A Simple Clinical Instrument to Predict the Survival Probability of Breast Cancer Patients Receiving Radiotherapy for Bone Metastases

Haus, Rapha; Schild, Steven E.; Rades, Dirk

doi:10.21873/anticanres.13961

Anticancer Res

2019

DOI: 10.21873/anticanres.13961

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A Simple Clinical Instrument to Predict the Survival Probability of Breast Cancer Patients Receiving Radiotherapy for Bone Metastases

Rapha Haus

Steven E. Schild

Dirk Rades

Abstract: Background/Aim: Individualization of treatment may improve the outcome of patients with bone metastases from breast cancer. To support physicians when selecting individualized programs for these patients, a simple instrument for predicting survival was created. Patients and Methods: In 126 female patients with breast cancer irradiated for bone metastases, 11 characteristics were evaluated with respect to survival. Results: On Cox regression analysis, Eastern Cooperative Oncology Group performance score (0-1 vs… Show more

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Cited by 3 publications

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References 26 publications

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“…A thorough prognostic assessment is recommended when choosing the dose/fractionation regimen (3)(4)(5). Mismatch between length of radiation treatment and remaining survival time should be avoided.…”

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confidence: 99%

Independent External Validation of the METSSS Model Predicting Survival After Palliative Radiotherapy

2022

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Background/Aim: An external validation study was performed to analyze the performance of the recently published METSSS model, which may predict survival after palliative radiotherapy. The model was developed from a large US database (2010-2014). It includes age, sex, cancer type, localization of distant metastases, Charlson-Deyo comorbidity score and radiotherapy site. Materials and Methods: Both 1-and 5-year survival was assessed in the Norwegian validation cohort from 2009-2014. Deviations between model-predicted survival and observed survival were analyzed. Results: The METSSS model predicted a 1-year survival of 29% (cohort median, predicted probability 0-74% in individual patients). The observed 1-year survival rate was 33% (median survival 5.3 months). The corresponding figures for predicted 5-year survival were 0% and 0-46%. The observed 5-year survival rate was 3%. Statistical comparison of the survival curves was possible for two of three strata (medium and high risk; insufficient number of low-risk patients) and the resulting pvalue was 0.045. Conclusion: A complete validation was hampered by imbalances in risk group size. More than 90% of the patients in the present study were classified as high risk. If this distribution, which deviates from the US study, is representative for other countries, the METSSS model might need additional adjustment. However, its general ability to predict survival appears promising..

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confidence: 99%

Independent External Validation of the METSSS Model Predicting Survival After Palliative Radiotherapy

2022

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“…This heterogeneity causes uncertainty regarding treatment decisions, such as the decision to irradiate at all and the choice of fractionation regimen (9)(10)(11). Several groups have proposed prognostic models, which may support decision making (12)(13)(14). Recently, new technology has resulted in improved opportunities to add complexity to such models, e.g.…”

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confidence: 99%

Independent Validation of a Comprehensive Machine Learning Approach Predicting Survival After Radiotherapy for Bone Metastases

2021

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Background/Aim: The aim of this study was to analyze the survival predictions obtained from a web platform allowing for computation of the so-called Bone Metastases Ensemble Trees for Survival (BMETS). This prediction model is based on a machine learning approach and considers 27 prognostic covariates. Patients and Methods: This was a retrospective single-institution analysis of 326 patients, managed with palliative radiotherapy for bone metastases. Deviations between model-predicted survival and observed survival were assessed. Results: The median actuarial survival was 7.5 months. In total, 59% of patients survived for a period shorter than predicted. Twenty percent of the predictions of the median survival deviated from the observed survival by at least 6 months. Regarding actual survival <3 months (99 of 326 patients), the BMETS-predicted median survival was <3 months, i.e. correct in 67 of 99 cases (68%), whereas the model predicted a median of 4-6 months in 16 (16%) and of >6 months in another 16 cases. Conclusion: The model predicted survival with high accuracy in a large number of patients. Nevertheless, if the model predicts a low likelihood of 3-month survival, actual survival may be very poor (often 1 month or less). Also, in patients who died within 3 months from the start of radiotherapy, the model often predicted longer survival (16% had >6 months predicted median survival). It would, therefore, be interesting to feed the U.S. database utilized to develop the BMETS with additional poor-prognosis patients to optimize the predictions.

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ACROPath Oligometastases: The American College of Radiation Oncology Clinical Pathway

Jahraus,

Wallner,

Heron

et al. 2024

Cureus

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Radiation oncology is among the most data-driven specialties in medicine. Recently, a wealth of peer-reviewed data has been published supporting the treatment of oligometastatic malignancies, demonstrating improved survival with metastasis-directed therapy, such as stereotactic body radiation therapy (SBRT), when combined with appropriate patient selection and treatment. However, there are currently few, if any, established guidelines that synthesize the abundance of data specific to radiotherapy into a single, easily accessed resource for clinicians. ACROPath® is a major initiative of the American College of Radiation Oncology (ACRO) that aims to present aggregated clinical pathway data in a highly usable format that is readily accessible to clinicians at the point of care in real time. The oligometastases pathway is the first published algorithm in this collection, with additional pathways anticipated in future publications. Clinical radiation oncologists with expertise in the treatment and management of oligometastatic disease were recruited from across ACRO’s diverse membership, including both academic and private practice physicians, to ensure a broad-based experience and insight. Individual participants were assigned subsections of the pathway for guideline development, and then, each subsection was presented to the full group for evaluation and consensus development based on published data. Rather than presenting an unstructured set of treatment options, as is common in other treatment guidelines, this initiative aimed to categorize appropriate treatments based on published clinical evidence in a hierarchy further ranked by efficacy, toxicity, and cost. Based on these strata, treatment recommendations were collated and grouped into three rank categories (gold, silver, or bronze) to denote the degree of applicability. The team assembled an interactive document that will eventually be available online, and it is summarized in detail here. Recommendations are grouped both by the anatomic site of metastasis and by the primary tumor type, recognizing that original histology might impact the treatment differently in different anatomic locations. After a review of available published clinical evidence, the committee reached a consensus on all recommendations presented, categorizing each option as gold, silver, or bronze to guide clinicians appropriately. This first iteration of ACROPath® Oligometastases represents one of the few comprehensive clinical decision support tools available for managing patients with limited metastatic disease. It presents available data in a highly accessible, easily used reference, which will be formally reviewed and updated by the committee as frequently as emerging data requires, likely at six- to 12-month intervals.

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A Simple Clinical Instrument to Predict the Survival Probability of Breast Cancer Patients Receiving Radiotherapy for Bone Metastases

Cited by 3 publications

References 26 publications

Independent External Validation of the METSSS Model Predicting Survival After Palliative Radiotherapy

Independent External Validation of the METSSS Model Predicting Survival After Palliative Radiotherapy

Independent Validation of a Comprehensive Machine Learning Approach Predicting Survival After Radiotherapy for Bone Metastases

ACROPath Oligometastases: The American College of Radiation Oncology Clinical Pathway

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