@article {55, title = {PET-based dose painting in non-small cell lung cancer: Comparing uniform dose escalation with boosting hypoxic and metabolically active sub-volumes.}, journal = {Radiother Oncol}, volume = {116}, year = {2015}, month = {2015 Aug}, pages = {281-6}, abstract = {

BACKGROUND AND PURPOSE: We compared two imaging biomarkers for dose-escalation in patients with advanced non-small cell lung cancer (NSCLC). Treatment plans boosting metabolically active sub-volumes defined by FDG-PET or hypoxic sub-volumes defined by HX4-PET were compared with boosting the entire tumour.

MATERIALS AND METHODS: Ten NSCLC patients underwent FDG- and HX4-PET/CT scans prior to radiotherapy. Three isotoxic dose-escalation plans were compared per patient: plan A, boosting the primary tumour (PTVprim); plan B, boosting sub-volume with FDG >50\% SUVmax (PTVFDG); plan C, boosting hypoxic volume with HX4 tumour-to-background >1.4 (PTVHX4).

RESULTS: Average boost volumes were 507 {\textpm} 466 cm(3) for PTVprim, 173 {\textpm} 127 cm(3) for PTVFDG and 114 {\textpm} 73 cm(3) for PTVHX4. The smaller PTVHX4 overlapped on average 87 {\textpm} 16\% with PTVFDG. Prescribed dose was escalated to 87 {\textpm} 10 Gy for PTVprim, 107 {\textpm} 20 Gy for PTVFDG, and 117 {\textpm} 15 Gy for PTVHX4, with comparable doses to the relevant organs-at-risk (OAR). Treatment plans are available online (https://www.cancerdata.org/10.1016/j.radonc.2015.07.013).

CONCLUSIONS: Dose escalation based on metabolic sub-volumes, hypoxic sub-volumes and the entire tumour is feasible. Highest dose was achieved for hypoxia plans, without increasing dose to OAR. For most patients, boosting the metabolic sub-volume also resulted in boosting the hypoxic volume, although to a lower dose, but not vice versa.

}, keywords = {Aged, Aged, 80 and over, Carcinoma, Non-Small-Cell Lung, Cell Hypoxia, Female, Humans, Lung Neoplasms, Male, Middle Aged, Positron-Emission Tomography, Radiotherapy Dosage, Radiotherapy Planning, Computer-Assisted}, issn = {1879-0887}, doi = {10.1016/j.radonc.2015.07.013}, author = {Even, Aniek J G and van der Stoep, Judith and Zegers, Catharina M L and Reymen, Bart and Troost, Esther G C and Lambin, Philippe and van Elmpt, Wouter} } @article {56, title = {A Validated Prediction Model for Overall Survival From Stage III Non-Small Cell Lung Cancer: Toward Survival Prediction for Individual Patients.}, journal = {Int J Radiat Oncol Biol Phys}, volume = {92}, year = {2015}, month = {2015 Jul 15}, pages = {935-44}, abstract = {

PURPOSE: Although patients with stage III non-small cell lung cancer (NSCLC) are homogeneous according to the TNM staging system, they form a heterogeneous group, which is reflected in the survival outcome. The increasing amount of information for an individual patient and the growing number of treatment options facilitate personalized treatment, but they also complicate treatment decision making. Decision support systems (DSS), which provide individualized prognostic information, can overcome this but are currently lacking. A DSS for stage III NSCLC requires the development and integration of multiple models. The current study takes the first step in this process by developing and validating a model that can provide physicians with a survival probability for an individual NSCLC patient.

METHODS AND MATERIALS: Data from 548 patients with stage III NSCLC were available to enable the development of a prediction model, using stratified Cox regression. Variables were selected by using a bootstrap procedure. Performance of the model was expressed as the c statistic, assessed internally and on 2 external data sets (n=174 and n=130).

RESULTS: The final multivariate model, stratified for treatment, consisted of age, gender, World Health Organization performance status, overall treatment time, equivalent radiation dose, number of positive lymph node stations, and gross tumor volume. The bootstrapped c statistic was 0.62. The model could identify risk groups in external data sets. Nomograms were constructed to predict an individual patient{\textquoteright}s survival probability (www.predictcancer.org). The data set can be downloaded at https://www.cancerdata.org/10.1016/j.ijrobp.2015.02.048.

CONCLUSIONS: The prediction model for overall survival of patients with stage III NSCLC highlights the importance of combining patient, clinical, and treatment variables. Nomograms were developed and validated. This tool could be used as a first building block for a decision support system.

}, keywords = {Age Factors, Aged, Analysis of Variance, Antineoplastic Combined Chemotherapy Protocols, Carboplatin, Carcinoma, Non-Small-Cell Lung, Chemoradiotherapy, Cisplatin, Deoxycytidine, Etoposide, Female, Gemcitabine, Humans, Lung Neoplasms, Male, Middle Aged, Models, Statistical, Neoplasm Staging, Nomograms, Probability, Radiotherapy Dosage, Regression Analysis, Severity of Illness Index, Sex Factors, Vinblastine, Vinorelbine}, issn = {1879-355X}, doi = {10.1016/j.ijrobp.2015.02.048}, author = {Oberije, Cary and De Ruysscher, Dirk and Houben, Ruud and van de Heuvel, Michel and Uyterlinde, Wilma and Deasy, Joseph O and Belderbos, Jose and Dingemans, Anne-Marie C and Rimner, Andreas and Din, Shaun and Lambin, Philippe} } @article {41, title = {Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.}, journal = {Nat Commun}, volume = {5}, year = {2014}, month = {2014 Jun 03}, pages = {4006}, abstract = {

Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.

}, keywords = {Adenocarcinoma, Carcinoma, Non-Small-Cell Lung, Carcinoma, Squamous Cell, Female, Head and Neck Neoplasms, Humans, Lung Neoplasms, Male, Multimodal Imaging, Phenotype, Positron-Emission Tomography, Prognosis, Tomography, X-Ray Computed, Tumor Burden}, issn = {2041-1723}, doi = {10.1038/ncomms5006}, author = {Aerts, Hugo J W L and Velazquez, Emmanuel Rios and Leijenaar, Ralph T H and Parmar, Chintan and Grossmann, Patrick and Carvalho, Sara and Bussink, Johan and Monshouwer, Ren{\'e} and Haibe-Kains, Benjamin and Rietveld, Derek and Hoebers, Frank and Rietbergen, Michelle M and Leemans, C Ren{\'e} and Dekker, Andre and Quackenbush, John and Gillies, Robert J and Lambin, Philippe} } @article {46, title = {Externally validated HPV-based prognostic nomogram for oropharyngeal carcinoma patients yields more accurate predictions than TNM staging.}, journal = {Radiother Oncol}, volume = {113}, year = {2014}, month = {2014 Dec}, pages = {324-30}, abstract = {

PURPOSE: Due to the established role of the human papillomavirus (HPV), the optimal treatment for oropharyngeal carcinoma is currently under debate. We evaluated the most important determinants of treatment outcome to develop a multifactorial predictive model that could provide individualized predictions of treatment outcome in oropharyngeal carcinoma patients.

METHODS: We analyzed the association between clinico-pathological factors and overall and progression-free survival in 168 OPSCC patients treated with curative radiotherapy or concurrent chemo-radiation. A multivariate model was validated in an external dataset of 189 patients and compared to the TNM staging system. This nomogram will be made publicly available at www.predictcancer.org.

RESULTS: Predictors of unfavorable outcomes were negative HPV-status, moderate to severe comorbidity, T3-T4 classification, N2b-N3 stage, male gender, lower hemoglobin levels and smoking history of more than 30 pack years. Prediction of overall survival using the multi-parameter model yielded a C-index of 0.82 (95\% CI, 0.76-0.88). Validation in an independent dataset yielded a C-index of 0.73 (95\% CI, 0.66-0.79. For progression-free survival, the model{\textquoteright}s C-index was 0.80 (95\% CI, 0.76-0.88), with a validation C-index of 0.67, (95\% CI, 0.59-0.74). Stratification of model estimated probabilities showed statistically different prognosis groups in both datasets (p<0.001).

CONCLUSION: This nomogram was superior to TNM classification or HPV status alone in an independent validation dataset for prediction of overall and progression-free survival in OPSCC patients, assigning patients to distinct prognosis groups. These individualized predictions could be used to stratify patients for treatment de-escalation trials.

}, keywords = {Adult, Aged, Aged, 80 and over, Carcinoma, Squamous Cell, Chemoradiotherapy, Disease-Free Survival, Female, Head and Neck Neoplasms, Humans, Male, Middle Aged, Neoplasm Staging, Nomograms, Oropharyngeal Neoplasms, Papillomaviridae, Polymerase Chain Reaction, Predictive Value of Tests, Prognosis, Reproducibility of Results, Severity of Illness Index, Sex Factors, Squamous Cell Carcinoma of Head and Neck, Treatment Outcome}, issn = {1879-0887}, doi = {10.1016/j.radonc.2014.09.005}, author = {Rios Velazquez, Emmanuel and Hoebers, Frank and Aerts, Hugo J W L and Rietbergen, Michelle M and Brakenhoff, Ruud H and Leemans, Ren{\'e} C and Speel, Ernst-Jan and Straetmans, Jos and Kremer, Bernd and Lambin, Philippe} } @article {47, title = {Prognostic value of metabolic metrics extracted from baseline positron emission tomography images in non-small cell lung cancer.}, journal = {Acta Oncol}, volume = {52}, year = {2013}, month = {2013 Oct}, pages = {1398-404}, abstract = {

BACKGROUND: Maximum, mean and peak SUV of primary tumor at baseline FDG-PET scans, have often been found predictive for overall survival in non-small cell lung cancer (NSCLC) patients. In this study we further investigated the prognostic power of advanced metabolic metrics derived from intensity volume histograms (IVH) extracted from PET imaging.

METHODS: A cohort of 220 NSCLC patients (mean age, 66.6 years; 149 men, 71 women), stages I-IIIB, treated with radiotherapy with curative intent were included (NCT00522639). Each patient underwent standardized pre-treatment CT-PET imaging. Primary GTV was delineated by an experienced radiation oncologist on CT-PET images. Common PET descriptors such as maximum, mean and peak SUV, and metabolic tumor volume (MTV) were quantified. Advanced descriptors of metabolic activity were quantified by IVH. These comprised five groups of features: absolute and relative volume above relative intensity threshold (AVRI and RVRI), absolute and relative volume above absolute intensity threshold (AVAI and RVAI), and absolute intensity above relative volume threshold (AIRV). MTV was derived from the IVH curves for volumes with SUV above 2.5, 3 and 4, and of 40\% and 50\% maximum SUV. Univariable analysis using Cox Proportional Hazard Regression was performed for overall survival assessment.

RESULTS: Relative volume above higher SUV (80\%) was an independent predictor of OS (p = 0.05). None of the possible surrogates for MTV based on volumes above SUV of 3, 40\% and 50\% of maximum SUV showed significant associations with OS [p (AVAI3) = 0.10, p (AVAI4) = 0.22, p (AVRI40\%) = 0.15, p (AVRI50\%) = 0.17]. Maximum and peak SUV (r = 0.99) revealed no prognostic value for OS [p (maximum SUV) = 0.20, p (peak SUV) = 0.22].

CONCLUSIONS: New methods using more advanced imaging features extracted from PET were analyzed. Best prognostic value for OS of NSCLC patients was found for relative portions of the tumor above higher uptakes (80\% SUV).

}, keywords = {Aged, Carcinoma, Non-Small-Cell Lung, Female, Fluorodeoxyglucose F18, Humans, Lung Neoplasms, Male, Neoplasm Staging, Positron-Emission Tomography, Prognosis, Radiopharmaceuticals, Radiotherapy Planning, Computer-Assisted, Radiotherapy, Image-Guided, Tumor Burden}, issn = {1651-226X}, doi = {10.3109/0284186X.2013.812795}, author = {Carvalho, Sara and Leijenaar, Ralph T H and Velazquez, Emmanuel Rios and Oberije, Cary and Parmar, Chintan and van Elmpt, Wouter and Reymen, Bart and Troost, Esther G C and Oellers, Michel and Dekker, Andre and Gillies, Robert and Aerts, Hugo J W L and Lambin, Philippe} } Error | CancerData.org

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