Machine Learning Streamlines the Morphometric Characterization and Multiclass Segmentation of Nuclei in Different Follicular Thyroid Lesions: Everything in a NUTSHELL.

The diagnostic assessment of thyroid nodules is hampered by the persistence of uncertainty in borderline cases and further complicated by the inclusion of noninvasive follicular tumor with papillary-like nuclear features (NIFTP) as a less aggressive alternative to papillary thyroid carcinoma (PTC). In this setting, computational methods might facilitate the diagnostic process by unmasking key nuclear characteristics of NIFTP. The main aims of this work were to (1) identify morphometric features of NIFTP and PTC that are interpretable for the human eye and (2) develop a deep learning model for multiclass segmentation as a support tool to reduce diagnostic variability. Our findings confirmed that nuclei in NIFTP and PTC share multiple characteristics, setting them apart from hyperplastic nodules (HP). The morphometric analysis identified 15 features that can be translated into nuclear alterations readily understandable by pathologists, such as a remarkable internuclear homogeneity for HP in contrast to a major complexity in the chromatin texture of NIFTP and to the peculiar pattern of nuclear texture variability of PTC. A few NIFTP cases with available next-generation sequencing data were also analyzed to initially explore the impact of RAS-related mutations on nuclear morphometry. Finally, a pixel-based deep learning model was trained and tested on whole-slide images of NIFTP, PTC, and HP cases. The model, named NUTSHELL (NUclei from Thyroid tumors Segmentation to Highlight Encapsulated Low-malignant Lesions), successfully detected and classified the majority of nuclei in all whole-slide image tiles, showing comparable results with already well-established pathology nuclear scores. NUTSHELL provides an immediate overview of NIFTP areas and can be used to detect microfoci of PTC within extensive glandular samples or identify lymph node metastases. NUTSHELL can be run inside WSInfer with an easy rendering in QuPath, thus facilitating the democratization of digital pathology.

Improving the Annotation Process in Computational Pathology: A Pilot Study with Manual and Semi-automated Approaches on Consumer and Medical Grade Devices.

The development of reliable artificial intelligence (AI) algorithms in pathology often depends on ground truth provided by annotation of whole slide images (WSI), a time-consuming and operator-dependent process. A comparative analysis of different annotation approaches is performed to streamline this process. Two pathologists annotated renal tissue using semi-automated (Segment Anything Model, SAM)) and manual devices (touchpad vs mouse). A comparison was conducted in terms of working time, reproducibility (overlap fraction), and precision (0 to 10 accuracy rated by two expert nephropathologists) among different methods and operators. The impact of different displays on mouse performance was evaluated. Annotations focused on three tissue compartments: tubules (57 annotations), glomeruli (53 annotations), and arteries (58 annotations). The semi-automatic approach was the fastest and had the least inter-observer variability, averaging 13.6 ± 0.2 min with a difference (Δ) of 2%, followed by the mouse (29.9 ± 10.2, Δ = 24%), and the touchpad (47.5 ± 19.6 min, Δ = 45%). The highest reproducibility in tubules and glomeruli was achieved with SAM (overlap values of 1 and 0.99 compared to 0.97 for the mouse and 0.94 and 0.93 for the touchpad), though SAM had lower reproducibility in arteries (overlap value of 0.89 compared to 0.94 for both the mouse and touchpad). No precision differences were observed between operators (p = 0.59). Using non-medical monitors increased annotation times by 6.1%. The future employment of semi-automated and AI-assisted approaches can significantly speed up the annotation process, improving the ground truth for AI tool development.

Galileo-an Artificial Intelligence tool for evaluating pre-implantation kidney biopsies.

BACKGROUND: Pre-transplant procurement biopsy interpretation is challenging, also because of the low number of renal pathology experts. Artificial intelligence (AI) can assist by aiding pathologists with kidney donor biopsy assessment. Herein we present the "Galileo" AI tool, designed specifically to assist the on-call pathologist with interpreting pre-implantation kidney biopsies. METHODS: A multicenter cohort of whole slide images acquired from core-needle and wedge biopsies of the kidney was collected. A deep learning algorithm was trained to detect the main findings evaluated in the pre-implantation setting (normal glomeruli, globally sclerosed glomeruli, ischemic glomeruli, arterioles and arteries). The model obtained on the Aiforia Create platform was validated on an external dataset by three independent pathologists to evaluate the performance of the algorithm. RESULTS: Galileo demonstrated a precision, sensitivity, F1 score and total area error of 81.96%, 94.39%, 87.74%, 2.81% and 74.05%, 71.03%, 72.5%, 2% in the training and validation sets, respectively. Galileo was significantly faster than pathologists, requiring 2 min overall in the validation phase (vs 25, 22 and 31 min by 3 separate human readers, p < 0.001). Galileo-assisted detection of renal structures and quantitative information was directly integrated in the final report. CONCLUSIONS: The Galileo AI-assisted tool shows promise in speeding up pre-implantation kidney biopsy interpretation, as well as in reducing inter-observer variability. This tool may represent a starting point for further improvements based on hard endpoints such as graft survival.