Gestional Trophoblastic Neoplasia Ultrasound Assessment: Titanium Study

NCT ID: NCT06284980

Last Updated: 2024-02-29

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

120 participants

Study Classification

OBSERVATIONAL

Study Start Date

2019-01-24

Study Completion Date

2025-12-31

Brief Summary

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Gestational trophoblast diseases are characterized by abnormal proliferation of trophoblastic tissue, which can occur consequent to any pregnancy event. Pre-malignant forms (partial hydatiform mola and complete mola) and malignant forms are distinguished, the latter also known as gestational trophoblast neoplasms (invasive hydatiform mola, choriocarcinoma, placental site trophoblastic tumor, and epithelioid trophoblastic tumor).

These are neoplasms associated with good prognosis, amenable to conservative treatment and highly sensitive to chemotherapy.

The identification of ultrasonographic and echofluximetric features typical of malignant forms as well as the identification of ultrasonographic parameters predictive of chemoresistance to single-drug treatments could help improve their management.

Detailed Description

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Primary aim

1. To describe typical gray scale and color Doppler ultrasound features of Gestational Troph-oblastic Neoplasia at the baseline US examination; the results will be presented as in Table 4
2. To assess if there are differences at the baseline US scan between low-risk and high-risk patients and, in the low-risk group, between responders and non-responders to the single drug therapy; the results will be presented as in Table 5
3. To identify ultrasound predictors of resistance to first-line single drug chemotherapy in low-risk GTN patients; statistical analysis will be performed using logistic regression mod-eling
4. To investigate if machine learning (ML) algorithms (radiomics, deep neural networks (DNN), and other machine learning algorithms), applied on ultrasound images, can dis-criminate between low-risk patients that will or will not develop chemoresistance to single-drug therapy.To investigate if ML algorithms, combined with the conventional risk-score improve prediction of resistance to first-line single drug chemotherapy in low risk GTN pa-tients.

Secondary aims:

I. To describe any longitudinal changes in ultrasound characteristics of Gestational Tropho-blastic Neoplasia during treatment.

I. To describe any longitudinal changes in ultrasound characteristics of Gestational Tropho-blastic Neoplasia after stopping treatment, when the uterine lesion is still visible despite the normalization of hCG.

Conditions

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Describe Typical Gray Scale/Color Doppler Ultrasound Features of Gestational Trophoblastic Neoplasia at the US Examination To Assess if There Are Differences at the Baseline US Scan Between Low-risk and High-risk Patients To Identify Ultrasound Predictors of Resistance to First-line Single Drug Chemotherapy in Low-risk GTN Patients To Investigate if Machine Learning (ML) Algorithms (Radiomics, Deep Neural Networks (DNN), and Other Machine Learning Algorithms

Study Design

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Observational Model Type

OTHER

Study Time Perspective

PROSPECTIVE

Eligibility Criteria

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Inclusion Criteria

* Patients with a diagnosis of Gestational Trophoblastic Neoplasia (GTN), after any kind of pregnancy:

1. Histological diagnosis of Placental Site Trophoblastic Tumor or Epithelioid Trophoblastic Tumor
2. International Federation of Gynecology and Obstetrics (FIGO) 2002 criteria for the diagnosis of Gestational Trophoblastic Neoplasia I. Histological evidence of invasive mole or choriocarcinoma II. Rising hCG after evacuation of hydatidiform mole, i.e. two consecutive rises in hCG of 10% or greater over at least 2 weeks (at least 10% between day 1 and 7 and then again at least 10% between day 7 and 14) III. Plateau of hCG after evacuation of hydatidiform mole, i.e. four or more equivalent values of hCG over at least 3 weeks (days 1, 7, 14 and 21) IV. The hCG level remains elevated for 6 months or more after evacuation even if decreasing
3. Charing Cross Hospital (UK) additional criteria for the diagnosis of Gestational Trophoblastic

Neoplasia:

I. Heavy vaginal bleeding or evidence of gastrointestinal or intraperitoneal hemorrhage in patients with histological diagnosis of GTD II. Serum hCG concentration of 20 000 IU/L or more 4 weeks or more after evacuation of a mole, because of the risk of uterine perforation III. Evidence of metastases in brain, liver, or gastrointestinal tract, or radiological opacities larger than 2 cm on chest radiograph in patients with GTD.

Referral Centers for Gestational Trophoblastic Disease with at least 5 cases of GTN per year, that routinely use ultrasound in clinical evaluation, could participate in the study adhering to the:
* BASIC PROTOCOL if they are able to guarantee only the baseline ultrasound evaluation at the diagnosis but NOT the follow-up ultrasound scans during and after chemotherapy
* FULL PROTOCOL if they are able to guarantee both the baseline and all the follow-up ultrasound examination according to the complete protocol schedule.

Exclusion Criteria

1. Previous or ongoing chemotherapy for GTN that started \>7 days earlier
2. Denial of informed consent.
Minimum Eligible Age

18 Years

Eligible Sex

FEMALE

Accepts Healthy Volunteers

No

Sponsors

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Fondazione Policlinico Universitario Agostino Gemelli IRCCS

OTHER

Sponsor Role lead

Responsible Party

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Mascilini Floriana

Floriana Mascilini

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Floriana Mascilini

Role: PRINCIPAL_INVESTIGATOR

Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome

Locations

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Fondazione Policlinico Universitario Agostino Gemelli, IRCCS

Rome, , Italy

Site Status RECRUITING

Countries

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Italy

Central Contacts

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Antonia Carla Testa

Role: CONTACT

0630156399

Facility Contacts

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Floriana Mascilini

Role: primary

0630156399

Other Identifiers

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2369

Identifier Type: -

Identifier Source: org_study_id

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