Gaussian process models of dynamic PET for functional volume definition in radiation oncology

Height and Weight Estimation From Anthropometric Measurements Using Machine Learning Regressions

Height and weight are measurements explored to tracking nutritional diseases, energy expenditure, clinical conditions, drug dosages, and infusion rates. Many patients are not ambulant or may be unable to communicate, and a sequence of these factors may not allow accurate estimation or measurements; in those cases, it can be estimated approximately by anthropometric means. Different groups have proposed different linear or non-linear equations which coefficients are obtained by using single or multiple linear regressions. In this paper, we present a complete study of the application of different learning models to estimate height and weight from anthropometric measurements: support vector regression, Gaussian process, and artificial neural networks. The predicted values are significantly more accurate than that obtained with conventional linear regressions. In all the cases, the predictions are non-sensitive to ethnicity, and to gender, if more than two anthropometric parameters are analyzed. The learning model analysis creates new opportunities for anthropometric applications in industry, textile technology, security, and health care.

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

PURPOSE

The purpose of this educational report is to provide an overview of the present state-of-the-art PET auto-segmentation (PET-AS) algorithms and their respective validation, with an emphasis on providing the user with help in understanding the challenges and pitfalls associated with selecting and implementing a PET-AS algorithm for a particular application.

APPROACH

A brief description of the different types of PET-AS algorithms is provided using a classification based on method complexity and type. The advantages and the limitations of the current PET-AS algorithms are highlighted based on current publications and existing comparison studies. A review of the available image datasets and contour evaluation metrics in terms of their applicability for establishing a standardized evaluation of PET-AS algorithms is provided. The performance requirements for the algorithms and their dependence on the application, the radiotracer used and the evaluation criteria are described and discussed. Finally, a procedure for algorithm acceptance and implementation, as well as the complementary role of manual and auto-segmentation are addressed.

FINDINGS

A large number of PET-AS algorithms have been developed within the last 20 years. Many of the proposed algorithms are based on either fixed or adaptively selected thresholds. More recently, numerous papers have proposed the use of more advanced image analysis paradigms to perform semi-automated delineation of the PET images. However, the level of algorithm validation is variable and for most published algorithms is either insufficient or inconsistent which prevents recommending a single algorithm. This is compounded by the fact that realistic image configurations with low signal-to-noise ratios (SNR) and heterogeneous tracer distributions have rarely been used. Large variations in the evaluation methods used in the literature point to the need for a standardized evaluation protocol.

CONCLUSIONS

Available comparison studies suggest that PET-AS algorithms relying on advanced image analysis paradigms provide generally more accurate segmentation than approaches based on PET activity thresholds, particularly for realistic configurations. However, this may not be the case for simple shape lesions in situations with a narrower range of parameters, where simpler methods may also perform well. Recent algorithms which employ some type of consensus or automatic selection between several PET-AS methods have potential to overcome the limitations of the individual methods when appropriately trained. In either case, accuracy evaluation is required for each different PET scanner and scanning and image reconstruction protocol. For the simpler, less robust approaches, adaptation to scanning conditions, tumor type, and tumor location by optimization of parameters is necessary. The results from the method evaluation stage can be used to estimate the contouring uncertainty. All PET-AS contours should be critically verified by a physician. A standard test, i.e., a benchmark dedicated to evaluating both existing and future PET-AS algorithms needs to be designed, to aid clinicians in evaluating and selecting PET-AS algorithms and to establish performance limits for their acceptance for clinical use. The initial steps toward designing and building such a standard are undertaken by the task group members.

Predicting the Interactive Rendering Time Threshold of Gaussian Process Models With HyperSlice

In this paper we present a method for predicting the rendering time to display multi-dimensional data for the analysis of computer simulations using the HyperSlice [36] method with Gaussian process model reconstruction. Our method relies on a theoretical understanding of how the data points are drawn on slices and then fits the formula to a user's machine using practical experiments. We also describe the typical characteristics of data when analyzing deterministic computer simulations as described by the statistics community. We then show the advantage of carefully considering how many data points can be drawn in real time by proposing two approaches of how this predictive formula can be used in a real-world system.

Multisite Experience of the Safety, Detection Rate and Diagnostic Performance of Fluciclovine (18F) Positron Emission Tomography/Computerized Tomography Imaging in the Staging of Biochemically Recurrent Prostate Cancer

PURPOSE

Sensitive detection of cancer foci in men experiencing biochemical recurrence following initial treatment of prostate cancer is of great clinical significance with a possible impact on subsequent treatment choice. We describe a multisite experience of the efficacy and safety of the positron emission tomography/computerized tomography agent fluciclovine (¹⁸F) after biochemical recurrence.

MATERIALS AND METHODS

A total of 596 patients underwent fluciclovine (¹⁸F) positron emission tomography/computerized tomography at 4 clinical sites. Detection rate determinations were stratified by the baseline prostate specific antigen value. Diagnostic performance was assessed against a histological reference standard in 143 scans.

RESULTS

The subject level fluciclovine (¹⁸F) positron emission tomography/computer tomography detection rate was 67.7% (403 of 595 scans). Positive findings were detected in the prostate/bed and pelvic lymph node regions in 38.7% (232 of 599) and 32.6% of scans (194 of 596), respectively. Metastatic involvement outside the pelvis was detected in 26.2% of scans (155 of 591). The subject level detection rate in patients in the lowest quartile for baseline prostate specific antigen (0.79 ng/ml or less) was 41.4% (53 of 128). Of these patients 13 had involvement in the prostate/bed only, 16 had pelvic lymph node involvement without distant disease and 24 had distant metastases. The positive predictive value of fluciclovine (¹⁸F) positron emission tomography/computerized tomography scanning for all sampled lesions was 62.2%, and it was 92.3% and 71.8% for extraprostatic and prostate/bed involvement, respectively. Fluciclovine (¹⁸F) was well tolerated and the safety profile was not altered following repeat administration.

CONCLUSIONS

Fluciclovine (¹⁸F) is well tolerated and able to detect local and distant prostate cancer recurrence across a wide range of prostate specific antigen values.

Radiotherapy volume delineation using dynamic [18F]-FDG PET/CT imaging in patients with oropharyngeal cancer: a pilot study

PURPOSE

Delineation of gross tumour volume in 3D is a critical step in the radiotherapy (RT) treatment planning for oropharyngeal cancer (OPC). Static [¹⁸F]-FDG PET/CT imaging has been suggested as a method to improve the reproducibility of tumour delineation, but it suffers from low specificity. We undertook this pilot study in which dynamic features in time-activity curves (TACs) of [¹⁸F]-FDG PET/CT images were applied to help the discrimination of tumour from inflammation and adjacent normal tissue.

METHODS

Five patients with OPC underwent dynamic [¹⁸F]-FDG PET/CT imaging in treatment position. Voxel-by-voxel analysis was performed to evaluate seven dynamic features developed with the knowledge of differences in glucose metabolism in different tissue types and visual inspection of TACs. The Gaussian mixture model and K-means algorithms were used to evaluate the performance of the dynamic features in discriminating tumour voxels compared to the performance of standardized uptake values obtained from static imaging.

RESULTS

Some dynamic features showed a trend towards discrimination of different metabolic areas but lack of consistency means that clinical application is not recommended based on these results alone.

CONCLUSIONS

Impact of inflammatory tissue remains a problem for volume delineation in RT of OPC, but a simple dynamic imaging protocol proved practicable and enabled simple data analysis techniques that show promise for complementing the information in static uptake values.

A review on segmentation of positron emission tomography images

Positron Emission Tomography (PET), a non-invasive functional imaging method at the molecular level, images the distribution of biologically targeted radiotracers with high sensitivity. PET imaging provides detailed quantitative information about many diseases and is often used to evaluate inflammation, infection, and cancer by detecting emitted photons from a radiotracer localized to abnormal cells. In order to differentiate abnormal tissue from surrounding areas in PET images, image segmentation methods play a vital role; therefore, accurate image segmentation is often necessary for proper disease detection, diagnosis, treatment planning, and follow-ups. In this review paper, we present state-of-the-art PET image segmentation methods, as well as the recent advances in image segmentation techniques. In order to make this manuscript self-contained, we also briefly explain the fundamentals of PET imaging, the challenges of diagnostic PET image analysis, and the effects of these challenges on the segmentation results.