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Cancers
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Decision-Support Systems for Cancer Diagnosis and Prognosis
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Decision-Support Systems for Cancer Diagnosis and Prognosis

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 14045

Special Issue Editor

Dr. Margarida Julia-Sape

E-Mail Website
Guest Editor
Departament de Bioquímica i Biologia Molecular, Institut de Biotecnologia I de Biomedicina, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Valles, Spain
Interests: precision medicine; prospective evaluation; clinical trial; decision-support tool; added value; non-invasive biomarkers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning and clinical data analysis hold promise for a facilitation of clinical workflows. Algorithms can potentially aid clinicians to screen large populations for the early detection of cancer, to personalize anticancer treatments or to identify prognostic biomarkers. Despite the large amount of literature devoted to machine learning in general, radiomics, deep learning, only few software tools make it to the clinical reality.

This issue welcomes contributions that demonstrate how decision-support systems in any area of oncology can help in clinical decision-making. The contributions should report on early (Phase I) to real world evidence (RWE) clinical development of such systems. Radiological decision support systems as well as those integrating multimodal data (clinical, imaging, molecular), are a main focus for this issue. Another focus is in the interpretability and explainability of the models presented to the clinician, from simple nomograms to data visualization tools. Studies with simulated data or simulated scenarios are not a target in this issue. 

Dr. Margarida Julia-Sape
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

  • personalized medicine
  • machine learning
  • explainability
  • interpretability
  • multimodality
  • decision making
  • prognostic biomarker
  • imaging biomarker
  • real-world evidence
  • clinical trial
  • cancer detection

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Published Papers (6 papers)

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Research

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21 pages, 1975 KiB  
Article
Enhancing Prostate Cancer Diagnosis with a Novel Artificial Intelligence-Based Web Application: Synergizing Deep Learning Models, Multimodal Data, and Insights from Usability Study with Pathologists
by Akarsh Singh, Shruti Randive, Anne Breggia, Bilal Ahmad, Robert Christman and Saeed Amal
Cancers 2023, 15(23), 5659; https://doi.org/10.3390/cancers15235659 - 30 Nov 2023
Cited by 4 | Viewed by 2724
Abstract
Prostate cancer remains a significant cause of male cancer mortality in the United States, with an estimated 288,300 new cases in 2023. Accurate grading of prostate cancer is crucial for ascertaining disease severity and shaping treatment strategies. Modern deep learning techniques show promise [...] Read more.
Prostate cancer remains a significant cause of male cancer mortality in the United States, with an estimated 288,300 new cases in 2023. Accurate grading of prostate cancer is crucial for ascertaining disease severity and shaping treatment strategies. Modern deep learning techniques show promise in grading biopsies, but there is a gap in integrating these advances into clinical practice. Our web platform tackles this challenge by integrating human expertise with AI-driven grading, incorporating diverse data sources. We gathered feedback from four pathologists and one medical practitioner to assess usability and real-world alignment through a survey and the NASA TLX Usability Test. Notably, 60% of users found it easy to navigate, rating it 5.5 out of 7 for ease of understanding. Users appreciated self-explanatory information in popup tabs. For ease of use, all users favored the detailed summary tab, rating it 6.5 out of 7. While 80% felt patient demographics beyond age were unnecessary, high-resolution biopsy images were deemed vital. Acceptability was high, with all users willing to adopt the app, and some believed it could reduce workload. The NASA TLX Usability Test indicated a low–moderate perceived workload, suggesting room for improved explanations and data visualization. Full article
(This article belongs to the Special Issue Decision-Support Systems for Cancer Diagnosis and Prognosis)
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20 pages, 1772 KiB  
Article
Tracking Therapy Response in Glioblastoma Using 1D Convolutional Neural Networks
by Sandra Ortega-Martorell, Ivan Olier, Orlando Hernandez, Paula D. Restrepo-Galvis, Ryan A. A. Bellfield and Ana Paula Candiota
Cancers 2023, 15(15), 4002; https://doi.org/10.3390/cancers15154002 - 7 Aug 2023
Viewed by 1830
Abstract
Background: Glioblastoma (GB) is a malignant brain tumour that is challenging to treat, often relapsing even after aggressive therapy. Evaluating therapy response relies on magnetic resonance imaging (MRI) following the Response Assessment in Neuro-Oncology (RANO) criteria. However, early assessment is hindered by phenomena [...] Read more.
Background: Glioblastoma (GB) is a malignant brain tumour that is challenging to treat, often relapsing even after aggressive therapy. Evaluating therapy response relies on magnetic resonance imaging (MRI) following the Response Assessment in Neuro-Oncology (RANO) criteria. However, early assessment is hindered by phenomena such as pseudoprogression and pseudoresponse. Magnetic resonance spectroscopy (MRS/MRSI) provides metabolomics information but is underutilised due to a lack of familiarity and standardisation. Methods: This study explores the potential of spectroscopic imaging (MRSI) in combination with several machine learning approaches, including one-dimensional convolutional neural networks (1D-CNNs), to improve therapy response assessment. Preclinical GB (GL261-bearing mice) were studied for method optimisation and validation. Results: The proposed 1D-CNN models successfully identify different regions of tumours sampled by MRSI, i.e., normal brain (N), control/unresponsive tumour (T), and tumour responding to treatment (R). Class activation maps using Grad-CAM enabled the study of the key areas relevant to the models, providing model explainability. The generated colour-coded maps showing the N, T and R regions were highly accurate (according to Dice scores) when compared against ground truth and outperformed our previous method. Conclusions: The proposed methodology may provide new and better opportunities for therapy response assessment, potentially providing earlier hints of tumour relapsing stages. Full article
(This article belongs to the Special Issue Decision-Support Systems for Cancer Diagnosis and Prognosis)
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24 pages, 6157 KiB  
Article
Using Single-Voxel Magnetic Resonance Spectroscopy Data Acquired at 1.5T to Classify Multivoxel Data at 3T: A Proof-of-Concept Study
by Gülnur Ungan, Albert Pons-Escoda, Daniel Ulinic, Carles Arús, Alfredo Vellido and Margarida Julià-Sapé
Cancers 2023, 15(14), 3709; https://doi.org/10.3390/cancers15143709 - 21 Jul 2023
Viewed by 1985
Abstract
In vivo magnetic resonance spectroscopy (MRS) has two modalities, single-voxel (SV) and multivoxel (MV), in which one or more contiguous grids of SVs are acquired. Purpose: To test whether MV grids can be classified with models trained with SV. Methods: Retrospective study. Training [...] Read more.
In vivo magnetic resonance spectroscopy (MRS) has two modalities, single-voxel (SV) and multivoxel (MV), in which one or more contiguous grids of SVs are acquired. Purpose: To test whether MV grids can be classified with models trained with SV. Methods: Retrospective study. Training dataset: Multicenter multiformat SV INTERPRET, 1.5T. Testing dataset: MV eTumour, 3T. Two classification tasks were completed: 3-class (meningioma vs. aggressive vs. normal) and 4-class (meningioma vs. low-grade glioma vs. aggressive vs. normal). Five different methods were tested for feature selection. The classification was implemented using linear discriminant analysis (LDA), random forest, and support vector machines. The evaluation was completed with balanced error rate (BER) and area under the curve (AUC) on both sets. The accuracy in class prediction was calculated by developing a solid tumor index (STI) and segmentation accuracy with the Dice score. Results: The best method was sequential forward feature selection combined with LDA, with AUCs = 0.95 (meningioma), 0.89 (aggressive), 0.82 (low-grade glioma), and 0.82 (normal). STI was 66% (4-class task) and 71% (3-class task) because two cases failed completely and two more had suboptimal STI as defined by us. Discussion: The reasons for failure in the classification of the MV test set were related to the presence of artifacts. Full article
(This article belongs to the Special Issue Decision-Support Systems for Cancer Diagnosis and Prognosis)
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28 pages, 2005 KiB  
Article
AI-Based Glioma Grading for a Trustworthy Diagnosis: An Analytical Pipeline for Improved Reliability
by Carla Pitarch, Vicent Ribas and Alfredo Vellido
Cancers 2023, 15(13), 3369; https://doi.org/10.3390/cancers15133369 - 27 Jun 2023
Cited by 5 | Viewed by 2367
Abstract
Glioma is the most common type of tumor in humans originating in the brain. According to the World Health Organization, gliomas can be graded on a four-stage scale, ranging from the most benign to the most malignant. The grading of these tumors from [...] Read more.
Glioma is the most common type of tumor in humans originating in the brain. According to the World Health Organization, gliomas can be graded on a four-stage scale, ranging from the most benign to the most malignant. The grading of these tumors from image information is a far from trivial task for radiologists and one in which they could be assisted by machine-learning-based decision support. However, the machine learning analytical pipeline is also fraught with perils stemming from different sources, such as inadvertent data leakage, adequacy of 2D image sampling, or classifier assessment biases. In this paper, we analyze a glioma database sourced from multiple datasets using a simple classifier, aiming to obtain a reliable tumor grading and, on the way, we provide a few guidelines to ensure such reliability. Our results reveal that by focusing on the tumor region of interest and using data augmentation techniques we significantly enhanced the accuracy and confidence in tumor classifications. Evaluation on an independent test set resulted in an AUC-ROC of 0.932 in the discrimination of low-grade gliomas from high-grade gliomas, and an AUC-ROC of 0.893 in the classification of grades 2, 3, and 4. The study also highlights the importance of providing, beyond generic classification performance, measures of how reliable and trustworthy the model’s output is, thus assessing the model’s certainty and robustness. Full article
(This article belongs to the Special Issue Decision-Support Systems for Cancer Diagnosis and Prognosis)
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Review

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55 pages, 723 KiB  
Review
Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology
by Carla Pitarch, Gulnur Ungan, Margarida Julià-Sapé and Alfredo Vellido
Cancers 2024, 16(2), 300; https://doi.org/10.3390/cancers16020300 - 10 Jan 2024
Cited by 1 | Viewed by 2093
Abstract
Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning [...] Read more.
Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. This paper reviews in detail some of the most recent advances in the use of Deep Learning in this field, from the broader topic of the development of Machine-Learning-based analytical pipelines to specific instantiations of the use of Deep Learning in neuro-oncology; the latter including its use in the groundbreaking field of ultra-low field magnetic resonance imaging. Full article
(This article belongs to the Special Issue Decision-Support Systems for Cancer Diagnosis and Prognosis)
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14 pages, 1122 KiB  
Review
Clinical Decision Support Systems for Brain Tumour Diagnosis and Prognosis: A Systematic Review
by Teesta Mukherjee, Omid Pournik, Sarah N. Lim Choi Keung and Theodoros N. Arvanitis
Cancers 2023, 15(13), 3523; https://doi.org/10.3390/cancers15133523 - 6 Jul 2023
Cited by 4 | Viewed by 1798
Abstract
CDSSs are being continuously developed and integrated into routine clinical practice as they assist clinicians and radiologists in dealing with an enormous amount of medical data, reduce clinical errors, and improve diagnostic capabilities. They assist detection, classification, and grading of brain tumours as [...] Read more.
CDSSs are being continuously developed and integrated into routine clinical practice as they assist clinicians and radiologists in dealing with an enormous amount of medical data, reduce clinical errors, and improve diagnostic capabilities. They assist detection, classification, and grading of brain tumours as well as alert physicians of treatment change plans. The aim of this systematic review is to identify various CDSSs that are used in brain tumour diagnosis and prognosis and rely on data captured by any imaging modality. Based on the 2020 preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol, the literature search was conducted in PubMed and Engineering Village Compendex databases. Different types of CDSSs identified through this review include Curiam BT, FASMA, MIROR, HealthAgents, and INTERPRET, among others. This review also examines various CDSS tool types, system features, techniques, accuracy, and outcomes, to provide the latest evidence available in the field of neuro-oncology. An overview of such CDSSs used to support clinical decision-making in the management and treatment of brain tumours, along with their benefits, challenges, and future perspectives has been provided. Although a CDSS improves diagnostic capabilities and healthcare delivery, there is lack of specific evidence to support these claims. The absence of empirical data slows down both user acceptance and evaluation of the actual impact of CDSS on brain tumour management. Instead of emphasizing the advantages of implementing CDSS, it is important to address its potential drawbacks and ethical implications. By doing so, it can promote the responsible use of CDSS and facilitate its faster adoption in clinical settings. Full article
(This article belongs to the Special Issue Decision-Support Systems for Cancer Diagnosis and Prognosis)
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