Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology.

Medical image processing and analysis (also known as Radiomics) is a rapidly growing discipline that maps digital medical images into quantitative data, with the end goal of generating imaging biomarkers as decision support tools for clinical practice. The use of imaging data from routine clinical work-up has tremendous potential in improving cancer care by heightening understanding of tumor biology and aiding in the implementation of precision medicine. As a noninvasive method of assessing the tumor and its microenvironment in their entirety, radiomics allows the evaluation and monitoring of tumor characteristics such as temporal and spatial heterogeneity. One can observe a rapid increase in the number of computational medical imaging publications-milestones that have highlighted the utility of imaging biomarkers in oncology. Nevertheless, the use of radiomics as clinical biomarkers still necessitates amelioration and standardization in order to achieve routine clinical adoption. This Review addresses the critical issues to ensure the proper development of radiomics as a biomarker and facilitate its implementation in clinical practice.

Investigation of radiomics based intra-patient inter-tumor heterogeneity and the impact of tumor subsampling strategies.

While radiomics analysis has been applied for localized cancer disease, its application to the metastatic setting involves a non-exhaustive lesion subsampling strategy which may sidestep the intrapatient tumoral heterogeneity, hindering the reproducibility and the therapeutic response performance. 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 and extracted radiomics features of all visible tumors from single acquisition pre-treatment computed tomography of patients with metastatic lung cancer (cohort L) and confirmed our results on a larger cohort of patients with metastatic melanoma (cohort M). To quantify the captured heterogeneity, the absolute coefficient of variation (CV) of each radiomics index was calculated at the patient-level and a sensitivity analysis was performed using only a subset of all extracted features robust to the segmentation step. The extent of information loss by six commonly used tumor sampling strategies was then assessed. A total of 602 lesions were segmented from 43 patients (median age 57, 4.9% female). 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. Image-based inter-tumor intra-patient heterogeneity can be successfully grasped by radiomics analyses. Failing to take into account this kind of heterogeneity will lead to inconsistent predictive algorithms. Guidelines to standardize the tumor sampling step and/or AI-driven tools to alleviate the segmentation effort are required.

[Computational medical imaging (radiomics) and potential for immuno-oncology]. / Imagerie médicale computationnelle (radiomique) et potentiel en immuno-oncologie.

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.

Public Health and Epidemiology Informatics.

OBJECTIVES: The aim of this manuscript is to provide a brief overview of the scientific challenges that should be addressed in order to unlock the full potential of using data from a general point of view, as well as to present some ideas that could help answer specific needs for data understanding in the field of health sciences and epidemiology. METHODS: A survey of uses and challenges of big data analyses for medicine and public health was conducted. The first part of the paper focuses on big data techniques, algorithms, and statistical approaches to identify patterns in data. The second part describes some cutting-edge applications of analyses and predictive modeling in public health. RESULTS: In recent years, we witnessed a revolution regarding the nature, collection, and availability of data in general. This was especially striking in the health sector and particularly in the field of epidemiology. Data derives from a large variety of sources, e.g. clinical settings, billing claims, care scheduling, drug usage, web based search queries, and Tweets. CONCLUSION: The exploitation of the information (data mining, artificial intelligence) relevant to these data has become one of the most promising as well challenging tasks from societal and scientific viewpoints in order to leverage the information available and making public health more efficient.

[Adaptive radiation therapy for non-small cell lung cancer]. / Radiothérapie adaptative du carcinome bronchique non à petites cellules.

Anatomical changes and tumor regression during thoracic radiotherapy may alter the treatment volumes. These modifications are not taken into account into set-up or motion margins used for treatment planning. Their dosimetric impact could be significant and a better understanding of the changes occurring during the 6 to 7 weeks of treatment could be useful in order to define quantitative thresholds before a new treatment planning is needed. Margins could also be reduced in order to better spare organs at risk and perform targeted dose escalation. This review assesses the potential of morphologic and metabolic imaging during treatment for adaptive radiotherapy in non-small cell lung cancer.

Assessment of calf muscle contraction by diffusion tensor imaging.

The goal of this study was to assess the changes of water diffusion during contraction and elongation of calf muscles using diffusion tensor (DT) MRI in normal volunteers. Twenty volunteers (mean age, 29+/-4 years) underwent DT MRI examination of the right calf. Echo planar imaging sequence was performed at rest, during dorsal flexion and during plantar flexion. The three eigenvalues (lambda1, lambda2, and lambda3), apparent diffusion coefficient (ADC) and fractional anisotropy (FA) of the diffusion tensor were calculated for medial gastrocnemius (mGM) and tibialis anterior (TA). A fiber tractography was performed on both muscles. Non-parametric Wilcoxon and Mann Whitney tests were used for statistical evaluation. At rest, lambda1, lambda2 and ADC of mGM were higher than their counterparts of TA (P<0.01). During dorsal flexion, the three eigenvalues and ADC of TA significantly increased (P<0.05) as their counterparts of mGM slightly decreased (P=NS). Opposite variations were detected during plantar flexion of the foot. Visual analysis evidenced a relationship between 3D representations of MRI fibers and physiological state of muscles. Contraction of calf muscles produces changes in DT parameters, which are related to the physiological state of the muscle.