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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

Example of a two-step approach towards the clinical use of ADNEX predicted risks.
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Figure 5: Example of a two-step approach towards the clinical use of ADNEX predicted risks.

Mentions: The ADNEX model is the first risk model that differentiates between benign and four subgroups of malignant adnexal tumors. The model consists of three clinical predictors and six ultrasound predictors, which can be evaluated by examiners familiar with the IOTA terms and definitions.Although the ADNEX model includes the serum CA-125 level, the online and mobile application allow for risk calculations without this measurement. A two-step approach could be adopted to make clinical use of the predicted risks from ADNEX (see Figure 5 for an example). First the risk calculation can be used to discriminate between benign and malignant masses based on the specific risk cut-off value used by individual centers to define malignancy, where the adopted cut-off may depend on the local healthcare policy. The discrimination between benign and malignant masses can be done without CA-125 should this be desired, because results indicate no loss of performance in terms of AUC. Second, we can differentiate between the four subgroups of malignant tumors using the predicted risks for these subgroups. In this step, absolute predicted risks as well as the relative change of these risks versus the baseline risks provide clinically useful information to select an appropriate patient-specific management strategy. We cannot propose a generally applicable algorithm with fixed cut-offs, because this depends on the specific clinical setting where the model will be used. Nevertheless, this paper provides guidance on how the ADNEX risk model may be adopted into clinical practice.


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)

Example of a two-step approach towards the clinical use of ADNEX predicted risks.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Example of a two-step approach towards the clinical use of ADNEX predicted risks.
Mentions: The ADNEX model is the first risk model that differentiates between benign and four subgroups of malignant adnexal tumors. The model consists of three clinical predictors and six ultrasound predictors, which can be evaluated by examiners familiar with the IOTA terms and definitions.Although the ADNEX model includes the serum CA-125 level, the online and mobile application allow for risk calculations without this measurement. A two-step approach could be adopted to make clinical use of the predicted risks from ADNEX (see Figure 5 for an example). First the risk calculation can be used to discriminate between benign and malignant masses based on the specific risk cut-off value used by individual centers to define malignancy, where the adopted cut-off may depend on the local healthcare policy. The discrimination between benign and malignant masses can be done without CA-125 should this be desired, because results indicate no loss of performance in terms of AUC. Second, we can differentiate between the four subgroups of malignant tumors using the predicted risks for these subgroups. In this step, absolute predicted risks as well as the relative change of these risks versus the baseline risks provide clinically useful information to select an appropriate patient-specific management strategy. We cannot propose a generally applicable algorithm with fixed cut-offs, because this depends on the specific clinical setting where the model will be used. Nevertheless, this paper provides guidance on how the ADNEX risk model may be adopted into clinical practice.

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