Prediction of Discharge Status and Readmissions after Resection of Intradural Spinal Tumors

Machine Learning in Spine Oncology: A Narrative Review

STUDY DESIGN

Narrative Review.

OBJECTIVE

Machine learning (ML) is one of the latest advancements in artificial intelligence used in medicine and surgery with the potential to significantly impact the way physicians diagnose, prognose, and treat spine tumors. In the realm of spine oncology, ML is utilized to analyze and interpret medical imaging and classify tumors with incredible accuracy. The authors present a narrative review that specifically addresses the use of machine learning in spine oncology.

METHODS

This study was conducted in accordance with the Preferred Reporting Items of Systematic Reviews and Meta-Analysis (PRISMA) methodology. A systematic review of the literature in the PubMed, EMBASE, Web of Science, Scopus, and Cochrane Library databases since inception was performed to present all clinical studies with the search terms '[[Machine Learning] OR [Artificial Intelligence]] AND [[Spine Oncology] OR [Spine Cancer]]'. Data included studies that were extracted and included algorithms, training and test size, outcomes reported. Studies were separated based on the type of tumor investigated using the machine learning algorithms into primary, metastatic, both, and intradural. A minimum of 2 independent reviewers conducted the study appraisal, data abstraction, and quality assessments of the studies.

RESULTS

Forty-five studies met inclusion criteria out of 480 references screened from the initial search results. Studies were grouped by metastatic, primary, and intradural tumors. The majority of ML studies relevant to spine oncology focused on utilizing a mixture of clinical and imaging features to risk stratify mortality and frailty. Overall, these studies showed that ML is a helpful tool in tumor detection, differentiation, segmentation, predicting survival, predicting readmission rates of patients with either primary, metastatic, or intradural spine tumors.

CONCLUSION

Specialized neural networks and deep learning algorithms have shown to be highly effective at predicting malignant probability and aid in diagnosis. ML algorithms can predict the risk of tumor recurrence or progression based on imaging and clinical features. Additionally, ML can optimize treatment planning, such as predicting radiotherapy dose distribution to the tumor and surrounding normal tissue or in surgical resection planning. It has the potential to significantly enhance the accuracy and efficiency of health care delivery, leading to improved patient outcomes.

The Impact of Neurophysiological Monitoring during Intradural Spinal Tumor Surgery

Surgery for spinal cord tumors poses a significant challenge due to the inherent risk of neurological deterioration. Despite being performed at numerous centers, there is an ongoing debate regarding the efficacy of pre- and intraoperative neurophysiological investigations in detecting and preventing neurological lesions. This study begins by providing a comprehensive review of the neurophysiological techniques commonly employed in this context. Subsequently, we present findings from a cohort of 67 patients who underwent surgery for intradural tumors. These patients underwent preoperative and intraoperative multimodal somatosensory evoked potentials (SSEPs) and motor evoked potentials (MEPs), with clinical evaluation conducted three months postoperatively. The study aimed to evaluate the neurophysiological, clinical, and radiological factors associated with neurological outcomes. In univariate analysis, preoperative and intraoperative potential alterations, tumor size, and ependymoma-type histology were linked to the risk of worsening neurological condition. In multivariate analysis, only preoperative and intraoperative neurophysiological abnormalities remained significantly associated with such neurological deterioration. Interestingly, transient alterations in intraoperative MEPs and SSEPs did not pose a risk of neurological deterioration. The machine learning model we utilized demonstrated the possibility of predicting clinical outcome, achieving 84% accuracy.

Limitations in Evaluating Machine Learning Models for Imbalanced Binary Outcome Classification in Spine Surgery: A Systematic Review

Clinical prediction models for spine surgery applications are on the rise, with an increasing reliance on machine learning (ML) and deep learning (DL). Many of the predicted outcomes are uncommon; therefore, to ensure the models' effectiveness in clinical practice it is crucial to properly evaluate them. This systematic review aims to identify and evaluate current research-based ML and DL models applied for spine surgery, specifically those predicting binary outcomes with a focus on their evaluation metrics. Overall, 60 papers were included, and the findings were reported according to the PRISMA guidelines. A total of 13 papers focused on lengths of stay (LOS), 12 on readmissions, 12 on non-home discharge, 6 on mortality, and 5 on reoperations. The target outcomes exhibited data imbalances ranging from 0.44% to 42.4%. A total of 59 papers reported the model's area under the receiver operating characteristic (AUROC), 28 mentioned accuracies, 33 provided sensitivity, 29 discussed specificity, 28 addressed positive predictive value (PPV), 24 included the negative predictive value (NPV), 25 indicated the Brier score with 10 providing a null model Brier, and 8 detailed the F1 score. Additionally, data visualization varied among the included papers. This review discusses the use of appropriate evaluation schemes in ML and identifies several common errors and potential bias sources in the literature. Embracing these recommendations as the field advances may facilitate the integration of reliable and effective ML models in clinical settings.

Current Applications of Machine Learning for Spinal Cord Tumors

Spinal cord tumors constitute a diverse group of rare neoplasms associated with significant mortality and morbidity that pose unique clinical and surgical challenges. Diagnostic accuracy and outcome prediction are critical for informed decision making and can promote personalized medicine and facilitate optimal patient management. Machine learning has the ability to analyze and combine vast amounts of data, allowing the identification of patterns and the establishment of clinical associations, which can ultimately enhance patient care. Although artificial intelligence techniques have been explored in other areas of spine surgery, such as spinal deformity surgery, precise machine learning models for spinal tumors are lagging behind. Current applications of machine learning in spinal cord tumors include algorithms that improve diagnostic precision by predicting genetic, molecular, and histopathological profiles. Furthermore, artificial intelligence-based systems can assist surgeons with preoperative planning and surgical resection, potentially reducing the risk of recurrence and consequently improving clinical outcomes. Machine learning algorithms promote personalized medicine by enabling prognostication and risk stratification based on accurate predictions of treatment response, survival, and postoperative complications. Despite their promising potential, machine learning models require extensive validation processes and quality assessments to ensure safe and effective translation to clinical practice.

Role of Posterior Longitudinal Ligament Complex in Spinal Deformity Secondary to Surgical Resection of the Intradural Tumor

OBJECTIVE

In most cases, complete resection of the intradural tumor is accompanied by long-term neurological complications. Postoperative spinal deformity is the most common complication after surgical resection of intradural tumors, and posterior longitudinal ligament complex (PLC) plays an important role in postoperative spinal deformity. In this study, we investigated the role of PLC in spinal deformity after the surgical treatment of intradural tumors.

METHODS

We analyzed the data of 218 consecutive patients who underwent intradural tumor resection from 2000 to 2018 in this retrospective study. Before 2010, patients underwent laminoplasty without maintaining the integrity of PLC (laminoplasty group, n = 155). After 2010, patients performed single-port laminoplasty to maintain the integrity of PLC (laminoplasty retain posterior ligament complex group, n = 63). The score of quality of life, painful cortex, spinal cord movement, progressive kyphosis or scoliosis, perioperative morbidity, and neurological results were analyzed in the laminoplasty group and laminoplasty retain posterior ligament complex group. The distributed variable was shown as mean ± standard deviation and an independent t-test or one-way analysis of variance was calculated.

RESULTS

There are 155 patients (71.1%) included in the laminoplasty group, and 63 patients (28.9%) in the laminoplasty retain posterior ligament complex group. The average age of patients was 42 ± 2.3 years, and the average modified McCormick score was 2. There were 158 (72.4%) patients with intramedullary tumors and 115 (52.7%) patients with extramedullary tumors. The length of hospital stays (8 days vs. 6 days; p = 0.023) and discharge to inpatient rehabilitation (48.4% vs. 26.9%; p = 0.012) were significantly lower in the laminoplasty retain posterior ligament complex group than the laminoplasty group. There was no significant difference in the risk of progressive deformity between the two groups at 18 months after surgery (relative risk 0.12; 95% confidence interval [CI] 0.43-1.25; p = 0.258) and at 20 months after surgery (relative risk 0.24; 95% CI 0.21-2.1).

CONCLUSION

Laminoplasty retains posterior ligament complex showed no impact on the spinal deformities compared with laminoplasty, but significantly improved the postoperative spinal activity, alleviated pain symptoms, and reduced hospital recovery time.

A Machine Learning-Based Online Prediction Tool for Predicting Short-Term Postoperative Outcomes Following Spinal Tumor Resections

Background: Preoperative prediction of short-term postoperative outcomes in spinal tumor patients can lead to more precise patient care plans that reduce the likelihood of negative outcomes. With this study, we aimed to develop machine learning algorithms for predicting short-term postoperative outcomes and implement these models in an open-source web application. Methods: Patients who underwent surgical resection of spinal tumors were identified using the American College of Surgeons, National Surgical Quality Improvement Program. Three outcomes were predicted: prolonged length of stay (LOS), nonhome discharges, and major complications. Four machine learning algorithms were developed and integrated into an open access web application to predict these outcomes. Results: A total of 3073 patients that underwent spinal tumor resection were included in the analysis. The most accurately predicted outcomes in terms of the area under the receiver operating characteristic curve (AUROC) was the prolonged LOS with a mean AUROC of 0.745 The most accurately predicting algorithm in terms of AUROC was random forest, with a mean AUROC of 0.743. An open access web application was developed for getting predictions for individual patients based on their characteristics and this web application can be accessed here: huggingface.co/spaces/MSHS-Neurosurgery-Research/NSQIP-ST. Conclusion: Machine learning approaches carry significant potential for the purpose of predicting postoperative outcomes following spinal tumor resections. Development of predictive models as clinically useful decision-making tools may considerably enhance risk assessment and prognosis as the amount of data in spinal tumor surgery continues to rise.