Title:Prediction of Microwave Ablation Recurrence in Pulmonary Malignancies
Using Preoperative Computed Tomography Radiomics Models
Volume: 20
Author(s): Fandong Zhu, Chen Yang, Jing Yang, Haijia Mao, Yanan Huang, Hao Liu and Zhenhua Zhao*
Affiliation:
- Department of Radiology, Shaoxing People’s Hospital (Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis
and Treatment of Shaoxing City), No.568, Zhongxing North Road, Shaoxing 312000, China
Keywords:
Computed tomography, Recurrence, Pulmonary malignancies, Microwave ablation, Radiomics, Pulmonary tumor.
Abstract:
Background:
Assessing the early efficacy of microwave ablation (MWA) for pulmonary malignancies is a challenge for interventionalists. However, performing
an accurate efficacy assessment at an earlier stage can significantly enhance clinical intervention and improve the patient’s prognosis.
Purpose:
This research aimed to create and assess non-invasive diagnostic techniques using pre-operative computed tomography (CT) radiomics models to
predict the recurrence of MWA in pulmonary malignancies.
Materials and Methods:
We retrospectively enrolled 116 eligible patients with pulmonary malignancies treated with MWA. we separated the patients into two groups: a
recurrence group (n = 28) and a non-recurrence group (n = 88), following the modified Response Evaluation Criteria in Solid Tumors (m-RECIST)
criteria. We segmented the preoperative tumor area manually. We expanded outward the tumor boundary 4 times, with a width of 3 mm, using the
tumor boundary as the baseline. Five groups of radiomics features were extracted and screened using max-relevance and min-redundancy (mRMR)
and least absolute shrinkage and selection operator (LASSO) regression. Weight coefficients of the aforementioned features were used to calculate
the Radscore and construct radiomics models for both tumoral and peritumoral areas. The Radscore from the radiomics model was combined with
clinical risk factors to construct a combined model. The performance and clinical usefulness of the combined models were assessed through the
evaluation of receiver operating characteristic (ROC) curves, the Delong test, calibration curves, and decision curve analysis (DCA) curves.
Results:
The clinical risk factor for recurrence after MWA was tumor diameter (P < 0.05). Both tumoral and four peritumoral radiomics models exhibited
high diagnostic efficacy. Furthermore, the combined 1 (C1)-RO model and the combined 2 (C2)-RO model showed higher efficacy with area under
the curve (AUCs) of 0.89 and 0.89 in the training cohort, and 0.93 and 0.94 in the validation cohort, respectively. Both combined models
demonstrated excellent predictive accuracy and clinical benefit.
Conclusion:
Preoperative CT radiomics models for both tumoral and peritumoral regions are capable of accurately predicting the recurrence of pulmonary
malignancies after MWA. The combination of both models may lead to better performance and may aid in devising more effective preoperative
treatment strategies.