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.

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.