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He Z, Zhu L, He J, Chen X, Li X, Yu J. Causal effect of sarcopenia-related traits on the occurrence and prognosis of breast cancer - A bidirectional and multivariable Mendelian randomization study. NUTR HOSP 2024; 41:657-665. [PMID: 38666335 DOI: 10.20960/nh.05139] [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] [Indexed: 06/28/2024] Open
Abstract
Introduction Background and aims: although sarcopenia is associated with several types of cancer, there is limited research regarding its effect on breast cancer. We aimed to explore the causality between sarcopenia-related traits and the incidence and prognosis of breast cancer. Methods: two-sample bidirectional and multivariate Mendelian randomization (MR) analyses were utilized in this study. Genome-wide association studies were used to genetically identify sarcopenia-related traits, such as appendicular lean mass, grip strength of both hands, and walking pace. Data on the incidence and prognosis of breast cancer were collected from two extensive cohort studies. Multivariate MR analysis was used to adjust for body mass index, waist circumference, and whole-body fat mass. The primary method used for analysis was inverse-variance weighted analysis. Results: a significant association was found between appendicular lean mass and ER- breast cancer (OR = 0.873, 95 % CI: 0.817-0.933, p = 6.570 × 10-5). Increased grip strength of the left hand was associated with a reduced risk of ER- breast cancer (OR = 0.744, 95 % CI: 0.579-0.958, p = 0.022). Stronger grip strength of the right hand was associated with prolonged survival time of ER+ breast cancer patients (OR = 0.463, 95 % CI: 0.242-0.882, p = 0.019). In the multivariable MR analysis, appendicular lean mass, grip strength of both hands, and walking pace were still genetically associated with the development of total breast cancer and ER-/+ breast cancer. Conclusions: several sarcopenia-related traits were genetically associated with the occurrence and prognosis of breast cancer. It is crucial for elderly women to increase their strength and muscle mass to help prevent breast cancer.
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Affiliation(s)
- Zhijian He
- Department of Thyroid and Breast Surgery. Wenzhou Central Hospital
| | - Lujia Zhu
- Department of Emergency. The First Affiliated Hospital of Wenzhou Medical University
| | - Jie He
- Department of Thyroid and Breast Surgery. Wenzhou Central Hospital
| | - Xinwei Chen
- Department of Thyroid and Breast Surgery. Wenzhou Central Hospital
| | - Xiaoyang Li
- Department of Thyroid and Breast Surgery. Wenzhou Central Hospital
| | - Jian Yu
- Department of Thyroid and Breast Surgery. Wenzhou Central Hospital
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Rozynek M, Tabor Z, Kłęk S, Wojciechowski W. Body composition radiomic features as a predictor of survival in patients with non-small cellular lung carcinoma: A multicenter retrospective study. Nutrition 2024; 120:112336. [PMID: 38237479 DOI: 10.1016/j.nut.2023.112336] [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: 08/14/2023] [Revised: 12/14/2023] [Accepted: 12/20/2023] [Indexed: 02/24/2024]
Abstract
OBJECTIVES This study combined two novel approaches in oncology patient outcome predictions-body composition and radiomic features analysis. The aim of this study was to validate whether automatically extracted muscle and adipose tissue radiomic features could be used as a predictor of survival in patients with non-small cell lung cancer. METHODS The study included 178 patients with non-small cell lung cancer receiving concurrent platinum-based chemoradiotherapy. Abdominal imaging was conducted as a part of whole-body positron emission tomography/computed tomography performed before therapy. Methods used included automated assessment of the volume of interest using densely connected convolutional network classification model - DenseNet121, automated muscle and adipose tissue segmentation using U-net architecture implemented in nnUnet framework, and radiomic features extraction. Acquired body composition radiomic features and clinical data were used for overall and 1-y survival prediction using machine learning classification algorithms. RESULTS The volume of interest detection model achieved the following metric scores: 0.98 accuracy, 0.89 precision, 0.96 recall, and 0.92 F1 score. Automated segmentation achieved a median dice coefficient >0.99 in all segmented regions. We extracted 330 body composition radiomic features for every patient. For overall survival prediction using clinical and radiomic data, the best-performing feature selection and prediction method achieved areas under the curve-receiver operating characteristic (AUC-ROC) of 0.73 (P < 0.05); for 1-y survival prediction AUC-ROC was 0.74 (P < 0.05). CONCLUSION Automatically extracted muscle and adipose tissue radiomic features could be used as a predictor of survival in patients with non-small cell lung cancer.
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Affiliation(s)
- Miłosz Rozynek
- Department of Radiology, Jagiellonian University Medical College, Krakow, Poland
| | - Zbisław Tabor
- AGH University of Science and Technology, Krakow, Poland
| | - Stanisław Kłęk
- Surgical Oncology Clinic, Maria Skłodowska-Curie National Cancer Institute, Krakow, Poland
| | - Wadim Wojciechowski
- Department of Radiology, Jagiellonian University Medical College, Krakow, Poland.
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Zhang X, Zhang G, Qiu X, Yin J, Tan W, Yin X, Yang H, Wang H, Zhang Y. Exploring non-invasive precision treatment in non-small cell lung cancer patients through deep learning radiomics across imaging features and molecular phenotypes. Biomark Res 2024; 12:12. [PMID: 38273398 PMCID: PMC10809593 DOI: 10.1186/s40364-024-00561-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 01/10/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND Accurate prediction of tumor molecular alterations is vital for optimizing cancer treatment. Traditional tissue-based approaches encounter limitations due to invasiveness, heterogeneity, and molecular dynamic changes. We aim to develop and validate a deep learning radiomics framework to obtain imaging features that reflect various molecular changes, aiding first-line treatment decisions for cancer patients. METHODS We conducted a retrospective study involving 508 NSCLC patients from three institutions, incorporating CT images and clinicopathologic data. Two radiomic scores and a deep network feature were constructed on three data sources in the 3D tumor region. Using these features, we developed and validated the 'Deep-RadScore,' a deep learning radiomics model to predict prognostic factors, gene mutations, and immune molecule expression levels. FINDINGS The Deep-RadScore exhibits strong discrimination for tumor molecular features. In the independent test cohort, it achieved impressive AUCs: 0.889 for lymphovascular invasion, 0.903 for pleural invasion, 0.894 for T staging; 0.884 for EGFR and ALK, 0.896 for KRAS and PIK3CA, 0.889 for TP53, 0.895 for ROS1; and 0.893 for PD-1/PD-L1. Fusing features yielded optimal predictive power, surpassing any single imaging feature. Correlation and interpretability analyses confirmed the effectiveness of customized deep network features in capturing additional imaging phenotypes beyond known radiomic features. INTERPRETATION This proof-of-concept framework demonstrates that new biomarkers across imaging features and molecular phenotypes can be provided by fusing radiomic features and deep network features from multiple data sources. This holds the potential to offer valuable insights for radiological phenotyping in characterizing diverse tumor molecular alterations, thereby advancing the pursuit of non-invasive personalized treatment for NSCLC patients.
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Affiliation(s)
- Xingping Zhang
- School of Medical Information Engineering, Gannan Medical University, 341000, Ganzhou, China
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006, Guangzhou, China
- School of Computer Science and Technology, Zhejiang Normal University, 321000, Jinhua, China
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Guijuan Zhang
- Department of Respiratory and Critical Care, First Affiliated Hospital of Gannan Medical University, 341000, Ganzhou, China
| | - Xingting Qiu
- Department of Radiology, First Affiliated Hospital of Gannan Medical University, 341000, Ganzhou, China
| | - Jiao Yin
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, 110189, Shenyang, China
| | - Xiaoxia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006, Guangzhou, China
| | - Hong Yang
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006, Guangzhou, China
| | - Hua Wang
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Yanchun Zhang
- School of Computer Science and Technology, Zhejiang Normal University, 321000, Jinhua, China.
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia.
- Department of New Networks, Peng Cheng Laboratory, 518000, Shenzhen, China.
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