Ultrasound RF Data for Discriminating Between Benign and Malignant Ovarian Masses

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 model.This 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 0.937 (95% CI: 0.915-0.954). 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%-75%2 and the discrimination between benign and malignant in this morphological category is challenging. Additionally, it has been estimated that 30% (25/84; 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Preliminary data (unpublished) on radiomics analysis and ovarian masses provided that benign and malignant ovarian masses with solid morphology have different radiomics features in a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.

Conditions

• Ovarian Cancer

Interventions

DIAGNOSTIC_TEST

RF data extraction

To will be acquired: 10 S-Harmonic images (5 in longitudinal plane, 5 in orthogonal plane), 10 B-mode fundamental images (without Harmonic), 1 gray-scale video clip, 1 gray-scale 3D vol will be stored in Harmonic settings and RF-preset. The Region of interest (ROI) of each image will be manually segmented by a trained gynecologist using the software Aliza version 1.48. The ROI will include only the solid component of the mass. Additional analysis will be performed by using a predefined ROI (area 2x2 cm2). Radiomic features will be extracted using the MODDICOM, an open-source in-house software solution developed by the Knowledge Based Oncology Labs (Rome, Italy) for quantitative imaging analysis fully compliant with the Image Biomarker Standardization Initiative recommendations. The features will be considered: intensity-based statistical and textural.

Sponsors & Collaborators

• Samsung Medison

  collaborator INDUSTRY

• Fondazione Policlinico Universitario Agostino Gemelli IRCCS

  lead OTHER

Principal Investigators

• Antonia Carla Testa, Professor · Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome

Study Design

Allocation

NA

Purpose

DIAGNOSTIC

Masking

NONE

Model

SINGLE_GROUP

Eligibility

Min Age

18 Years

Sex

FEMALE

Healthy Volunteers

No

Timeline & Regulatory

Start

2024-07-15

Primary Completion

2025-07-31

Completion

2025-09-30

Countries

• Italy

Study Locations