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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)
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
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
UOG Journal Club: Improving strategies for diagnosing ovarian cancer: a summary of the IOTA studies
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
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
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
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
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
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
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.
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
Descriptors of an ovarian mass used to make a diagnosis




BD, benign descriptor; MD, malignant descriptor.
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
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
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
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.
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
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?

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UOG Journal Club: Improving strategies for diagnosing ovarian cancer: a summary of the IOTA studies

  • 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?