Roberto Negro1, Matteo Rucco2, Annalisa Creanza3, Alberto Mormile3, Paolo Piero Limone3, Roberto Garberoglio4, Stefano Spiezia5, Salvatore Monti6, Christian Cugini7, Ghassan El Dalati8, Maurilio Deandrea3. 1. Division of Endocrinology, V. Fazzi Hospital, Lecce, Italy. 2. United Technology Research Center, Trento, Italy. 3. Division of Endocrinology and Metabolism, Mauriziano Hospital Umberto I, Turin, Italy. 4. Division of Endocrinology and Metabolism, Molinette Hospital, Turin, Italy. 5. Endocrine Surgery, Ospedale del Mare, ASL NA1 Centro, Naples, Italy. 6. Endocrinology Unit, Azienda Ospedaliera Universitaria Sant'Andrea, Rome, Italy. 7. Radiology Department, Villa Salus Hospital, Venice, Italy. 8. Radiology Department, Policlinico G.B. Rossi, Verona, Italy.
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.
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.
Authors: Jörg-P Ritz; Kai S Lehmann; Urte Zurbuchen; Verena Knappe; Thomas Schumann; Heinz J Buhr; Christoph Holmer Journal: Lasers Surg Med Date: 2009-09 Impact factor: 4.025
Authors: F Magri; S Chytiris; M Molteni; L Croce; F Coperchini; M Rotondi; R Fonte; L Chiovato Journal: J Endocrinol Invest Date: 2019-07-18 Impact factor: 4.256