Publications by authors named "T Mirtti"
Article Synopsis
- Machine learning, particularly deep learning with convolutional neural networks (CNNs), is being used to detect prostate cancer in tissue slides, but sample type differences affect model accuracy.
- Research tested whether CNNs trained on one type of sample (biopsy or radical prostatectomy) could effectively analyze the other type, revealing a significant drop in performance across sample types.
- Results indicated that models performed well on their own sample but poorly on the alternative type, highlighting the need to consider morphological differences in training to improve cancer detection accuracy in clinical settings.*
Article Synopsis
- Prostate cancer treatment resistance is a major challenge, with genomic studies revealing how cancer cells evade therapies, yet the tumor microenvironment's (TME) role remains unclear.
- A study using advanced techniques on samples from 120 patients offers a detailed transcriptomic profile of the prostate TME throughout the treatment process.
- The research highlights a unique cell type called club-like cells that interact with the immune system, suggesting their involvement in inflammation and resistance to androgen deprivation therapy, indicating they could be potential targets for new treatments.
Background: The transcription factor Signal Transducer and Activator of Transcription 3 (STAT3) plays a role in carcinogenesis and is involved in processes, such as proliferation, differentiation, drug resistance and immunosuppression. STAT3 can be activated by phosphorylation of tyrosine at position 705 (pSTAT3) or serine at 727 (pSTAT3). High expression levels of pSTAT3 are implicated in advanced stages of prostate cancer (PCa) and are known to interact with the androgen receptor signaling pathway.
View Article and Find Full Text PDFClear-cell renal cell carcinoma (ccRCC) is the most common origin of pancreatic metastases (PM). Distinct genomic aberrations, favorable prognosis, and clinical observations on high angiogenesis, and succeeding tyrosine kinase inhibitor (TKI) sensitivity have been reported in PM-ccRCC. However, no functional or single-cell studies have been conducted thus far.
View Article and Find Full Text PDFBackground: The changes in the tumor microenvironment of high-grade serous ovarian carcinomas following neoadjuvant chemotherapy are a complex area of study. Previous research underscores the importance of investigating the immune and collagen components within the tumor microenvironment for prognostic implications.
Methods: In this study, we utilized computational pathology techniques with Hematoxylin and Eosin-stained images to quantitatively characterize the immune and collagen architecture within the tumor microenvironment of patients with high-grade serous ovarian carcinoma.