Pierre Fontaine
University of Rennes
AlgorithmMachine learningCancerInternal medicineRadiologyMagnetic resonance imagingOncologyFeature selectionLogistic regressionArtificial intelligenceRandom forestPattern recognitionNearest neighbour algorithmMitotic catastropheVisual WordRegion of interestOversamplingIn silicoExternal beam radiotherapyLiver cancerProstateHead and neck cancerMultiple kernel learningBiochemical recurrenceCirrhosisHepatocellular carcinomaRadiomicsModalitiesProstate cancerRadiosurgeryAngiogenesisPopulationStereotactic body radiation therapyArea under the roc curveFeature aggregationMinority classPlanning target volumeTumor responseClassification methodsOverall survivalComputer sciencePredictive modellingRadiation therapyVoxelLinear discriminant analysisMedicineFeature (computer vision)Selection (genetic algorithm)Image processing
7Publications
3H-index
3Citations
Publications 7
Newest
#2Pierre FontaineH-Index: 3
Last. Oscar AcostaH-Index: 21
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In prostate cancer radiotherapy, biochemical recurrence has been traditionally predicted using radiomics approaches with however limited performance. The purpose of this work was to use a mechanistic in silico model of tumor growth and response to irradiation to obtain better predictions. A cohort of 76 patients with localized prostate adenocarcinoma having undergone external beam radiotherapy was used. Analogous digital tissues were built from pre-treatment MRI. The prescribed irradiation proto...
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OBJECTIVE Purposes of this work were i) to develop an in silico model of tumor response to radiotherapy, ii) to perform an exhaustive sensitivity analysis in order to iii) propose a simplified version and iv) to predict biochemical recurrence with both the comprehensive and the reduced model. METHODS A multiscale computational model of tumor response to radiotherapy was developed. It integrated the following radiobiological mechanisms: oxygenation, including hypoxic death; division of tumor cell...
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#1Pierre FontaineH-Index: 3
#2Oscar Acosta (French Institute of Health and Medical Research)H-Index: 21
Last. Adrien Depeursinge (University of Applied Sciences Western Switzerland)H-Index: 26
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In standard radiomics studies the features extracted from clinical images are mostly quantified with simple statistics such as the average or variance per Region of Interest (ROI). Such approaches may smooth out any intra-region heterogeneity and thus hide some tumor aggressiveness that may hamper predictions. In this paper we study the importance of feature aggregation within the standard radiomics workflow, which allows to take into account intra-region variations. Feature aggregation methods ...
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Jul 1, 2020 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
#1Pierre FontaineH-Index: 3
Last. Oscar Acosta (French Institute of Health and Medical Research)H-Index: 21
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Hepatocellular carcinoma (HCC) is the sixth more frequent cancer worldwide. This type of cancer has a poor overall survival rate mainly due to underlying cirrhosis and risk of recurrence outside the treated lesion. Quantitative imaging within a radiomics workflow may help assessing the probability of survival and potentially may allow tailoring personalized treatments. In radiomics a large amount of features can be extracted, which may be correlated across a population and very often can be surr...
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#2Pierre FontaineH-Index: 3
Last. Oscar AcostaH-Index: 21
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Radiomics refers to the quantification of images by the extraction and analysis of a large number of features from different modalities, aiming to establish potential links between them and disease phenotypes. It can potentially predict the free-disease survival or allow the selection of patients at risk, thereby leading to the development of more personalized treatments. The development of robust prediction models is cumbersome as we deal with a high multidimensional problem, where a high numbe...
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#1Eugenia MylonaH-Index: 4
#2Clement LebretonH-Index: 1
Last. Oscar AcostaH-Index: 21
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Prostate cancer radiotherapy unavoidably involves the irradiation not only of the target volume, but also of healthy organs-at-risk, neighboring the prostate, likely causing adverse, toxicity-related side-effects. Specifically, in the case of urinary toxicity, these side effects might be associated with a variety of dosimetric, clinical and genetic factors, making its prediction particularly challenging. Given the inconsistency of available data concerning radiation-induced toxicity, it is cruci...
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#1Pierre FontaineH-Index: 3
#2Oscar AcostaH-Index: 21
Last. R. de CrevoisierH-Index: 20
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