Scoring Algorithms for Predicting Survival Prognosis in Patients With Metastatic Spinal Disease

Cui, Yunpeng; Lei, Mingxing; Pan, Yuanxing; Lin, Yunfei; Shi, Xuedong

doi:10.1097/bsd.0000000000001031

Cited by 21 publications

(13 citation statements)

References 68 publications

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“… 11 12 13 14) The scores have also been created using the variables input at the time of study, which may not include modern treatment modalities influencing patient survival, such as target therapies or advanced radiation techniques. 15 16) Thus, the aim of the present study was to evaluate both the overall predictive accuracy of such scoring systems and the predictive accuracy based on the primary tumor using our patient database.…”

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confidence: 99%

Validation of Traditional Prognosis Scoring Systems and Skeletal Oncology Research Group Nomogram for Predicting Survival of Spinal Metastasis Patients Undergoing Surgery

Chanplakorn

Jaipanya

et al. 2022

Clin Orthop Surg

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Background Many scoring systems that predict overall patient survival are based on clinical parameters and primary tumor type. To date, no consensus exists regarding which scoring system has the greatest predictive survival accuracy, especially when applied to specific primary tumors. Additionally, such scores usually fail to include modern treatment modalities, which influence patient survival. This study aimed to evaluate both the overall predictive accuracy of such scoring systems and the predictive accuracy based on the primary tumor. Methods A retrospective review on spinal metastasis patients who were aged more than 18 years and underwent surgical treatment was conducted between October 2008 and August 2018. Patients were scored based on data before the time of surgery. A survival probability was calculated for each patient using the given scoring systems. The predictive ability of each scoring system was assessed using receiver operating characteristic analysis at postoperative time points; area under the curve was then calculated to quantify predictive accuracy. Results A total of 186 patients were included in this analysis: 101 (54.3%) were men and the mean age was 57.1 years. Primary tumors were lung in 37 (20%), breast in 26 (14%), prostate in 20 (10.8%), hematologic malignancy in 18 (9.7%), thyroid in 10 (5.4%), gastrointestinal tumor in 25 (13.4%), and others in 40 (21.5%). The primary tumor was unidentified in 10 patients (5.3%). The overall survival was 201 days. For survival prediction, the Skeletal Oncology Research Group (SORG) nomogram showed the highest performance when compared to other prognosis scores in all tumor metastasis but a lower performance to predict survival with lung cancer. The revised Katagiri score demonstrated acceptable performance to predict death for breast cancer metastasis. The Tomita and revised Tokuhashi scores revealed acceptable performance in lung cancer metastasis. The New England Spinal Metastasis Score showed acceptable performance for predicting death in prostate cancer metastasis. SORG nomogram demonstrated acceptable performance for predicting death in hematologic malignancy metastasis at all time points. Conclusions The results of this study demonstrated inconsistent predictive performance among the prediction models for the specific primary tumor types. The SORG nomogram revealed the highest predictive performance when compared to previous survival prediction models.

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confidence: 99%

Validation of Traditional Prognosis Scoring Systems and Skeletal Oncology Research Group Nomogram for Predicting Survival of Spinal Metastasis Patients Undergoing Surgery

Chanplakorn

Jaipanya

et al. 2022

Clin Orthop Surg

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“…Prognostically, pathology-speci c actuarial scales are widely available to help surgeons determine estimated survival. [42][43][44][45][46][47][48] As patients are surviving longer with metastatic disease, these scoring systems will continue to evolve to re ect treatment options and outcomes data for cancer patients. The NOMS framework has been modi ed recently to include a molecular typing category, re ecting the impact of targeted therapeutics on prognosis and treatment planning.…”

Section: Advancements In Radiation Therapymentioning

confidence: 99%

Evolution of surgical treatment of metastatic spine tumors

et al. 2022

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The treatment of cancer has transformed over the past forty years, with medical oncologists, radiation oncologists and surgeons working together to prolong survival times and minimize treatment related morbidity. With each advancement, the risk-bene t scale has been calibrated to provide an accurate assessment of surgical hazard. The goal of this review is to look back at how the role of surgery has evolved with each new medical advance, and to explore the role of surgeons in the future of cancer care. MethodsA literature review was conducted, highlighting the key papers guiding surgical management of spinal metastatic lesions. ConclusionThe roles of surgery, medical therapy and radiation have evolved over the past forty years, with new advances requiring complex multidisciplinary care.

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“…Survival estimation seemed to be a critical step and was able to guide the management of bone metastases. Broadly speaking, it was no longer advised to perform surgery on patients with a life expectancy of <3 months ( 14 – 16 ). Consequently, accurate survival prediction is crucial since over- or underestimating the likelihood of survival might give rise to ineffective treatment, such as either overly aggressive or insufficient therapies.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approaches for prediction of early death among lung cancer patients with bone metastases using routine clinical characteristics: An analysis of 19,887 patients

Cui

Shi

Wang

et al. 2022

Front. Public Health

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PurposeBone is one of the most common sites for the spread of malignant tumors. Patients with bone metastases whose prognosis was shorter than 3 months (early death) were considered as surgical contraindications. However, the information currently available in the literature limits our capacity to assess the risk likelihood of 3 month mortality. As a result, the study's objective is to create an accurate prediction model utilizing machine-learning techniques to predict 3 month mortality specifically among lung cancer patients with bone metastases according to easily available clinical data.MethodsThis study enrolled 19,887 lung cancer patients with bone metastases between 2010 and 2018 from a large oncologic database in the United States. According to a ratio of 8:2, the entire patient cohort was randomly assigned to a training (n = 15881, 80%) and validation (n = 4,006, 20%) group. In the training group, prediction models were trained and optimized using six approaches, including logistic regression, XGBoosting machine, random forest, neural network, gradient boosting machine, and decision tree. There were 13 metrics, including the Brier score, calibration slope, intercept-in-large, area under the curve (AUC), and sensitivity, used to assess the model's prediction performance in the validation group. In each metric, the best prediction effectiveness was assigned six points, while the worst was given one point. The model with the highest sum score of the 13 measures was optimal. The model's explainability was performed using the local interpretable model-agnostic explanation (LIME) according to the optimal model. Predictor importance was assessed using H2O automatic machine learning. Risk stratification was also evaluated based on the optimal threshold.ResultsAmong all recruited patients, the 3 month mortality was 48.5%. Twelve variables, including age, primary site, histology, race, sex, tumor (T) stage, node (N) stage, brain metastasis, liver metastasis, cancer-directed surgery, radiation, and chemotherapy, were significantly associated with 3 month mortality based on multivariate analysis, and these variables were included for developing prediction models. With the highest sum score of all the measurements, the gradient boosting machine approach outperformed all the other models (62 points), followed by the XGBooting machine approach (59 points) and logistic regression (53). The area under the curve (AUC) was 0.820 (95% confident interval [CI]: 0.807–0.833), 0.820 (95% CI: 0.807–0.833), and 0.815 (95% CI: 0.801–0.828), respectively, calibration slope was 0.97, 0.95, and 0.96, respectively, and accuracy was all 0.772. Explainability of models was conducted to rank the predictors and visualize their contributions to an individual's mortality outcome. The top four important predictors in the population according to H2O automatic machine learning were chemotherapy, followed by liver metastasis, radiation, and brain metastasis. Compared to patients in the low-risk group, patients in the high-risk group were more than three times the odds of dying within 3 months (P < 0.001).ConclusionsUsing machine learning techniques, this study offers a number of models, and the optimal model is found after thoroughly assessing and contrasting the prediction performance of each model. The optimal model can be a pragmatic risk prediction tool and is capable of identifying lung cancer patients with bone metastases who are at high risk for 3 month mortality, informing risk counseling, and aiding clinical treatment decision-making. It is better advised for patients in the high-risk group to have radiotherapy alone, the best supportive care, or minimally invasive procedures like cementoplasty.

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Scoring Algorithms for Predicting Survival Prognosis in Patients With Metastatic Spinal Disease

Cited by 21 publications

References 68 publications

Validation of Traditional Prognosis Scoring Systems and Skeletal Oncology Research Group Nomogram for Predicting Survival of Spinal Metastasis Patients Undergoing Surgery

Validation of Traditional Prognosis Scoring Systems and Skeletal Oncology Research Group Nomogram for Predicting Survival of Spinal Metastasis Patients Undergoing Surgery

Evolution of surgical treatment of metastatic spine tumors

Machine learning approaches for prediction of early death among lung cancer patients with bone metastases using routine clinical characteristics: An analysis of 19,887 patients

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