Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges

Elhalawani, Hesham; Mohamed, A.S.R.; White, Arica; Zafereo, James; Wong, Andrew; Berends, Joel; Abohashem, Shady; Williams, Bowman; Aymard, Jeremy M.; Kanwar, Aasheesh; Perni, Subha; Rock, Crosby D.; Cooksey, Luke C; Campbell, Shauna; Ding, Yao; Lai, Stephen Y.; Marai, Elisabeta; Vock, David M.; Canahuate, Guadalupe; Freymann, John; Farahani, Keyvan; Kalpathy–Cramer, Jayashree; Fuller, Clifton D.

doi:10.1038/sdata.2017.77

Cited by 57 publications

(32 citation statements)

References 37 publications

(38 reference statements)

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“…Scientific papers analyzing the individual performances of winning algorithms submitted to the Challenge, along with database descriptor have been or will be published ( 14 , 21 ). In general, we also recommend publishing a post-challenge data descriptor that details data configuration as a guide for future dataset usage ( 14 ).…”

Section: Discussionmentioning

confidence: 99%

“…We also provided contestants with a list of suggested open-source infrastructure software that supports common radiomics workflow tasks such as image data import and review as well as radiomics feature computation, along with links to download the software. After completion of the challenge, a complete digital repository was deposited (figshare: https://doi.org/10.6084/m9.figshare.c.3757403.v1 and https://doi.org/10.6084/m9.figshare.c.3757385.v1 ) ( 18 , 19 ) and registered as a public access data descriptor ( 14 ).…”

Section: Materials and Methods For Challengesmentioning

confidence: 99%

“…Using manually-segmented contrast-enhanced computed tomography (CECT) images and matched clinical data, contestants were tasked with building one of 2 models. These included: (i) a classification model of human papillomavirus (HPV) status; and (ii) a predictive model of local tumor recurrence, following intensity-modulated radiation treatment (IMRT) ( 14 ).…”

Section: Introductionmentioning

confidence: 99%

“…The main aim of this review is to detail the mechanics and outcomes of our experience of using a large standardized database for radiomics machine-learning challenges. We previously detailed the data included in both our challenges in a recently published data descriptor ( 14 ). Here, we will continue to outline the “challenge within a challenge” to provide a template workflow for initiating substantial platforms for facilitating “multi-user” radiomics endeavors.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges

et al. 2018

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Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the “HPV” challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the “local recurrence” challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings.

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Section: Discussionmentioning

confidence: 99%

Section: Materials and Methods For Challengesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges

et al. 2018

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“…In an approach designed as a competition with scoring, different researchers could test their algorithms regarding the evaluation of the HPV status and the local recurrences. During the annual meeting of the MICCAI 2106, the winners were presented 70 .…”

Section: Radiomics and Tumorsmentioning

confidence: 99%

Radiomics: Big Data statt Biopsie in der Zukunft?

Scheckenbach

2018

Laryngo-Rhino-Otol

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Precision medicine is increasingly pushed forward, also with respect to upcoming new targeted therapies. Individual characterization of diseases on the basis of biomarkers is a prerequisite for this development. So far, biomarkers are characterized clinically, histologically or on a molecular level. The implementation of broad screening methods ("Omics") and the analysis of big data - in addition to single markers - allow to define biomarker signatures. Next to "Genomics", "Proteomics", and "Metabolicis", "Radiomics" gained increasing interest during the last years. Based on radiologic imaging, multiple radiomic markers are extracted with the help of specific algorithms. These are correlated with clinical, (immuno-) histopathological, or genomic data. Underlying structural differences are based on the imaging metadata and are often not visible and therefore not detectable without specific software. Radiomics are depicted numerically or by graphs. The fact that radiomic information can be extracted from routinely performed imaging adds a specific appeal to this method. Radiomics could potentially replace biopsies and additional investigations. Alternatively, radiomics could complement other biomarkers and thus lead to a more precise, multimodal prediction. Until now, radiomics are primarily used to investigate solid tumors. Some promising studies in head and neck cancer have already been published.

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Application of radiomics for the prediction of HPV status for patients with head and neck cancers

et al. 2020

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Purpose To perform radiomic analysis of primary tumors extracted from pretreatment contrast‐enhanced computed tomography (CE‐CT) images for patients with oropharyngeal cancers to identify discriminant features and construct an optimal classifier for the characterization and prediction of human papilloma virus (HPV) status. Materials and methods One hundred and eighty seven patients with oropharyngeal cancers with known HPV status (confirmed by immunohistochemistry—p16 protein testing) were retrospectively studied as follows: Group A: 95 patients (19HPV− and 76HPV+) from the MICAII grand challenge. Group B: 92 patients (52HPV− and 40HPV+) from our institution. Radiomic features (172) were extracted from pretreatment diagnostic CE‐CT images of the gross tumor volume (GTV). Levene and Kolmogorov–Smirnov's tests with absolute biserial correlation (>0.48) were used to identify the discriminant features between the HPV+ and HPV− groups. The discriminant features were used to train and test eight different classifiers. Area under receiver operating characteristic (AUC), positive predictive and negative predictive values (PPV and NPV, respectively) were used to evaluate the performance of the classifiers. Principal component analysis (PCA) was applied on the discriminant feature set and seven PCs were used to train and test a generalized linear model (GLM) classifier. Results Among 172 radiomic features only 12 radiomic features (from 3 categories) were significantly different (P < 0.05, |BSC| > 0.48) between the HPV+ and HPV− groups. Among the eight classifiers trained and applied for prediction of HPV status, the GLM showed the highest performance for each discriminant feature and the combined 12 features: AUC/PPV/NPV = 0.878/0.834/0.811. The GLM high prediction power was AUC/PPV/NPV = 0.849/0.731/0.788 and AUC/PPV/NPV = 0.869/0.807/0.870 for unseen test datasets for groups A and B, respectively. After eliminating the correlation among discriminant features by applying PCA analysis, the performance of the GLM was improved by 3.3%, 2.2%, and 1.8% for AUC, PPV, and NPV, respectively. Conclusions Results imply that GTV's for HPV+ patients exhibit higher intensities, smaller lesion size, greater sphericity/roundness, and higher spatial intensity‐variation/heterogeneity. Results are suggestive that radiomic features primarily associated with the spatial arrangement and morphological appearance of the tumor on contrast‐enhanced diagnostic CT datasets may be potentially used for classification of HPV status.

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Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges

Cited by 57 publications

References 37 publications

Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges

Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges

Radiomics: Big Data statt Biopsie in der Zukunft?

Application of radiomics for the prediction of HPV status for patients with head and neck cancers

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