Improving
strategies for diagnosing ovarian cancer: a summary of the International
Ovarian Tumor Analysis (IOTA) studies
First published: 15 October 2012
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
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