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Long term ultrasonic follow up of choroidal naevi and their transformation to melanomas
  1. I Kaiserman1,
  2. N Kaiserman2,
  3. J Pe’er2
  1. 1Department of Ophthalmology, Barzilai Medical Center, Ashkelon, Israel
  2. 2Department of Ophthalmology, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
  1. Correspondence to: Dr Igor Kaiserman Department of Ophthalmology, Barzilai Medical Center, Ashkelon 78306, Israel; igor{at}dr-kaiserman.com

Abstract

Aims: To compare ultrasonographic (US) predicting factors for conversion of choroidal naevi into melanomas.

Methods: 659 consecutive eyes with choroidal naevi were examined between 1984 and 2004. 165 clinically suspicious naevi were followed clinically and ultrasonographically (thickness, base diameters, internal reflectivity and location in the eye) for 5.08 (SE 0.24) years.

Results: 17 naevi (2.6% of all naevi, 10.3% of suspicious naevi) converted to small choroidal melanomas. The thickness of benign and premalignant naevi differed significant only after 1.5 years of follow up. The mean initial thickness of benign and premalignant naevi was significantly different (p = 0.001), as was mean initial internal reflectivity (p = 0.002) and mean initial largest base diameter (LBD, p = 0.05). Posterior pole and nasally located naevi were more likely to become malignant. A thickness of ⩾2 mm and a LBD ⩾7 mm were most predictive of conversion to melanoma, as was a combined KI index of ⩾14.5 (KI =  LBD + 4 × thickness + 1 (for nasal location) + 1 (for posterior pole location)). An artificial neural network did not have a better forecasting accuracy than the KI index. Logistic regression found the only significant parameters to influence the risk of conversion to melanoma to be the KI value and the initial tumour thickness.

Conclusions: A follow up of at least 1.5 years is necessary to detect conversion of naevi to choroidal melanomas. The thickness and LBD of the lesion can be used for predicting the risk.

  • ANNs, artificial neural networks
  • AUC, area under the ROC curve
  • LBD, largest base diameter
  • LR, likelihood ratio
  • ROC, receiver operating characteristics
  • US, ultrasonography
  • choroidal neoplasm
  • naevus
  • melanoma
  • ophthalmic ultrasonography
  • ANNs, artificial neural networks
  • AUC, area under the ROC curve
  • LBD, largest base diameter
  • LR, likelihood ratio
  • ROC, receiver operating characteristics
  • US, ultrasonography
  • choroidal neoplasm
  • naevus
  • melanoma
  • ophthalmic ultrasonography

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Footnotes

  • No competing interests exist for any of the authors.

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