Accuracy of conventional MRI for preoperative diagnosis of intracranial tumors: A retrospective cohort study of 762 cases

Yan, Pingkun; Ling, Yan To; Zhang, Zhen; Salim, Adnan; Wang, Lei; Hu, Tingting; Zhao, Hongyang

doi:10.1016/j.ijsu.2016.10.023

Cited by 28 publications

(26 citation statements)

References 35 publications

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“…However, the poor quality of MR images compound with the shortage of experienced radiologists make the manual analysis of MRI data difficult, biased and time-consuming. It has been reported that the current diagnostic sensitivity for primary and recurrent pituitary microadenoma is only 47% and 39%, respectively [5,6] . Therefore, an accurate and efficient auxiliary diagnostic method for the diagnosis of pituitary microadenomas is highly needed.…”

Section: Discussionmentioning

confidence: 99%

“…High-quality MR images are critical for the accurate diagnosis of pituitary adenoma. The increased use and accessibility of imaging techniques and improved spatial resolution of modern devices has led to a rise in incidental detection of pituitary microadenomas [3,6,18] . Importantly, accurate diagnosis of pituitary microadenoma is largely relied on the radiologist's professional experience.…”

Section: Discussionmentioning

confidence: 99%

“…The increase in the demand for senior radiologist is an inevitable trend, but the supply of radiologist has not increased proportionally. A shortage of radiologists, especially senior radiologists, restricts the continuity in radiology services and causes a delay in diagnosis, compromising the overall quality of service to patients [1,6] . Recently, we have encountered several missed diagnosis pituitary microadenomas in our hospital after further detailed diagnosis (Fig 1).…”

Section: Introductionmentioning

confidence: 99%

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Automatic detection of pituitary microadenoma from magnetic resonance imaging using deep learning algorithms

Zhu

Chen

et al. 2020

Preprint

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The risks of misdiagnosed pituitary microadenoma is high. We designed a convolutional neural network (CNN) based computer-aided diagnosis (CAD) system to retrospectively diagnose patients with pituitary microadenoma. A total 5,540 pituitary magnetic resonance (MR) images from 1,108 participants were recruited. MRI images were randomly stratified into non-overlapping cohorts (training set, validation set and test set) to establish five different CNN models. The best CNN model is the ResNet with a diagnostic accuracy of 94%, which outperforms the diagnosis accuracy of our radiologists (64%-85%). The accuracy of our CAD system is further confirmed in additional MR datasets. The diagnostic accuracy of our ResNet model is comparable to the proficiency of a radiologist with 5-10 years’ experience. In summary, this is the first report showing that the CAD system is a viable tool for diagnosing pituitary microadenoma. CAD system is applicable to radiology departments, especially in primary health care institutions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic detection of pituitary microadenoma from magnetic resonance imaging using deep learning algorithms

Zhu

Chen

et al. 2020

Preprint

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“…The capabilities of our proposed methodology is tested against a real-world application, namely, a brain tumour MRS data, with the purpose of elucidating and understanding the associations between brain tumour types and a range of observed metabolites. Improving the knowledge and understanding of these associations is a relevant matter, since the anatomical information provided by magnetic resonance imaging (MRI) is not always accurate, especially in case of heterogeneous, glial tumours [ 22 – 24 ]. Although high resolution magnetic resonance techniques with tumour samples are described in the literature for validating in vivo MRS findings [ 25 , 26 ], this requires invasive (surgical) obtention of additional samples from patients, since tissue used for histopathology analysis is not suitable.…”

Section: Datamentioning

confidence: 99%

Robust Conditional Independence maps of single-voxel Magnetic Resonance Spectra to elucidate associations between brain tumours and metabolites

Casana-Eslava¹,

Ortega‐Martorell²,

Lisboa³

et al. 2020

PLoS ONE

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The aim of the paper is twofold. First, we show that structure finding with the PC algorithm can be inherently unstable and requires further operational constraints in order to consistently obtain models that are faithful to the data. We propose a methodology to stabilise the structure finding process, minimising both false positive and false negative error rates. This is demonstrated with synthetic data. Second, to apply the proposed structure finding methodology to a data set comprising single-voxel Magnetic Resonance Spectra of normal brain and three classes of brain tumours, to elucidate the associations between brain tumour types and a range of observed metabolites that are known to be relevant for their characterisation. The data set is bootstrapped in order to maximise the robustness of feature selection for nominated target variables. Specifically, Conditional Independence maps (CI-maps) built from the data and their derived Bayesian networks have been used. A Directed Acyclic Graph (DAG) is built from CI-maps, being a major challenge the minimization of errors in the graph structure. This work presents empirical evidence on how to reduce false positive errors via the False Discovery Rate, and how to identify appropriate parameter settings to improve the False Negative Reduction. In addition, several node ordering policies are investigated that transform the graph into a DAG. The obtained results show that ordering nodes by strength of mutual information can recover a representative DAG in a reasonable time, although a more accurate graph can be recovered using a random order of samples at the expense of increasing the computation time.

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“…The current gold standard investigation for patients with a suspected brain tumour is MRI, however determining the exact brain tumour type is not possible from imaging alone [ 5 , 6 ]. Some brain tumours pose particular imaging challenges, e.g.…”

Section: Introductionmentioning

confidence: 99%

Stratifying Brain Tumour Histological Sub-Types: The Application of ATR-FTIR Serum Spectroscopy in Secondary Care

Cameron

Rinaldi

Butler

et al. 2020

Cancers

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Patients living with brain tumours have the highest average years of life lost of any cancer, ultimately reducing average life expectancy by 20 years. Diagnosis depends on brain imaging and most often confirmatory tissue biopsy for histology. The majority of patients experience non-specific symptoms, such as headache, and may be reviewed in primary care on multiple occasions before diagnosis is made. Sixty-two per cent of patients are diagnosed on brain imaging performed when they deteriorate and present to the emergency department. Histological diagnosis from invasive surgical biopsy is necessary prior to definitive treatment, because imaging techniques alone have difficulty in distinguishing between several types of brain cancer. However, surgery itself does not necessarily control tumour growth, and risks morbidity for the patient. Due to their similar features on brain scans, glioblastoma, primary central nervous system lymphoma and brain metastases have been known to cause radiological confusion. Non-invasive tests that support stratification of tumour subtype would enhance early personalisation of treatment selection and reduce the delay and risks associated with surgery for many patients. Techniques involving vibrational spectroscopy, such as attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, have previously demonstrated analytical capabilities for cancer diagnostics. In this study, infrared spectra from 641 blood serum samples obtained from brain cancer and control patients have been collected. Firstly, we highlight the capability of ATR-FTIR to distinguish between healthy controls and brain cancer at sensitivities and specificities above 90%, before defining subtle differences in protein secondary structures between patient groups through Amide I deconvolution. We successfully differentiate several types of brain lesions (glioblastoma, meningioma, primary central nervous system lymphoma and metastasis) with balanced accuracies >80%. A reliable blood serum test capable of stratifying brain tumours in secondary care could potentially avoid surgery and speed up the time to definitive therapy, which would be of great value for both neurologists and patients.

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Accuracy of conventional MRI for preoperative diagnosis of intracranial tumors: A retrospective cohort study of 762 cases

Cited by 28 publications

References 35 publications

Automatic detection of pituitary microadenoma from magnetic resonance imaging using deep learning algorithms

Automatic detection of pituitary microadenoma from magnetic resonance imaging using deep learning algorithms

Robust Conditional Independence maps of single-voxel Magnetic Resonance Spectra to elucidate associations between brain tumours and metabolites

Stratifying Brain Tumour Histological Sub-Types: The Application of ATR-FTIR Serum Spectroscopy in Secondary Care

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