Deciphering the Morphology of Tumor-Stromal Features in Invasive Breast Cancer Using Artificial Intelligence

被引：6

作者：

Atallah, Nehal M. ^{[1
,2
]}

Wahab, Noorul ^{[3
]}

Toss, Michael S. ^{[1
,4
]}

Makhlouf, Shorouk ^{[1
,5
]}

Ibrahim, Asmaa Y. ^{[1
,6
]}

Lashen, Ayat G. ^{[1
,2
]}

Ghannam, Suzan ^{[1
,7
]}

Mongan, Nigel P. ^{[8
,9
]}

Jahanifar, Mostafa ^{[3
]}

Graham, Simon ^{[3
]}

Bilal, Mohsin ^{[3
]}

Bhalerao, Abhir ^{[3
]}

Raza, Shan E. Ahmed ^{[3
]}

Snead, David ^{[10
]}

Minhas, Fayyaz ^{[3
]}

Rajpoot, Nasir ^{[3
]}

Rakha, Emad ^{[1
,2
,11
]}

机构：

[1] Univ Nottingham, Sch Med, Acad Unit Translat Med Sci, Nottingham, England

[2] Menoufia Univ, Fac Med, Dept Pathol, Shibin Al Kawm, Egypt

[3] Univ Warwick, Tissue Image Analyt Ctr, Coventry, England

[4] Sheffield Teaching Hosp NHS Fdn Trust, Royal Hallamshire Hosp, Histopathol Dept, Sheffield, England

[5] Assiut Univ, Fac Med, Dept Pathol, Assiut, Egypt

[6] Suez Canal Univ, Fac Med, Dept Pathol, Ismailia, Egypt

[7] Suez Canal Univ, Fac Med, Dept Histol & Cell Biol, Ismailia, Egypt

[8] Univ Nottingham, Biodiscovery Inst, Sch Vet Med & Sci, Loughborough, England

[9] Weill Cornell Med, Dept Pharmacol, New York, NY USA

[10] Univ Hosp Coventry & Warwickshire NHS Trust, Cellular Pathol, Coventry, England

[11] Hamad Med Corp, Pathol Dept, Doha, Qatar

来源：

MODERN PATHOLOGY | 2023年 / 36卷 / 10期

基金：

英国科研创新办公室;

关键词：

artificial intelligence; tumor-associated stroma; stroma-to-tumor ratio; ER-positive breast cancer; PROGNOSTIC-SIGNIFICANCE; RATIO; VALIDATION; CARCINOMA; SURVIVAL; CELLS;

D O I：

10.1016/j.modpat.2023.100254

中图分类号：

R36 [病理学];

学科分类号：

100104 ;

摘要：

Tumor-associated stroma in breast cancer (BC) is complex and exhibits a high degree of heterogeneity. To date, no standardized assessment method has been established. Artificial intelligence (AI) could provide an objective morphologic assessment of tumors and stroma, with the potential to identify new features not discernible by visual microscopy. In this study, we used AI to assess the clinical signifi-cance of (1) stroma-to-tumor ratio (S:TR) and (2) the spatial arrangement of stromal cells, tumor cell density, and tumor burden in BC. Whole-slide images of a large cohort (n = 1968) of well -characterized luminal BC cases were examined. Region and cell-level annotation was performed, and supervised deep learning models were applied for automated quantification of tumor and stromal features. S:TR was calculated in terms of surface area and cell count ratio, and the S:TR heterogeneity and spatial distribution were also assessed. Tumor cell density and tumor size were used to estimate tumor burden. Cases were divided into discovery (n = 1027) and test (n = 941) sets for validation of the findings. In the whole cohort, the stroma-to-tumor mean surface area ratio was 0.74, and stromal cell density heterogeneity score was high (0.7/1). BC with high S:TR showed features characteristic of good prognosis and longer patient survival in both the discovery and test sets. Heterogeneous spatial distribution of S:TR areas was predictive of worse outcome. Higher tumor burden was associated with aggressive tumor behavior and shorter survival and was an independent predictor of worse outcome (BC-specific survival; hazard ratio: 1.7, P = .03, 95% CI, 1.04-2.83 and distant metastasis-free survival; hazard ratio: 1.64, P =.04, 95% CI, 1.01-2.62) superior to absolute tumor size. The study concludes that AI provides a tool to assess major and subtle morphologic stromal features in BC with prognostic implications. Tumor burden is more prognostically informative than tumor size.& COPY; 2023 THE AUTHORS. Published by Elsevier Inc. on behalf of the United States & Canadian Academy of Pathology. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/).

引用

页数：12