Efficiency of 18F-FDG and 99mTc-depreotide SPECT in the diagnosis of malignancy of solitary pulmonary nodules

Halley, Arnaud; Hugentobler, Alexis; Icard, Philippe; Porret, E.; Sobrio, Franck; Lerochais, Jean-Philippe; Bouvard, G.; Zalcman, Gérard; Denis, Arnaud

doi:10.1007/s00259-005-1812-1

Cited by 43 publications

(43 citation statements)

References 22 publications

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“…Moreover the precise mechanism of depreotide uptake in tumour cells is not completely clear, and there is an ongoing debate about whether this is due to somatostatin receptors on tumour cells or to tumour-infiltrating lymphocytes [9] . The evidence of somatostatin receptors in non-small cell lung cancer cells is still controversial, while there are no data regarding their expression profile in adenocarcinomas [8] that some authors reported to be falsely negative in depreotide tests [8,10] . A potential advantage of SPECT over PET could be the higher accuracy in detecting carcinoid tumours that may not be detectable at FDG-PET scan, but the major diagnostic problem in the evaluation of SPN concerns the identification of lesions !…”

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confidence: 99%

“…This technique is based on a somatostatin analogue (depreotide) scintigraphy. A recent paper by Halley et al [8] Tc-depreotide SPECT were found to be equally sensitive (92.3%) for large and equally specific (85.7%) for small SPNs. The reported higher sensitivity of FDG-PET in small-size ( !…”

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confidence: 99%

“…1 cm. Halley et al [8] showed that FDG-PET is superior to depreotide SPECT in the detection of lesions ! 1.5 cm in diameter, with an accuracy of 91.7 versus 83.3% and a sensitivity of 100 versus 80%, while they presented the same specificity of 85.7%.…”

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confidence: 99%

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Is There an Ideal Diagnostic Algorithm in Solitary Pulmonary Nodules?

Ambrosini

Rubello

2006

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Is There an Ideal Diagnostic Algorithm in Solitary Pulmonary Nodules?

Ambrosini

Rubello

2006

Respiration

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“…Solitary pulmonary nodules (SPNs) are single, spherical, well-circumscribed, radiographic opacity that measures ≤ 3 cm in diameter. Provided a visually validated diagnostics of the SPNs in PET images, these studies [339][340][341][342][343][344][345][346] have reported an SPN diagnostic accuracy with a sensitivity of 88-96% and a specificity of 70-90% for malignant cells (see Table 9 for more detail).…”

Section: Pet/ct Nodule Diagnosismentioning

confidence: 99%

“…In PET images, the malignant cells have unregulated metabolism that results in having higher FDG uptake that permits malignancy to be detected. Reported studies [339][340][341][342][343][344][345][346] used this characteristic to detect malignant solitary pulmonary nodules (SPNs) in PET. Solitary pulmonary nodules (SPNs) are single, spherical, well-circumscribed, radiographic opacity that measures ≤ 3 cm in diameter.…”

Section: Pet/ct Nodule Diagnosismentioning

confidence: 99%

Computational methods for the analysis of functional 4D-CT chest images.

Soliman¹

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DEDICATIONThis dissertation is dedicated to my father's soul.iii ACKNOWLEDGMENTS In the name of Allah the most merciful, the most compassionate. All deepest thanks are due to Almighty Allah for the uncountable gifts given to me. Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients.v The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decisionmaking. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification.This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today's clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered...

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