Naveena Singh Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

High Impact

Researcher

University of Arkansas – Fort Smith

faculty

64 h-index 436 pubs 16,741 cited

Research Areas

Links

ORCID

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OverviewAI-generated summary

Naveena Singh's research focuses on the molecular and diagnostic aspects of gynecological cancers, particularly endometrial and ovarian cancers. Her work involves the application of advanced computational techniques, including deep learning, to analyze histopathological images for cancer classification and prognosis. She has investigated the role of specific molecular markers, such as p53, and pathological features, like tertiary lymphoid structures, in predicting patient outcomes in endometrial cancer.

Singh has also contributed to the understanding of tumor classification, including updates to the World Health Organization (WHO) classification of female genital tumors. Her research extends to the challenges in differentiating primary ovarian tumors from metastatic lesions, particularly those originating from the gastrointestinal tract. Collaborations with researchers like David N. Church have resulted in shared publications in these areas.

With a substantial publication record (436 publications) and high citation count (16,741), Singh is recognized as a highly cited researcher. Her work contributes to the fields of medical imaging, cancer diagnostics, and molecular pathology, with a specific emphasis on improving diagnostic accuracy and prognostic assessment in gynecological malignancies.

Metrics

h-index: 64
Publications: 436
Citations: 16,741

Selected Publications

Estimating the ovarian cancer CA-125 preclinical detectable phase, in-vivo tumour doubling time, and window for detection in early stage: an exploratory analysis of UKCTOCS (2025) DOI
The genomic trajectory of ovarian high‐grade serous carcinoma can be observed in <scp>STIC</scp> lesions (2024) DOI
AI-based histopathology image analysis reveals a distinct subset of endometrial cancers (2024) DOI
The spectrum of oestrogen receptor expression in endometrial carcinomas of no specific molecular profile (2024) DOI
Prognostic impact and causality of age on oncological outcomes in women with endometrial cancer: a multimethod analysis of the randomised PORTEC-1, PORTEC-2, and PORTEC-3 trials (2024) DOI
High concordance of molecular subtyping between pre-surgical biopsy and surgical resection specimen (matched-pair analysis) in patients with vulvar squamous cell carcinoma using p16- and p53-immunostaining (2024) DOI
Deep learning-based segmentation of multisite disease in ovarian cancer (2023) DOI
Ovarian cancer symptoms in pre-clinical invasive epithelial ovarian cancer – An exploratory analysis nested within the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) (2023) DOI
FIGO 2023 endometrial cancer staging: too much, too soon? (2023) DOI
Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer (2023) DOI
Clinical Behavior and Molecular Landscape of Stage I p53-Abnormal Low-Grade Endometrioid Endometrial Carcinomas (2023) DOI
Perceptions of Controversies and Unresolved Issues in the 2014 FIGO Staging System for Endometrial Cancer: Survey Results From Members of the International Society of Gynecological Pathologists and International Gynecologic Cancer Society (2023) DOI
Tumour stage, treatment, and survival of women with high-grade serous tubo-ovarian cancer in UKCTOCS: an exploratory analysis of a randomised controlled trial (2023) DOI
Biallelic <i>Dicer1</i> Mutations in the Gynecologic Tract of Mice Drive Lineage-Specific Development of <i>DICER1</i> Syndrome–Associated Cancer (2023) DOI
Microsatellite instability in non-endometrioid ovarian epithelial tumors: a study of 400 cases comparing immunohistochemistry, PCR, and NGS based testing with mutation status of MMR genes (2023) DOI