AI Age Discrepancy: A Novel Parameter for Frailty Assessment in Kidney Tumor Patients

Rikhil Seshadri, Jayant Siva, Angelica Bartholomew, Clara Goebel, Gabriel Wallerstein-King, Beatriz López Morato, Nicholas Heller, Jason Scovell, Rebecca Campbell, Andrew Wood, Michal Ozery-Flato, Vesna Barros, Maria Gabrani, Michal Rosen-Zvi, Resha Tejpaul, Vidhyalakshmi Ramesh, Nikolaos Papanikolopoulos, Subodh Regmi, Ryan Ward, Robert AbouassalySteven C. Campbell, Erick Remer, Christopher Weight

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Kidney cancer is a global health concern, and accurate assessment of patient frailty is crucial for optimizing surgical outcomes. This paper introduces AI Age Discrepancy, a novel metric derived from machine learning analysis of preoperative abdominal CT scans, as a potential indicator of frailty and postoperative risk in kidney cancer patients. This retrospective study of 599 patients from the 2023 Kidney Tumor Segmentation (KiTS) challenge dataset found that a higher AI Age Discrepancy is significantly associated with longer hospital stays and lower overall survival rates, independent of established factors. This suggests that AI Age Discrepancy may provide valuable insights into patient frailty and could thus inform clinical decision-making in kidney cancer treatment.

Original languageEnglish (US)
Title of host publicationCancer Prevention, Detection, and Intervention - 3rd MICCAI Workshop, CaPTion 2024, Held in Conjunction with MICCAI 2024, Proceedings
EditorsSharib Ali, Fons van der Sommen, Iris Kolenbrander, Bartłomiej Władysław Papież, Noha Ghatwary, Yueming Jin
PublisherSpringer Science and Business Media Deutschland GmbH
Pages167-175
Number of pages9
ISBN (Print)9783031733758
DOIs
StatePublished - 2025
Event3rd International Workshop on Cancer Prevention, detection and intervenTion, CaPTion 2024, held in Conjunction with 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024 - Marrakesh, Morocco
Duration: Oct 6 2024Oct 6 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume15199 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Workshop on Cancer Prevention, detection and intervenTion, CaPTion 2024, held in Conjunction with 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024
Country/TerritoryMorocco
CityMarrakesh
Period10/6/2410/6/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Keywords

  • Frailty
  • Kidney Cancer
  • Machine Learning

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