Prognostic Value of MR Imaging Texture Analysis in Brain Non-Small Cell Lung Cancer Oligo-Metastases Undergoing Stereotactic Irradiation

Nardone, Valerio; Tini, Paolo; Biondi, M.; Sebaste, Lucio; Vanzi, E.; Otto, G. De; Rubino, Giovanni; Carfagno, Tommaso; Battaglia, Giuseppe; Pastina, Pierpaolo; Cerase, Alfonso; Mazzoni, Lorenzo Nicola; Buonamici, F. Banci; Pirtoli, Luigi

doi:10.7759/cureus.584

Cited by 20 publications

(20 citation statements)

References 27 publications

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“…Diagnosis of brain tumors by conventional magnetic resonance imaging (MRI) is based on non-contrast T1weighted images (T1WI) and T2-weighted images (T2WI) and post-contrast (T1WI). Conventional MRI techniques are considered not enough for grading, classifying, and detecting the aggressiveness of brain tumors [3].…”

Section: Introductionmentioning

confidence: 99%

Role of diffusion-weighted imaging in differentiation between posterior fossa brain tumors

Mustafa

Abbas

Elsorougy

2020

Egypt J Neurol Psychiatry Neurosurg

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Background: Diffusion-weighted imaging (DWI) is an imaging modality using multi-section single-shot spin echo planar imaging (EPI) sequence which is extremely sensitive for detection of water motion within intra, extra, and transcellular regions. This character is important to differentiate between brain tumors either low (benign) or highly (malignant) cellular tumors. Objective: To evaluate the role of DWI and apparent diffusion coefficient (ADC) in evaluation and differentiation between different brain posterior fossa tumors in children and adults.Patients and methods: The study included 34 patients with different brain posterior fossa tumors for evaluation by conventional MRI (using 1.5 T MRI PHILIPS Achieva 2.1 Best Netherland) and DWI. Results: Our study showed that mean ADC values were significantly different between the four groups of posterior fossa tumors in children: juvenile pilocytic astrocytoma (JPA), medulloblastoma, ependymoma, and brain stem glioma while mean ADC values were not significantly different between posterior fossa tumors in the adult group. Regions of interest were manually positioned, and all values were automatically calculated and expressed in 10 −3 mm 2 /s. Conclusion: DWI is an ideal additional imaging technique, which is a rapid, easy, non-invasive imaging modality, with no contrast injection needed. It has been widely applied in the differentiation between posterior fossa brain tumors and in the diagnosis of various intracranial diseases.

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Section: Introductionmentioning

confidence: 99%

Role of diffusion-weighted imaging in differentiation between posterior fossa brain tumors

Mustafa

Abbas

Elsorougy

2020

Egypt J Neurol Psychiatry Neurosurg

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“…The hotspot method will underestimate the heterogeneity of gliomas. MRI-based texture analysis (TA) can give a quantitative measure to assess the distribution of gray levels within an image to obtain texture features of intralesional heterogeneity (8). The TA of DCE-MRI based on the volume of interest (VOI) covering the entire tumor allows for a deeper analysis of the microvascular permeability and heterogeneity of the brain tumor.…”

Section: Introductionmentioning

confidence: 99%

“…The extreme sensitivity of SWI for the detection of neovascularity, hemorrhage, and calcification has been an indispensable tool for characterization of the internal architecture of glioma (9). Additionally, the intratumoral susceptibility signal (ITSS) is an important imaging surrogate for neovascularity (8). DCE-MRI derived parameters and ITSS degree have been reported to correlate with glioma grades (6,9,10).…”

Section: Introductionmentioning

confidence: 99%

Intergrating conventional MRI, texture analysis of dynamic contrast-enhanced MRI, and susceptibility weighted imaging for glioma grading

Han

et al. 2018

Acta Radiol

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Background The application of conventional magnetic resonance imaging (MRI) in glioma grading is limited and non-specific. Purpose To investigate the application values of MRI, texture analysis (TA) of dynamic contrast-enhanced MRI (DCE-MRI) and intratumoral susceptibility signal (ITSS) on susceptibility weighted imaging (SWI), alone and in combination, for glioma grading. Material and Methods Fifty-two patients with pathologically confirmed gliomas who underwent DCE-MRI and SWI were enrolled in this retrospective study. Conventional MRIs were evaluated by the VASARI scoring system. TA of DCE-MRI-derived parameters and the degree of ITSS were compared between low-grade gliomas (LGGs) and high-grade gliomas (HGGs). The diagnostic ability of each parameter and their combination for glioma grading were analyzed. Results Significant statistical differences in VASARI features were observed between LGGs and HGGs (P < 0.05), of which the enhancement quality had the highest area under the curve (AUC) (0.873) with 93.3% sensitivity and 80% specificity. The TA of DCE-MRI derived parameters were significantly different between LGGs and HGGs (P < 0.05), of which the uniformity of Ktrans had the highest AUC (0.917) with 93.3% sensitivity and 90% specificity. The degree of ITSS was significantly different between LGGs and HGGs (P < 0.001). The AUC of the ITSS was 0.925 with 93.3% sensitivity and 90% specificity. The best discriminative power was obtained from a combination of enhancement quality, Ktrans- uniformity, and ITSS, resulting in 96.7% sensitivity, 100.0% specificity, and AUC of 0.993. Conclusion Combining conventional MRI, TA of DCE-MRI, and ITSS on SWI may help to improve the differentiation between LGGs and HGGs.

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“…The pathologic response prediction, assessed on CECT images using texture analysis, was also improved in patients affected by softtissue sarcoma, with respect to classical indices, such as tumour size, density and perfusion. 98 Finally, the assessment of prognostic value of MRI texture analysis in brain NSCLC oligometastases undergoing stereotactic irradiation was explored by Nardone et al 99 to help drive the best treatment in these patients.…”

Section: Application Of Texture Analysis In Radiotherapymentioning

confidence: 99%

Texture analysis of medical images for radiotherapy applications

Scalco¹,

Rizzo²

2017

The British Journal of Radiology

125

108

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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.

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Prognostic Value of MR Imaging Texture Analysis in Brain Non-Small Cell Lung Cancer Oligo-Metastases Undergoing Stereotactic Irradiation

Cited by 20 publications

References 27 publications

Role of diffusion-weighted imaging in differentiation between posterior fossa brain tumors

Role of diffusion-weighted imaging in differentiation between posterior fossa brain tumors

Intergrating conventional MRI, texture analysis of dynamic contrast-enhanced MRI, and susceptibility weighted imaging for glioma grading

Texture analysis of medical images for radiotherapy applications

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