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. 2020 Jul;9(4):205-212.
doi: 10.1159/000504882. Epub 2019 Dec 19.

Machine Learning Prediction of Radiofrequency Thermal Ablation Efficacy: A New Option to Optimize Thyroid Nodule Selection

Roberto Negro  1 , Matteo Rucco  2 , Annalisa Creanza  3 , Alberto Mormile  3 , Paolo Piero Limone  3 , Roberto Garberoglio  4 , Stefano Spiezia  5 , Salvatore Monti  6 , Christian Cugini  7 , Ghassan El Dalati  8 , Maurilio Deandrea  3
Affiliations

Machine Learning Prediction of Radiofrequency Thermal Ablation Efficacy: A New Option to Optimize Thyroid Nodule Selection

Roberto Negro et al. Eur Thyroid J. 2020 Jul.

Abstract

Background: Radiofrequency (RF) is a therapeutic modality for reducing the volume of large benign thyroid nodules. If thermal therapies are interpreted as an alternative strategy to surgery, critical issues in their use are represented by the extent of nodule reduction and by the durability of nodule reduction over a long period of time.

Objective: To assess the ability of machine learning to discriminate nodules with volume reduction rate (VRR) < or ≥50% at 12 months following RF treatment.

Methods: A machine learning model was trained with a dataset of 402 cytologically benign thyroid nodules subjected to RF at six Italian Institutions. The model was trained with the following variables: baseline nodule volume, echostructure, macrocalcalcifications, vascularity, and 12-month VRR.

Results: After training, the model could distinguish between nodules having VRR <50% from those having VRR ≥50% in 85% of cases (accuracy: 0.85; 95% confidence interval [CI]: 0.80-0.90; sensitivity: 0.70; 95% CI: 0.62-0.75; specificity: 0.99; 95% CI: 0.98-1.0; positive predictive value: 0.95; 95% CI: 0.92-0.98; negative predictive value: 0.95; 95% CI: 0.92-0.98).

Conclusions: This study demonstrates that a machine learning model can reliably identify those nodules that will have VRR < or ≥50% at 12 months after one RF treatment session. Predicting which nodules will be poor or good responders represents valuable data that may help physicians and patients decide on the best treatment option between thermal ablation and surgery or in predicting if more than one session might be necessary to obtain a significant volume reduction.

Keywords: Artificial Intelligence; Laser; Machine learning; Nodules; Radiofrequency; Thermal ablation; Thyroid.

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Conflict of interest statement

The authors have no conflicts of interest to declare.

Figures

Fig. 1
Fig. 1
a Distribution of nodule volume at baseline and at 12 months after treatment, divided by reduction ≥ or <50%. b Accuracy of the performances of the trained classifiers on the test set.
Fig. 2
Fig. 2
Confusion matrix. The 2 × 2 contingency table reports the number of true positives, false positives, false negatives, and true negatives; the test set contains 201 patients.
Fig. 3
Fig. 3
Simulated probability for a given nodule to reduce ≥50% at 12-month follow-up.
Fig. 4
Fig. 4
Input variables sorted by their relative weight.
See this image and copyright information in PMC

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