Improving strategies for diagnosing ovarian cancer: a summary of the International Ovarian Tumor Analysis (IOTA) studies J. Kaijser, T. Bourne, L. Valentin,

Improving strategies for diagnosing ovarian cancer: a summary of the International Ovarian Tumor Analysis (IOTA) studies

J. Kaijser, T. Bourne, L. Valentin, A. Sayasneh, C. Van Holsbeke, I. Vergote,

A. Testa, D.Franchi, B. Van Calster, D. Timmerman

Volume 41, Issue 1, Date: January 2013, pages 9–20

Journal Club slides prepared by Ligita Jokubkiene(UOG Editor for Trainees)

UOG Journal Club: January 2013

Previous studies limited by: – small sample size

– single-center population

– different tumor types

– not standardized ultrasound terms and definitions

– lack of consistency in histological reports

Correct discrimination between benign and malignant ovarian masses

• To develope rules and models to characterize ovarian pathology

• To test the diagnostic performance of rules and models by external validation with examiners of different levels of ultrasound experience

• To establish the role of CA 125 and other serum tumor markers for the diagnosis of ovarian cancer

• To identify the characteristics of ovarian tumors that are difficult to classify as benign or malignant

• To validate these models or rules in non-operated patients by studying the outcome of adnexal masses classified as benign

Aims of the IOTA studies


Kaijser et al., UOG 2013

• 1066 non-pregnant women• At least one persistent adnexal mass• Nine clinical centers in five countries

Training set

754 (71%) patients

Test set

312 (29%) patients

Two logistic regressions models developed

(LR1 and LR2)

Timmerman et al, J Clin Oncol, 2005

IOTA phase 1

1. Personal history of ovarian cancer

2. Current hormonal therapy

3. Age of the patient*

4. Maximum diameter of the lesion

5. Pain during examination

6. Ascites*

7. Blood flow within a solid papillary projection*

8. Purely solid tumor

9. Maximum diameter of the solid component*

10. Irregular internal cyst wall*

11. Acoustic shadows*

12. Color score

Timmerman et al, J Clin Oncol, 2005

LR1(12 variables)

Variables used in the logistic regression models

*LR2(6 variables)

• 507 consecutive women• Three centers• Prospective validation of the models

• 997 patients in twelve new centers and• 941 patients in seven centers from phase 1• External validation of the models

JVan Holsbeke et al, Clin Cancer Res, 2009 and 2012; Timmerman et al, UOG 2010

IOTA phase 1b

IOTA phase 2

• Based on subjective assessment of ultrasound images

• Rules could be applied to 77% of ovarian tumors

• Classify tumors as benign, malignant or inconclusive

• Included into RCOG guideline for evaluating ovarian pathology in premenopausal women

Timmerman et al, UOG, 2008

Simple ultrasound-based rules

Features of a benign mass (B-features)

A mass is classified as benign if at least

one B-feature is present and no M-

features are present

Features of a malignancy (M-features)

A mass is classified as malignant if at

least one M-feature is present and no B-

features are present

If the rules are inconclusive if no B/M-features are present or both B and M features are present...

... rely on subjective assessment by an expert ultrasound examiner as a second stage test

Simple ultrasound-based rules

LR2 cut-off 10%

LR1cut-off 10%

Simples rules*

ROC AUC Sensitivity Specificity LR+ LR-

RMI

External validation

* Simple rules supplemented with subjective assessment of ultrasound findings when the rules could not be applied. IOTA phase 2.

0.96 92% 87% 6.8 0.09

0.95 92% 86% 6.4 0.10

N/A 90% 93% 12.6 0.11

0.91 67% 95% 12.7 0.34

Similar diagnostic performance between LR1 and LR2

Diagnostic performance of the models and rules

Timmerman et al, BMJ, 2010

LR1, LR2 and simple rules had similar diagnostic performance in IOTA phase 1b and phase 2 datasets

Diagnostic performance of the models and rules

Descriptors of an ovarian mass used to make a diagnosis

BD, benign descriptor; MD, malignant descriptor.

Timmerman et al, J Clin Oncol, 2007; Van Calster et al, J Natl Cancer Inst, 2007,Valentin et al, UOG, 2009

• CA 125 has no significant impact on performance of logistic regression model for women at any age

• Adding information on serum CA 125 level to subjective assessment of ultrasound findings does not improve diagnostic performance of experienced ultrasound examiner

The role of CA 125 in diagnosing ovariancancer according to IOTA results

LR1 and LR2 had higher detection rate of Stage 1 primary ovarian cancer than RMI

Simple rules combined with subjective assessment when rules did not apply missclassified fewer Stage 1 ovarian cancer than RMI and CA 125

JVan Holsbeke et al, Clin Cancer Res, 2012; Timmerman et al, BMJ, 2010

Diagnostic performance of the models and simple rules to detect Stage 1 ovarian cancer

Pattern recognition of ultrasound features of an ovarian mass by an experienced examiner is the best way to characterize ovarian pathology

A small proportion of solid tissue makes a malignant mass more likely to be a borderline tumor or a Stage 1 primary invasive epithelial ovarian cancer

CA 125 does not improve diagnostic performance of assessment by experienced ultrasonographers

Two main approaches to classify ovarian masses have been developed:

1. Risk prediction models – LR1 and LR2

2. Simple rules or ”easy descriptors”

Multiclass models have been created to distinguish between benign, borderline, primary invasive and metastatic disease



Summary of the IOTA project



Recommendations for clinical practice

1. IOTA simple rules can be used as a triage test in 75% of all adnexal masses for estimating the risk of malignancy

2. A two-step strategy with referral to a specialist in gynecological ultrasound of unclassifiable masses rules has excellent diagnostic performance

3. An alternative to the simple rules is the LR2 model

4. LR2 or the simple rules should be adopted as the principal test to characterize masses as benign and malignant in premenopausal women

5. Measurement of serum CA 125 marker is not necessary for characterization of ovarian pathology in premenopausal women and is unlikely to improve the performance of experienced ultrasound examiners even in postmenopausal women.



Different approaches to estimate risk of malignancy

• Does serum CA 125 level help to discriminate between benign and malignant ovarian tumors?

• Which test should be used for discriminating between benign and malignant ovarian tumors by a non-expert ultrasound examiner?

• Can logistic regression models better predict malignancy than the IOTA simple rules or subjective evaluation by an experienced ultrasound examiner?

• Do we need to use IOTA simple rules or logistic regression models when classifying an adnexal mass as benign and malignant?

• Should we use the same models and rules for both premenopausal and postmenopausal patients?

• Are the IOTA logistic regression model and simple rules superior to the Risk of Malignancy Index (RMI) in discriminating between benign and malignant ovarian tumors?

Discussion points



Documents

Improving strategies for diagnosing ovarian cancer: a summary of the International Ovarian Tumor Analysis (IOTA) studies J. Kaijser, T. Bourne, L. Valentin,