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Publications

Journal Article Ahead of print

PubMed: 42658735 DOI: 10.1097/BRS.0000000000005843

Deep Learning Versus LASSO-Based Machine Learning for 1-Year Survival After Surgery for Spinal Metastases: A JASA Multicenter Prospective Cohort Analysis.

Spine (Phila Pa 1976)

Authors: Sadayuki Ito, Hiroaki Nakashima, Naoki Segi, Jun Ouchida, Yuki Shiratani, Akinobu Suzuki, Hidetomi Terai, 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, Yuta Goto, Masashi Miyazaki, Kazuyuki Watanabe, Toshio Nakamae, Takashi Kaito, Narihito Nagoshi … and 5 other authors

Affiliations

  • Department of Orthopaedic Surgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Show-ku, Nagoya City, 466-8550, Japan.
  • Department of Orthopaedic Surgery, Chiba University Hospital, 1-8-1 Inohana, Chuo-ku, Chiba-shi, 260-8670, Japan.
  • Department of Orthopaedic Surgery, Osaka Metropolitan University, 1-4-3 Asahimachi, Abeno-ku, Osaka, 545-8585, Japan.
  • Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kanazawa University, 13-1 Takara-machi, Kanazawa, Ishikawa, 920-8641, Japan.
  • Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-cho, Chuo-ku, Kobe, 650-0017, Japan.
  • Department of Orthopaedic Surgery, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8520, Japan.
  • Department of Orthopaedic Surgery, Kansai Medical University Hospital, 2-5-1 Shinmachi, Hirakata, Osaka, 573-1010, Japan.
  • Department of Orthopaedic Surgery, Keio University School of Medicine, 35 Shinanomachi, Hiroshima Tokyo, 160-8582, Japan.
  • Department of Orthopaedic Surgery, Institute of Medicine, University of Tsukuba 1-1-1 Tennodai, Tsukuba, Ibaraki 3058575, Japan.
  • Department of Orthopaedic Surgery, Kitasato University School of Medicine 1-15-1 Kitazato, Minami-ku, Sagamihara, Kanagawa, 252-0374, Japan.
  • Rehabilitation Center, Jichi Medical University Hospital, 3311-1 Yakushiji, Shimotuke-shi, 329-0498, Japan.
  • Department of Orthopaedics, Jichi Medical University, 3311-1 Yakushiji, Shimotuke-shi, 329-0498, Japan.
  • Department of Orthopaedic Surgery, Dokkyo Medical University, 880 Kitakobayashi, Mibu, Shimotsugagun, Tochigi, 321-0293, Japan.
  • Department of Orthopaedics and Rehabilitation Medicine, University of Fukui Faculty of Medical Sciences, 23-3 Matsuoka Shimoaizuki, Eiheiji-cho, Yoshida-gun, Fukui, 910-1193, Japan.
  • Department of Orthopaedic Surgery, Mie University Graduate School of Medicine, 2-174 Edobashi, Tsu city, 514-8507, Japan.
  • Department of Orthopaedic Surgery, Kawasaki Medical School, 577 Matsushima, Kurashiki, Okayama, 701-0192, Japan.
  • Department of Orthopaedic Surgery, Nihon University School of Medicine, 30-1 oyaguchi-kamimachi, Itabashi-ku, Tokyo, 173-8610, Japan.
  • Department of Orthopaedics Surgery, Yamaguchi University Graduate School of Medicine, 1-1-1 Minamikogushi, Ube City, Yamaguchi, 755-8505, Japan.
  • Department of Orthopaedic Surgery, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita City, Chiba, 286-8520, Japan.
  • Department of Orthopaedic Surgery, Sapporo Medical University School of Medicine, S1 W16, Chuo-ku, Sapporo-shi, Hokkaido, 060-8543, Japan.
  • Department of Orthopaedics, Institute of Science Tokyo, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan.
  • Department of Orthopaedic Surgery, Kyoto University Hospital, 54 Kawaharamachi, Syogoin, Sakyo, Kyoto, 606-8397, Japan.
  • Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
  • Department of Orthopaedic Surgery, Okayama University Hospital, 2-5-1 Shikata-cho, Kita-ku, Okayama-shi, 700-8558, Japan.
  • Department of Orthopedics, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan.
  • Division of Orthopedic Surgery, Department of Sensory and Motor Organs, School of Medicine, Faculty of Medicine, Tottori University, 36-1 Nishi-cho, Yonago 683-8504, Japan.
  • Department of Orthopaedic Surgery, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.
  • Department of Orthopaedic Surgery, Gifu University Hospital, 1-1 Yanagido, Gifu, 501-1194, Japan.
  • Department of Orthopaedic Surgery, Iwate Medical University, 2-1-1 Idaidori, Yahaba-cho, Shiwagun, Iwate, 028-3609, Japan.
  • Department of Orthopaedic Surgery, Tokai University School of Medicine, 143 Shimokasuya, Isehara, Kanagawa, 259-1193, Japan.
  • Department of Orthopaedic Surgery, University of Toyama 2630 Sugitani, Toyama-shi, 930-0194, Japan.
  • Department of Orthopaedic Surgery, Nagoya City University, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, Aichi, 467-8601, Japan.
  • Department of Orthopaedic Surgery, Faculty of Medicine, Oita University, 1-1 Idaigaoka, Hasama-machi, Yufu-shi, Oita 879-5593, Japan.
  • Department of Orthopaedic Surgery, Fukushima Medical University School of Medicine, 1 Hikarigaoka, Fukushima City, 960-1295, Japan.
  • Department of Orthopaedic Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
  • Department of Orthopedic Surgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita 565-0871, Japan.

Abstract

STUDY DESIGN

Multicenter prospective cohort study; secondary analysis.

OBJECTIVE

To evaluate predictors associated with 1-year survival after surgery for spinal metastases by comparing a comprehensive 50-variable deep learning (DL) model with a previously published 5-variable LASSO-based machine learning (ML) model and applying DL-based permutation feature importance as an exploratory analytic lens.

SUMMARY OF BACKGROUND DATA

Surgical decision-making for spinal metastases requires reliable survival estimates. Traditional scores such as those of Tokuhashi and Tomita and contemporary tools such as SORG and NESMS support prognostication, but performance and calibration may vary across cohorts. A parsimonious 5-variable JASA ML model is clinically practical, whereas DL may help identify prognostic signals embedded in detailed activities of daily living (ADLs), patient-reported outcomes (PROs), and scoring-system components.

METHODS

We analyzed 401 complete-case patients who underwent surgery for spinal metastases at 35 Japanese institutions (2018-2021). A feed-forward neural network incorporating 50 preoperative variables was evaluated using five repeated random 8:2 train-test splits. Accuracy, AUROC, Brier score, and calibration summaries were reported and descriptively compared with the previously published 5-variable LASSO-based ML model.

RESULTS

At 1 year, 269 of 401 patients were alive. The DL model achieved 75.5+/- 3.0% accuracy (95% confidence interval [CI], 71.8%-79.2%), held-out AUROC 0.789 (95% CI, 0.681-0.886), and Brier score 0.214. The ML model achieved 71.8% accuracy (Wilson 95% CI, 67.2%-76.0%) and apparent AUROC 0.762. Because the comparison was descriptive rather than paired, formal statistical superiority was not claimed. DL feature importance highlighted Vitality Index-On and Off Toilet, EQ-5D-5L total score and pain/discomfort, and individual Tokuhashi/Tomita components; the ML-selected Vitality Index-Wake Up item ranked 38th.

CONCLUSIONS

The 50-variable DL model provided reasonable prediction and generated clinically plausible feature-importance hypotheses, but it did not demonstrate a clearly meaningful performance advantage over the simpler 5-variable ML model. DL may be most useful for research-based feature discovery and refinement of future parsimonious prognostic tools, whereas validated simple models remain more practical for bedside prognostication.

LEVEL OF EVIDENCE

2.

MeSH

Activities Of Daily Living, Machine Learning, Neoplasm Metastasis, Deep Learning, Learning, Spine