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Giannopoulos F, Gillstedt M, Lindskogen S, Paoli J, Polesie S. Performance of a Machine Learning Algorithm on Lesions with a High Preoperative Suspicion of Invasive Melanoma. Acta Derm Venereol 2024; 104:adv40023. [PMID: 39023145 PMCID: PMC11262359 DOI: 10.2340/actadv.v104.40023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 06/10/2024] [Indexed: 07/20/2024] Open
Abstract
Abstract is missing (Short communication)
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Affiliation(s)
- Filippos Giannopoulos
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden.
| | - Martin Gillstedt
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden
| | - Sofia Lindskogen
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - John Paoli
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden
| | - Sam Polesie
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden
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Liu X, Qin X, Luo Q, Qiao J, Xiao W, Zhu Q, Liu J, Zhang C. 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; 31:2818-2826. [PMID: 38182443 DOI: 10.1016/j.acra.2023.12.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 12/19/2023] [Accepted: 12/20/2023] [Indexed: 01/07/2024]
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.
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Affiliation(s)
- Xiaoling Liu
- 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.)
| | - Xiachuan Qin
- 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.)
| | - Qi Luo
- 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.)
| | - Jing Qiao
- Department of Ultrasound, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China (J.Q.)
| | - Weihan Xiao
- North Sichuan Medical College, Nanchong, China (W.X.)
| | - Qiwei Zhu
- 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.)
| | - Jian Liu
- Department of Ultrasound, The First Affiliated Hospital of Chengdu Medical College, Chengdu, China (J.L.)
| | - Chaoxue Zhang
- 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.).
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