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| Image Credit: Scientific Frontline / AI generated (Gemini) |
Scientific Frontline: "At a Glance" Summary
- Main Discovery: Researchers developed a machine learning-based prognostic scoring system for spinal metastasis that accurately predicts one-year survival using modern clinical data.
- Methodology: The team employed Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression to analyze prospective data from 401 patients undergoing surgery at 35 medical institutions.
- Key Data: The model demonstrated high accuracy with an AUROC of 0.762, distinguishing one-year survival rates between low-risk (82.2%), intermediate-risk (67.2%), and high-risk (34.2%) groups.
- Significance: This tool resolves the limitations of traditional scoring systems based on obsolete 1990s data by integrating outcomes from contemporary treatments like molecularly targeted therapies and immunotherapies.
- Future Application: Clinical deployment to guide surgical versus palliative care decisions, with ongoing plans to validate the model's efficacy using international datasets.
- Branch of Science: Orthopedics, Oncology, and Data Science
- Additional Detail: Prognostic stratification relies on five non-invasive variables: vitality index, age, performance status, bone metastasis presence, and preoperative opioid usage.
Spinal metastasis, the spread of cancer to the spine, is a frequent complication in advanced cancer. It often causes severe pain and paralysis, significantly impacting quality of life.
Surgery may be an option for patients with a favorable prognosis, while palliative care may be recommended for patients with limited life expectancy. An accurate prognosis is essential for selecting appropriate treatment. Traditional scoring systems, however, rely on outdated data and do not reflect recent advances in cancer therapy that have improved survival rates.
In a recent study published in the journal Spine, researchers at Nagoya University Graduate School of Medicine introduced a simple, highly accurate prognostic prediction system, developed using large-scale prospective data from spinal metastasis patients who received modern cancer treatments.
“Traditional survival prediction models in clinical practice use data from the 1990s and 2000s,” said Assistant Professor Sadayuki Ito, the study’s first author. “Those models don’t fully reflect the impact of modern oncologic therapies, such as molecularly targeted therapies and immune checkpoint inhibitors.”
Most conventional prediction models also use retrospective medical records, while surgical decisions require accurate, real-time models based on prospective data. Although collecting prospective data is time-consuming and costly, it allows physicians and nurses to make objective evaluations using standardized criteria.
From this perspective, Dr. Ito, Professor Shiro Imagama, Associate Professor Hiroaki Nakashima, and their colleagues worked to develop a highly accurate, real-time model based on prospective data.
A Modern Approach to Data
The researchers conducted a large-scale, multicenter prospective study. They analyzed 401 patients who underwent surgery for spinal metastasis at 35 medical institutions across Japan between 2018 and 2021.
The team used Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, a machine learning method, to identify significant predictors of one-year survival. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC) and calibration plots.
Five key predictors
The model used five preoperative factors that physicians can assess without specialized electronic devices:
- Vitality index (“Wake Up” component): Reflects patient motivation and psychological health;
- Age: Specifically, whether the patient is 75 years or older;
- ECOG performance status: Measures the patient’s functional impairment;
- Bone metastases: Presence of cancer in bones outside the spine; and
- Opioid use: Preoperative opioid use, as high doses may cause immunosuppression and accelerate tumor progression.
Results and risk stratification
The model achieved a high predictive accuracy (AUROC = 0.762) and classified patients into three risk groups:
- Low-risk: 82.2% one-year survival rate
- Intermediate-risk: 67.2% one-year survival rate
- High-risk: 34.2% one-year survival rate
This simple scoring system allows surgeons to make more informed decisions about who should undergo surgery and how to tailor post-operative care.
Future Outlook
Although the current model is based on Japanese clinical data, the researchers aim to apply it globally. “Our next step is to validate this system with data from medical institutions worldwide to ensure it can help patients globally,” concluded Dr. Ito.
Published in journal: Spine
Title: Machine Learning-Based Prognostic Scoring for Spinal Metastases
Authors: Ito, Sadayuki MDa; Nakashima, Hiroaki MDa; Segi, Naoki MDa; Ouchida, Jun MDa; Shiratani, Yuki MDb; Suzuki, Akinobu MDc; Terai, Hidetomi MDc; Shimizu, Takaki MDd; Kakutani, Kenichiro MDe; Kanda, Yutaro MDe; Tominaga, Hiroyuki MDf; Kawamura, Ichiro MDf; Ishihara, Masayuki MDg; Paku, Masaaki MDg; Takahashi, Yohei MDh; Funayama, Toru MDi; Miura, Kousei MDi; Shirasawa, Eiki MDj; Inoue, Hirokazu MDk; Kimura, Atsushi MDl; Iimura, Takuya MDm; Moridaira, Hiroshi MDm; Nakajima, Hideaki MDn; Watanabe, Shuji MDn; Akeda, Koji MDo; Takegami, Norihiko MDo; Nakanishi, Kazuo MDp; Sawada, Hirokatsu MDq; Matsumoto, Koji MDq; Funaba, Masahiro MDr; Suzuki, Hidenori MDr; Funao, Haruki MDs; Oshigiri, Tsutomu MDt; Hirai, Takashi MDu; Otsuki, Bungo MDv; Kobayakawa, Kazu MDw; Uotani, Koji MDx; Manabe, Hiroaki MDy; Tanishima, Shinji MDz; Hashimoto, Ko MDaa; Iwai, Chizuo MDab; Yamabe, Daisuke MDac; Hiyama, Akihikoad; Seki, Shoji MDae; Goto, Yuta MDaf; Miyazaki, Masashi MDag; Watanabe, Kazuyuki MDah; Nakamae, Toshio MDai; Kaito, Takashi MDaj; Nagoshi, Narihito MDh; Kato, Satoshi MDd; Watanabe, Kota MDh; Imagama, Shiro MDa; Inoue, Gen MDj; Furuya, Takeo MDb; JASA Study Group
Source/Credit: Nagoya University
Reference Number: ongy012626_01