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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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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Training Group | A primary cohort of eligible patients from the cancer center of Zhejiang Provincial People's Hospital is used for developing the radiomics-based nomogram prediction model. In the training cohort, a sample size of 88 was required to accept the hypothesis that the prediction accuracy of the radiomics-based nomogram model was greater than 45% with 90% power and to reject the hypothesis that the prediction accuracy rate was less than 30% with an α error of 5%. Initially, we planned to enroll 77 patients in the first stage. If 27 or more prediction accuracy rates were observed, we planned to continue to the second stage for a total of 88 patients for the analysis. Considering some deviant cases, the preplanned accrual number was set to 100 patients in the training cohort. |
| |
| Validation Group | An independent cohort of eligible patients is used for external validation. we are planning to enroll an additional 50 patients to further validate this radiomics-based nomogram prediction model. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| There are no interventions. | Other | There are no interventions. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Construction and Validation | Construction and validation of a radiomics-based nomogram for the prediction of malnutrition based on the PG-SGA scores in cervical cancer patients who underwent postoperative radiotherapy/chemoradiotherapy. | 06/30/2023-10/31/2026 |
| Measure | Description | Time Frame |
|---|---|---|
| GLIM criteria | Predict malnutrition assessed by the new Global Leadership Initiative on Malnutrition (GLIM) criteria. | 02/01/2023-10/31/2026 |
| Measure | Description | Time Frame |
|---|---|---|
| Correlation | Analyze the correlation among NRS-2002 score, PG-SGA score and the GLIM criteria. | 02/01/2023-10/31/2026 |
Inclusion Criteria:
Exclusion Criteria:
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After radical surgery, CC patients are recommended to undergo adjuvant pelvic RT/CRT based on their pathological risk factors. The patients are immobilized in an immobilization device prior to RT, and a scheduled abdomen-pelvis CT scan is routinely conducted to plan RT. Radiomic features are extracted from the non-enhanced CT images and used for building the nomogram prediction model.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Hong'en Xu, M.D. | Contact | +86-571-85893638 | xuhongenzpph@sina.com | |
| Huafeng Shou, M.D. | Contact | +86-571-85893385 | hfshou@126.com |
| Name | Affiliation | Role |
|---|---|---|
| Yongshi Jia, M.D. | Zhejiang Provincial People's Hospital | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Zhejiang Provincial People's Hospital | Recruiting | Hangzhou | Zhejiang | 310000 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 35283649 | Result | Song T, Xu H, Shi L, Yan S. Prognostic Analysis and Comparison of the 2014 and 2018 International Federation of Gynecology and Obstetrics Staging System on Overall Survival in Patients with Stage IIB-IVA Cervix Carcinoma. Int J Womens Health. 2022 Mar 6;14:333-344. doi: 10.2147/IJWH.S348074. eCollection 2022. |
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| ID | Term |
|---|---|
| D002583 | Uterine Cervical Neoplasms |
| D044342 | Malnutrition |
| ID | Term |
|---|---|
| D014594 | Uterine Neoplasms |
| D005833 | Genital Neoplasms, Female |
| D014565 | Urogenital Neoplasms |
| D009371 | Neoplasms by Site |
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| D009369 |
| Neoplasms |
| D002577 | Uterine Cervical Diseases |
| D014591 | Uterine Diseases |
| D005831 | Genital Diseases, Female |
| D052776 | Female Urogenital Diseases |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D000091662 | Genital Diseases |
| D009748 | Nutrition Disorders |
| D009750 | Nutritional and Metabolic Diseases |