The Role of Ultrasound in the Assessment of Uterine Cervical Cancer

Alcázar, Juan Luis; Arribas, Sara; Mínguez, J.; Jurado, M.

doi:10.1007/s13224-014-0622-4

Cited by 27 publications

(16 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is particularly relevant as ultrasound technology becomes more accessible and integral to the management of women's health. Pelvic floor dysfunction, endometriosis and cervical cancer are only some of the conditions for which there is substantial evidence that ultrasound has the potential to optimize patient care. The real‐time, dynamic nature of ultrasound lends itself well to being performed and interpreted by obstetricians/gynecologists to facilitate timely and appropriate treatment decisions.…”

Section: International Society Of Ultrasound In Obstetrics and Gynecomentioning

confidence: 99%

Ultrasound curricula in obstetrics and gynecology training programs

Leonardi

Murji

D’Souza

2018

Ultrasound in Obstet & Gyne

View full text Add to dashboard Cite

Section: International Society Of Ultrasound In Obstetrics and Gynecomentioning

confidence: 99%

Ultrasound curricula in obstetrics and gynecology training programs

Leonardi

Murji

D’Souza

2018

Ultrasound in Obstet & Gyne

View full text Add to dashboard Cite

“…This modality had limitations in assessing lymph nodes. USG could be useful for monitoring and predicting response to therapy (14).…”

Section: Tod Et Al Formulated the Manchester System In 1938mentioning

confidence: 99%

Ultrasound Image Based High Dose Rate (HDR) Brachytherapy in Locally Advanced Cancer Cervix

et al. 2017

View full text Add to dashboard Cite

Background: Brachytherapy is an integral component of the definitive treatment of locally advanced cancer cervix. Cervical Brachytherapy has evolved from the time when dose prescription was largely empirical, to the image based conformal brachytherapy advocated by Groupe Europeen de Curie Therapie-European society for therapeutic radiology and oncology (GEC-ESTRO) and American brachytherapy society (ABS).

show abstract

“…Ovarian cancer remains the second most common gynecological malignancy and the leading cause of death in women with gynecological cancer ( 1 ). Several imaging modalities, such as computed tomography (CT), ultrasonography (US), positron emission tomography (PET), and magnetic resonance imaging (MRI) have been used as diagnostic and treatment assessment tools for gynecological cancer all over the world ( 2 , 3 ). US is a well recognized and most common applied image modality for diagnosis and assessment of ovarian cancer due to its advantage characteristics of non-invasive, no radiation, cheap and affordable ( 4 , 5 ).…”

Section: Introductionmentioning

confidence: 99%

Multiple U-Net-Based Automatic Segmentations and Radiomics Feature Stability on Ultrasound Images for Patients With Ovarian Cancer

et al. 2021

View full text Add to dashboard Cite

Few studies have reported the reproducibility and stability of ultrasound (US) images based radiomics features obtained from automatic segmentation in oncology. The purpose of this study is to study the accuracy of automatic segmentation algorithms based on multiple U-net models and their effects on radiomics features from US images for patients with ovarian cancer. A total of 469 US images from 127 patients were collected and randomly divided into three groups: training sets (353 images), validation sets (23 images), and test sets (93 images) for automatic segmentation models building. Manual segmentation of target volumes was delineated as ground truth. Automatic segmentations were conducted with U-net, U-net++, U-net with Resnet as the backbone (U-net with Resnet), and CE-Net. A python 3.7.0 and package Pyradiomics 2.2.0 were used to extract radiomic features from the segmented target volumes. The accuracy of automatic segmentations was evaluated by Jaccard similarity coefficient (JSC), dice similarity coefficient (DSC), and average surface distance (ASD). The reliability of radiomics features were evaluated by Pearson correlation and intraclass correlation coefficients (ICC). CE-Net and U-net with Resnet outperformed U-net and U-net++ in accuracy performance by achieving a DSC, JSC, and ASD of 0.87, 0.79, 8.54, and 0.86, 0.78, 10.00, respectively. A total of 97 features were extracted from the delineated target volumes. The average Pearson correlation was 0.86 (95% CI, 0.83–0.89), 0.87 (95% CI, 0.84–0.90), 0.88 (95% CI, 0.86–0.91), and 0.90 (95% CI, 0.88–0.92) for U-net++, U-net, U-net with Resnet, and CE-Net, respectively. The average ICC was 0.84 (95% CI, 0.81–0.87), 0.85 (95% CI, 0.82–0.88), 0.88 (95% CI, 0.85–0.90), and 0.89 (95% CI, 0.86–0.91) for U-net++, U-net, U-net with Resnet, and CE-Net, respectively. CE-Net based segmentation achieved the best radiomics reliability. In conclusion, U-net based automatic segmentation was accurate enough to delineate the target volumes on US images for patients with ovarian cancer. Radiomics features extracted from automatic segmented targets showed good reproducibility and for reliability further radiomics investigations.

show abstract

The Role of Ultrasound in the Assessment of Uterine Cervical Cancer

Cited by 27 publications

References 42 publications

Ultrasound curricula in obstetrics and gynecology training programs

Ultrasound curricula in obstetrics and gynecology training programs

Ultrasound Image Based High Dose Rate (HDR) Brachytherapy in Locally Advanced Cancer Cervix

Multiple U-Net-Based Automatic Segmentations and Radiomics Feature Stability on Ultrasound Images for Patients With Ovarian Cancer

Contact Info

Product

Resources

About