1. UOG Journal Club: January 2013
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)

2. Correct discrimination between benign and
malignant ovarian masses
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

3. Improving strategies for diagnosing ovarian cancer: a summary of the
International Ovarian Tumor Analysis (IOTA) studies
Kaijser et al., UOG 2013
Aims of the IOTA studies
• 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

5. IOTA phase 1
• 1066 non-pregnant women
• At least one persistent adnexal mass
• Nine clinical centers in five countries
Training set Test set
754 (71%) patients 312 (29%) patients
Two logistic regressions models developed
(LR1 and LR2)
Timmerman et al, J Clin Oncol, 2005

6. Variables used in the logistic regression models
1. Personal history of ovarian cancer
2. Current hormonal therapy LR1
3. Age of the patient* (12 variables)
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* *LR2
12. Color score (6 variables)
Timmerman et al, J Clin Oncol, 2005

7. IOTA phase 1b
• 507 consecutive women
• Three centers
• Prospective validation of the models
IOTA phase 2
• 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

8. Simple ultrasound-based rules
• 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

9. 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

10. 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

11. Simple ultrasound-based rules
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

12. Diagnostic performance of the models and rules
External
ROC AUC Sensitivity Specificity LR+ LR-
validation
Similar diagnostic performance between LR1 and LR2
LR1
cut-off 10% 0.96 92% 87% 6.8 0.09
LR2
0.95 92% 86% 6.4 0.10
cut-off 10%
Simples
rules* N/A 90% 93% 12.6 0.11
RMI 0.91 67% 95% 12.7 0.34
* Simple rules supplemented with subjective assessment of ultrasound
findings when the rules could not be applied. IOTA phase 2.

13. Diagnostic performance of the models and rules
LR1, LR2 and simple rules had similar diagnostic performance in
IOTA phase 1b and phase 2 datasets
Timmerman et al, BMJ, 2010

14. Descriptors of an ovarian mass used to make a diagnosis
BD, benign descriptor; MD, malignant descriptor.

15. The role of CA 125 in diagnosing ovarian
cancer according to IOTA results
• 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
Timmerman et al, J Clin Oncol, 2007; Van Calster et al, J Natl Cancer Inst, 2007,
Valentin et al, UOG, 2009

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

17. Improving strategies for diagnosing ovarian cancer: a summary of the
International Ovarian Tumor Analysis (IOTA) studies
Kaijser et al., UOG 2013
Summary of the IOTA project
 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

18. Improving strategies for diagnosing ovarian cancer: a summary of the
International Ovarian Tumor Analysis (IOTA) studies
Kaijser et al., UOG 2013
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.

19. Improving strategies for diagnosing ovarian cancer: a summary of the
International Ovarian Tumor Analysis (IOTA) studies
Kaijser et al., UOG 2013
Different approaches to estimate risk of malignancy

20. Improving strategies for diagnosing ovarian cancer: a summary of the
International Ovarian Tumor Analysis (IOTA) studies
Kaijser et al., UOG 2013
Discussion points
• 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?