Prediction of prognosis in brain metastasis with artificial-intelligence-driven methods for whole brain radiotherapy
Abstract
Purpose: Inferentially, 24%–45% of cancer patients develop brain metastases in their course. Individual survival estimation for these patients is crucial to identify the subset that may not benefit from whole-brain irradiation (WBI) due to a short survival time. This study aimed to identify variables and evaluate an artificial intelligence algorithm to determine which patients would benefit from WBI.
Materials and Methods: The data of 345 patients with brain metastasis who were treated with 30 Gy in 10 fractions of WBI were retrospectively analyzed. In this cohort, a total of 15 clinical / laboratory factors are evaluated with 15 models of machine learning algorithms using Python 2.3, Pycaret library.
Results: The Gradient Boosting Regressor was found to be the most accurate model, with a 0.68 R2 an R² value of 0.68, and a mean absolute error (MAE) of 12.90.The prediction error for the gradient Boosting Regressor was calculated as R2: 0.841. When the importance of features was investigated, time from diagnosis to metastasis was found to be the most important predictive variable for survival.
Conclusion: The results of this study enable us to identify patients who may have an early death and provide a consequential decision guide in terms of whole-brain radiotherapy or additional labor-intensive techniques.
Keywords
brain metastases , machine learning , prognosis , radiotherapy
Kaynakça
- Eichler AF, Loeffler JS. Multidisciplinary management of brain metastases. Oncologist. 2007;12:884–98.
- Sacks P, Rahman M. Epidemiology of brain metastases. Neurosurg Clin N Am. 2020;31:481-88.
- Hirose T, Deguchi S, Yasui K, Inoue M, Onoe T, Ogawa H et al. The indication of palliative whole-brain radiotherapy for patients with brain metastases: a simple prognostic scoring system in the era of stereotactic radiosurgery. BMC Cancer. 2024;24:940.
- Cardinal T, Pangal D, Strickland BA, Newton P, Mahmoodifar S, Mason J et al. Anatomical and topographical variations in the distribution of brain metastases based on primary cancer origin and molecular subtypes: a systematic review. Neurooncol Adv. 2021;4:vdab170.
- Nieder C, Norum J, Dalhaug A, Aandahl G, Pawinski A. Radiotherapy versus best supportive care in patients with brain metastases and adverse prognostic factors. Clin Exp Metastasis. 2013;30:723-29.
- Li Z, Shen D, Zhang J, Zhang J, Yang F, Kong D et al. Relationship between WBRT total dose, intracranial tumor control, and overall survival in NSCLC patients with brain metastases - a single-center retrospective analysis. BMC Cancer. 2019;19:1104.
- Rades D, Panzner A, Dziggel L, Haatanen T, Lohynska R, Schild SE. Dose-escalation of whole-brain radiotherapy for brain metastasis in patients with a favorable survival prognosis. Cancer. 2012;118:3852-59.
- Sperduto PW, Chao ST, Sneed PK, Luo X, Suh J, Roberge D et al. Diagnosis-specific prognostic factors, indexes, and treatment outcomes for patients with newly diagnosed brain metastases: a multi-institutional analysis of 4,259 patients. Int J Radiat Oncol Biol Phys. 2010;77:655-61.
- Rades D, Dziggel L, Nagy V, Segedin B, Lohynska R, Veninga T et al. A new survival score for patients with brain metastases who received whole-brain radiotherapy (WBRT) alone. Radiother Oncol. 2013;108:123-27.
- Kennedy, E.D, Simunovic M, Jhaveri K, Kirsch R, Brierley J, Drolet S et al. Safety and feasibility of using magnetic resonance imaging criteria to identify patients with “good prognosis” rectal cancer eligible for primary surgery: The phase 2 nonrandomized QuickSilver clinical trial. JAMA Oncol. 2019;5:961–66.
