Evaluation of Prognostic Factors that Affect Survival Outcomes of Breast Cancer Patients with Brain Metastases: A Single Institutional Experience

Pandit, Prakash; Palwe, Vijay; Kate, Shruti; Gandhe, Sucheta; Patil, Rahul; Nagarkar, Raj

doi:10.4274/ejbh.2020.5969

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…The survival predictors identified in this study are widely acknowledged prognostic factors both specifically for breast cancer patients (such as HER2 status [11,15,17,25], and time interval between breast cancer and brain metastases [58][59][60]), and for brain metastases patients regardless the cancer type: patients' age [26,27,36,38], number of brain metastases [37,41,58,61,62], and preoperative KPS [26,38,47].…”

Section: Survival Prediction After Brain Metastases Surgery: From Sin...mentioning

confidence: 99%

Prediction of Short and Long Survival after Surgery for Breast Cancer Brain Metastases

Michel

Oppong

Rauschenbach

et al. 2022

Cancers

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Background: Brain metastases requiring surgical treatment determine the prognosis of patients with breast cancer. We aimed to develop the scores for the prediction of short (<6 months) and long (≥3 years) survival after BCBM surgery. Methods: Female patients with BCBM surgery between 2008 and 2019 were included. The new scores were constructed upon independent predictors for short and long postoperative survival. Results: In the final cohort (n = 95), 18 (18.9%) and 22 (23.2%) patients experienced short and long postoperative survival, respectively. Breast-preserving surgery, presence of multiple brain metastases and age ≥65 years at breast cancer diagnosis were identified as independent predictors of short postoperative survival. In turn, positive HER2 receptor status in brain metastases, time interval ≥3 years between breast cancer and brain metastases diagnosis and KPS ≥ 90% independently predicted long survival. The appropriate short and long survival scores showed higher diagnostic accuracy for the prediction of short (AUC = 0.773) and long (AUC = 0.775) survival than the breast Graded Prognostic Assessment score (AUC = 0.498/0.615). A cumulative survival score (total score) showed significant association with overall survival (p = 0.001). Conclusions: We identified predictors independently impacting the prognosis after BCBM surgery. After external validation, the presented scores might become useful tools for the selection of proper candidates for BCBM surgery.

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Section: Survival Prediction After Brain Metastases Surgery: From Sin...mentioning

confidence: 99%

Prediction of Short and Long Survival after Surgery for Breast Cancer Brain Metastases

Michel

Oppong

Rauschenbach

et al. 2022

Cancers

View full text Add to dashboard Cite

show abstract

“…The related literature has shown PS to be a reliable indicator of survival outcomes in cancer patients [ 8 , 9 ]. Numerous studies have demonstrated that PS evaluations along with clinical symptoms and signs can improve survival prediction in cancer patients [ 4 , 10 , 11 , 12 ]. PS has also been frequently employed as a criterion for evaluating a patient’s suitability for participation in clinical trials and modifying treatment strategies [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Prediction Model for Patient Survival Outcomes in Palliative Care Using Actigraphy Data and Clinical Information

Huang

Roy

Dhar

et al. 2023

Cancers

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(1) Background: Predicting the survival of patients in end-of-life care is crucial, and evaluating their performance status is a key factor in determining their likelihood of survival. However, the current traditional methods for predicting survival are limited due to their subjective nature. Wearable technology that provides continuous patient monitoring is a more favorable approach for predicting survival outcomes among palliative care patients. (2) Aims and objectives: In this study, we aimed to explore the potential of using deep learning (DL) model approaches to predict the survival outcomes of end-stage cancer patients. Furthermore, we also aimed to compare the accuracy of our proposed activity monitoring and survival prediction model with traditional prognostic tools, such as the Karnofsky Performance Scale (KPS) and the Palliative Performance Index (PPI). (3) Method: This study recruited 78 patients from the Taipei Medical University Hospital’s palliative care unit, with 66 (39 male and 27 female) patients eventually being included in our DL model for predicting their survival outcomes. (4) Results: The KPS and PPI demonstrated an overall accuracy of 0.833 and 0.615, respectively. In comparison, the actigraphy data exhibited a higher accuracy at 0.893, while the accuracy of the wearable data combined with clinical information was even better, at 0.924. (5) Conclusion: Our study highlights the significance of incorporating clinical data alongside wearable sensors to predict prognosis. Our findings suggest that 48 h of data is sufficient for accurate predictions. The integration of wearable technology and the prediction model in palliative care has the potential to improve decision making for healthcare providers and can provide better support for patients and their families. The outcomes of this study can possibly contribute to the development of personalized and patient-centered end-of-life care plans in clinical practice.

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Real-World Experience in Hispanic Patients With Breast Cancer and Brain Metastases Using Different Prognostic Tools

Cacho-Díaz,

Tripathy,

Arrieta

et al. 2024

International Journal of Radiation Oncology*Biology*Physics

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Evaluation of Prognostic Factors that Affect Survival Outcomes of Breast Cancer Patients with Brain Metastases: A Single Institutional Experience

Cited by 5 publications

References 22 publications

Prediction of Short and Long Survival after Surgery for Breast Cancer Brain Metastases

Prediction of Short and Long Survival after Surgery for Breast Cancer Brain Metastases

Deep Learning Prediction Model for Patient Survival Outcomes in Palliative Care Using Actigraphy Data and Clinical Information

Real-World Experience in Hispanic Patients With Breast Cancer and Brain Metastases Using Different Prognostic Tools

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