The impact of virtual simulation in palliative radiotherapy for non-small-cell lung cancer.

Background and purpose: Radiotherapy is widely used to palliate local symptoms in non-small-cell lung cancer. Using conventional X-ray simulation, it is often difficult to accurately localize the extent of the tumour. We report a randomized, double blind trial comparing target localization with conventional and virtual simulation.Methods: Eighty-six patients underwent both conventional and virtual simulation. The conventional simulator films were compared with digitally reconstructed radiographs (DRRs) produced from the computed tomography (CT) data. The treatment fields defined by the clinicians using each modality were compared in terms of field area, position and the implications for target coverage.Results: Comparing fields defined by each study arm, there was a major mis-match in coverage between fields in 66.2% of cases, and a complete match in only 5.2% of cases. In 82.4% of cases, conventional simulator fields were larger (mean 24.5+/-5.1% (95% confidence interval)) than CT-localized fields, potentially contributing to a mean target under-coverage of 16.4+/-3.5% and normal tissue over-coverage of 25.4+/-4.2%.Conclusions: CT localization and virtual simulation allow more accurate definition of the target volume. This could enable a reduction in geographical misses, while also reducing treatment-related toxicity.

Generation of a non-small cell lung cancer transcriptome microarray

Background: Non-small cell lung cancer (NSCLC) is the leading cause of cancer mortality worldwide. At present no reliable biomarkers are available to guide the management of this condition. Microarray technology may allow appropriate biomarkers to be identified but present platforms are lacking disease focus and are thus likely to miss potentially vital information contained in patient tissue samples.

(18)F-FDG PET-CT simulation for non-small-cell lung cancer: effect in patients already staged by PET-CT.

Purpose: Positron emission tomography (PET), in addition to computed tomography (CT), has an effect in target volume definition for radical radiotherapy (RT) for non–small-cell lung cancer (NSCLC). In previously PET-CT staged patients with NSCLC, we assessed the effect of using an additional planning PET-CT scan for gross tumor volume (GTV) definition. Methods and Materials: A total of 28 patients with Stage IA-IIIB NSCLC were enrolled. All patients had undergone staging PET-CT to ensure suitability for radical RT. Of the 28 patients, 14 received induction chemotherapy. In place of a RT planning CT scan, patients underwent scanning on a PET-CT scanner. In a virtual planning study, four oncologists independently delineated the GTVon the CT scan alone and then on the PET-CTscan. Intraobserver and interobserver variability were assessed using the concordance index (CI), and the results were compared using the Wilcoxon signed ranks test. Results: PET-CT improved the CI between observers when defining the GTVusing the PET-CT images compared with using CTalone for matched cases (median CI, 0.57 for CTand 0.64 for PET-CT, p = .032). The median of the mean percentage of volume change from GTVCT to GTVFUSED was 5.21% for the induction chemotherapy group and 18.88% for the RT-alone group. Using the Mann-Whitney U test, this was significantly different (p = .001). Conclusion: PET-CT RT planning scan, in addition to a staging PET-CT scan, reduces interobserver variability in GTV definition for NSCLC. The GTV size with PET-CT compared with CT in the RT-alone group increased and was reduced in the induction chemotherapy group.

Raman microscopy in the diagnosis and prognosis of surgically resected nonsmall cell lung cancer

The main curative therapy for patients with nonsmall cell lung cancer is surgery. Despite this, the survival rate is only 50%, therefore it is important to more efficiently diagnose and predict prognosis for lung cancer patients. Raman spectroscopy is useful in the diagnosis of malignant and premalignant lesions. The aim of this study is to investigate the ability of Raman microscopy to diagnose lung cancer from surgically resected tissue sections, and predict the prognosis of these patients. Tumor tissue sections from curative resections are mapped by Raman microscopy and the spectra analzsed using multivariate techniques. Spectra from the tumor samples are also compared with their outcome data to define their prognostic significance. Using principal component analysis and random forest classification, Raman microscopy differentiates malignant from normal lung tissue. Principal component analysis of 34 tumor spectra predicts early postoperative cancer recurrence with a sensitivity of 73% and specificity of 74%. Spectral analysis reveals elevated porphyrin levels in the normal samples and more DNA in the tumor samples. Raman microscopy can be a useful technique for the diagnosis and prognosis of lung cancer patients receiving surgery, and for elucidating the biochemical properties of lung tumors. (C) 2010 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3323088]