We systematically reviewed prognostic models for recurrence after curative-intent locoregional treatment of colorectal liver metastases (CRLM) and quantitatively synthesized prognostic factors associated with recurrence-free survival (RFS). From 2,208 records across 26 years of literature, 293 studies were included, encompassing 85,150 patients in development cohorts and 5,549 patients in validation cohorts. Most models targeted risk stratification rather than clinically actionable prediction, and Cox proportional hazards regression remained the dominant modelling approach despite increasing use of artificial intelligence and machine learning terminology. External validation was uncommon (33/293 studies), performance reporting was heterogeneous, and overall methodological quality was limited, restricting cross-study comparability and clinical translation. Imaging-based modelling represented a small subset (19 studies; 23 RFS models), generally characterized by small cohorts and predominantly conventional regression-based pipelines. Meta-analysis identified several clinicopathological, molecular, and treatment-related predictors consistently associated with recurrence, including primary lymph node positivity (HR 1.58), multiple liver metastases (HR 1.49), positive resection margins (HR 1.73), postoperative carcinoembryonic antigen >5ng/mL (HR 2.79), poor response to neoadjuvant therapy according to RECIST criteria (HR 2.76), and postoperative circulating tumor DNA positivity (HR 4.78), the strongest prognostic factor identified. Adjuvant and peri-operative systemic therapies were associated with lower recurrence risk. The study showed that current prognostic models incompletely capture the biological heterogeneity underlying CRLM recurrence. Dynamic biomarkers and treatment-response indicators may support future multimodal prognostic frameworks and improve risk stratification following curative-intent treatment.
L. Manganaro, Tommaso Russo, L. Novello et al.· Critical reviews in oncology...· 0 citations
Background The AI-HOPE Lung Cancer study is a multicenter initiative designed to integrate artificial intelligence (AI) and real-world data to improve outcome prediction in patients with metastatic non-small-cell lung cancer treated with first-line immunotherapy-based regimens. AI-HOPE aims to leverage machine learning (ML) models to generate individualized predictions of progression-free survival (PFS), overall survival (OS), and treatment-related toxicity in a broad, unselected population. Materials and methods Clinical and imaging data are harmonized and stored within a privacy-compliant infrastructure (San Raffaele Ai CEnter [S-RACE] platform), promoting FAIR (Findable, Accessible, Interoperable and Reusable) data principles and minimizing manual workload. The primary objective is the development of time-to-event models for PFS and OS. Complementary binary classification models will explore early progression, long-term survival, and clinically relevant toxicities. Results The study includes retrospective (from 2017) and prospective (until 2027) phases across 21 European centers. So far, 920 patients have been recruited for the study, of whom 621 have baseline imaging scans available for centralized analysis. In the AI-HOPE study, a flexible methodological approach integrates multiple ML models tailored to specific clinical questions, complemented by explainable AI tools. Multimodal models combining clinical variables with computed tomography and [18F]2-fluoro-2-deoxy-d-glucose–positron emission tomography imaging features (when available) are supported through the S-RACE platform, which provides a partially automated imaging analysis workflow. Conclusions By combining structured clinical variables and multimodal imaging data, the AI-HOPE Lung Cancer study aims to support refined risk stratification and treatment personalization, ultimately facilitating the responsible integration of AI into routine thoracic oncology practice.
F. Ogliari, M. Ferrara, J. Huijs et al.· ESMO real world data and dig...· 0 citations
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