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Cancers
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Computational Pathology for Breast Cancer and Gynecologic Cancer
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Computational Pathology for Breast Cancer and Gynecologic Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 7600

Special Issue Editor

Prof. Dr. Ching-Wei Wang

E-Mail Website
Guest Editor
Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
Interests: computational pathology; precision pathology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of deep learning methods and techniques in the last decade, numerous challenges in the biomedical field have been tackled, which has drastically improved healthcare quality at an unprecedented speed. Consequently, computational pathology is growing in cancer, including diagnosis, phenotyping, subtype classification, early detection, prognostication, assessment of sensitivity to chemotherapy and immunotherapy, and identification of suitable targeted therapies.

Several studies have reportod on the utility of computational imaging to automate cancer diagnoses without compromising accuracy. For example, a study that assessed the ability of deep learning algorithms to accurately detect breast cancer metastases in H&E slides of lymph node sections reported that the algorithms were superior in the detection of micrometastases and equivalent to the best-performing pathologists when under time constraints in detecting macrometastases. Another study of the quantitative characterization of the architecture of tumor-infiltrating lymphocytes and their interplay with cancer cells from H&E slides of three different gynecologic cancer types (ovarian, cervical, and endometrial) and across three different treatment approaches (platinum, radiation, and immunotherapy) showed that the geospatial profile was prognostic of disease progression and survival, irrespective of the treatment modality.

This Special Issue will summarize the recent developments in computational pathology in cancer. It will interpret the complexity of computational pathology for breast cancer and gynecologic cancer. Purely computational/informatics (analysis) papers should include sufficient experimental validation.  Furthermore, the reader will recieve an update on the approaches to new insights obtained through computational approaches applied to the breast and gynecologic cancer datasets.

Prof. Dr. Ching-Wei Wang
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

  • computational pathology
  • digital pathology
  • breast cancer
  • gynecologic cancer
  • precision pathology
  • cancer diagnosis
  • cancer prognosis

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Published Papers (3 papers)

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Editorial

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4 pages, 196 KiB  
Editorial
Computational Pathology for Breast Cancer and Gynecologic Cancer
by Ching-Wei Wang and Hikam Muzakky
Cancers 2023, 15(3), 942; https://doi.org/10.3390/cancers15030942 - 2 Feb 2023
Viewed by 1714
Abstract
Advances in computation pathology have continued at an impressive pace in recent years [...] Full article
(This article belongs to the Special Issue Computational Pathology for Breast Cancer and Gynecologic Cancer)

Research

Jump to: Editorial

19 pages, 21644 KiB  
Article
Efficient Convolution Network to Assist Breast Cancer Diagnosis and Target Therapy
by Ching-Wei Wang, Kai-Lin Chu, Hikam Muzakky, Yi-Jia Lin and Tai-Kuang Chao
Cancers 2023, 15(15), 3991; https://doi.org/10.3390/cancers15153991 - 6 Aug 2023
Cited by 3 | Viewed by 2186
Abstract
Breast cancer is the leading cause of cancer-related deaths among women worldwide, and early detection and treatment has been shown to significantly reduce fatality rates from severe illness. Moreover, determination of the human epidermal growth factor receptor-2 (HER2) gene amplification by Fluorescence in [...] Read more.
Breast cancer is the leading cause of cancer-related deaths among women worldwide, and early detection and treatment has been shown to significantly reduce fatality rates from severe illness. Moreover, determination of the human epidermal growth factor receptor-2 (HER2) gene amplification by Fluorescence in situ hybridization (FISH) and Dual in situ hybridization (DISH) is critical for the selection of appropriate breast cancer patients for HER2-targeted therapy. However, visual examination of microscopy is time-consuming, subjective and poorly reproducible due to high inter-observer variability among pathologists and cytopathologists. The lack of consistency in identifying carcinoma-like nuclei has led to divergences in the calculation of sensitivity and specificity. This manuscript introduces a highly efficient deep learning method with low computing cost. The experimental results demonstrate that the proposed framework achieves high precision and recall on three essential clinical applications, including breast cancer diagnosis and human epidermal receptor factor 2 (HER2) amplification detection on FISH and DISH slides for HER2 target therapy. Furthermore, the proposed method outperforms the majority of the benchmark methods in terms of IoU by a significant margin (p<0.001) on three essential clinical applications. Importantly, run time analysis shows that the proposed method obtains excellent segmentation results with notably reduced time for Artificial intelligence (AI) training (16.93%), AI inference (17.25%) and memory usage (18.52%), making the proposed framework feasible for practical clinical usage. Full article
(This article belongs to the Special Issue Computational Pathology for Breast Cancer and Gynecologic Cancer)
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15 pages, 3456 KiB  
Article
NRK-ABMIL: Subtle Metastatic Deposits Detection for Predicting Lymph Node Metastasis in Breast Cancer Whole-Slide Images
by Usama Sajjad, Mostafa Rezapour, Ziyu Su, Gary H. Tozbikian, Metin N. Gurcan and M. Khalid Khan Niazi
Cancers 2023, 15(13), 3428; https://doi.org/10.3390/cancers15133428 - 30 Jun 2023
Cited by 8 | Viewed by 2669
Abstract
The early diagnosis of lymph node metastasis in breast cancer is essential for enhancing treatment outcomes and overall prognosis. Unfortunately, pathologists often fail to identify small or subtle metastatic deposits, leading them to rely on cytokeratin stains for improved detection, although this approach [...] Read more.
The early diagnosis of lymph node metastasis in breast cancer is essential for enhancing treatment outcomes and overall prognosis. Unfortunately, pathologists often fail to identify small or subtle metastatic deposits, leading them to rely on cytokeratin stains for improved detection, although this approach is not without its flaws. To address the need for early detection, multiple-instance learning (MIL) has emerged as the preferred deep learning method for automatic tumor detection on whole slide images (WSIs). However, existing methods often fail to identify some small lesions due to insufficient attention to small regions. Attention-based multiple-instance learning (ABMIL)-based methods can be particularly problematic because they may focus too much on normal regions, leaving insufficient attention for small-tumor lesions. In this paper, we propose a new ABMIL-based model called normal representative keyset ABMIL (NRK-ABMIL), which addresseses this issue by adjusting the attention mechanism to give more attention to lesions. To accomplish this, the NRK-ABMIL creates an optimal keyset of normal patch embeddings called the normal representative keyset (NRK). The NRK roughly represents the underlying distribution of all normal patch embeddings and is used to modify the attention mechanism of the ABMIL. We evaluated NRK-ABMIL on the publicly available Camelyon16 and Camelyon17 datasets and found that it outperformed existing state-of-the-art methods in accurately identifying small tumor lesions that may spread over a few patches. Additionally, the NRK-ABMIL also performed exceptionally well in identifying medium/large tumor lesions. Full article
(This article belongs to the Special Issue Computational Pathology for Breast Cancer and Gynecologic Cancer)
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