Nunes, N;
Ambler, G;
Foo, X;
Widschwendter, M;
Jurkovic, D;
(2018)
A prospective evaluation of the IOTA Logistic Regression Models (LR1 and LR2) in comparison to Subjective Pattern Recognition for the diagnosis of ovarian cancer in the outpatient setting.
Ultrasound in Obstetrics & Gynecology
, 51
(6)
pp. 829-835.
10.1002/uog.18918.
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Abstract
OBJECTIVES: To determine whether IOTA diagnostic models developed for pre-operative diagnosis of ovarian cancer could also be used to differentiate between benign and malignant adnexal tumours in the population of women attending gynaecology outpatient clinics. METHODS: All women referred to our outpatient clinic were first examined by a Level II ultrasound operator. In those diagnosed with adnexal tumours the IOTA LR1/2 protocol was used to evaluate the masses. The LR1 and LR2 models were then used to assess the risk of malignancy. Subsequently women were also examined by a Level 3 examiner who used pattern recognition to differentiate between benign and malignant tumours. Women with an ultrasound diagnosis of malignancy were offered surgery whilst asymptomatic women with presumed benign lesions were offered conservative management with a minimum follow-up of 12 months. The initial diagnosis was compared with two reference standards: histological findings and/or a comparative assessment of tumour morphology on follow-up ultrasound scans. All women in whom tumour classification on follow-up changed from benign to malignant were offered surgery. RESULTS: 489 women who had either or both of the reference standards were included into the final analysis. Their mean age was 50 years (range 16-91) and 45% of them were menopausal. 342/489 (69.9%) women had surgery and 147/489 (30.1%) were managed conservatively. The malignancy rate was 137/489 (28.0%). Overall sensitivities of LR1 and LR2 for the diagnosis of malignancy were 97.1% (95% CI: 92.7-99.2) and 94.9% (95%CI: 89.8-97.9) and specificities were 77.3% (95%CI: 72.5-81.5) and 76.7% (95%CI; 71.9-81.0) respectively (p>0.05). In comparison to pattern recognition [Sensitivity 94.2% (95% CI: 88.8 to 97.4); specificity 96.3% (95% CI: 93.8 to 98.0)], the specificities of IOTA models were significantly lower. (p < 0.0001) A significantly higher number of women would have been offered surgery for suspected cancer if women were assessed using the IOTA models instead of pattern recognition [213/489 (43.6%) versus 142/489 (29.0%)] (p<0.001). CONCLUSIONS: IOTA models maintained their high sensitivity when used in the outpatient setting. Specificity was relatively low which indicates that a significant proportion of women would have been offered unnecessary surgery for suspected ovarian cancer. These findings show that IOTA models could be used as a first stage test to diagnose ovarian cancer in the outpatient setting but a different second stage test is required to minimise the number of false positive findings.
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