Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer

Cheng, Nai Ming; Fang, Yiqun; Lee, Li Yu; Chang, Joseph T.; Tsan, Din-Li; Ng, Shu Hang; Liao, Chun Ta; Yang, Lan-Yan; Hsu, Charles Ching–Hsiang; Yen, Tzu Chen

doi:10.1007/s00259-014-2933-1

Cited by 105 publications

(67 citation statements)

References 38 publications

(51 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Clinical outcome (DFS or OS) was significantly different in the 3 risk groups. These findings were confirmed in an independent series of 88 oropharyngeal cancer patients (Cheng et al, 2015).…”

Section: Texture and Shape Analysissupporting

confidence: 64%

“…The choice of the threshold for either method may affect the absolute value of the MTV. Six studies compared the predictive value of MTV and/or TLG computed with different thresholds (Cheng et al, 2015;Kao et al, 2012;Lin et al, 2015;Schinagl et al, 2011;Yabuki et al, 2015). In the study by (Schinagl et al, 2011), 4 thresholds (2.5, 40%, 50% and adaptive threshold based on liver uptake) were compared for 77 patients treated with RT ± CT. MTV 40% was the strongest predictor of DFS and OS.…”

Section: Suvmax and Metabolic Tumor Volumementioning

confidence: 99%

See 1 more Smart Citation

Overview of the predictive value of quantitative 18 FDG PET in head and neck cancer treated with chemoradiotherapy

Castelli

Bari

Depeursinge

et al. 2016

Critical Reviews in Oncology/Hematology

View full text Add to dashboard Cite

International audience18 F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) allows to quantify the metabolic activity of a tumor (glycolysis) and has become a reference tool in oncology for the staging, restaging, radiotherapy planning and monitoring response in many cancers. Quantitative analyses have been introduced in order to overcome some of the limits of the visual methods, allowing an easier and more objective comparison of the inter- and intra-patients variations. The aims of this review were to report available evidences on the clinical value of quantitative PET/CT parameters in HNC. Forty-five studies, for a total of 2928 patients, were analyzed. Most of the data available dealt with the intensity of the metabolism, calculated from the Standard Uptake Value (SUV). Metabolic Tumor Volume (MTV) was well correlated with overall survival and disease free survival, with a higher predictive value than the maximum SUV. Spatial distribution of metabolism and textural analyses seems promising

show abstract

Section: Texture and Shape Analysissupporting

confidence: 64%

Section: Suvmax and Metabolic Tumor Volumementioning

confidence: 99%

Overview of the predictive value of quantitative 18 FDG PET in head and neck cancer treated with chemoradiotherapy

Castelli

Bari

Depeursinge

et al. 2016

Critical Reviews in Oncology/Hematology

View full text Add to dashboard Cite

show abstract

“…Textural features have been correlated to clinical data such as survival, clinical response and prognostic pathological features in cervical, head and neck, lung, esophageal, rectal, and breast cancers. [9][10][11][12][13][14][15][16] Despite the increased use of these metrics for PET imaging, relatively little is known about the impact of fundamental data acquisition and image reconstruction parameters on metric variability. The few studies to date have appropriately focused on test-retest or sensitivity studies in patient data.…”

Section: Introductionmentioning

confidence: 99%

Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards

et al. 2015

View full text Add to dashboard Cite

Abstract. Image heterogeneity metrics such as textural features are an active area of research for evaluating clinical outcomes with positron emission tomography (PET) imaging and other modalities. However, the effects of stochastic image acquisition noise on these metrics are poorly understood. We performed a simulation study by generating 50 statistically independent PET images of the NEMA IQ phantom with realistic noise and resolution properties. Heterogeneity metrics based on gray-level intensity histograms, co-occurrence matrices, neighborhood difference matrices, and zone size matrices were evaluated within regions of interest surrounding the lesions. The impact of stochastic variability was evaluated with percent difference from the mean of the 50 realizations, coefficient of variation and estimated sample size for clinical trials. Additionally, sensitivity studies were performed to simulate the effects of patient size and image reconstruction method on the quantitative performance of these metrics. Complex trends in variability were revealed as a function of textural feature, lesion size, patient size, and reconstruction parameters. In conclusion, the sensitivity of PET textural features to normal stochastic image variation and imaging parameters can be large and is feature-dependent. Standards are needed to ensure that prospective studies that incorporate textural features are properly designed to measure true effects that may impact clinical outcomes.

show abstract

“…Not every radiomic feature that significantly predicted the survival of lung cancer patients could also predict the survival of head-and-neck cancer patients and vice versa. Radiomic features are better at predicting treatment response than conventional measures, such as tumor volume and diameter, and the maximum radiotracer uptake on positron emission tomography (PET) imaging [19][20][21][22][23][24][25]. Metastatic latency of tumors could also be predicted by radiomic features [26][27].…”

Section: Discussionmentioning

confidence: 86%

Quantitative Radiomic Phenotyping of Cervix Cancer

Anbumani¹

2018

ICPJL

View full text Add to dashboard Cite

Radiomics aims to extract huge amount of quantifiable features from medical images with data-characterization algorithms [1][2][3][4][5]. Radiologists cannot appreciate all the disease characteristics in generic diagnostic images [6]. Using these radiomic features, it is possible to uncover the physiognomies of disease progression. The hypothesis of radiomics is that the distinctive imaging features between disease forms may be useful for predicting prognosis and therapeutic response for various conditions, thus providing valuable information for personalized therapy [1,7,8]. Radiomics emerged from oncology [3] is the most cutting-edge innovation. However, the technique can be useful in any medical study involving tomographic imaging. This article analyzes currently available phenotyping algorithms for Lung and Head & Neck malignancies. And It can help researchers to understand the concept to apply the same for cervix cancer.

show abstract

Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer

Abstract: ZSNU is an independent predictor of outcome in patients with advanced T-stage OPSCC, and may improve their prognostic stratification.

Cited by 105 publications

References 38 publications

Overview of the predictive value of quantitative 18 FDG PET in head and neck cancer treated with chemoradiotherapy

Overview of the predictive value of quantitative 18 FDG PET in head and neck cancer treated with chemoradiotherapy

Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards

Quantitative Radiomic Phenotyping of Cervix Cancer

Contact Info

Product

Resources

About