Brain Tumor Volume Measurement: Comparison of Manual and Semiautomated Methods

Joe, Bonnie N.; Fukui, Melanie B.; Meltzer, Carolyn C.; Huang, Qingshou; Day, Roger; Greer, Phil J.; Bozik, Michael E.

doi:10.1148/radiology.212.3.r99se22811

Cited by 101 publications

(62 citation statements)

References 19 publications

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“…Furthermore, although not a focus of our study, this result also indicates that both the manual and automatic segmentation routine used in our study may not be an adequate measure of tumor volume for quantitative assessment of tumor growth and for aiding neurosurgeons intraoperatively. For quantification of tumor volume, growth and invasion, alternative methods such as threshold-based, semiautomated methods (37) or adaptive template-moderated classification routines (21) may be more suitable and should be evaluated in future studies. Nevertheless, when grading gliomas presurgically, it has been shown that potentially imperfect tumor volume definition has little influence on the diagnostic accuracy and reproducibility of the histogram method (10).…”

Section: Discussionmentioning

confidence: 99%

Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge‐based fuzzy clustering

Emblem

Nedregaard

Hald

et al. 2009

Magnetic Resonance Imaging

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Purpose:To assess whether glioma volumes from knowledge-based fuzzy c-means (FCM) clustering of multiple MR image classes can provide similar diagnostic efficacy values as manually defined tumor volumes when characterizing gliomas from dynamic susceptibility contrast (DSC) imaging. Materials and Methods:Fifty patients with newly diagnosed gliomas were imaged using DSC MR imaging at 1.5 Tesla. To compare our results with manual tumor definitions, glioma volumes were also defined independently by four neuroradiologists. Using a histogram analysis method, diagnostic efficacy values for glioma grade and expected patient survival were assessed. Results:The areas under the receiver operator characteristics curves were similar when using manual and automated tumor volumes to grade gliomas (P ϭ 0.576 -0.970). When identifying a high-risk patient group (expected survival Ͻ2 years) and a low-risk patient group (expected survival Ͼ2 years), a higher log-rank value from Kaplan-Meier survival analysis was observed when using automatic tumor volumes (14.403; P Ͻ 0.001) compared with the manual volumes (10.650 -12.761; P ϭ 0.001-0.002). Conclusion:Our results suggest that knowledge-based FCM clustering of multiple MR image classes provides a completely automatic, user-independent approach to selecting the target region for presurgical glioma characterization

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Section: Discussionmentioning

confidence: 99%

Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge‐based fuzzy clustering

Emblem

Nedregaard

Hald

et al. 2009

Magnetic Resonance Imaging

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“…These data were acquired after collection of all other magnetic resonance data sets. Volume measurements were determined as described before using the MORPH software (40,41). Autoradiography.…”

Section: Animalsmentioning

confidence: 99%

PET imaging of brain macrophages using the peripheral benzodiazepine receptor in a macaque model of neuroAIDS

Venneti¹,

Lopresti²,

Wang³

et al. 2004

J. Clin. Invest.

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HIV infection in humans and simian immunodeficiency virus (SIV) infection in macaques result in encephalitis in approximately one-quarter of infected individuals and is characterized by infiltration of the brain with infected and activated macrophages. 1-(2-chlorphenyl)-N-methyl-N-(1-methylpropyl)-3-isoquinoline-carboxamide (PK11195) is a ligand specific for the peripheral benzodiazepine receptor abundant on macrophages and is expressed in low levels in the noninfected brain. We hypothesized that positron-emission tomography (PET) with the carbon-11–labeled, R-enantiomer form of PK11195 ([11C](R)-PK11195) could image brain macrophages and hence the development of encephalitis in vivo. [11C](R)-PK11195 binding was assessed in the brain using PET in 11 SIV infected macaques, six of which showed increased binding in vivo. Postmortem examination of the brain in these six macaques demonstrated encephalitis, while macaques that did not show an increase in [11C](R)-PK11195 binding did not develop SIV encephalitis. Brain tissue from SIV encephalitic macaques also showed increased [3H](R)-PK11195 binding compared with binding in nonencephalitic macaques. Increased PK11195 binding in vivo and in postmortem brain tissue correlated with abundance of macrophages but not astrocytes. Our results suggest that PET [11C](R)-PK11195 imaging can detect the presence of macrophages in SIV encephalitis in vivo and may be useful to predict the development of HIV encephalitis and in studies of the pathogenesis and treatment of HIV dementia

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“…Tumor volume has been the basic criterion for deciding the treatment outcome in radiotherapy (5). Measuring the tumor volume in the brain allows making a decision on the prognosis and treatment of the patient (7). Tumor volume is the best prognostic factor that was confirmed in prostate cancer in a study by Bettendorf et al (21), in which a significant concordance was found between the tumor volume and prognostic parameters such as preoperative prostate-specific antigen level, histological grade, lymph node metastasis, and malignant cell differentiation.…”

Section: Discussionmentioning

confidence: 99%

“…Tumor volume calculated using three-dimensional (3D) imaging techniques is accepted as independent marker in predicting the outcome in pharyngolaryngeal, lung, and breast cancers (5). Tumor volume is the basic criterion for deciding the treatment outcome in radiotherapy (7). Measuring the volume of brain tumors allows making decision on the prognosis and treatment of the patient (8).…”

mentioning

confidence: 99%

The effect of tumor volume on survival in renal cell carcinoma patients

Seçil

Çullu²,

Aslan³

et al. 2012

Diagn Interv Radiol

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480R enal cell carcinoma (RCC) constitutes 85% of all primary malignant tumors arising from kidney and nearly 3% of adult cancers (1-6). Approximately 20%-30% of patients diagnosed with RCC have metastasis at the time of diagnosis (1). In patients with recurrent RCC, progression of the disease, the period of survival, and the disease process can be predicted owing to many morphological, clinical, histological, and molecular parameters (3). The prognostic markers of RCC include tumor node metastasis (TNM) stage as morphological marker; histological type, nuclear grade, tumor necrosis, and sarcomatoid change as histological markers; and factors such as adhesion molecules, molecules stimulating immune response, growth factor receptors, and molecules inducing hypoxia as molecular and genetic markers (3). Clinical and laboratory prognostic factors are represented by the following parameters: patient performance at tumor presentation, the erythrocyte sedimentation rate, thrombocyte count, as well as serum calcium, hemoglobin, and lactate dehydrogenase levels. Among these, tumor stage, grade, and patient performance are the most widely used. Many studies on molecular and cytogenetic markers have been performed but none has been found to be better than tumor stage and nuclear grade to estimate prognosis. For this reason, efforts for determining new prognostic factors that indicate the proliferation and progression of RCC still persist (2-4). Several trials have been conducted concerning the diameter of tumors in RCC; however, few trials exist regarding the tumor volume. Tumor volume calculated using three-dimensional (3D) imaging techniques is accepted as independent marker in predicting the outcome in pharyngolaryngeal, lung, and breast cancers (5). Tumor volume is the basic criterion for deciding the treatment outcome in radiotherapy (7). Measuring the volume of brain tumors allows making decision on the prognosis and treatment of the patient (8).The aim of our study was to investigate the effect of tumor volume on prognosis and the relation of tumor volume to other prognostic markers in patients with RCC. Materials and methodsThe study included 46 retrospectively assessed patients (32 males and 14 females; mean age, 58.13±10.47 years; age range, 33-81 years) who underwent surgery between January 2002 and January 2009 and received a diagnosis of RCC. Considering January 1, 2009 as the deadline, the clinical information and the last health status of all patients were obtained from the hospital information system and/or by contacting the patients or their relatives who agreed to communicate by phone. The study was approved by the local ethics committee of our institution. GENITOURINARY IMAGING ORIGINAL ARTICLE The effect of tumor volume on survival in patients with renal cell carcinomaMustafa Seçil, Neşat Çullu, Güven Aslan, Uğur Mungan, Fatma Uysal, Burçin Tuna, Kutsal Yörükoğlu MATERIALS AND METHODSThe study included 46 retrospectively assessed patients with RCC (32 males and 14 females; mean age, 58.13±10.47 years) ...

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Brain Tumor Volume Measurement: Comparison of Manual and Semiautomated Methods

Abstract: The semiautomated computer method of measuring tumor volume was faster than the manual trace method. Semiautomated computer approaches offer an alternative to manual tracing for measuring serial tumor volumes in patients with high-grade brain neoplasms.

Cited by 101 publications

References 19 publications

Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge‐based fuzzy clustering

Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge‐based fuzzy clustering

PET imaging of brain macrophages using the peripheral benzodiazepine receptor in a macaque model of neuroAIDS

The effect of tumor volume on survival in renal cell carcinoma patients

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