A Novel Digital Score for Abundance of Tumour Infiltrating Lymphocytes Predicts Disease Free Survival in Oral Squamous Cell Carcinoma

被引:122
作者
Shaban, Muhammad [1 ]
Khurram, Syed Ali [2 ]
Fraz, Muhammad Moazam [1 ,3 ,4 ]
Alsubaie, Najah [1 ,5 ]
Masood, Iqra [6 ]
Mushtaq, Sajid [6 ]
Hassan, Mariam [6 ]
Loya, Asif [6 ]
Rajpoot, Nasir M. [1 ,4 ,7 ]
机构
[1] Univ Warwick, Dept Comp Sci, Coventry CV4 7AL, W Midlands, England
[2] Univ Sheffield, Sch Clin Dent, Sheffield, S Yorkshire, England
[3] Natl Univ Sci & Technol, Sch Elect Engn & Comp Sci, H-12, Islamabad, Pakistan
[4] Alan Turing Inst, London NW1 2DB, England
[5] Princess Nourah Univ, Dept Comp Sci, Riyadh, Saudi Arabia
[6] Shaukat Khanum Mem Canc Hosp Res Ctr, Lahore, Pakistan
[7] Univ Hosp Coventry, Dept Pathol, Coventry, W Midlands, England
基金
英国工程与自然科学研究理事会;
关键词
DEEP; HETEROGENEITY; SEGMENTATION; SUBSITE; NUCLEI; CAVITY;
D O I
10.1038/s41598-019-49710-z
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Oral squamous cell carcinoma (OSCC) is the most common type of head and neck (H&N) cancers with an increasing worldwide incidence and a worsening prognosis. The abundance of tumour infiltrating lymphocytes (TILs) has been shown to be a key prognostic indicator in a range of cancers with emerging evidence of its role in OSCC progression and treatment response. However, the current methods of TIL analysis are subjective and open to variability in interpretation. An automated method for quantification of TIL abundance has the potential to facilitate better stratification and prognostication of oral cancer patients. We propose a novel method for objective quantification of TIL abundance in OSCC histology images. 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.
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页数:13
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