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A Nomogram Based on Radiomics with Mammography Texture Analysis for the Prognostic Prediction in Patients with Triple-Negative Breast Cancer.
Jiang, Xian; Zou, Xiuhe; Sun, Jing; Zheng, Aiping; Su, Chao.
Afiliación
  • Jiang X; Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
  • Zou X; Laboratory of Tumor Targeted and Immune Therapy, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, China.
  • Sun J; Department of Thyroid Surgery, West China Hospital, Sichuan University, Chengdu, China.
  • Zheng A; Department of Integrated Chinese and Western Medicine, Qingdao Central Hospital, Qingdao University, Qingdao, Shandong, China.
  • Su C; West China School of Medicine, Sichuan University, Chengdu, China.
Contrast Media Mol Imaging ; 2020: 5418364, 2020.
Article en En | MEDLINE | ID: mdl-32922222
Objectives: To develop and validate a radiomics-based nomogram with texture features from mammography for the prognostic prediction in patients with early-stage triple-negative breast cancer (TNBC). Methods: The study included 200 consecutive patients with TNBC (training cohort: n = 133, validation cohort: n = 67). A total of 136 mammography-derived textural features were extracted, and LASSO (least absolute shrinkage and selection operator) was applied to select features for building the radiomics score (Rad-score). After univariate and multivariate logistic regression, a radiomics-based nomogram was constructed with independent prognostic factors. The discrimination and calibration power were assessed, and further the clinical applicability of the nomograms was evaluated. Results: Among the 136 mammography-derived textural features, fourteen were used to build the Rad-score after LASSO regression. A radiomics nomogram that incorporates Rad-score and pN stage was constructed. This nomogram achieved a C-index of 0.873 (95% CI: 0.758-0.989) for predicting iDFS (invasive disease-free survival), which outperformed the clinical model. Moreover, it is feasible to stratify patients into high-risk and low-risk groups based on the optimal cut-off point of Rad-score. The validations of the nomogram confirmed favorable discrimination and considerable predictive efficiency. Conclusions: The radiomics nomogram that incorporates Rad-score and pN stage exhibited favorable performance in the prediction of iDFS in patients with early-stage TNBCs.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Mamografía / Nomogramas / Neoplasias de la Mama Triple Negativas Tipo de estudio: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans / Middle aged Idioma: En Revista: Contrast Media Mol Imaging Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2020 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Mamografía / Nomogramas / Neoplasias de la Mama Triple Negativas Tipo de estudio: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans / Middle aged Idioma: En Revista: Contrast Media Mol Imaging Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2020 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido