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Diagnostics
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Machine Learning Advances in MRI of Cancer
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Machine Learning Advances in MRI of Cancer

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (31 January 2021) | Viewed by 23171

Special Issue Editor

Dr. Peter Gibbs

E-Mail Website
Guest Editor
Memorial Sloan-Kettering Cancer Center, New York, NY, USA
Interests: quantitative MRI; image processing

Special Issue Information

Dear Colleagues,

Magnetic resonance (MR) imaging is traditionally seen as a qualitative imaging technique, providing high spatial resolution images of human anatomy in exquisite detail. The extraction and utilization of radiomics features from within lesions, thus acting as quantitative representations of lesion heterogeneity, has seen a rapid expansion in the 21st century. However, the optimal features to extract and the most appropriate level of image pre-processing required to accentuate feature-based lesion differences are still unclear. The emergence of advanced machine learning methods and artificial intelligence in the form of neural network deep learning techniques has further stimulated the field and boosted attempts to improve the diagnostic and prognostic capabilities of MR images.

More recently, advanced imaging techniques have been developed enabling the quantification of fundamental MR parameters, including the spin–lattice and spin–spin relaxation rates in clinically acceptable scan times. The generation of robust quantitative information empowers the expansion of diagnostic and prognostic algorithms, utilizing data acquired on multiple scanners from multiple institutions. The employment of so-called ‘big data’ will help to drive the development of more generalizable algorithms for use in the wider community.

Therefore, contributions that add to the knowledge base on machine learning applications in the diagnosis and prognosis of cancer are welcome, particularly in the areas of deep learning, quantitative imaging, and multimodality imaging.

Dr. Peter Gibbs
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 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. Diagnostics 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 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

  • Machine learning
  • Radiomics
  • Deep learning
  • Quantitative imaging
  • Artificial intelligence
  • Big Data
  • Cancer imaging
  • Multimodal imaging

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Published Papers (4 papers)

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Research

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12 pages, 734 KiB  
Article
BrainSeg-Net: Brain Tumor MR Image Segmentation via Enhanced Encoder–Decoder Network
by Mobeen Ur Rehman, SeungBin Cho, Jeehong Kim and Kil To Chong
Diagnostics 2021, 11(2), 169; https://doi.org/10.3390/diagnostics11020169 - 25 Jan 2021
Cited by 88 | Viewed by 4156
Abstract
Efficient segmentation of Magnetic Resonance (MR) brain tumor images is of the utmost value for the diagnosis of tumor region. In recent years, advancement in the field of neural networks has been used to refine the segmentation performance of brain tumor sub-regions. The [...] Read more.
Efficient segmentation of Magnetic Resonance (MR) brain tumor images is of the utmost value for the diagnosis of tumor region. In recent years, advancement in the field of neural networks has been used to refine the segmentation performance of brain tumor sub-regions. The brain tumor segmentation has proven to be a complicated task even for neural networks, due to the small-scale tumor regions. These small-scale tumor regions are unable to be identified, the reason being their tiny size and the huge difference between area occupancy by different tumor classes. In previous state-of-the-art neural network models, the biggest problem was that the location information along with spatial details gets lost in deeper layers. To address these problems, we have proposed an encoder–decoder based model named BrainSeg-Net. The Feature Enhancer (FE) block is incorporated into the BrainSeg-Net architecture which extracts the middle-level features from low-level features from the shallow layers and shares them with the dense layers. This feature aggregation helps to achieve better performance of tumor identification. To address the problem associated with imbalance class, we have used a custom-designed loss function. For evaluation of BrainSeg-Net architecture, three benchmark datasets are utilized: BraTS2017, BraTS 2018, and BraTS 2019. Segmentation of Enhancing Core (EC), Whole Tumor (WT), and Tumor Core (TC) is carried out. The proposed architecture have exhibited good improvement when compared with existing baseline and state-of-the-art techniques. The MR brain tumor segmentation by BrainSeg-Net uses enhanced location and spatial features, which performs better than the existing plethora of brain MR image segmentation approaches. Full article
(This article belongs to the Special Issue Machine Learning Advances in MRI of Cancer)
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16 pages, 3570 KiB  
Article
A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI
by Mohammed R. S. Sunoqrot, Kirsten M. Selnæs, Elise Sandsmark, Gabriel A. Nketiah, Olmo Zavala-Romero, Radka Stoyanova, Tone F. Bathen and Mattijs Elschot
Diagnostics 2020, 10(9), 714; https://doi.org/10.3390/diagnostics10090714 - 18 Sep 2020
Cited by 20 | Viewed by 5331
Abstract
Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of magnetic resonance imaging (MRI). Fully automated segmentation of the prostate is a crucial step of CAD for prostate cancer, but visual inspection is [...] Read more.
Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of magnetic resonance imaging (MRI). Fully automated segmentation of the prostate is a crucial step of CAD for prostate cancer, but visual inspection is still required to detect poorly segmented cases. The aim of this work was therefore to establish a fully automated quality control (QC) system for prostate segmentation based on T2-weighted MRI. Four different deep learning-based segmentation methods were used to segment the prostate for 585 patients. First order, shape and textural radiomics features were extracted from the segmented prostate masks. A reference quality score (QS) was calculated for each automated segmentation in comparison to a manual segmentation. A least absolute shrinkage and selection operator (LASSO) was trained and optimized on a randomly assigned training dataset (N = 1756, 439 cases from each segmentation method) to build a generalizable linear regression model based on the radiomics features that best estimated the reference QS. Subsequently, the model was used to estimate the QSs for an independent testing dataset (N = 584, 146 cases from each segmentation method). The mean ± standard deviation absolute error between the estimated and reference QSs was 5.47 ± 6.33 on a scale from 0 to 100. In addition, we found a strong correlation between the estimated and reference QSs (rho = 0.70). In conclusion, we developed an automated QC system that may be helpful for evaluating the quality of automated prostate segmentations. Full article
(This article belongs to the Special Issue Machine Learning Advances in MRI of Cancer)
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Review

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26 pages, 2141 KiB  
Review
Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review
by Jasper J. Twilt, Kicky G. van Leeuwen, Henkjan J. Huisman, Jurgen J. Fütterer and Maarten de Rooij
Diagnostics 2021, 11(6), 959; https://doi.org/10.3390/diagnostics11060959 - 26 May 2021
Cited by 58 | Viewed by 6957
Abstract
Due to the upfront role of magnetic resonance imaging (MRI) for prostate cancer (PCa) diagnosis, a multitude of artificial intelligence (AI) applications have been suggested to aid in the diagnosis and detection of PCa. In this review, we provide an overview of the [...] Read more.
Due to the upfront role of magnetic resonance imaging (MRI) for prostate cancer (PCa) diagnosis, a multitude of artificial intelligence (AI) applications have been suggested to aid in the diagnosis and detection of PCa. In this review, we provide an overview of the current field, including studies between 2018 and February 2021, describing AI algorithms for (1) lesion classification and (2) lesion detection for PCa. Our evaluation of 59 included studies showed that most research has been conducted for the task of PCa lesion classification (66%) followed by PCa lesion detection (34%). Studies showed large heterogeneity in cohort sizes, ranging between 18 to 499 patients (median = 162) combined with different approaches for performance validation. Furthermore, 85% of the studies reported on the stand-alone diagnostic accuracy, whereas 15% demonstrated the impact of AI on diagnostic thinking efficacy, indicating limited proof for the clinical utility of PCa AI applications. In order to introduce AI within the clinical workflow of PCa assessment, robustness and generalizability of AI applications need to be further validated utilizing external validation and clinical workflow experiments. Full article
(This article belongs to the Special Issue Machine Learning Advances in MRI of Cancer)
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27 pages, 1672 KiB  
Review
Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)
by Rima Hajjo, Dima A. Sabbah, Sanaa K. Bardaweel and Alexander Tropsha
Diagnostics 2021, 11(5), 742; https://doi.org/10.3390/diagnostics11050742 - 21 Apr 2021
Cited by 16 | Viewed by 5801
Abstract
The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. [...] Read more.
The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types. Full article
(This article belongs to the Special Issue Machine Learning Advances in MRI of Cancer)
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