Robustness of Radiomic Features in [11C]Choline and [18F]FDG PET/CT Imaging of Nasopharyngeal Carcinoma: Impact of Segmentation and Discretization

Lu, Lijun; Lv, Wenbing; Jiang, Jun; Ma, Jianhua; Feng, Qianjin; Rahmim, Arman; Chen, Wufan

doi:10.1007/s11307-016-0973-6

Cited by 110 publications

(110 citation statements)

References 53 publications

Supporting

Mentioning

105

Contrasting

Unclassified

Order By: Relevance

“…quantization, [51][52][53] SUV discretization, 54 pre-processing steps 55 and the choice of respiration-averaged CT for attenuation correction. 56,57 The impact of tumour delineation on textural features computation was also assessed in PET images.…”

Section: Positron Emission Tomography Imagesmentioning

confidence: 99%

“…56,57 The impact of tumour delineation on textural features computation was also assessed in PET images. [50][51][52]55 Finally, an attempt of interpreting PET textural indices was carried out in a very recent work, where heterogeneity quantified by texture analysis was investigated on simulated patient data. 58 Here, the authors found that homogeneity, entropy, short-run emphasis and long-run emphasis were sensitive to the presence of uptake heterogeneity, whereas high grey-level zone emphasis and low grey-level zone emphasis were mostly sensitive to the average uptake.…”

Section: Positron Emission Tomography Imagesmentioning

confidence: 99%

See 1 more Smart Citation

Texture analysis of medical images for radiotherapy applications

Scalco¹,

Rizzo²

2017

The British Journal of Radiology

136

108

View full text Add to dashboard Cite

The high-throughput extraction of quantitative information from medical images, known as radiomics, has grown in interest due to the current necessity to quantitatively characterize tumour heterogeneity. In this context, texture analysis, consisting of a variety of mathematical techniques that can describe the grey-level patterns of an image, plays an important role in assessing the spatial organization of different tissues and organs. For these reasons, the potentiality of texture analysis in the context of radiotherapy has been widely investigated in several studies, especially for the prediction of the treatment response of tumour and normal tissues. Nonetheless, many different factors can affect the robustness, reproducibility and reliability of textural features, thus limiting the impact of this technique. In this review, an overview of the most recent works that have applied texture analysis in the context of radiotherapy is presented, with particular focus on the assessment of tumour and tissue response to radiations. Preliminary, the main factors that have an influence on features estimation are discussed, highlighting the need of more standardized image acquisition and reconstruction protocols and more accurate methods for region of interest identification. Despite all these limitations, texture analysis is increasingly demonstrating its ability to improve the characterization of intratumour heterogeneity and the prediction of clinical outcome, although prospective studies and clinical trials are required to draw a more complete picture of the full potential of this technique.

show abstract

Section: Positron Emission Tomography Imagesmentioning

confidence: 99%

Section: Positron Emission Tomography Imagesmentioning

confidence: 99%

Texture analysis of medical images for radiotherapy applications

Scalco¹,

Rizzo²

2017

The British Journal of Radiology

136

108

View full text Add to dashboard Cite

show abstract

“…By calculating how frequently pairs of pixels with specific values in specific spatial relationships occur throughout the ROI, descriptors such as energy, entropy, and correlation were computed . Studies that have investigated the robustness of GLCM features over varying region segmentations and bin sizes for discretization also found that GLCM features demonstrate high robustness …”

Section: Discussionmentioning

confidence: 99%

Radiomics robustness assessment and classification evaluation: A two‐stage method demonstrated on multivendor FFDM

Robinson

et al. 2019

Medical Physics

View full text Add to dashboard Cite

Purpose Radiomic texture analysis is typically performed on images acquired under specific, homogeneous imaging conditions. These controlled conditions may not be representative of the range of imaging conditions implemented clinically. We aim to develop a two‐stage method of radiomic texture analysis that incorporates the reproducibility of individual texture features across imaging conditions to guide the development of texture signatures which are robust across mammography unit vendors. Methods Full‐field digital mammograms were retrospectively collected for women who underwent screening mammography on both a Hologic Lorad Selenia and GE Senographe 2000D system. Radiomic features were calculated on manually placed regions of interest in each image. In stage one (robustness assessment), we identified a set of nonredundant features that were reproducible across the two different vendors. This was achieved through hierarchical clustering and application of robustness metrics. In stage two (classification evaluation), we performed stepwise feature selection and leave‐one‐out quadratic discriminant analysis (QDA) to construct radiomic signatures. We refer to this two‐state method as robustness assessment, classification evaluation (RACE). These radiomic signatures were used to classify the risk of breast cancer through receiver operator characteristic (ROC) analysis, using the area under the ROC curve as a figure of merit in the task of distinguishing between women with and without high‐risk factors present. Generalizability was investigated by comparing the classification performance of a feature set on the images from which they were selected (intravendor) to the classification performance on images from the vendor on which it was not selected (intervendor). Intervendor and intravendor performances were also compared to the performance obtained by implementing ComBat, a feature‐level harmonization method and to the performance by implementing ComBat followed by RACE. Results Generalizability, defined as the difference between intervendor and intravendor classification performance, was shown to monotonically decrease as the number of clusters used in stage one increased (Mann–Kendall P < 0.001). Intravendor performance was not shown to be statistically different from ComBat harmonization while intervendor performance was significantly higher than ComBat. No significant difference was observed between either of the single methods and the use of ComBat followed by RACE. Conclusions A two‐stage method for robust radiomic signature construction is proposed and demonstrated in the task of breast cancer risk assessment. The proposed method was used to assess generalizability of radiomic texture signatures at varying levels of feature robustness criteria. The results suggest that generalizability of feature sets monotonically decreases as reproducibility of features decreases. This trend suggests that considerations of feature robustness in feature selection methodology could improve classifier generalizability in multif...

show abstract

“…To the best of our knowledge, this is the first series that studied many parameters (31 textural indices) in such a large cohort of HNSCC (284 patients). Comparing results should take into account the methodological choices underlying indices calculation . In a series of 9 HN and cervical cancers, El Naqa et al showed the prognostic interest of Contrast textural index using both manual contouring and fixed threshold (40% of SUV max ).…”

Section: Discussionmentioning

confidence: 99%

“…Comparing results should take into account the methodological choices underlying indices calculation. 32,33 In a series of 9 HN and cervical cancers, El Naqa et al 34 showed the prognostic interest of Contrast textural index using both manual contouring and fixed threshold (40% of SUV max ). Moreover, Cheng et al 21,22 studied the prognostic value of 22 textural indices with different segmentation methods (fixed SUV >2.5 and 42% of SUV max and relative thresholds) in 88 patients with locally advanced oropharynx cancers.…”

Section: Discussionmentioning

confidence: 99%

Prognostic value of textural indices extracted from pretherapeutic 18‐F FDG‐PET/CT in head and neck squamous cell carcinoma

et al. 2018

View full text Add to dashboard Cite

Background This study aimed at assessing the prognostic value of textural indices extracted from 18F‐fluorodeoxyglucose positron‐emission tomography (FDG‐PET)/CT in a large cohort of patients with head and neck squamous cell carcinomas (HNSCC) of any anatomic subsite and staging. Methods Consecutive patients with HNSCC referred for a pretreatment FDG‐PET/CT were retrospectively included and followed up for a minimum of 2 years. Standardized uptake value, metabolic tumor volume (MTV), and textural indices were calculated using LIFEx software. Prognostic significance of parameters was assessed in univariate and multivariate analysis. Results Textural indices were extracted in 284 patients (mean age = 63.7±9.6 years). In univariate analysis, MTV and 4 textural indices—Correlation, Entropy, Energy, and Coarseness—were significantly correlated with overall survival (OS). In multivariate analysis, MTV (P = .008) and Correlation (P = .028) remained independently correlated to OS. Conclusion This study showed that MTV and 1 textural index extracted from pretherapeutic FDG‐PET/CT (Correlation) were independent prognostic factors of OS in patients with HNSCC.

show abstract

Robustness of Radiomic Features in [11C]Choline and [18F]FDG PET/CT Imaging of Nasopharyngeal Carcinoma: Impact of Segmentation and Discretization

Abstract: Discretization generated larger effects on features than segmentation in both tracers. Features extracted from [C]choline were more robust than [F]FDG for segmentation. Discretization had very similar effects on features extracted from both tracers.

Cited by 110 publications

References 53 publications

Texture analysis of medical images for radiotherapy applications

Texture analysis of medical images for radiotherapy applications

Radiomics robustness assessment and classification evaluation: A two‐stage method demonstrated on multivendor FFDM

Prognostic value of textural indices extracted from pretherapeutic 18‐F FDG‐PET/CT in head and neck squamous cell carcinoma

Contact Info

Product

Resources

About