Unsupervised supervoxel-based lung tumor segmentation across patient scans in hybrid PET/MRI

Tumor segmentation is a crucial but difficult task in treatment planning and follow-up of cancerous patients. The challenge of automating the tumor segmentation has recently received a lot of attention, but the potential of utilizing hybrid positron emission tomography (PET)/magnetic resonance imaging (MRI), a novel and promising imaging modality in oncology, is still under-explored. Recent approaches have either relied on manual user input and/or performed the segmentation patient-by-patient, whereas a fully unsupervised segmentation framework that exploits the available information from all patients is still lacking. We present an unsupervised across-patients supervoxel-based clustering framework for lung tumor segmentation in hybrid PET/MRI. The method consists of two steps: First, each patient is represented by a set of PET/ MRI supervoxel-features. Then the data points from all patients are transformed and clustered on a population level into tumor and non-tumor supervoxels. The proposed framework is tested on the scans of 18 non-small cell lung cancer patients with a total of 19 tumors and evaluated with respect to manual delineations provided by clinicians. Experiments study the performance of several commonly used clustering algorithms within the framework and provide analysis of (i) the effect of tumor size, (ii) the segmentation errors, (iii) the benefit of across-patient clustering, and (iv) the noise robustness. The proposed framework detected 15 out of 19 tumors in an unsupervised manner. Moreover, performance increased considerably by segmenting across patients, with the mean dice score increasing from 0.169 ± 0.295 (patient-by-patient) to 0.470 ± 0.308 (across-patients). Results demonstrate that both spectral clustering and Manhattan hierarchical clustering have the potential to segment tumors in PET/MRI with a low number of missed tumors and a low number of false-positives, but that spectral clustering seems to be more robust to noise

The interplay of matrix metalloproteinase-8, transforming growth factor-beta 1 and vascular endothelial growth factor-C cooperatively contributes to the aggressiveness of oral tongue squamous cell carcinoma

Background: Matrix metalloproteinase-8 (MMP-8) has oncosuppressive properties in various cancers. We attempted to assess MMP-8 function in oral tongue squamous cell carcinoma (OTSCC). Methods: MMP-8 overexpressing OTSCC cells were used to study the effect of MMP-8 on proliferation, apoptosis, migration, invasion and gene and protein expression. Moreover, MMP-8 functions were assessed in the orthotopic mouse tongue cancer model and by immunohistochemistry in patient samples. Results: MMP-8 reduced the invasion and migration of OTSCC cells and decreased the expression of MMP-1, cathepsin-K and vascular endothelial growth factor-C (VEGF-C). VEGF-C was induced by transforming growth factor-beta 1 (TGF-beta 1) in control cells, but not in MMP-8 overexpressing cells. In human OTSCC samples, low MMP-8 in combination with high VEGF-C was an independent predictor of poor cancer-specific survival. TGF-beta 1 treatment also restored the migration of MMP-8 overexpressing cells to the level of control cells. In mouse tongue cancer, MMP-8 did not inhibit metastasis, possibly because it was eliminated in the peripheral carcinoma cells. Conclusions: The suppressive effects of MMP-8 in OTSCC may be mediated through interference of TGF-beta 1 and VEGF-C function and altered proteinase expression. Together, low MMP-8 and high VEGF-C expression have strong independent prognostic value in OTSCC.Peer reviewe

Changing times: tactics

info:eu-repo/semantics/publishedVersio

Gender Differences in Russian Colour Naming

In the present study we explored Russian colour naming in a web-based psycholinguistic experiment (http://www.colournaming.com). Colour singletons representing the Munsell Color Solid (N=600 in total) were presented on a computer monitor and named using an unconstrained colour-naming method. Respondents were Russian speakers (N=713). For gender-split equal-size samples (NF=333, NM=333) we estimated and compared (i) location of centroids of 12 Russian basic colour terms (BCTs); (ii) the number of words in colour descriptors; (iii) occurrences of BCTs most frequent non-BCTs. We found a close correspondence between females’ and males’ BCT centroids. Among individual BCTs, the highest inter-gender agreement was for seryj ‘grey’ and goluboj ‘light blue’, while the lowest was for sinij ‘dark blue’ and krasnyj ‘red’. Females revealed a significantly richer repertory of distinct colour descriptors, with great variety of monolexemic non-BCTs and “fancy” colour names; in comparison, males offered relatively more BCTs or their compounds. Along with these measures, we gauged denotata of most frequent CTs, reflected by linguistic segmentation of colour space, by employing a synthetic observer trained by gender-specific responses. This psycholinguistic representation revealed females’ more refined linguistic segmentation, compared to males, with higher linguistic density predominantly along the redgreen axis of colour space

The interplay of matrix metalloproteinase-8, transforming growth factor-beta 1 and vascular endothelial growth factor-C cooperatively contributes to the aggressiveness of oral tongue squamous cell carcinoma

Background: Matrix metalloproteinase-8 (MMP-8) has oncosuppressive properties in various cancers. We attempted to assess MMP-8 function in oral tongue squamous cell carcinoma (OTSCC). Methods: MMP-8 overexpressing OTSCC cells were used to study the effect of MMP-8 on proliferation, apoptosis, migration, invasion and gene and protein expression. Moreover, MMP-8 functions were assessed in the orthotopic mouse tongue cancer model and by immunohistochemistry in patient samples. Results: MMP-8 reduced the invasion and migration of OTSCC cells and decreased the expression of MMP-1, cathepsin-K and vascular endothelial growth factor-C (VEGF-C). VEGF-C was induced by transforming growth factor-beta 1 (TGF-beta 1) in control cells, but not in MMP-8 overexpressing cells. In human OTSCC samples, low MMP-8 in combination with high VEGF-C was an independent predictor of poor cancer-specific survival. TGF-beta 1 treatment also restored the migration of MMP-8 overexpressing cells to the level of control cells. In mouse tongue cancer, MMP-8 did not inhibit metastasis, possibly because it was eliminated in the peripheral carcinoma cells. Conclusions: The suppressive effects of MMP-8 in OTSCC may be mediated through interference of TGF-beta 1 and VEGF-C function and altered proteinase expression. Together, low MMP-8 and high VEGF-C expression have strong independent prognostic value in OTSCC.Peer reviewe

Advancing Quantitative PET Imaging with Machine Learning

Medical imaging with positron emission tomography (PET) plays an important role in the detection, staging, and treatment response assessment in cancer, neurological and cardiovascular conditions, inflammation and infection. PET imaging is based on measuring the distribution of injected radioactive tracers, designed to follow specific biological pathways. One of the main advantages with PET is that it allows, not only visualization of regional tracer uptake, but also quantification of the underlying biological process. There are many challenges associated with PET imaging, which, unless accounted for, may reduce the accuracy and precision in PET-based quantification. This thesis addresses the impact of imaging artifacts and subject motion on static PET-based tumor quantification and on machine-learning-based prediction models. Furthermore, the challenge of arterial blood sampling, required for quantification in dynamic PET is addressed. To this end, four papers are presented, suggesting methodology for improved PET-based quantification. In the first two papers, the impact of imaging artifacts (Paper I) and respiratory motion (Paper II) on tumor quantification is investigated in two lung cancer PET/magnetic resonance imaging cohorts. This knowledge is important, not only for PET-based quantification on a patient level, but also as a pre-processing step in machine-learning-based prediction models. In the next two papers, a non-invasive machine-learning-based approach is proposed to replace the arterial input function, required for tracer kinetic modelling in dynamic PET applications. The approach is evaluated in a pre-clinical mouse PET cohort (Paper III) and in a human clinical PET cohort (Paper IV). The proposed methodology may considerably simplify the acquisition and analysis workflow in future pre-clinical and clinical dynamic PET studies, by avoiding the need for invasive blood sampling

Quantitative PET/MR imaging of lung cancer in the presence of artifacts in the MR-based attenuation correction maps

Background - Positron emission tomography (PET)/magnetic resonance (MR) imaging may become increasingly important for assessing tumor therapy response. A prerequisite for quantitative PET/MR imaging is reliable and repeatable MR-based attenuation correction (AC). Purpose - To investigate the frequency and test–retest reproducibility of artifacts in MR-AC maps in a lung cancer patient cohort and to study the impact of artifact corrections on PET-based tumor quantification. Material and Methods - Twenty-five lung cancer patients underwent single-day, test–retest, 18F-fluorodeoxyglucose (FDG) PET/MR imaging. The acquired MR-AC maps were inspected for truncation, susceptibility, and tissue inversion artifacts. An anatomy-based bone template and a PET-based estimation of truncated arms were employed, while susceptibility artifacts were corrected manually. We report the frequencies of artifacts and the relative difference (RD) on standardized uptake value (SUV) based quantification in PET images reconstructed with the corrected AC maps. Results - Truncation artifacts were found in all 50 acquisitions (100%), while susceptibility and tissue inversion artifacts were observed in six (12%) and 26 (52%) of the scans, respectively. The RD in lung tumor SUV was  Conclusion - The absence of bone and truncation artifacts have limited effect on the PET quantification of lung lesions. In contrast, susceptibility artifacts caused significant and inconsistent underestimations of the lung tumor SUVs, between test–retest scans. This may have clinical implications for patients undergoing serial imaging for tumor therapy response assessment

Unsupervised supervoxel-based lung tumor segmentation across patient scans in hybrid PET/MRI

Tumor segmentation is a crucial but difficult task in treatment planning and follow-up of cancerous patients. The challenge of automating the tumor segmentation has recently received a lot of attention, but the potential of utilizing hybrid positron emission tomography (PET)/magnetic resonance imaging (MRI), a novel and promising imaging modality in oncology, is still under-explored. Recent approaches have either relied on manual user input and/or performed the segmentation patient-by-patient, whereas a fully unsupervised segmentation framework that exploits the available information from all patients is still lacking. We present an unsupervised across-patients supervoxel-based clustering framework for lung tumor segmentation in hybrid PET/MRI. The method consists of two steps: First, each patient is represented by a set of PET/MRI supervoxel-features. Then the data points from all patients are transformed and clustered on a population level into tumor and non-tumor supervoxels. The proposed framework is tested on the scans of 18 non-small cell lung cancer patients with a total of 19 tumors and evaluated with respect to manual delineations provided by clinicians. Experiments study the performance of several commonly used clustering algorithms within the framework and provide analysis of (i) the effect of tumor size, (ii) the segmentation errors, (iii) the benefit of across-patient clustering, and (iv) the noise robustness. The proposed framework detected 15 out of 19 tumors in an unsupervised manner. Moreover, performance increased considerably by segmenting across patients, with the mean dice score increasing from (patient-by-patient) to (across-patients). Results demonstrate that both spectral clustering and Manhattan hierarchical clustering have the potential to segment tumors in PET/MRI with a low number of missed tumors and a low number of false-positives, but that spectral clustering seems to be more robust to noise

Unsupervised supervoxel-based lung tumor segmentation across patient scans in hybrid PET/MRI

Tumor segmentation is a crucial but difficult task in treatment planning and follow-up of cancerous patients. The challenge of automating the tumor segmentation has recently received a lot of attention, but the potential of utilizing hybrid positron emission tomography (PET)/magnetic resonance imaging (MRI), a novel and promising imaging modality in oncology, is still under-explored. Recent approaches have either relied on manual user input and/or performed the segmentation patient-by-patient, whereas a fully unsupervised segmentation framework that exploits the available information from all patients is still lacking. We present an unsupervised across-patients supervoxel-based clustering framework for lung tumor segmentation in hybrid PET/MRI. The method consists of two steps: First, each patient is represented by a set of PET/ MRI supervoxel-features. Then the data points from all patients are transformed and clustered on a population level into tumor and non-tumor supervoxels. The proposed framework is tested on the scans of 18 non-small cell lung cancer patients with a total of 19 tumors and evaluated with respect to manual delineations provided by clinicians. Experiments study the performance of several commonly used clustering algorithms within the framework and provide analysis of (i) the effect of tumor size, (ii) the segmentation errors, (iii) the benefit of across-patient clustering, and (iv) the noise robustness. The proposed framework detected 15 out of 19 tumors in an unsupervised manner. Moreover, performance increased considerably by segmenting across patients, with the mean dice score increasing from 0.169 ± 0.295 (patient-by-patient) to 0.470 ± 0.308 (across-patients). Results demonstrate that both spectral clustering and Manhattan hierarchical clustering have the potential to segment tumors in PET/MRI with a low number of missed tumors and a low number of false-positives, but that spectral clustering seems to be more robust to noise