AIMC Topic: Ovarian Neoplasms
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Interpretable machine learning model based on clinical factors for predicting muscle radiodensity loss after treatment in ovarian cancer.
Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Jul 24, 2024
PURPOSE: Muscle radiodensity loss after surgery and adjuvant chemotherapy is associated with poor outcomes in ovarian cancer. Assessing muscle radiodensity is a real-world clinical challenge owing to the requirement for computed tomography (CT) with ...
Artificial Intelligence-Based Histopathological Subtyping of High-Grade Serous Ovarian Cancer.
The American journal of pathology
Jul 19, 2024
Four subtypes of ovarian high-grade serous carcinoma (HGSC) have previously been identified, each with different prognoses and drug sensitivities. However, the accuracy of classification depended on the assessor's experience. This study aimed to deve...
Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review.
International journal of cancer
Jul 11, 2024
The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The ove...
Developing a deep learning model for predicting ovarian cancer in Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US) Category 4 lesions: A multicenter study.
Journal of cancer research and clinical oncology
Jul 9, 2024
PURPOSE: To develop a deep learning (DL) model for differentiating between benign and malignant ovarian tumors of Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US) Category 4 lesions, and validate its diagnostic performance.
Ovarian cancer identification technology based on deep learning and second harmonic generation imaging.
Journal of biophotonics
Jul 2, 2024
Ovarian cancer is among the most common gynecological cancers and the eighth leading cause of cancer-related deaths among women worldwide. Surgery is among the most important options for cancer treatment. During surgery, a biopsy is generally require...
Machine Learning-Enhanced Extraction of Biomarkers for High-Grade Serous Ovarian Cancer from Proteomics Data.
Scientific data
Jun 25, 2024
Comprehensive biomedical proteomic datasets are accumulating exponentially, warranting robust analytics to deconvolute them for identifying novel biological insights. Here, we report a strategic machine learning (ML)-based feature extraction workflow...
Fragmentomics features of ovarian cancer.
International journal of cancer
May 20, 2024
Ovarian cancer (OC) is a major cause of cancer mortality in women worldwide. Due to the occult onset of OC, its nonspecific clinical symptoms in the early phase, and a lack of effective early diagnostic tools, most OC patients are diagnosed at an adv...
Histopathologic image-based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer.
Nature communications
May 18, 2024
Platinum-based chemotherapy is the cornerstone treatment for female high-grade serous ovarian carcinoma (HGSOC), but choosing an appropriate treatment for patients hinges on their responsiveness to it. Currently, no available biomarkers can promptly ...
The Promise and Challenges of AI Integration in Ovarian Cancer Screenings.
Reproductive sciences (Thousand Oaks, Calif.)
May 15, 2024
PURPOSE: Ovarian cancer is oftendiagnosed late due to vague symptoms, leading to poor survival rate. Improved screening tests could mitigate this issue. This narrative review examines the potential and challenges of integrating artificial intelligenc...
Advancing ovarian cancer diagnosis: harnessing artificial intelligence and novel biomarker strategies.
Asia-Pacific journal of clinical oncology
May 15, 2024
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