No abstract

In this phase II study, long-term PFS was found in a subgroup of NSCLC patients with synchronous oligometastases when treated radically. Identification of this favorable subgroup before therapy is needed.

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.

The objectives of this study were to investigate the relationship between CT-and 18 F-FDG PET-based tumor volumes in nonsmall cell lung cancer (NSCLC) and the impact of tumor size and uptake heterogeneity on various approaches to delineating uptake on PET images. Methods: Twenty-five NSCLC cancer patients with 18 F-FDG PET/CT were considered. Seventeen underwent surgical resection of their tumor, and the maximum diameter was measured. Two observers manually delineated the tumors on the CT images and the tumor uptake on the corresponding PET images, using a fixed threshold at 50% of the maximum (T 50 ), an adaptive threshold methodology, and the fuzzy locally adaptive Bayesian (FLAB) algorithm. Maximum diameters of the delineated volumes were compared with the histopathology reference when available. The volumes of the tumors were compared, and correlations between the anatomic volume and PET uptake heterogeneity and the differences between delineations were investigated. Results: All maximum diameters measured on PET and CT images significantly correlated with the histopathology reference (r . 0.89, P , 0.0001). Significant differences were observed among the approaches: CT delineation resulted in large overestimation (132% 6 37%), whereas all delineations on PET images resulted in underestimation (from 215% 6 17% for T 50 to 24% 6 8% for FLAB) except manual delineation (18% 6 17%). Overall, CT volumes were significantly larger than PET volumes (55 6 74 cm 3 for CT vs. from 18 6 25 to 47 6 76 cm 3 for PET). A significant correlation was found between anatomic tumor size and heterogeneity (larger lesions were more heterogeneous). Finally, the more heterogeneous the tumor uptake, the larger was the underestimation of PET volumes by threshold-based techniques. Conclusion: Volumes based on CT images were larger than those based on PET images. Tumor size and tracer uptake heterogeneity have an impact on threshold-based methods, which should not be used for the delineation of cases of large heterogeneous NSCLC, as these methods tend to largely underestimate the spatial extent of the functional tumor in such cases. For an accurate delineation of PET volumes in NSCLC, advanced image segmentation algorithms able to deal with tracer uptake heterogeneity should be preferred. Theuseof 18 F-FDG PET, with the addition of CT since the development of PET/CT devices, has been increasing for staging non-small cell lung cancer (NSCLC) (1). In addition, the use of 18 F-FDG PET/CT in radiotherapy treatment planning for the definition of gross tumor volume has been similarly growing (2). Manual contouring of the tumor boundaries on the CT images is still the conventional methodology for target volume definition. On the other hand, and despite a high spatial resolution, the delineation on CT alone may be biased by insufficient contrast between tumor and healthy tissues (e.g., in cases of atelectasis, pleural effusion, and fibrosis or for tumors attached to the chest wall or mediastinum). Several studies have investigated the impact of...