Pathobiology : journal of immunopathology, molecular and cellular biology · 2025
Artificial Intelligence-Driven Quantification of Tumor-Stroma Ratio and Fibroblasts Enables Precise Classification of Stroma Quality and Quantity in Predicting Colorectal Cancer Recurrence.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Jung Minsun, Kim Jun Yong, Jeong Hoein, Valero Puche Aaron, Song Sanghoon, Cho Soo Ick, Jung Minsun
Аннотация
INTRODUCTION: The tumor microenvironment plays a crucial role in the progression and prognosis of colorectal cancer (CRC). Among its components, the tumor-stroma ratio (TSR) and cancer-associated fibroblasts (CAFs) have emerged as significant prognostic markers. However, conventional assessments of TSR and CAF density remain subjective and labor-intensive, limiting their clinical applicability.
METHODS: We utilized an artificial intelligence (AI)-based whole slide image analysis platform, Lunit SCOPE IO, to objectively quantify TSR and CAF density in tissue samples from 207 treatment-naïve patients with stage II and III CRC.
RESULTS: Our analysis demonstrated that both TSR (log-rank p < 0.0001) and CAF density (log-rank p = 0.017) were independently associated with disease-free survival (DFS). These AI-derived markers outperformed conventional prognostic factors. Furthermore, integrating TSR and CAF density with existing high-risk criteria enabled reclassification of additional patients as high risk, enhancing DFS prediction and reducing false-negative rates.
CONCLUSION: AI-powered histopathological quantification of TSR and CAF density improves prognostic accuracy in CRC and offers a promising approach for refining risk stratification. These findings support the integration of AI-based pathology into clinical practice to enhance diagnostic precision and patient management.
METHODS: We utilized an artificial intelligence (AI)-based whole slide image analysis platform, Lunit SCOPE IO, to objectively quantify TSR and CAF density in tissue samples from 207 treatment-naïve patients with stage II and III CRC.
RESULTS: Our analysis demonstrated that both TSR (log-rank p < 0.0001) and CAF density (log-rank p = 0.017) were independently associated with disease-free survival (DFS). These AI-derived markers outperformed conventional prognostic factors. Furthermore, integrating TSR and CAF density with existing high-risk criteria enabled reclassification of additional patients as high risk, enhancing DFS prediction and reducing false-negative rates.
CONCLUSION: AI-powered histopathological quantification of TSR and CAF density improves prognostic accuracy in CRC and offers a promising approach for refining risk stratification. These findings support the integration of AI-based pathology into clinical practice to enhance diagnostic precision and patient management.