Comparative Evaluation of Breast Ductal Carcinoma Grading: A Deep-Learning Model and General Pathologists' Assessment Approach.

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.

Unique Growth Pattern Presentation of a Papillary Renal Cell Carcinoma.

Papillary renal cell carcinoma (PRCC) is defined by the WHO 2022 classification as a malignant tumor derived from the renal tubular epithelium. However, the WHO 2016 classification subdivided PRCC into two types, with type 1 PRCC showing papillae covered by a single layer of neoplastic cells, and type II PRCC, which can show multiple types of histologies and is more aggressive. The WHO 2022 classification eliminated the subcategorization of PRCC. Here, we present a histopathological case study with a 4-year follow-up diagnosed in 2018 as type I PRCC (WHO 2016) with intra-pyelocalyceal growth pattern in a 59-year-old male patient with a history of Type II diabetes mellitus, left-sided renal-ureteral lithiasis, and benign hypertrophy of the prostate. Microscopically the tumor was composed of small cuboidal cells with inconspicuous nucleoli, arranged on a single layer of tubulo-papillary cores, and scant, foamy macrophages. The tumor had a non-infiltrative, expansive pyelocalyceal growth pattern. Immunohistochemically (IHC), the tumor cells were CK7-intense and diffusely positive, and stained granular for AMACR. Next-generation sequencing (NGS) was performed for the tumor and the normal adjacent tissue for in-depth pathological characterization. To our knowledge, this is the first reported case where a PRCC displays this unique intra-pyelocalyceal growth pattern, mimicking a urothelial cell carcinoma of the renal pelvis system.

Analysis of Tumor Depth Invasion With Anti-Smoothelin Antibody in Equivocal Transurethral Resection of Urinary Bladder Tumor Surgical Specimens.

OBJECTIVE: To examine the expression and value of the smoothelin marker in control cases, to standardize the working method, and to analyze its application in pathologic staging process of problematic transurethral resection of bladder tumor (TURBT) cases. MATERIAL AND METHODS: Immunohistochemical (IHC) staining was performed on tumor-free bladder wall sections, tumor-free large bowel sections, TURBTs with unequivocal tumor stage, and TURBTs with equivocal stage. The IHC staining of muscularis mucosa (MM), muscularis propria (MP), and blood vessels was evaluated semiquantitatively. RESULTS: Smoothelin IHC staining pattern ranged from negative (30% to 67% cases) to 2+ (0% to 15% cases) in MM and from 1+ (10% to 50% cases) to 3+ (9% to 48% cases) in MP. When compared on the same slide, the smoothelin expression of MP showed a stronger staining intensity than the one of the MM in all the analyzed cases. Blood vessel muscle cells stained in a constant intensity as the MM (r = 0.9808; r = 0.9604). Smoothelin determined restaging of 33% of the problematic TURBT cases. CONCLUSION: Smoothelin is an IHC marker that shows differential staining between coexistent MM and MP; however, variations in staining intensity and pattern may occur, aspects that can be influenced by different technique variables. We recommend using this marker as a diagnostic tool in problematic TURBT cases only when there is sufficient experience in control cases with this antibody.

The natural adaptive evolution of cancer: The metastatic ability of cancer cells.

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.