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Advancements in Oncology with Artificial Intelligence—A Review Article
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In: Cancers; Volume 14; Issue 5; Pages: 1349 (2022)
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Management and Outcome of Young Women (≤40 Years) with Breast Cancer in Switzerland
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In: Cancers; Volume 14; Issue 5; Pages: 1328 (2022)
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Chemotherapy-Related Cognitive Impairment in Patients with Breast Cancer Based on Functional Assessment and NIRS Analysis
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In: Journal of Clinical Medicine; Volume 11; Issue 9; Pages: 2363 (2022)
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Metaphors of cancer in the Arabic language: An analysis of the use of metaphors in the online narratives of breast cancer patients
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In: Open Linguistics, Vol 8, Iss 1, Pp 27-45 (2022) (2022)
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Prevalence of ankyloglossia and factors that impact on exclusive breastfeeding in neonates
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In: Revista CEFAC, Vol 24, Iss 1 (2022) (2022)
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BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning.
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BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning.
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Assessment of the Relevance of a Breast Cancer Rehabilitation Program based on a Neutrosophic Linguistic Scale ...
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Assessment of the Relevance of a Breast Cancer Rehabilitation Program based on a Neutrosophic Linguistic Scale ...
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Assessment of the Relevance of a Breast Cancer Rehabilitation Program based on a Neutrosophic Linguistic Scale ...
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Assessment of the Relevance of a Breast Cancer Rehabilitation Program based on a Neutrosophic Linguistic Scale ...
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Incorporating the Breast Imaging Reporting and Data System Lexicon with a Fully Convolutional Network for Malignancy Detection on Breast Ultrasound
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In: Diagnostics; Volume 12; Issue 1; Pages: 66 (2021)
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Assessing PD-L1 Expression Status Using Radiomic Features from Contrast-Enhanced Breast MRI in Breast Cancer Patients: Initial Results
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In: Cancers; Volume 13; Issue 24; Pages: 6273 (2021)
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Abstract:
The purpose of this retrospective study was to assess whether radiomics analysis coupled with machine learning (ML) based on standard-of-care dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict PD-L1 expression status in patients with triple negative breast cancer, and to compare the performance of this approach with radiologist review. Patients with biopsy-proven triple negative breast cancer who underwent pre-treatment breast MRI and whose PD-L1 status was available were included. Following 3D tumor segmentation and extraction of radiomic features, radiomic features with significant differences between PD-L1+ and PD-L1− patients were determined, and a final predictive model to predict PD-L1 status was developed using a coarse decision tree and five-fold cross-validation. Separately, all lesions were qualitatively assessed by two radiologists independently according to the BI-RADS lexicon. Of 62 women (mean age 47, range 31–81), 27 had PD-L1− tumors and 35 had PD-L1+ tumors. The final radiomics model to predict PD-L1 status utilized three MRI parameters, i.e., variance (FO), run length variance (RLM), and large zone low grey level emphasis (LZLGLE), for a sensitivity of 90.7%, specificity of 85.1%, and diagnostic accuracy of 88.2%. There were no significant associations between qualitative assessed DCE-MRI imaging features and PD-L1 status. Thus, radiomics analysis coupled with ML based on standard-of-care DCE-MRI is a promising approach to derive prognostic and predictive information and to select patients who could benefit from anti-PD-1/PD-L1 treatment.
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Keyword:
breast cancer; magnetic resonance imaging; PD-L1; radiomics
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URL: https://doi.org/10.3390/cancers13246273
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Barriers and Facilitators of Breast Cancer Screening amongst Culturally and Linguistically Diverse Women in South Western Sydney: A Qualitative Explorative Study
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In: International Journal of Environmental Research and Public Health ; Volume 18 ; Issue 17 (2021)
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Culture and breast cancer surgical decisions and experiences
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The effects of lingual frenotomy on breastfeeding: an integrative review
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In: Revista CEFAC, Vol 24, Iss 1 (2021) (2021)
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Characterization of circulating breast cancer cells with tumorigenic and metastatic capacity
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In: ISSN: 1757-4676 ; EISSN: 1757-4684 ; EMBO Molecular Medicine ; https://hal.archives-ouvertes.fr/hal-03602489 ; EMBO Molecular Medicine, Wiley Open Access, 2020, 12 (9), pp.e11908. ⟨10.15252/emmm.201911908⟩ (2020)
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Breast cancer patients' language use across four stages ...
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