relevant radiomic features, and two GP-based feature construction methods whose fitness functions are based on measuring the constructed features’ quality. The experimental results show that survival prediction models trained on GP-based constructed features outperform feature selection methods. Also, maximizing the classification performance gain output-to-input ratio produces features with higher discriminative power than only maximizing the classification accuracy from constructed features. Furthermore, a survival analysis demonstrated statistically significant differences between survival and non-survival groups in the Kaplan–Meier curves. Therefore, the proposed approach can be used as a complementary method for oncologists in determining the clinical management of NSCLC patients. Full article
(This article belongs to the Special Issue Computational Approaches for Cancer Research)
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13 pages, 3569 KiB  
Article
Optimization of Replanning Processes for Volumetric Modulated Arc Therapy Plans at Risk of QA Failure Predicted by a Machine Learning Model
by Nicola Lambri, Caterina Zaccone, Monica Bianchi, Andrea Bresolin, Damiano Dei, Pasqualina Gallo, Francesco La Fauci, Francesca Lobefalo, Lucia Paganini, Marco Pelizzoli, Giacomo Reggiori, Stefano Tomatis, Marta Scorsetti, Cristina Lenardi and Pietro Mancosu
Appl. Sci. 2024, 14(14), 6103; https://doi.org/10.3390/app14146103 - 12 Jul 2024
Viewed by 818
Abstract
Patient-specific quality assurance (PSQA) procedures ensure the safe delivery of volumetric modulated arc therapy (VMAT) plans. PSQA requires extensive time and resources and may cause treatment delays if replanning is needed due to failures. Recently, our group developed a machine learning (ML) model [...] Read more.
Patient-specific quality assurance (PSQA) procedures ensure the safe delivery of volumetric modulated arc therapy (VMAT) plans. PSQA requires extensive time and resources and may cause treatment delays if replanning is needed due to failures. Recently, our group developed a machine learning (ML) model predicting gamma passing rate (GPR) for VMAT arcs. This study explores automatable replanning strategies for plans identified at risk of failure, aiming to improve deliverability while maintaining dosimetric quality. Between 2022 and 2023, our ML model analyzed 1252 VMAT plans. Ten patients having a predicted GPR (pGPR) <95% were selected. Replanning strategies consisted of limiting monitor units (MUlimit) and employing the aperture shape controller (ASC) tool. Re-optimized plans were compared with the originals in terms of dose volume constraints (DVCs) for the target and organs-at-risk (OARs), and deliverability using the modulation complexity score (MCS), pGPR, and measured GPR (mGPR). Forty-five re-optimizations were performed. Replanning led to an increase in DVCs for OARs and a reduction for the target. Complexity decreased, reflected by the increase in the MCS from 0.17 to 0.21 (MUlimit) and 0.20 (ASC). The deliverability improved, with the pGPR increasing from 93.3% to 94.4% (MUlimit) and 95.1% (ASC), and the mGPR from 99.3% to 99.7% (MUlimit) and 99.8% (ASC). Limiting the MUs or utilizing the ASC reduced the complexity of plans and improved the GPR without compromising the dosimetric quality. These strategies can be used to automate replanning procedures, reduce the workload related to PSQA, and improve patient safety. Full article
(This article belongs to the Special Issue Computational Approaches for Cancer Research)
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15 pages, 4923 KiB  
Article
Prediction of Consolidation Tumor Ratio on Planning CT Images of Lung Cancer Patients Treated with Radiotherapy Based on Deep Learning
by Yizhi Tong, Hidetaka Arimura, Tadamasa Yoshitake, Yunhao Cui, Takumi Kodama, Yoshiyuki Shioyama, Ronnie Wirestam and Hidetake Yabuuchi
Appl. Sci. 2024, 14(8), 3275; https://doi.org/10.3390/app14083275 - 13 Apr 2024
Cited by 1 | Viewed by 1122
Abstract
This study aimed to propose an automated prediction approach of the consolidation tumor ratios (CTRs) of part-solid tumors of patients treated with radiotherapy on treatment planning computed tomography images using deep learning segmentation (DLS) models. For training the DLS model for cancer regions, [...] Read more.
This study aimed to propose an automated prediction approach of the consolidation tumor ratios (CTRs) of part-solid tumors of patients treated with radiotherapy on treatment planning computed tomography images using deep learning segmentation (DLS) models. For training the DLS model for cancer regions, a total of 115 patients with non-small cell lung cancer (NSCLC) who underwent stereotactic body radiation therapy were selected as the training dataset, including solid, part-solid, and ground-glass opacity tumors. For testing the automated prediction approach of CTRs based on segmented tumor regions, 38 patients with part-solid tumors were selected as an internal test dataset A (IN) from a same institute as the training dataset, and 49 patients as an external test dataset (EX) from a public database. The CTRs for part-solid tumors were predicted as ratios of the maximum diameters of solid components to those of whole tumors. Pearson correlations between reference and predicted CTRs for the two test datasets were 0.953 (IN) and 0.926 (EX) for one of the DLS models (p < 0.01). Intraclass correlation coefficients between reference and predicted CTRs for the two test datasets were 0.943 (IN) and 0.904 (EX) for the same DLS models. The findings suggest that the automated prediction approach could be robust in calculating the CTRs of part-solid tumors. Full article
(This article belongs to the Special Issue Computational Approaches for Cancer Research)
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17 pages, 1979 KiB  
Systematic Review
Ultrasound-Based Deep Learning Models Performance versus Expert Subjective Assessment for Discriminating Adnexal Masses: A Head-to-Head Systematic Review and Meta-Analysis
by Mariana Lourenço, Teresa Arrufat, Elena Satorres, Sara Maderuelo, Blanca Novillo-Del Álamo, Stefano Guerriero, Rodrigo Orozco and Juan Luis Alcázar
Appl. Sci. 2024, 14(7), 2998; https://doi.org/10.3390/app14072998 - 3 Apr 2024
Viewed by 1255
Abstract
(1) Background: Accurate preoperative diagnosis of ovarian masses is crucial for optimal treatment and postoperative outcomes. Transvaginal ultrasound is the gold standard, but its accuracy depends on operator skill and technology. In the absence of expert imaging, pattern-based approaches have been proposed. The [...] Read more.
(1) Background: Accurate preoperative diagnosis of ovarian masses is crucial for optimal treatment and postoperative outcomes. Transvaginal ultrasound is the gold standard, but its accuracy depends on operator skill and technology. In the absence of expert imaging, pattern-based approaches have been proposed. The integration of artificial intelligence, specifically deep learning (DL), shows promise in improving diagnostic precision for adnexal masses. Our meta-analysis aims to evaluate DL’s performance compared to expert evaluation in diagnosing adnexal masses using ultrasound images. (2) Methods: Studies published between 2000 and 2023 were searched in PubMed, Scopus, Cochrane and Web of Science. The study quality was assessed using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2). Pooled sensitivity and specificity for both methods were estimated and compared. (3) Results: From 1659 citations, we selected four studies to include in this meta-analysis. The mean prevalence of ovarian cancer was 30.6%. The quality of the studies was good with low risk of bias for index and reference tests, but with high risk of bias for patient selection domain. Pooled sensitivity and specificity were 86.0% and 90.0% for DL and 86.0% and 89.0% for expert accuracy (p = 0.9883). (4) Conclusion: We found no significant differences between DL systems and expert evaluations in detecting and differentially diagnosing adnexal masses using ultrasound images. Full article
(This article belongs to the Special Issue Computational Approaches for Cancer Research)
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17 pages, 3186 KiB  
Systematic Review
Melanoma Brain Metastases: Immunotherapy or Targeted Therapy? A Systematic Review and Meta-Analyses
by Livia Onofrio, Aurora Gaeta, Oriana D’Ecclesiis, Giovanni Cugliari, Sara Gandini and Paola Queirolo
Appl. Sci. 2024, 14(6), 2222; https://doi.org/10.3390/app14062222 - 7 Mar 2024
Viewed by 1916
Abstract
Background. Brain metastases are one of the leading causes of death in melanoma patients. This systematic review and meta-analysis aimed to look at the variables that affect melanoma patients’ intracranial treatment responses to immunotherapy and targeted therapy. Methods. A systematic search [...] Read more.
Background. Brain metastases are one of the leading causes of death in melanoma patients. This systematic review and meta-analysis aimed to look at the variables that affect melanoma patients’ intracranial treatment responses to immunotherapy and targeted therapy. Methods. A systematic search of PubMed and Scopus up to December 2023 was conducted to identify trials investigating treatment response of melanoma brain metastasis. This meta-analysis presents summary estimates (SEs) of treatment response and odd ratios (ORs) for the comparison between symptomatic and asymptomatic metastases. Generalised linear mixed models were used for the SE of the proportion of clinical responses and 95% CIs are reported. We investigated between-study heterogeneity using meta-regression. Results. We included 19 independent clinical trials for a total of 1074 patients with brain metastases. The SE of the overall response was 36% 95%CI [27%; 47%], I2 = 84%, similar to the SE for symptomatic metastases: SE = 29% 95%CI [16%; 47%], I2 = 80%. A significantly higher response of symptomatic metastases was observed between patients who had previously received immunotherapy compared to those who had not (47% vs. 9%, p-value = 0.001). The SE was greater for asymptomatic metastases (38% 95%CI [29%; 49%], I2 = 80%), and among these, patients that received the combo-immunotherapy importantly responded more than those who had received monotherapy (45% vs. 26.1%, p-value = 0.002). The major limit of our analysis is the absence of data about the specific intracranial response separately in asymptomatic and symptomatic patients in seven studies. Conclusions. This study shows the importance of starting immunotherapy as early as possible in asymptomatic patients. Randomised trials with greater statistical power are needed to find the best strategies for symptomatic and asymptomatic brain metastases. Full article
(This article belongs to the Special Issue Computational Approaches for Cancer Research)
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