Diagnostic accuracy of qualitative MRI in 550 paediatric brain tumours: evaluating current practice in the computational era

Dixon, Luke; Jandu, Gurpreet Kaur; Sidpra, Jai; Mankad, Kshitij

doi:10.21037/qims-20-1388

Cited by 4 publications

(5 citation statements)

References 18 publications

(26 reference statements)

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“…It should however be considered that the supratentorial categorization of the tumors is slightly different in these two studies. Our results also agree with the report by Dixon et al, 7 in that tumors in the posterior fossa were easier to diagnose.…”

Section: Location Of the Tumorssupporting

confidence: 93%

“…The latter have not been reported earlier. The results for the supratentorial tumors obtained in this study show the potential of deep learning models 9/13 trained on MRI data for supporting radiologists and improving the diagnosis of pediatric supratentorial tumors which have been shown to be hard to classify 7 . It should however be considered that the supratentorial categorization of the tumors is slightly different in these two studies.…”

Section: Location Of the Tumorsmentioning

confidence: 74%

“…Brain tumor treatment procedures are usually complicated where tumor detection and preliminary diagnosis are based on magnetic resonance images (MRI), and treatment planning also uses histopathological and molecular analysis of the tissue sample 6 . Diagnosis by radiologists, when comparing the first MRI diagnosis to the final histology diagnosis, varies greatly among tumor types and locations, with an overall accuracy of 72% for broad tumor type classification, which shows the need for computational methods to improve qualitative assessments 7 . With recent technological advances, deep learning algorithms can be trained to assist radiologists in diagnosing brain tumors based on MR images.…”

Section: Introductionmentioning

confidence: 99%

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Pediatric brain tumor classification using deep learning on MR-images from the children’s brain tumor network

Bianchessi,

Tampu,

Blystad

et al. 2023

Preprint

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Brain tumors are among the leading causes of cancer deaths in children. Initial diagnosis based on MR images can be a challenging task for radiologists, depending on the tumor type and location. Deep learning methods could support the diagnosis by predicting the tumor type. A subset (181 subjects) of the data from "Children's Brain Tumor Network" (CBTN) was used, including infratentorial and supratentorial tumors, with the main tumor types being low-grade astrocytomas, ependymomas, and medulloblastomas. T1w-Gd, T2-w, and ADC MR sequences were used separately. Classification was performed on 2D MR images using four different off-the-shelf deep learning models and a custom-designed shallow network all pre-trained on adult MR images. Joint fusion was implemented to combine image and age data, and tumor type prediction was computed volume-wise. Matthew's correlation coefficient (MCC), accuracy, and F1 scores were used to assess the models' performance. Model explainability, using gradient-weighted class activation mapping (Grad-CAM), was implemented and the network's attention on the tumor region was quantified. The shallow custom network resulted in the highest classification performance when trained on T2-w or ADC MR images fused with age information, when considering infratentorial tumors only (MCC: 0.71 for ADC and 0.57 for T2-w), and both infra- and supratentorial tumors (MCC: 0.70 for ADC and 0.57 for T2-w). Classification of pediatric brain tumors on MR images could be accomplished using deep learning, and the fusion of age information improved model performance.

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Section: Location Of the Tumorssupporting

confidence: 93%

Section: Location Of the Tumorsmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

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Pediatric brain tumor classification using deep learning on MR-images from the children’s brain tumor network

Bianchessi,

Tampu,

Blystad

et al. 2023

Preprint

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“…[4][5][6][7] These methods rely on accelerating data acquisition using parallel imaging [8], compressed sensing [9][10][11][12][13], non-Cartesian imaging [14], and simultaneous multi-slice imaging. [15] Quantitative imaging: Dixon et al [16] analyzed 550 pediatric patients with brain tumors and showed the accuracy of qualitative diagnostic imaging. Consequently, quantitative MR (qMR) imaging plays a crucial role in diagnosing.…”

Section: Introductionmentioning

confidence: 99%

Tailored magnetic resonance fingerprinting of post-operative pediatric brain tumor patients

et al. 2023

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“…Dixon et al [16] analyzed 550 pediatric patients with brain tumors and showed the accuracy of qualitative diagnostic imaging. Consequently, quantitative MR (qMR) imaging plays a crucial role in diagnosing.…”

Section: Introductionmentioning

confidence: 99%

Tailored magnetic resonance fingerprinting of post-operative pediatric brain tumor patients

Poojar

Qian

Jin

et al. 2022

Preprint

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Purpose: Brain and spinal cord tumors are the second most common cancer in children and account for one out of four cancers diagnosed. However, the long acquisition times associated with acquiring both data types prohibit using quantitative MR (qMR) in pediatric imaging protocols. This study aims to demonstrate the tailored magnetic resonance fingerprinting's (TMRF) ability to simultaneously provide quantitative maps (T1, T2) and multi-contrast qualitative images (T1 weighted, T1 FLAIR, T2 weighted) rapidly in pediatric brain tumor patients. Methods: In this work, we imaged five pediatric patients with brain tumors (resected/residual) using TMRF at 3T. We compared the TMRF-derived T2 weighted images with those from the vendor-supplied sequence (as the gold standard, GS) for healthy and pathological tissue signal intensities. The relaxometric maps from TMRF were subjected to a region of interest (ROI) analysis to differentiate between healthy and pathological tissues. We performed the Wilcoxon rank sum test to check for significant differences between the two tissue types. Results: We found significant differences (P < 0.05) in both T1 and T2 ROI values between the two tissue types. A strong correlation was found between the TMRF-based T2 weighted and GS signal intensities for the healthy (correlation coefficient, r = 0.99) and pathological tissues (r = 0.88). Conclusion: The TMRF implementation provides the two relaxometric maps and can potentially save ~2 minutes if it replaces the T2-weighted imaging in the current protocol.

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Diagnostic accuracy of qualitative MRI in 550 paediatric brain tumours: evaluating current practice in the computational era

Cited by 4 publications

References 18 publications

Pediatric brain tumor classification using deep learning on MR-images from the children’s brain tumor network

Pediatric brain tumor classification using deep learning on MR-images from the children’s brain tumor network

Tailored magnetic resonance fingerprinting of post-operative pediatric brain tumor patients

Tailored magnetic resonance fingerprinting of post-operative pediatric brain tumor patients

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