ESTRO 36 Abstract Book

S906 ESTRO 36 2017 _______________________________________________________________________________________________

1 Karolinska Institutet, Medical Radiation Physics- Department of Oncology-Pathology, Stockholm, Swede 2 RaySearch Laboratories AB, RaySearch Laboratories AB, Stockholm, Sweden 3 GROW-School for Oncology and Developmental Biology- Maastricht University Medical Center, Department of Radiation Oncology, Maastricht, The Netherlands 4 The Skandion Clinic, The Skandion Clinic, Uppsala, Sweden 5 Stockholm University, Medical Radiation Physics- Department of Physics, Stockholm, Sweden Purpose or Objective A previous study has shown that the early response to treatment in NSCLC can be evaluated by stratifying the patients in good and poor responders based on calculations of the effective radiosensitivity, αeff, derived from two FDG-PET scans taken before the treatment and during the second week of radiotherapy [1]. However, the optimal window during the treatment for assessing αeff was not investigated. This study aims at assessing αeff of NSCLC tumours on a new cohort of patients for which the second scan was taken during the third week of treatment. The optimal window for response assessment could be determined by investigating the ability of the method to predict treatment outcome through a comparison of the results of a ROC analysis for the new cohort of patients, imaged at three weeks, with the results of the previous study in which patients were imaged at two weeks. Material and Methods Twenty-eight NSCLC patients were imaged with FDG-PET before the treatment and during the third week of radiotherapy. The patients received 45 Gy in 1.5 Gy fractions twice-daily followed by a dose-escalation up to maximum 69 Gy in daily fractions of 2 Gy. The outcome of the treatment was reported as overall survival (OS) at two years. αeff was determined at the voxel level taking into account the voxel SUV in the two images and the dose delivered until the second scan. Correlations were sought between the average (a_αeff) or negative fraction (nf_αeff) of αeff values and the OS. The AUC and the p- value resulting from the ROC analysis were compared to the corresponding values reported for the case when the second scan was taken during the second week of treatment. Results The ROC curves in Figure 1 show the correlation between a_αeff and OS and also the correlation between nf_αeff and OS in the present and the earlier analysis. The results expressed as AUC and p-value show the lack of correlation between either a_αeff (AUC=0.5, p=0.7) or nf_αeff (AUC=0.5, p=0.8) and the OS for the scan at 3 weeks. This contrasts with the case when the second image was taken during the second week of treatment (AUC=0.9, p<0.0001). From the comparison of the ROC curves it results that the values of αeff can be used for predicting the OS if the second scan is taken during the second week, but not during the third week.

the obtained results with ImageJ, was implemented in Moddicom, an open-source software developed in our Institution to perform radiomic analysis. Fractal analysis was performed applying the Box Counting method on T2-weighted images of magnetic resonance. The FD computation was carried out slice by slice, for each patient of the study: values regarding mean, median, standard deviation, maximum and minimum of the FD distribution were considered as fractal features characterizing the patient. Fractal analysis was moreover extended on sub- populations inside GTV, defined by considering the pixels whose intensities were above a threshold calculated as percentage of the maximum intensity value occurred inside GTV. A logistic regression model was then developed and its predictive performances were tested in terms of ROC analysis. An external validation, based on 25 patients provided by MAASTRO clinic, was also performed. The details on imaging parameters adopted are listed in table 1.

Results The predictive model developed is characterized by 3 features: the tumor clinical stage, the entropy of the GTV histogram (calculated after the application of a Laplacian of Gaussian filter with σ=0.34 mm) and the maximum FD (maxFD) calculated for the sub-population whose intensities are higher than 40% of the GTV maximum value. MaxFD is the most significant parameter of the model: higher maxFD value, typical of a more complex structure, is correlated with less pCR probability. The model developed showed an AUC of ROC equal to 0.77± 0.07. The model reliability has been confirmed by the external validation, providing an AUC equal to 0.80 ± 0.09.

Conclusion Fractal analysis can play an important role in Radiomics: the fractal features provide important spatial information not only about the GTV structure, but also about its sub- populations.Further investigations are needed to investigate the spatial localization of these sub- populations and their potential connection with biological structures. EP-1684 Optimal window for assessing treatment responsiveness on repeated FDG-PET scans in NSCLC patients M. Lazzeroni 1 , J. Uhrdin 2 , S. Carvalho 3 , W. Van Elmpt 3 , P. Lambin 3 , A. Dasu 4 , I. Toma-Dasu 5

Conclusion