A Deep Learning Model for Preoperative Differentiation of Glioblastoma, Brain Metastasis and Primary Central Nervous System Lymphoma: A Pilot Study

Tariciotti, Leonardo; Caccavella, Valerio Maria; Fiore, Giorgio; Schisano, Luigi; Carrabba, Giorgio; Borsa, Stefano; Giordano, Martina; Palmisciano, Paolo; Remoli, Giulia; Remore, Luigi Gianmaria; Pluderi, Mauro; Caroli, Manuela; Conte, Giorgio; Triulzi, Fabio; Locatelli, Marco; Bertani, Giulio

doi:10.3389/fonc.2022.816638

Cited by 19 publications

(12 citation statements)

References 39 publications

(41 reference statements)

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“…Normal vs. LGG vs. HGG Custom CNN model 5-fold CV 95 2. MEN vs. Glioma vs. PT Custom CNN model 5-fold CV 94.74 Guo et al [ 128 ] 2022 GBM vs. AST vs. OLI Custom CNN 3-fold CV SEN = 0.772, SPE = 93.0%, AUC = 0.902 87.8% Rizwan et al [ 130 ] 2022 Grade I vs. Grade II vs. Grade III Custom CNN No info shared 97.14% Tariciotti et al [ 123 ] 2022 Metastasis vs. GBM vs. PCNSL Resnet101 Hold-out PRE = 91.88%, SEN = 90.84%, SPE = 96.34%, F1 score = 91.0%, AUC = 0.92 94.72% 4 classes Ahammed et al [ 72 ] 2019 Grade I vs. Grade II vs. Grade III vs. Grade IV VGG19 No info shared PRE = 94.71%, SEN = 92.72%, SPE = 98.13%, F1 score = 93.71% 98.25 Mohammed et al [ 51 ] 2020 …”

Section: Resultsmentioning

confidence: 99%

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives

et al. 2022

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Convolutional neural networks (CNNs) constitute a widely used deep learning approach that has frequently been applied to the problem of brain tumor diagnosis. Such techniques still face some critical challenges in moving towards clinic application. The main objective of this work is to present a comprehensive review of studies using CNN architectures to classify brain tumors using MR images with the aim of identifying useful strategies for and possible impediments in the development of this technology. Relevant articles were identified using a predefined, systematic procedure. For each article, data were extracted regarding training data, target problems, the network architecture, validation methods, and the reported quantitative performance criteria. The clinical relevance of the studies was then evaluated to identify limitations by considering the merits of convolutional neural networks and the remaining challenges that need to be solved to promote the clinical application and development of CNN algorithms. Finally, possible directions for future research are discussed for researchers in the biomedical and machine learning communities. A total of 83 studies were identified and reviewed. They differed in terms of the precise classification problem targeted and the strategies used to construct and train the chosen CNN. Consequently, the reported performance varied widely, with accuracies of 91.63–100% in differentiating meningiomas, gliomas, and pituitary tumors (26 articles) and of 60.0–99.46% in distinguishing low-grade from high-grade gliomas (13 articles). The review provides a survey of the state of the art in CNN-based deep learning methods for brain tumor classification. Many networks demonstrated good performance, and it is not evident that any specific methodological choice greatly outperforms the alternatives, especially given the inconsistencies in the reporting of validation methods, performance metrics, and training data encountered. Few studies have focused on clinical usability.

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Section: Resultsmentioning

confidence: 99%

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives

et al. 2022

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“…Although multiparametric MRI represents the current imaging gold standard in primary and metastatic brain neoplasms, PET studies have the unique and complementary ability to evaluate and characterize the metabolic patterns within the tumor and non-tumor tissues through the use of selected radiolabeled tracers [ 84 , 85 , 86 ]. The roles of different PET tracers in tumor diagnosis and post-treatment response assessment have been largely discussed and validated by international consensuses and recommendations [ 87 , 88 , 89 , 90 ].…”

Section: Discussionmentioning

confidence: 99%

The Role of [68Ga]Ga-DOTA-SSTR PET Radiotracers in Brain Tumors: A Systematic Review of the Literature and Ongoing Clinical Trials

Palmisciano

Watanabe

Conching

et al. 2022

Cancers

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Background: The development of [68Ga]Ga-DOTA-SSTR PET tracers has garnered interest in neuro-oncology, to increase accuracy in diagnostic, radiation planning, and neurotheranostics protocols. We systematically reviewed the literature on the current uses of [68Ga]Ga-DOTA-SSTR PET in brain tumors. Methods: PubMed, Scopus, Web of Science, and Cochrane were searched in accordance with the PRISMA guidelines to include published studies and ongoing trials utilizing [68Ga]Ga-DOTA-SSTR PET in patients with brain tumors. Results: We included 63 published studies comprising 1030 patients with 1277 lesions, and 4 ongoing trials. [68Ga]Ga-DOTA-SSTR PET was mostly used for diagnostic purposes (62.5%), followed by treatment planning (32.7%), and neurotheranostics (4.8%). Most lesions were meningiomas (93.6%), followed by pituitary adenomas (2.8%), and the DOTATOC tracer (53.2%) was used more frequently than DOTATATE (39.1%) and DOTANOC (5.7%), except for diagnostic purposes (DOTATATE 51.1%). [68Ga]Ga-DOTA-SSTR PET studies were mostly required to confirm the diagnosis of meningiomas (owing to their high SSTR2 expression and tracer uptake) or evaluate their extent of bone invasion, and improve volume contouring for better radiotherapy planning. Some studies reported the uncommon occurrence of SSTR2-positive brain pathology challenging the diagnostic accuracy of [68Ga]Ga-DOTA-SSTR PET for meningiomas. Pre-treatment assessment of tracer uptake rates has been used to confirm patient eligibility (high somatostatin receptor-2 expression) for peptide receptor radionuclide therapy (PRRT) (i.e., neurotheranostics) for recurrent meningiomas and pituitary carcinomas. Conclusion: [68Ga]Ga-DOTA-SSTR PET studies may revolutionize the routine neuro-oncology practice, especially in meningiomas, by improving diagnostic accuracy, delineation of radiotherapy targets, and patient eligibility for radionuclide therapies.

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“…Another meta-analysis of 10 articles by You et al suggested that arterial spin labeling could differentiate between PCNSL and HGG with an AUC of 0.86 ( 27 ). Additionally, a diagnostic model constructed with a deep learning algorithm based on MR imaging was used to differentiate PCNSLs, glioblastoma and brain metastases, with an AUC of 0.98 in the differential diagnosis of PCNSL ( 28 ). However, PET-CT still has great diagnostic advantages over other techniques in terms of imaging detection methods that rely on a single index.…”

Section: Discussionmentioning

confidence: 99%

Use of 18F-FDG-PET/CT in differential diagnosis of primary central nervous system lymphoma and high-grade gliomas: A meta-analysis

Zhang

Li²,

Hui³

2022

Front. Neurol.

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BackgroundPrimary central nervous system lymphoma (PCNSL) and high-grade glioma (HGG) appear similar under imaging. However, since the two tumors vary in their treatment methods, their differential diagnosis is crucial. The use of 18F-fluorodeoxyglucose positron emission tomography computed tomography (18F-FDG-PET/CT) imaging to effectively distinguish between the two tumors is not clear; therefore, a meta-analysis was carried out to determine its effectiveness.Materials and methodsThe databases PubMed, EMBASE, Cochrane, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang, China Science, and Technology Journal Database (CQVIP) were exhaustively searched using stringent inclusion and exclusion criteria to select high-quality literature. The Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS-2) was used for the qualitative assessment of the included literature. The bivariate effect model was used to combine statistics such as sensitivity (SEN) and specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) [95% confidence intervals (CI)], plot summary receiver operating characteristic (SROC) curve, and calculate the area under the curve (AUC) value. Sensitivity analysis was used to evaluate the stability of the results, and Deek's test was used to assess publication bias. Meta-regression and subgroup analysis was used to determine the sources of heterogeneity.ResultsA total of nine studies were included in this study. For differential diagnosis of PCNSL and HGG, the combined SEN was 0.91 (95% CI: 0.80–0.96; I2 = 46.73%), combined SPE was 0.88 (95% CI: 0.82–0.93; I2 = 56.30%), the combined PLR was 7.83 (95% CI: 4.96–12.37; I2 = 15.57%), combined NLR was 0.10 (95% CI: 0.05–0.23; I2 = 31.99%), combined DOR was 77.36 (95% CI: 32.74–182.77; I2 = 70.70%). The AUC of SROC was 0.95 (95% CI: 0.93–0.97). No publication bias was found and the sample size and different parameters were the primary reason for heterogeneity.ConclusionThe 18F-FDG-PET/CT imaging technique has a high diagnostic accuracy in the differential diagnosis of PNCSL and HGG. Patients suspected to have the above two tumors are suggested to be examined by 18F-FDG-PET / CT to help in the clinical distinction and further treatment modalities.

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A Deep Learning Model for Preoperative Differentiation of Glioblastoma, Brain Metastasis and Primary Central Nervous System Lymphoma: A Pilot Study

Cited by 19 publications

References 39 publications

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives

The Role of [68Ga]Ga-DOTA-SSTR PET Radiotracers in Brain Tumors: A Systematic Review of the Literature and Ongoing Clinical Trials

Use of 18F-FDG-PET/CT in differential diagnosis of primary central nervous system lymphoma and high-grade gliomas: A meta-analysis

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