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: 30 June 2024 | Viewed by 2140

Special Issue Editors


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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

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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

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Mathematics Research Centre, Academy of Athens, 10679 Athens, Greece
Interests: tomography; inverse problems; mathematical optimisation; cancer informatics; physics
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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

Published Papers (3 papers)

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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
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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
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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
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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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