Metastatic Lymph Node Detection on Ultrasound Images Using YOLOv7 in Patients with Head and Neck Squamous Cell Carcinoma
Abstract
:Simple Summary
Abstract
1. Introduction
- Incorporates a trainable bag-of-freebies to improve real-time object detection performance without increasing inference costs;
- Integrates extended and composite scaling to effectively reduce model parameters and calculations for faster detection; and
2. Materials and Methods
2.1. Patients
- Both axial-oriented B- and D-mode US images were available;
- Short-axis diameter >2 mm (the longest nodal axis perpendicular to the long axis of the node with the maximal nodal area in an axially oriented US image);
- Identifiable on dissection specimens; and
- Histologically proven metastasis or non-metastasis.
2.2. US Image Acquisition
2.3. Preparation of US Datasets
2.4. YOLOv7 Model Procedure
- A low threshold (confidence score ≥ 0.1) to obtain a higher recall (B-mode model-1 and D-mode model-1), and
- An investigated threshold to obtain the largest area under the receiver operating characteristic curve (AUC) for the test images (B-mode model-2 and D-mode model-2).
2.5. Evaluation of Detection Performance and Comparison with Observers
2.6. Statistical Analysis
3. Results
3.1. Detection Performance for Metastatic LNs
3.2. Recall at Each Cervical Level
3.3. Agreement on the Identification of Metastatic LNs between the Models and Observers
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
References
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Training Datasets | Validation Datasets | Testing Datasets | Total | |
---|---|---|---|---|
No. of images | 324 | 92 | 46 | 462 |
No. of LNs | 365 | 116 | 59 | 540 |
No. of metastatic LNs | 183 | 50 | 28 | 261 |
No. of non-metastatic LNs | 182 | 66 | 31 | 279 |
Detection Result | ||||
---|---|---|---|---|
Detected as Metastatic LNs | Not Detected as Metastatic LNs | |||
Pathology | Metastatic LNs | N (A + B) = 28 | True positive: A | False negative: B |
Non-metastatic LNs | N (C + D) = 31 | False positive: C | True negative: D | |
Non-LN areas detected as metastatic LNs | False positive for non-LN area: E |
B-mode images | B-mode model-1 | B-mode model-2 | Radiologists | Residents |
Recall | 0.75 | 0.75 | 0.821 | 0.536 |
Precision | 0.618 | 0.724 | 0.958 | 0.517 |
F1-score | 0.677 | 0.737 | 0.885 | 0.526 |
False-positive rate for non-LN area | 0.087 | 0.043 | 0 | 0.261 |
Accuracy | 0.729 | 0.78 | 0.898 | 0.746 |
AUC (95% CI) | 0.73 (0.601–0.829) | 0.778 (0.652–0.868) | 0.895 (0.786–0.951) | 0.736 (0.62–0.826) |
D-mode images | D-mode model-1 | D-mode model-2 | Radiologists | Residents |
Recall | 0.821 | 0.75 | 0.75 | 0.714 |
Precision | 0.719 | 0.84 | 0.875 | 0.606 |
F1-score | 0.767 | 0.792 | 0.808 | 0.656 |
False-positive rate for non-LN area | 0.022 | 0 | 0 | 0.022 |
Accuracy | 0.78 | 0.814 | 0.831 | 0.661 |
AUC (95% CI) | 0.782 (0.657–0.870) | 0.81 (0.689–0.892) | 0.827 (0.707–0.904) | 0.664 (0.533–0.773) |
Comparison of accuracy, recall, and AUC between B-mode and D-mode images | ||||
p value for recall * | 0.687 | 1 | 0.687 | 0.063 |
p value for accuracy * | 0.549 | 0.727 | 0.289 | 0.359 |
p value for AUC ** | 0.365 | 0.518 | 0.169 | 0.270 |
B-mode images | B-mode model-1 | B-mode model-2 | Radiologists | Residents | ||||
B-mode model-1 | Recall | 1 | 0.727 | 0.146 | ||||
Accuracy/AUC | ||||||||
B-mode model-2 | 0.250/0.073 | Recall | 0.727 | 0.146 | ||||
Accuracy/AUC | ||||||||
Radiologists | 0.031/0.0166 | 0.118/0.0747 | Recall | 0.008 | ||||
Accuracy/AUC | ||||||||
Residents | 1/0.9351 | 0.804/0.5242 | 0.012/0.0022 | Recall | ||||
Accuracy/AUC | ||||||||
D-mode images | D-mode model-1 | D-mode model-2 | Radiologists | Residents | ||||
D-mode model-1 | Recall | 0.5 | 0.727 | 0.508 | ||||
Accuracy/AUC | ||||||||
D-mode model-2 | 0.687/0.4645 | Recall | 1 | 1 | ||||
Accuracy/AUC | ||||||||
Radiologists | 0.607/0.4914 | 1/0.7951 | Recall | 1 | ||||
Accuracy/AUC | ||||||||
Residents | 0.230/0.1591 | 0.093/0.0615 | 0.064/0.0419 | Recall | ||||
Accuracy/AUC |
B-mode images | B-mode model-1 | B-mode model-2 | Radiologists | Residents |
Level I | 0.909 | 0.909 | 0.909 | 0.364 |
Level II | 0.8 | 0.8 | 0.9 | 0.7 |
Level III + IV | 0.429 | 0.429 | 0.571 | 0.571 |
D-mode images | D-mode model-1 | D-mode model-2 | Radiologists | Residents |
Level I | 0.727 | 0.636 | 0.727 | 0.636 |
Level II | 1 | 1 | 1 | 0.7 |
Level III + IV | 0.714 | 0.571 | 0.429 | 0.857 |
B-Mode Images | Kappa Value | D-Mode Images | Kappa Value |
---|---|---|---|
B-mode model-1 vs. B-mode model-2 | 0.898 | D-mode model-1 vs. D-mode model-2 | 0.798 |
B-mode model-1 vs. Radiologists | 0.392 | D-mode model-1 vs. Radiologists | 0.496 |
B-mode model-1 vs. Residents | 0.361 | D-mode model-1 vs. Residents | 0.149 |
B-mode model-2 vs. Radiologists | 0.483 | D-mode model-2 vs. Radiologists | 0.546 |
B-mode mode-2 vs. Residents | 0.437 | D-mode model-2 vs. Residents | 0.230 |
Radiologists vs. Residents | 0.595 | Radiologists vs. Residents | 0.199 |
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Eida, S.; Fukuda, M.; Katayama, I.; Takagi, Y.; Sasaki, M.; Mori, H.; Kawakami, M.; Nishino, T.; Ariji, Y.; Sumi, M. Metastatic Lymph Node Detection on Ultrasound Images Using YOLOv7 in Patients with Head and Neck Squamous Cell Carcinoma. Cancers 2024, 16, 274. https://doi.org/10.3390/cancers16020274
Eida S, Fukuda M, Katayama I, Takagi Y, Sasaki M, Mori H, Kawakami M, Nishino T, Ariji Y, Sumi M. Metastatic Lymph Node Detection on Ultrasound Images Using YOLOv7 in Patients with Head and Neck Squamous Cell Carcinoma. Cancers. 2024; 16(2):274. https://doi.org/10.3390/cancers16020274
Chicago/Turabian StyleEida, Sato, Motoki Fukuda, Ikuo Katayama, Yukinori Takagi, Miho Sasaki, Hiroki Mori, Maki Kawakami, Tatsuyoshi Nishino, Yoshiko Ariji, and Misa Sumi. 2024. "Metastatic Lymph Node Detection on Ultrasound Images Using YOLOv7 in Patients with Head and Neck Squamous Cell Carcinoma" Cancers 16, no. 2: 274. https://doi.org/10.3390/cancers16020274