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Diagnostics
Special Issues
Imaging of Cervical Cancer
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Imaging of Cervical 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 March 2023) | Viewed by 10099

Special Issue Editor

Dr. Zoard T. Krasznai

E-Mail Website
Guest Editor
Department of Obstetrics and Gynecology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
Interests: clinical oncology; gynecological oncology; gyneco-oncology surgery; HPV
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In contrast to other solid tumours, the FIGO staging of cervical cancer historically relied on clinical features. With the 2018 revision of the FIGO staging system of cervical cancer, imaging has a major role in the diagnosis and management of the disease. Although this new system includes several subdivisions, there are limited data on the prognostic value and diagnostic accuracy of the revised system. The widespread use of nuclear imaging (PET-CT and PET-MRI) in the diagnostics, treatment and follow-up of the disease inspired new perspectives. The advancement of artificial intelligence technology in combination with imaging modalities and with colposcopy also imply the possibilities of future advances. Since chemo-radiotherapy has a major role in the treatment of the disease from stage IB3, the advances and possibilities of precise image-guided radiotherapy, including brachytherapy, are also of great importance.  

The topics of this Special Issue aim to focus on, but are not limited to, the following:  

  • the role of imaging in the diagnostics of cervical cancer;
  • limitations and accuracy of the selected methods;
  • novel imaging biomarkers in cervical cancer;
  • the predictive value of imaging on survival;
  • the potential role of artificial intelligence in data processing including the future aspects of colposcopy;
  • the prospects of image-guided radiotherapy, with a special focus on brachytherapy.

Submissions of original preclinical or clinical papers contributing to the advances in this field as well as reviews are encouraged.

Dr. Zoard T Krasznai
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

  • cervical cancer
  • survival analysis
  • image-guided radiotherapy
  • diagnostic imaging
  • multimodal imaging
  • colposcopy
  • artificial intelligence
  • PET-CT
  • biomarkers
  • lymph node metastasis

Published Papers (5 papers)

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Research

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16 pages, 2460 KiB  
Article
Hybridization of Deep Learning Pre-Trained Models with Machine Learning Classifiers and Fuzzy Min–Max Neural Network for Cervical Cancer Diagnosis
by Madhura Kalbhor, Swati Shinde, Daniela Elena Popescu and D. Jude Hemanth
Diagnostics 2023, 13(7), 1363; https://doi.org/10.3390/diagnostics13071363 - 6 Apr 2023
Cited by 13 | Viewed by 2440
Abstract
Medical image analysis and classification is an important application of computer vision wherein disease prediction based on an input image is provided to assist healthcare professionals. There are many deep learning architectures that accept the different medical image modalities and provide the decisions [...] Read more.
Medical image analysis and classification is an important application of computer vision wherein disease prediction based on an input image is provided to assist healthcare professionals. There are many deep learning architectures that accept the different medical image modalities and provide the decisions about the diagnosis of various cancers, including breast cancer, cervical cancer, etc. The Pap-smear test is the commonly used diagnostic procedure for early identification of cervical cancer, but it has a high rate of false-positive results due to human error. Therefore, computer-aided diagnostic systems based on deep learning need to be further researched to classify the pap-smear images accurately. A fuzzy min–max neural network is a neuro fuzzy architecture that has many advantages, such as training with a minimum number of passes, handling overlapping class classification, supporting online training and adaptation, etc. This paper has proposed a novel hybrid technique that combines the deep learning architectures with machine learning classifiers and fuzzy min–max neural network for feature extraction and Pap-smear image classification, respectively. The deep learning pretrained models used are Alexnet, ResNet-18, ResNet-50, and GoogleNet. Benchmark datasets used for the experimentation are Herlev and Sipakmed. The highest classification accuracy of 95.33% is obtained using Resnet-50 fine-tuned architecture followed by Alexnet on Sipakmed dataset. In addition to the improved accuracies, the proposed model has utilized the advantages of fuzzy min–max neural network classifiers mentioned in the literature. Full article
(This article belongs to the Special Issue Imaging of Cervical Cancer)
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16 pages, 1111 KiB  
Article
ColpoClassifier: A Hybrid Framework for Classification of the Cervigrams
by Madhura Kalbhor and Swati Shinde
Diagnostics 2023, 13(6), 1103; https://doi.org/10.3390/diagnostics13061103 - 14 Mar 2023
Cited by 8 | Viewed by 1629
Abstract
Colposcopy plays a vital role in detecting cervical cancer. Artificial intelligence-based methods have been implemented in the literature for the classification of colposcopy images. However, there is a need for a more effective method that can accurately classify cervigrams. In this paper, ColpoClassifier, [...] Read more.
Colposcopy plays a vital role in detecting cervical cancer. Artificial intelligence-based methods have been implemented in the literature for the classification of colposcopy images. However, there is a need for a more effective method that can accurately classify cervigrams. In this paper, ColpoClassifier, a hybrid framework for the classification of cervigrams, is proposed, which consists of feature extraction followed by classification. This paper uses a Gray-level co-occurrence matrix (GLCM), a Gray-level run length matrix (GLRLM), and a histogram of gradients (HOG) for feature extraction. These features are combined to form a feature fusion vector of the form GLCM + GLRLM + HOG. The different machine learning classifiers are used for classification by using individual feature vectors as well as feature fusion vectors. The dataset used in this paper is compiled by downloading images from the WHO website. Two variants of this dataset are created, Dataset-I contains images of the aceto-whitening effect, green filter, iodine application, and raw cervigram while Dataset-II only contains images of the aceto-whitening effect. This paper presents the classification performance on all kinds of images with the individual as well as hybrid feature fusion vector and concludes that hybrid feature fusion vectors on aceto-whitening images have given the best results. Full article
(This article belongs to the Special Issue Imaging of Cervical Cancer)
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12 pages, 2398 KiB  
Article
In Vivo Preclinical Assessment of the VEGF Targeting Potential of the Newly Synthesized [52Mn]Mn-DOTAGA-Bevacizumab Using Experimental Cervix Carcinoma Mouse Model
by Csaba Csikos, Adrienn Vágner, Gábor Nagy, Ibolya Kálmán-Szabó, Judit P. Szabó, Minh Toan Ngo, Zoltán Szoboszlai, Dezső Szikra, Zoárd Tibor Krasznai, György Trencsényi and Ildikó Garai
Diagnostics 2023, 13(2), 236; https://doi.org/10.3390/diagnostics13020236 - 8 Jan 2023
Cited by 2 | Viewed by 1607
Abstract
Among humanized monoclonal antibodies, bevacizumab specifically binds to vascular endothelial growth factor A (VEGF-A). VEGF-A is an overexpressed biomarker in cervix carcinoma and is involved in the development and maintenance of tumor-associated neo-angiogenesis. The non-invasive positron emission tomography using radiolabeled target-specific antibodies (immuno-PET) [...] Read more.
Among humanized monoclonal antibodies, bevacizumab specifically binds to vascular endothelial growth factor A (VEGF-A). VEGF-A is an overexpressed biomarker in cervix carcinoma and is involved in the development and maintenance of tumor-associated neo-angiogenesis. The non-invasive positron emission tomography using radiolabeled target-specific antibodies (immuno-PET) provides the longitudinal and quantitative assessment of tumor target expression. Due to antibodies having a long-circulating time, radioactive metal ions (e.g., 52Mn) with longer half-lives are the best candidates for isotope conjugation. The aim of our preclinical study was to assess the biodistribution and tumor-targeting potential of 52Mn-labeled DOTAGA-bevacizumab. The VEGF-A targeting potential of the new immuno-PET ligand was assessed by using the VEGF-A expressing KB-3-1 (human cervix carcinoma) tumor-bearing CB17 SCID mouse model and in vivo PET/MRI imaging. Due to the high and specific accumulation found in the subcutaneously located experimental cervix carcinoma tumors, [52Mn]Mn-DOTAGA-bevacizumab is a promising PET probe for the detection of VEGF-A positive gynecological tumors, for patient selection, and monitoring the efficacy of therapies targeting angiogenesis. Full article
(This article belongs to the Special Issue Imaging of Cervical Cancer)
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16 pages, 2342 KiB  
Article
Cervical Cell/Clumps Detection in Cytology Images Using Transfer Learning
by Chuanyun Xu, Mengwei Li, Gang Li, Yang Zhang, Chengjie Sun and Nanlan Bai
Diagnostics 2022, 12(10), 2477; https://doi.org/10.3390/diagnostics12102477 - 13 Oct 2022
Cited by 5 | Viewed by 2447
Abstract
Cervical cancer is one of the most common and deadliest cancers among women and poses a serious health risk. Automated screening and diagnosis of cervical cancer will help improve the accuracy of cervical cell screening. In recent years, there have been many studies [...] Read more.
Cervical cancer is one of the most common and deadliest cancers among women and poses a serious health risk. Automated screening and diagnosis of cervical cancer will help improve the accuracy of cervical cell screening. In recent years, there have been many studies conducted using deep learning methods for automatic cervical cancer screening and diagnosis. Deep-learning-based Convolutional Neural Network (CNN) models require large amounts of data for training, but large cervical cell datasets with annotations are difficult to obtain. Some studies have used transfer learning approaches to handle this problem. However, such studies used the same transfer learning method that is the backbone network initialization by the ImageNet pre-trained model in two different types of tasks, the detection and classification of cervical cell/clumps. Considering the differences between detection and classification tasks, this study proposes the use of COCO pre-trained models when using deep learning methods for cervical cell/clumps detection tasks to better handle limited data set problem at training time. To further improve the model detection performance, based on transfer learning, we conducted multi-scale training according to the actual situation of the dataset. Considering the effect of bounding box loss on the precision of cervical cell/clumps detection, we analyzed the effects of different bounding box losses on the detection performance of the model and demonstrated that using a loss function consistent with the type of pre-trained model can help improve the model performance. We analyzed the effect of mean and std of different datasets on the performance of the model. It was demonstrated that the detection performance was optimal when using the mean and std of the cervical cell dataset used in the current study. Ultimately, based on backbone Resnet50, the mean Average Precision (mAP) of the network model is 61.6% and Average Recall (AR) is 87.7%. Compared to the current values of 48.8% and 64.0% in the used dataset, the model detection performance is significantly improved by 12.8% and 23.7%, respectively. Full article
(This article belongs to the Special Issue Imaging of Cervical Cancer)
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Review

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10 pages, 237 KiB  
Review
Controversies in the Staging of Patients with Locally Advanced Cervical Cancer
by Dimitrios Nasioudis, Erin M. George and Janos L. Tanyi
Diagnostics 2023, 13(10), 1747; https://doi.org/10.3390/diagnostics13101747 - 16 May 2023
Viewed by 1244
Abstract
Approximately 10–25% of patients with locally advanced cervical cancer harbor metastases to the para-aortic lymph nodes. Staging of patients with locally advanced cervical cancer can be performed with imaging techniques, such as PET-CT; however, false negative rates can be as high as 20%, [...] Read more.
Approximately 10–25% of patients with locally advanced cervical cancer harbor metastases to the para-aortic lymph nodes. Staging of patients with locally advanced cervical cancer can be performed with imaging techniques, such as PET-CT; however, false negative rates can be as high as 20%, especially for patients with pelvic lymph node metastases. Surgical staging can identify patients with microscopic lymph nodes metastases and aid in accurate treatment planning with the administration of extended-field radiation therapy. Data from retrospective studies investigating the impact of para-aortic lymphadenectomy on the oncological outcomes of patients with locally advanced cervical cancer are mixed, while data from randomized controlled trials do not demonstrate a progression-free survival benefit. In the present review, we explore controversies in the staging of patients with locally advanced cervical cancer and summarize the available literature. Full article
(This article belongs to the Special Issue Imaging of Cervical Cancer)
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