AIMC Topic: Ovarian Neoplasms

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An artificial intelligence-enhanced early ovarian cancer diagnosis biosensor.

Journal of materials chemistry. B
In early cancer diagnosis, extracellular vesicles (EVs) are more advantageous than circulating tumor cells due to their smaller size, greater stability, and enhanced tissue penetration. These qualities lead to higher EV concentrations in body fluids,...

Recent Advances in Diagnostic Strategies and Nanotechnology-Based Therapies for Ovarian Cancer Treatment.

ACS applied bio materials
Ovarian cancer is a global silent killer in women and is the second most common cause of gynecologic cancer-related deaths. Despite significant research and advances in treatment, ovarian cancer treatment remains a challenge, as it is diagnosed in an...

Discovery of Novel 4,5-Dihydropyrrolo[3,4-]pyrazol-6()-one-Based Tubulin Inhibitors Targeting Colchicine Binding Site with Potent Anti-Ovarian Cancer Activity.

Journal of medicinal chemistry
To address the toxicity of current microtubule inhibitors, we employed the GeminiMol deep learning model to screen the Zinc20 database, identifying a novel 4,5-dihydropyrrolo[3,4-]pyrazol-6(2)-one scaffold () targeting the colchicine binding site. Su...

Circulating proteins and metabolites panel for noninvasive preoperative diagnosis of epithelial ovarian cancer.

BMC medicine
BACKGROUND: Existing biomarkers for epithelial ovarian cancer (EOC) have demonstrated limited sensitivity and specificity. This study aimed to investigate plasma protein and metabolite characteristics of EOC and identify novel biomarker candidates fo...

Development and validation of deep learning for predicting the growth of ovarian cancer organoids.

Chinese medical journal
BACKGROUND: Organoids have attracted enormous interest in disease modeling, drug screening, and precision medicine. However, developing robust patient-derived organoids (PDOs) was time-consuming, costly, and had low success rates for certain cancer t...

KS-TMIL: A K-Stage Transformer approach with multiple instance learning model for ovarian cancer subtype classification.

Computers in biology and medicine
Existing multiple instance learning (MIL) methods treat whole slide image (WSI) as collections of independent patches, neglecting these crucial spatial and morphological connections. This paper proposes K-Stage Transformer with multiple instance lear...

Integration of label-free surface enhanced Raman spectroscopy (SERS) of extracellular vesicles (EVs) with Raman tagged labels to enhance ovarian cancer diagnostics.

Biosensors & bioelectronics
We report a proof-of-concept diagnostic strategy that integrates multiplexed Raman-tagged antibody labeling with label-free surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) to improve the detection of ovarian cancer via extracellu...

An enhanced deep learning model for accurate classification of ovarian cancer from histopathological images.

Scientific reports
Ovarian Cancer is a malignancy that develops from ovarian cells and is frequently characterized by aberrant cell proliferation that leads to the creation of tumors within the ovaries. The high death rate and often delayed discovery of Ovarian Cancer ...

Identifying ovarian cancer with machine learning DNA methylation pattern analysis.

Scientific reports
The majority of patients with epithelial ovarian cancer (EOC) continue to be diagnosed at an advanced stage despite great advances in this disease treatment. To impact overall survival, we need better methods of EOC early diagnosis. We performed a ca...