Pretreatment MR imaging radiomics signatures for response prediction to induction chemotherapy in patients with nasopharyngeal carcinoma

Wang, Guangyi; He, Lan; Yuan, Cai; Huang, Yanqi; Liu, Zaiyi; Liang, Changhong

doi:10.1016/j.ejrad.2017.11.007

Cited by 114 publications

(94 citation statements)

References 29 publications

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“…While multiple prior research studies have investigated the use of a texture analysis applied to MR images the underlying influence of MRI scan parameters on texture analysis features are not entirely understood. Furthermore, despite the increasing use of texture analysis in the field of radiology, a fundamental understanding of the histopathologic and biologic correlation between tissue and texture analysis features remains in its infancy.…”

Section: Discussionmentioning

confidence: 99%

“…The use of a texture analysis applied to imaging studies including CT and MRI have been previously performed for the evaluation of multiple nonneoplastic disorders including the evaluation for mesial temporal sclerosis on MRI, evaluation of intervertebral disc disease on MRI, evaluation of hepatic fibrosis on both CT and MRI, evaluation of subchondral bone on MRI . Prior oncologic studies have also employed texture analyses to evaluate specific tumor features including the assessment of HPV status of oropharyngeal squamous cell carcinomas, prognosis of head and neck neoplasms, classification of gastric and colorectal tumors on CT, genomic mapping and predictive marker identification of gliomas on MRI, the identification of potentially prognostic predictors in lung cancer, evaluation of genitourinary neoplasms on both CT and MRI, and for the radiomic classifications of breast carcinoma subtypes …”

Section: Introductionmentioning

confidence: 99%

“…In an effort to study specific patterns of tumor biology correlating with different imaging appearances, multi‐institutional centers have worked toward pooling resources to make publicly available cancer imaging databases, such as The Cancer Imaging Archive (TCIA) and The Cancer Genomic Atlas (TCGA), to help facilitate research efforts in the arena of tumor genotype‐phenotype analyses . Prior research studies have used a radiomics approach for investigating prostate cancer radiotherapy responses, responsiveness of neoadjuvant chemotherapy in breast cancer, and prognostic predictions of advanced nasopharyngeal carcinoma . However, larger studies and systematic reviews on radiomics have noted methodological variations as a source of difficulty precluding an accurate and collective interpretation of data …”

Section: Introductionmentioning

confidence: 99%

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Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model

Buch

Kuno

Qureshi

et al. 2018

J Applied Clin Med Phys

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ObjectivesTo evaluate the influence of MRI scanning parameters on texture analysis features.MethodsPublicly available data from the Reference Image Database to Evaluate Therapy Response (RIDER) project sponsored by The Cancer Imaging Archive included MRIs on a phantom comprised of 18 25‐mm doped, gel‐filled tubes, and 1 20‐mm tube containing 0.25 mM Gd‐DTPA (EuroSpinII Test Object5, Diagnostic Sonar, Ltd, West Lothian, Scotland). MRIs performed on a 1.5 T GE HD, 1.5 T Siemens Espree (VB13), or 3.0 T GE HD with TwinSpeed gradients with an eight‐channel head coil included T1WIs with multiple flip angles (flip‐angle = 2,5,10,15,20,25,30), TR/TE = 4.09–5.47/0.90–1.35 ms, NEX = 1 and DCE with 30° flip‐angle, TR/TE=4.09–5.47/0.90–1.35, and NEX = 1,4. DICOM data were imported into an in‐house developed texture analysis program which extracted 41‐texture features including histogram, gray‐level co‐occurrence matrix (GLCM), and gray‐level run‐length (GLRL). Two‐tailed t tests, corrected for multiple comparisons (Q values) were calculated to compare changes in texture features with variations in MRI scanning parameters (magnet strength, flip‐angle, number of excitations (NEX), scanner platform).ResultsSignificant differences were seen in histogram features (mean, median, standard deviation, range) with variations in NEX (Q = 0.003–0.045) and scanner platform (Q < 0.0001), GLCM features (entropy, contrast, energy, and homogeneity) with NEX (Q = 0.001–0.018) and scanner platform (Q < 0.0001), GLRL features (long‐run emphasis, high gray‐level run emphasis, high gray‐level emphasis) with magnet strength (Q = 0.0003), NEX (Q = 0.003–0.022) and scanner platform (Q < 0.0001).ConclusionSignificant differences were seen in many texture features with variations in MRI acquisition emphasizing the need for standardized MRI technique.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model

Buch

Kuno

Qureshi

et al. 2018

J Applied Clin Med Phys

View full text Add to dashboard Cite

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“…Radiomics is one of the most recent fields in medical image analysis and it consists in the extraction of a large number of features from radiological images such as computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) with the final aim of providing “imaging biomarkers” that can be acquired in an inexpensive and non‐invasive way . Research on radiomics has already been performed in oncology for different purposes like tumor prognosis, staging, and prediction of response to treatment, often with success.…”

Section: Introductionmentioning

confidence: 99%

Technical Note: Virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI‐radiomics of the brain

2019

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Purpose: The purpose of the paper was to use a virtual phantom to identify a set of radiomic features from T1-weighted and T2-weighted magnetic resonance imaging (MRI) of the brain which is stable to variations in image acquisition parameters and to evaluate the effect of image preprocessing on radiomic features stability. Methods: Stability to different sources of variability (time of repetition and echo, voxel size, random noise and intensity non-uniformity) was evaluated for both T1-weighted and T2-weighted MRI images. A set of 107 radiomic features, accounting for shape and size, first order statistics, and textural features was used. Feature stability was quantified using intraclass correlation coefficient (ICC). For each source of variability, stability was evaluated before and after preprocessing (Z-score normalization, resampling, gaussian filtering and bias field correction). Features that have ICC > 0.75 in all the analysis of variability are selected as stable features. Last, the robust feature sets were tested on images acquired with random simulation parameters to assess their generalizability to unseen conditions. Results: Preprocessing significantly increased the robustness of radiomic features to the different sources of variability. When preprocessing is applied, a set of 67 and 61 features resulted as stable for T1-weighted and T2-wieghted images respectively, over 80% of which were confirmed by the analysis on the images acquired with random simulation parameters. Conclusion: A set of MRI-radiomic features, robust to changes in TR/TE/PS/ST, was identified. This set of features may be used in radiomic analyses based on T1-weighted and T2-weighted MRI images.

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“…Combining pre-and early-treatment MRI data for analysis, unlike in another recent study [29], was another factor that contributed to the robustness of our model. To the best of our knowledge, combining texture analyses at different time points to predict the response of rectal cancer to nCRT has not previously been reported.…”

Section: Notementioning

confidence: 99%

Prediction of efficacy of neoadjuvant chemoradiotherapy for rectal cancer: the value of texture analysis of magnetic resonance images

Shu

Fang

et al. 2019

Abdom Radiol

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Purpose To explore the clinical feasibility of predicting the efficacy of neoadjuvant chemoradiotherapy (nCRT) for rectal cancer on the basis of texture analysis (TA) of T2-weighted imaging (T2WI). Methods The cohort for this prospective study comprised 136 patients with rectal cancer to be treated with nCRT, all of whom underwent three MR scans (pre-, early, and post-nCRT). Treatment efficacy was assessed on the basis of the outcomes of pathologic complete response (pCR) and non-pCR as determined by postoperative pathological examination. Extraction and analysis of texture features in T2WI of defined tumor regions were performed by AK software. Pre-and early-nCRT texture features were selected as potential predictors of outcomes by logistic regression analysis, and a prediction model for pCR was developed. A receiver operating characteristic (ROC) curve was used to assess the predictive power of texture features in pre-and early-nCRT images.Results Univariate logistic regression analysis demonstrated that the pre-nCRT features of energy, entropy, and skewness, and early-nCRT features of variance, kurtosis, energy, and entropy were independent predictors of pCR. A prediction model incorporating these predictors was constructed by multivariate logistic regression, The AUCs of pre-nCRT, early, and combined models were 0.751, 0.831, and 0.873, respectively; the sensitivities 66, 71, and 75%, respectively; and the specificities 87.22, 86.11, and 91.67%, respectively. Conclusions TA of T2WI images can predict the efficacy of nCRT for rectal cancer, possibly providing a new marker of tumor biological response in clinical practice.

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Pretreatment MR imaging radiomics signatures for response prediction to induction chemotherapy in patients with nasopharyngeal carcinoma

Cited by 114 publications

References 29 publications

Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model

Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model

Technical Note: Virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI‐radiomics of the brain

Prediction of efficacy of neoadjuvant chemoradiotherapy for rectal cancer: the value of texture analysis of magnetic resonance images

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