AIMC Topic: Ovarian Neoplasms

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Advancing adoptive T cell therapy in ovarian cancer: barriers, innovations, and emerging platforms.

Journal for immunotherapy of cancer
Adoptive cell therapy (ACT) has demonstrated curative potential in select cancers, but its translation to solid tumors such as ovarian cancer (OC) has been hindered by multiple factors, including tumor heterogeneity, immune exclusion, and a profoundl...

Artificial intelligence-based machine learning models for preoperative diagnosis and staging of ovarian tumors.

Journal of the Egyptian National Cancer Institute
BACKGROUND: Ovarian cancer remains the most lethal gynecological malignancy, necessitating precise diagnostic strategies to improve patient outcomes. This study aims to develop and evaluate machine learning models that utilize patient history, imagin...

Machine learning driven multiomics analysis identifies disulfidptosis associated molecular subtypes in ovarian cancer.

Scientific reports
Precision oncology enables molecularly guided cancer therapy through multi-omics profiling, AI-driven classification, and biomarker-targeted interventions. Disulfidptosis has emerged as a promising therapeutic target, yet no ovarian cancer classifica...

Identification of novel biomarkers for epithelial ovarian cancer through machine learning and explainable artificial intelligence using in silico and in vitro analysis.

Scientific reports
Epithelial ovarian cancer (EOC) is a lethal gynecological malignancy. Ongoing research aimed to identify novel biomarkers and develop combined algorithms to improve diagnosis and prognosis prediction for EOC. RNA-seq related to EOC were obtained from...

Development of a diagnostic model for ovarian cancer based on machine learning algorithms and functional analysis of key biomarker SOX17.

Journal of ovarian research
BACKGROUND: Ovarian cancer (OC) demonstrates the poorest prognosis among gynecological malignancies, with five-year survival rates below 45%, primarily due to late-stage diagnosis. To address this challenge, we systematically identified OC-specific d...

Personalized Cancer-Specific Protein-Aptamer Corona for Orthogonal Multiplex Cancer Diagnosis.

Journal of the American Chemical Society
Aptamers are powerful synthetic recognition elements for biosensing, yet their application in complex biofluids, such as human serum, is critically limited by enzymatic degradation. To overcome this fundamental challenge, we introduce a novel analyti...

Application of multimodal integration to develop preoperative diagnostic models for borderline and malignant ovarian tumors.

Scientific reports
Malignant ovarian tumors (MOTs) and borderline ovarian tumors (BOTs) differ in treatment strategies and prognosis. However, accurate preoperative diagnosis remains challenging, and improving diagnostic accuracy is crucial. We developed and validated ...

Global DNA methylation signatures associated with chemoresistance and poor prognosis of high grade serous ovarian cancer.

Scientific reports
Ovarian cancer (OVCA) is third most lethal gynecologic cancers and acquired chemoresistance is the key link in the high mortality rate of OVCA patients. Currently, there are no reliable methods to predict chemoresistance in OVCA. In our study, we ide...

Serum Fingerprinting-Based Integrative Dual-Omics Machine Learning for Endometriosis-Associated Ovarian Cancer.

Analytical chemistry
Dual-omics, by integrating molecular information from two distinct dimensions, can offer more comprehensive perspective for complex disease. Herein, we developed an efficient functionalized mesoporous nanoparticle-coupled laser desorption/ionization ...