Evaluating the risk of ovarian cancer before surgery using the ADNEX model to differentiate between benign, borderline, early and advanced stage invasive, and secondary metastatic tumours: prospective multicentre diagnostic study

Calster, Ben Van; Hoorde, Kirsten Van; Valentin, Lil; Testa, Antonia Carla; Fischerová, D.; Holsbeke, C. Van; Savelli, L.; Franchi, D.; Epstein, Elisabeth; Kaijser, Jeroen; Belle, Vanya Van; Czekierdowski, Artur; Guerriero, S.; Fruscio, Robert; Lanzani, Chiara; Scala, Felice; Bourne, Tom; Timmerman, Dirk

doi:10.1136/bmj.g5920

Cited by 371 publications

(463 citation statements)

References 45 publications

(61 reference statements)

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“…Published by Sciedu Press 27 compared to laparotomy (29.5% vs. 13.1%) [21] . Cystectomy is associated with a higher recurrence rate (up to 31%) [23] .…”

Section: Discussionmentioning

confidence: 99%

“…This prediction model is able to discriminate between five types of adnexal tumor (benign, borderline, stage I cancer, stage II-IV cancer, and secondary metastatic cancer), while still showing excellent overall discriminative capacity between benign and all malignant tumors, resulted in sensitivity of 96.5% and specificity of 71.3%. Unfortunately this index is not still validated for pregnant women [21] . In patients with suspicious ultrasound of malignant ovarian tumor, the surgical staging procedure is threefold.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Management of a borderline ovarian tumor in a pregnant woman in a rural hospital: a case report

Álvarez-Ponce

López-Gómez

Figueroa-Ruiz

et al. 2015

CRCP

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Borderline ovarian tumors (BOTs) are neoplasms with benign and malignant characteristics, affecting young women in childbearing age. Finding incidental adnexal mass in pregnancy is more common with the use of ultrasound, incidence is about 2% to 10% of pregnancies. A great majority of adnexal masses excised during pregnancy are in fact BOTs. Treatment varies depending of gestational age: before 24 gestational week is usually conservative and surgical staging. Radical management is reserved to patients with suspected malignancy and satisfied parity. A 38-years-old woman presented to external consultation in a rural hospital with 36 gestational weeks of her fourth pregnancy, without antenatal control and asymptomatic. Obstetric ultrasound reported: right ovarian tumor with cystic and solid areas. Laboratory data were normal. She underwent elective cesarean and exploratory laparotomy, surgical findings were: a term female baby with no obstetric complications and a right ovarian tumor with trans-operatory report of mucinous epithelial cells with atypia, suggestive of ovarian borderline tumor. Based on this, a hysterectomy with bilateral oophorectomy was performed. No complications were reported. Post-operatory evolution was normal. Definitive histopathological study reported a borderline ovarian tumor.

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“…Published by Sciedu Press 27 compared to laparotomy (29.5% vs. 13.1%) [21] . Cystectomy is associated with a higher recurrence rate (up to 31%) [23] .…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Management of a borderline ovarian tumor in a pregnant woman in a rural hospital: a case report

Álvarez-Ponce

López-Gómez

Figueroa-Ruiz

et al. 2015

CRCP

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“…The ADNEX model, simple rules and the logistic regression models of the IOTA group are available at www.iotagroup. org and as mobile applications [25].…”

Section: Prediction Modelmentioning

confidence: 99%

Clinical, ultrasound parameters and tumor marker-based mathematical models and scoring systems in pre-surgical diagnosis of adnexal tumors

et al. 2016

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The choice of management for patients with adnexal tumors requires careful pre-surgical assessment. In case of adnexal masses, the diagnostic difficulties arise from the heterogenic nature of the adnexal diseases, presence of multiple functional changes, and lack of early symptoms of malignancy. A reliable pre-surgical differentiation cannot be performed using clinical features, ultrasound examination, or tumor markers alone. New diagnostic techniques and novel markers are under investigations, however no single test can be used to conclusively differentiate between malignant and non-malignant adnexal masses. Mathematical models and scoring systems based on different clinical, ultrasonographic and laboratory parameters alone or together may facilitate the diagnosis. Selected mathematical models and scoring systems are presented in this article. Models using only ultrasound features include simple rules, regression models, Gynecologic Imaging Report and Data System, and various morphologic scores. Some logistic regression models are based on multiple clinical and ultrasound data. The OVA1 test is based on five tumor markers without using other data. The Risk of Malignancy Algorithm uses two tumor markers with one clinical parameter. i.e. the menopausal status. Some models used clinical, ultrasound and tumor marker data together. This group of models includes risk of malignancy indices, artificial neural networks, and the ADNEX model. Although some of these models have been compared in the literature, more prospective studies are needed to select the most effective model, to develop the existing models, or to create new more effective models of oncological assessment of the adnexal tumors.

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“…However, to gain a more accurate classification of tumour type, International Ovarian Tumour Analysis (IOTA) researchers created a model system that utilised sonography. These models include the Simple Rules, Risk of Malignancy Index (RMI), Logistic Regression models (LR1 and LR2) and the most recent ADNEX risk model [4].…”

Section: -Introductionmentioning

confidence: 99%

Multi-level Trainable Segmentation for Measuring Gestational and Yolk Sacs from Ultrasound Images

Ibrahim

Al-Assam

Jassim

et al. 2017

Communications in Computer and Information Science

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Ovarian masses are categorised into different types of malignant and benign. In order to optimize patient treatment, it is necessary to carry out pre-operational characterisation of the suspect ovarian mass to determine its category. Ultrasound imaging has been widely used in differentiating malignant from benign cases due to its safe and non-intrusive nature, and can be used for determining the number of cysts in the ovary. Presently, the gynaecologist is tasked with manually counting the number of cysts shown on the ultrasound image. This paper proposes, a new approach that automatically segments the ovarian masses and cysts from a static B-mode image. Initially, the method uses a trainable segmentation procedure and a trained neural network classifier to accurately identify the position of the masses and cysts. After that, the borders of the masses can be appraised using watershed transform. The effectiveness of the proposed method has been tested by comparing the number of cysts identified by the method against the manual examination by a gynaecologist. A total of 65 ultrasound images were used for the comparison, and the results showed that the proposed solution is a viable alternative to the manual counting method for accurately determining the number of cysts in a US ovarian image.

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Evaluating the risk of ovarian cancer before surgery using the ADNEX model to differentiate between benign, borderline, early and advanced stage invasive, and secondary metastatic tumours: prospective multicentre diagnostic study

Abstract: 13.04.15 KB. Ok to add published OA paper to spira

Cited by 371 publications

References 45 publications

Management of a borderline ovarian tumor in a pregnant woman in a rural hospital: a case report

Management of a borderline ovarian tumor in a pregnant woman in a rural hospital: a case report

Clinical, ultrasound parameters and tumor marker-based mathematical models and scoring systems in pre-surgical diagnosis of adnexal tumors

Multi-level Trainable Segmentation for Measuring Gestational and Yolk Sacs from Ultrasound Images

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