Cervical Cancer Clinical Trial
Official title:
Development and Validation of a CT-Based Radiomics Nomogram for the Prediction of Postoperative Malnutrition in Stage IB1-IIA2 Cervical Cancer (Nutrition Helper-1).
Loss of skeletal muscle, is one of the most prevalent symptoms of malnutrition, and has been frequently reported as a negative factor in cancer patients at any disease stage. In this study, we are planning to firstly analyze the radiomics features of psoas extracted at the level of the third lumbar vertebra (L3) and then, develop a CT-based radiomics nomogram prediction model for predicting malnutrition based on their Patient-Generated Subjective Global Assessment (PG-SGA) scores in patients with International Federation of Gynecology and Obstetrics (FIGO, 2014 version) stage IB1-IIA2 cervical cancer (CC) who received postoperative radiotherapy/chemoradiotherapy (RT/CRT).
Cervical cancer is still a significant health problem worldwide. Based on the pathological findings after surgery, patients with intermediate or high risk factors for recurrence are recommended to receive adjuvant pelvic RT and/or platinum (cisplatin or carboplatin) based CRT to reduce the risk of tumor recurrence. However, around 30% of individuals with CC will still eventually develop tumor relapse, necessitating the investigation of better supportive care, like nutritional support, to improve therapeutic tolerance and reduce toxic reactions in these patients. In this respect, how to early identification of malnutrition by PG-SGA tool is crucial. Meanwhile, CT-based radiomics approaches have been successfully applied to generate imaging biomarkers as decision support tools for clinical practice. In our recently accepted research (not yet publish on line, abstract available at https://www.frontiersin.org/articles/10.3389/fnut.2023.1113588/abstract), we firstly analyzed the radiomics features of psoas extracted at the level of L3 and then, developed a nomogram prediction model for patients with FIGO stage IB1-IIA2 CC who received postoperative RT/CRT. Our results demonstrated that this nomogram prediction model showed promising ability for detecting malnutrition based on their PG-SGA scores. The aim of the current study is designed to verify the prediction accuracy of the developed radiomics-based nomogram prospectively. ;
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