The most common histological type of urinary bladder cancer is urothelial carcinoma (UC). In contrast, the clear cell variant of urothelial carcinoma (CCUC) is quite a rare neoplasm. In this study, we report a case of an 81-year-old male, presenting with gross hematuria and acute urinary retention, which was subsequently diagnosed with CCUC at our pathology department. Furthermore, we provide a short systematic review of the literature (PubMed, Scopus, Science Citation Index) for this rare histopathological entity and a brief discussion about its morphological and immunohistochemical (IHC) characteristics.
Colorectal cancer (CRC) remains a major public health burden worldwide, despite increased knowledge on its pathogenesis and advances in therapy. We aimed to evaluate a new histological grading system based on poorly differentiated clusters (PDCs) counting -the PDCs grade (PDCs-G), and its clinicopathological and prognostic significance, compared to the World Health Organisation (WHO) grading system (WHO grade). We reviewed 71 surgical resection specimens for CRC from the Emergency County Hospital "Pius Brînzeu" Timisoara. The cases were graded using the WHO grade and the PDCs-G, with further analysis of their association with the other recognised prognostic parameters. Using the WHO grade, 9% of the analysed cases were G1, 80% G2, 11% G3, and none of the tumours was graded G4, while in the PDCs-G 16% were G1, 45% G2, and 39% G3. In multivariate analysis PDCs-G was significantly associated with the American Joint Committee on Cancer stage of the disease (AJCC stage) (p = 0.0003), depth of invasion (pT) (p = 0.0084), nodal status (LNM) (p < 0.0001), lymphovascular invasion (LVI) (p < 0.0001), perineural invasion (PNI) (p < 0.0052), and tumour border configuration (p < 0.0001). The novel grading system based on PDCs counting is an additional histological tool in the evaluation of CRC and a promising new prognostic factor for these patients.
The ability of cancer to adapt renders it one of the most challenging pathologies of all time. It is the most dreaded pathological entity because of its capacity to metastasize to distant sites in the body, and 90% of all cancer-related deaths recorded to date are attributed to metastasis. Currently, three main theories have been proposed to explain the metastatic pathway of cancer: the epithelial–mesenchymal transition (EMT) and mesenchymal–epithelial transition (MET) hypothesis (1), the cancer stem cell hypothesis (2), and the macrophage–cancer cell fusion hybrid hypothesis (3). We propose a new hypothesis, i.e., under the effect of particular biochemical and/or physical stressors, cancer cells can undergo nuclear expulsion with subsequent macrophage engulfment and fusion, with the formation of cancer fusion cells (CFCs). The existence of CFCs, if confirmed, would represent a novel metastatic pathway and a shift in the extant dogma of cancer; consequently, new treatment targets would be available for this adaptive pathology.
Breast cancer is the most prevalent neoplasia among women, with early and accurate diagnosis critical for effective treatment. In clinical practice, however, the subjective nature of histological grading of infiltrating ductal adenocarcinoma of the breast (DAC-NOS) often leads to inconsistencies among pathologists, posing a significant challenge to achieving optimal patient outcomes. Our study aimed to address this reproducibility problem by leveraging artificial intelligence (AI). We trained a deep-learning model using a convolutional neural network-based algorithm (CNN-bA) on 100 whole slide images (WSIs) of DAC-NOS from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset. Our model demonstrated high precision, sensitivity, and F1 score across different grading components in about 17.5 h with 19,000 iterations. However, the agreement between the model’s grading and that of general pathologists varied, showing the highest agreement for the mitotic count score. These findings suggest that AI has the potential to enhance the accuracy and reproducibility of breast cancer grading, warranting further refinement and validation of this approach.
Several alternatives to formalin-stored physical specimens have been described in medical literature, but only a few studies have addressed the issue of learning outcomes when these materials were employed. The aim of this study was to conduct a prospective controlled study to assess student performance in learning anatomic pathology when adding three-dimensional (3D) virtual models as adjunct teaching materials in the study of macroscopic lesions. Third-year medical students (n = 501) enrolled at the Victor Babes University of Medicine and Pharmacy in Timisoara, Romania, were recruited to participate. Student performance was assessed through questionnaires. Students performed worse with new method, with poorer results in terms of overall (mean 77.6% ±SD 11.8% vs. 83.6% ±10.5) and individual question scores (percentage of questions with maximum score 34.6% ±25.6 vs. 47.7 ± 24.6). This decreased performance was generalizable, as it was observed across all language divisions and was independent of the teaching assistant involved in the process. In an open-ended feedback evaluation of the new 3D specimens, most students agreed that the new method was better, bringing arguments both for and against these models. Although subjectively the students found the novel teaching materials to be more helpful, their learning performance decreased. A wider implementation as well as exposure to the technique and use of virtual specimens in medical teaching could improve the students' performance outcome by accommodating the needs for novel teaching materials for digital natives. Anat Sci Educ 15: 115-126.
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