Comparing the effectiveness of 2D and 3D features on predicting the response to chemotherapy for ovarian cancer patients

Abstract

Ovarian carcinoma is the most lethal malignancy in all kinds of gynecologic cancers, and radiomics based image marker is an effective tool for the early-stage prediction of the chemotherapies applied on ovarian cancer patients. This investigation aims to compare and evaluate the predicting performance of the 2D and 3D radiomics features. During the experiment, the tumors were first segmented from the CT slices, based on which a total of 1032 2D radiomics features and 1595 3D radiomics features were extracted. These features are related to tumor shape, density and texture properties. Next, a least absolute shrinkage and selection operator (LASSO) feature selection method was adopted to determine optimal features clusters for 2D and 3D feature pools respectively, which were used as the input of support vector machine (SVM) based prediction models. During the experiment, a total of 99 cases were selected from a previously established dataset at our medical center. The model performance was assessed by receiver operating characteristic (ROC) curve. The results indicated that the 2D and 3D feature based models achieved an area under the curve (AUC) of 0.85±0.03 and 0.89±0.02, while the overall accuracies were 0.76 and 0.81 respectively. These results indicate that the overall performance of the 3D feature is higher than the 2D features. But the sensitivity of the 2D model is higher at some certain specificity range. This study initially reveals the difference between the 2D and 3D features, which should be meaningful for the optimization of the radiomics based clinical decision support tools.

Developing a CT-image-based clinical marker to predict vasospasm for aneurysmal subarachnoid hemorrhage patients

“…During the last decade, radiomics and deep learning technologies have been widely used in the medical imaging field [8,9]. Radiomics method utilizes a large amount of quantitative image features to characterize the underlying tumor patterns which cannot be visually identified by radiologists [10,11], while deep learning approaches use deep neural networks to achieve a similar goal [12]. Despite their wide application, few studies have been conducted on combining these two methods for vasospasm prediction.…”