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Radiotherapy (RT) for head and neck cancer is now guided by cone-beam computed tomography (CBCT). We aim to identify a CBCT radiomic signature predictive of progression to RT. A cohort of 93 patients was split into training (n = 60) and testing (n = 33) sets. A total of 88 features were extracted from the gross tumor volume (GTV) on each CBCT. Receiver operating characteristic (ROC) curves were used to determine the power of each feature at each week of treatment to predict progression to radio(chemo)therapy. Only features with AUC > 0.65 at each week were pre-selected. Absolute differences were calculated between features from each weekly CBCT and baseline CBCT1 images. The smallest detectable change (C = 1.96 × SD, SD being the standard deviation of differences between feature values calculated on CBCT1 and CBCTn) with its confidence interval (95% confidence interval [CI]) was determined for each feature. The features for which the change was larger than C for at least 5% of patients were then selected. A radiomics-based model was built at the time-point that showed the highest AUC and compared with models relying on clinical variables. Seven features had an AUC > 0.65 at each week, and six exhibited a change larger than the predefined CI 95%. After exclusion of inter-correlated features, only one parameter remains, Coarseness. Among clinical variable, only hemoglobin value was significant. AUC for predicting the treatment response were 0.78 (p =.006), 0.85 (p <.001), and 0.99 (p <.001) for clinical, CBCT4-radiomics (Coarseness) and clinical + radiomics based models respectively. The mean AUC of this last model on a 5-fold cross-validation was 0.80 (±0.09). On the testing cohort, the best prediction was given by the combined model (balanced accuracy [BAcc] 0.67 , p <.001). We described a feature selection methodology for delta-radiomics that is able to select reproducible features which are informative due to their change during treatment. A selected delta radiomics feature may improve clinical-based prediction models. [ABSTRACT FROM AUTHOR]

Background and Purpose: Positron-emission tomography (PET) with [(18)F]-fluoromisonidazole (FMISO) permits consideration of radiotherapy dose escalation to hypoxic volumes in head and neck cancers (HNC). However, the definition of FMISO volumes remains problematic. The aims of this study are to confirm that delayed acquisition at 4 h is most appropriate for FMISO-PET imaging and to assess different methods of volume segmentation.Patients and Methods: A total of 15 HNC patients underwent several FMISO-PET/computed tomography (CT) acquisitions 2, 3 and 4 h after FMISO injection. Three automatic methods of PET image segmentation were tested: fixed threshold, adaptive threshold based on the ratio between tumour-derived and background activities (R(T/B)) and the fuzzy locally adaptive Bayesian (FLAB) method. The hypoxic fraction (HF), which is defined as the ratio between the FMISO and CT volumes, was also calculated.Results: The R(T/B) for images acquired at 2, 3 and 4 h differed significantly, with mean values of 2.5 (1.7-2.9), 3 (2-4.5) and 3.4 (2.3-6.1), respectively. The mean tumour volume, as defined manually using CT images, was 39.1 ml (1.2-116 ml). After 4 h, the mean FMISO volumes were 18.9 (0.1-81), 9.5 (0.9-33.1) and 12.5 ml (0.9-38.4 ml) with fixed threshold, adaptive threshold and the FLAB method, respectively; median HF values were 0.47 (0.1-1.93), 0.25 (0.11-0.75) and 0.35 (0.14-1.05), respectively. FMISO volumes were significantly different.Conclusion: The best contrast is obtained at the 4-hour acquisition time. Large discrepancies were found between the three tested methods of volume segmentation. [ABSTRACT FROM AUTHOR]

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Copyright of IRBM is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Presents a general theorization of a historical account of Hamo Thornycroft's sculpture Mower, exhibited at the Royal Academy in 1884. Examination of the statue; Assessment of the relationship between the homoerotic and labor; Exploration of the relationship between desire and politics.

Le but de ce travail était de décrire une méthodologie de sélection longitudinale de radiomique extraite des tomographies coniques embarquées de patients atteint d'un carcinome épidermoïde de la sphère ORL et d'établir une signature radiomique permettant de prédire la réponse thérapeutique à 3 mois. Chez 102 patients (validation n = 68, entraînement n = 34), les volumes tumoraux primitifs et ganglionnaires issus de la planification ont été importés sur les tomographies coniques hebdomadaires. Quatre-vingt-huit radiomiques ont été extraits de chaque volume primitifs. Des courbes ROC ont été construites pour chaque radiomiques, sur chacunes des tomographies coniques afin d'évaluer leur pouvoir prédictif sur la réponse thérapeutique. Les radiomiques significatifs à chaque semaine ont été présélectionnés. Le plus petit changement détectable avec son intervalle de confiance à 95 % (IC95 %) a été déterminé (C = 1,96 × sd) pour chaque radiomique (SD = déviation standard des valeurs des calculs [tomographie conique 2 − tomographie conique 1]). Les différences absolues des calculs de [tomographie conique n − tomographie conique 1] (Δ radiomiques) ont été calculées. Les radiomiques présentant un changement au-delà de l'IC95 % chez plus de 10 % des patients ont été conservés. Une signature radiomique a été recherchée à la semaine durant laquelle se produisait le plus de changements. trois modèles ont été comparés : clinique, radiomique et combiné. La signature radiomique a été établie à la semaine 3. Pour le volume tumoral primitif : les AUC (aire sous la courbe, prédiction de la réponse thérapeutique) étaient de 0,78 (p < 0,001), 0,71 (p = 0,006) et 0,74 (p < 0,001) pour les modèles cliniques, radiomique et combiné, respectivement ; pour le volume primitif ganglionnaire : le modèle clinique (AUC : 0,74, p < 0,001) était plus performant que les deux autres. Sur la cohorte de validation, la radiomique avait le meilleur pouvoir prédictif pour le volume tumoral primitif (AUC = 0,64) tandis que la clinique était plus performante pour le volume ganglionnaire. Il s'agit de la première étude décrivant une méthodologie de sélection longitudinale de radiomiques extraits de tomographies coniques de patients avec un cancer de la sphère ORL et évaluant leur valeur pronostique. Le modèle radiomique à la troisième semaine semble avoir un potentiel prédictif pour le volume tumoral primitif mais pas pour le volume ganglionnaire. [ABSTRACT FROM AUTHOR]

Introduction Radiomics [1] has been a very active field of research during the last five years. There have been very promising results in CT, MRI and PET/CT image modalities. There are, however, also numerous issues and limitations that have been identified. Methods This talk will review the currently available results of radiomics with a focus on PET/CT imaging, and highlights the identified limitations, issues and potential solutions for the future [2,3] . Results Numerous encouraging results regarding PET/CT radiomics have been published in various types of tumors. However, major limitations include (but are not limited to) statistical power of the studies, lack of methodological standardization, and complexity of the workflow. Conclusions Although radiomics is a fast evolving field of research and encouraging results have been published, caution is required and in order to ensure the future of radiomics, efforts should be focused on methodological standardization, large prospective validation studies, and automatization/simplification of the analysis workflow. [ABSTRACT FROM AUTHOR]

PURPOSE/OBJECTIVE(S): (Chemo)-radiotherapy is the standard treatment for patients with locally advanced lung cancer (LALC) not accessible to surgery. Despite strict application of dose constraints, acute toxicities such as acute pulmonary toxicity (APT) remain frequent, and may impact treatment's compliance and patients' quality of life. Previously, on a population treated with intensity-modulated photon therapy or passive scattering proton therapy, spatial dose patterns associated with APT were identified in the lower lungs, especially in the lower right lung. In the present study, we aim to define these spatial dose patterns on a retrospective cohort treated by volumetric-arc therapy (VMAT) and to validate our findings prospectively.Materials/methods: For the training cohort, we retrospectively included all patients treated in our institution by VMAT for a LALC between 2015 and 2018. APT was scored according to the CTCAE v4.0 scale. All dose maps were registered to a thorax phantom using a segmentation-based elastic registration. Voxel-based analysis of local dose differences was performed with a non-parametric permutation test accounting for n = 10.000 permutations, producing a 3-dimensional significance maps on which clusters of voxels that exhibited significant dose differences (P < 0.05) between the two toxicity groups (APT ≥ grade 2 vs APT < grade 2) were identified. A dose cut-off was defined by maximizing the Youden-Index derived from the Receiver Operative Curve. For the validation cohort, we applied the predefined spatial dose pattern and the dose cut-off to an observational prospective cohort. The model was evaluated using the Area under the curve (AUC) and the balanced accuracy (Bacc: mean of the sensitivity and specificity).Results: 167 and 42 patients were included in the training and validation cohorts, with respective APT rates of 22.2% and 21.4%. In the training cohort, a cluster of voxels was identified in the lower right lung with a cut off of 30.3 Gy for the Dmean(mean-dose). This spatial-based model resulted in an AUC of 0.69 and a Bacc of 0.71. Using the same voxel cluster and the Dmeancut-off on the validation cohort, the model resulted in an AUC of 0.74 and a Bacc of 0.69.Conclusion: Our model corroborates previous findings regarding the possible role of the lower right lung regarding the development of APT and successfully evaluates its robustness in a prospective cohort treated by VMAT. Regional radiosensitivity should be considered in usual lung dose constraints. [ABSTRACT FROM AUTHOR]

• The miscalibration of pulmonary and esophageal toxicities in patients with lung cancer treated by (chemo)-radiotherapy is frequent. • Usual dose-volume histograms do not account for dose spatial distribution. • Radiomics based models contribute to a significant improvement in acute and late pulmonary toxicities prediction. For acute esophageal toxicity, dosimetric and radiomics based models achieve similar results. (Chemo)–radiotherapy (RT) is the gold standard treatment for patients with locally advanced lung cancer non accessible for surgery. However, current toxicity prediction models rely on clinical and dose volume histograms (DVHs) and remain unsufficient. The goal of this work is to investigate the added predictive value of the radiomics approach applied to dose maps regarding acute and late toxicities in both the lungs and esophagus. Acute and late toxicities scored using the CTCAE v4.0 were retrospectively collected on patients treated with RT in our institution. Radiomic features were extracted from 3D dose maps considering Gy values as grey-levels in images. DVH and usual clinical factors were also considered. Three toxicity prediction models (clinical only, clinical + DVH and combined , i.e., including clinical + DVH + radiomics) were incrementally trained using a neural network on 70% of the patients for prediction of grade ≥2 acute and late pulmonary toxicities (APT/LPT) and grade ≥2 acute esophageal toxicities (AET). After bootstrapping (n = 1000), optimal cut-off values were determined based on the Youden Index. The trained models were then evaluated in the remaining 30% of patients using balanced accuracy (BAcc). 167 patients were treated from 2015 to 2018: 78% non small-cell lung cancers, 14% small-cell lung cancers and 8% other histology with a median age at treatment of 66 years. Respectively, 22.2%, 16.8% and 30.0% experienced APT, LPT and AET. In the training set (n = 117), the corresponding BAcc for clinical only/clinical + DVH/combined were 0.68/0.79/0.92, 0.66/0.77/0.87 and 0.68/0.73/0.84. In the testing evaluation (n = 50), these trained models obtained a corresponding BAcc of 0.69/0.69/0.92, 0.76/0.80/0.89 and 0.58/0.73/0.72. In patients with a lung cancer treated with RT, radiomic features extracted from 3D dose maps seem to surpass usual models based on clinical factors and DVHs for the prediction of APT and LPT. [ABSTRACT FROM AUTHOR]

Résumé Objectif Évaluer la valeur pronostique de différents facteurs préthérapeutiques définis avec la tomographie par émission de positons au ( 18 F)-fluorodésoxyglucose (TEP-FDG) pour le cancer de l’œsophage localisé. Patients et méthodes Nous avons réalisé une étude rétrospective monocentrique à partir des dossiers de 83 patients atteints de cancers de l’œsophage traités à visée curative. La chimioradiothérapie a été soit exclusive, soit suivie de chirurgie. Les critères constitutifs, carcinologiques et dérivés de la TEP-FDG ( maximum standardized uptake value [SUV max ], volume tumoral métabolique de la lésion primitive [ metabolically active tumour volume ] défini soit avec le logiciel « fuzzy locally adaptive bayesian » [MATV FLAB ], soit avec un seuillage [MATV seuil ], et le total lesion glycolysis [TLG FLAB et TLG seuil ]) ont été corrélés avec la survie. Résultats La durée médiane de suivi était de 21,8 mois (0,16–104). La durée médiane de survie était de 22 mois (intervalle de confiance à 95 % [IC95 %] : 15,2–28,9). Il y a eu 67 décès, 49 liés au cancer et 18 à une autre cause. Aucun des facteurs testés n’avait un impact significatif sur la survie globale. Pour la survie spécifique, en analyse unifactorielle, le MATV FLAB ( p = 0,025), le TLG FLAB ( p = 0,04) et le TLG seuil ( p = 0,04) avaient un impact significatif. En analyse multifactorielle, seul le MATV FLAB avait un impact significatif ( p = 0,049) : MATV FLAB inférieur à 18 cm 3 : 31,2 mois (IC95 % : 21,7–non atteint) et MATV FLAB supérieur à 18 cm 3 : 20 mois (IC95 % : 11,1–228,9). Conclusion Sur la TEP-FDG initiale, le volume métabolique défini avec l’algorithme de segmentation automatique FLAB était un facteur pronostique intéressant qui mérite d’être évalué sur une cohorte plus importante de patients. Purpose The purpose of this study was to assess the prognostic value of different parameters on pretreatment fluorodeoxyglucose [( 18 F)-FDG] positron emission tomography-computed tomography (PET-CT) in patients with localized oesophageal cancer. Patients and method We retrospectively reviewed 83 cases of localised oesophageal cancer treated in our institution. Patients were treated with curative intent and have received chemoradiotherapy alone or followed by surgery. Different prognostic parameters were correlated to survival: cancer-related factors, patient-related factors and parameters derived from PET-CT (maximum standardized uptake value [SUV max], metabolically active tumor volume either measured with an automatic segmentation software [“fuzzy locally adaptive bayesian”: MATV FLAB ] or with an adaptive threshold method [MATV seuil ] and total lesion glycolysis [TLG FLAB and TLG seuil ]). Results The median follow-up was 21.8 months (range: 0.16–104). The median overall survival was 22 months (95% confidence interval [95%CI]: 15.2–28.9). There were 67 deaths: 49 associated with cancer and 18 from intercurrent causes. None of the tested factors was significant on overall survival. In univariate analysis, the following three factors affected the specific survival: MATV FLAB ( P = 0.025), TLG FLAB ( P = 0.04) and TLG seuil ( P = 0.04). In multivariate analysis, only MATV FLAB had a significant impact on specific survival ( P = 0.049): MATV FLAB < 18 cm 3 : 31.2 months (95%CI: 21.7–not reached) and MATV FLAB > 18 cm 3 : 20 months (95%CI: 11.1–228.9). Conclusion The metabolically active tumour volume measured with the automatic segmentation software FLAB on baseline PET-CT was a significant prognostic factor, which should be tested on a larger cohort. [ABSTRACT FROM AUTHOR]

Chez les patients atteints de cancer du rectum localement évolué pris en charge par chimioradiothérapie néoadjuvante, la chirurgie pourrait être omise chez ceux présentant une réponse pathologique complète sans compromettre la survie sans progression et globale. Notre objectif était de développer un modèle radiomique basé sur l'imagerie par résonance magnétique (IRM) et la tomodensitométrie avec contraste pour prédire la réponse pathologique complète à la chimioradiothérapie néoadjuvante dans les cas de cancer du rectum. Tous les patients pris en charge pour un cancer du rectum localement évolué par une chimioradiothérapie néoadjuvante puis une chirurgie dans deux centres de l'Ouest de la France (Institut de cancérologie Bretagne Occidentale et Institut de cancérologie de l'Ouest) ont été considérés. Les IRM pelviennes avant la chimioradiothérapie néoadjuvante et les scanographies avec injection de produit de contraste étaient obligatoires pour l'inclusion. La tumeur a été segmentée manuellement sur les séquences axiales pondérées en T2 et en diffusion et sur la scanographie avec injection de produit de contraste. Quatre-vingt-huit paramètres radiomiques ont été extraits de chaque séquence à l'aide du logiciel interne Miras©, avec un total de 822 caractéristiques par patient. La cohorte globale a été divisée au hasard en deux cohortes indépendantes (apprentissage : 70 % et test : 30 %). Une sélection stricte des caractéristiques basée sur le coefficient de corrélation de Spearman et l'aire sous la courbe (AUC) a été développée. Trois modèles de prédiction de la réponse pathologique complète (clinique ou radiomique ou combiné des deux) ont été développés sur la cohorte d'entraînement uniquement avec une approche de forêt aléatoire et une validation interne Boostrap avec n = 1000 réplications. Chaque modèle a ensuite été évalué sur l'ensemble de test, sur la base de l'AUC et du C-statistique calculé. Enfin, une harmonisation a posteriori, utilisant l'approche ComBat a été appliquée pour tenir compte de l'hétérogénéité des modalités d'imagerie. Sur les 124 patients inclus, 14 avaient une réponse pathologique complète (11,3 %). Sur la cohorte d'apprentissage, le modèle clinique basé sur deux paramètres (stade T initial et degré de différenciation) a obtenu une AUC de 0,67 et un C-statistique de 0,65. Le modèle radiomique basé sur un paramètre (entropie de l'histogramme dérivé de la séquence T2) a obtenu une AUC de 1 et un C-statistique de 1. Le modèle combiné était strictement identique au modèle radiomique et avait donc les mêmes performances. Sur la cohorte de test, les modèles ont donné un C-statistique de 0,70, 0,73 et 0,73 pour les modèles clinique, radiomique et combiné, respectivement. Enfin, après harmonisation, le modèle radiomique a obtenu un C-statistique de 0,77 sur la cohorte de test, tandis que le modèle combiné a donné un C-statistique de 0,75. Le modèle radiomique basé sur les séquences d'IRM préthérapeutiques pondérées en T2 pourrait aider à prédire la réponse pathologique complète après la chimioradiothérapie néoadjuvante des cancers du rectum localement évolués. [ABSTRACT FROM AUTHOR]

Monitoring the spatial and temporal plant availability of nitrogen (N) in agroecosystems is a key step to improve the synchronization between N fertilizer application and crop N demand, consequently reducing the risk of N emissions to the environment. Using a winter wheat N fertilization dataset from six site-years, we linked dynamic nitrate data measured in the soil solution to standard soil and crop analyses data and multispectral imagery acquired by an unmanned aerial vehicle. Wheat N uptake was determined as remotely estimated N uptake (REN) from the spectral data with a power regression model (mean absolute error = 17 kg N ha−1). The nitrate-N in the soil solution (NSS), extracted by means of suction cups, was measured with an ion-selective electrode. The REN proved to be suitable for monitoring the accumulation of N in the plants along the season. The NSS was characterized by low values and found of limited use as a direct indicator for potentially plant-available N. The N balances resulted in N surplus in the range of 43–100 kg N ha−1 over the six site-years. The most important contribution to the N balances was the soil N supply (67–143 kg N ha−1; mineralization and atmospheric input). Including this factor in the fertilization strategy was investigated post-season by calculating the 'adjusted N fertilization norm', reflecting the current best fertilization practice in Switzerland. The approach suggested lower N fertilization rates in the fields with higher N surplus. However, such static empirical strategies do not allow to react to in-season changes. Sensor-based monitoring could help to overcome this shortcoming. • The remotely estimated N uptake (REN) was suitable for N uptake monitoring. • Nitrate-N in soil solution (NSS) was of limited use as indicator of soil N supply. • Differences in soil N supplies explained variation in N surplus in the N balance. [ABSTRACT FROM AUTHOR]

Poster: Physics track: Radiobiological and predictive modelling, and radiomics PO-1553: Development of a CT based radiomic model predictive of non-response to chemotherapy in larynx cancer I. Masson, R. Da-ano, F. Lucia, M. Doré, J. Castelli, C. Goislard de Monsabert, J. Ramée, S. Sellami, D. Visvikis, M. Hatt, U. Schick. [Extracted from the article]

Poster: Physics track: Radiobiological and predictive modelling, and radiomics PO-1551: Deep CNN on PET/CT images for NSCLC automated tumor detection and outcome prediction M. Ibrahim, J. Castelli, C. Cheze Le Rest, O. Acosta, D. Visvikis, R. De Crevoisier, M. Hatt. [Extracted from the article]

Poster: Physics track: Radiobiological and predictive modelling, and radiomics PO-1530: Pulmonary toxicity in lung cancer treated by (chemo)-radiotherapy: a radiomics-based NTCP. V. Bourbonne, F. Lucia, G. Dissaux, B. Julien, D. Visvikis, O. Pradier, M. Hatt, U. Schick, PO-1530: Pulmonary toxicity in lung cancer treated by (chemo)-radiotherapy: a radiomics-based NTCP. [Extracted from the article]