A CT-Based Radiomics Nomogram to Predict Complete Ablation of Pulmonary Malignancy: A Multicenter Study

Abstract

ObjectiveThermal ablation is a minimally invasive procedure for the treatment of pulmonary malignancy, but the intraoperative measure of complete ablation of the tumor is mainly based on the subjective judgment of clinicians without quantitative criteria. This study aimed to develop and validate an intraoperative computed tomography (CT)-based radiomic nomogram to predict complete ablation of pulmonary malignancy.MethodsThis study enrolled 104 individual lesions from 92 patients with primary or metastatic pulmonary malignancies, which were randomly divided into training cohort (n=74) and verification cohort (n=30). Radiomics features were extracted from the original CT images when the study clinicians determined the completion of the ablation surgery. Minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) were adopted for the dimensionality reduction of high-dimensional data and feature selection. The prediction model was developed based on the radiomics signature combined with the independent clinical predictors by multiple logistic regression analysis. The area under the curve (AUC), accuracy, sensitivity, and specificity were calculated. Receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the predictive performance of the model. Decision curve analysis (DCA) was applied to estimate the clinical usefulness and net benefit of the nomogram for decision making.ResultsThirteen CT features were selected to construct radiomics prediction model, which exhibits good predictive performance for determination of complete ablation of pulmonary malignancy. The AUCs of a CT-based radiomics nomogram that integrated the radiomics signature and the clinical predictors were 0.88 (95% CI 0.80-0.96) in the training cohort and 0.87 (95% CI: 0.71–1.00) in the validation cohort, respectively. The radiomics nomogram was well calibrated in both the training and validation cohorts, and it was highly consistent with complete tumor ablation. DCA indicated that the nomogram was clinically useful.ConclusionA CT-based radiomics nomogram has good predictive value for determination of complete ablation of pulmonary malignancy intraoperatively, which can assist in decision-making.

Robotics in Interventional Radiology: Review of Current and Future Applications

“…AI and specifically radiomics, with the extraction of quantitative features from radiological images and the combination of these with clinical and genetic/molecular models, have been improving the prognostic assessment prior to perform both percutaneous and endovascular procedures. 74 – 79 …”

“…Many studies show the role of radiomics in terms of prognostic evaluation of recurrence after MWA of liver, lung and kidney diseases. 74 – 79 …”

“…Many studies show the role of radiomics in terms of prognostic evaluation of recurrence after MWA of liver, lung and kidney diseases. [74][75][76][77][78][79] Among the fields of application of AI, augmented reality (AR) or virtual reality (VR) can be supportive tools during the IR procedures. Through advanced 3D rendering and spatial image manipulation, AR and VR allow practitioners to conceptualize difficult anatomy, increase realism in procedural planning (compared to standard 2D images) and improve procedural skills in a previously simulated environment.…”

Imaging in interventional oncology, the better you see, the better you treat

“…[89][90][91][92][93] Furthermore, good results were found for CT-radiomics predicting completeness of ablation in colorectal liver metastases, adrenal metastases and pulmonary malignancies. 94,95 In addition, Ma et al 96 used radiomics from contrast-enhanced ultrasound (CEUS) for prediction of recurrence in HCC lesions. Finally, the use of verification software for ablation evaluation and standardization of ablation margins needs to be implemented regularly in the clinical practice mostly in big lesions and/or with complex locations to optimize results.…”

“…Only some pilot studies have suggested radiomics features might be associated with outcome after TARE for HCC, liver metastases and intrahepatic cholangiocarcinoma 89–93 . Furthermore, good results were found for CT‐radiomics predicting completeness of ablation in colorectal liver metastases, adrenal metastases and pulmonary malignancies 94,95 . In addition, Ma et al 96 .…”