Abstract
Tumour infiltrating lymphocytes (TIL) influence the prognosis of Ductal carcinoma in situ (DCIS). Currently, manual assessment of TIL by expert pathologists is considered a gold standard. However, there are issues with a shortage of expert pathologists and inter-observer variability. A reliable automated scoring method is yet to be developed due to the inherent complexity of DCIS duct morphology and the assessment strategy. We developed a new deep learning and spatial analysis pipeline to automatically score DCIS stromal TIL (AI-TIL) from 243 diagnostic haematoxylin and eosin-stained whole slide images from 127 patients. To automatically identify and segment DCIS ducts, we implemented a generative adversarial network. To identify lymphocytes, we used a pre-trained deep learning model. Our DCIS segmentation model achieved a dice overlap of 0.94 (\({\pm }0.01\)) and the cell classifier model achieved 92% accuracy compared to pathologists’ annotations. Subsequently, we automatically delineated a stromal boundary and computed the percentage of the boundary area occupied by lymphocytes for each DCIS duct. Finally, we computed TIL score as the average of all duct level scores within the slide. We observe a higher correlation between AI-TIL and pathologists (average) score for wider stomal boundaries (r = 0.66, p = \(6.0 \times 10^{-7}\), W = 0.3 mm) compared with smaller boundary (r = 0.23, p = 0.12, W = 0.03 mm). Using multivariate analysis, a low AI-TIL score was associated with an increased risk of recurrence independent of age, grade, estrogen receptor (ER) status, progesterone receptor (PR) status, and necrosis (hazard ratio = 0.14, 95% CI 0.038–0.51, p = 0.003, W = 0.03 mm). These results suggest that our pipeline could be used to automatically quantify stromal TIL in DCIS and integrating AI-TIL with pathologists’ visual assessment may improve DCIS recurrence risk estimation.
Y. B. Hagos and F. Sobhani—Equally contributed.
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Acknowledgement
We are grateful for the funding support to the TBCRC from The Breast Cancer Research Foundation and Susan G. Komen. We also recognize the contributions of the investigators who participated in TBCRC 038, including Shi Wei, Angela DeMichele, Tari King, Priscilla McAuliffe, Julie Nangia, Joanna Lee, Jennifer Tseng, Anna Maria Storniolo, Alastair M Thompson, Gaorav P Gupta, Antonio Wolff, and Ian Krop.
Y.H.B received funding from European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie (No766030). Y.Y. acknowledges funding from Cancer Research UK Career Establishment Award (C45982/A21808), Breast Cancer Now (2015NovPR638), Children’s Cancer and Leukaemia Group (CCLGA201906), NIH U54 CA217376 and R01 CA185138, CDMRP Breast Cancer Research Program Award BC132057, CRUK Brain Tumour Awards (TARGET-GBM), European Commission ITN (H2020-MSCA-ITN-2019), Wellcome Trust (105104/Z/14/Z), and The Royal Marsden/ICR National Institute of Health Research Biomedical Research Centre. ESH received funding from the DoD (BC132057) and the NIH (1U2CCA233254-01, R01 CA185138-01) as well as from the Breast Cancer Research Foundation (BCRF 19-074).
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Hagos, Y.B. et al. (2022). DCIS AI-TIL: Ductal Carcinoma In Situ Tumour Infiltrating Lymphocyte Scoring Using Artificial Intelligence. In: Kakileti, S.T., et al. Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Biomarker Discovery. MIABID AIIIMA 2022 2022. Lecture Notes in Computer Science, vol 13602. Springer, Cham. https://doi.org/10.1007/978-3-031-19660-7_16
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