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Cancers
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Radiomics/Radiogenomics in Cancer
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Radiomics/Radiogenomics in Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 38328

Special Issue Editor

Dr. Nikolaos Papanikolaou

E-Mail Website
Guest Editor
1. Computational Clinical Imaging Group, Champalimaud Foundation, Lisbon, Portugal
2. Computational Medicine Lab, Institute of Computer Science, FORTH, Crete, Greece
3. Karolinska Institute, Stockholm, Sweden
Interests: radiomics in oncology; AI for medical applications; quantitative imaging biomarkers (diffusion, perfusion, magnetization transfer); advanced neuroimaging applications in brain tumors (tractography, fMRI)

Special Issue Information

Dear Colleagues,

Recently, radiomics has raised the hope of improving the reproducibility of imaging-based diagnosis and facilitating the discovery of novel biomarkers that can be used to improve the early detection and characterization of neoplastic disease. The potential for creating clinical value for the oncologic patient has been demonstrated, but the rate of the translation to clinical practice is very low. The trustworthiness of radiomics should be achieved mostly though explainable radiomic signatures. The current Special Issue will welcome submissions on radiomics that can demonstrate a high possibility of clinical application by assessing explainability, generalizability, and trustworthiness.

Physicians do not rely exclusively on a single input for the clinical assessment of complex tasks, including the prediction of treatment response and the stratification of patients into groups with different prognosis. They integrate heterogeneous information to reach a clinical decision. In the same way, more integrative approaches to model tumor biology have been proposed. This combinatory approach can be applied using two different strategies, either to identify associations between different data sources or to evaluate their complementarity to improve diagnostic performance further. In particular, there is strong interest in combining radiomics and genomics in various types of cancers, including but not limited to the brain, breast, lung, and prostate.

The purpose of the present Special Issue is to present solutions to unmet clinical problems, mostly focusing on predicting treatment response and assessing disease prognosis employing radiogenomic models. 

Dr. Nikolaos Papanikolaou
Guest Editor

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

  • radiogenomics
  • machine learning
  • deep learning
  • oncologic imaging
  • genomics
  • explainable AI
  • radiomic signatures

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Published Papers (8 papers)

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Research

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17 pages, 3846 KiB  
Article
Prediction of Prostate Cancer Disease Aggressiveness Using Bi-Parametric Mri Radiomics
by Ana Rodrigues, João Santinha, Bernardo Galvão, Celso Matos, Francisco M. Couto and Nickolas Papanikolaou
Cancers 2021, 13(23), 6065; https://doi.org/10.3390/cancers13236065 - 1 Dec 2021
Cited by 22 | Viewed by 4387
Abstract
Prostate cancer is one of the most prevalent cancers in the male population. Its diagnosis and classification rely on unspecific measures such as PSA levels and DRE, followed by biopsy, where an aggressiveness level is assigned in the form of Gleason Score. Efforts [...] Read more.
Prostate cancer is one of the most prevalent cancers in the male population. Its diagnosis and classification rely on unspecific measures such as PSA levels and DRE, followed by biopsy, where an aggressiveness level is assigned in the form of Gleason Score. Efforts have been made in the past to use radiomics coupled with machine learning to predict prostate cancer aggressiveness from clinical images, showing promising results. Thus, the main goal of this work was to develop supervised machine learning models exploiting radiomic features extracted from bpMRI examinations, to predict biological aggressiveness; 288 classifiers were developed, corresponding to different combinations of pipeline aspects, namely, type of input data, sampling strategy, feature selection method and machine learning algorithm. On a cohort of 281 lesions from 183 patients, it was found that (1) radiomic features extracted from the lesion volume of interest were less stable to segmentation than the equivalent extraction from the whole gland volume of interest; and (2) radiomic features extracted from the whole gland volume of interest produced higher performance and less overfitted classifiers than radiomic features extracted from the lesions volumes of interest. This result suggests that the areas surrounding the tumour lesions offer relevant information regarding the Gleason Score that is ultimately attributed to that lesion. Full article
(This article belongs to the Special Issue Radiomics/Radiogenomics in Cancer)
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19 pages, 2881 KiB  
Article
Prediction of Radiation-Induced Hypothyroidism Using Radiomic Data Analysis Does Not Show Superiority over Standard Normal Tissue Complication Models
by Urszula Smyczynska, Szymon Grabia, Zuzanna Nowicka, Anna Papis-Ubych, Robert Bibik, Tomasz Latusek, Tomasz Rutkowski, Jacek Fijuth, Wojciech Fendler and Bartlomiej Tomasik
Cancers 2021, 13(21), 5584; https://doi.org/10.3390/cancers13215584 - 8 Nov 2021
Cited by 6 | Viewed by 2697
Abstract
State-of-art normal tissue complication probability (NTCP) models do not take into account more complex individual anatomical variations, which can be objectively quantitated and compared in radiomic analysis. The goal of this project was development of radiomic NTCP model for radiation-induced hypothyroidism (RIHT) using [...] Read more.
State-of-art normal tissue complication probability (NTCP) models do not take into account more complex individual anatomical variations, which can be objectively quantitated and compared in radiomic analysis. The goal of this project was development of radiomic NTCP model for radiation-induced hypothyroidism (RIHT) using imaging biomarkers (radiomics). We gathered CT images and clinical data from 98 patients, who underwent intensity-modulated radiation therapy (IMRT) for head and neck cancers with a planned total dose of 70.0 Gy (33–35 fractions). During the 28-month (median) follow-up 27 patients (28%) developed RIHT. For each patient, we extracted 1316 radiomic features from original and transformed images using manually contoured thyroid masks. Creating models based on clinical, radiomic features or a combination thereof, we considered 3 variants of data preprocessing. Based on their performance metrics (sensitivity, specificity), we picked best models for each variant ((0.8, 0.96), (0.9, 0.93), (0.9, 0.89) variant-wise) and compared them with external NTCP models ((0.82, 0.88), (0.82, 0.88), (0.76, 0.91)). We showed that radiomic-based models did not outperform state-of-art NTCP models (p > 0.05). The potential benefit of radiomic-based approach is that it is dose-independent, and models can be used prior to treatment planning allowing faster selection of susceptible population. Full article
(This article belongs to the Special Issue Radiomics/Radiogenomics in Cancer)
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16 pages, 5715 KiB  
Article
Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer
by Viet-Huan Le, Quang-Hien Kha, Truong Nguyen Khanh Hung and Nguyen Quoc Khanh Le
Cancers 2021, 13(14), 3616; https://doi.org/10.3390/cancers13143616 - 19 Jul 2021
Cited by 40 | Viewed by 4927
Abstract
This study aimed to create a risk score generated from CT-based radiomics signatures that could be used to predict overall survival in patients with non-small cell lung cancer (NSCLC). We retrospectively enrolled three sets of NSCLC patients (including 336, 84, and 157 patients [...] Read more.
This study aimed to create a risk score generated from CT-based radiomics signatures that could be used to predict overall survival in patients with non-small cell lung cancer (NSCLC). We retrospectively enrolled three sets of NSCLC patients (including 336, 84, and 157 patients for training, testing, and validation set, respectively). A total of 851 radiomics features for each patient from CT images were extracted for further analyses. The most important features (strongly linked with overall survival) were chosen by pairwise correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and univariate Cox proportional hazard regression. Multivariate Cox proportional hazard model survival analysis was used to create risk scores for each patient, and Kaplan–Meier was used to separate patients into two groups: high-risk and low-risk, respectively. ROC curve assessed the prediction ability of the risk score model for overall survival compared to clinical parameters. The risk score, which developed from ten radiomics signatures model, was found to be independent of age, gender, and stage for predicting overall survival in NSCLC patients (HR, 2.99; 95% CI, 2.27–3.93; p < 0.001) and overall survival prediction ability was 0.696 (95% CI, 0.635–0.758), 0.705 (95% CI, 0.649–0.762), 0.657 (95% CI, 0.589–0.726) (AUC) for 1, 3, and 5 years, respectively, in the training set. The risk score is more likely to have a better accuracy in predicting survival at 1, 3, and 5 years than clinical parameters, such as age 0.57 (95% CI, 0.499–0.64), 0.552 (95% CI, 0.489–0.616), 0.621 (95% CI, 0.544–0.689) (AUC); gender 0.554, 0.546, 0.566 (AUC); stage 0.527, 0.501, 0.459 (AUC), respectively, in 1, 3 and 5 years in the training set. In the training set, the Kaplan–Meier curve revealed that NSCLC patients in the high-risk group had a lower overall survival time than the low-risk group (p < 0.001). We also had similar results that were statistically significant in the testing and validation set. In conclusion, risk scores developed from ten radiomics signatures models have great potential to predict overall survival in NSCLC patients compared to the clinical parameters. This model was able to stratify NSCLC patients into high-risk and low-risk groups regarding the overall survival prediction. Full article
(This article belongs to the Special Issue Radiomics/Radiogenomics in Cancer)
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22 pages, 3564 KiB  
Article
Impact of Preprocessing and Harmonization Methods on the Removal of Scanner Effects in Brain MRI Radiomic Features
by Yingping Li, Samy Ammari, Corinne Balleyguier, Nathalie Lassau and Emilie Chouzenoux
Cancers 2021, 13(12), 3000; https://doi.org/10.3390/cancers13123000 - 15 Jun 2021
Cited by 56 | Viewed by 5509
Abstract
In brain MRI radiomics studies, the non-biological variations introduced by different image acquisition settings, namely scanner effects, affect the reliability and reproducibility of the radiomics results. This paper assesses how the preprocessing methods (including N4 bias field correction and image resampling) and the [...] Read more.
In brain MRI radiomics studies, the non-biological variations introduced by different image acquisition settings, namely scanner effects, affect the reliability and reproducibility of the radiomics results. This paper assesses how the preprocessing methods (including N4 bias field correction and image resampling) and the harmonization methods (either the six intensity normalization methods working on brain MRI images or the ComBat method working on radiomic features) help to remove the scanner effects and improve the radiomic feature reproducibility in brain MRI radiomics. The analyses were based on in vitro datasets (homogeneous and heterogeneous phantom data) and in vivo datasets (brain MRI images collected from healthy volunteers and clinical patients with brain tumors). The results show that the ComBat method is essential and vital to remove scanner effects in brain MRI radiomic studies. Moreover, the intensity normalization methods, while not able to remove scanner effects at the radiomic feature level, still yield more comparable MRI images and improve the robustness of the harmonized features to the choice among ComBat implementations. Full article
(This article belongs to the Special Issue Radiomics/Radiogenomics in Cancer)
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19 pages, 2146 KiB  
Article
Integrated Multi-Tumor Radio-Genomic Marker of Outcomes in Patients with High Serous Ovarian Carcinoma
by Harini Veeraraghavan, Herbert Alberto Vargas, Alejandro Jimenez-Sanchez, Maura Micco, Eralda Mema, Yulia Lakhman, Mireia Crispin-Ortuzar, Erich P. Huang, Douglas A. Levine, Rachel N. Grisham, Nadeem Abu-Rustum, Joseph O. Deasy, Alexandra Snyder, Martin L. Miller, James D. Brenton and Evis Sala
Cancers 2020, 12(11), 3403; https://doi.org/10.3390/cancers12113403 - 17 Nov 2020
Cited by 29 | Viewed by 4270
Abstract
Purpose: Develop an integrated intra-site and inter-site radiomics-clinical-genomic marker of high grade serous ovarian cancer (HGSOC) outcomes and explore the biological basis of radiomics with respect to molecular signaling pathways and the tumor microenvironment (TME). Method: Seventy-five stage III-IV HGSOC patients from internal [...] Read more.
Purpose: Develop an integrated intra-site and inter-site radiomics-clinical-genomic marker of high grade serous ovarian cancer (HGSOC) outcomes and explore the biological basis of radiomics with respect to molecular signaling pathways and the tumor microenvironment (TME). Method: Seventy-five stage III-IV HGSOC patients from internal (N = 40) and external factors via the Cancer Imaging Archive (TCGA) (N = 35) with pre-operative contrast enhanced CT, attempted primary cytoreduction, at least two disease sites, and molecular analysis performed within TCGA were retrospectively analyzed. An intra-site and inter-site radiomics (cluDiss) measure was combined with clinical-genomic variables (iRCG) and compared against conventional (volume and number of sites) and average radiomics (N = 75) for prognosticating progression-free survival (PFS) and platinum resistance. Correlation with molecular signaling and TME derived using a single sample gene set enrichment that was measured. Results: The iRCG model had the best platinum resistance classification accuracy (AUROC of 0.78 [95% CI 0.77 to 0.80]). CluDiss was associated with PFS (HR 1.03 [95% CI: 1.01 to 1.05], p = 0.002), negatively correlated with Wnt signaling, and positively to immune TME. Conclusions: CluDiss and the iRCG prognosticated HGSOC outcomes better than conventional and average radiomic measures and could better stratify patient outcomes if validated on larger multi-center trials. Full article
(This article belongs to the Special Issue Radiomics/Radiogenomics in Cancer)
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Review

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13 pages, 577 KiB  
Review
Radiomics/Radiogenomics in Lung Cancer: Basic Principles and Initial Clinical Results
by Athanasios K. Anagnostopoulos, Anastasios Gaitanis, Ioannis Gkiozos, Emmanouil I. Athanasiadis, Sofia N. Chatziioannou, Konstantinos N. Syrigos, Dimitris Thanos, Achilles N. Chatziioannou and Nikolaos Papanikolaou
Cancers 2022, 14(7), 1657; https://doi.org/10.3390/cancers14071657 - 25 Mar 2022
Cited by 23 | Viewed by 4989
Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide, and elucidation of its complicated pathobiology has been traditionally targeted by studies incorporating genomic as well other high-throughput approaches. Recently, a collection of methods used for cancer imaging, supplemented by quantitative aspects leading [...] Read more.
Lung cancer is the leading cause of cancer-related deaths worldwide, and elucidation of its complicated pathobiology has been traditionally targeted by studies incorporating genomic as well other high-throughput approaches. Recently, a collection of methods used for cancer imaging, supplemented by quantitative aspects leading towards imaging biomarker assessment termed “radiomics”, has introduced a novel dimension in cancer research. Integration of genomics and radiomics approaches, where identifying the biological basis of imaging phenotypes is feasible due to the establishment of associations between molecular features at the genomic–transcriptomic–proteomic level and radiological features, has recently emerged termed radiogenomics. This review article aims to briefly describe the main aspects of radiogenomics, while discussing its basic limitations related to lung cancer clinical applications for clinicians, researchers and patients. Full article
(This article belongs to the Special Issue Radiomics/Radiogenomics in Cancer)
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19 pages, 337 KiB  
Review
Radiogenomics in Colorectal Cancer
by Bogdan Badic, Florent Tixier, Catherine Cheze Le Rest, Mathieu Hatt and Dimitris Visvikis
Cancers 2021, 13(5), 973; https://doi.org/10.3390/cancers13050973 - 26 Feb 2021
Cited by 29 | Viewed by 3895
Abstract
The steady improvement of high-throughput technologies greatly facilitates the implementation of personalized precision medicine. Characterization of tumor heterogeneity through image-derived features—radiomics and genetic profile modifications—genomics, is a rapidly evolving field known as radiogenomics. Various radiogenomics studies have been dedicated to colorectal cancer so [...] Read more.
The steady improvement of high-throughput technologies greatly facilitates the implementation of personalized precision medicine. Characterization of tumor heterogeneity through image-derived features—radiomics and genetic profile modifications—genomics, is a rapidly evolving field known as radiogenomics. Various radiogenomics studies have been dedicated to colorectal cancer so far, highlighting the potential of these approaches to enhance clinical decision-making. In this review, a general outline of colorectal radiogenomics literature is provided, discussing the current limitations and suggested further developments. Full article
(This article belongs to the Special Issue Radiomics/Radiogenomics in Cancer)
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Other

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15 pages, 1147 KiB  
Perspective
Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine
by Anahita Fathi Kazerooni, Stephen J. Bagley, Hamed Akbari, Sanjay Saxena, Sina Bagheri, Jun Guo, Sanjeev Chawla, Ali Nabavizadeh, Suyash Mohan, Spyridon Bakas, Christos Davatzikos and MacLean P. Nasrallah
Cancers 2021, 13(23), 5921; https://doi.org/10.3390/cancers13235921 - 25 Nov 2021
Cited by 45 | Viewed by 5179
Abstract
Machine learning (ML) integrated with medical imaging has introduced new perspectives in precision diagnostics of high-grade gliomas, through radiomics and radiogenomics. This has raised hopes for characterizing noninvasive and in vivo biomarkers for prediction of patient survival, tumor recurrence, and genomics and therefore [...] Read more.
Machine learning (ML) integrated with medical imaging has introduced new perspectives in precision diagnostics of high-grade gliomas, through radiomics and radiogenomics. This has raised hopes for characterizing noninvasive and in vivo biomarkers for prediction of patient survival, tumor recurrence, and genomics and therefore encouraging treatments tailored to individualized needs. Characterization of tumor infiltration based on pre-operative multi-parametric magnetic resonance imaging (MP-MRI) scans may allow prediction of the loci of future tumor recurrence and thereby aid in planning the course of treatment for the patients, such as optimizing the extent of resection and the dose and target area of radiation. Imaging signatures of tumor genomics can help in identifying the patients who benefit from certain targeted therapies. Specifying molecular properties of gliomas and prediction of their changes over time and with treatment would allow optimization of treatment. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the promise of radiomics and radiogenomics for allowing personalized treatments of patients with gliomas and discuss the challenges and limitations of these methods in multi-institutional clinical trials and suggestions to mitigate the issues and the future directions. Full article
(This article belongs to the Special Issue Radiomics/Radiogenomics in Cancer)
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