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Distribution of SUVpeak, Entropy, HGZE, and SRE in healthy tissues and in tumors as a function of the voxel size: in blue for 4x4x4 mm3, in green for 2x2x2 mm3 and in pink for 1x1x1 mm3.

Textural and shape analysis is gaining considerable interest in medical imaging, particularly to identify parameters characterizing tumor heterogeneity and to feed radiomic models. Here, we present a free, multiplatform, and easy-to-use freeware called LIFEx, which enables the calculation of conventional, histogram-based, textural, and shape features from PET, SPECT, MR, CT, and US images, or from any combination of imaging modalities. The application does not require any programming skills and was developed for medical imaging professionals. The goal is that independent and multicenter evidence of the usefulness and limitations of radiomic features for characterization of tumor heterogeneity and subsequent patient management can be gathered. Many options are offered for interactive textural index calculation and for increasing the reproducibility among centers. The software already benefits from a large user community (more than 800 registered users), and interactions within that community are part of the development strategy.Significance: This study presents a user-friendly, multi-platform freeware to extract radiomic features from PET, SPECT, MR, CT, and US images, or any combination of imaging modalities. Cancer Res; 78(16); 4786–9. ©2018 AACR.

A real-time complete example of radiomic feature calculation from a PET/MR scan, where the same volumes of interest are used to extract PET and MR radiomic features.

Well-known software programs that enable the calculation of radiomic features and associated characteristics.

Objective: To compare the performance of conventional and machinelearning approaches for the diagnosis of tumor recurrence after radiation therapy of brain metastases. Methods: 184 symptomatic patients with solitary metastatic brain lesions treated with radiation therapy were enrolled in a monocentric retrospective study from June 2013 to May 2018. The diagnosis was tumor recurrence (n=71) and radiation necrosis (n=113) using as reference standard expert-consensus derived from pathology and long-term follow-up. 37 potential predictors were recorded at the time of radiological progression (7-15 months after therapy): 6 clinical features and 31 imaging features including 20 radiomics features derived from standard of care 3D T1-gadolinium sequences. We compared four approaches (A, B, C, D): expert report using MRI sequences without (A) and with delayedcontrast MRI (TRAM) sequences (B), 11 non-Radiomics imaging features alone (C) and a signature combining variables selected using unsupervised machinelearning algorithms (D), training:validation sets: n=144:40 pts). Results: Overall (n=184), approaches B and C (using TRAM sequence alone) reached comparable performances with respective AUCs [95% CI] of 78.7% [72.3%-85.1%] and 76.8% [70.3%-83.3%]. Both significantly outperformed approach A with AUC [95% CI] of 57.4% [50.7%-64.1%] (DeLong’s test, p-value=10-7). In the validation set (n=40), the signature reached an AUC [95% CI] of 92% [87%-97%]. Conclusion: A quantitative analysis of TRAM sequence seems the best approach for the diagnosis of recurrent tumor after radiation therapy. It is parsimonious, objective and less time-consuming than interpreting all sequences. A signature derived from the analysis of standard of care 3D T1- gadolinium sequence showed promising results that warrant prospective validation.

Radiomics extracts high-throughput quantitative data from medical images to contribute to precision medicine. Radiomic shape features have been shown to correlate with patient outcomes. However, how radiomic shape features vary in function of tumor complexity and tumor volume, as well as with method used for meshing and voxel resampling, remains unknown. The aims of this study are to create tumor models with varying degrees of complexity, or spiculatedness, and evaluate their relationship with quantitatively extracted shape features. Twenty-eight tumor models were mathematically created using spherical harmonics with the spiculatedness degree d being increased by increments of 3 (d = 11 to d = 92). Models were 3D printed with identical bases of 5 cm, imaged with a CT scanner with two different slice thicknesses, and semi-automatically delineated. Resampling of the resulting masks on a 1 × 1 × 1 mm3 grid was performed, and the voxel size of each model was then calculated to eliminate volume differences. Four MATLAB-based algorithms (isosurface (M1), isosurface filter (M2), isosurface remeshing (M3), and boundary (M4)) were used to extract nine 3D features (Volume, Surface area, Surface-to-volume, Compactness1, Compactness2, Compactness3, Spherical Disproportion, Sphericity and Fractional Concavity). To quantify the impact of 3D printing, acquisition, segmentation and meshing, features were computed directly from the stereolithography (STL) file format that was used for 3D printing, and compared to those computed. Changes in feature values between 0.6 and 2 mm slice acquisitions were also compared. Spearman’s rank-order correlation coefficients were computed to determine the relationship of each shape feature with spiculatedness for each of the four meshing algorithms. Percent changes were calculated between shape features extracted from the original and resampled contoured images to evaluate the influence of spatial resampling. Finally, the percent change in shape features when the volume was changed from 25% to 150% of their original volume was quantified for three distinct tumor models and compared to the percent change observed when modifying the spiculatedness of the model from d = 11 to d = 92. Values extracted using isosurface remeshing method are the closest to the STL reference ones, with mean differences less than 10.8% (Compactness2) for all features. Seven of the eight features had strong significant correlations with tumor model complexity irrespective of the meshing algorithm (r > 0.98, p < 10-4), with fractional concavity having the lowest correlation coefficient (r = 0.83, p < 10-4, M2). Comparisons of features extracted from the 0.6 and 2 mm slice thicknesses showed that mean differences were from 2.1% (Compactness3) to 12.7% (Compactness2) for the isosurface remeshing method. Resampling on a 1 × 1 × 1 mm3 grid resulted in between 1.3% (Compactness3) to 9.5% (Fractional Concavity) mean changes in feature values. Compactness2, Compactness3, Spherical Disproportion, Sphericity and Fractional Concavity were the features least affected by volume changes. Compactness1 had a 90.4% change with volume, which was greater than the change between the least and most spiculated models. This is the first methodological study that directly demonstrates the relationship of tumor spiculatedness with radiomic shape features, that also produced 3D tumor models, which may serve as reference phantoms for future radiomic studies. Surface Area, Surface-to-volume, and Spherical Disproportion had direct relationships with spiculatedness while the three formulas for Compactness, Sphericity and Fractional Concavity had inverse relationships. The features Compactness2, Compactness3, Spherical Disproportion, and Sphericity should be prioritized as these have minimal variations with volume changes, slice thickness and resampling.

The arrival of immunotherapy has profoundly changed the management of multiple cancers, obtaining unexpected tumour responses. However, until now, the majority of patients do not respond to these new treatments. The identification of biomarkers to determine precociously responding patients is a major challenge. Computational medical imaging (also known as radiomics) is a promising and rapidly growing discipline. This new approach consists in the analysis of high-dimensional data extracted from medical imaging, to further describe tumour phenotypes. This approach has the advantages of being non-invasive, capable of evaluating the tumour and its microenvironment in their entirety, thus characterising spatial heterogeneity, and being easily repeatable over time. The end goal of radiomics is to determine imaging biomarkers as decision support tools for clinical practice and to facilitate better understanding of cancer biology, allowing the assessment of the changes throughout the evolution of the disease and the therapeutic sequence. This review will develop the process of computational imaging analysis and present its potential in immuno-oncology.

Textural and shape analysis is gaining considerable interest in medical imaging, particularly to identify parameters characterizing tumor heterogeneity and to feed radiomic models. Here, we present a free, multiplatform, and easy-to-use freeware called LIFEx, which enables the calculation of conventional, histogram-based, textural, and shape features from PET, SPECT, MR, CT, and US images, or from any combination of imaging modalities. The application does not require any programming skills and was developed for medical imaging professionals. The goal is that independent and multicenter evidence of the usefulness and limitations of radiomic features for characterization of tumor heterogeneity and subsequent patient management can be gathered. Many options are offered for interactive textural index calculation and for increasing the reproducibility among centers. The software already benefits from a large user community (more than 800 registered users), and interactions within that community are part of the development strategy. Significance: This study presents a user-friendly, multi-platform freeware to extract radiomic features from PET, SPECT, MR, CT, and US images, or any combination of imaging modalities. Cancer Res; 78(16); 4786–9. ©2018 AACR.

// Sylvain Reuze 1, 2, 3, 4 , Fanny Orlhac 1, 5 , Cyrus Chargari 1, 2, 3, 6, 7 , Christophe Nioche 5 , Elaine Limkin 1 , Francois Riet 3 , Alexandre Escande 3 , Christine Haie-Meder 3 , Laurent Dercle 8, 9 , Sebastien Gouy 10 , Irene Buvat 5 , Eric Deutsch 1, 2, 3 and Charlotte Robert 1, 2, 3, 4 1 INSERM, U1030, F-94805, Villejuif, France 2 Universite Paris-Sud, Universite Paris-Saclay, F-94270, Le Kremlin-Bicetre, France 3 Gustave Roussy, Universite Paris-Saclay, Department of Radiotherapy, F-94805, Villejuif, France 4 Gustave Roussy, Universite Paris-Saclay, Department of Medical Physics, F-94805, Villejuif, France 5 IMIV, CEA, INSERM, CNRS, Universite Paris-Sud, Universite Paris-Saclay, CEA-SHFJ, Orsay, France 6 French Military Health Services Academy, Ecole du Val-de-Grâce, Paris, France 7 Institut de Recherche Biomedicale des Armees, Bretigny-sur-Orge, France 8 INSERM, U1015, F-94805, Villejuif, France 9 Gustave Roussy, Universite Paris-Saclay, Department of Nuclear Medicine and Endocrine Oncology, F-94805, Villejuif, France 10 Gustave Roussy, Universite Paris-Saclay, Department of Gynecologic Surgery, F-94805, Villejuif, France Correspondence to: Charlotte Robert, email: ch.robert@gustaveroussy.fr Keywords: radiomics, cervical cancer, texture, PET imaging Received: February 24, 2017 Accepted: April 11, 2017 Published: May 15, 2017 ABSTRACT Objectives: To identify an imaging signature predicting local recurrence for locally advanced cervical cancer (LACC) treated by chemoradiation and brachytherapy from baseline 18 F-FDG PET images, and to evaluate the possibility of gathering images from two different PET scanners in a radiomic study. Methods: 118 patients were included retrospectively. Two groups (G1, G2) were defined according to the PET scanner used for image acquisition. Eleven radiomic features were extracted from delineated cervical tumors to evaluate: (i) the predictive value of features for local recurrence of LACC, (ii) their reproducibility as a function of the scanner within a hepatic reference volume, (iii) the impact of voxel size on feature values. Results: Eight features were statistically significant predictors of local recurrence in G1 (p < 0.05). The multivariate signature trained in G2 was validated in G1 (AUC=0.76, p

The aim of this study was to evaluate if bone marrow (BM) SUVmax measured on pre-treatment 18F-FDG PET/CT predicts the clinical outcome of locally advanced cervical cancer (LACC). We recruited retrospectively patients with LACC who underwent staging 18F-FDG PET/CT and had baseline blood tests, then treated by chemoradiation therapy (CRT), followed by image-guided adaptive brachytherapy (IGABT). BM SUVmax was calculated and correlated to inflammatory blood markers. Tumor size and pelvic lymph node involvement were evaluated on baseline MRI. Prognostic value of SUV uptake and blood markers regarding overall survival (OS), pelvic and extra-pelvic recurrence-free survival (PRFS and EPRFS respectively) was assessed using Cox models with adjusted p-values. 116 patients with FIGO stage Ib-IVa cervical cancer, treated between 2005 and 2014, were analyzed. The median follow-up was 75.5 months. BM SUVmax was significantly correlated to tumor SUVmax. In multivariate analysis, PRFS was significantly poorer in patients with high BM SUVmax (>2.8) and neutrophilia (p 5 vs ≤5 cm) could predict PRFS, EPRFS and OS (p 12 vs ≤12) were not prognostic for OS or pelvic and extra-pelvic relapses. Patients with LACC and high BM SUVmax on 18F-FDG PET have worse PFRS following CRT plus IGABT. These results can be potentially explained by the pro-inflammatory role of the tumor microenvironment and G-CSF expressed by tumor cells. These data support the role of PET as a potential indicator of disease aggressiveness beyond tumor staging.

Background: The discovery of biomarkers identifying responders to immunotherapy is a major challenge. Tumor and peritumoral immune infiltration has been shown to be associated with response to anti-PD-1/PD-L1. The aim of this study was to develop a radiomics-based imaging tool of tumor immune infiltrate and to assess whether such a tool could predict clinical outcomes of patients treated with anti-PD1/PDL1. Methods: A predictive radiomics-based model of tumor-infiltrating CD8+ T cells was trained using data from the head and neck cohort of The Cancer Imaging Archive (HNSC-TCIA). Two cohorts from our institute were used for validation. Contrast-enhanced CTs of 57 patients from the HNSC-TCIA were manually segmented (tumor and surrounding tissue) and 76 radiomics features extracted. A radiomics-based score was build using radiomics features to predict tumor-infiltrating CD8+ T-cells' abundance, which was estimated using RNA-sequencing data from The Cancer Genome Atlas, and the Microenvironment Cell Populations-counter signature. As a first validation, this signature was applied to an independent cohort of 100 patients for whom the pathologic tumor immune infiltrate was postulated as either favorable (lymphoma, melanoma, lung, bladder, renal, MSI+ cancers, and adenopathy; 70 patients) or unfavorable (adenoid cystic carcinoma, low-grade neuroendocrine tumors, uterine leiomyoma; 30 patients). The signature was then applied on baseline-CTs of a second external cohort of 139 patients prospectively enrolled in anti PD-1/PD-L1 phase 1 trials. The median of the radiomics-based CD8+ score was used to separate patients into two groups (high and low score). Survival was estimated using Cox-proportional hazards model. Results: We developed a radiomics-based CD8+ signature using the six radiomics features that had highest performance on random forest. In the first external cohort, the radiomics-based CD8 T-cells score was associated with the postulated tumor immune infiltrate (Wilcoxon test, P < 0.001). In the second external cohort of patients treated with anti-PD-1/anti-PD-L1, median (±SD) radiomics score was 109.6±61.3. Patients with high-predicted score had significantly better OS (HR= 0.55, 95%CI=0.36-0.86, P= 0.009). The radiomics-based CD8+ predicted score remained significant in a multivariate Cox regression analysis including RMH score (HR= 0.50, 95%CI=0.32-0.78, P= 0.003). Conclusions: The radiomics-based signature of CD8+ T cells appears as a promising tool to estimate tumor immune infiltrate and to infer the outcome of patients treated with anti-PD-1/PD-L1. Citation Format: Roger Sun, Elaine Johanna Limkin, Laurent Dercle, Sylvain Reuzé, Stéphane Champiat, David Brandao, Loic Verlingue, Samy Ammari, Sandrine Aspeslagh, Antoine Hollebecque, Christophe Massard, Aurélien Marabelle, Jean-Yves Scoazec, Charlotte Robert, Jean-Charles Soria, Eric Deutsch, Charles Ferté. Prediction of clinical outcomes of cancer patients treated with anti-PD-1/PD-L1 using a radiomics-based imaging score of immune infiltrate [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr A051.