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| Name | Class |
|---|---|
| University of Sao Paulo | OTHER |
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Desmoid tumors (DT) are uncommon tumors that arise from musculoaponeurotic structures. Despite benign, they can cause pain and disability due to their tendency to be locally aggressive. Cryoablation, a technique used in interventional radiology, has gained popularity in recent years as a treatment option for sporadic DT. This involves repeated cycles of freezing, leading to cell death. Recent studies showed that percutaneous image-guided cryoablation appears to be safe and effective for local control for patients with extra-abdominal desmoid tumors.Although changes in the heterogeneity of tumors are commonly known, they are often ignored in response criteria that only evaluate tumor size in a single dimension, such as Response Evaluation Criteria in Solid Tumors (RECIST 1.1). Nevertheless, MRI can reveal early changes in tumor heterogeneity in responding tumors, resulting from a reduction in cellular area and an increase in fibro-necrotic content, before any dimensional changes occur. These changes in heterogeneity can be quantified using a radiomics approach. The aim of this study is to develop radiomics response criteria dedicated to the evaluation of DT treated with cryoablation as a first line treatment and to compare their performance with those of alternative radiologic response criteria for predicting progression according to RECIST 1.1.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Performed cryotherapy | Patients who received cryoablation for desmoid tumor and no chemotherapy |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Cryoablation | Procedure | Cryoablation, a technique used in interventional radiology, involves repeated cycles of freezing, leading to cell death. Recent studies showed that percutaneous image-guided cryoablation appears to be safe and effective for local control for patients with extra-abdominal desmoid tumors |
| Measure | Description | Time Frame |
|---|---|---|
| Progression of disease using mRECIST 1.1 criteria | The aim of this study is to develop radiomics response criteria dedicated to the evaluation of DT treated with cryoablation based on quantification of early changes in heterogeneity sign at MRI images and to compare their performance with those of alternative radiologic response criteria for predicting progression according to mRECIST 1.1. Description of mRECIST 1.1 criteria: CR = complete response, disappearing of contrast enhancement of all lesions; PR= partial response, ≥30% dicrease in the sum of the diameters of contrast enhancement of target lesions; SD= stable disease, neither partial response nor progressive disease; PD= progressive disease, ≥20% size increase in contrast enhancement of the target lesions or new disease. | 6 months |
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Inclusion Criteria:
Exclusion Criteria:
- not meeting inclusion criteria
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Patients who were treated with cryoablation for desmoid tumor
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Giancarlo Facchini | Recruiting | Bologna | 40136 | Italy |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 33709291 | Background | Auloge P, Garnon J, Robinson JM, Thenint MA, Koch G, Caudrelier J, Weiss J, Cazzato RL, Kurtz JE, Gangi A. Percutaneous cryoablation for advanced and refractory extra-abdominal desmoid tumors. Int J Clin Oncol. 2021 Jun;26(6):1147-1158. doi: 10.1007/s10147-021-01887-y. Epub 2021 Mar 11. | |
| 28168275 | Background |
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| ID | Term |
|---|---|
| D018222 | Desmoid Tumors |
| ID | Term |
|---|---|
| D005350 | Fibroma |
| D018218 | Neoplasms, Fibrous Tissue |
| D009372 | Neoplasms, Connective Tissue |
| D018204 | Neoplasms, Connective and Soft Tissue |
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| ID | Term |
|---|---|
| D003452 | Cryosurgery |
| ID | Term |
|---|---|
| D055011 | Ablation Techniques |
| D013514 | Surgical Procedures, Operative |
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| Limkin EJ, Sun R, Dercle L, Zacharaki EI, Robert C, Reuze S, Schernberg A, Paragios N, Deutsch E, Ferte C. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol. 2017 Jun 1;28(6):1191-1206. doi: 10.1093/annonc/mdx034. |
| D009370 | Neoplasms by Histologic Type |
| D009369 | Neoplasms |