A Transvaginal Ultrasound-Based Deep Learning Model for the Noninvasive Diagnosis of Myometrial Invasion in Patients with Endometrial Cancer: Comparison with Radiologists

Xiaoling Liu; Xiachuan Qin; Qi Luo; Jing Qiao; Weihan Xiao; Qiwei Zhu; Jian Liu; Chaoxue Zhang

doi:10.1016/j.acra.2023.12.035

A Transvaginal Ultrasound-Based Deep Learning Model for the Noninvasive Diagnosis of Myometrial Invasion in Patients with Endometrial Cancer: Comparison with Radiologists

Acad Radiol. 2024 Jan 4:S1076-6332(23)00713-4. doi: 10.1016/j.acra.2023.12.035. Online ahead of print.

Authors

Xiaoling Liu¹, Xiachuan Qin¹, Qi Luo², Jing Qiao³, Weihan Xiao⁴, Qiwei Zhu², Jian Liu⁵, Chaoxue Zhang⁶

Affiliations

¹ Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Rd, Shushan District, Hefei, 230022, Anhui, China (X.L., X.Q., Q.L., Q.Z., C.Z.); Department of Ultrasound, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College (University), Nanchong, Sichuan, China (X.L., X.Q.).
² Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Rd, Shushan District, Hefei, 230022, Anhui, China (X.L., X.Q., Q.L., Q.Z., C.Z.).
³ Department of Ultrasound, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China (J.Q.).
⁴ North Sichuan Medical College, Nanchong, China (W.X.).
⁵ Department of Ultrasound, The First Affiliated Hospital of Chengdu Medical College, Chengdu, China (J.L.).
⁶ Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Rd, Shushan District, Hefei, 230022, Anhui, China (X.L., X.Q., Q.L., Q.Z., C.Z.). Electronic address: zcxay@163.com.

PMID: 38182443
DOI: 10.1016/j.acra.2023.12.035

Abstract

Rationale and objectives: This study aimed to determine the feasibility of using the deep learning (DL) method to determine the degree (whether myometrial invasion [MI] >50%) of MI in patients with endometrial cancer (EC) based on ultrasound (US) images.

Materials and methods: From September 2017 to April 2023, 1289 US images of 604 patients with EC who underwent surgical resection at center 1, center 2 or center 3 were obtained and divided into a training set and an internal validation set. Ninety-five patients from center 4 and center 5 were randomly selected as the external testing set according to the same criteria as those for the primary cohort. This study evaluated three DL models trained on the training set and tested them on the validation and testing sets. The models' performance was analyzed based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), and the performance of the models was subsequently compared with that of 15 radiologists.

Results: In the final clinical diagnosis of MI in patients with EC, EfficientNet-B6 showed the best performance in the testing set in terms of area under the curve (AUC) [0.814, 95% CI (0.746-0.882]; accuracy [0.802, 95% CI (0.733-0.855]; sensitivity [0.623]; specificity [0.879]; positive likelihood ratio (PLR) [6.750]; and negative likelihood ratio (NLR) [0.389]. The diagnostic efficacy of EfficientNet-B6 was significantly better than that of the 15 radiologists, with an average diagnostic accuracy of 0.681, average AUC of 0.678, AUC of the best performance of 0.739, accuracy of 0.716, sensitivity of 0.806, specificity 0.672, PLR2.457, and NLR 0.289.

Conclusion: Based on the preoperative US images of patients with EC, the DL model can accurately determine the degree of endometrial MI; the performance of this model is significantly better than that of radiologists, and it can effectively assist in clinical treatment decisions.

Keywords: Deep learning; Endometrial cancer; Myometrial invasion; Ultrasonography.