Machine Learning for Imaging-Based Cancer Diagnostics

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Causes, Screening and Diagnosis".

Deadline for manuscript submissions: closed (23 February 2023) | Viewed by 7992

Special Issue Editors

National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
Interests: cancers; machine learning; artificial intelligence; image processing; computer vision; biomedical informatics; data science
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Guest Editor
National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
Interests: machine learning; artificial intelligence; medical image analysis; image informatics; multimodal data analysis; data science; NCI (cervical cancer)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medical imaging has been ubiquitously used in clinical point-of-care and plays an important role in cancer screening, diagnosis, and treatment. There are many types of medical imaging modalities, for example, MRI, CT, X-ray, PET, mammography, ultrasound, colposcopy, microscopy, endoscopy, and fluoroscopy. Innovations in machine learning and computer-aided techniques to medical imaging in combination with various omics applications can provide valuable information to clinicians to aid in decision-making as well as improve the quality and efficiency of medical care. Although promising results have been demonstrated in the literature, especially with deep learning techniques, a great deal of work and effort remain to further advance this research field. Progress has been limited by the lack of sufficiently large and diverse datasets —especially those enriched by multiple expert annotations, relatively high intra- and inter-observer variance in annotations, inadequately defined truth standards, poor correlation in annotations among data collected at different sources, and noisy labels. Further, advances are also limited by poor image quality control, insufficiently meaningful and usable AI model explanations that adversely impact clinical interpretation of machine predictions, inadequate integration with non-imaging biomarkers, and data imbalance due to the varying prevalence of cases, among others. Through this Special Issue, we aim to highlight advances in machine learning and artificial intelligence methods that address or overcome some of the listed challenges and limitations toward improving the state of the art in image-based cancer diagnostics, treatment, and predictive risk assessment.

We are pleased to invite you to submit your related original research articles and reviews to this Special Issue, “Machine learning for imaging-based cancer diagnostics”, of the open-access MDPI journal Cancers (impact factor: 6.639). This Special Issue aims to promote and advance machine learning techniques in medical applications, especially regarding the use of intelligent medical imaging to aid the diagnostics, therapeutics, and predictive risk assessment of cancers of various organs. Research areas may include (but are not limited to) the following:

  • Computer-aided diagnosis;
  • Medical image analysis;
  • Medical image reconstruction;
  • Medical image registration;
  • Medical image enhancement;
  • Medical image segmentation;
  • Medical image classification;
  • Medical image fusion;
  • Medical image retrieval;
  • Image-guided interventions and surgery;
  • Network interpretability and explainability for medical applications;
  • Visualization in medical imaging;
  • Automatic medical data cleaning;
  • Statistical pattern analysis for medical applications;
  • Multi-model medical data analysis;
  • Medical data bias mitigation;
  • Multiomics with imaging applications for cancer diagnostics and therapeutics.

We look forward to receiving your contributions.

You may choose our Joint Special Issue in Current Oncology.

Dr. Zhiyun Xue
Dr. Sameer Antani
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. 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

  • machine learning
  • deep learning
  • cancer diagnosis
  • medical image analysis
  • enhancement
  • segmentation
  • registration
  • classification
  • network explanation

Published Papers (3 papers)

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Research

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18 pages, 4792 KiB  
Article
Evaluation of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma Using Clinical-Ultrasound Radiomic Machine Learning-Based Model
by Enock Adjei Agyekum, Yong-Zhen Ren, Xian Wang, Sashana Sashakay Cranston, Yu-Guo Wang, Jun Wang, Debora Akortia, Fei-Ju Xu, Leticia Gomashie, Qing Zhang, Dongmei Zhang and Xiaoqin Qian
Cancers 2022, 14(21), 5266; https://doi.org/10.3390/cancers14215266 - 26 Oct 2022
Cited by 7 | Viewed by 1821
Abstract
We aim to develop a clinical-ultrasound radiomic (USR) model based on USR features and clinical factors for the evaluation of cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC). This retrospective study used routine clinical and US data from 205 [...] Read more.
We aim to develop a clinical-ultrasound radiomic (USR) model based on USR features and clinical factors for the evaluation of cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC). This retrospective study used routine clinical and US data from 205 PTC patients. According to the pathology results, the enrolled patients were divided into a non-CLNM group and a CLNM group. All patients were randomly divided into a training cohort (n = 143) and a validation cohort (n = 62). A total of 1046 USR features of lesion areas were extracted. The features were reduced using Pearson’s Correlation Coefficient (PCC) and Recursive Feature Elimination (RFE) with stratified 15-fold cross-validation. Several machine learning classifiers were employed to build a Clinical model based on clinical variables, a USR model based solely on extracted USR features, and a Clinical-USR model based on the combination of clinical variables and USR features. The Clinical-USR model could discriminate between PTC patients with CLNM and PTC patients without CLNM in the training (AUC, 0.78) and validation cohorts (AUC, 0.71). When compared to the Clinical model, the USR model had higher AUCs in the validation (0.74 vs. 0.63) cohorts. The Clinical-USR model demonstrated higher AUC values in the validation cohort (0.71 vs. 0.63) compared to the Clinical model. The newly developed Clinical-USR model is feasible for predicting CLNM in patients with PTC. Full article
(This article belongs to the Special Issue Machine Learning for Imaging-Based Cancer Diagnostics)
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Review

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14 pages, 590 KiB  
Review
Endocrine Tumor Classification via Machine-Learning-Based Elastography: A Systematic Scoping Review
by Ye-Jiao Mao, Li-Wen Zha, Andy Yiu-Chau Tam, Hyo-Jung Lim, Alyssa Ka-Yan Cheung, Ying-Qi Zhang, Ming Ni, James Chung-Wai Cheung and Duo Wai-Chi Wong
Cancers 2023, 15(3), 837; https://doi.org/10.3390/cancers15030837 - 29 Jan 2023
Cited by 6 | Viewed by 2280
Abstract
Elastography complements traditional medical imaging modalities by mapping tissue stiffness to identify tumors in the endocrine system, and machine learning models can further improve diagnostic accuracy and reliability. Our objective in this review was to summarize the applications and performance of machine-learning-based elastography [...] Read more.
Elastography complements traditional medical imaging modalities by mapping tissue stiffness to identify tumors in the endocrine system, and machine learning models can further improve diagnostic accuracy and reliability. Our objective in this review was to summarize the applications and performance of machine-learning-based elastography on the classification of endocrine tumors. Two authors independently searched electronic databases, including PubMed, Scopus, Web of Science, IEEEXpress, CINAHL, and EMBASE. Eleven (n = 11) articles were eligible for the review, of which eight (n = 8) focused on thyroid tumors and three (n = 3) considered pancreatic tumors. In all thyroid studies, the researchers used shear-wave ultrasound elastography, whereas the pancreas researchers applied strain elastography with endoscopy. Traditional machine learning approaches or the deep feature extractors were used to extract the predetermined features, followed by classifiers. The applied deep learning approaches included the convolutional neural network (CNN) and multilayer perceptron (MLP). Some researchers considered the mixed or sequential training of B-mode and elastographic ultrasound data or fusing data from different image segmentation techniques in machine learning models. All reviewed methods achieved an accuracy of ≥80%, but only three were ≥90% accurate. The most accurate thyroid classification (94.70%) was achieved by applying sequential training CNN; the most accurate pancreas classification (98.26%) was achieved using a CNN–long short-term memory (LSTM) model integrating elastography with B-mode and Doppler images. Full article
(This article belongs to the Special Issue Machine Learning for Imaging-Based Cancer Diagnostics)
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24 pages, 363 KiB  
Review
The Use of Artificial Intelligence in the Diagnosis and Classification of Thyroid Nodules: An Update
by Maksymilian Ludwig, Bartłomiej Ludwig, Agnieszka Mikuła, Szymon Biernat, Jerzy Rudnicki and Krzysztof Kaliszewski
Cancers 2023, 15(3), 708; https://doi.org/10.3390/cancers15030708 - 24 Jan 2023
Cited by 12 | Viewed by 2922
Abstract
The incidence of thyroid nodules diagnosed is increasing every year, leading to a greater risk of unnecessary procedures being performed or wrong diagnoses being made. In our paper, we present the latest knowledge on the use of artificial intelligence in diagnosing and classifying [...] Read more.
The incidence of thyroid nodules diagnosed is increasing every year, leading to a greater risk of unnecessary procedures being performed or wrong diagnoses being made. In our paper, we present the latest knowledge on the use of artificial intelligence in diagnosing and classifying thyroid nodules. We particularly focus on the usefulness of artificial intelligence in ultrasonography for the diagnosis and characterization of pathology, as these are the two most developed fields. In our search of the latest innovations, we reviewed only the latest publications of specific types published from 2018 to 2022. We analyzed 930 papers in total, from which we selected 33 that were the most relevant to the topic of our work. In conclusion, there is great scope for the use of artificial intelligence in future thyroid nodule classification and diagnosis. In addition to the most typical uses of artificial intelligence in cancer differentiation, we identified several other novel applications of artificial intelligence during our review. Full article
(This article belongs to the Special Issue Machine Learning for Imaging-Based Cancer Diagnostics)
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