Evaluation of tumor measurements in oncology: Use of film-based and electronic techniques
ABSTRACT To evaluate the variability in bidimensional computed tomography (CT) measurements obtained of actual tumors and of tumor phantoms by use of three measurement techniques: hand-held calipers on film, electronic calipers on a workstation, and an autocontour technique on a workstation.
Three radiologists measured 45 actual tumors (in the lung, liver, and lymph nodes) on CT images, using each of the three techniques. Bidimensional measurements were recorded, and their cross-products calculated. The coefficient of variation was calculated to assess interobserver variability. CT images of 48 phantoms were measured by three radiologists with each of the techniques. In addition to the coefficient of variation, the differences between the cross-product measurements of tumor phantoms themselves and the measurements obtained with each of the techniques were calculated.
The differences between the coefficients of variation were statistically significantly different for the autocontour technique, compared with the other techniques, both for actual tumors and for tumor phantoms. There was no statistically significant difference in the coefficient of variation between measurements obtained with hand-held calipers and electronic calipers. The cross-products for tumor phantoms were 12% less than the actual cross-product when calipers on film were used, 11% less using electronic calipers, and 1% greater using the autocontour technique.
Tumor size is obtained more accurately and consistently between readers using an automated autocontour technique than between those using hand-held or electronic calipers. This finding has substantial implications for monitoring tumor therapy in an individual patient, as well as for evaluating the effectiveness of new therapies under development.
- SourceAvailable from: Jinzi Zheng
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- "The growth curves of each group of tumors were shown in figure 2. On day 14 and later time points, group A tumors were consistently larger than that of group B or group C (p<0.01). The tumor doubling times were 1.2 days, 3.6 days and 18.8 days for groups A, B and C during the first 2 weeks after tumor inoculation , . "
ABSTRACT: The rabbit VX2 lung cancer model is a large animal model useful for preclinical lung cancer imaging and interventional studies. However, previously reported models had issues in terms of invasiveness of tumor inoculation, control of tumor aggressiveness and incidence of complications. We aimed to develop a minimally invasive rabbit VX2 lung cancer model suitable for imaging and transbronchial interventional studies. New Zealand white rabbits and VX2 tumors were used in the study. An ultra-thin bronchoscope was inserted through a miniature laryngeal mask airway into the bronchus. Different numbers of VX2 tumor cells were selectively inoculated into the lung parenchyma or subcarinal mediastinum to create a uniform tumor with low incidence of complications. The model was characterized by CT, FDG-PET, and endobronchial ultrasound (EBUS). Liposomal dual-modality contrast agent was used to evaluate liposome drug delivery system in this model. Both peripheral and mediastinal lung tumor models were created. The tumor making success rate was 75.8% (25/33) in the peripheral lung tumor model and 60% (3/5) in the mediastinal tumor model. The group of 1.0×10(6) of VX2 tumor cells inoculation showed a linear growth curve with less incidence of complications. Radial probe EBUS visualized the internal structure of the tumor and the size measurement correlated well with CT measurements (r(2) = 0.98). Over 7 days of continuous enhancement of the lung tumor by liposomal contrast in the lung tumor was confirmed both CT and fluorescence imaging. Our minimally invasive bronchoscopic rabbit VX2 lung cancer model is an ideal platform for lung cancer imaging and preclinical bronchoscopic interventional studies.PLoS ONE 06/2013; 8(6):e67355. DOI:10.1371/journal.pone.0067355 · 3.23 Impact Factor
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- "The volume of tumor foci was calculated according to the following formula: tumor volume (mm3) = (major axis) × (minor axis)2 × 0.52 [39,40]. The major axis is the longest diameter of the tumor, and the minor axis is the maximal line drawn perpendicular to the major axis [41,42]. "
ABSTRACT: In the animal model of brain metastasis using human lung squamous cell carcinoma-derived cells (HARA-B) inoculated into the left ventricle of the heart of nude mice, metastasized tumor cells and brain resident cells interact with each other. Among them, tumor cells and astrocytes have been reported to stimulate each other, releasing soluble factors from both sides, subsequently promoting tumor growth significantly. Among the receptors for soluble factors released from astrocytes, only IL-6 receptor (IL-6R) on tumor cells was up-regulated during the activation with astrocytes. Application of monoclonal antibody against human IL-6R (tocilizumab) to the activated HARA-B cells, the growth of HARA-B cells stimulated by the conditioned medium of HARA-B/astrocytes was significantly inhibited. Injecting tocilizumab to animal models of brain metastasis starting at three weeks of inoculation of HARA-B cells, two times a week for three weeks, significantly inhibited the size of the metastasized tumor foci. The up-regulated expression of IL-6R on metastasized lung tumor cells was also observed in the tissue from postmortem patients. These results suggest that IL-6R on metastasized lung tumor cells would be a therapeutic target to inhibit the growth of the metastasized lung tumor cells in the brain.International Journal of Molecular Sciences 12/2012; 14(1):515-26. DOI:10.3390/ijms14010515 · 2.86 Impact Factor
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- "It is expected that the increased automation of our approach should improve the reproducibility of calculated tumor volumes, but this still needs to be studied and validated prospectively. In support of this assumption, Schwartz et al. found that in CT assessment of solid tumors, techniques that employed increased automation obtained results that were more accurate and consistent than manual methods. Other studies using automated CT volumetric methods in pulmonary tumors suggest superiority when compared to manual RECIST measurements. "
ABSTRACT: Current radiographic response criteria for brain tumors have difficulty describing changes surrounding postoperative resection cavities. Volumetric techniques may offer improved assessment, however usually are time-consuming, subjective and require expert opinion and specialized magnetic resonance imaging (MRI) sequences. We describe the application of a novel volumetric software algorithm that is nearly fully automated and uses standard T1 pre- and post-contrast MRI sequences. T1-weighted pre- and post-contrast images are automatically fused and normalized. The tumor region of interest is grossly outlined by the user. An atlas of the nasal mucosa is automatically detected and used to normalize levels of enhancement. The volume of enhancing tumor is then automatically calculated. We tested the ability of our method to calculate enhancing tumor volume with resection cavity collapse and when the enhancing tumor is obscured by subacute blood in a resection cavity. To determine variability in results, we compared narrowly-defined tumor regions with tumor regions that include adjacent meningeal enhancement and also compared different contrast enhancement threshold levels used for the automatic calculation of enhancing tumor volume. Our method quantified enhancing tumor volume despite resection cavity collapse. It detected tumor volume increase in the midst of blood products that incorrectly caused decreased measurements by other techniques. Similar trends in volume changes across scans were seen with inclusion or exclusion of meningeal enhancement and despite different automated thresholds for tissue enhancement. Our approach appears to overcome many of the challenges with response assessment of enhancing brain tumors and warrants further examination and validation.PLoS ONE 01/2011; 6(1):e16031. DOI:10.1371/journal.pone.0016031 · 3.23 Impact Factor