Imaging and clinical data archive for head and neck squamous cell carcinoma patients treated with radiotherapy

Grossberg, Aaron J.; Mohamed, A.S.R.; Halawani, Hesham El; Bennett, William; Smith, Kirk E.; Nolan, Tracy S.; Williams, Bowman; Chamchod, Sasikarn; Heukelom, J.; Kantor, Michael; Browne, Theodora; Hutcheson, Katherine A.; Gunn, G. Brandon; Garden, Adam S.; Morrison, William H.; Frank, Steven J.; Rosenthal, D.I.; Freymann, John; Fuller, Clifton D.

doi:10.1038/sdata.2018.173

Cited by 64 publications

(60 citation statements)

References 21 publications

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“…org/10.7937/K9/TCIA.2015.7AKGJUPZ), 22,30,46,60,66 Head-Neck-PET-CT (https://doi.org/10.7937/K9/TCIA.2017.8oje 5q00), 22,30,46,114 TCGA-HNSC (https://doi.org/10.7937/K9/ TCIA.2016.LXKQ47MS) 22,60 and Data from Head and Neck Cancer CT Atlas (https://doi.org/10.7937/K9/TCIA.2017. umz8dv6s) 22,115 CT image databases, the RT-MAC (https://d oi.org/10.7937/tcia.2019.bcfjqfqb) 116 MR image database, or TABLE I. Image modalities used for auto-segmentation of organs at risk in the head and neck region for the purpose of radiotherapy planning, and the corresponding references.…”

Section: C Image Databasementioning

confidence: 99%

Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods

et al. 2020

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Radiotherapy (RT) is one of the basic treatment modalities for cancer of the head and neck (H&N), which requires a precise spatial description of the target volumes and organs at risk (OARs) to deliver a highly conformal radiation dose to the tumor cells while sparing the healthy tissues. For this purpose, target volumes and OARs have to be delineated and segmented from medical images. As manual delineation is a tedious and time-consuming task subjected to intra/interobserver variability, computerized auto-segmentation has been developed as an alternative. The field of medical imaging and RT planning has experienced an increased interest in the past decade, with new emerging trends that shifted the field of H&N OAR auto-segmentation from atlas-based to deep learning-based approaches. In this review, we systematically analyzed 78 relevant publications on auto-segmentation of OARs in the H&N region from 2008 to date, and provided critical discussions and recommendations from various perspectives: image modalityboth computed tomography and magnetic resonance image modalities are being exploited, but the potential of the latter should be explored more in the future; OARthe spinal cord, brainstem, and major salivary glands are the most studied OARs, but additional experiments should be conducted for several less studied soft tissue structures; image databaseseveral image databases with the corresponding ground truth are currently available for methodology evaluation, but should be augmented with data from multiple observers and multiple institutions; methodologycurrent methods have shifted from atlas-based to deep learning autosegmentation, which is expected to become even more sophisticated; ground truthdelineation guidelines should be followed and participation of multiple experts from multiple institutions is recommended; performance metricsthe Dice coefficient as the standard volumetric overlap metrics should be accompanied with at least one distance metrics, and combined with clinical acceptability scores and risk assessments; segmentation performancethe best performing methods achieve clinically acceptable auto-segmentation for several OARs, however, the dosimetric impact should be also studied to provide clinically relevant endpoints for RT planning.

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Section: C Image Databasementioning

confidence: 99%

Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods

et al. 2020

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“…We retrospectively acquired clinical and imaging data from (1) Yale's Smilow Hospital cancer registry from 2009-2019, and (2) two publicly available TCIA collections [36]: the "Head-Neck-PET-CT" collection from four Canadian institutions ("Canadian" cohort) [37,38] and the "Data from Head and Neck Cancer CT Atlas" collection from MD Anderson Cancer Center ("MD Anderson" cohort) [39,40]. Institutional review board approval was obtained from the Yale University ethics committee (IRB protocol #2000024295) and informed consent was waived, given the retrospective study design.…”

Section: Data Acquisitionmentioning

confidence: 99%

Potential Added Value of PET/CT Radiomics for Survival Prognostication beyond AJCC 8th Edition Staging in Oropharyngeal Squamous Cell Carcinoma

Haider

Zeevi

Baumeister

et al. 2020

Cancers

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Accurate risk-stratification can facilitate precision therapy in oropharyngeal squamous cell carcinoma (OPSCC). We explored the potential added value of baseline positron emission tomography (PET)/computed tomography (CT) radiomic features for prognostication and risk stratification of OPSCC beyond the American Joint Committee on Cancer (AJCC) 8th edition staging scheme. Using institutional and publicly available datasets, we included OPSCC patients with known human papillomavirus (HPV) status, without baseline distant metastasis and treated with curative intent. We extracted 1037 PET and 1037 CT radiomic features quantifying lesion shape, imaging intensity, and texture patterns from primary tumors and metastatic cervical lymph nodes. Utilizing random forest algorithms, we devised novel machine-learning models for OPSCC progression-free survival (PFS) and overall survival (OS) using “radiomics” features, “AJCC” variables, and the “combined” set as input. We designed both single- (PET or CT) and combined-modality (PET/CT) models. Harrell’s C-index quantified survival model performance; risk stratification was evaluated in Kaplan–Meier analysis. A total of 311 patients were included. In HPV-associated OPSCC, the best “radiomics” model achieved an average C-index ± standard deviation of 0.62 ± 0.05 (p = 0.02) for PFS prediction, compared to 0.54 ± 0.06 (p = 0.32) utilizing “AJCC” variables. Radiomics-based risk-stratification of HPV-associated OPSCC was significant for PFS and OS. Similar trends were observed in HPV-negative OPSCC. In conclusion, radiomics imaging features extracted from pre-treatment PET/CT may provide complimentary information to the current AJCC staging scheme for survival prognostication and risk-stratification of HPV-associated OPSCC.

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“…We analyzed PET images and patient data from Xiamen, China and Charité Berlin, Germany plus additional images and patient data from three American databases, available in the cancer imaging archive [11][12][13][14]. Data for the Chinese patients and the patients of the cancer imaging archive have been published previously [15][16][17][18].…”

Section: Patientsmentioning

confidence: 99%

“…Several publications suggest that 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) parameters bear prognostic value in NPC and could potentially be used for treatment individualization. Two meta-analyses investigated the prognostic role of FDG-PET in NPC and found that the parameters maximum standardized uptake value (SUV max ), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) bear a significant prognostic value for various important clinical endpoints, including event-free survival (EFS) and overall survival (OS) [4,5].…”

Section: Introductionmentioning

confidence: 99%

Prognostic value of baseline [18F]-fluorodeoxyglucose positron emission tomography parameters MTV, TLG and asphericity in an international multicenter cohort of nasopharyngeal carcinoma patients

Zschaeck

et al. 2020

PLoS ONE

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Purpose [ 18 F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) parameters have shown prognostic value in nasopharyngeal carcinomas (NPC), mostly in monocenter studies. The aim of this study was to assess the prognostic impact of standard and novel PET parameters in a multicenter cohort of patients. Methods The established PET parameters metabolic tumor volume (MTV), total lesion glycolysis (TLG) and maximal standardized uptake value (SUV max) as well as the novel parameter tumor asphericity (ASP) were evaluated in a retrospective multicenter cohort of 114 NPC patients with FDG-PET staging, treated with (chemo)radiation at 8 international institutions. Uni-and multivariable Cox regression and Kaplan-Meier analysis with respect to overall survival (OS), event-free survival (EFS), distant metastases-free survival (FFDM), and locoregional control (LRC) was performed for clinical and PET parameters. Results When analyzing metric PET parameters, ASP showed a significant association with EFS (p = 0.035) and a trend for OS (p = 0.058). MTV was significantly associated with EFS

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Imaging and clinical data archive for head and neck squamous cell carcinoma patients treated with radiotherapy

Cited by 64 publications

References 21 publications

Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods

Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods

Potential Added Value of PET/CT Radiomics for Survival Prognostication beyond AJCC 8th Edition Staging in Oropharyngeal Squamous Cell Carcinoma

Prognostic value of baseline [18F]-fluorodeoxyglucose positron emission tomography parameters MTV, TLG and asphericity in an international multicenter cohort of nasopharyngeal carcinoma patients

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