Abstract 276: Artificial intelligence-powered analysis of tumor-stroma ratio and fibroblast density as prognostic indicators in colorectal cancer

Abstract Introduction: The tumor microenvironment (TME) plays a critical role in cancer prognosis. In colorectal cancer (CRC), the tumor-stroma ratio (TSR) and cancer-associated fibroblast (CAF) density are emerging as prognostic markers. However, accurately quantifying them remains challenging. Recent studies have shown that artificial intelligence (AI) -powered analyzers can quantify features in the TME including these. Methods: Lunit SCOPE IO, an AI-powered H0. 001 High (N = 173) 0. 32 (0. 19 - 0. 56) 67. 2 CAF density of cancer area (CA) (cutoff: 46. 8) Low (N = 62) Reference 69. 3 0. 056 High (N = 145) 1. 86 (0. 98 - 3. 50) 60. 7 CAF density of cancer stromal area (CS) (cutoff: 1, 901) Low (N = 116) Reference 58. 2 0. 015 High (N = 91) 0. 50 (0. 29 - 0. 88) 69. 9 The ratio of CAF density of CS to CA (CAF ratio) (cutoff: 53. 1) Low (N = 187) Reference 61. 5 0. 043 High (N = 20) 0. 13 (0. 02 - 0. 94) 80. 9 Combination of TSR and CAF ratio Both low (N = 32) Reference 41. 2 0. 001 TSR high, CAF ratio low (N = 155) 0. 32 (0. 19 - 0. 56) 65. 8 TSR low, CAF ratio high (N = 2) 0. 00 (0. 00 - ∞) 83. 4 Both high (N = 18) 0. 06 (0. 01 - 0. 45) 80. 2 Conclusions: In this study, we found a favorable prognosis in the group with higher TSR or higher density of CAF in CS compared to CA. This result suggests that AI-enhanced analysis of TSR and CAF can effectively predict prognosis in CRC, highlighting the potential roles of these markers in cancer prognosis. Citation Format: Minsun Jung, Jun Yong Kim, Mingu Kang, Jinhee Lee, Sanghoon Song, Taebum Lee, Wonkyung Jung, Soo Ick Cho. Artificial intelligence-powered analysis of tumor-stroma ratio and fibroblast density as prognostic indicators in colorectal cancer abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 276.