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The Dynamic Changes of COL11A1 Expression During the Carcinogenesis and Development of Breast Cancer and as a Candidate Diagnostic and Prognostic Marker.

Breast J

January 2025

Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin 300052, China.

Collagen type XI alpha 1 (COL11A1), a critical member of the collagen superfamily, is essential for tissue structure and integrity. This study aimed to validate previously identified variations in COL11A1 expression during breast cancer carcinogenesis and progression, as well as elucidate their clinical implications. COL11A1 mRNA expression levels were assessed using real-time reverse transcription-PCR (RT-PCR) in 30 pairs of normal breast tissue and primary breast cancer, 30 pairs of primary breast cancer and lymph node metastases, 30 benign tumors, and 107 primary breast cancers.

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Objective: Phyllodes tumor (PT) is a variant of fibroepithelial proliferations of the breast, histologically demonstrating a leaf-like pattern. The WHO has categorized PTs as benign, borderline, or malignant based on their histological characteristics. The objective of this paper is to assess the clinicopathological factors with malignancy in PT of the breast.

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Aim: Superb microvascular imaging (SMI) is a new ultrasound tool that can detect small blood vessels while cancelling out artefacts. It may be useful in detecting the vascularity associated with malignant breast lesions. This study evaluated the reproducibility and diagnostic performance of SMI's Vascular Index (VI) in differentiating benign from malignant solid breast lesions.

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Phyllodes tumours of the breast are rare fibroepithelial neoplasms classified histologically into benign, borderline, or malignant; each requiring different treatment strategies. The infrequency of presentation can result in diagnostic and management variability. The aim is to provide evidence-based or expert consensus recommendations for multidisciplinary teams managing patients with phyllodes tumours.

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Background And Objective: Accurate identification and prioritization of driver-mutations in cancer is critical for effective patient management. Despite the presence of numerous bioinformatic algorithms for estimating mutation pathogenicity, there is significant variation in their assessments. This inconsistency is evident even for well-established cancer driver mutations.

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