METHODS: This work proposes a convolutional neural network (CNN) model for classifying IDC and metastatic breast cancer. The study utilized a large-scale dataset of microscopic histopathological images to automatically perceive a hierarchical manner of learning and understanding.
RESULTS: It is evident that using machine learning techniques significantly (15%-25%) boost the effectiveness of determining cancer vulnerability, malignancy, and demise. The results demonstrate an excellent performance ensuring an average of 95% accuracy in classifying metastatic cells against benign ones and 89% accuracy was obtained in terms of detecting IDC.
CONCLUSIONS: The results suggest that the proposed model improves classification accuracy. Therefore, it could be applied effectively in classifying IDC and metastatic cancer in comparison to other state-of-the-art models.
METHODS: In the present studies, the consequence of PPARβ/δ inhibition either by global genetic deletion or by a specific PPARβ/δ antagonist, 10h, on malignant transformation of melanoma cells and melanoma metastasis was examined using both in vitro and in vivo models.
RESULTS: Our study showed that 10h promotes epithelial-mesenchymal transition (EMT), migration, adhesion, invasion and trans-endothelial migration of mouse melanoma B16/F10 cells. We further demonstrated an increased tumour cell extravasation in the lungs of wild-type mice subjected to 10h treatment and in Pparβ/δ-/- mice in an experimental mouse model of blood-borne pulmonary metastasis by tail vein injection. This observation was further supported by an increased tumour burden in the lungs of Pparβ/δ-/- mice as demonstrated in the same animal model.
CONCLUSION: These results indicated a protective role of PPARβ/δ in melanoma progression and metastasis.
CASE REPORT: Herein, we describe and compare three cases of CASTLE, including a case with distant metastasis despite administering postoperative chemotherapy. Thus, the mechanisms underlying metastasis of CASTLE are unclear. This case study helps to elucidate the histopathological risk factors of metastasis in CASTLE.
DISCUSSION: We found that prominent lymphovascular invasion and higher proliferative activities might be risk factors of metastasis in CASTLE. In addition, we have summarised the cytological, morphological, and immunohistochemical features of CASTLE for an accurate diagnosis.