AI-powered model advances treatment planning for patients with spinal metastasis

Credit: Unsplash/CC0 Public Domain

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 Traditional scoring systems, however, rely on outdated data and do not reflect recent advances in cancer therapy that have improved survival rates.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.

Integrating anatomy and biology for a better pancreatic cancer prognosis