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Palavani LB, de Barros Oliveira L, Reis PA, Batista S, Santana LS, de Freitas Martins LP, Rabelo NN, Bertani R, Welling LC, Figueiredo EG, Paiva WS, Neville IS. Efficacy and Safety of Intraoperative Radiotherapy for High-Grade Gliomas: A Systematic Review and Meta-Analysis. Neurosurg Rev 2024; 47:47. [PMID: 38221545 DOI: 10.1007/s10143-024-02279-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/27/2023] [Accepted: 01/03/2024] [Indexed: 01/16/2024]
Abstract
BACKGROUND AND OBJECTIVES High-grade gliomas (HGGs) are aggressive tumors of the central nervous system that cause significant morbidity and mortality. Despite advances in surgery and radiation therapy (RT), HGG still has a high incidence of recurrence and treatment failure. Intraoperative radiotherapy (IORT) has emerged as a promising therapeutic approach to achieve local tumor control while sparing normal brain tissue from radiation-induced damage. METHODS A systematic review and meta-analysis were conducted following PRISMA guidelines to evaluate the use of IORT for HGG. Eligible studies were included based on specific criteria, and data were independently extracted. Outcomes of interest included complications, IORT failure, survival rates at 12 and 24 months, and mortality. RESULTS Sixteen studies comprising 436 patients were included. The overall complication rate after IORT was 17%, with significant heterogeneity observed. The IORT failure rate was 77%, while the survival rates at 12 and 24 months were 74% and 24%, respectively. The mortality rate was 62%. CONCLUSION This meta-analysis suggests that IORT may be a promising adjuvant treatment for selected patients with HGG. Despite the high rate of complications and treatment failures, the survival outcomes were comparable or even superior to conventional methods. However, the limitations of the study, such as the lack of a control group and small sample sizes, warrant further investigation through prospective randomized controlled trials to better understand the specific patient populations that may benefit most from IORT. However, the limitations of the study, such as the lack of a control group and small sample sizes, warrant further investigation. Notably, the ongoing RP3 trial (NCT02685605) is currently underway, with the aim of providing a more comprehensive understanding of IORT. Moreover, future research should focus on managing complications associated with IORT to improve its safety and efficacy in treating HGG.
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Affiliation(s)
| | | | - Pedro Abrahão Reis
- Faculty of Medicine, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Savio Batista
- Faculty of Medicine, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
| | | | | | - Nicollas Nunes Rabelo
- Division of Neurosurgery, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Raphael Bertani
- Division of Neurosurgery, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | | | - Eberval Gadelha Figueiredo
- Division of Neurosurgery, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Wellingson S Paiva
- Division of Neurosurgery, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Iuri Santana Neville
- Division of Neurosurgery, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
- Instituto do Câncer do Estado de São Paulo - Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
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Bao H, Ai S, Wang G, Yi L, Lai J, Wang S, Lv Z, Li C, Liu Q, Zhao X, Wu C, Liu C, Mi S, Sun X, Hao C, Liang P. Intraoperative radiotherapy in recurrent IDH-wildtype glioblastoma with gross total resection: A single-center retrospective study. Clin Neurol Neurosurg 2024; 236:108103. [PMID: 38199118 DOI: 10.1016/j.clineuro.2023.108103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/08/2023] [Accepted: 12/22/2023] [Indexed: 01/12/2024]
Abstract
BACKGROUND Isocitrate dehydrogenase-wildtype (IDHwt) glioblastoma (GBM) is one of the most aggressive primary brain tumors. The recurrence of GBM is almost inevitable. As an adjuvant option to surgery, intraoperative radiotherapy (IORT) is gaining increasing attention in the treatment of glioma. This study is aimed to evaluate the therapeutic efficacy of IORT on recurrent IDHwt GBM. METHODS In total, 34 recurrent IDHwt GBM patients who received a second surgery were included in the analysis (17 in the surgery group and 17 in the surgery + IORT group). RESULTS The progression-free survival and overall survival after the second surgery were defined as PFS2 and OS2, respectively. The median PFS2 was 7.3 months (95% CI: 6.3-10.5) and 10.6 months (95% CI: 9.3-14.6) for those patients who received surgery and surgery + IORT, respectively. Patients in the surgery + IORT group also had a longer OS2 (12.8 months, 95% CI: 11.4-17.2) than those in the surgery group (9.3 months, 95% CI: 8.9-12.9). The Kaplan-Meier survival curves, analyzed by log-rank test, revealed a statistically significant difference in PFS2 and OS2 between both groups, suggesting that IORT plays an active role in the observed benefits for PFS2 and OS2. The effects of IORT on PFS2 and OS2 were further confirmed by multivariate Cox hazards regression analysis. Two patients in the surgery group developed distant glioma metastases, and no radiation-related complications were observed in the IORT group. CONCLUSIONS This study suggests that low-dose IORT may improve the prognosis of recurrent IDHwt GBM patients. Future prospective large-scale studies are needed to validate the efficacy and safety of IORT.
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Affiliation(s)
- Hongbo Bao
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China; Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Siqi Ai
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China; Department of Neurosurgery, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Gang Wang
- Department of Radiotherapy, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
| | - Liye Yi
- Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Jiacheng Lai
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
| | - Shuai Wang
- Department of Imaging Center, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
| | - Zhonghua Lv
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
| | - Chenlong Li
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
| | - Qing Liu
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
| | - Xinyu Zhao
- Department of Radiotherapy, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
| | - Chou Wu
- Department of Radiotherapy, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
| | - Chang Liu
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
| | - Shan Mi
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
| | - Xiaoyang Sun
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
| | - Chuncheng Hao
- Department of Radiotherapy, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China.
| | - Peng Liang
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China.
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Yearley AG, Goedmakers CMW, Panahi A, Doucette J, Rana A, Ranganathan K, Smith TR. FDA-approved machine learning algorithms in neuroradiology: A systematic review of the current evidence for approval. Artif Intell Med 2023; 143:102607. [PMID: 37673576 DOI: 10.1016/j.artmed.2023.102607] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 05/30/2023] [Accepted: 06/05/2023] [Indexed: 09/08/2023]
Abstract
Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as "medical devices" to ensure patient safety. However, recent work has shown that the FDA approval process may be deficient. In this study, we evaluate the evidence supporting FDA-approved neuroalgorithms, the subset of machine learning algorithms with applications in the central nervous system (CNS), through a systematic review of the primary literature. Articles covering the 53 FDA-approved algorithms with applications in the CNS published in PubMed, EMBASE, Google Scholar and Scopus between database inception and January 25, 2022 were queried. Initial searches identified 1505 studies, of which 92 articles met the criteria for extraction and inclusion. Studies were identified for 26 of the 53 neuroalgorithms, of which 10 algorithms had only a single peer-reviewed publication. Performance metrics were available for 15 algorithms, external validation studies were available for 24 algorithms, and studies exploring the use of algorithms in clinical practice were available for 7 algorithms. Papers studying the clinical utility of these algorithms focused on three domains: workflow efficiency, cost savings, and clinical outcomes. Our analysis suggests that there is a meaningful gap between the FDA approval of machine learning algorithms and their clinical utilization. There appears to be room for process improvement by implementation of the following recommendations: the provision of compelling evidence that algorithms perform as intended, mandating minimum sample sizes, reporting of a predefined set of performance metrics for all algorithms and clinical application of algorithms prior to widespread use. This work will serve as a baseline for future research into the ideal regulatory framework for AI applications worldwide.
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Affiliation(s)
- Alexander G Yearley
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA.
| | - Caroline M W Goedmakers
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; Department of Neurosurgery, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, Netherlands
| | - Armon Panahi
- The George Washington University School of Medicine and Health Sciences, 2300 I St NW, Washington, DC 20052, USA
| | - Joanne Doucette
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; School of Pharmacy, MCPHS University, 179 Longwood Ave, Boston, MA 02115, USA
| | - Aakanksha Rana
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
| | - Kavitha Ranganathan
- Division of Plastic Surgery, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115, USA
| | - Timothy R Smith
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA
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Roques M, Catalaa I, Raveneau M, Attal J, Siegfried A, Darcourt J, Cognard C, de Champfleur NM, Bonneville F. Assessment of the hypervascularized fraction of glioblastomas using a volume analysis of dynamic susceptibility contrast-enhanced MRI may help to identify pseudoprogression. PLoS One 2022; 17:e0270216. [PMID: 36227862 PMCID: PMC9560146 DOI: 10.1371/journal.pone.0270216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 06/07/2022] [Indexed: 11/22/2022] Open
Abstract
PURPOSE Although perfusion magnetic resonance imaging (MRI) is widely used to identify pseudoprogression, this advanced technique lacks clinical reliability. Our aim was to develop a parameter assessing the hypervascularized fraction of glioblastomas based on volume analysis of dynamic susceptibility contrast-enhanced MRI and evaluate its performance in the diagnosis of pseudoprogression. METHODS Patients with primary glioblastoma showing lesion progression on the first follow-up MRI after chemoradiotherapy were enrolled retrospectively. On both initial and first follow-up MRIs, the leakage-corrected cerebral blood volume (CBV) maps were post-processed using the conventional hot-spot method and a volume method, after manual segmentation of the contrast-enhanced delineated lesion. The maximum CBV (rCBVmax) was calculated with both methods. Secondly, the threshold of 2 was applied to the CBV values contained in the entire segmented volume, defining our new parameter: %rCBV>2. The probability of pseudoprogression based on rCBVmax and %rCBV>2 was calculated in logistic regression models and diagnostic performance assessed by receiving operator characteristic curves. RESULTS Out of 25 patients, 11 (44%) were classified with pseudoprogression and 14 (56%) with true progression based on the Response Assessement in Neuro-Oncology criteria. rCBVmax was lower for pseudoprogression (3.4 vs. 7.6; p = 0.033) on early follow-up MRI. %rCBV>2, was lower for pseudoprogression on both initial (57.5% vs. 71.3%; p = 0.033) and early follow-up MRIs (22.1% vs. 51.8%; p = 0.0006). On early follow-up MRI, %rCBV>2 had the largest area under the curve for the diagnosis of pseudoprogression: 0.909 [0.725-0.986]. CONCLUSION The fraction of hypervascularization of glioblastomas as assessed by %rCBV>2 was lower in tumours that subsequently developed pseudoprogression both on the initial and early follow-up MRIs. This fractional parameter may help identify pseudoprogression with greater accuracy than rCBVmax.
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Affiliation(s)
- Margaux Roques
- Department of Neuroradiology, Toulouse Hospital, Toulouse, France
- * E-mail:
| | - Isabelle Catalaa
- Department of Neuroradiology, Toulouse Hospital, Toulouse, France
| | - Magali Raveneau
- Department of Neuroradiology, Toulouse Hospital, Toulouse, France
| | - Justine Attal
- Department of Radiotherapy, IUCT Toulouse (Toulouse University Cancer Institute), Toulouse, France
| | | | - Jean Darcourt
- Department of Neuroradiology, Toulouse Hospital, Toulouse, France
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Mammadov O, Akkurt BH, Musigmann M, Ari AP, Blömer DA, Kasap DN, Henssen DJ, Nacul NG, Sartoretti E, Sartoretti T, Backhaus P, Thomas C, Stummer W, Heindel W, Mannil M. Radiomics for pseudoprogression prediction in high grade gliomas: added value of MR contrast agent. Heliyon 2022; 8:e10023. [PMID: 35965975 PMCID: PMC9364026 DOI: 10.1016/j.heliyon.2022.e10023] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 05/04/2022] [Accepted: 07/18/2022] [Indexed: 10/31/2022] Open
Abstract
Objective Material & methods Results Conclusion Radiomics allows for prediction of pseudoprogression in high-grade gliomas. Use of contrast media boosts the performance of the Radiomics prediction model.
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