Predicting Postoperative Neurological Outcomes in Metastatic Spinal Tumor Surgery Using Machine Learning.

IF 2.6 2区 医学 Q2 CLINICAL NEUROLOGY
Spine Pub Date : 2025-03-03 DOI:10.1097/BRS.0000000000005322
Satoshi Maki, Yuki Shiratani, Sumihisa Orita, Akinobu Suzuki, Koji Tamai, Takaki Shimizu, Kenichiro Kakutani, Yutaro Kanda, Hiroyuki Tominaga, Ichiro Kawamura, Masayuki Ishihara, Masaaki Paku, Yohei Takahashi, Toru Funayama, Kousei Miura, Eiki Shirasawa, Hirokazu Inoue, Atsushi Kimura, Takuya Iimura, Hiroshi Moridaira, Hideaki Nakajima, Shuji Watanabe, Koji Akeda, Norihiko Takegami, Kazuo Nakanishi, Hirokatsu Sawada, Koji Matsumoto, Masahiro Funaba, Hidenori Suzuki, Haruki Funao, Tsutomu Oshigiri, Takashi Hirai, Bungo Otsuki, Kazu Kobayakawa, Koji Uotani, Hiroaki Manabe, Shinji Tanishima, Ko Hashimoto, Chizuo Iwai, Daisuke Yamabe, Akihiko Hiyama, Shoji Seki, Kenji Kato, Masashi Miyazaki, Kazuyuki Watanabe, Toshio Nakamae, Takashi Kaito, Hiroaki Nakashima, Narihito Nagoshi, Gen Inoue, Shiro Imagama, Kota Watanabe, Satoshi Kato, Seiji Ohtori, Takeo Furuya
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引用次数: 0

Abstract

Study design: Retrospective analysis of data collected across multiple centers.

Objective: To develop machine learning models for predicting neurological outcomes one month postoperatively in patients with metastatic spinal tumors undergoing surgery, and to identify key factors influencing neurological recovery.

Summary of background data: The increasing prevalence of spinal metastases has led to a growing need for surgical intervention to address mechanical instability and neurological deficits. Predicting postoperative neurological status, as assessed by the Frankel classification, can provide valuable insights for surgical planning and patient counseling. Traditional prognostic models have shown limitations in capturing the complexity of neurological recovery patterns.

Methods: We analyzed data from 244 patients who underwent spinal surgery for metastatic disease across 38 institutions. The primary outcome was functional ambulation, defined as Frankel grades D or E at one month postoperatively. Four machine learning algorithms (Random Forest, XGBoost, LightGBM, and CatBoost) were used to build predictive models. Feature selection employed the Boruta algorithm and Variance Inflation Factor analysis to reduce multicollinearity.

Results: Among the 244 patients, the proportion of ambulatory patients (Frankel grades D or E) increased from 36.8% preoperatively to 63.1% at one month postoperatively. The Random Forest model achieved the highest area under the receiver operating characteristic curve (AUC-ROC) of 0.8516, followed by XGBoost (0.8351), CatBoost (0.8331), and LightGBM (0.8098). SHapley Additive exPlanations analysis identified preoperative Frankel classification, transfer ability, inflammatory markers (C-reactive protein, white blood cell-lymphocyte), and surgical timing as the most important predictors of postoperative outcomes.

Conclusions: Machine learning models showed strong predictive performance in assessing postoperative neurological status for patients with metastatic spinal tumors. Key factors including preoperative neurological function, functional ability, and inflammation markers significantly influenced outcomes. These findings could inform surgical decision-making and help set realistic postoperative expectations while potentially improving patient care through more accurate outcome prediction.

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来源期刊
Spine
Spine 医学-临床神经学
CiteScore
5.90
自引率
6.70%
发文量
361
审稿时长
6.0 months
期刊介绍: Lippincott Williams & Wilkins is a leading international publisher of professional health information for physicians, nurses, specialized clinicians and students. For a complete listing of titles currently published by Lippincott Williams & Wilkins and detailed information about print, online, and other offerings, please visit the LWW Online Store. Recognized internationally as the leading journal in its field, Spine is an international, peer-reviewed, bi-weekly periodical that considers for publication original articles in the field of Spine. It is the leading subspecialty journal for the treatment of spinal disorders. Only original papers are considered for publication with the understanding that they are contributed solely to Spine. The Journal does not publish articles reporting material that has been reported at length elsewhere.
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