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Cacho-Díaz B, Tripathy D, Arrieta VA, Escamilla-Ramirez A, Alvarado-Miranda A, Rodríguez-Mayoral O. Real-World Experience in Hispanic Patients With Breast Cancer and Brain Metastases Using Different Prognostic Tools. Int J Radiat Oncol Biol Phys 2024:S0360-3016(24)00305-5. [PMID: 38364945 DOI: 10.1016/j.ijrobp.2024.01.222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/29/2023] [Accepted: 01/28/2024] [Indexed: 02/18/2024]
Abstract
PURPOSE Only a small percentage of Hispanic patients have been included in studies that developed prognostic models for breast cancer and brain metastases. Therefore, there is a clear need for tools tailored to this demographic. This study assesses the efficacy of common prognostic tools in a Hispanic population. METHODS AND MATERIALS We retrospectively analyzed a data set of Hispanic patients with breast cancer and newly diagnosed brain metastases from 2009 to 2023 at a single referral center. For each prognostic tool, Kaplan-Meier curves were built. The performances of the models were compared using the area under the curve (AUC), C-statistic, and Akaike information criteria (AIC). RESULTS Of 492 patients, the median time from breast cancer to brain metastasis diagnosis was 22.7 months (IQR, 12.1-53.3). The median overall survival was 11.6 months (95% CI, 9.9-13.4). All models were validated as prognostic tools (P < .001). The model with the better performance was the breast graded prognostic assessment (GPA; AIC, 402; AUC, 0.65), followed by the modified GPA (AIC, 406; AUC, 0.64), the disease-specific GPA (AIC, 407; AUC, 0.62), recursive partitioning analysis (AIC, 421; AUC, 0.62), and GPA (AIC, 422; AUC, 0.60). CONCLUSIONS The breast GPA demonstrated superior accuracy in prognosticating outcomes for Hispanic patients with breast cancer and brain metastases. This underscores the critical importance of incorporating racial and ethnic diversity in creating and validating medical prognostic tools.
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Affiliation(s)
- Bernardo Cacho-Díaz
- Neuro-Oncology Unit, Instituto Nacional de Cancerologia, Mexico City, Mexico.
| | - Debu Tripathy
- Department of Breast Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Victor A Arrieta
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
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Huang Y, Roy N, Dhar E, Upadhyay U, Kabir MA, Uddin M, Tseng CL, Syed-Abdul S. Deep Learning Prediction Model for Patient Survival Outcomes in Palliative Care Using Actigraphy Data and Clinical Information. Cancers (Basel) 2023; 15:cancers15082232. [PMID: 37190161 DOI: 10.3390/cancers15082232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/07/2023] [Accepted: 04/07/2023] [Indexed: 05/17/2023] Open
Abstract
(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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Affiliation(s)
- Yaoru Huang
- Department of Radiation Oncology, Taipei Medical University Hospital, Taipei 110, Taiwan
- Graduate Institute of Biomedical Materials and Tissue Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei 110, Taiwan
| | - Nidita Roy
- Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chittagong 4349, Bangladesh
| | - Eshita Dhar
- Graduate Institute of Biomedical Informatics, College of Medical Sciences and Technology, Taipei Medical University, Taipei 106, Taiwan
- International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei 106, Taiwan
| | - Umashankar Upadhyay
- Graduate Institute of Biomedical Informatics, College of Medical Sciences and Technology, Taipei Medical University, Taipei 106, Taiwan
- International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei 106, Taiwan
- Faculty of Applied Sciences and Biotechnology, Shoolini University of Biotechnology and Management Sciences, Solan 173229, Himachal Pradesh, India
| | - Muhammad Ashad Kabir
- School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW 2678, Australia
| | - Mohy Uddin
- Research Quality Management Section, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard-Health Affairs, Riyadh 11481, Saudi Arabia
| | - Ching-Li Tseng
- Graduate Institute of Biomedical Materials and Tissue Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei 110, Taiwan
- International Ph.D. Program in Biomedical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei 110, Taiwan
| | - Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, College of Medical Sciences and Technology, Taipei Medical University, Taipei 106, Taiwan
- International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei 106, Taiwan
- School of Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, Taipei 110, Taiwan
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Michel A, Darkwah Oppong M, Rauschenbach L, Dinger TF, Barthel L, Pierscianek D, Wrede KH, Hense J, Pöttgen C, Junker A, Schmidt T, Iannaccone A, Kimmig R, Sure U, Jabbarli R. Prediction of Short and Long Survival after Surgery for Breast Cancer Brain Metastases. Cancers (Basel) 2022; 14:cancers14061437. [PMID: 35326590 PMCID: PMC8946189 DOI: 10.3390/cancers14061437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 03/02/2022] [Indexed: 12/09/2022] Open
Abstract
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). Conclusion: 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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Affiliation(s)
- Anna Michel
- Department of Neurosurgery and Spine Surgery, University Hospital Essen, 45147 Essen, Germany; (M.D.O.); (L.R.); (T.F.D.); (L.B.); (D.P.); (K.H.W.); (U.S.); (R.J.)
- Correspondence: ; Tel.: +49-201-723-1230; Fax: +49-201-723-1220
| | - Marvin Darkwah Oppong
- Department of Neurosurgery and Spine Surgery, University Hospital Essen, 45147 Essen, Germany; (M.D.O.); (L.R.); (T.F.D.); (L.B.); (D.P.); (K.H.W.); (U.S.); (R.J.)
| | - Laurèl Rauschenbach
- Department of Neurosurgery and Spine Surgery, University Hospital Essen, 45147 Essen, Germany; (M.D.O.); (L.R.); (T.F.D.); (L.B.); (D.P.); (K.H.W.); (U.S.); (R.J.)
| | - Thiemo Florin Dinger
- Department of Neurosurgery and Spine Surgery, University Hospital Essen, 45147 Essen, Germany; (M.D.O.); (L.R.); (T.F.D.); (L.B.); (D.P.); (K.H.W.); (U.S.); (R.J.)
| | - Lennart Barthel
- Department of Neurosurgery and Spine Surgery, University Hospital Essen, 45147 Essen, Germany; (M.D.O.); (L.R.); (T.F.D.); (L.B.); (D.P.); (K.H.W.); (U.S.); (R.J.)
| | - Daniela Pierscianek
- Department of Neurosurgery and Spine Surgery, University Hospital Essen, 45147 Essen, Germany; (M.D.O.); (L.R.); (T.F.D.); (L.B.); (D.P.); (K.H.W.); (U.S.); (R.J.)
| | - Karsten H. Wrede
- Department of Neurosurgery and Spine Surgery, University Hospital Essen, 45147 Essen, Germany; (M.D.O.); (L.R.); (T.F.D.); (L.B.); (D.P.); (K.H.W.); (U.S.); (R.J.)
| | - Jörg Hense
- Department of Medical Oncology, University Hospital Essen, 45147 Essen, Germany;
| | - Christoph Pöttgen
- Department of Radiotherapy, University Hospital Essen, 45147 Essen, Germany;
| | - Andreas Junker
- Department of Neuropathology, University Hospital Essen, 45147 Essen, Germany;
| | - Teresa Schmidt
- Department of Neurooncology, University Hospital Essen, 45147 Essen, Germany;
| | - Antonella Iannaccone
- Department of Obstetrics and Gynecology, University Hospital Essen, 45147 Essen, Germany; (A.I.); (R.K.)
| | - Rainer Kimmig
- Department of Obstetrics and Gynecology, University Hospital Essen, 45147 Essen, Germany; (A.I.); (R.K.)
| | - Ulrich Sure
- Department of Neurosurgery and Spine Surgery, University Hospital Essen, 45147 Essen, Germany; (M.D.O.); (L.R.); (T.F.D.); (L.B.); (D.P.); (K.H.W.); (U.S.); (R.J.)
| | - Ramazan Jabbarli
- Department of Neurosurgery and Spine Surgery, University Hospital Essen, 45147 Essen, Germany; (M.D.O.); (L.R.); (T.F.D.); (L.B.); (D.P.); (K.H.W.); (U.S.); (R.J.)
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