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Practical guidance for applying the ADNEX model from the IOTA group to discriminate between different subtypes of adnexal tumors.

Van Calster B, Van Hoorde K, Froyman W, Kaijser J, Wynants L, Landolfo C, Anthoulakis C, Vergote I, Bourne T, Timmerman D - Facts Views Vis Obgyn (2015)

Bottom Line: This approach is novel compared to existing tools that only differentiate between benign and malignant tumors, and therefore questions may arise on how ADNEX can be used in clinical practice.This is illustrated with a few example patients.We cannot propose a generally applicable algorithm with fixed cut-offs, because (as with any risk model) this depends on the specific clinical setting in which the model will be used.

View Article: PubMed Central - PubMed

Affiliation: KU Leuven, Department of Development & Regeneration, 3000 Leuven, Belgium.

ABSTRACT
All gynecologists are faced with ovarian tumors on a regular basis, and the accurate preoperative diagnosis of these masses is important because appropriate management depends on the type of tumor. Recently, the International Ovarian Tumor Analysis (IOTA) consortium published the Assessment of Different NEoplasias in the adneXa (ADNEX) model, the first risk model that differentiates between benign and four types of malignant ovarian tumors: borderline, stage I cancer, stage II-IV cancer, and secondary metastatic cancer. This approach is novel compared to existing tools that only differentiate between benign and malignant tumors, and therefore questions may arise on how ADNEX can be used in clinical practice. In the present paper, we first provide an in-depth discussion about the predictors used in ADNEX and the ability for risk prediction with different tumor histologies. Furthermore, we formulate suggestions about the selection and interpretation of risk cut-offs for patient stratification and choice of appropriate clinical management. This is illustrated with a few example patients. We cannot propose a generally applicable algorithm with fixed cut-offs, because (as with any risk model) this depends on the specific clinical setting in which the model will be used. Nevertheless, this paper provides a guidance on how the ADNEX model may be adopted into clinical practice.

No MeSH data available.


Related in: MedlinePlus

Illustration of the ADNEX model for case 1.
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Figure 3: Illustration of the ADNEX model for case 1.

Mentions: We assess a 59-year-old woman in a gynecological oncology center. The CA125 level is 153 U/ml. On transvaginal ultrasound, we describe a solid ovarian mass with a maximal lesion diameter of 59 mm, a maximal diameter of the largest solid component of 59 mm as well, and without acoustic shadowing. There is presence of fluid outside the pouch of Douglas (ascites). If we introduce these parameters in the ADNEX model (in this case, application for smartphone), we obtain the results and column charts as illustrated in Figure 3. First we add the risk predictions for the four malignant subgroups to obtain the total risk of malignancy, which is 95.3% for this patient. Thus the tumor is likely to be malignant. Then, we look at the differentiation between four malignant subgroups and observe a predicted risk for secondary metastatic cancer of 21.2% (compared to a baseline risk of 4.0%) and a risk for stage II-IV ovarian cancer of 67.9% (compared to a baseline risk of 14.1%). The predicted risks for the other subgroups are lower (4.6% for stage I cancer and 1.6% for borderline) and are also smaller than the baseline risks.


Practical guidance for applying the ADNEX model from the IOTA group to discriminate between different subtypes of adnexal tumors.

Van Calster B, Van Hoorde K, Froyman W, Kaijser J, Wynants L, Landolfo C, Anthoulakis C, Vergote I, Bourne T, Timmerman D - Facts Views Vis Obgyn (2015)

Illustration of the ADNEX model for case 1.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4402441&req=5

Figure 3: Illustration of the ADNEX model for case 1.
Mentions: We assess a 59-year-old woman in a gynecological oncology center. The CA125 level is 153 U/ml. On transvaginal ultrasound, we describe a solid ovarian mass with a maximal lesion diameter of 59 mm, a maximal diameter of the largest solid component of 59 mm as well, and without acoustic shadowing. There is presence of fluid outside the pouch of Douglas (ascites). If we introduce these parameters in the ADNEX model (in this case, application for smartphone), we obtain the results and column charts as illustrated in Figure 3. First we add the risk predictions for the four malignant subgroups to obtain the total risk of malignancy, which is 95.3% for this patient. Thus the tumor is likely to be malignant. Then, we look at the differentiation between four malignant subgroups and observe a predicted risk for secondary metastatic cancer of 21.2% (compared to a baseline risk of 4.0%) and a risk for stage II-IV ovarian cancer of 67.9% (compared to a baseline risk of 14.1%). The predicted risks for the other subgroups are lower (4.6% for stage I cancer and 1.6% for borderline) and are also smaller than the baseline risks.

Bottom Line: This approach is novel compared to existing tools that only differentiate between benign and malignant tumors, and therefore questions may arise on how ADNEX can be used in clinical practice.This is illustrated with a few example patients.We cannot propose a generally applicable algorithm with fixed cut-offs, because (as with any risk model) this depends on the specific clinical setting in which the model will be used.

View Article: PubMed Central - PubMed

Affiliation: KU Leuven, Department of Development & Regeneration, 3000 Leuven, Belgium.

ABSTRACT
All gynecologists are faced with ovarian tumors on a regular basis, and the accurate preoperative diagnosis of these masses is important because appropriate management depends on the type of tumor. Recently, the International Ovarian Tumor Analysis (IOTA) consortium published the Assessment of Different NEoplasias in the adneXa (ADNEX) model, the first risk model that differentiates between benign and four types of malignant ovarian tumors: borderline, stage I cancer, stage II-IV cancer, and secondary metastatic cancer. This approach is novel compared to existing tools that only differentiate between benign and malignant tumors, and therefore questions may arise on how ADNEX can be used in clinical practice. In the present paper, we first provide an in-depth discussion about the predictors used in ADNEX and the ability for risk prediction with different tumor histologies. Furthermore, we formulate suggestions about the selection and interpretation of risk cut-offs for patient stratification and choice of appropriate clinical management. This is illustrated with a few example patients. We cannot propose a generally applicable algorithm with fixed cut-offs, because (as with any risk model) this depends on the specific clinical setting in which the model will be used. Nevertheless, this paper provides a guidance on how the ADNEX model may be adopted into clinical practice.

No MeSH data available.


Related in: MedlinePlus