Digital Biomarker Identification for Cancer Prognosis
The proposed TIL abundance (TILAb) score is calculated by first segmenting the whole slide images (WSIs) into underlying tissue types (tumour, lymphocytes, etc.) and then quantifying the co-localization of lymphocytes and tumour areas in a novel fashion. We investigate the prognostic significance of TILAb score on digitized WSIs of Hematoxylin and Eosin (H&E) stained slides of OSCC patients. Our deep learning-based tissue segmentation achieves high accuracy of 96.31%, which paves the way for reliable downstream analysis. We show that the TILAb score is a strong prognostic indicator (p = 0.0006) of disease-free survival (DFS) on our OSCC test cohort. The automated TILAb score has a significantly higher prognostic value than the manual TIL score (p = 0.0024). In summary, the proposed TILAb score is a digital biomarker which is based on more accurate classification of tumour and lymphocytic regions, is motivated by the biological definition of TILs as tumour infiltrating lymphocytes, with the added advantages of objective and reproducible quantification.
The proposed TIL abundance (TILAb) score is calculated by first segmenting the whole slide images (WSIs) into underlying tissue types (tumour, lymphocytes, etc.) and then quantifying the co-localization of lymphocytes and tumour areas in a novel fashion. We investigate the prognostic significance of TILAb score on digitized WSIs of Hematoxylin and Eosin (H&E) stained slides of OSCC patients. Our deep learning-based tissue segmentation achieves high accuracy of 96.31%, which paves the way for reliable downstream analysis. We show that the TILAb score is a strong prognostic indicator (p = 0.0006) of disease-free survival (DFS) on our OSCC test cohort. The automated TILAb score has a significantly higher prognostic value than the manual TIL score (p = 0.0024). In summary, the proposed TILAb score is a digital biomarker which is based on more accurate classification of tumour and lymphocytic regions, is motivated by the biological definition of TILs as tumour infiltrating lymphocytes, with the added advantages of objective and reproducible quantification.
Methdology
Collaborators
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Dr Muhammad Moazam FarazDr Muhammad Moazam Faraz
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Prof Nasir M Rajpoot, TIA Lab, University of Warwick, UKProf Nasir M Rajpoot, TIA Lab, University of Warwick, UK
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Dr Syed Ali Khurram, University of Sheffield, UKDr Syed Ali Khurram, University of Sheffield, UK
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Muhammad Shaban, University of Warwick, UKMuhammad Shaban, University of Warwick, UK
Publications
- M. Shaban, S.A. Khurram, M. M. Fraz , N. Alsubaie, I. Masood, S. Mushtaq, M. Hassan, A. Loya & N. M. Rajpoot , “A Novel Digital Score for Abundance of Tumour Infiltrating Lymphocytes Predicts Disease Free Survival in Oral Squamous Cell Carcinoma”, Nature Scientific Reports, Vol. 9 , No. 1, PP. 13341 (2019) , Sep, 2019. IF: 4.525