{
  "abstract": "Background Patients with resectable colorectal liver metastasis (CRLM) show heterogeneous outcomes after neoadjuvant chemotherapy (NAC) and resection. Overall survival (OS), the gold-standard endpoint for assessing surgical benefit, requires prolonged follow-up, delaying treatment evaluation. Currently, no guideline-recommended prognostic indicators exist for this population, and existing pathology-based surrogates lack standardized quantification and validated cut-offs. Residual viable tumor percentage (RVT%) offers a quantitative, objective, and reproducible alternative. This study developed a deep learning (DL) system to automatically derive RVT% from whole-slide images (WSIs) and evaluated its value as a surrogate OS marker.Methods A total of 511 CRLM patients who underwent resection after NAC were retrospectively included across three cohorts: discovery (n=122, Sun Yat-sen University Cancer Center [SYSUCC]), internal validation (n=136, SYSUCC), and external validation (n=253, Beijing Cancer Hospital). A UNI encoder-based model classified WSIs into six tissue classes. RVT% was defined as tumor area divided by regression bed area, with regression bed defined as regions having a combined proportion of tumor, stroma, lymphocyte, mucus, and debris ≥90% and a hepatocyte proportion ≤50%. The optimal RVT% cut-off (20%) was determined using survival analysis. Prognostic surrogate value was assessed using Kaplan-Meier and Cox regression.Results We developed a DL-based WSI analysis framework that enabled fully automated six-class tissue classification and RVT% quantification as a surrogate endpoint for OS. Using the 20% cut-off optimized, high RVT% was consistently associated with worse OS across all cohorts (discovery: HR 2.32, 95% CI 1.41–3.82, P=0.0009; internal: HR 1.85, 95% CI 1.18–2.90, P=0.0074; external: HR 2.79, 95% CI 1.47–5.29, P=0.0017). After adjustment for Clinical Risk Score, RVT% remained an independent surrogate predictor of OS in all cohorts (all P<0.001), supporting the robustness and cross-cohort generalizability of this artificial intelligence-driven biomarker.Conclusions This multicenter study established a DL-based framework for automated RVT% quantification and validated RVT% as a robust, independent surrogate prognostic biomarker for resectable CRLM after NAC. By enabling objective, reproducible, and early prognostic stratification, this approach supports more fine-grained pathological response assessment, improved risk stratification, and may inform risk-adaptive trial design, accelerate treatment evaluation, guide subsequent clinical decision-making, and help optimize follow-up intensity.",
  "authors": [
    {
      "affiliations": [
        "Department of Surgery, The Chinese University of Hong Kong, Hong Kong"
      ],
      "name": "Xin Wang"
    },
    {
      "affiliations": [
        "Department of Surgery, The Chinese University of Hong Kong, Hong Kong"
      ],
      "name": "Lingli He"
    },
    {
      "affiliations": [
        "State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China"
      ],
      "name": "Jianli Duan"
    },
    {
      "affiliations": [
        "Department of Surgery, The Chinese University of Hong Kong, Hong Kong"
      ],
      "name": "Hang Jiang"
    },
    {
      "affiliations": [
        "State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China"
      ],
      "name": "Shaoyan Xi"
    },
    {
      "affiliations": [
        "Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of HepatoBiliaryPancreatic Surgery I, Peking University Cancer Hospital and Institute, Beijing, China"
      ],
      "name": "Baocai Xing"
    },
    {
      "affiliations": [
        "State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China"
      ],
      "name": "Yuhong Li"
    }
  ],
  "title": "IDDF2026-ABS-0185 Deep learning-based residual viable tumor percentage (RVT%) as a surrogate for prognosis in resectable colorectal liver metastases after neoadjuvant chemotherapy: a multicenter study",
  "uid": "b8debbaf-d97c-5db9-b9f7-9110dd2bcb15"
}