Dataset Information

Preliminary study of AI-assisted diagnosis using FDG-PET/CT for axillary lymph node metastasis in patients with breast cancer.

ABSTRACT:

Background

To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[¹⁸F]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 2-[¹⁸F]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 2-[¹⁸F]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.

SUBMITTER: Li Z

PROVIDER: S-EPMC7835273 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

Preliminary study of AI-assisted diagnosis using FDG-PET/CT for axillary lymph node metastasis in patients with breast cancer.

Li Zongyao Z Kitajima Kazuhiro K Hirata Kenji K Togo Ren R Takenaka Junki J Miyoshi Yasuo Y Kudo Kohsuke K Ogawa Takahiro T Haseyama Miki M

EJNMMI research 20210125 1

<h4>Background</h4>To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[<sup>18</sup>F]FDG-PET/CT, we constructed an artificial intelligence (AI)-assisted diagnosis system that uses deep-learning technologies.<h4>Materials and methods</h4>Two clinicians and the new AI system retrospectively analyzed and diagnosed 414 axillae of 407 patients with biopsy-proven breast cancer who had undergone 2-[<sup>18</sup>F]FDG-PET/CT before a mastectomy o ...[more]

PMID: 33492478

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Project description:BackgroundThe aim of this study was to assess the diagnostic utility of metabolic parameters on fluorine-18-fluoro-2-deoxy-D-glucose-positron emission tomography (FDG-PET)/computed tomography (CT) for predicting lymph node (LN) metastasis in patients with cN2 non-small cell lung cancer (NSCLC).MethodsWe retrospectively reviewed patients who underwent surgery for cN2 NSCLC between 2007 and 2020. Those who had clinically diagnosed positive hilar and mediastinal LNs by routine CT and PET/CT imaging were investigated. To measure the metabolic parameters of LNs, the data according to maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and LN-to-primary tumor ratio of SUVmax (LPR) were examined. The diagnosis of each retrieved LN was confirmed based on histopathological examination of surgical tissue specimens. Receiver operating characteristics (ROC) curves with area under the curve (AUC) calculations and multivariate analysis by logistic regression were performed.ResultsForty-five patients with 84 clinically diagnosed positive hilar or mediastinal LNs were enrolled in the present study. Of the 84 LNs, 63 LNs were pathologically proven as positive (75%). The SUVmax, MTV, TLG, and LPR of LN metastasis were significantly higher than those of benign nodes. In the ROC analysis, the AUC value of LPR [AUC, 0.776; 95% confidence interval (CI), 0.640-0.913] was higher than that of LN SUVmax (AUC, 0.753; 95% CI, 0.626-0.880) or LN TLG3.5 (AUC, 0.746; 95% CI, 0.607-0.885). Using the optimal LPR cutoff value of 0.47, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 84.1, 66.7, 88.3, 58.3, and 79.8%, respectively. Multivariate analysis by logistic regression showed that LPR was an independent predictor for LN metastasis (odds ratio, 6.45; 95% CI, 1.785-23.301; P = 0.004). In the subgroup analysis of adenocarcinoma patients (n = 18; 32 LNs), TLG3.5 was a better predictor (AUC, 0.816; 95% CI, 0.639-0.985) than LPR (AUC, 0.792; 95% CI, 0.599-0.986) or LN SUVmax (AUC, 0.792; 95% CI, 0.625-0.959).ConclusionsOur findings suggest that LPR on FDG-PET is a useful predictor for LN metastasis in patients with cN2 NSCLC. TLG can be a good predictor for LN metastasis in patients with adenocarcinoma.

Dataset Information

Preliminary study of AI-assisted diagnosis using FDG-PET/CT for axillary lymph node metastasis in patients with breast cancer.

Background

Materials and methods

Results

Conclusions

Publications

Preliminary study of AI-assisted diagnosis using FDG-PET/CT for axillary lymph node metastasis in patients with breast cancer.

Similar Datasets

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