About this Research Topic
The availability of digitized histopathology slides combined with recent discoveries in molecular pathology and computational imaging provide an opportunity to revisit conventional approaches to the diagnosis and prediction of clinical outcomes. Applications of methods such as artificial intelligence- and deep learning-assisted image analyses allow for the identification of thousands of image features and non-biased generation of continuous quantifiable measurement data that can be readily integrated with other -omic platforms. Moreover, such analyses allow for the identification of features that are invisible to the human eye and beyond human comprehension. Future clinical application of such features associated with the underlying molecular pathways will assist pathologists in diagnosis and guide patient treatment and management decisions. From a predictive molecular pathology point of view, more advances have been made in terms of targeted therapies allowing for significant improvement in clinical outcomes of ovarian cancer patients.
Our goal is to compile publications that focus on current advances in molecular and computational pathology in gynecologic malignancies. Molecular and computational pathology has already shown marked success in assisting with diagnosis, tumor classification, and predicting patient prognosis in a variety of cancer types. However, much remains to be discovered about the biology underpinning molecular, visual and sub-visual features and whether these features are associated with clinical parameters (for the identification of biomarkers for diagnosis, chemo-responsiveness, and prognosis) and/or specific drivers and carcinogenic pathways (for the identification of biomarkers or targets for cancer prevention and treatment).
We welcome Original Research, Reviews and Hypotheses in all types of gynecological cancers and premalignant lesions that focus on one or more of the following topics:
1) The role of molecular and digital pathology in gynecologic malignancies
2) Artificial intelligence, deep learning, and machine learning strategies applied to pathology slides and other clinical images
3) Development of algorithms, feature databases, and other innovative tools to advance molecular and computational pathology
4) Multi-dimensional integration of molecular and computational pathology data with other -omic platforms
5) The role of different BRCA alterations as predictors of response to PARP inhibitors
6) Immunohistochemistry biomarkers for the accurate diagnosis of gynecologic malignancies
7) Biomarkers associated with immunotherapy
8) Biomarkers for the prediction of drug resistance
9) Next-generation sequencing and its application in diagnosis of gynecologic malignancies.
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
Our goal is to compile publications that focus on current advances in molecular and computational pathology in gynecologic malignancies. Molecular and computational pathology has already shown marked success in assisting with diagnosis, tumor classification, and predicting patient prognosis in a variety of cancer types. However, much remains to be discovered about the biology underpinning molecular, visual and sub-visual features and whether these features are associated with clinical parameters (for the identification of biomarkers for diagnosis, chemo-responsiveness, and prognosis) and/or specific drivers and carcinogenic pathways (for the identification of biomarkers or targets for cancer prevention and treatment).
We welcome Original Research, Reviews and Hypotheses in all types of gynecological cancers and premalignant lesions that focus on one or more of the following topics:
1) The role of molecular and digital pathology in gynecologic malignancies
2) Artificial intelligence, deep learning, and machine learning strategies applied to pathology slides and other clinical images
3) Development of algorithms, feature databases, and other innovative tools to advance molecular and computational pathology
4) Multi-dimensional integration of molecular and computational pathology data with other -omic platforms
5) The role of different BRCA alterations as predictors of response to PARP inhibitors
6) Immunohistochemistry biomarkers for the accurate diagnosis of gynecologic malignancies
7) Biomarkers associated with immunotherapy
8) Biomarkers for the prediction of drug resistance
9) Next-generation sequencing and its application in diagnosis of gynecologic malignancies.
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
Keywords: Artificial intelligence, Convolutional neural network, Deep learning, Machine learning, Ovarian cancer, Cervical cancer, Endometrial cancer, Molecular pathology
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
