Lifen Yan1,2, Huasheng Yao2,3, Ruichun Long4, Lei Wu2,3, Haotian Xia2,3, Jinglei Li2, Zaiyi Liu1,2, Changhong Liang1,2. 1. The Second School of Clinical Medical, Southern Medical University, 1023 Shatai Nan Road, Baiyun District, Guangzhou 510515, Guangdong, China. 2. Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 ZhongshanEr Road, Guangzhou 510080, Guangdong, China. 3. School of Medicine, South China University of Technology, Guangzhou 510006, Guangdong. 4. Department of anesthesiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 ZhongshanEr Road, Guangzhou 510080, Guangdong, China.
Abstract
OBJECTIVES: To develop and validate a radiomics model for preoperative identification of lymph node metastasis (LNM) in patients with early-stage cervical squamous cell carcinoma (CSCC). METHODS: Total of 190 eligible patients were randomly divided into training (n = 100) and validation (n = 90) cohorts. Handcrafted features and deep-learning features were extracted from T2W fat suppression images. The minimum redundancy maximum relevance algorithm and LASSO regression with 10-fold cross-validation were used for key features selection. A radiomics model that incorporated the handcrafted-signature, deep-signature, and squamous cell carcinoma antigen (SCC-Ag) levels was developed by logistic regression. The model performance was assessed and validated with respect to its calibration, discrimination, and clinical usefulness. RESULTS: Three handcrafted features and three deep-learning features were selected and used to build handcrafted- and deep-signature. The model, which incorporated the handcrafted-signature, deep-signature, and SCC-Ag, showed satisfactory calibration and discrimination in the training cohort (AUC: 0.852, 95% CI: 0.761-0.943) and the validation cohort (AUC: 0.815, 95% CI: 0.711-0.919). Decision curve analysis indicated the clinical usefulness of the radiomics model. The radiomics model yielded greater AUCs than either the radiomics signature (AUC = 0.806 and 0.779, respectively) or the SCC-Ag (AUC = 0.735 and 0.688, respectively) alone in both the training and validation cohorts. CONCLUSION: The presented radiomics model can be used for preoperative identification of LNM in patients with early-stage CSCC. Its performance outperforms that of SCC-Ag level analysis alone. ADVANCES IN KNOWLEDGE: A radiomics model incorporated radiomics signature and SCC-Ag levels demonstrated good performance in identifying LNM in patients with early-stage CSCC.
OBJECTIVES: To develop and validate a radiomics model for preoperative identification of lymph node metastasis (LNM) in patients with early-stage cervical squamous cell carcinoma (CSCC). METHODS: Total of 190 eligible patients were randomly divided into training (n = 100) and validation (n = 90) cohorts. Handcrafted features and deep-learning features were extracted from T2W fat suppression images. The minimum redundancy maximum relevance algorithm and LASSO regression with 10-fold cross-validation were used for key features selection. A radiomics model that incorporated the handcrafted-signature, deep-signature, and squamous cell carcinoma antigen (SCC-Ag) levels was developed by logistic regression. The model performance was assessed and validated with respect to its calibration, discrimination, and clinical usefulness. RESULTS: Three handcrafted features and three deep-learning features were selected and used to build handcrafted- and deep-signature. The model, which incorporated the handcrafted-signature, deep-signature, and SCC-Ag, showed satisfactory calibration and discrimination in the training cohort (AUC: 0.852, 95% CI: 0.761-0.943) and the validation cohort (AUC: 0.815, 95% CI: 0.711-0.919). Decision curve analysis indicated the clinical usefulness of the radiomics model. The radiomics model yielded greater AUCs than either the radiomics signature (AUC = 0.806 and 0.779, respectively) or the SCC-Ag (AUC = 0.735 and 0.688, respectively) alone in both the training and validation cohorts. CONCLUSION: The presented radiomics model can be used for preoperative identification of LNM in patients with early-stage CSCC. Its performance outperforms that of SCC-Ag level analysis alone. ADVANCES IN KNOWLEDGE: A radiomics model incorporated radiomics signature and SCC-Ag levels demonstrated good performance in identifying LNM in patients with early-stage CSCC.
Authors: F Landoni; A Maneo; A Colombo; F Placa; R Milani; P Perego; G Favini; L Ferri; C Mangioni Journal: Lancet Date: 1997-08-23 Impact factor: 79.321
Authors: Susan J Freeman; Ahmed M Aly; Masako Y Kataoka; Helen C Addley; Caroline Reinhold; Evis Sala Journal: Radiographics Date: 2012-10 Impact factor: 5.333
Authors: J M Duk; K H Groenier; H W de Bruijn; H Hollema; K A ten Hoor; A G van der Zee; J G Aalders Journal: J Clin Oncol Date: 1996-01 Impact factor: 44.544
Authors: Jan C Peeken; Matthew B Spraker; Carolin Knebel; Hendrik Dapper; Daniela Pfeiffer; Michal Devecka; Ahmed Thamer; Mohamed A Shouman; Armin Ott; Rüdiger von Eisenhart-Rothe; Fridtjof Nüsslin; Nina A Mayr; Matthew J Nyflot; Stephanie E Combs Journal: EBioMedicine Date: 2019-09-12 Impact factor: 8.143