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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.

PurposeWhile there is still a significant need to identify potential biomarkers that can predict which patients are most likely to respond to immunotherapy treatments, radiomic approaches have shown promising results. The objectives of this study were to evaluate whether a previously validated radiomics signature of CD8 T-cells could predict progressions at a lesion level and whether the spatial heterogeneity of this radiomics score could be used at a patient level to assess the clinical response and survival of melanoma patients.MethodsClinical data from patients with advanced melanoma treated in our center with immunotherapy were retrieved. Radiomic features were extracted and the CD8 radiomics signature was applied. A progressive lesion was defined by an increase in lesion size of 20% or more. Dispersion metrics of the radiomics signature were estimated to evaluate the impact of interlesion heterogeneity on patient’s response. Fine-tuned cut-offs for predicting overall survival were evaluated after splitting data into training and test sets.ResultsA total of 136 patients were included in this study, with 1120 segmented lesions at baseline, and 1052 lesions at first evaluation. A low CD8 radiomics score at baseline was associated with a significantly higher risk of lesion progression (AUC=0.55, p=0.0091), especially for lesions larger than >1 mL (AUC=0.59 overall, p=0.0035, with AUC=0.75, p=0.002 for subcutaneous lesions, AUC=0.68, p=0.01, for liver lesions and AUC=0.62, p=0.03 for nodes). The least infiltrated lesion according to the radiomics score of CD8 T-cells was positively associated with overall survival (training set HR=0.31, p=0.00062, test set HR=0.28, p=0.016), which remained significant in a multivariate analysis including clinical and biological variables.ConclusionsThese results confirm the predictive value at a lesion level of the biologically inspired CD8 radiomics score in melanoma patients treated with anti-PD1-based immunotherapy and may be interesting to assess the disease spatial heterogeneity to evaluate the patient prognosis with potential clinical implication such as tumor selection for focal ablative therapies.

Background During the COVID-19 pandemic, national and local measures were implemented on the island of Mayotte, a French overseas department in the Indian Ocean with critical socioeconomic and health indicators. Aim We aimed to describe the COVID-19 outbreak in Mayotte from March 2020 to March 2021, with two waves from 9 March to 31 December 2020 and from 1 January to 14 March 2021, linked to Beta (20H/501Y.V2) variant. Methods To understand and assess the dynamic and the severity of the COVID-19 outbreak in Mayotte, surveillance and investigation/contact tracing systems were set up including virological, epidemiological, hospitalisation and mortality indicators. Results In total, 18,131 cases were laboratory confirmed, with PCR or RAT. During the first wave, incidence rate (IR) peaked in week 19 2020 (133/100,000). New hospitalisations peaked in week 20 (54 patients, including seven to ICU). Testing rate increased tenfold during the second wave. Between mid-December 2020 and mid-January 2021, IR doubled (851/100,000 in week 5 2021) and positivity rate tripled (28% in week 6 2021). SARS-CoV-2 Beta variant (Pangolin B.1.351) was detected in more than 80% of positive samples. Hospital admissions peaked in week 6 2021 with 225 patients, including 30 to ICU. Conclusion This massive second wave could be linked to the high transmissibility of the Beta variant. The increase in the number of cases has naturally led to a higher number of severe cases and an overburdening of the hospital. This study shows the value of a real-time epidemiological surveillance for better understanding crisis situations.

After promising results for localized cancer disease, radiomics analysis has been applied to the metastatic setting, sidestepping the potential intertumoral intrapatient heterogeneity captured by radiomics features. Our aim was to evaluate if radiomics features can capture intertumoral intrapatient heterogeneity, and the impact of tumor subsampling on the computed heterogeneity. To this end, We delineated all visible tumors from single acquisition pre-treatment computed tomography of patients with metastatic lung cancer or melanoma, and extracted tumor radiomics features. All robust radiomics indexes exhibited at least 20% of variation with significant heterogeneity both in heavily and oligo metastasized patients, and also at the organ level. None of the segmentation subsampling strategies were able to recover the true tumoral heterogeneity obtained by exhaustive tumor sampling. Further, we discussed potential limitations of predictive algorithms when this kind of heterogeneity is not taken into account.

Radiation therapy is a pivotal cancer treatment that has significantly progressed over the last decade due to numerous technological breakthroughs. Imaging is now playing a critical role on deployment of the clinical workflow, both for treatment planning and treatment delivery. Machine-learning analysis of predefined features extracted from medical images, i.e. radiomics, has emerged as a promising clinical tool for a wide range of clinical problems addressing drug development, clinical diagnosis, treatment selection and implementation as well as prognosis. Radiomics denotes a paradigm shift redefining medical images as a quantitative asset for data-driven precision medicine. The adoption of machine-learning in a clinical setting and in particular of radiomics features requires the selection of robust, representative and clinically interpretable biomarkers that are properly evaluated on a representative clinical data set. To be clinically relevant, radiomics must not only improve patients' management with great accuracy but also be reproducible and generalizable. Hence, this review explores the existing literature and exposes its potential technical caveats, such as the lack of quality control, standardization, sufficient sample size, type of data collection, and external validation. Based upon the analysis of 165 original research studies based on PET, CT-scan, and MRI, this review provides an overview of new concepts, and hypotheses generating findings that should be validated. In particular, it describes evolving research trends to enhance several clinical tasks such as prognostication, treatment planning, response assessment, prediction of recurrence/relapse, and prediction of toxicity. Perspectives regarding the implementation of an AI-based radiotherapy workflow are presented.

To assess the impact of delayed hemothorax on outcomes in blunt chest trauma patients without life-threatening condition at admission and characterize the predictive value of predefined anatomical factors for delayed hemothorax. In a single-centre retrospective study, every spontaneous breathing patient admitted for a blunt chest trauma without significant pleural effusion at ICU admission was included. A multivariable regression model was used to determine the covariate-adjusted odd of secondary respiratory complications in patients with delayed hemothorax ≥ 500 ml. The characteristics of rib fractures (number, location and displacement) were integrated into a logistic regression model to determine variables associated with delayed hemothorax in multivariate analysis. Over the study period, 109 patients were included and the rate of delayed hemothorax ≥ 500 ml was 36%. Patients with delayed hemothorax had higher rates of pulmonary infections (OR 4.8 [1.6–16.4]) but no statistical association between delayed hemothorax and secondary respiratory failure (OR 2.0 [0.4–9.4]). A posterior location and a displaced rib fracture were independent predictors of delayed hemothorax (OR 3.4 [1.3–8.6] and OR 2.3 [1.1–5.1], respectively). At least one displaced rib fracture was more specific of delayed hemothorax than the commonly used threshold of three or more rib fractures (81.3 vs. 51.5%). Delayed hemothorax is a frequent complication associated with increased risk of pulmonary infection. The posterior location and the displacement of at least one rib fracture in the initial CT scan were independent risk factors for predicting the occurrence of delayed hemothorax.

The use of multicentric data is becoming essential for developing generalizable radiomic signatures. In particular, Magnetic Resonance Imaging (MRI) data used in brain oncology are often heterogeneous in terms of scanners and acquisitions, which significantly impact quantitative radiomic features. Various methods have been proposed to decrease dependency, including methods acting directly on MR images, i.e., based on the application of several preprocessing steps before feature extraction or the ComBat method, which harmonizes radiomic features themselves. The ComBat method used for radiomics may be misleading and presents some limitations, such as the need to know the labels associated with the “batch effect”. In addition, a statistically representative sample is required and the applicability of a signature whose batch label is not present in the train set is not possible. This work aimed to compare a priori and a posteriori radiomic harmonization methods and propose a code adaptation to be machine learning compatible. Furthermore, we have developed AutoComBat, which aims to automatically determine the batch labels, using either MRI metadata or quality metrics as inputs of the proposed constrained clustering. A heterogeneous dataset consisting of high and low-grade gliomas coming from eight different centers was considered. The different methods were compared based on their ability to decrease relative standard deviation of radiomic features extracted from white matter and on their performance on a classification task using different machine learning models. ComBat and AutoComBat using image-derived quality metrics as inputs for batch assignment and preprocessing methods presented promising results on white matter harmonization, but with no clear consensus for all MR images. Preprocessing showed the best results on the T1w-gd images for the grading task. For T2w-flair, AutoComBat, using either metadata plus quality metrics or metadata alone as inputs, performs better than the conventional ComBat, highlighting its potential for data harmonization. Our results are MRI weighting, feature class and task dependent and require further investigations on other datasets.

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.

Objective. Reliable radionuclide production yield data are a prerequisite for positron-emission-tomography (PET) based in vivo proton treatment verification. In this context, activation data acquired at two different treatment facilities with different imaging systems were analyzed to provide experimentally determined radionuclide yields in thick targets and were compared with each other to investigate the impact of the respective imaging technique. Approach. Homogeneous thick targets (PMMA, gelatine, and graphite) were irradiated with mono-energetic proton pencil-beams at two distinct energies. Material activation was measured (i) in-beam during and after beam delivery with a double-head prototype PET camera and (ii) offline shortly after beam delivery with a commercial full-ring PET/CT scanner. Integral as well as depth-resolved β +-emitter yields were determined for the dominant positron-emitting radionuclides 11C, 15O, 13N and (in-beam only) 10C. In-beam data were used to investigate the qualitative impact of different monitoring time schemes on activity depth profiles and their quantitative impact on count rates and total activity. Main results. Production yields measured with the in-beam camera were comparable to or higher compared to respective offline results. Depth profiles of radionuclide-specific yields obtained from the double-head camera showed qualitative differences to data acquired with the full-ring camera with a more convex profile shape. Considerable impact of the imaging timing scheme on the activity profile was observed for gelatine only with a range variation of up to 3.5 mm. Evaluation of the coincidence rate and the total number of observed events in the considered workflows confirmed a strongly decreasing rate in targets with a large oxygen fraction. Significance. The observed quantitative and qualitative differences between the datasets underline the importance of a thorough system commissioning. Due to the lack of reliable cross-section data, in-house phantom measurements are still considered a gold standard for careful characterization of the system response and to ensure a reliable beam range verification.

International audience; Purpose: Severe pneumonia and acute respiratory distress syndrome (ARDS) have been described in patients with severe coronavirus disease 2019 (COVID-19). Recently, early clinical data reported the feasibility of low doses of radiation therapy (RT) in the treatment of ARDS in patients with severe COVID-19. However, the involved mechanisms remained unknown. Methods and Materials: Here, we used airways-instilled lipopolysaccharide (LPS) and influenza virus (H1N1) as murine models of pneumonia, and toll-like receptor (TLR)-3 stimulation in human lung macrophages. Results: Low doses of RT (0.5-1 Gray) decreased LPS-induced pneumonia, and increased the percentage of nerve- and airway-associated macrophages producing interleukin (IL) 10. During H1N1 viral infection, we observed decreased lung tissue damage and immune cell infiltration in irradiated animals. Low doses of RT increased IL-10 production by infiltrating immune cells into the lung. Irradiation of TLR-3 ligand-stimulated human lung macrophages ex vivo increased IL-10 secretion and decreased interferon γ production in the culture supernatant. The percentage of human lung macrophages producing IL-6 was also decreased. Conclusions: Our data highlight a mechanism by which low doses of RT regulate lung inflammation and skew lung macrophages toward an anti-inflammatory profile. These data provide a preclinical mechanistic support to clinical trials evaluating low doses of RT, such as COVID-19-induced ARDS.

Brain tumor segmentation is a critical task for patient’s disease management. In order to automate and standardize this task, we trained multiple U-net like neural networks, mainly with deep supervision and stochastic weight averaging, on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 training dataset. Two independent ensembles of models from two different training pipelines were trained, and each produced a brain tumor segmentation map. These two labelmaps per patient were then merged, taking into account the performance of each ensemble for specific tumor subregions. Our performance on the online validation dataset with test time augmentation were as follows: Dice of 0.81, 0.91 and 0.85; Hausdorff (95%) of 20.6, 4, 3, 5.7 mm for the enhancing tumor, whole tumor and tumor core, respectively. Similarly, our solution achieved a Dice of 0.79, 0.89 and 0.84, as well as Hausdorff (95%) of 20.4, 6.7 and 19.5 mm on the final test dataset, ranking us among the top ten teams. More complicated training schemes and neural network architectures were investigated without significant performance gain at the cost of greatly increased training time. Overall, our approach yielded good and balanced performance for each tumor subregion. Our solution is open sourced at https://github.com/lescientifik/open_brats2020.

In radiotherapy, patient-specific quality assurance is very time-consuming and causes machine downtime. It consists of testing (using measurement with a phantom and detector) if a modulated plan is correctly delivered by a treatment unit. Artificial intelligence and in particular machine learning algorithms were mentioned in recent reports as promising solutions to reduce or eliminate the patient-specific quality assurance workload. Several teams successfully experienced a virtual patient-specific quality assurance by training a machine learning tool to predict the results. Training data are generally composed of previous treatment plans and associated patient-specific quality assurance results. However, other training data types were recently introduced such as actual positions and velocities of multileaf collimators, metrics of the plan's complexity, and gravity vectors. Different types of machine learning algorithms were investigated (Poisson regression algorithms, convolutional neural networks, support vector classifiers) with sometimes promising results. These tools are being used for treatment units' quality assurance as well, in particular to analyse the results of imaging devices. Most of these reports were feasibility studies. Using machine learning in clinical routines as a tool that could fully replace quality assurance tests conducted by physics teams has yet to be implemented.

Image registration is one of the most challenging problems in medical image analysis. In the recent years, deep learning based approaches became quite popular, providing fast and performing registration strategies. In this short paper, we summarise our work presented on Learn2Reg challenge 2020. The main contributions of our work rely on (i) a symmetric formulation, predicting the transformations from source to target and from target to source simultaneously, enforcing the trained representations to be similar and (ii) integration of variety of publicly available datasets used both for pretraining and for augmenting segmentation labels. Our method reports a mean dice of 0.64 for task 3 and 0.85 for task 4 on the test sets, taking third place on the challenge. Our code and models are publicly available at https://github.com/TheoEst/abdominal_registration and https://github.com/TheoEst/hippocampus_registration.

Monte-Carlo simulation of radiotherapy dose remains an extremely time-consuming task, despite being still the most precise tool for radiation transport calculation. To circumvent this issue, deep learning offers promising avenues. In this paper, we extend ConvLSTM to handle 3D data and introduce a 3D recurrent and fully convolutional neural network architecture. Our model’s purpose is to infer a computationally expensive Monte Carlo dose calculation result for VMAT plans with a high number of particles from a sequence of simulations with a low number of particles. We benchmark our framework against other learning methods commonly used for denoising and other medical tasks. Our model outperforms the other methods with regards to several evaluation metrics used to assess the clinical viability of the predictions. Code is available at https://git.io/JcbxD.

BackgroundCombining radiotherapy (RT) with immuno-oncology (IO) therapy (IORT) may enhance IO-induced antitumor response. Quantitative imaging biomarkers can be used to provide prognosis, predict tumor response in a non-invasive fashion and improve patient selection for IORT. A biologically inspired CD8 T-cells-associated radiomics signature has been developed on previous cohorts. We evaluated here whether this CD8 radiomic signature is associated with lesion response, whether it may help to assess disease spatial heterogeneity for predicting outcomes of patients treated with IORT. We also evaluated differences between irradiated and non-irradiated lesions.MethodsClinical data from patients with advanced solid tumors in six independent clinical studies of IORT were investigated. Immunotherapy consisted of 4 different drugs (antiprogrammed death-ligand 1 or anticytotoxic T-lymphocyte-associated protein 4 in monotherapy). Most patients received stereotactic RT to one lesion. Irradiated and non-irradiated lesions were delineated from baseline and the first evaluation CT scans. Radiomic features were extracted from contrast-enhanced CT images and the CD8 radiomics signature was applied. A responding lesion was defined by a decrease in lesion size of at least 30%. Dispersion metrices of the radiomics signature were estimated to evaluate the impact of tumor heterogeneity in patient’s response.ResultsA total of 94 patients involving multiple lesions (100 irradiated and 189 non-irradiated lesions) were considered for a statistical interpretation. Lesions with high CD8 radiomics score at baseline were associated with significantly higher tumor response (area under the receiving operating characteristic curve (AUC)=0.63, p=0.0020). Entropy of the radiomics scores distribution on all lesions was shown to be associated with progression-free survival (HR=1.67, p=0.040), out-of-field abscopal response (AUC=0.70, p=0.014) and overall survival (HR=2.08, p=0.023), which remained significant in a multivariate analysis including clinical and biological variables.ConclusionsThese results enhance the predictive value of the biologically inspired CD8 radiomics score and suggests that tumor heterogeneity should be systematically considered in patients treated with IORT. This CD8 radiomics signature may help select patients who are most likely to benefit from IORT.

Severe pneumonia and acute respiratory distress syndrome (ARDS) have been described in patients with severe COVID-19. Recently, early clinical data reported the efficacy of low doses of radiation therapy (RT) in the treatment of ARDS in patients with severe COVID-19. However, the involved mechanisms remained unknown. Here, we used airways-instilled lipopolysaccharide (LPS) and influenza virus (H1N1) as murine models of pneumonia, and Tolllike receptor (TLR)-3 stimulation in human lung macrophages. Low doses RT (0.5-1 Gy) decreased LPS induced pneumonia, and increased the percentage of Nerve- and Airway-associated Macrophages (NAMs) producing IL-10. During H1N1 viral infection, we observed decreased lung tissue damage and immune cell infiltration in irradiated animals. Low doses RT increased IL-10 production by infiltrating immune cells into the lung. Irradiation of TLR-3 ligand-stimulated human lung macrophages ex vivo increased IL-10 secretion and decreased IFNγ production in the culture supernatant. The percentage of human lung macrophages producing IL-6 was also decreased. Our data highlight one of the mechanisms by which low doses RT regulate lung inflammation and skew lung macrophages towards an anti-inflammatory profile. These data provide the preclinical rationale for the use and for the optimization of low doses RT in situations such as COVID-19-induced ARDS.

Neuroimaging and especially MRI has emerged as a necessary imaging modality to detect, measure, characterize and monitor brain tumors. Advanced MRI sequences such as perfusion MRI, diffusion MRI and spectroscopy as well as new post-processing techniques such as automatic segmentation of tumors and radiomics play a crucial role in characterization and follow up of brain tumors. The purpose of this review is to provide an overview on anatomical and functional MR imaging use for brain tumors boundaries determination and tumor characterization in the specific context of radiotherapy. The usefulness of anatomical and functional MR imaging on particular challenges posed by radiotherapy such as pseudoprogression and pseudoresponse and new treatment strategies such as dose painting is also described., Les avancées en neuro-imagerie, principalement liées au développement de l’IRM, ont rendu cette modalité centrale dans la prise en charge des patients porteurs de tumeur cérébrale. L’IRM, par son approche anatomique, permet de détecter, localiser et caractériser les lésions. L’application de séquences avancées d’IRM de type imagerie de perfusion, de spectroscopie ou de diffusion, ainsi que les nouveaux post-traitements permettant une segmentation ou une caractérisation automatique des lésions, apportent de nouvelles possibilités pour affiner la caractérisation des tumeurs tant au moment du diagnostic initial que lors du traitement par radiothérapie et du suivi. Le but de cette revue de littérature est de donner un aperçu de l’utilisation des imageries IRM anatomique et fonctionnelle utilisées pour la détermination des contours des différentes tumeurs cérébrales dans le contexte particulier de la radiothérapie. L'utilité de l'IRM anatomique et fonctionnelle est également examinée, en portant une attention particulière aux défis posés par la radiothérapie, tels que la pseudoprogression et la pseudoréponse, ainsi que par de nouvelles stratégies de traitement personnalisées, comme la dose painting.

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.