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

Rikhil Seshadri, Jayant Siva, Angelica Bartholomew, Clara Goebel, Gabriel Wallerstein-King +18 more
6/29/2024
cs.CV

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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.

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@article{seshadri2024discrepancy,
  title  = {AI Age Discrepancy: A Novel Parameter for Frailty Assessment in Kidney Tumor Patients},
  author = {Rikhil Seshadri and Jayant Siva and Angelica Bartholomew and Clara Goebel and Gabriel Wallerstein-King and Beatriz López Morato and Nicholas Heller and Jason Scovell and Rebecca Campbell and Andrew Wood and Michal Ozery-Flato and Vesna Barros and Maria Gabrani and Michal Rosen-Zvi and Resha Tejpaul and Vidhyalakshmi Ramesh and Nikolaos Papanikolopoulos and Subodh Regmi and Ryan Ward and Robert Abouassaly and Steven C. Campbell and Erick Remer and Christopher Weight},
  year   = {2024},
  eprint = {2407.00438},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.00438v2}
}

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