Prediction of Recurrence in Thyroid Cancer Patients Using Plotkin’s Least General Generalisation LGG as a Method of Inductive Logic Programming ILP

  • Background:
    Although thyroid cancers are known to have a relative low risk of recurrence, there are factors associated with a higher risk of recurrence. However, predicting disease recurrence and prognosis in patients undergoing thyroidectomy is clinically difficult. To detect new algorithms to predict recurrence, inductive logic programming was used.

    From January 2009 to June 2010, 797 cases of thyroid cancer patients who underwent bilateral total thyroidectomy with following radio-iodine treatment from our database were studied. 638 (80%) cases were used to create algorithms to detect recurrence. 159 (20%) cases were analyzed for validation of created rules. Least Generalized Generalization LGG was chosen as a method of inductive logic programming to extract rules which represents algorithms to predict recurrence. Delmia PRD was used for analysis.

    Of the 638 cases, there were 46 (7.2%) cases with recurrence. There were 4 rules detected which could predict all of the cases with recurrence. Postoperative thyroglobulin was the most powerful variable which correlated with recurrence. When the 4 recognized rules were applied to 159 cases for validation, it was possible to predict 72.7% (8 cases among 11 of the recurrences). When factors known for high and intermediate risk were selected for creating rules, the most optimal combination could only predict 66.7% of the recurrence cases.

    Discussion & Conclusion:
    From our database, rules to predict recurrence were identified which were able to predict recurrence more precise than already known high and intermediate risk factors.


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