Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: A preliminary study

Banzato, Tommaso; Causin, Francesco; Puppa, Alessandro Della; Cester, Giacomo; Mazzai, Linda; Zotti, Alessandro

doi:10.1002/jmri.26723

Cited by 39 publications

(33 citation statements)

References 31 publications

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“…Twelve papers investigated the role of AI in the diagnosis of neurological neoplasms (Table 2). The main objectives of the papers involved: pathological or molecular classification of the tumors (N = 7) [38][39][40][41][42][43][44] solely detection of tumor in a Computer Aided Diagnosis (CAD) fashion (N = 1) [45] and the combination of detection and segmentation of the lesions (N = 4) [46][47][48][49]. The number of patients used in the studies span from a minimum of 33 patients [47] to a maximum of 266 patients [49].…”

Section: Diagnosismentioning

confidence: 99%

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Current applications of deep-learning in neuro-oncological MRI

et al. 2021

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Magnetic Resonance Imaging (MRI) provides an essential contribution in the screening, detection, diagnosis, staging, treatment and follow-up in patients with a neurological neoplasm. Deep learning (DL), a subdomain of artificial intelligence has the potential to enhance the characterization, processing and interpretation of MRI images. The aim of this review paper is to give an overview of the current state-of-art usage of DL in MRI for neuro-oncology. Methods: We reviewed the Pubmed database by applying a specific search strategy including the combination of MRI, DL, neuro-oncology and its corresponding search terminologies, by focussing on Medical Subject Headings (Mesh) or title/abstract appearance. The original research papers were classified based on its application, into three categories: technological innovation, diagnosis and follow-up.Results: Forty-one publications were eligible for review, all were published after the year 2016. The majority (N = 22) was assigned to technological innovation, twelve had a focus on diagnosis and seven were related to patient follow-up. Applications ranged from improving the acquisition, synthetic CT generation, autosegmentation, tumor classification, outcome prediction and response assessment. The majority of publications made use of standard (T1w, cT1w, T2w and FLAIR imaging), with only a few exceptions using more advanced MRI technologies. The majority of studies used a variation on convolution neural network (CNN) architectures. Conclusion:Deep learning in MRI for neuro-oncology is a novel field of research; it has potential in a broad range of applications. Remaining challenges include the accessibility of large imaging datasets, the applicability across institutes/vendors and the validation and implementation of these technologies in clinical practise.

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Section: Diagnosismentioning

confidence: 99%

“…Half of the studies used data collected from a single institution and the other half collected data from at least two different institutions (minimum 2, maximum 37 centres). Four studies were focused on meningioma [38,42,43,46], two studies on glioblastoma [47,48]. Two studies used glioma patients [44,45].…”

Section: Diagnosismentioning

confidence: 99%

Current applications of deep-learning in neuro-oncological MRI

et al. 2021

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“…The main tasks in neurosurgical oncology attempted to be solved using AI technologies were: segmentation and volumetry of brain structures [2, 3]; noninvasive tissue and molecular genetic differential diagnosis [4][5][6][7]; predicting complications and treatment outcomes [8,9]. One of the unconventional AI applications in neurooncology was the analysis of research trends in neurooncology based on scientific publications.…”

Section: Application Of Artificial Intelligence In Neurooncology (133mentioning

confidence: 99%

Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives

Danilov¹,

Shifrin²,

Kotik³

et al. 2020

Sovrem Tehnol Med

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The current increase in the number of publications on the use of artificial intelligence (AI) technologies in neurosurgery indicates a new trend in clinical neuroscience. The aim of the study was to conduct a systematic literature review to highlight the main directions and trends in the use of AI in neurosurgery. Methods. Using the PubMed search engine, 327 original journal articles published from 1996 to July 2019 and related to the use of AI technologies in neurosurgery, were selected. The typical issues addressed by using AI were identified for each area of neurosurgery. Results. The typical AI applications within each of the five main areas of neurosurgery (neuro-oncology, functional, vascular, spinal neurosurgery, and traumatic brain injury) were defined. Conclusion. The article highlights the main areas and trends in the up-to-date AI research in neurosurgery, which might be helpful in planning new scientific projects.

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“…The second task was a 'di cult' task of differentiating between low-vs. high-grade meningioma, with reported accuracies of less than 76% by conventional MRI. 13,14 The second dataset consisted of 258 adult patients with a low grade (n=163) or high grade (n=95) meningioma diagnosed from February 2008 to September 2018.…”

Section: Subjectsmentioning

confidence: 99%

Radiomics machine learning study with a small sample size: single random training-test set split may result in unreliable results

Park

Ahn

et al. 2020

Preprint

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Objective: To determine how the estimated performance of a machine learning model varies according to how a dataset is split into training and test sets using brain tumor radiomics data, under different conditions.Materials and Methods: Two binary tasks with different levels of difficulty ('simple’ task, glioblastoma [GBM, n=109] vs. brain metastasis [n=58]; 'difficult’ task, low- [n=163] vs. high grade [n=95] meningiomas) were performed using radiomics features from magnetic resonance imaging (MRI). For each trial of the 1,000 different training-test set splits with a ratio of 7:3, a least absolute shrinkage and selection operator (LASSO) model was trained by 5-fold cross-validation (CV) in the training set and tested in the test set. The model stability and performance was evaluated according to the number of input features (from 1 to 50), the sample size (full vs. undersampled), and the level of difficulty. In addition to 5-fold CV without a repetition, three other CV methods were compared: 5-fold CV with 100 repetitions, nested CV, and nested CV with 100 repetitions.Results: The highest mean cross-validated area under the receiver operating characteristics curve (AUC) and the higher stability (lower AUC differences between training and testing) was achieved with 6 and 13 features from the GBM and meningioma task, respectively. For the simple task, simple task with undersampling, difficult task, and difficult task with undersampling, average mean AUCs were 0.947, 0.923, 0.795, and 0.764, and average AUC differences between training and testing were 0.029, 0.054, 0.053, and 0.108, respectively. Among four CV models, the most conservative method (i.e., lowest AUC and highest relative standard deviation [RSD]) was nested CV with 100 repetitions.Conclusions: A single random split of a dataset into training and test sets may lead to an unreliable report of model performance in radiomics machine learning studies, and reporting the mean and standard deviation of model performance metrics by performing nested and/or repeated CV on the entire dataset is suggested.

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Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: A preliminary study

Cited by 39 publications

References 31 publications

Current applications of deep-learning in neuro-oncological MRI

Current applications of deep-learning in neuro-oncological MRI

Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives

Radiomics machine learning study with a small sample size: single random training-test set split may result in unreliable results

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