UOG Journal Club: January 2013 Improving strategies for diagnosing ovarian   cancer: a summary of the International   Ovar...
Correct discrimination between benign and          malignant ovarian massesPrevious studies limited by:  –   small sample ...
Improving strategies for diagnosing ovarian cancer: a summary of the        International Ovarian Tumor Analysis (IOTA) st...
IOTA phase 1• 1066 non-pregnant women• At least one persistent adnexal mass• Nine clinical centers in five countries     T...
Variables used in the logistic regression models1.    Personal history of ovarian cancer2.    Current hormonal therapy    ...
IOTA phase 1b • 507 consecutive women • Three centers • Prospective validation of the models                           IOT...
Simple ultrasound-based rules• Based on subjective assessment of ultrasound  images• Rules could be applied to 77% of ovar...
Features of a benign mass (B-features)           A mass is classified           as benign if at least             one B-fe...
Features of a malignancy (M-features)           A mass is classified             as malignant if at            least one M...
Simple ultrasound-based rulesIf the rules are inconclusive if no B/M-featuresare present or both B and M features areprese...
Diagnostic performance of the models and rules External               ROC AUC     Sensitivity   Specificity   LR+     LR-v...
Diagnostic performance of the models and rulesLR1, LR2 and simple rules had similar diagnostic performance inIOTA phase 1b...
Descriptors of an ovarian mass used to make a diagnosisBD, 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...
Diagnostic performance of the models and simple     rules to detect Stage 1 ovarian cancer  LR1 and LR2 had higher detecti...
Improving strategies for diagnosing ovarian cancer: a summary of the         International Ovarian Tumor Analysis (IOTA) s...
Improving strategies for diagnosing ovarian cancer: a summary of the             International Ovarian Tumor Analysis (IOT...
Improving strategies for diagnosing ovarian cancer: a summary of the        International Ovarian Tumor Analysis (IOTA) st...
Improving strategies for diagnosing ovarian cancer: a summary of the            International Ovarian Tumor Analysis (IOTA...
UOG Journal Club: Improving strategies for diagnosing ovarian cancer: a summary of the IOTA studies
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UOG Journal Club: Improving strategies for diagnosing ovarian cancer: a summary of the IOTA studies

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

  1. 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. 2. Correct discrimination between benign and malignant ovarian massesPrevious 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. 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 externalvalidation with examiners of different levels of ultrasound experience• To establish the role of CA 125 and other serum tumor markers forthe diagnosis of ovarian cancer• To identify the characteristics of ovarian tumors that are difficult toclassify as benign or malignant• To validate these models or rules in non-operated patients bystudying the outcome of adnexal masses classified as benign
  4. 4. 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%) patientsTwo logistic regressions models developed(LR1 and LR2) Timmerman et al, J Clin Oncol, 2005
  5. 5. Variables used in the logistic regression models1. Personal history of ovarian cancer2. Current hormonal therapy LR13. Age of the patient* (12 variables)4. Maximum diameter of the lesion5. Pain during examination6. Ascites*7. Blood flow within a solid papillary projection*8. Purely solid tumor9. Maximum diameter of the solid component*10. Irregular internal cyst wall*11. Acoustic shadows* *LR212. Color score (6 variables) Timmerman et al, J Clin Oncol, 2005
  6. 6. 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 modelsJVan Holsbeke et al, Clin Cancer Res, 2009 and 2012; Timmerman et al, UOG 2010
  7. 7. 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
  8. 8. 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
  9. 9. 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
  10. 10. Simple ultrasound-based rulesIf the rules are inconclusive if no B/M-featuresare present or both B and M features arepresent...... rely on subjective assessment by an expertultrasound examiner as a second stage test
  11. 11. Diagnostic performance of the models and rules External ROC AUC Sensitivity Specificity LR+ LR-validation Similar diagnostic performance between LR1 and LR2 LR1cut-off 10% 0.96 92% 87% 6.8 0.09 LR2 0.95 92% 86% 6.4 0.10cut-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.
  12. 12. Diagnostic performance of the models and rulesLR1, LR2 and simple rules had similar diagnostic performance inIOTA phase 1b and phase 2 datasets Timmerman et al, BMJ, 2010
  13. 13. Descriptors of an ovarian mass used to make a diagnosisBD, benign descriptor; MD, malignant descriptor.
  14. 14. 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 examinerTimmerman et al, J Clin Oncol, 2007; Van Calster et al, J Natl Cancer Inst, 2007,Valentin et al, UOG, 2009
  15. 15. 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
  16. 16. 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
  17. 17. Improving strategies for diagnosing ovarian cancer: a summary of the International Ovarian Tumor Analysis (IOTA) studies Kaijser et al., UOG 2013 Recommendations for clinical practice1. IOTA simple rules can be used as a triage test in 75% of all adnexal masses forestimating the risk of malignancy2. A two-step strategy with referral to a specialist in gynecological ultrasound ofunclassifiable masses rules has excellent diagnostic performance3. An alternative to the simple rules is the LR2 model4. LR2 or the simple rules should be adopted as the principal test to characterize massesas benign and malignant in premenopausal women5. Measurement of serum CA 125 marker is not necessary for characterization of ovarianpathology in premenopausal women and is unlikely to improve the performance ofexperienced ultrasound examiners even in postmenopausal women.
  18. 18. 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
  19. 19. 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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