A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models.

Spitzoid tumors (ST) are a group of melanocytic tumors of high diagnostic complexity. Since 1948, when Sophie Spitz first described them, the diagnostic uncertainty remains until now, especially in the intermediate category known as Spitz tumor of unknown malignant potential (STUMP) or atypical Spitz tumor. Studies developing deep learning (DL) models to diagnose melanocytic tumors using whole slide imaging (WSI) are scarce, and few used ST for analysis, excluding STUMP. To address this gap, we introduce SOPHIE: the first ST dataset with WSIs, including labels as benign, malignant, and atypical tumors, along with the clinical information of each patient. Additionally, we explain two DL models implemented as validation examples using this database.

Renal Histologic Findings in Necropsies of Type 2 Diabetes Mellitus Patients.

Background: Very few studies have analyzed early histologic lesions of diabetic nephropathy (DN) in patients without signs of clinical involvement (microalbuminuria). In this study, we analyzed renal histologic lesions in necropsies of diabetic patients with or without previous signs of DN. Methods: Histological material was analyzed from 21 autopsies of type 2 diabetes mellitus (T2DM) patients (9 with albuminuria and 12 without albuminuria) and 4 controls. Histologic lesions were evaluated according to the Tervaert classification. Results: Kidneys of diabetic patients presented significantly higher scores in most histologic indices analyzed (glomerular basal membrane thickening, mild and severe mesangial expansion, nodular sclerosis, interstitial fibrosis, and tubular atrophy) than in nondiabetic controls (p < 0.01 in all cases). In contrast, no significant differences were detected between histologic scores when comparing the 21 diabetic patients with and without albuminuria. A significant percentage of cases without albuminuria showed moderate to severe histologic lesions, particularly severe mesangial expansion and severe glomerular vascular lesions. No significant differences were found in age, blood pressure, diabetes vintage, BMI, HbA1c, cholesterol, triglycerides, or treatments between the two (albuminuric vs. nonalbuminuric) T2DM patient groups. Conclusions: Our data suggest that histologic lesions of DN are present in the early stages of the disease, even without albuminuria presence. More precise and earlier metabolic control is recommended in T2DM, and monitoring of risk factors can play a role in DN development.

An attention-based weakly supervised framework for spitzoid melanocytic lesion diagnosis in whole slide images.

Melanoma is an aggressive neoplasm responsible for the majority of deaths from skin cancer. Specifically, spitzoid melanocytic tumors are one of the most challenging melanocytic lesions due to their ambiguous morphological features. The gold standard for its diagnosis and prognosis is the analysis of skin biopsies. In this process, dermatopathologists visualize skin histology slides under a microscope, in a highly time-consuming and subjective task. In the last years, computer-aided diagnosis (CAD) systems have emerged as a promising tool that could support pathologists in daily clinical practice. Nevertheless, no automatic CAD systems have yet been proposed for the analysis of spitzoid lesions. Regarding common melanoma, no system allows both the selection of the tumor region and the prediction of the benign or malignant form in the diagnosis. Motivated by this, we propose a novel end-to-end weakly supervised deep learning model, based on inductive transfer learning with an improved convolutional neural network (CNN) to refine the embedding features of the latent space. The framework is composed of a source model in charge of finding the tumor patch-level patterns, and a target model focuses on the specific diagnosis of a biopsy. The latter retrains the backbone of the source model through a multiple instance learning workflow to obtain the biopsy-level scoring. To evaluate the performance of the proposed methods, we performed extensive experiments on a private skin database with spitzoid lesions. Test results achieved an accuracy of 0.9231 and 0.80 for the source and the target models, respectively. In addition, the heat map findings are directly in line with the clinicians' medical decision and even highlight, in some cases, patterns of interest that were overlooked by the pathologist.