AI and imaging-based cancer screening: getting ready for prime time

Kleeff, Jörg; Ronellenfitsch, Ulrich

doi:10.1038/s41591-023-02630-y

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…In the past decade, radiomics [ 15 ], machine learning (ML) [ 16 ] and deep learning (DL) [ 17 – 19 ] have been proposed as effective approaches for feature extraction and classification of radiologic images [ 20 ]. DL models automatically learn feature representations, reducing the need for manual feature engineering.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network-based magnetic resonance image differentiation of filum terminale ependymomas from schwannomas

Gu,

Dai,

Chen

et al. 2024

BMC Cancer

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Purpose Preoperative diagnosis of filum terminale ependymomas (FTEs) versus schwannomas is difficult but essential for surgical planning and prognostic assessment. With the advancement of deep-learning approaches based on convolutional neural networks (CNNs), the aim of this study was to determine whether CNN-based interpretation of magnetic resonance (MR) images of these two tumours could be achieved. Methods Contrast-enhanced MRI data from 50 patients with primary FTE and 50 schwannomas in the lumbosacral spinal canal were retrospectively collected and used as training and internal validation datasets. The diagnostic accuracy of MRI was determined by consistency with postoperative histopathological examination. T1-weighted (T1-WI), T2-weighted (T2-WI) and contrast-enhanced T1-weighted (CE-T1) MR images of the sagittal plane containing the tumour mass were selected for analysis. For each sequence, patient MRI data were randomly allocated to 5 groups that further underwent fivefold cross-validation to evaluate the diagnostic efficacy of the CNN models. An additional 34 pairs of cases were used as an external test dataset to validate the CNN classifiers. Results After comparing multiple backbone CNN models, we developed a diagnostic system using Inception-v3. In the external test dataset, the per-examination combined sensitivities were 0.78 (0.71–0.84, 95% CI) based on T1-weighted images, 0.79 (0.72–0.84, 95% CI) for T2-weighted images, 0.88 (0.83–0.92, 95% CI) for CE-T1 images, and 0.88 (0.83–0.92, 95% CI) for all weighted images. The combined specificities were 0.72 based on T1-WI (0.66–0.78, 95% CI), 0.84 (0.78–0.89, 95% CI) based on T2-WI, 0.74 (0.67–0.80, 95% CI) for CE-T1, and 0.81 (0.76–0.86, 95% CI) for all weighted images. After all three MRI modalities were merged, the receiver operating characteristic (ROC) curve was calculated, and the area under the curve (AUC) was 0.93, with an accuracy of 0.87. Conclusions CNN based MRI analysis has the potential to accurately differentiate ependymomas from schwannomas in the lumbar segment.

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