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RESOLVE-DWI-based deep learning nomogram for prediction of normal-sized lymph node metastasis in cervical cancer: a preliminary study.
Qian, Weiliang; Li, Zhisen; Chen, Weidao; Yin, Hongkun; Zhang, Jibin; Xu, Jianming; Hu, Chunhong.
Afiliação
  • Qian W; Department of Radiology, The First Affiliated Hospital of Soochow University, No.188 Shizi Street, Suzhou, 215006, Jiangsu, People's Republic of China.
  • Li Z; Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, No.26 Daoqian Street, Suzhou, 215002, Jiangsu, People's Republic of China.
  • Chen W; Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, No.26 Daoqian Street, Suzhou, 215002, Jiangsu, People's Republic of China.
  • Yin H; Beijing Infervision Technology Co., Ltd, No.60 Dongsihuan Middle Road, Chaoyang District, Beijing, 100020, People's Republic of China.
  • Zhang J; Beijing Infervision Technology Co., Ltd, No.60 Dongsihuan Middle Road, Chaoyang District, Beijing, 100020, People's Republic of China.
  • Xu J; Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, No.26 Daoqian Street, Suzhou, 215002, Jiangsu, People's Republic of China.
  • Hu C; Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, No.26 Daoqian Street, Suzhou, 215002, Jiangsu, People's Republic of China.
BMC Med Imaging ; 22(1): 221, 2022 12 17.
Article em En | MEDLINE | ID: mdl-36528577
BACKGROUND: It is difficult to predict normal-sized lymph node metastasis (LNM) in cervical cancer clinically. We aimed to investigate the feasibility of using deep learning (DL) nomogram based on readout segmentation of long variable echo-trains diffusion weighted imaging (RESOLVE-DWI) and related patient information to preoperatively predict normal-sized LNM in patients with cervical cancer. METHODS: A dataset of MR images [RESOLVE-DWI and apparent diffusion coefficient (ADC)] and patient information (age, tumor size, International Federation of Gynecology and Obstetrics stage, ADC value and squamous cell carcinoma antigen level) of 169 patients with cervical cancer between November 2013 and January 2022 were retrospectively collected. The LNM status was determined by final histopathology. The collected studies were randomly divided into a development cohort (n = 126) and a test cohort (n = 43). A single-channel convolutional neural network (CNN) and a multi-channel CNN based on ResNeSt architectures were proposed for predicting normal-sized LNM from single or multi modalities of MR images, respectively. A DL nomogram was constructed by incorporating the clinical information and the multi-channel CNN. These models' performance was analyzed by the receiver operating characteristic analysis in the test cohort. RESULTS: Compared to the single-channel CNN model using RESOLVE-DWI and ADC respectively, the multi-channel CNN model that integrating both two MR modalities showed improved performance in development cohort [AUC 0.848; 95% confidence interval (CI) 0.774-0.906] and test cohort (AUC 0.767; 95% CI 0.613-0.882). The DL nomogram showed the best performance in development cohort (AUC 0.890; 95% CI 0.821-0.938) and test cohort (AUC 0.844; 95% CI 0.701-0.936). CONCLUSION: The DL nomogram incorporating RESOLVE-DWI and clinical information has the potential to preoperatively predict normal-sized LNM of cervical cancer.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias do Colo do Útero / Aprendizado Profundo Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias do Colo do Útero / Aprendizado Profundo Idioma: En Ano de publicação: 2022 Tipo de documento: Article