Maurya P, Sirohiya P, Sahoo M, Puri S, Ratre BK, Singh R, Kumar B. Radiomics and anaesthetic planning: Quantitative imaging as a new frontier in preoperative risk assessment. World J Radiol 2026; 18(8): 121065 [DOI: 10.4329/wjr.121065]
Corresponding Author of This Article
Prashant Sirohiya, MD, Assistant Professor, Department of Onco-Anaesthesia and Palliative Medicine, Dr. B.R. Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, Delhi, India. sirohiyaprashant@gmail.com
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Anesthesiology
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review-article
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Maurya P, Sirohiya P, Sahoo M, Puri S, Ratre BK, Singh R, Kumar B. Radiomics and anaesthetic planning: Quantitative imaging as a new frontier in preoperative risk assessment. World J Radiol 2026; 18(8): 121065 [DOI: 10.4329/wjr.121065]
Prateek Maurya, Prashant Sirohiya, Brajesh Kumar Ratre, Department of Onco-Anaesthesia and Palliative Medicine, Dr. B.R. Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi 110029, Delhi, India
Manisha Sahoo, Ram Singh, Department of Anaesthesiology, Pain Medicine and Critical Care, All India Institute of Medical Sciences, New Delhi 110029, Delhi, India
Sidharth Puri, Department of Critical Care Medicine, SGHS Hospital, Mohali 160055, Punjab, India
Balbir Kumar, Department of Onco-Anaesthesia and Palliative Medicine, National Cancer Institute (Jhajjar), All India Institute of Medical Sciences, New Delhi 110029, Delhi, India
Author contributions: Maurya P and Sirohiya P conceptualized and designed the review; Maurya P, Sahoo M and Puri S performed the literature search and data extraction; Maurya P, Ratre BK and Singh R analyzed the literature and drafted the manuscript; Sirohiya P, Singh R and Kumar B critically revised the manuscript for important intellectual content; all authors have read and approved the final version of the manuscript.
AI contribution statement: Paperpal (paid version) was used solely for language polishing and grammar correction during manuscript preparation. No AI tool was used to design the review, generate research data, analyze or interpret the literature, or formulate the conclusions. All AI-assisted text and all references were manually reviewed, verified, and revised by the authors, who take full responsibility for the accuracy, originality, and integrity of the manuscript, in accordance with the recommendations of the International Committee of Medical Journal Editors and the Committee on Publication Ethics.
Conflict-of-interest statement: All authors declare that they have no conflicts of interest related to this manuscript.
Corresponding author: Prashant Sirohiya, MD, Assistant Professor, Department of Onco-Anaesthesia and Palliative Medicine, Dr. B.R. Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, Delhi, India. sirohiyaprashant@gmail.com
Received: March 16, 2026 Revised: June 11, 2026 Accepted: July 28, 2026 Published online: August 28, 2026 Processing time: 166 Days and 4.8 Hours
Abstract
Radiomics-the high-throughput extraction of quantitative features from standard medical images-has transformed oncologic imaging by revealing subvisual patterns linked to tissue biology, yet its role in perioperative medicine remains largely unexplored. This narrative review summarises current evidence linking imaging-derived radiomic biomarkers to perioperative outcomes and proposes a conceptual framework for integrating radiomics into precision anaesthesia. Quantitative assessment of body composition, organ function, and vascular morphology from routine preoperative computed tomography and magnetic resonance imaging can provide objective indicators of physiologic reserve, drug-handling capacity, and recovery potential. Across heterogeneous, largely oncological cohorts, combined radiomic-clinical models have reported higher discrimination (area under the curve 0.84-0.93) than conventional risk scores for selected postoperative complications, and artificial intelligence-based airway assessment has shown sensitivity and specificity exceeding traditional bedside tests; these figures are pooled from methodologically diverse studies rather than single validated estimates. The Image Biomarker Standardisation Initiative has substantially reduced cross-platform feature variability. However, perioperative-specific, prospectively validated evidence remains scarce. Prospective multicentre trials, standardized and automated feature-extraction pipelines, transparent cost and equity appraisal, and integration with electronic health records are critical priorities before radiomics-driven preoperative assessment can enter routine anaesthetic practice.
Core Tip: This minireview proposes radiomics-the extraction of high-dimensional quantitative features from routine medical images-as a novel tool for precision anaesthesia. By quantifying body composition, organ function, and vascular morphology from preoperative computed tomography and magnetic resonance imaging, clinicians can obtain objective biomarkers of physiologic reserve that augment traditional preoperative evaluation. Integration of radiomic features with clinical data through machine learning may enable personalized anaesthetic planning, optimized drug dosing, and improved identification of high-risk surgical patients.
