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EP-1683: Fractals in Radiomics: implementation of new features based on fractal analysis
S918 ESTRO 36 _______________________________________________________________________________________________
were acquired using 3D or 4D gated PET(average image) according to institutional settings. 14 SUV(mean) metrics were obtained per acquisition varying VOI/ ROI shape and location. Three ROIs and three VOIs with respective radii of 0.5, 0.6 and 0.8cm were investigated. These ROIs/VOIs were first centred on the maximum activity voxel; a second analysis was made changing the location from the voxel to the region (ROI5voxels) or the volume (VOI7voxels) with the maximum value. Two additional VOIs were defined as 3D isocontours respectively at 70% and 50% of the maximum voxel value. The SUV metrics were normalized by the corresponding 3D static SUV. Converting to recovery coefficients (RC) allowed us to pool data from all institutions, while maintaining focus solely on motion. For each RC from each motion setting we calculated the mean over institutions, we then looked at the standard deviation (Sd) and spread of each averaged RC over each motion setting. Results For the institutions visited we found that RCVOI70% and RCVOI50%, yielded over the 14 metrics the lowest variability to motion with Sd of 0.04 and 0.03 respectively. The RCs based on ROIs/VOIs centered on a single voxel were less impacted by motion (Sd: 0.08) compared to region RCs (Sd: 0.14). The averaged Sd over the RCs based on VOIs and ROIs was 0.12 and 0.11 respectively.
Conclusion Quantification over breathing types depends on ROI/VOI definition. Variables based on SUV max thresholds were found the most robust against respiratory noise. EP-1683 Fractals in Radiomics: implementation of new features based on fractal analysis D. Cusumano1, N. Dinapoli2, R. Gatta2, C. Masciocchi2, J. Lenkowicz2, G. Chilorio2, L. Azario1, J. Van Soest3, A. Dekker3, P. Lambin3, M. De Spirito4, V. Valentini5 1 Fondazione Policlinico Universitario A.Gemelli, Unità Complessa di Fisica Sanitaria, Roma, Italy 2 Fondazione Policlinico Universitario A.Gemelli, Divisione di Radioterapia Oncologica- Gemelli ART, Roma, Italy 3 Maastricht University Medical Center, Department of Radiation Oncology, Maastricht, The Netherlands 4 Università Cattolica del Sacro Cuore, Istituto di Fisica, Roma, Italy 5 Università Cattolica del Sacro Cuore, Department of Radiotherapy - Gemelli ART, Roma, Italy Purpose or Objective A fractal object is characterized by a repeating pattern that it displays at different size scales: this property, known as self-similarity, is typical of many structures in nature or inside human body (a snow flake and the neural networks are just some examples). The fractal self-similarity can be measured by Fractal Dimension (FD), a parameter able to quantify the geometric complexity of the object under analysis. Aim of this study is to introduce in Radiomics new features based on fractal analysis, in order to obtain new indicators able to detect tumor spatial heterogeneity. These fractal features have been used to develop a predictive model able to calculate the probability of pathological complete response (pCR) after neoadjuvant chemo-radiotherapy for
patients affected by locally advanced rectal cancer (LARC).Material and Methods An home-made R software was developed to calculate the FD of the Gross Tumor Volume (GTV) of 173 patients affected by LARC. The software, validated by comparing 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 subpopulations 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 subpopulations.Further investigations are needed to investigate the spatial localization of these subpopulations and their potential connection with biological structures.
were acquired using 3D or 4D gated PET(average image) according to institutional settings. 14 SUV(mean) metrics were obtained per acquisition varying VOI/ ROI shape and location. Three ROIs and three VOIs with respective radii of 0.5, 0.6 and 0.8cm were investigated. These ROIs/VOIs were first centred on the maximum activity voxel; a second analysis was made changing the location from the voxel to the region (ROI5voxels) or the volume (VOI7voxels) with the maximum value. Two additional VOIs were defined as 3D isocontours respectively at 70% and 50% of the maximum voxel value. The SUV metrics were normalized by the corresponding 3D static SUV. Converting to recovery coefficients (RC) allowed us to pool data from all institutions, while maintaining focus solely on motion. For each RC from each motion setting we calculated the mean over institutions, we then looked at the standard deviation (Sd) and spread of each averaged RC over each motion setting. Results For the institutions visited we found that RCVOI70% and RCVOI50%, yielded over the 14 metrics the lowest variability to motion with Sd of 0.04 and 0.03 respectively. The RCs based on ROIs/VOIs centered on a single voxel were less impacted by motion (Sd: 0.08) compared to region RCs (Sd: 0.14). The averaged Sd over the RCs based on VOIs and ROIs was 0.12 and 0.11 respectively.
Conclusion Quantification over breathing types depends on ROI/VOI definition. Variables based on SUV max thresholds were found the most robust against respiratory noise. EP-1683 Fractals in Radiomics: implementation of new features based on fractal analysis D. Cusumano1, N. Dinapoli2, R. Gatta2, C. Masciocchi2, J. Lenkowicz2, G. Chilorio2, L. Azario1, J. Van Soest3, A. Dekker3, P. Lambin3, M. De Spirito4, V. Valentini5 1 Fondazione Policlinico Universitario A.Gemelli, Unità Complessa di Fisica Sanitaria, Roma, Italy 2 Fondazione Policlinico Universitario A.Gemelli, Divisione di Radioterapia Oncologica- Gemelli ART, Roma, Italy 3 Maastricht University Medical Center, Department of Radiation Oncology, Maastricht, The Netherlands 4 Università Cattolica del Sacro Cuore, Istituto di Fisica, Roma, Italy 5 Università Cattolica del Sacro Cuore, Department of Radiotherapy - Gemelli ART, Roma, Italy Purpose or Objective A fractal object is characterized by a repeating pattern that it displays at different size scales: this property, known as self-similarity, is typical of many structures in nature or inside human body (a snow flake and the neural networks are just some examples). The fractal self-similarity can be measured by Fractal Dimension (FD), a parameter able to quantify the geometric complexity of the object under analysis. Aim of this study is to introduce in Radiomics new features based on fractal analysis, in order to obtain new indicators able to detect tumor spatial heterogeneity. These fractal features have been used to develop a predictive model able to calculate the probability of pathological complete response (pCR) after neoadjuvant chemo-radiotherapy for
patients affected by locally advanced rectal cancer (LARC).Material and Methods An home-made R software was developed to calculate the FD of the Gross Tumor Volume (GTV) of 173 patients affected by LARC. The software, validated by comparing 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 subpopulations 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 subpopulations.Further investigations are needed to investigate the spatial localization of these subpopulations and their potential connection with biological structures.