Project description:Lung cancer can be classified into two main categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are different in treatment strategy and survival probability. The lung CT images of SCLC and NSCLC are similar such that their subtle differences are hardly visually discernible by the human eye through conventional imaging evaluation. We hypothesize that SCLC/NSCLC differentiation could be achieved via computerized image feature analysis and classification in feature space, as termed a radiomic model. The purpose of this study was to use CT radiomics to differentiate SCLC from NSCLC adenocarcinoma. Patients with primary lung cancer, either SCLC or NSCLC adenocarcinoma, were retrospectively identified. The post-diagnosis pre-treatment lung CT images were used to segment the lung cancers. Radiomic features were extracted from histogram-based statistics, textural analysis of tumor images and their wavelet transforms. A minimal-redundancy-maximal-relevance method was used for feature selection. The predictive model was constructed with a multilayer artificial neural network. The performance of the SCLC/NSCLC adenocarcinoma classifier was evaluated by the area under the receiver operating characteristic curve (AUC). Our study cohort consisted of 69 primary lung cancer patients with SCLC (n = 35; age mean ± SD = 66.91± 9.75 years), and NSCLC adenocarcinoma (n = 34; age mean ± SD = 58.55 ± 11.94 years). The SCLC group had more male patients and smokers than the NSCLC group (P < 0.05). Our SCLC/NSCLC classifier achieved an overall performance of AUC of 0.93 (95% confidence interval = [0.85, 0.97]), sensitivity = 0.85, and specificity = 0.85). Adding clinical data such as smoking history could improve the performance slightly. The top ranking radiomic features were mostly textural features. Our results showed that CT radiomics could quantitatively represent tumor heterogeneity and therefore could be used to differentiate primary lung cancer subtypes with satisfying results. CT image processing with the wavelet transformation technique enhanced the radiomic features for SCLC/NSCLC classification. Our pilot study should motivate further investigation of radiomics as a non-invasive approach for early diagnosis and treatment of lung cancer.

Project description:BACKGROUND:The role of postoperative radiotherapy (PORT) in the treatment of patients with completely resected non-small cell lung cancer (NSCLC) was not clear. A systematic review and individual participant data meta-analysis was undertaken to evaluate available evidence from randomised controlled trials (RCTs). These results were first published in Lung Cancer in 2013. OBJECTIVES:To evaluate the effects of PORT on survival and recurrence in patients with completely resected NSCLC. To investigate whether predefined patient subgroups benefit more or less from PORT. SEARCH METHODS:We supplemented MEDLINE and CANCERLIT searches (1965 to 8 July 2016) with information from trial registers, handsearching of relevant meeting proceedings and discussion with trialists and organisations. SELECTION CRITERIA:We included trials of surgery versus surgery plus radiotherapy, provided they randomised participants with NSCLC using a method that precluded prior knowledge of treatment assignment. DATA COLLECTION AND ANALYSIS:We carried out a quantitative meta-analysis using updated information from individual participants from all randomised trials. We sought data on all participants from those responsible for the trial. We obtained updated individual participant data (IPD) on survival and date of last follow-up, as well as details on treatment allocation, date of randomisation, age, sex, histological cell type, stage, nodal status and performance status. To avoid potential bias, we requested information on all randomised participants, including those excluded from investigators' original analyses. We conducted all analyses on intention-to-treat on the endpoint of survival. MAIN RESULTS:We identified 14 trials evaluating surgery versus surgery plus radiotherapy. Individual participant data were available for 11 of these trials, and our analyses are based on 2343 participants (1511 deaths). Results show a significant adverse effect of PORT on survival, with a hazard ratio of 1.18, or an 18% relative increase in risk of death. This is equivalent to an absolute detriment of 5% at two years (95% confidence interval (CI) 2% to 9%), reducing overall survival from 58% to 53%. Subgroup analyses showed no differences in effects of PORT by any participant subgroup covariate.We did not undertake analysis of the effects of PORT on quality of life and adverse events. Investigators did not routinely collect quality of life information during these trials, and it was unlikely that any benefit of PORT would offset the observed survival disadvantage. We considered risk of bias in the included trials to be low. AUTHORS' CONCLUSIONS:Results from 11 trials and 2343 participants show that PORT is detrimental to those with completely resected non-small cell lung cancer and should not be used in the routine treatment of such patients. Results of ongoing RCTs will clarify the effects of modern radiotherapy in patients with N2 tumours.

Project description:BackgroundLymphopenia during radiotherapy (RT) may have an adverse effect on treatment outcome. The aim of this study is to investigate associations between lymphopenia and RT parameters in patients with advanced lung cancer. Moreover, to investigate the prognostic role of lymphopenia, blood protein levels, and treatment and patient-related factors.Material and methodsSixty-two advanced stage non-small cell lung cancer (NSCLC) patients were retrospectively analyzed. Blood counts were available prior to, during, and after RT (3Gyx10). For each patient, a thoracic volume of interest (VOI) -including thoracic soft tissue and trabecular bone- was obtained by applying a CT window of -500 to 1200 HU in the planning CT. Dose parameters from thoracic VOI and other regions including lungs and vertebrae were calculated. Association between risk of lymphopenia ≥ G3 (lymphocytes at nadir according to CTCAE v4.0) and therapeutic parameters was investigated using Logistic regression. Relationships between overall survival (OS) and RT dose parameters, baseline blood counts and protein levels, and clinical factors were evaluated using Log-rank and Cox models.ResultMean thoracic RT dose (odds ratio [OR] 1.67; p = 0.04), baseline lymphocytes (OR 0.65; p = 0.01), and corticosteroids use (OR 6.07; p = 0.02) were significantly associated with increased risk of lymphopenia ≥ G3 in multivariable analysis. Worse OS was associated with high mean thoracic RT dose, high CRP/Albumin, large tumor volume and corticosteroids use (p < 0.05, univariate analysis), but not with lymphopenia ≥ G3. CRP/Albumin ratio > 0.12 (hazard ratio [HR] 2.28, p = 0.03) and corticosteroid use (HR 2.52, p = 0.01) were independently associated with worse OS.ConclusionHigh thoracic RT dose gave a higher risk of lymphopenia ≥ G3; hence limiting dose volume to the thorax may be valuable in preventing severe lymphopenia for patients receiving palliative fractionated RT. Still, lymphopenia ≥ G3 was not associated with worse OS. however, high baseline CRP/Albumin was associated with poorer OS and may carry important information as a prognostic factor of OS in advanced NSCLC receiving palliative RT.

Dataset Information

A radiomic approach for adaptive radiotherapy in non-small cell lung cancer patients.

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A radiomic approach for adaptive radiotherapy in non-small cell lung cancer patients.

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