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 7490
Special Issue Editor
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
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Keywords
- computational pathology
- digital pathology
- breast cancer
- gynecologic cancer
- precision pathology
- cancer diagnosis
- cancer prognosis
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