Ignite Creation Date:
2024-07-17 @ 11:46 AM
Last Modification Date:
2024-10-26 @ 3:33 PM
Study NCT ID:
NCT06473766
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-06-25
First Post:
2024-06-19
Brief Title:
Ultrasound RF Data for Discriminating Between Benign and Malignant Ovarian Masses
Sponsor:
Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Organization:
Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Study Overview
Official Title:
Ultrasound RF Data for Discriminating Between Benign and Malignant Ovarian Masses With Solid Ultrasound Morphology a Feasibility Study
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
RFDATA
Brief Summary:
Ultrasound imaging provides useful information for the characterization of ovarian masses as benign or malignant The most accurate mathematical model to categorize ovarian masses is the IOTA ADNEX modelThis model estimates the risk of malignancy and performs similarly to subjective assessment by an experienced ultrasound examiner for discriminating between benign and malignant adnexal masses The ability of IOTA ADNEX to discriminate between benign and malignant masses is very good area under the receiver operator characteristic curve 0937 95 CI 0915-0954 The ADNEX model maintains its accuracy even in the hands of operators with different experience and training
According to IOTA terminology 13 of ovarian masses detected on ultrasound examination are classified as solid Solid ovarian masses have a risk of malignancy of 60-752 and the discrimination between benign and malignant in this morphological category is challenging Additionally it has been estimated that 30 2584 95 CI 18 to 44 of solid malignant ovarian masses are metastases from non-ovarian tumors The discrimination between primary ovarian cancer and metastatic tumors in the ovary is also clinically important for planning adequate therapeutic procedures It is worth exploring the predictive performance of the diagnostic tools in identifying ovarian masses with ultrasound solid morphology
Preliminary data unpublished on radiomics analysis and ovarian masses provided that benign and malignant ovarian masses with solid morphology have different radiomics features in a monocentric retrospective study However no statistically significant differences have been observed between primary ovarian cancer and metastases to the ovary
A new technology is emerging in engineering ultrasound field the analysis of ultrasound summed RF data- raw data generated by the interface of ultrasound beams with human tissues To date raw data are not utilized for conventional imaging and their eventual role in clinical practice is unknown Indeed summed RF data could better correlate with biological parameters then parameters identifiable in B-mode images Summed RF data could also improve radiomic analysis
According to IOTA terminology 13 of ovarian masses detected on ultrasound examination are classified as solid Solid ovarian masses have a risk of malignancy of 60-752 and the discrimination between benign and malignant in this morphological category is challenging Additionally it has been estimated that 30 2584 95 CI 18 to 44 of solid malignant ovarian masses are metastases from non-ovarian tumors The discrimination between primary ovarian cancer and metastatic tumors in the ovary is also clinically important for planning adequate therapeutic procedures It is worth exploring the predictive performance of the diagnostic tools in identifying ovarian masses with ultrasound solid morphology
Preliminary data unpublished on radiomics analysis and ovarian masses provided that benign and malignant ovarian masses with solid morphology have different radiomics features in a monocentric retrospective study However no statistically significant differences have been observed between primary ovarian cancer and metastases to the ovary
A new technology is emerging in engineering ultrasound field the analysis of ultrasound summed RF data- raw data generated by the interface of ultrasound beams with human tissues To date raw data are not utilized for conventional imaging and their eventual role in clinical practice is unknown Indeed summed RF data could better correlate with biological parameters then parameters identifiable in B-mode images Summed RF data could also improve radiomic analysis
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None