Accurate,robust and harmonized implementation of morpho-functional imaging in treatment planning for personalized radiotherapy

In this work we present a methodology able to use harmonized PET/CT imaging in dose painting by number (DPBN) approach by means of a robust and accurate treatment planning system. Image processing and treatment planning were performed by using a Matlab-based platform, called CARMEN, in which a full Monte Carlo simulation is included. Linear programming formulation was developed for a voxel-by-voxel robust optimization and a specific direct aperture optimization was designed for an efficient adaptive radiotherapy implementation. DPBN approach with our methodology was tested to reduce the uncertainties associated with both, the absolute value and the relative value of the information in the functional image. For the same H&N case, a single robust treatment was planned for dose prescription maps corresponding to standardized uptake value distributions from two different image reconstruction protocols: One to fulfill EARL accreditation for harmonization of [18F]FDG PET/CT image, and the other one to use the highest available spatial resolution. Also, a robust treatment was planned to fulfill dose prescription maps corresponding to both approaches, the dose painting by contour based on volumes and our voxel-by-voxel DPBN. Adaptive planning was also carried out to check the suitability of our proposal. Different plans showed robustness to cover a range of scenarios for implementation of harmonizing strategies by using the highest available resolution. Also, robustness associated to discretization level of dose prescription according to the use of contours or numbers was achieved. All plans showed excellent quality index histogram and quality factors below 2%. Efficient solution for adaptive radiotherapy based directly on changes in functional image was obtained. We proved that by using voxel-by-voxel DPBN approach it is possible to overcome typical drawbacks linked to PET/CT images, providing to the clinical specialist confidence enough for routinely implementation of functional imaging for personalized radiotherapy.Junta de Andalucía (FISEVI, reference project CTS 2482)European Regional Development Fund (FEDER

Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups. This could have important clinical impact, notably by allowing for a better personalization of chemo-radiation treatments for head-and-neck cancer patients from different risk groups.Comment: (1) Paper: 33 pages, 4 figures, 1 table; (2) SUPP info: 41 pages, 7 figures, 8 table

CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma

BACKGROUND AND PURPOSE: Radiomics provides opportunities to quantify the tumor phenotype non-invasively by applying a large number of quantitative imaging features. This study evaluates computed-tomography (CT) radiomic features for their capability to predict distant metastasis (DM) for lung adenocarcinoma patients. MATERIAL AND METHODS: We included two datasets: 98 patients for discovery and 84 for validation. The phenotype of the primary tumor was quantified on pre-treatment CT-scans using 635 radiomic features. Univariate and multivariate analysis was performed to evaluate radiomics performance using the concordance index (CI). RESULTS: Thirty-five radiomic features were found to be prognostic (CI > 0.60, FDR < 5%) for DM and twelve for survival. It is noteworthy that tumor volume was only moderately prognostic for DM (CI=0.55, p-value=2.77 × 10(−5)) in the discovery cohort. A radiomic-signature had strong power for predicting DM in the independent validation dataset (CI=0.61, p-value=1.79 ×10(−17)). Adding this radiomic-signature to a clinical model resulted in a significant improvement of predicting DM in the validation dataset (p-value=1.56 × 10(−11)). CONCLUSIONS: Although only basic metrics are routinely quantified, this study shows that radiomic features capturing detailed information of the tumor phenotype can be used as a prognostic biomarker for clinically-relevant factors such as DM. Moreover, the radiomic-signature provided additional information to clinical data

Multi-observation PET image analysis for patient follow-up quantitation and therapy assessment.: Multi observation PET image fusion for patient follow-up quantitation and therapy response

International audienceIn positron emission tomography (PET) imaging, an early therapeutic response is usually characterized by variations of semi-quantitative parameters restricted to maximum SUV measured in PET scans during the treatment. Such measurements do not reflect overall tumor volume and radiotracer uptake variations. The proposed approach is based on multi-observation image analysis for merging several PET acquisitions to assess tumor metabolic volume and uptake variations. The fusion algorithm is based on iterative estimation using a stochastic expectation maximization (SEM) algorithm. The proposed method was applied to simulated and clinical follow-up PET images. We compared the multi-observation fusion performance to threshold-based methods, proposed for the assessment of the therapeutic response based on functional volumes. On simulated datasets the adaptive threshold applied independently on both images led to higher errors than the ASEM fusion and on clinical datasets it failed to provide coherent measurements for four patients out of seven due to aberrant delineations. The ASEM method demonstrated improved and more robust estimation of the evaluation leading to more pertinent measurements. Future work will consist in extending the methodology and applying it to clinical multi-tracer datasets in order to evaluate its potential impact on the biological tumor volume definition for radiotherapy applications

2012 Activity Report of the Regional Research Programme on Hadrontherapy for the ETOILE Center

2012 is the penultimate year of financial support by the CPER 2007-2013 for ETOILE's research program, sustained by the PRRH at the University Claude Bernard. As with each edition we make the annual review of the research in this group, so active for over 12 years now. Over the difficulties in the decision-making process for the implementation of the ETOILE Center, towards which all our efforts are focussed, some "themes" (work packages) were strengthened, others have progressed, or have been dropped. This is the case of the eighth theme (technological developments), centered around the technology for rotative beam distribution heads (gantries) and, after being synchronized with the developments of ULICE's WP6, remained so by ceasing its activities, coinciding also with the retirement of its historic leader at IPNL, Marcel Bajard. Topic number 5 ("In silico simulations") has suffered the departure of its leader, Benjamin Ribba, although the work has still been provided by Branka Bernard, a former postdoctoral fellow in Lyon Sud, and now back home in Croatia, still in contract with UCBL for the ULICE project. Aside from these two issues (and the fact that the theme "Medico-economical simulations" is now directly linked to the first one ("Medical Project"), the rest of the teams are growing, as evidenced by the publication statistics at the beginning of this report. This is obviously due to the financial support of our always faithful regional institutions, but also to the synergy that the previous years, the European projects, the arrival of the PRIMES LabEx, and the national France Hadron infrastructure have managed to impulse. The Rhone-Alpes hadron team, which naturally includes the researchers of LPC at Clermont, should also see its influence result in a strong presence in France Hadron's regional node, which is being organized. The future of this regional research is not yet fully guaranteed, especially in the still uncertain context of ETOILE, but the tracks are beginning to emerge to allow past and present efforts translate into a long future that we all want to see established. Each of the researchers in PRRH is aware that 2013 will be (and already is) the year of great challenge : for ETOILE, for the PRRH, for hadron therapy in France, for French hadrontherapy in Europe (after the opening and beginning of treatments in the German [HIT Heidelberg, Marburg], Italian [CNAO, Pavia] and Austrian [MedAustron, Wien Neuerstadt]) centers. Let us meet again in early 2014 for a comprehensive review of the past and a perspective for the future ..