Non-Hodgkin lymphoma response evaluation with MRI texture classification

Harrison, Lara; Luukkaala, Tiina; Pertovaara, Hannu; Saarinen, Tuure; Heinonen, T.; Järvenpää, Ritva; Soimakallio, Seppo; Kellokumpu-Lehtinen, Pirkko-Liisa; Eskola, Hannu; Dastidar, Prasun

doi:10.1186/1756-9966-28-87

Cited by 44 publications

(32 citation statements)

References 34 publications

(29 reference statements)

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“…Third, GLCM continue to outperform other methods of texture classification [27]. Fourth, these quantitative imaging features have been shown to improve tumor diagnosis [11, 14], and to provide measures of response assessment [28, 29] and radiation-induced gland injury [30], and are reproducible across multiple imaging units [31]. Lastly, and perhaps most relevant to the present study, recent investigations have shown that CT texture relates to fibrosis staging [15, 16] so the extension to liver insufficiency is not unexpected.…”

Section: Discussionmentioning

confidence: 99%

Texture Analysis of Preoperative CT Images for Prediction of Postoperative Hepatic Insufficiency: A Preliminary Study

Simpson

Adams

Allen

et al. 2015

Journal of the American College of Surgeons

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Background Texture analysis is a promising method of analyzing imaging data to potentially enhance diagnostic capability. This approach involves automated measurement of pixel intensity variation that may offer further insight into disease progression than standard imaging techniques alone. We postulate that postoperative liver insufficiency, a major source of morbidity and mortality, correlates with preoperative heterogeneous parenchymal enhancement that can be quantified with texture analysis of cross-sectional imaging. Study Design A retrospective case-matched study (waiver of informed consent and HIPAA authorization, approved by the institutional review board) was performed comparing patients who underwent major hepatic resection and developed liver insufficiency (n=12) to a matched group of patients with no postoperative liver insufficiency (n=24) by procedure, remnant volume, and year of procedure. Texture analysis (with gray-level co-occurrence matrices) was used to quantify the heterogeneity of liver parenchyma on preoperative computed tomography (CT) scans. Statistical significance was evaluated using Wilcoxon’s signed rank and Pearson’s chi-squared tests. Results No statistically significant differences were found between study groups for preoperative patient demographics and clinical characteristics, with the exception of gender (p<0.05). Two texture features differed significantly between the groups: correlation (linear dependency of gray levels on neighboring pixels) and entropy (randomness of brightness variation) (p<0.05). Conclusions In this preliminary study, the texture of liver parenchyma on preoperative CT, was significantly more varied, less symmetric, and less homogeneous in patients with postoperative liver insufficiency; thus texture analysis has the potential to provide an additional means of preoperative risk stratification.

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Section: Discussionmentioning

confidence: 99%

Texture Analysis of Preoperative CT Images for Prediction of Postoperative Hepatic Insufficiency: A Preliminary Study

Simpson

Adams

Allen

et al. 2015

Journal of the American College of Surgeons

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“…The texture parameters were derived, respectively, from image histogram (information about the intensity of pixels and without any spatial relations between the pixels on the image), gradient (information about the image intensity distribution and describes the histogram of the absolute gradient values of 3 Â 3 neighborhoods of pixels in the ROI), run-length matrix (information about pixel runs with the specified gray-level values in a given direction and describes intensity homogeneity in specific directions in the ROI), co-occurrence matrix (information about changes of SI with increasing distance, describes the gray-level value distribution of pixel pairs along all directions at different distances in the ROI), autoregressive model (description of texture based on the statistical correlation between neighboring pixels), and wavelet transform (information about the frequency of similar SIs and describes the wavelet transform of the pixels in the ROI) (16,18,21).…”

Section: Conventional Imaging Analysismentioning

confidence: 99%

“…TA allows the computation of hundreds of texture features based on the SI of pixels in the ROI (15,16), which may yield potential information. Successful applications of TA have been reported in discriminating pathologic stages of hepatic fibrosis (15) and glioma (17), differentiating invasive ductal carcinoma from invasive lobular carcinoma (18) and hepatic cysts from hemangiomas (19), evaluating the therapeutic efficacy of metastatic RCC (20), non-Hodgkin lymphoma (21), and mammary carcinomas (22). Moreover, TA can be performed with commercial software and does not require highly specialized computer knowledge.…”

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confidence: 99%

Angiomyolipoma with Minimal Fat

Lu¹,

Liu²,

Wang³

et al. 2015

Academic Radiology

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“…There is currently considerable interest in probing intralesional heterogeneity by evaluating the intensity of acquired imaging voxels (termed texture analysis) [18]. Textural analysis is being applied to predict treatment response on CT [19], PET imaging and both conventional [20] and dynamic contrast-enhanced MRI [21,22].…”

Section: Tumour Response Assessment Using Imagingmentioning

confidence: 99%

Childhood extracranial neoplasms: the role of imaging in drug development and clinical trials

Fowkes

Koh

Collins³

et al. 2015

Pediatr Radiol

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Cancer is the leading cause of death in children older than 1 year of age and new drugs are necessary to improve outcomes. Imaging is crucial to the drug development process and assessment of therapeutic response. In adults, tumours are often assessed with CT using size criteria. Unfortunately, techniques established in adults are not necessarily applicable in children due to differing pathophysiology, ability to cooperate and increased susceptibility to ionising radiation. MRI, in particular quantitative MRI, has to date not been fully utilised in children with extracranial neoplasms. The specific challenges of imaging in children, the potential for functional imaging techniques to inform upon and their inclusion in clinical trials are discussed.

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Non-Hodgkin lymphoma response evaluation with MRI texture classification

Abstract: Background: To show magnetic resonance imaging (MRI) texture appearance change in nonHodgkin lymphoma (NHL) during treatment with response controlled by quantitative volume analysis.

Cited by 44 publications

References 34 publications

Texture Analysis of Preoperative CT Images for Prediction of Postoperative Hepatic Insufficiency: A Preliminary Study

Texture Analysis of Preoperative CT Images for Prediction of Postoperative Hepatic Insufficiency: A Preliminary Study

Angiomyolipoma with Minimal Fat

Childhood extracranial neoplasms: the role of imaging in drug development and clinical trials

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