PET-based prognostic survival model after radiotherapy for head and neck cancer.

PURPOSE: The aims of this multicentre retrospective study of locally advanced head and neck cancer (LAHNC) treated with definitive radiotherapy were to (1) identify positron emission tomography (PET)-18F-fluorodeoxyglucose (18F-FDG) parameters correlated with overall survival (OS) in a training cohort, (2) compute a prognostic model, and (3) externally validate this model in an independent cohort. MATERIALS AND METHODS: A total of 237 consecutive LAHNC patients divided into training (n = 127) and validation cohorts (n = 110) were retrospectively analysed. The following PET parameters were analysed: SUVMax, metabolic tumour volume (MTV), total lesion glycolysis (TLG), and SUVMean for the primary tumour and lymph nodes using a relative SUVMax threshold or an absolute SUV threshold. Cox analyses were performed on OS in the training cohort. The c-index was used to identify the highly prognostic parameters. A prognostic model was subsequently identified, and a nomogram was generated. The model was externally tested in the validation cohort. RESULTS: In univariate analysis, the significant PET parameters for the primary tumour included MTV (relative thresholds from 6 to 83% and absolute thresholds from 1.5 to 6.5) and TLG (relative thresholds from 1 to 82% and absolute thresholds from 0.5 to 4.5). For the lymph nodes, the significant parameters included MTV and TLG regardless of the threshold value. In multivariate analysis, tumour site, p16 status, MTV35% of the primary tumour, and MTV44% of the lymph nodes were independent predictors of OS. Based on these four parameters, a prognostic model was identified with a c-index of 0.72. The corresponding nomogram was generated. This prognostic model was externally validated, achieving a c-index of 0.66. CONCLUSIONS: A prognostic model of OS based on primary tumour and lymph node MTV, tumour site, and p16 status was proposed and validated. The corresponding nomogram may be used to tailor individualized treatment.

(18F)-FDG PET/CT parameters to predict survival and recurrence in patients with locally advanced cervical cancer treated with chemoradiotherapy.

PURPOSE: To identify predictive (18F)-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT)-based parameters for locoregional control, disease-free survival and overall survival, by testing different thresholds of metabolic tumor volume and total lesion glycolysis in patients with locally-advanced cervical cancer. PATIENTS AND METHODS: Thirty-seven patients treated with standard chemoirradiation underwent a pretreatment (18F)-FDG-PET/CT. Using different thresholds of maximum standardized uptake value, the following PET parameters were computed: maximum standardized uptake value, mean standardized uptake value, metabolic tumor volume and total lesion glycolysis for primary tumor and lymph nodes and a new parameter combining the metabolic tumor volume and the distance between lymph nodes and the primary tumor, namely metabolic node distance. Correlation between PET and clinical parameters with clinical outcome (overall survival, disease-free survival, and locoregional control) was assessed using univariate and multivariate analyses (Cox model). RESULTS: In univariate analyses, PET/CT parameters associated with overall survival and disease-free survival were: metabolic tumor volume and total lesion glycolysis of the primary tumor, total lesion glycolysis of lymph nodes and metabolic node distance. The most predictive threshold segmentation for metabolic tumor volume and total lesion glycolysis was 48% of maximum standardized uptake value for the primary tumor and 30% for the lymph nodes. In multivariate Cox analysis, the total lesion glycolysis of primary tumor 48% and metabolic node distance were the two independent risk factors for overall survival (P<0.01), disease-free survival (P<0.01) and locoregional control (P=0.046). CONCLUSION: Total lesion glycolysis of primary tumor and distance between the invaded positive lymph node and the primary tumor seem to have the highest predictive value when compared to classical clinical prognostic parameters and may be useful to identify high risk groups at time of diagnosis and to tailor the therapeutic approach in locally-advanced cervical cancer.

A PET-based nomogram for oropharyngeal cancers.

PURPOSE: In the context of locally advanced oropharyngeal cancer (LAOC) treated with definitive radiotherapy (RT) (combined with chemotherapy or cetuximab), the aims of this study were: (1) to identify PET-FDG parameters correlated with overall survival (OS) from a first cohort of patients; then (2) to compute a prognostic score; and (3) finally to validate this scoring system in a second independent cohort of patients. MATERIALS AND METHODS: A total of 76 consecutive patients (training cohort from Rennes) treated with chemoradiotherapy or RT with cetuximab for LAOC were used to build a predictive model of locoregional control (LRC) and OS based on PET-FDG parameters. After internal calibration and validation of this model, a nomogram and a scoring system were developed and tested in a validation cohort of 46 consecutive patients treated with definitive RT for LAOC in Lausanne. RESULTS: In multivariate analysis, the metabolic tumour volume (MTV) of the primary tumour and the lymph nodes were independent predictive factors for LRC and OS. Internal calibration showed a very good adjustment between the predicted OS and the observed OS at 24 months. Using the predictive score, two risk groups were identified (median OS 42 versus 14 months, p < 0.001) and confirmed in the validation cohort from Lausanne (median OS not reached versus 26 months, p=0.008). CONCLUSIONS: This is the first report of a PET-based nomogram in oropharyngeal cancer. Interestingly, it appeared stronger than the classical prognostic factors and was validated in independent cohorts markedly diverging in many aspects, which suggest that the observed signal was robust.

Overview of the predictive value of quantitative 18 FDG PET in head and neck cancer treated with chemoradiotherapy.

18 F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) allows to quantify the metabolic activity of a tumor (glycolysis) and has become a reference tool in oncology for the staging, restaging, radiotherapy planning and monitoring response in many cancers. Quantitative analyses have been introduced in order to overcome some of the limits of the visual methods, allowing an easier and more objective comparison of the inter- and intra-patients variations. The aims of this review were to report available evidences on the clinical value of quantitative PET/CT parameters in HNC. Forty-five studies, for a total of 2928 patients, were analyzed. Most of the data available dealt with the intensity of the metabolism, calculated from the Standard Uptake Value (SUV). Metabolic Tumor Volume (MTV) was well correlated with overall survival and disease free survival, with a higher predictive value than the maximum SUV. Spatial distribution of metabolism and textural analyses seems promising.

Sensors, medical images and signal processing: ubiquitous personalized health monitoring.

OBJECTIVES: To summarize excellent research in the field of medical sensor, signal and imaging informatics published in the year 2011. METHODS: Synopsis of the articles selected for the IMIA (International Medical Informatics Association) Yearbook 2012 through a manual initial selection and a peer review process to find the best paper in this domain published in 2011. RESULTS: Current research in the field of sensors, signal, and imaging informatics is characterized by theoretically sound techniques and evaluations with focus in imaging informatics. An increased number of systems with embedded signal processing where sensors include signal processing were observed in 2011. In all domains, pragmatic solutions with the goal of clinical impact have grown, including in developing countries where simple, robust techniques are combined to address primary and simple medical problems with potentially high impact. Finally, recent advances in image and signal processing are moving towards patient-based modeling. CONCLUSIONS: The best paper selection of articles on sensors, signal, and imaging informatics shows examples of excellent research on methods concerning theoretically sound original development in this field in the year 2012.

Sensors, medical images and signal processing: the growth of clinicians' expectations.

OBJECTIVES: To summarize excellent research in the field of medical sensor, signal and imaging informatics published in the year 2010. METHOD: Synopsis of the articles selected for the IMIA (International Medical Informatics Association) Yearbook 2011. RESULTS: Current research in the field of sensors, signal, and imaging informatics is characterized by theoretically sound techniques and evaluations with focus in imaging informatics. When compared to research on sensors and signals, imaging research represent the majority of published papers in 2010. Research published in 2010 was characterized by an increased participation of the clinicians in the study design, implementation and validation of computerized diagnosis aid tools. CONCLUSIONS: The best paper selection of articles on sensors, signal, and imaging informatics shows examples of excellent research on methods concerning theoretically sound original development in this field.

Sensors, medical images and signal processing: comprehensive multi-modal diagnosis aid frameworks.

OBJECTIVES: To summarize current excellent research in the field of medical sensor, signal and imaging informatics. METHOD: Synopsis of the articles selected for the IMIA (International Medical Informatics Association) Yearbook 2010. RESULTS: Current research in the field of sensor, signal, and imaging informatics is characterized by theoretically sound techniques and evaluations with focus in imaging informatics. CONCLUSIONS: The best paper selection of articles on sensors, signal, and imaging informatics shows examples of excellent research on methods concerning theoretically sound original development in this field. Research published in 2009 was characterized by the emergence of mature computerized diagnosis aid frameworks with assessment of input and output quality. The purpose of these systems is clearly to bring new image and signal interpretation tools to clinicians.

Sensors, medical images and signal processing: exploring multi-dimensional medical imaging data.

OBJECTIVES: To summarize current excellent research in the field of medical sensor, signal and imaging informatics. METHOD: Synopsis of the articles selected for the IMIA (International Medical Informatics Association) Yearbook 2009. RESULTS: Current research in the field of sensors, signal, and imaging informatics is characterized by theoretically sound techniques and evaluations with focus in imaging informatics. CONCLUSIONS: The best paper selection of articles on sensors, signal, and imaging informatics shows examples of excellent research on methods concerning theoretically sound original development in this field. Imaging and particularly multi-dimensional imaging has had in 2008 by far the largest number of publications compared to signals and sensors.

Clinical data mining: a review.

OBJECTIVE: Clinical data mining is the application of data mining techniques using clinical data. We review the literature in order to provide a general overview by identifying the status-of-practice and the challenges ahead. METHODS: The nine data mining steps proposed by Fayyad in 1996 [4] were used as the main themes of the review. MEDLINE was used as primary source and 84 papers were retained based on our inclusion criteria. RESULTS: Clinical data mining has three objectives: understanding the clinical data, assist healthcare professionals, and develop a data analysis methodology suitable for medical data. Classification is the most frequently used data mining function with a predominance of the implementation of Bayesian classifiers, neural networks, and SVMs (Support Vector Machines). A myriad of quantitative performance measures were proposed with a predominance of accuracy, sensitivity, specificity, and ROC curves. The latter are usually associated with qualitative evaluation. CONCLUSION: Clinical data mining respects its commitment to extracting new and previously unknown knowledge from clinical databases. More efforts are still needed to obtain a wider acceptance from the healthcare professionals and for generalization of the knowledge and reproducibility of its extraction process: better description of variables, systematic report of algorithm parameters including the method to obtain them, use of easy-to-understand models and comparisons of the efficiency of clinical data mining with traditional statistical analyses. More and more data will be available for data miners and they have to develop new methodologies and infrastructures to analyze the increasingly complex medical data.