Volumetric CT-based segmentation of NSCLC using 3D-Slicer

Abstract

Accurate volumetric assessment in non-small cell lung cancer (NSCLC) is critical for adequately informing treatments. In this study we assessed the clinical relevance of a semiautomatic computed tomography (CT)-based segmentation method using the competitive region-growing based algorithm, implemented in the free and public available 3D-Slicer software platform. We compared the 3D-Slicer segmented volumes by three independent observers, who segmented the primary tumour of 20 NSCLC patients twice, to manual slice-by-slice delineations of five physicians. Furthermore, we compared all tumour contours to the macroscopic diameter of the tumour in pathology, considered as the “gold standard”. The 3D-Slicer segmented volumes demonstrated high agreement (overlap fractions > 0.90), lower volume variability (p = 0.0003) and smaller uncertainty areas (p = 0.0002), compared to manual slice-by-slice delineations. Furthermore, 3D-Slicer segmentations showed a strong correlation to pathology (r = 0.89, 95%CI, 0.81–0.94). Our results show that semiautomatic 3D-Slicer segmentations can be used for accurate contouring and are more stable than manual delineations. Therefore, 3D-Slicer can be employed as a starting point for treatment decisions or for high-throughput data mining research, such as Radiomics, where manual delineating often represent a time-consuming bottleneck.

Hybrid positron emission tomography segmentation of heterogeneous lung tumors using 3D Slicer: improved GrowCut algorithm with threshold initialization

“…7 To overcome this methodological shortcoming, we used the interactive GrowCut algorithm implemented in the 3D Slicer. Velazquez et al 26 have shown some robust results for CT-based segmentation of NSCLC and we tried to extend it to segment the functional volume from FDG PET images. However, the algorithm is dependent on the correctness of user-marked labels.…”

“…An uncertainty region, as described in Ref. 26, was calculated as the difference between the union and intersection volumes of the three runs (represented as A, B, and C) for the GrowCut and IGC segmentation methods:…”

Quantitative Radiomic Phenotyping of Cervix Cancer

“…They evaluated the predictive values of more than 400 textural and shape-and intensity-based features extracted from the computed tomography (CT) images acquired before treatment. Tumor volumes were delineated either by radiation oncologists or using semiautomatic segmentation methods [16,17]. A subset of radiomic features were identified for predicting patient survival and describing intra-tumoral heterogeneity.…”

3D Printing and Personalized Airway Stents

“…Using 3D Slicer, COPDGene investigators evaluated over 3600 subjects' CT scans and found that airway wall thickness increased with bronchodilator responsiveness [21][22][23][24][25]. In a recent study of patients with NSCLC, Velazquez et al extracted volumetric data on NSCLC size using 3D Slicer extensions [26]. The ability to provide accurate tumor growth assessment allows more precise treatment response monitoring.…”