MiThyCA: A Computational Pathology Pipeline for the Identification of Microscopic Foci of Papillary Thyroid Carcinoma-Like Nuclear Features with AI in Whole-Slide Histological Images

The histological identification of papillary thyroid carcinoma (PTC) is straightforward for experienced endocrine pathologists. The increase in radical thyroidectomies led to a raise in the rate of postoperative incidental subcentimeter PTC foci and the recent introduction of the Non-Invasive Follicular Thyroid Neoplasm with Papillary-like Nuclear Features (NIFTP) as a less aggressive mimicker of PTC, which significantly complicated the histology screening of thyroid histology specimens. Artificial Intelligence (AI) applied to Whole Slide Images (WSI) can speed up these processes, aiding pathologists to improve diagnostic accuracy and turnaround times. Here we present a computational pathology pipeline for the identification of Microscopic foci of papillary Thyroid Carcinoma-like nuclear features using Artificial intelligence (MiThyCA). This algorithm relies on a tandem architecture consisting of a Convolutional Neural Network (CNN) designed to identify neoplastic areas within thyroid specimens, and a Vision Transformer (TinyViT) focused on detecting PTC-like areas within the neoplastic regions identified by the first model. The study was conducted on a multi-institutional cohort of 73 WSIs from 67 patients with normal thyroid tissue (n = 22 patients, 33%), NIFTP (n = 19, 28%), PTC (n = 23, 34%), and lymph nodes (n = 3, 5%). Cases were divided into training (n = 40 patients, 41 WSIs), validation (n = 13 patients, 14 WSIs) and test (n = 14 patients, 18 WSIs) sets. Each model singly demonstrated excellent performance at the tile-level on the validation set (accuracy = 0.95 and AUC-ROC = 0.95 for CNN, accuracy = 0.86 and AUC-ROC = 0.84 for TinyViT), with their tandem combination in MiThyCA showing accuracy = 0.85 and F1 score = 0.8 on the validation set at the whole WSI-level. The average total execution time of MiThyCA on the test set WSIs was 51 ± 27 s on average on workstations not equipped with GPU, and up to 16 ± 6 s and 11 ± 4 s per WSI with Nvidia GPU and Apple’s laptop chip, respectively. Worthy of note, WSIs dimension did not significantly impact the algorithm processing time. Given its speed and accessibility, MiThyCA is a promising AI-based computer-aided diagnostic tool for the detection of subcentimeter PTC foci in histology.