Textural features of cervical cancers on FDG-PET/CT associate with survival and local relapse in patients treated with definitive chemoradiotherapy

Chen, Shang-Wen; Shen, Wei; Hsieh, Te Chun; Liang, Ji An; Hung, Yao-Ching; Yeh, Lian Shung; Chang, Wei-Chun; Lin, Wu-Chou; Yen, Kuo‐Yang; Kao, Chia Hung

doi:10.1038/s41598-018-30336-6

Cited by 30 publications

(32 citation statements)

References 40 publications

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“…In each CT or SUV image, the intensity was first normalized into 64 gray levels. The texture features were computed based on the gray level co-occurrence matrix (GLCM) (16–18) and gray level run-length matrix (GLRM) (18, 19).…”

Section: Methodsmentioning

confidence: 99%

“…An element of a GLCM measures the number of two specified gray levels separated by a given distance in a specified direction (16–18). After the construction of the GLCM, the following eight frequently used features were computed (16–18): Energy, entropy, correlation, inverse difference moment, inertia, cluster shade, cluster prominence, Haralick correlation. Each GLCM feature was computed in 13 directions (in 3D) with a distance of one voxel between the pair of voxels.…”

Section: Methodsmentioning

confidence: 99%

“…An element of a GLRM measures the number of “runs” with a specific length and specific gray level (18, 19), where a “run” is defined as a block of consecutive voxels with the same gray level in a specific direction. In this study, we set the maximum allowed length of runs to the length of the minimum bounding box of the GTV.…”

Section: Methodsmentioning

confidence: 99%

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Prediction of Anal Cancer Recurrence After Chemoradiotherapy Using Quantitative Image Features Extracted From Serial 18F-FDG PET/CT

et al. 2019

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We extracted image features from serial 18F-labeled fluorodeoxyglucose (FDG) positron emission tomography (PET) / computed tomography (CT) scans of anal cancer patients for the prediction of tumor recurrence after chemoradiation therapy (CRT). Seventeen patients (4 recurrent and 13 non-recurrent) underwent three PET/CT scans at baseline (Pre-CRT), in the middle of the treatment (Mid-CRT) and post-treatment (Post-CRT) were included. For each patient, Mid-CRT and Post-CRT scans were aligned to Pre-CRT scan. Comprehensive image features were extracted from CT and PET (SUV) images within manually delineated gross tumor volume, including geometry features, intensity features and texture features. The difference of feature values between two time points were also computed and analyzed. We employed univariate logistic regression model, multivariate model, and naïve Bayesian classifier to analyze the image features and identify useful tumor recurrent predictors. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the accuracy of the prediction. In univariate analysis, six geometry, three intensity, and six texture features were identified as significant predictors of tumor recurrence. A geometry feature of Roundness between Post-CRT and Pre-CRT CTs was identified as the most important predictor with an AUC value of 1.00 by multivariate logistic regression model. The difference of Number of Pixels on Border (geometry feature) between Post-CRT and Pre-CRT SUVs and Elongation (geometry feature) of Post-CRT CT were identified as the most useful feature set (AUC = 1.00) by naïve Bayesian classifier. To investigate the early prediction ability, we used features only from Pre-CRT and Mid-CRT scans. Orientation (geometry feature) of Pre-CRT SUV, Mean (intensity feature) of Pre-CRT CT, and Mean of Long Run High Gray Level Emphasis (LRHGLE) (texture feature) of Pre-CRT CT were identified as the most important feature set (AUC = 1.00) by multivariate logistic regression model. Standard deviation (intensity feature) of Mid-CRT SUV and difference of Mean of LRHGLE (texture feature) between Mid-CRT and Pre-CRT SUVs were identified as the most important feature set (AUC = 0.86) by naïve Bayesian classifier. The experimental results demonstrated the potential of serial PET/CT scans in early prediction of anal tumor recurrence.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Prediction of Anal Cancer Recurrence After Chemoradiotherapy Using Quantitative Image Features Extracted From Serial 18F-FDG PET/CT

et al. 2019

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“…Most of the PET prognostic studies for cervix cancer are qualitative correlative studies 36 - 38 or have focused on single quantitative measures like textural analysis. 39 Using a cohort of 14 cervical cancer patients treated at a single institution, El Naqa et al found that a combination of intensity-volume histogram (IVH) metrics and texture features extracted from PET images had high predictive power for response to treatment. 11 The models in our study were constructed using a larger dataset consisting of 75 patients for model training and testing.…”

Section: Discussionmentioning

confidence: 99%

Quantitative PET Imaging and Clinical Parameters as Predictive Factors for Patients With Cervical Carcinoma: Implications of a Prediction Model Generated Using Multi-Objective Support Vector Machine Learning

Zhou

Maquilan

Thomas

et al. 2020

Technol Cancer Res Treat

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Purpose: Quantitative features from pre-treatment positron emission tomography (PET) have been used to predict treatment outcomes for patients with cervical carcinoma. The purpose of this study is to use quantitative PET imaging features and clinical parameters to construct a multi-objective machine learning predictive model. Materials/Methods: Seventy-five patients with stage IB2-IVA disease treated at our institution from 2009–2012 were analyzed. Models predicting locoregional and distant failure were generated using clinical parameters (age, race, stage, histology, tumor size, nodal status) and imaging features (12 textural, 9 intensity, 8 geometric features, 2 additional imaging features) from pre-treatment PET. Model features were selected based on a multi-objective evolutionary algorithm to maximize specificity given a fixed moderately high sensitivity using support vector machine learning methods. Model 1 used clinical parameters only (C), Model 2 used imaging features only (I), and Model 3 used clinical and imaging features (C+I). Sensitivity, specificity, area under a receiver-operating characteristic curve (AUC), and p-values were compared to assess ability to predict locoregional and distant failure. Results: C+I had the highest performance for both locoregional failure (AUC 0.84, p < 0.01; specificity: 0.86; sensitivity: 0.79) and distant failure (AUC 0.75, p < 0.01; specificity: 0.75; sensitivity: 0.75). Conclusions: Based on a moderately high fixed sensitivity and optimized for specificity, the model using both clinical parameters and imaging features (C+I) had the best performance in predicting both locoregional failure and distant failure.

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“…The treatment was described previously [18,21]. All patients were treated with intensity-modulated radiotherapy.…”

Section: Treatmentmentioning

confidence: 99%

Quantitative Differences in Levels of Immunohistochemical Biomarkers between Squamous Cell Carcinoma and Adenocarcinoma of the Uterine Cervix: Implications for Treatment Outcomes after Chemoradiotherapy

Chen

Lin

Liang

et al. 2020

Preprint

Self Cite

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Introduction: This study compared the quantitative differences in immunohistochemical markers between uterine cervical squamous cell carcinoma (SCC) and adenocarcinoma (AC) and assessed the impact of these biomarkers on outcomes in patients treated with chemoradiotherapy (CRT).Method: This retrospective study included 118 patients (SCC in 76, AC in 42) who received definitive CRT. According to the International Federation of Gynecology and Obstetrics staging system, 14, 34, and 70 patients were classified as having stage IB3, II, and III disease, respectively. Baseline immunohistochemical biomarkers, including hypoxia, cell proliferation, cell adhesion, immunogenicity, inflammatory, and evasion of apoptosis biomarkers, were analyzed using tissue microarrays from biopsy specimens. The Mann-Whitney U test was carried out for quantitative analysis between SCC and AC. Cox regression analysis was used to examine the effects of the biomarkers and clinical parameters on treatment outcomes.Results: Using the H-scores of the biomarkers for SCC as a reference, increased expression of E-cadherin, calretinin, CAIX, and c-Myc and decreased levels of HIF-1α, VEGF, tumor necrosis factor-α, galectin-9, chemokine ligand 5, Bax, EGFR, and insulin-like growth factor 1 receptor were found in the patients with AC. A high E-cadherin (P = 0.002) and low Bax (P = 0.001) H-score were associated with inferior pelvic relapse-free survival. Conclusion: Cervical SCC exhibited strong expression of baseline immunohistochemical inflammatory, hypoxic, and angiogenesis biomarkers whereas the intensity of cell adhesion markers was more distinct in cervical AC. A high E-cadherin and a low Bax H-score were associated with a high rate of local relapse.

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Textural features of cervical cancers on FDG-PET/CT associate with survival and local relapse in patients treated with definitive chemoradiotherapy

Cited by 30 publications

References 40 publications

Prediction of Anal Cancer Recurrence After Chemoradiotherapy Using Quantitative Image Features Extracted From Serial 18F-FDG PET/CT

Prediction of Anal Cancer Recurrence After Chemoradiotherapy Using Quantitative Image Features Extracted From Serial 18F-FDG PET/CT

Quantitative PET Imaging and Clinical Parameters as Predictive Factors for Patients With Cervical Carcinoma: Implications of a Prediction Model Generated Using Multi-Objective Support Vector Machine Learning

Quantitative Differences in Levels of Immunohistochemical Biomarkers between Squamous Cell Carcinoma and Adenocarcinoma of the Uterine Cervix: Implications for Treatment Outcomes after Chemoradiotherapy

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