Clinical Trial Details
— Status: Active, not recruiting
Administrative data
NCT number |
NCT05387460 |
Other study ID # |
2022-TJ-R-RBM |
Secondary ID |
|
Status |
Active, not recruiting |
Phase |
|
First received |
|
Last updated |
|
Start date |
October 1, 2021 |
Est. completion date |
July 1, 2023 |
Study information
Verified date |
May 2022 |
Source |
Tongji Hospital |
Contact |
n/a |
Is FDA regulated |
No |
Health authority |
|
Study type |
Observational
|
Clinical Trial Summary
Retrospectively collect preoperative transvaginal B-mode ultrasound (BMUS), color Doppler
flow imaging (CDFI) and three-dimensional ultrasound (3D-US) images and clinical data in
patients with non-endometrial cancer diseases and endometrial cancer confirmed by pathology.
They were grouped as training set(Tongji Hospital of Tongji Medical College, Huazhong
University of Science and Technology) and external validation set(Women's Hospital, School of
Medicine, Zhejiang University) . Radiomics features were extracted from corresponding
transvaginal ultrasound images. Then, the minimum redundancy maximum relevance (mRMR)
algorithm and the least absolute shrinkage and selection operator (LASSO) regression were
used to select the non- malignant or malignant status-related features and cervical stromal
invasion (CSI) status or non-CSI status features and construct the transvaginal ultrasound
radiomics score (Rad-score). Multivariate logistic regression was performed using the three
radiomics score together with clinical data, and subsequently develop a nomogram to diagnosis
endometrial cancer and CSI respectively. The performance of the nomogram was assessed by
discrimination, calibration, and clinical usefulness in the training and external validation
set.
Description:
Endometrial Cancer is the second most common gynecological cancer in China and the first in
Western countries. The common clinical symptom of endometrial cancer is vaginal bleeding,
which occurs in about 10% of postmenopausal women. Most patients with postmenopausal vaginal
bleeding are diagnosed with benign diseases, and less than 10% of patients are diagnosed with
endometrial cancer. Early diagnosis is crucial for the prognosis of patients with endometrial
cancer. The 5-year survival rate of patients with endometrial cancer which lesions localized
to the uterus is about 95%, while the survival rate of patients with regional and distant
metastasis is reduced to less than 70% and 20%.
Surgery is the main treatment of endometrial cancer. CSI is one of the main criteria for
determining the follow-up treatment. According to NCCN guidelines, Total Hysterectomy and
Bilateral Salpingo-Oophorectomy (THBSO) are standard treatments for patients with
endometrioid carcinoma without CSI. While extensive hysterectomy or surgery after
radiotherapy is appropriate for patients with CSI. Therefore, accurate assessment of CSI
status in patients with EC before operation is important for the formulation of accurate
surgical strategies.
Endometrial biopsy has been considered the gold standard for assessing endometrial cancer.
However, it is limited because of increased cost, sample errors, related complications such
as pain, bleeding, inability to evaluate the extent of tumor invasion and easy to cause tumor
spread. CT/MR are alternative ways with high cost and complications. Transvaginal ultrasound
examination is considered as the first imaging investigation for endometrial cancer.
ESGO/ESTRO/ESP guidelines for the management of patients with endometrial carcinoma indicate
that transvaginal ultrasound can be used instead of magnetic resonance imaging to detect
cervical stromal infiltration under the operation of experienced doctors. Improving the
performance of ultrasonic diagnosis is significant for how to choose the follow-up treatment
and reduce the cost and risk of overtreatment.
Radiomics refers to high-throughput mining of quantitative image features from medical
imaging. Radiomics derived data, when combined with other pertinent clinicopathological
features, can produce accurate and robust evidence-based decision-making systems. Multimodal
radiomics can provide more imaging feature information than single modal radiomics, which
showed better diagnostic performance in previous study of kinds of cancer diseases.