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Current Oncology
Special Issues
Cancer Screening, Diagnosis and Theragnosis Using Artificial Intelligence
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Cancer Screening, Diagnosis and Theragnosis Using Artificial Intelligence

A special issue of Current Oncology (ISSN 1718-7729).

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 31160

Special Issue Editor

Dr. William T. Tran

E-Mail Website
Guest Editor
Department of Radiation Oncology, Sunnybrook Health Sciences Centre & University of Toronto, Toronto, Canada
Interests: breast cancer; radiomics; biomarkers; artificial intelligence; imaging biomarkers; therapy response markers

Special Issue Information

Dear Colleagues,

It is clear that we are heading into an era where medicine and artificial intelligence are converging. In oncology, the combination of digital imaging platforms, quantitative radiology digital pathology, and computational frameworks have the potential to enhance cancer screening, diagnosis and to measure the efficacy of anticancer therapies (i.e., theragnosis). Such technological advancements will inherently impact oncology practice and present unique challenges in the clinic.

In this Special Issue of Current Oncology, we invite research reports focused on artificial intelligence-based cancer screening, diagnosis and theragnosis; specifically, these topics may include:

  • Original research articles using machine learning frameworks, computer vision, and deep learning for predictive and prognostic modelling in oncology;
  • Methodological papers related to medical image analysis in oncology to enhance screening and diagnosis;
  • Reviews on the impact in clinical practice.

Dr. William T. Tran
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. Current Oncology is an international peer-reviewed open access monthly 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 2200 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

  • artificial intelligence
  • machine learning
  • computer aided diagnosis
  • computer aided theragnosis
  • medical imaging
  • quantitative digital pathology imaging
  • radiomics
  • imaging biomarkers

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Published Papers (7 papers)

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Editorial

Jump to: Research, Review, Other

4 pages, 185 KiB  
Editorial
Towards Precision Oncology: Enhancing Cancer Screening, Diagnosis and Theragnosis Using Artificial Intelligence
by William T. Tran
Curr. Oncol. 2022, 29(8), 5698-5701; https://doi.org/10.3390/curroncol29080449 - 12 Aug 2022
Viewed by 1860
Abstract
Highly complex and multi-dimensional medical data containing clinical, radiologic, pathologic, and sociodemographic information have the potential to advance precision oncology [...] Full article
(This article belongs to the Special Issue Cancer Screening, Diagnosis and Theragnosis Using Artificial Intelligence)

Research

Jump to: Editorial, Review, Other

12 pages, 2455 KiB  
Article
Artificial Intelligence System for Predicting Prostate Cancer Lesions from Shear Wave Elastography Measurements
by Ciprian Cosmin Secasan, Darian Onchis, Razvan Bardan, Alin Cumpanas, Dorin Novacescu, Corina Botoca, Alis Dema and Ioan Sporea
Curr. Oncol. 2022, 29(6), 4212-4223; https://doi.org/10.3390/curroncol29060336 - 10 Jun 2022
Cited by 12 | Viewed by 3276
Abstract
(1) Objective: To design an artificial intelligence system for prostate cancer prediction using the data obtained by shear wave elastography of the prostate, by comparing it with the histopathological exam of the prostate biopsy specimens. (2) Material and methods: We have conducted a [...] Read more.
(1) Objective: To design an artificial intelligence system for prostate cancer prediction using the data obtained by shear wave elastography of the prostate, by comparing it with the histopathological exam of the prostate biopsy specimens. (2) Material and methods: We have conducted a prospective study on 356 patients undergoing transrectal ultrasound-guided prostate biopsy, for suspicion of prostate cancer. All patients were examined using bi-dimensional shear wave ultrasonography, which was followed by standard systematic transrectal prostate biopsy. The mean elasticity of each of the twelve systematic biopsy target zones was recorded and compared with the pathological examination results in all patients. The final dataset has included data from 223 patients with confirmed prostate cancer. Three machine learning classification algorithms (logistic regression, a decision tree classifier and a dense neural network) were implemented and their performance in predicting the positive lesions from the elastographic data measurements was assessed. (3) Results: The area under the curve (AUC) results were as follows: for logistic regression—0.88, for decision tree classifier—0.78 and for the dense neural network—0.94. Further use of an upsampling strategy for the training set of the neural network slightly improved its performance. Using an ensemble learning model, which combined the three machine learning models, we have obtained a final accuracy of 98%. (4) Conclusions: Bi-dimensional shear wave elastography could be very useful in predicting prostate cancer lesions, especially when it benefits from the computational power of artificial intelligence and machine learning algorithms. Full article
(This article belongs to the Special Issue Cancer Screening, Diagnosis and Theragnosis Using Artificial Intelligence)
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19 pages, 3465 KiB  
Article
Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade
by Andrew Lagree, Audrey Shiner, Marie Angeli Alera, Lauren Fleshner, Ethan Law, Brianna Law, Fang-I Lu, David Dodington, Sonal Gandhi, Elzbieta A. Slodkowska, Alex Shenfield, Katarzyna J. Jerzak, Ali Sadeghi-Naini and William T. Tran
Curr. Oncol. 2021, 28(6), 4298-4316; https://doi.org/10.3390/curroncol28060366 - 27 Oct 2021
Cited by 8 | Viewed by 3671
Abstract
Background: Evaluating histologic grade for breast cancer diagnosis is standard and associated with prognostic outcomes. Current challenges include the time required for manual microscopic evaluation and interobserver variability. This study proposes a computer-aided diagnostic (CAD) pipeline for grading tumors using artificial intelligence. Methods: [...] Read more.
Background: Evaluating histologic grade for breast cancer diagnosis is standard and associated with prognostic outcomes. Current challenges include the time required for manual microscopic evaluation and interobserver variability. This study proposes a computer-aided diagnostic (CAD) pipeline for grading tumors using artificial intelligence. Methods: There were 138 patients included in this retrospective study. Breast core biopsy slides were prepared using standard laboratory techniques, digitized, and pre-processed for analysis. Deep convolutional neural networks (CNNs) were developed to identify the regions of interest containing malignant cells and to segment tumor nuclei. Imaging-based features associated with spatial parameters were extracted from the segmented regions of interest (ROIs). Clinical datasets and pathologic biomarkers (estrogen receptor, progesterone receptor, and human epidermal growth factor 2) were collected from all study subjects. Pathologic, clinical, and imaging-based features were input into machine learning (ML) models to classify histologic grade, and model performances were tested against ground-truth labels at the patient-level. Classification performances were evaluated using receiver-operating characteristic (ROC) analysis. Results: Multiparametric feature sets, containing both clinical and imaging-based features, demonstrated high classification performance. Using imaging-derived markers alone, the classification performance demonstrated an area under the curve (AUC) of 0.745, while modeling these features with other pathologic biomarkers yielded an AUC of 0.836. Conclusion: These results demonstrate an association between tumor nuclear spatial features and tumor grade. If further validated, these systems may be implemented into pathology CADs and can assist pathologists to expeditiously grade tumors at the time of diagnosis and to help guide clinical decisions. Full article
(This article belongs to the Special Issue Cancer Screening, Diagnosis and Theragnosis Using Artificial Intelligence)
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18 pages, 3083 KiB  
Article
Detection of Cervical Cancer Cells in Whole Slide Images Using Deformable and Global Context Aware Faster RCNN-FPN
by Xia Li, Zhenhao Xu, Xi Shen, Yongxia Zhou, Binggang Xiao and Tie-Qiang Li
Curr. Oncol. 2021, 28(5), 3585-3601; https://doi.org/10.3390/curroncol28050307 - 16 Sep 2021
Cited by 49 | Viewed by 4658
Abstract
Cervical cancer is a worldwide public health problem with a high rate of illness and mortality among women. In this study, we proposed a novel framework based on Faster RCNN-FPN architecture for the detection of abnormal cervical cells in cytology images from a [...] Read more.
Cervical cancer is a worldwide public health problem with a high rate of illness and mortality among women. In this study, we proposed a novel framework based on Faster RCNN-FPN architecture for the detection of abnormal cervical cells in cytology images from a cancer screening test. We extended the Faster RCNN-FPN model by infusing deformable convolution layers into the feature pyramid network (FPN) to improve scalability. Furthermore, we introduced a global contextual aware module alongside the Region Proposal Network (RPN) to enhance the spatial correlation between the background and the foreground. Extensive experimentations with the proposed deformable and global context aware (DGCA) RCNN were carried out using the cervical image dataset of “Digital Human Body” Vision Challenge from the Alibaba Cloud TianChi Company. Performance evaluation based on the mean average precision (mAP) and receiver operating characteristic (ROC) curve has demonstrated considerable advantages of the proposed framework. Particularly, when combined with tagging of the negative image samples using traditional computer-vision techniques, 6–9% increase in mAP has been achieved. The proposed DGCA-RCNN model has potential to become a clinically useful AI tool for automated detection of cervical cancer cells in whole slide images of Pap smear. Full article
(This article belongs to the Special Issue Cancer Screening, Diagnosis and Theragnosis Using Artificial Intelligence)
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Review

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23 pages, 1963 KiB  
Review
Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer
by Hang Qiu, Shuhan Ding, Jianbo Liu, Liya Wang and Xiaodong Wang
Curr. Oncol. 2022, 29(3), 1773-1795; https://doi.org/10.3390/curroncol29030146 - 7 Mar 2022
Cited by 29 | Viewed by 9719
Abstract
Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early detection and diagnosis, comprehensive assessment of treatment response, and precise prediction of prognosis are essential to improve the patients’ survival rate. In recent years, due to the explosion of clinical [...] Read more.
Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early detection and diagnosis, comprehensive assessment of treatment response, and precise prediction of prognosis are essential to improve the patients’ survival rate. In recent years, due to the explosion of clinical and omics data, and groundbreaking research in machine learning, artificial intelligence (AI) has shown a great application potential in clinical field of CRC, providing new auxiliary approaches for clinicians to identify high-risk patients, select precise and personalized treatment plans, as well as to predict prognoses. This review comprehensively analyzes and summarizes the research progress and clinical application value of AI technologies in CRC screening, diagnosis, treatment, and prognosis, demonstrating the current status of the AI in the main clinical stages. The limitations, challenges, and future perspectives in the clinical implementation of AI are also discussed. Full article
(This article belongs to the Special Issue Cancer Screening, Diagnosis and Theragnosis Using Artificial Intelligence)
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Other

9 pages, 261 KiB  
Opinion
The Use of Artificial Intelligence in Clinical Care: A Values-Based Guide for Shared Decision Making
by Rosanna Macri and Shannon L. Roberts
Curr. Oncol. 2023, 30(2), 2178-2186; https://doi.org/10.3390/curroncol30020168 - 9 Feb 2023
Cited by 5 | Viewed by 2831
Abstract
Clinical applications of artificial intelligence (AI) in healthcare, including in the field of oncology, have the potential to advance diagnosis and treatment. The literature suggests that patient values should be considered in decision making when using AI in clinical care; however, there is [...] Read more.
Clinical applications of artificial intelligence (AI) in healthcare, including in the field of oncology, have the potential to advance diagnosis and treatment. The literature suggests that patient values should be considered in decision making when using AI in clinical care; however, there is a lack of practical guidance for clinicians on how to approach these conversations and incorporate patient values into clinical decision making. We provide a practical, values-based guide for clinicians to assist in critical reflection and the incorporation of patient values into shared decision making when deciding to use AI in clinical care. Values that are relevant to patients, identified in the literature, include trust, privacy and confidentiality, non-maleficence, safety, accountability, beneficence, autonomy, transparency, compassion, equity, justice, and fairness. The guide offers questions for clinicians to consider when adopting the potential use of AI in their practice; explores illness understanding between the patient and clinician; encourages open dialogue of patient values; reviews all clinically appropriate options; and makes a shared decision of what option best meets the patient’s values. The guide can be used for diverse clinical applications of AI. Full article
(This article belongs to the Special Issue Cancer Screening, Diagnosis and Theragnosis Using Artificial Intelligence)
14 pages, 1930 KiB  
Brief Report
Feasibility on the Use of Radiomics Features of 11[C]-MET PET/CT in Central Nervous System Tumours: Preliminary Results on Potential Grading Discrimination Using a Machine Learning Model
by Giorgio Russo, Alessandro Stefano, Pierpaolo Alongi, Albert Comelli, Barbara Catalfamo, Cristina Mantarro, Costanza Longo, Roberto Altieri, Francesco Certo, Sebastiano Cosentino, Maria Gabriella Sabini, Selene Richiusa, Giuseppe Maria Vincenzo Barbagallo and Massimo Ippolito
Curr. Oncol. 2021, 28(6), 5318-5331; https://doi.org/10.3390/curroncol28060444 - 12 Dec 2021
Cited by 23 | Viewed by 3642
Abstract
Background/Aim: Nowadays, Machine Learning (ML) algorithms have demonstrated remarkable progress in image-recognition tasks and could be useful for the new concept of precision medicine in order to help physicians in the choice of therapeutic strategies for brain tumours. Previous data suggest that, in [...] Read more.
Background/Aim: Nowadays, Machine Learning (ML) algorithms have demonstrated remarkable progress in image-recognition tasks and could be useful for the new concept of precision medicine in order to help physicians in the choice of therapeutic strategies for brain tumours. Previous data suggest that, in the central nervous system (CNS) tumours, amino acid PET may more accurately demarcate the active disease than paramagnetic enhanced MRI, which is currently the standard method of evaluation in brain tumours and helps in the assessment of disease grading, as a fundamental basis for proper clinical patient management. The aim of this study is to evaluate the feasibility of ML on 11[C]-MET PET/CT scan images and to propose a radiomics workflow using a machine-learning method to create a predictive model capable of discriminating between low-grade and high-grade CNS tumours. Materials and Methods: In this retrospective study, fifty-six patients affected by a primary brain tumour who underwent 11[C]-MET PET/CT were selected from January 2016 to December 2019. Pathological examination was available in all patients to confirm the diagnosis and grading of disease. PET/CT acquisition was performed after 10 min from the administration of 11C-Methionine (401–610 MBq) for a time acquisition of 15 min. 11[C]-MET PET/CT images were acquired using two scanners (24 patients on a Siemens scan and 32 patients on a GE scan). Then, LIFEx software was used to delineate brain tumours using two different semi-automatic and user-independent segmentation approaches and to extract 44 radiomics features for each segmentation. A novel mixed descriptive-inferential sequential approach was used to identify a subset of relevant features that correlate with the grading of disease confirmed by pathological examination and clinical outcome. Finally, a machine learning model based on discriminant analysis was used in the evaluation of grading prediction (low grade CNS vs. high-grade CNS) of 11[C]-MET PET/CT. Results: The proposed machine learning model based on (i) two semi-automatic and user-independent segmentation processes, (ii) an innovative feature selection and reduction process, and (iii) the discriminant analysis, showed good performance in the prediction of tumour grade when the volumetric segmentation was used for feature extraction. In this case, the proposed model obtained an accuracy of ~85% (AUC ~79%) in the subgroup of patients who underwent Siemens tomography scans, of 80.51% (AUC 65.73%) in patients who underwent GE tomography scans, and of 70.31% (AUC 64.13%) in the whole patients’ dataset (Siemens and GE scans). Conclusions: This preliminary study on the use of an ML model demonstrated to be feasible and able to select radiomics features of 11[C]-MET PET with potential value in prediction of grading of disease. Further studies are needed to improve radiomics algorithms to personalize predictive and prognostic models and potentially support the medical decision process. Full article
(This article belongs to the Special Issue Cancer Screening, Diagnosis and Theragnosis Using Artificial Intelligence)
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