Prediction of outcome using pretreatment 18F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy

Lucia, François; Visvikis, Dimitris; Desseroit, Marie-Charlotte; Miranda, O.; Malhaire, Jean-Pierre; Robin, Philippe; Pradier, Olivier; Hatt, Mathieu; Schick, Ulrike

doi:10.1007/s00259-017-3898-7

Cited by 204 publications

(187 citation statements)

References 33 publications

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“…There is increasing evidence for in vivo tissue characterization with both PET/CT and PET/MRI hybrid imaging [11,146,174,175]. In one study, PET, CT, and PET/CT features were used to predicting local tumor control in head and neck cancer [129] by multivariate cox regression with a confidence interval (CI) CI CT and CI PET/CT of 0.73, however, CT-based radiomics overestimated the probability of tumor control in the poor prognostic groups.…”

Section: Radiomicsmentioning

confidence: 99%

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Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

et al. 2018

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Medical imaging has evolved from a pure visualization tool to representing a primary source of analytic approaches toward in vivo disease characterization. Hybrid imaging is an integral part of this approach, as it provides complementary visual and quantitative information in the form of morphological and functional insights into the living body. As such, non-invasive imaging modalities no longer provide images only, but data, as stated recently by pioneers in the field. Today, such information, together with other, non-imaging medical data creates highly heterogeneous data sets that underpin the concept of medical big data. While the exponential growth of medical big data challenges their processing, they inherently contain information that benefits a patient-centric personalized healthcare. Novel machine learning approaches combined with high-performance distributed cloud computing technologies help explore medical big data. Such exploration and subsequent generation of knowledge require a profound understanding of the technical challenges. These challenges increase in complexity when employing hybrid, aka dual-or even multi-modality image data as input to big data repositories. This paper provides a general insight into medical big data analysis in light of the use of hybrid imaging information. First, hybrid imaging is introduced (see further contributions to this special Research Topic), also in the context of medical big data, then the technological background of machine learning as well as state-of-the-art distributed cloud computing technologies are presented, followed by the discussion of data preservation and data sharing trends. Joint data exploration endeavors in the context of in vivo radiomics and hybrid imaging will be presented. Standardization challenges of imaging protocol, delineation, feature engineering, and machine learning evaluation will be detailed. Last, the paper will provide an outlook into the future role of hybrid imaging in view of personalized medicine, whereby a focus will be given to the derivation of prediction models as part of clinical decision support systems, to which machine learning approaches and hybrid imaging can be anchored.

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Section: Radiomicsmentioning

confidence: 99%

“…Outcome prediction of locally advanced cervical cancer based on PET/CT and MRI is a topic of ongoing research [174]. The combined analysis of PET and ADC features resulted in an accuracy of 94% for predicting recurrence and 100% for predicting lack of loco-regional control compared to clinical parameters (51-60% accuracy).…”

Section: Radiomicsmentioning

confidence: 99%

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

et al. 2018

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“…A previous study performed by Lucia et al. proved that GLNU from GLRLM extracted from PET images can be used as a predictor of locoregional control with higher prognostic power than conventional quantitative parameters . As observed in Table , the GLNU value was markedly decreased in the tumor treated with AuNPs + ultrasound (10.12) compared to the tumors subjected to ultrasound (41.30) and AuNPs (202.84), alone.…”

Section: Discussionmentioning

confidence: 82%

Gold nanoparticle‐induced sonosensitization enhances the antitumor activity of ultrasound in colon tumor‐bearing mice

et al. 2018

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“…Currently, many free software packages are available for radiomic feature calculation in the form of stand‐alone programs, modules and libraries such as MaZda, CGITA, IBEX, LIFEx, MITK Phenotyping, RaCaT, CERR radiomic extension, and Pyradiomics . Moreover, several studies report in‐house developed radiomic programs …”

Section: Introductionmentioning

confidence: 99%

“…8 Moreover, several studies report in-house developed radiomic programs. [9][10][11][12][13][14][15] Many challenges hamper the reproducibility and validation of radiomic studies, 16,17 including ambiguous feature nomenclature and variability of feature definitions between software platforms. Indeed, standardization of radiomic software is a fundamental step to ease the comparison and validation of studies across different institutions and for a possible translation of radiomics into clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Technical Note: An IBEX adaption toward image biomarker standardization

et al. 2020

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Purpose Interest in the field of radiomics is rapidly growing because of its potential to characterize tumor phenotype and provide predictive and prognostic information. Nevertheless, the reproducibility and robustness of radiomics studies are hampered by the lack of standardization in feature definition and calculation. In the context of the image biomarker standardization initiative (IBSI), we investigated the grade of compliance of the image biomarker explorer (IBEX), a free open‐source radiomic software, and we developed and validated standardized‐IBEX (S‐IBEX), an adaptation of IBEX to IBSI. Methods Image biomarker explorer source code was checked against IBSI standard. Both the feature implementation and the overall image preprocessing chain were evaluated. Sections were re‐implemented wherever differences emerged: in particular, contour‐to‐binary‐mask conversion, image sub‐portion extraction, re‐segmentation, gray‐level discretization and interpolation were aligned to IBSI. All reported IBSI features were implemented in S‐IBEX. On a patient phantom, S‐IBEX was validated by benchmarking five different preprocessing configurations proposed by IBSI. Results Most IBEX feature definitions are IBSI compliant; however, IBEX preprocessing introduces non‐negligible nonconformities, resulting in feature values not aligned with the corresponding IBSI benchmarks. On the contrary, S‐IBEX features are in agreement with the standard regardless of preprocessing configurations: the percentage of features equal to their benchmark values ranges from 98.1% to 99.5%, with overall maximum percentage error below 1%. Moreover, the impact of noncompliant preprocessing steps has been assessed: in these cases, the percentage of features equal to the standard drops below 35%. Conclusions The use of standardized software for radiomic feature extraction is essential to ensure the reproducibility of results across different institutions, easing at the same time their external validation. This work presents and validates S‐IBEX, a free IBSI‐compliant software, developed upon IBEX, for feature extraction that is both easy to use and quantitatively accurate.

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Prediction of outcome using pretreatment 18F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy

Cited by 204 publications

References 33 publications

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

Gold nanoparticle‐induced sonosensitization enhances the antitumor activity of ultrasound in colon tumor‐bearing mice

Technical Note: An IBEX adaption toward image biomarker standardization

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