Analysis of tumor texture on a pre-treatment CT scan predicts treatment outcome in NSCLC patients

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Combining Clinical, Pathological, and Demographic Factors Refines Prognosis of Lung Cancer: A Population-Based Study

In the treatment of lung cancer, an accurate estimation of patient clinical outcome is essential for choosing an appropriate course of therapy. It is important to develop a prognostic stratification model which combines clinical, pathological and demographic factors for individualized clinical decision making.A total of 234,412 patients diagnosed with adenocarcinomas or squamous cell carcinomas of the lung or bronchus between 1988 and 2006 were retrieved from the SEER database to construct a prognostic model. A model was developed by estimating a Cox proportional hazards model on 500 bootstrapped samples. Two models, one using stage alone and another comprehensive model using additional covariates, were constructed. The comprehensive model consistently outperformed the model using stage alone in prognostic stratification and on Harrell's C, Nagelkerke's R(2), and Brier Scores in the whole patient population as well as in specific treatment modalities. Specifically, the comprehensive model generated different prognostic groups with distinct post-operative survival (log-rank P<0.001) within surgical stage IA and IB patients in Kaplan-Meier analyses. Two additional patient cohorts (n = 1,991) were used as an external validation, with the comprehensive model again outperforming the model using stage alone with regards to prognostic stratification and the three evaluated metrics.These results demonstrate the feasibility of constructing a precise prognostic model combining multiple clinical, pathologic, and demographic factors. The comprehensive model significantly improves individualized prognosis upon AJCC tumor staging and is robust across a range of treatment modalities, the spectrum of patient risk, and in novel patient cohorts

EORTC 22972-26991/MRC BR10 trial: Fractionated stereotactic boost following conventional radiotherapy of high grade gliomas - Clinical and quality-assurance results of the stereotactic boost arm

Background and purpose: The EORTC trial No. 22972 investigated the role of an additional fractionated stereotactic boost (fSRT) to conventional radiotherapy for patients with high grade gliomas. A quality-assurance (QA) programme was run in conjunction with the study and was the first within the EORTC addressing the quality of a supposedly highly accurate treatment technique such as stereotactic radiotherapy. A second aim was to investigate a possible relation between the clinical results of the stereotactic boost arm and the results of the QA. Materials and methods: The trial was closed in 2001 due to low accrual. In total, 25 patients were randomized: 14 into the experimental arm and 11 into the control arm. Six centres randomized patients, 8 centres had completed the dummy run (DR) for the stereotactic boost part. All participating centres (9) were asked to complete a quality-assurance questionnaire. The DR consisted of treatment planning according to the guidelines of the protocol on 3 different tumour volumes drawn on CT images of a humanized phantom. The SRT technique to be used was evaluated by the questionnaire. Clinical data from patients recruited to the boost arm from 6 participating centres were analysed. Results: There was a full compliance to the protocol requirements for 5 centres. Major and minor deviations in conformality were observed for 2 and 3 centres, respectively. Of the 8 centres which completed the DR, one centre did not comply with the requirements of stereotactic radiotherapy concerning accuracy, dosimetry and planning. Median follow-up and median overall survival were 39.2 and 21.4 months, respectively. Acute and late toxicities of the stereotactic boost were low. One radiation necrosis was seen for a patient who has not received the SRT boost. Three reported serious adverse events were all seizures and probably therapy-related. Conclusions: Overall compliance was good but not ideal from the point of view of this highly precise radiation technique. Survival in the subgroup of patients with small volume disease was encouraging, but the study does not provide sufficient information about the potential value of fSRT boost in patients with malignant glioma.Toxicity due to an additional stereotactic boost of 20 Gy in 4 fractions was low and may be considered as a safe treatment option for patients with small tumours. (C) 2008 Elsevier Ireland Ltd. All rights reserved

Comparison of Bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy

Purpose: Classic statistical and machine learning models such as support vector machines (SVMs) can be used to predict cancer outcome, but often only perform well if all the input variables are known, which is unlikely in the medical domain. Bayesian network (BN) models have a natural ability to reason under uncertainty and might handle missing data better. In this study, the authors hypothesize that a BN model can predict two-year survival in non-small cell lung cancer (NSCLC) patients as accurately as SVM, but will predict survival more accurately when data are missing. Methods: A BN and SVM model were trained on 322 inoperable NSCLC patients treated with radiotherapy from Maastricht and validated in three independent data sets of 35, 47, and 33 patients from Ghent, Leuven, and Toronto. Missing variables occurred in the data set with only 37, 28, and 24 patients having a complete data set. Results: The BN model structure and parameter learning identified gross tumor volume size, performance status, and number of positive lymph nodes on a PET as prognostic factors for two-year survival. When validated in the full validation set of Ghent, Leuven, and Toronto, the BN model had an AUC of 0.77, 0.72, and 0.70, respectively. A SVM model based on the same variables had an overall worse performance (AUC 0.71, 0.68, and 0.69) especially in the Ghent set, which had the highest percentage of missing the important GTV size data. When only patients with complete data sets were considered, the BN and SVM model performed more alike. Conclusions: Within the limitations of this study, the hypothesis is supported that BN models are better at handling missing data than SVM models and are therefore more suitable for the medical domain. Future works have to focus on improving the BN performance by including more patients, more variables, and more diversity