Analysis of 3D pathology samples using weakly supervised AI

Song, Andrew H.; Williams, Mane; Williamson, Drew F.K.; Chow, Sarah S.L.; Jaume, Guillaume; Gao, Gan; Zhang, Andrew; Chen, Bowen; Baras, Alexander S.; Serafin, Robert; Colling, Richard; Downes, Michelle R.; Farré, Xavier; Humphrey, Peter; Verrill, Clare; True, Lawrence D.; Parwani, Anil V.; Liu, Jonathan T.C.; Mahmood, Faisal

doi:10.1016/j.cell.2024.03.035

Cited by 8 publications

(1 citation statement)

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“…In terms of machine learning applications, light-sheet microscopy has been coupled with algorithms for intensity leveling and digital staining, also known as virtual staining or false-coloring ( Serafin et al, 2020 ). Additionally, it has been integrated with prognostication software based on weakly supervised learning, capable of predicting clinical outcomes in patients with prostate cancer ( Song et al, 2024 ). Recently, light-sheet imaging has been combined with an algorithm for automated vasculature analysis, enabling the quantification of blood vessel trees in various organs after staining with fluorescein isothiocyanate-labeled albumin and optical clearing ( Spangenberg et al, 2023 ).…”

Section: Discussionmentioning

confidence: 99%

ML-driven segmentation of microvascular features during histological examination of tissue-engineered vascular grafts

Danilov,

Laptev,

Klyshnikov

et al. 2024

Front. Bioeng. Biotechnol.

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IntroductionThe development of next-generation tissue-engineered medical devices such as tissue-engineered vascular grafts (TEVGs) is a leading trend in translational medicine. Microscopic examination is an indispensable part of animal experimentation, and histopathological analysis of regenerated tissue is crucial for assessing the outcomes of implanted medical devices. However, the objective quantification of regenerated tissues can be challenging due to their unusual and complex architecture. To address these challenges, research and development of advanced ML-driven tools for performing adequate histological analysis appears to be an extremely promising direction.MethodsWe compiled a dataset of 104 representative whole slide images (WSIs) of TEVGs which were collected after a 6-month implantation into the sheep carotid artery. The histological examination aimed to analyze the patterns of vascular tissue regeneration in TEVGs in situ. Having performed an automated slicing of these WSIs by the Entropy Masker algorithm, we filtered and then manually annotated 1,401 patches to identify 9 histological features: arteriole lumen, arteriole media, arteriole adventitia, venule lumen, venule wall, capillary lumen, capillary wall, immune cells, and nerve trunks. To segment and quantify these features, we rigorously tuned and evaluated the performance of six deep learning models (U-Net, LinkNet, FPN, PSPNet, DeepLabV3, and MA-Net).ResultsAfter rigorous hyperparameter optimization, all six deep learning models achieved mean Dice Similarity Coefficients (DSC) exceeding 0.823. Notably, FPN and PSPNet exhibited the fastest convergence rates. MA-Net stood out with the highest mean DSC of 0.875, demonstrating superior performance in arteriole segmentation. DeepLabV3 performed well in segmenting venous and capillary structures, while FPN exhibited proficiency in identifying immune cells and nerve trunks. An ensemble of these three models attained an average DSC of 0.889, surpassing their individual performances.ConclusionThis study showcases the potential of ML-driven segmentation in the analysis of histological images of tissue-engineered vascular grafts. Through the creation of a unique dataset and the optimization of deep neural network hyperparameters, we developed and validated an ensemble model, establishing an effective tool for detecting key histological features essential for understanding vascular tissue regeneration. These advances herald a significant improvement in ML-assisted workflows for tissue engineering research and development.

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Section: Discussionmentioning

confidence: 99%

ML-driven segmentation of microvascular features during histological examination of tissue-engineered vascular grafts

Danilov,

Laptev,

Klyshnikov

et al. 2024

Front. Bioeng. Biotechnol.

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Precision Cancer Medicine 2.0—Oncology in the postgenomic era

Masina,

Caldas

2024

Molecular Oncology

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Genomic medicine has transformed the lives of patients with cancer by enabling individualised and evidence‐based clinical decision‐making. Despite this progress, the implementation of precision cancer medicine is limited by its dependence on isolated biomarkers. The development of bulk and single‐cell multiomic technologies has revealed the enormous complexity of the cancer ecosystem. Beyond the cancer cell, the tumour microenvironment, macroenvironment and host factors, including the microbiome, profoundly influence the cancer phenotype, and accounting for these enhances the resolution of precision medicine. The advent of robust multiomic profiling and interpretable machine learning algorithms mark the dawn of a new postgenomic era of personalised cancer medicine. In Precision Cancer Medicine 2.0, high‐resolution personalised clinical decision‐making is informed by the comprehensive multiomic profiling of tumour and host, integrated using artificial intelligence.

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