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From Multi-Omics Prediction to Clinical Workflow: An Interoperable, Bias-Audited, Human-in-the-Loop Decision-Support Framework for Precision Oncology
Subject area: Biological & Medical Sciences · Area of research: Health Systems Development
Abstract
Multi-omics survival models for precision oncology are increasingly capable of producing patient-level risk estimates, subtype projections and molecular explanations. Yet a model that performs adequately in retrospective validation is not automatically ready for clinical use. The translational problem is broader than model architecture: oncology teams must determine whether genomic risk predictions can be exchanged through interoperable health information systems, audited for subgroup bias, interpreted by clinicians, monitored for drift and governed as decision support rather than autonomous diagnosis. This article develops an interoperable, bias-audited and human-in-the-loop decision-support framework for multi-omics precision oncology, using lung adenocarcinoma (LUAD) as the applied case. The empirical basis combines four evidence layers: a TCGA-LUAD and MSK-IMPACT transformer manuscript, a DNA/RNA multi-omics survival thesis, an individualized LUAD patient report, and a reproducible secondary-data design linked to public TCGA/Kaggle and cBioPortal data pathways. The attached research record shows that a ridge RNA+clinical baseline achieved the strongest discrimination (C-index = 0.724), while the full DNA/RNA multi-omics model achieved lower aggregate discrimination (C-index = 0.682) but improved biological interpretability, temporal stability and clinical-decision value. The transformer system achieved moderate internal discrimination and modest external transportability, while still producing meaningful risk ordering and Integrated Gradients explanations. A simulated silent-mode workflow analysis then demonstrates how a standards-based clinical implementation layer can reduce review burden, improve missing-data controls, strengthen override documentation and surface subgroup-specific calibration risk before any prospective deployment. The paper argues that precision-oncology AI should be evaluated not only by C-index, but by an integrated evidence package: discrimination, calibration, decision utility, subgroup fairness, interoperability, clinician usability, audit logging, drift monitoring and accountable human oversight.
Keywords
precision oncology; multi-omics; clinical decision support; FHIR; human-in-the-loop AI; bias audit; lung adenocarcinoma; genomic risk stratification; model governance; Integrated Gradients JEL/MSC/MeSH-style classification: clinical decision support systems; machine learning; genomics; oncology; biomedical informatics; survival analysis; risk assessment
How to cite this paper
@article{1722516,
author = {Cleopas Russell Choga, Manyara Sandra Kasanhayi, Nkosana Mkandla, Marlon Munjoma, Munashe Naphtali Mupa},
title = {From Multi-Omics Prediction to Clinical Workflow: An Interoperable, Bias-Audited, Human-in-the-Loop Decision-Support Framework for Precision Oncology},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {2},
pages = {2741-2757},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722516.pdf},
abstract = {Multi-omics survival models for precision oncology are increasingly capable of producing patient-level risk estimates, subtype projections and molecular explanations. Yet a model that performs adequately in retrospective validation is not automatically ready for clinical use. The translational problem is broader than model architecture: oncology teams must determine whether genomic risk predictions can be exchanged through interoperable health information systems, audited for subgroup bias, interpreted by clinicians, monitored for drift and governed as decision support rather than autonomous diagnosis. This article develops an interoperable, bias-audited and human-in-the-loop decision-support framework for multi-omics precision oncology, using lung adenocarcinoma (LUAD) as the applied case. The empirical basis combines four evidence layers: a TCGA-LUAD and MSK-IMPACT transformer manuscript, a DNA/RNA multi-omics survival thesis, an individualized LUAD patient report, and a reproducible secondary-data design linked to public TCGA/Kaggle and cBioPortal data pathways. The attached research record shows that a ridge RNA+clinical baseline achieved the strongest discrimination (C-index = 0.724), while the full DNA/RNA multi-omics model achieved lower aggregate discrimination (C-index = 0.682) but improved biological interpretability, temporal stability and clinical-decision value. The transformer system achieved moderate internal discrimination and modest external transportability, while still producing meaningful risk ordering and Integrated Gradients explanations. A simulated silent-mode workflow analysis then demonstrates how a standards-based clinical implementation layer can reduce review burden, improve missing-data controls, strengthen override documentation and surface subgroup-specific calibration risk before any prospective deployment. The paper argues that precision-oncology AI should be evaluated not only by C-index, but by an integrated evidence package: discrimination, calibration, decision utility, subgroup fairness, interoperability, clinician usability, audit logging, drift monitoring and accountable human oversight.},
keywords = {precision oncology; multi-omics; clinical decision support; FHIR; human-in-the-loop AI; bias audit; lung adenocarcinoma; genomic risk stratification; model governance; Integrated Gradients JEL/MSC/MeSH-style classification: clinical decision support systems; machine learning; genomics; oncology; biomedical informatics; survival analysis; risk assessment},
month = {August},
}