Artificial intelligence assisted genomic profiling of ovarian cancer liver metastases
- 1Department of Hepatobiliary Pancreatic Surgery, Shaoxing Second Hospital , ,
Abstract
Liver involvement in ovarian cancer is often treated as a single clinical entity, although liver-surface implants and parenchymal deposits occupy different anatomical settings and may not share the same molecular history. Primary-tumor profiling is informative, but can miss lesion-specific evolution, treatment-associated cell states, and microenvironmental structure. This Mini Review examines how germline susceptibility, somatic change, spatial biology, and artificial intelligence might be combined in high-grade serous ovarian cancer. The central task is not simply outcome prediction. It is to distinguish constitutional from tumor-acquired variation, retain lesion identity and treatment timing, and relate molecular programs to defined spatial compartments. Current data support marked clonal and microenvironmental heterogeneity in ovarian cancer, but direct molecular evidence from hepatic lesions remains sparse. AI-based integration is therefore best viewed as a way to formulate and test reproducible hypotheses, rather than as a validated route to select liver-directed therapy. Paired sampling, orthogonal assays, patient-level validation, and decision-specific clinical endpoints will be needed before these models can influence management.
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BibTeX
@article{sun2026artificial,
title = {{Artificial intelligence assisted genomic profiling of ovarian cancer liver metastases}},
author = {Louxun Sun and Zhongshan Li and Shiming Chen},
journal = {Frontiers in genetics},
year = {2026},
month = oct,
doi = {10.3389/fgene.2026.2004540},
url = {https://europepmc.org/article/PMC/PMC13727318},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial intelligence assisted genomic profiling of ovarian cancer liver metastases
AU - Louxun Sun
AU - Zhongshan Li
AU - Shiming Chen
PY - 2026
DA - 2026/10/20
JO - Frontiers in genetics
DO - 10.3389/fgene.2026.2004540
UR - https://europepmc.org/article/PMC/PMC13727318
AB - Liver involvement in ovarian cancer is often treated as a single clinical entity, although liver-surface implants and parenchymal deposits occupy different anatomical settings and may not share the same molecular history. Primary-tumor profiling is informative, but can miss lesion-specific evolution, treatment-associated cell states, and microenvironmental structure. This Mini Review examines how germline susceptibility, somatic change, spatial biology, and artificial intelligence might be combined in high-grade serous ovarian cancer. The central task is not simply outcome prediction. It is to distinguish constitutional from tumor-acquired variation, retain lesion identity and treatment timing, and relate molecular programs to defined spatial compartments. Current data support marked clonal and microenvironmental heterogeneity in ovarian cancer, but direct molecular evidence from hepatic lesions remains sparse. AI-based integration is therefore best viewed as a way to formulate and test reproducible hypotheses, rather than as a validated route to select liver-directed therapy. Paired sampling, orthogonal assays, patient-level validation, and decision-specific clinical endpoints will be needed before these models can influence management.
SN - 1664-8021
ER -