Artificial Intelligence for the Treatment and Management of Colorectal Liver Metastases.
2/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
환자: colorectal liver metastases (CRLM), whose management requires a multidisciplinary approach encompassing diagnosis, systemic therapy, surgery, and surveillance
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
Despite its promise, clinical translation of AI in CRLM remains limited by the retrospective nature of many studies, challenges with external validation, and limitations in the interpretability of model decisions. Still, AI has the potential to be a transformative tool in the treatment of CRLM by supporting precision, standardization, and personalization across the treatment spectrum.
OpenAlex 토픽 ·
Radiomics and Machine Learning in Medical Imaging
Hepatocellular Carcinoma Treatment and Prognosis
AI in cancer detection
Colorectal cancer (CRC) is the third most commonly diagnosed malignancy worldwide.
APA
Kaitlyn Weyman, Anai N. Kothari (2026). Artificial Intelligence for the Treatment and Management of Colorectal Liver Metastases.. Clinics in colon and rectal surgery, 39(3), 235-241. https://doi.org/10.1055/a-2769-1413
MLA
Kaitlyn Weyman, et al.. "Artificial Intelligence for the Treatment and Management of Colorectal Liver Metastases.." Clinics in colon and rectal surgery, vol. 39, no. 3, 2026, pp. 235-241.
PMID
41948161 ↗
Abstract 한글 요약
Colorectal cancer (CRC) is the third most commonly diagnosed malignancy worldwide. Prognosis is significantly worsened in patients with colorectal liver metastases (CRLM), whose management requires a multidisciplinary approach encompassing diagnosis, systemic therapy, surgery, and surveillance. Artificial intelligence (AI) offers the potential to improve treatment processes and outcomes across all aspects of CRLM care. This review summarizes current and future applications of AI throughout the CRLM treatment continuum. In diagnostics, radiomics-based AI models have demonstrated improved sensitivity in detecting small or ambiguous liver lesions, supporting radiologist interpretation, and improving efficiency. Similarly, AI models are increasingly employed to predict systemic treatment response, using deep learning (DL) to extract imaging-derived features that correlate with genomic and histopathologic profiles relevant to therapy selection. In surgical planning, AI tools can assist in preoperative preparation and optimization by measuring tumor volume and transection planes. Intraoperatively, computer vision and augmented reality are emerging to support tumor localization, margin assessment, and real-time anatomical navigation. Postoperatively, advanced AI models can integrate clinical, radiologic, and molecular data to stratify recurrence risk and inform individualized follow-up strategies. Despite its promise, clinical translation of AI in CRLM remains limited by the retrospective nature of many studies, challenges with external validation, and limitations in the interpretability of model decisions. Still, AI has the potential to be a transformative tool in the treatment of CRLM by supporting precision, standardization, and personalization across the treatment spectrum.
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🏷️ 같은 키워드 · 무료전문 — 이 논문 MeSH/keyword 기반
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