MRI and FDG-PET/CT based assessment of axillary lymph node metastasis in early breast cancer: a meta-analysis

Liang, Xiaolong; Yu, Jiegen; Wen, Bing; Xie, Jia; Cai, Qing-Qing; Yang, Quan

doi:10.1016/j.crad.2016.12.001

Cited by 60 publications

(56 citation statements)

References 44 publications

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“…From another systematic review by Liang et al . [ 33 ], it can be concluded that MRI shows superiority over 18F-FDG PET/CT in assessing nodal status with fewer false positive and false negative results. In addition, a recent prospective study by Bruckmann et al .…”

Section: Discussionmentioning

confidence: 99%

Diagnostic performance of PET/computed tomography versus PET/MRI and diffusion-weighted imaging in the N- and M-staging of breast cancer patients

Mooij

Amador

Mitea

et al. 2020

Nuclear Medicine Communications

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Section: Discussionmentioning

confidence: 99%

Diagnostic performance of PET/computed tomography versus PET/MRI and diffusion-weighted imaging in the N- and M-staging of breast cancer patients

Mooij

Amador

Mitea

et al. 2020

Nuclear Medicine Communications

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Section: Discussionmentioning

confidence: 99%

“…Breast MRI is widely used in the preoperative evaluation of ALN status in breast cancer patients, showing superior performance than some other techniques such as ultrasound or positron emission tomography / computed tomography (PET-CT). 21,22 While breast MRI is useful to diagnose ALN status via visual assessment, it suffers from some limitations. In most cases of breast MR scanning, the breast surface coil is mainly centered on the breast region, [23][24][25] but some of the positive ALNs may locate at the border of the coil and some may even be out of the imaging FOV.…”

Section: Discussionmentioning

confidence: 99%

Differentiating axillary lymph node metastasis in invasive breast cancer patients: A comparison of radiomic signatures from multiparametric breast MR sequences

Chai

et al. 2019

Magnetic Resonance Imaging

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Background The axillary lymph node status is critical for breast cancer staging and individualized treatment planning. Purpose To assess the effect of determining axillary lymph node (ALN) metastasis by breast MRI‐derived radiomic signatures, and compare the discriminating abilities of different MR sequences. Study Type Retrospective. Population In all, 120 breast cancer patients, 59 with ALN metastasis and 61 without metastasis, all confirmed by pathology. Field Strength/Sequence 3 .0T scanner with T1‐weighted imaging, T2‐weighted imaging, diffusion‐weighted imaging, and dynamic contrast‐enhanced (DCE) sequences. Assessment Typical morphological and texture features of the segmented tumor were extracted from four sequences, ie, T1WI, T2WI, DWI, and the second postcontrast phase (CE2) of the dynamic contrast‐enhanced sequences. Additional contrast enhancement kinetic features were extracted from all DCE sequences (one pre‐ and seven postcontrast phases). Linear discriminant analysis classifiers were built and compared when using features from an individual sequence or the combination of the sequences in differentiating the ALN metastasis status. Statistical Tests Mann–Whitney U‐test, Fisher's exact test, least absolute shrinkage selection operator (LASSO) regression, and receiver operating characteristic analysis were performed. Results The accuracy/AUC of the four sequences was 79%/0.87, 77%/0.85, 74%/0.79, and 79%/0.85 for the T1WI, CE2, T2WI, and DWI, respectively. When CE2 was augmented by adding kinetic features, the model achieved the highest performance (accuracy = 0.86 and AUC = 0.91). When all features from the four sequences and the kinetics were combined, it did not lead to a further increase in the performance (P = 0.48). Data Conclusion Breast tumor's radiomic signatures from preoperative breast MRI sequences are associated with the ALN metastasis status, where CE2 phase and the contrast enhancement kinetic features lead to the highest classification effect. Level of Evidence 3 Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2019;50:1125–1132.

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“…The 'gold standard' for diagnosing axillary LN involvement is a pathological examination of aspiration cytology, a sentinel LN biopsy (SLNB), and an axillary LN dissection (ALND); however, these are invasive methods. In contrast, the utility of noninvasive 18 F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) for the diagnosis of axillary LN metastasis in patients with breast cancer has been described by several research groups [3][4][5][6][7][8][9], one of which achieved a relatively low pooled sensitivity value of 60% and a quite high pooled specificity value of 97% [8].…”

Section: Introductionmentioning

confidence: 99%

Preliminary Study of Ai-assisted Diagnosis Using FDG-PET/CT for Axillary Lymph Node Metastasis in Patients With Breast Cancer

Kitajima

Hirata

et al. 2020

Preprint

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Background: To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using FDG-PET/CT, we constructed an artificial intelligence (AI)-assisted diagnosis system that uses deep-learning technologies.Materials and Methods: Two clinicians and the new AI system retrospectively analyzed and diagnosed 414 axillae of 407 patients with biopsy-proven breast cancer who had undergone FDG-PET/CT before a mastectomy or breast-conserving surgery with a sentinel lymph node (LN) biopsy and/or axillary LN dissection. We designed and trained a deep 3D convolutional neural network (CNN) as the AI model. The diagnoses from the clinicians were blended with the diagnoses from the AI model to improve the diagnostic accuracy.Results: Although the AI model did not outperform the clinicians, the diagnostic accuracies of the clinicians were considerably improved by collaborating with the AI model: the two clinicians' sensitivities of 59.8% and 57.4% increased to 68.6% and 64.2%, respectively, whereas the clinicians' specificities of 99.0% and 99.5% remained unchanged.Conclusions: It is expected that AI using deep-learning technologies will be useful in diagnosing axillary LN metastasis using FDG-PET/CT. Even if the diagnostic performance of AI is not better than that of clinicians, taking AI diagnoses into consideration may positively impact the overall diagnostic accuracy.

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MRI and FDG-PET/CT based assessment of axillary lymph node metastasis in early breast cancer: a meta-analysis

Cited by 60 publications

References 44 publications

Diagnostic performance of PET/computed tomography versus PET/MRI and diffusion-weighted imaging in the N- and M-staging of breast cancer patients

Diagnostic performance of PET/computed tomography versus PET/MRI and diffusion-weighted imaging in the N- and M-staging of breast cancer patients

Differentiating axillary lymph node metastasis in invasive breast cancer patients: A comparison of radiomic signatures from multiparametric breast MR sequences

Preliminary Study of Ai-assisted Diagnosis Using FDG-PET/CT for Axillary Lymph Node Metastasis in Patients With Breast Cancer

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MRI and FDG-PET/CT based assessment of axillary lymph node metastasis in early breast cancer: a meta-analysis

Cited by 60 publications

References 44 publications

Diagnostic performance of PET/computed tomography versus PET/MRI and diffusion-weighted imaging in the N- and M-staging of breast cancer patients

Diagnostic performance of PET/computed tomography versus PET/MRI and diffusion-weighted imaging in the N- and M-staging of breast cancer patients

Differentiating axillary lymph node metastasis in invasive breast cancer patients: A comparison of radiomic signatures from multiparametric breast MR sequences

Preliminary Study of Ai-assisted Diagnosis Using FDG-PET/CT&nbsp;for Axillary Lymph Node Metastasis in Patients With Breast Cancer

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Preliminary Study of Ai-assisted Diagnosis Using FDG-PET/CT for Axillary Lymph Node Metastasis in Patients With Breast Cancer