The presence of tumor-infiltrating lymphocytes (TILs) is associated with a favorable prognosis of pri-mary melanoma (PM). Recently, artificial intelligence (AI)-based approach in digital pathology was proposed for the standardized assessment of TILs on hematoxylin and eosin-stained whole slide images (WSIs). Herein, the study applied a new convolution neural network (CNN) analysis of PM WSIs to automatically assess the infiltration of TILs and extract a TIL score. A CNN was trained and validated in a retrospective cohort of 307 PMs including a training set (237 WSIs, 57,758 patches) and an inde-pendent testing set (70 WSIs, 29,533 patches). An AI-based TIL density index (AI-TIL) was identified after the classification of tumor patches by the presence or absence of TILs. The proposed CNN showed high performance in recognizing TILs in PM WSIs, showing 100% specificity and sensitivity on the testing set. The AI-based TIL index correlated with conventional TIL evaluation and clinical outcome. The AI-TIL index was an independent prognostic marker associated directly with a favorable prognosis. A fully automated and standardized AI-TIL appeared to be superior to conventional methods at differentiating the PM clinical outcome. Further studies are required to develop an easy-to-use tool to assist pathologists to assess TILs in the clinical evaluation of solid tumors. (Am J Pathol 2023, 193: 2099-2110; https://doi.org/10.1016/j.ajpath.2023.08.013)

Ugolini, F., De Logu, F., Iannone, L.F., Brutti, F., Simi, S., Maio, V., et al. (2023). Tumor-Infiltrating Lymphocyte Recognition in Primary Melanoma by Deep Learning Convolutional Neural Network. THE AMERICAN JOURNAL OF PATHOLOGY, 193(12), 2099-2110 [10.1016/j.ajpath.2023.08.013].

Tumor-Infiltrating Lymphocyte Recognition in Primary Melanoma by Deep Learning Convolutional Neural Network

Di Giacomo A.;Miracco C.;
2023-01-01

Abstract

The presence of tumor-infiltrating lymphocytes (TILs) is associated with a favorable prognosis of pri-mary melanoma (PM). Recently, artificial intelligence (AI)-based approach in digital pathology was proposed for the standardized assessment of TILs on hematoxylin and eosin-stained whole slide images (WSIs). Herein, the study applied a new convolution neural network (CNN) analysis of PM WSIs to automatically assess the infiltration of TILs and extract a TIL score. A CNN was trained and validated in a retrospective cohort of 307 PMs including a training set (237 WSIs, 57,758 patches) and an inde-pendent testing set (70 WSIs, 29,533 patches). An AI-based TIL density index (AI-TIL) was identified after the classification of tumor patches by the presence or absence of TILs. The proposed CNN showed high performance in recognizing TILs in PM WSIs, showing 100% specificity and sensitivity on the testing set. The AI-based TIL index correlated with conventional TIL evaluation and clinical outcome. The AI-TIL index was an independent prognostic marker associated directly with a favorable prognosis. A fully automated and standardized AI-TIL appeared to be superior to conventional methods at differentiating the PM clinical outcome. Further studies are required to develop an easy-to-use tool to assist pathologists to assess TILs in the clinical evaluation of solid tumors. (Am J Pathol 2023, 193: 2099-2110; https://doi.org/10.1016/j.ajpath.2023.08.013)
2023
Ugolini, F., De Logu, F., Iannone, L.F., Brutti, F., Simi, S., Maio, V., et al. (2023). Tumor-Infiltrating Lymphocyte Recognition in Primary Melanoma by Deep Learning Convolutional Neural Network. THE AMERICAN JOURNAL OF PATHOLOGY, 193(12), 2099-2110 [10.1016/j.ajpath.2023.08.013].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1266234