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Kalasauskas D, Kosterhon M, Kurz E, Schmidt L, Altmann S, Grauhan NF, Sommer C, Othman A, Brockmann MA, Ringel F, Keric N. Preoperative prediction of CNS WHO grade and tumour aggressiveness in intracranial meningioma based on radiomics and structured semantics. Sci Rep 2024; 14:20586. [PMID: 39232068 PMCID: PMC11374997 DOI: 10.1038/s41598-024-71200-0] [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: 04/11/2024] [Accepted: 08/26/2024] [Indexed: 09/06/2024] Open
Abstract
Preoperative identification of intracranial meningiomas with aggressive behaviour may help in choosing the optimal treatment strategy. Radiomics is emerging as a powerful diagnostic tool with potential applications in patient risk stratification. In this study, we aimed to compare the predictive value of conventional, semantic based and radiomic analyses to determine CNS WHO grade and early tumour relapse in intracranial meningiomas. We performed a single-centre retrospective analysis of intracranial meningiomas operated between 2007 and 2018. Recurrence within 5 years after Simpson Grade I-III resection was considered as early. Preoperative T1 CE MRI sequences were analysed conventionally by two radiologists. Additionally a semantic feature score based on systematic analysis of morphological characteristics was developed and a radiomic analysis were performed. For the radiomic model, tumour volume was extracted manually, 791 radiomic features were extracted. Eight feature selection algorithms and eight machine learning methods were used. Models were analysed using test and training datasets. In total, 226 patients were included. There were 21% CNS WHO grade 2 tumours, no CNS WHO grade 3 tumour, and 25 (11%) tumour recurrences were detected in total. In ROC analysis the best radiomic models demonstrated superior performance for determination of CNS WHO grade (AUC 0.930) and early recurrence (AUC 0.892) in comparison to the semantic feature score (AUC 0.74 and AUC 0.65) and conventional radiological analysis (AUC 0.65 and 0.54). The combination of human classifiers, semantic score and radiomic analysis did not markedly increase the model performance. Radiomic analysis is a promising tool for preoperative identification of aggressive and atypical intracranial meningiomas and could become a useful tool in the future.
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Affiliation(s)
- Darius Kalasauskas
- Department of Neurosurgery, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Michael Kosterhon
- Department of Neurosurgery, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany.
| | - Elena Kurz
- Department of Neurosurgery, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Leon Schmidt
- Department of Neurosurgery, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Sebastian Altmann
- Department of Neuroradiology, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Nils F Grauhan
- Department of Neuroradiology, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Clemens Sommer
- Institute of Neuropathology, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Ahmed Othman
- Department of Neuroradiology, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Marc A Brockmann
- Department of Neuroradiology, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Florian Ringel
- Department of Neurosurgery, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Naureen Keric
- Department of Neurosurgery, University Medical Center, Johannes Gutenberg University, Langenbeckstr. 1, 55131, Mainz, Germany
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Karperien AL, Jelinek HF. Morphology and Fractal-Based Classifications of Neurons and Microglia in Two and Three Dimensions. ADVANCES IN NEUROBIOLOGY 2024; 36:149-172. [PMID: 38468031 DOI: 10.1007/978-3-031-47606-8_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Microglia and neurons live physically intertwined, intimately related structurally and functionally in a dynamic relationship in which microglia change continuously over a much shorter timescale than do neurons. Although microglia may unwind and depart from the neurons they attend under certain circumstances, in general, together both contribute to the fractal topology of the brain that defines its computational capabilities. Both neuronal and microglial morphologies are well-described using fractal analysis complementary to more traditional measures. For neurons, the fractal dimension has proved valuable for classifying dendritic branching and other neuronal features relevant to pathology and development. For microglia, fractal geometry has substantially contributed to classifying functional categories, where, in general, the more pathological the biological status, the lower the fractal dimension for individual cells, with some exceptions, including hyper-ramification. This chapter provides a review of the intimate relationships between neurons and microglia, by introducing 2D and 3D fractal analysis methodology and its applications in neuron-microglia function in health and disease.
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Affiliation(s)
- Audrey L Karperien
- School of Community Health, Charles Sturt University, Albury, NSW, Australia
| | - Herbert F Jelinek
- Department of Medical Sciences and Biotechnology Center, Khalifa University, Abu Dhabi, UAE
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Donato I, Velpula KK, Tsung AJ, Tuszynski JA, Sergi CM. Demystifying neuroblastoma malignancy through fractal dimension, entropy, and lacunarity. TUMORI JOURNAL 2023:3008916221146208. [PMID: 36645143 DOI: 10.1177/03008916221146208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
PURPOSE Neuroblastoma is a pediatric solid tumor with a prognosis associated with histology and age of the patient, which are the parameters of the well-established current classification (Shimada classification). Despite the development of new treatment options, the prognosis of high-risk neuroblastoma patients is still poor. Therefore, there is a continuous need to stratify the children suffering from this tumor. A mathematical and computational approach is proposed to enable automatic and precise cancer diagnosis on the histological slide. METHODS We targeted the complexity of neuroblastoma by calculating its image entropy (S), fractal dimension (FD), and lacunarity (λ) in a combined mathematical code. First, we tested the proposed method for patient-derived glioma images. It allowed distinguishing between normal brain tissue, grade II, and grade III glioma, which harbor different outcomes. RESULTS In neuroblastoma, our analysis of image's FD, S, and λ combined with a machine learning algorithm automatically predicted tumor malignancy with a receiver operating characteristic curve of 0.82. FD, S, and λ distinguish between neuroblastoma and ganglioneuroma, but they only partially differentiate between the normal samples and the other classes. Ganglioneuroma, the most differentiated form, and poorly-differentiated neuroblastoma display different values of FD, S, and λ. CONCLUSIONS FD, S, and λ of imaging recognize groups in neuroblastic tumors. We suggest that future studies including these features may challenge the current Shimada classification of neuroblastoma with categories of favorable and unfavorable histology. It is expected that this methodology could trigger multicenter studies and potentially find practical use in the clinical setting of children's hospitals worldwide.
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Affiliation(s)
- Irene Donato
- Department of Oncology, University of Alberta, Cross Cancer Institute, Edmonton, AB, Canada
| | - Kiran K Velpula
- Departments of Cancer Biology and Pharmacology, Neurosurgery, University of Illinois College of Medicine at Peoria, Peoria, IL, USA
| | - Andrew J Tsung
- Departments of Cancer Biology and Pharmacology, Neurosurgery, University of Illinois College of Medicine at Peoria, Peoria, IL, USA
| | - Jack A Tuszynski
- Department of Oncology, University of Alberta, Cross Cancer Institute, Edmonton, AB, Canada.,Department of Physics, University of Alberta, Centennial Centre for Interdisciplinary Science, Edmonton, AB, Canada.,Dipartimento di Ingegneria Meccanica e Aerospaziale (DIMEAS), Polytechnic University of Turin, Turin, Italy
| | - Consolato M Sergi
- Department of Laboratory Medicine and Pathology, University of Alberta, Stollery Children's Hospital, Edmonton, AB, Canada.,Division of Anatomic Pathology, Children's Hospital of Eastern Ontario, University of Ottawa, Ottawa, ON, Canada.,Institute of Pathology, Medical University of Innsbruck, Innsbruck, Tyrol, Austria
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Three-dimensional fractal dimension and lacunarity features may noninvasively predict TERT promoter mutation status in grade 2 meningiomas. PLoS One 2022; 17:e0276342. [PMID: 36264940 PMCID: PMC9584385 DOI: 10.1371/journal.pone.0276342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 10/04/2022] [Indexed: 11/07/2022] Open
Abstract
PURPOSE The 2021 World Health Organization classification includes telomerase reverse transcriptase promoter (TERTp) mutation status as a factor for differentiating meningioma grades. Therefore, preoperative prediction of TERTp mutation may assist in clinical decision making. However, no previous study has applied fractal analysis for TERTp mutation status prediction in meningiomas. The purpose of this study was to assess the utility of three-dimensional (3D) fractal analysis for predicting the TERTp mutation status in grade 2 meningiomas. METHODS Forty-eight patients with surgically confirmed grade 2 meningiomas (41 TERTp-wildtype and 7 TERTp-mutant) were included. 3D fractal dimension (FD) and lacunarity values were extracted from the fractal analysis. A predictive model combining clinical, conventional, and fractal parameters was built using logistic regression analysis. Receiver operating characteristic curve analysis was used to assess the ability of the model to predict TERTp mutation status. RESULTS Patients with TERTp-mutant grade 2 meningiomas were older (P = 0.029) and had higher 3D FD (P = 0.026) and lacunarity (P = 0.004) values than patients with TERTp-wildtype grade 2 meningiomas. On multivariable logistic analysis, higher 3D FD values (odds ratio = 32.50, P = 0.039) and higher 3D lacunarity values (odds ratio = 20.54, P = 0.014) were significant predictors of TERTp mutation status. The area under the curve, accuracy, sensitivity, and specificity of the multivariable model were 0.84 (95% confidence interval 0.71-0.93), 83.3%, 71.4%, and 85.4%, respectively. CONCLUSION 3D FD and lacunarity may be useful imaging biomarkers for predicting TERTp mutation status in grade 2 meningiomas.
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Kalasauskas D, Kosterhon M, Keric N, Korczynski O, Kronfeld A, Ringel F, Othman A, Brockmann MA. Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors. Cancers (Basel) 2022; 14:cancers14030836. [PMID: 35159103 PMCID: PMC8834271 DOI: 10.3390/cancers14030836] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/30/2022] [Accepted: 02/04/2022] [Indexed: 02/05/2023] Open
Abstract
Simple Summary Tumor qualities, such as growth rate, firmness, and intrusion into healthy tissue, can be very important for operation planning and further treatment. Radiomics is a promising new method that allows the determination of some of these qualities on images performed before surgery. In this article, we provide a review of the use of radiomics in various tumors of the central nervous system, such as metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors. Abstract The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we performed an analysis of publications on PubMed and Web of Science databases, focusing on radiomics in CNS metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.
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Affiliation(s)
- Darius Kalasauskas
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Michael Kosterhon
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Naureen Keric
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Oliver Korczynski
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Andrea Kronfeld
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Florian Ringel
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Ahmed Othman
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Marc A. Brockmann
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
- Correspondence:
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Friconnet G, Baudouin M, Brinjikji W, Saleme S, Espíndola Ala VH, Boncoeur-Martel MP, Mounayer C, Rouchaud A. Advanced MRI shape analysis as a predictor of histologically aggressive supratentorial meningioma. J Neuroradiol 2021; 49:275-280. [PMID: 33421448 DOI: 10.1016/j.neurad.2020.12.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 12/29/2020] [Accepted: 12/29/2020] [Indexed: 10/22/2022]
Abstract
BACKGROUND AND PURPOSE A subset of aggressive meningioma is associated with higher morbidity and requires a different therapeutic management. This subset consists of World Health Organization (WHO) grade II and III meningioma, characterized particularly with microscopic brain invasion. Numerous studies tried to screen aggressive meningioma on pre-operative MRI. The objective of the study was to determine if an advanced shape analysis of supratentorial meningioma outlines could reliably predict WHO II-III grade and histological brain invasion. MATERIALS AND METHODS We performed a retrospective analysis for all consecutive patients who underwent surgery for supratentorial histologically-proven meningioma from 2010 to 2018. Pre-operative MRI T1WI contrast enhanced axial, coronal and sagittal slices were collected from 101 patients. Advanced shape analysis including fractal analysis and topological skeleton analysis was performed. Shape analysis parameters were correlated with histopathological WHO grading and brain invasion on surgical pieces. RESULTS Shape analysis features such as a low circularity, a low solidity, a high fractal dimension and a high number of skeleton's branches were significantly correlated with both WHO II-III meningioma and histological brain invasion. Cross-validated regression models including these features were predictive of WHO II-III meningioma and brain invasion with respective AUC of 0.71 and 0.72. CONCLUSIONS MRI shape analysis provides informative imaging biomarkers to predict high WHO grade and histological brain invasion of supratentorial meningioma. Further prospective studies including the evaluation of a fully-automatized and totally reproducible process are required to confirm the results.
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Affiliation(s)
- Guillaume Friconnet
- Department of Radiology, Centre Hospitalier et Universitaire Dupuytren, Limoges, France.
| | - Maxime Baudouin
- Department of Radiology, Centre Hospitalier et Universitaire Dupuytren, Limoges, France
| | - Waleed Brinjikji
- Department of the Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Suzana Saleme
- Department of Radiology, Centre Hospitalier et Universitaire Dupuytren, Limoges, France
| | | | | | - Charbel Mounayer
- Department of Radiology, Centre Hospitalier et Universitaire Dupuytren, Limoges, France; CNRS, XLIM, UMR 7252, F_87000, Limoges, France
| | - Aymeric Rouchaud
- Department of Radiology, Centre Hospitalier et Universitaire Dupuytren, Limoges, France; CNRS, XLIM, UMR 7252, F_87000, Limoges, France
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