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

Deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data

BACKGROUND

Artificial intelligence (AI), specifically Deep learning (DL), has revolutionized biomedical image analysis, but its efficacy is limited by the need for representative, high-quality large datasets with manual annotations. While latest research on synthetic data using AI-based generative models has shown promising results to tackle this problem, several challenges such as lack of interpretability and need for vast amounts of real data remain. This study aims to introduce a new approach-SYNTA-for the generation of photo-realistic synthetic biomedical image data to address the challenges associated with state-of-the art generative models and DL-based image analysis.

METHODS

The SYNTA method employs a fully parametric approach to create photo-realistic synthetic training datasets tailored to specific biomedical tasks. Its applicability is tested in the context of muscle histopathology and skeletal muscle analysis. This new approach is evaluated for two real-world datasets to validate its applicability to solve complex image analysis tasks on real data.

RESULTS

Here we show that SYNTA enables expert-level segmentation of unseen real-world biomedical data using only synthetic training data. By addressing the lack of representative and high-quality real-world training data, SYNTA achieves robust performance in muscle histopathology image analysis, offering a scalable, controllable and interpretable alternative to generative models such as Generative Adversarial Networks (GANs) or Diffusion Models.

CONCLUSIONS

SYNTA demonstrates great potential to accelerate and improve biomedical image analysis. Its ability to generate high-quality photo-realistic synthetic data reduces reliance on extensive collection of data and manual annotations, paving the way for advancements in histopathology and medical research.

The puzzling Spitz tumours: is artificial intelligence the key to their understanding?

Since their first description in 1948, Spitz tumours remain one of the most challenging diagnostic entities in dermatopathology due to their complex histological features and ambiguous clinical behaviour. In recent years, artificial intelligence (AI) solutions have demonstrated significant potential across a wide range of medical applications, including computational pathology, for decision-making in diagnosis, along with promising advances in prognosis and tumour classification. However, the application of AI to Spitz tumours remains relatively underexplored, with few studies addressing this field. Yet in this evolving technological landscape, could AI provide the insights needed to help resolve the diagnostic uncertainties surrounding Spitz tumours? How could this technology be leveraged to bridge the gap between histopathological uncertainty and clinical accuracy? This review aims to provide an overview of the current state of AI applications in Spitz tumour analysis, identify existing research gaps, and propose future directions to optimize the use of AI in understanding and diagnosing these complex tumours.

Weakly supervised deep learning image analysis can differentiate melanoma from naevi on haematoxylin and eosin-stained histopathology slides

BACKGROUND

The broad histomorphological spectrum of melanocytic pathologies requires large data sets to develop accurate and generalisable deep learning (DL)-based diagnostic pathology classifiers. Weakly supervised DL promotes utilisation of larger training data sets compared to fully supervised (patch annotation) approaches.

OBJECTIVES

To evaluate weakly supervised DL image classifiers for discriminating melanomas from naevi on haematoxylin and eosin (H&E)-stained pathology slides.

METHODS

A representative H&E slide for 260 naevi and 260 melanomas from mucocutaneous sites at one tertiary institution was digitized. Clinicopathological features were recorded for each case including thickness and histological subtype. Whole-slide or whole-tissue section labels were applied. The ground truth was established by consensus diagnosis from two pathologists. Multiple-instance learning models, Trans-MIL, CLAM and DTFD-MIL were evaluated at 10×, 20× and 40× magnifications using stratified fivefold Monte Carlo cross-validation, with 80/10/10 splits for training/validation/test groups, to predict melanoma from naevus. Heatmaps were generated to understand model performance.

RESULTS

Naevi cases were younger (median age: 51 years; melanoma median age: 71.5 years), with more balanced sex distribution (males: 48.8%, melanoma male subgroup: 64.2%). The most frequent histological subtypes of naevi and melanomas were dysplastic compound (n = 99, 38.1%) and superficial spreading (n = 124, 47.7%), respectively. Average AUC (±1 SD) for Trans-MIL, CLAM and DTFD-MIL across test groups were 0.9952 ± 0.006, 0.9925 ± 0.0052 and 0.9708 ± 0.0328, at 20× magnification, respectively. Performance of the models varied according to the magnification used. Heatmaps from the two best performing models, Trans-MIL and CLAM, generally indicated attention on appropriate tissue regions for interpretation.

CONCLUSIONS

Weakly supervised DL on pathological slides of common mucocutaneous melanocytic tumours provides highly accurate diagnostic value for discrimination of melanomas and naevi. External validation and further assessment on less frequently occurring histologic subtypes and borderline cases using this method is required.

Spitz Melanocytic Tumors: A Fascinating 75-Year Journey

Over the last 75 years, our understanding of Spitz lesions has undergone substantial evolution. Initially considered a specific type of melanoma, the perception has shifted towards recognizing Spitz lesions as a spectrum comprising Spitz nevi, Spitz melanocytomas, and Spitz melanomas. Spitz lesions are known for posing a significant diagnostic challenge regarding the distinction between benign neoplasms displaying atypical traits and melanomas. A comprehensive understanding of their molecular basis and genomic aberrations has significantly improved precision in classifying and diagnosing these challenging lesions. The primary aim of this review is to encapsulate the current understanding of the molecular pathogenesis and distinct clinicopathologic characteristics defining this intriguing set of tumors.