Ovarian
Ca & relevance of USG:--Ultrasound has always been pivotal in diagnosing pelvic
masses and can fairly differentiate cystic vs solid lesions, provide accurate
assessment of size, follow changes in appearance, and assess vascular supply
and flow.The International
Ovarian Tumor Analysis (IOTA) algorithms à
Simple Rules from the International Ovarian Tumor
Analysis to differentiate benign vs malignant adnexal masses.
Clinicians have incorporated the simple Rules in
practice and the Royal College of Obstetricians and Gynacologists in the United
Kingdom has included the Simple Rules in their Green Top guideline on the assessment and management of ovarian masses in premenopausal
women.
A recent study published in the American Journal of
Obstetrics and Gynecology April, 2016 issue aims to develop and validate a
model based on IOTA
Simple Rules to estimate the risk of malignancy in adnexal masses.
5 simple rules to suggest benign tumor
(B-rules):
(1) unilocular cyst;
(2) presence of solid components where the largest solid component is < 7 mm in largest diameter;
(1) unilocular cyst;
(2) presence of solid components where the largest solid component is < 7 mm in largest diameter;
5 simple rules to predict malignancy (M-rules):
(1) irregular solid tumor;
(2) ascites;
(1) irregular solid tumor;
(2) ascites;
How USG does
differentiates Beningn ovarian tumour to Ca of Ovary?? Simple Rules from the International Ovarian
Tumor Analysis to differentiate benign vs malignant adnexal masses.
Ovarian cancer is the seventh most common cancer in women worldwide (18 most common cancers
overall). The highest incidence of ovarian cancer is seen Europe and
Northern America; and the lowest incidence in Africa and Asia.
The American Cancer Society estimates that
in 2016, there will be 22,280 new cases of ovary cancer and an estimated 14,240
people will die of this disease.
Owing to slow progression, relatively
asymptomatic in early stage and with no single reliable screening test it is
mostly diagnosed at late stage.Few specific tumor markers and imaging studies
have made it possible to diagnose it at fairly early stages. Ultrasound has
always been pivotal in diagnosing pelvic masses and can fairly differentiate
cystic vs solid lesions, provide accurate assessment of size, follow changes in
appearance, and assess vascular supply and flow.
Over the years, researchers have developed
different models to accurately characterize adnexal masses as benign or
malignant preoperatively. A recent meta-analysis confirmed that the International Ovarian Tumor Analysis (IOTA) algorithms such as the Simple Rules are
very effective to preoperatively classify adnexal masses as benign or
malignant.
IOTA developed the simple rules and
mathematical models based on logistic regression (LR 1-2), which are very easy
to use in clinical practice to discriminate between benign and malignant
tumors. Logistic regression model 2 is a mathematical risk prediction
model which also includes age beside the 5 ultrasound variables (presence
of blood flow in a papillary structure, irregular cyst walls, ascites, acoustic
shadows, and maximum diameter of the largest solid component).
Clinicians have incorporated the Simple Rules in practice
and the Royal College of Obstetricians and Gynecologists in the United Kingdom
has included the Simple Rules in their Green Top guideline on
the assessment and management of ovarian masses in premenopausal women.
In spite of all the simplicity and ease of
use in practice, the simple rules have the limitations of inconclusive results
in many cases and absence of risk stratification for malignancy preoperatively.
A recent study published in the American Journal of Obstetrics and
Gynecology April, 2016 issue aims to develop and
validate a model based on IOTA Simple Rules to estimate the risk of malignancy
in adnexal masses.
It is an international cross sectional
study with 22 oncology centers, referral centers for ultrasonography, and
general hospitals. Data on 5020 patients were recorded in 3 phases from
2002 through 2012. The 5 simple rules of being benign or malignant was based on
presence of ascites, tumor morphology, and degree of vascularity at
ultrasonography.
All patient underwent a standard
transvaginal sonography by an experienced sonologist/gynecologist.
Transabdominal Scan was added as and when required particularly in very large
tumors. The reference standard was the histopathologic diagnosis after
the surgical removal of the tumor.
The quantifying predictive value of each of
the 10 features of Simple Rules was estimated. In addition, area under the
receiver operating characteristic curve (AUC), sensitivity, specificity, and
predictive values, Positive likelihood ratio (LR+) and negative likelihood
ratio (LR–) were also derived by multivariate logistic regression.
Data on 4848 patients were analyzed by
logistic regression. The observed malignancy rate was 34% overall (43% in
oncology centers, and 17% in other centers). The median age for benign tumor
was 42 vs 57 for malignant tumors.
A simple unilocular cyst was most
predictive of a benign tumor, while presence of ascites was most predictive of
malignancy and (irregular multilocular-solid tumor with largest diameter ≥100
mm) was least predictive.
When an ovarian mass is detected on
clinical examination, the risk of malignancy at an oncology center is 48.7% and
27.5% for patients at other centers. After sonography if more of
M-features than B-features were present the risk of malignancy was 42% and was
at most 0.29% when ≥2 B-features and no M-features were present. Based on these
findings a simple classification of adnexal masses can be used in clinical
practice to determine the risk of malignancy for an individual patient and her
management subsequently.
Over the years’ various mathematical models based on clinical and
pathological markers are being used to aid in clinical decision making. In 2014
a met analysis by Kaijser Jet alconfirmed the
superiority of IOTA simple rules and
What are the USG signs of beningn ovarian cyst?? Ans:- simple
rules to suggest benign tumor (B-rules):
(1) Unilocular cyst;
(2) presence of solid components where the largest solid component
is < 7 mm in largest diameter;
(3) acoustic shadows;
(4) smooth multilocular tumor less than 100 mm in largest
diameter; and
(5) no detectable blood flow on Doppler examination.
What are the simple rules to predict malignancy by USG (M-rules):
(1) irregular solid tumor;
(2) ascites;
(3) at least four papillary structures;
(4) irregular multilocular-solid tumor with a largest diameter of
at least 100 mm; and
(5) Very high color content on color Doppler examination.
The cancer antigen-125 is not one of the variables in the Simple
Rules, it is not included in the Simple Rules risk
classification and adding serum levels of CA 125 to the Logistic regression
model does not help us to discriminate between benign and malignant adnexal
masses.
In phase 3 the model was validated and it seen that it works well both
in hands of oncologists as well as general gynaecology practitioners. In low
risk patients a ‘wait and watch’ policy could be adapted, with close monitoring
and avoiding unnecessary surgeries whereas in high risk patients it leads to
early diagnosis and improved survival.
This was an international cross-sectional cohort study
involving 22 oncology centers, referral centers for ultrasonography, and
general hospitals. We included consecutive patients with an adnexal tumor who
underwent a standardized transvaginal ultrasound examination and were selected
for surgery. Data on 5020 patients were recorded in 3 phases from 2002 through
2012. The 5 Simple Rules features indicative of a benign tumor
(B-features) and the 5 features indicative of malignancy (M-features) are based
on the presence of ascites, tumor morphology, and degree of vascularity at
ultrasonography. Gold standard was the histopathologic diagnosis of the adnexal
mass (pathologist blinded to ultrasound findings). Logistic regression analysis
was used to estimate the risk of malignancy based on the 10 ultrasound features
and type of center. The diagnostic performance was evaluated by area under the
receiver operating characteristic curve, sensitivity, specificity, positive
likelihood ratio (LR+), negative likelihood ratio (LR–), positive predictive
value (PPV), negative predictive value (NPV), and calibration curves.
Results
Data on 4848 patients were analyzed. The malignancy rate was 43% (1402/3263) in oncology centers and
17% (263/1585) in other centers. The area under the receiver operating
characteristic curve on validation data was very similar in oncology centers
(0.917; 95% confidence interval, 0.901–0.931) and other centers (0.916; 95%
confidence interval, 0.873–0.945). Risk estimates showed good calibration. In
all, 23% of patients in the validation data set had a very low estimated risk
(<1%) and 48% had a high estimated risk (≥30%). For the 1% risk cutoff,
sensitivity was 99.7%, specificity 33.7%, LR+ 1.5, LR– 0.010, PPV 44.8%,
and NPV 98.9%. For the 30% risk cutoff, sensitivity was 89.0%, specificity
84.7%, LR+ 5.8, LR– 0.13, PPV 75.4%, and NPV 93.9%.
Conclusion
Quantification of the risk of malignancy
based on the Simple Rules has good diagnostic performance both
in oncology centers and other centers. A simple classification
based on these risk estimates may form the basis of a clinical management
system. Patients with a high risk may benefit from surgery by a gynecological
oncologist, while patients with a lower risk may be managed locally.
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