Utility of artificial intelligence in a binary classification of soft tissue tumors

Abstract

Soft tissue tumors (STTs) pose diagnostic and therapeutic challenges due to their rarity, complexity, and morphological overlap. Accurate differentiation between benign and malignant STTs is important to set treatment directions, however, this task can be difficult. The integration of machine learning and artificial intelligence (AI) models can potentially be helpful in classifying these tumors. The aim of this study was to investigate AI and machine learning tools in the classification of STT into benign and malignant categories. This study consisted of three components: (1) Evaluation of whole-slide images (WSIs) to classify STT into benign and malignant entities. Five specialized soft tissue pathologists from different medical centers independently reviewed 100 WSIs, representing 100 different cases, with limited clinical information and no additional workup. The results showed an overall concordance rate of 70.4% compared to the reference diagnosis. (2) Identification of cell-specific parameters that can distinguish benign and malignant STT. Using an image analysis software (QuPath) and a cohort of 95 cases, several cell-specific parameters were found to be statistically significant, most notably cell count, nucleus/cell area ratio, nucleus hematoxylin density mean, and cell max caliper. (3) Evaluation of machine learning library (Scikit-learn) in differentiating benign and malignant STTs. A total of 195 STT cases (156 cases in the training group and 39 cases in the validation group) achieved approximately 70% sensitivity and specificity, and an AUC of 0.68. Our limited study suggests that the use of WSI and AI in soft tissue pathology has the potential to enhance diagnostic accuracy and identify parameters that can differentiate between benign and malignant STTs. We envision the integration of AI as a supportive tool to augment the pathologists' diagnostic capabilities.

Keywords: Artificial intelligence; Deep learning; Diagnosis; Digital pathology; Sarcoma; Soft tissue tumors; Whole-slide images.

Fig. 1

Representative whole-slide image uploaded to OMERO for annotation of ROI.

Fig. 2

Representative case divided into 768-pixel tiles at the highest layer of resolution. 38 cell-specific parameters were detected using QuPath, which were averaged per tile.

Fig. 3

Expert pathologists’ review (study arm 1). This was performed using one slide only, no ancillary testing, and limited clinical data. Malignant cases had the highest concordance rate (82%), benign cases had the highest major discordance rate (7%), and intermediate cases had the highest minor discordance (28%) and uncertainty rates (22%).