A Pragmatic Machine Learning Approach to Quantify Tumor-Infiltrating Lymphocytes in Whole Slide Images.
Nikita ShvetsovMorten GrønnesbyEdvard PedersenKajsa MøllersenLill-Tove Rasmussen BusundRuth SchwienbacherLars Ailo BongoThomas Karsten KilvaerPublished in: Cancers (2022)
Increased levels of tumor-infiltrating lymphocytes (TILs) indicate favorable outcomes in many types of cancer. The manual quantification of immune cells is inaccurate and time-consuming for pathologists. Our aim is to leverage a computational solution to automatically quantify TILs in standard diagnostic hematoxylin and eosin-stained sections (H&E slides) from lung cancer patients. Our approach is to transfer an open-source machine learning method for the segmentation and classification of nuclei in H&E slides trained on public data to TIL quantification without manual labeling of the data. Our results show that the resulting TIL quantification correlates to the patient prognosis and compares favorably to the current state-of-the-art method for immune cell detection in non-small cell lung cancer (current standard CD8 cells in DAB-stained TMAs HR 0.34, 95% CI 0.17-0.68 vs. TILs in HE WSIs: HoVer-Net PanNuke Aug Model HR 0.30, 95% CI 0.15-0.60 and HoVer-Net MoNuSAC Aug model HR 0.27, 95% CI 0.14-0.53). Our approach bridges the gap between machine learning research, translational clinical research and clinical implementation. However, further validation is warranted before implementation in a clinical setting.
Keyphrases
- machine learning
- deep learning
- big data
- artificial intelligence
- healthcare
- primary care
- convolutional neural network
- electronic health record
- induced apoptosis
- peripheral blood
- randomized controlled trial
- case report
- squamous cell carcinoma
- papillary thyroid
- emergency department
- metabolic syndrome
- clinical trial
- optical coherence tomography
- skeletal muscle
- study protocol
- resistance training
- signaling pathway
- adipose tissue
- cell death
- label free
- oxidative stress
- endoplasmic reticulum stress
- squamous cell
- cell proliferation
- lymph node metastasis
- weight loss