Association between increased Subcutaneous Adipose Tissue Radiodensity and cancer mortality: Automated computation, comparison of cancer types, gender, and scanner bias.

PURPOSE: Body composition analysis using computed tomography (CT) is proposed as a predictor of cancer mortality. An association between subcutaneous adipose tissue radiodensity (SATr) and cancer-specific mortality was established, while gender effects and equipment bias were estimated. METHODS: 7,475 CT studies were selected from 17 cohorts containing CT images of untreated cancer patients who underwent follow-up for a period of 2.1-118.8 months. SATr measures were collected from published data (n = 6,718) or calculated according to CT images using a deep-learning network (n = 757). The association between SATr and mortality was ascertained for each cohort and gender using the p-value from either logistic regression or ROC analysis. The Kruskal-Wallis test was used to analyze differences between gender distributions, and automatic segmentation was evaluated using the Dice score and five-point Likert quality scale. Gender effect, scanner bias and changes in the Hounsfield unit (HU) to detect hazards were also estimated. RESULTS: Higher SATr was associated with mortality in eight cancer types (p < 0.05). Automatic segmentation produced a score of 0.949 while the quality scale measurement was good to excellent. The extent of gender effect was 5.2 HU while the scanner bias was 10.3 HU. The minimum proposed HU change to detect a patient at risk of death was between 5.6 and 8.3 HU. CONCLUSIONS: CT imaging provides valuable assessments of body composition as part of the staging process for several cancer types, saving both time and cost. Gender specific scales and scanner bias adjustments should be carried out to successfully implement SATr measures in clinical practice.

Multi-center Integrating Radiomics, Structured Reports, and Machine Learning Algorithms for Assisted Classification of COVID-19 in Lung Computed Tomography.

Purpose: To evaluate the classification performance of structured report features, radiomics, and machine learning (ML) models to differentiate between Coronavirus Disease 2019 (COVID-19) and other types of pneumonia using chest computed tomography (CT) scans. Methods: Sixty-four COVID-19 subjects and 64 subjects with non-COVID-19 pneumonia were selected. The data was split into two independent cohorts: one for the structured report, radiomic feature selection and model building (n = 73), and another for model validation (n = 55). Physicians performed readings with and without machine learning support. The model's sensitivity and specificity were calculated, and inter-rater reliability was assessed using Cohen's Kappa agreement coefficient. Results: Physicians performed with mean sensitivity and specificity of 83.4 and 64.3%, respectively. When assisted with machine learning, the mean sensitivity and specificity increased to 87.1 and 91.1%, respectively. In addition, machine learning improved the inter-rater reliability from moderate to substantial. Conclusion: Integrating structured reports and radiomics promises assisted classification of COVID-19 in CT chest scans.

Protocols for Harmonized Quantification and Noise Reduction in Low-Dose Oncologic 18F-FDG PET/CT Imaging.

Oncologic 18F-FDG PET/CT acquisition and reconstruction protocols need to be optimized for both quantitative and detection tasks. To date, most studies have focused on either quantification or noise, leading to quantitative harmonization guidelines or appropriate noise levels. We developed and evaluated protocols that provide harmonized quantitation with optimal amounts of noise as a function of acquisition parameters and body mass. Methods: Multiple image acquisitions (n = 17) of the International Electrotechnical Commission/National Electrical Manufacturers Association PET image-quality phantom were performed with variable counting statistics. Phantom images were reconstructed with 3-dimensional ordered-subset expectation maximization (OSEM3D) and point-spread function (PSF) for harmonized quantification of the contrast recovery coefficient of the maximum pixel value (CRC max ). The lowest counting statistics that resulted in compliance with European Association of Nuclear Medicine recommendations for CRC max and CRC max variability were used as optimization metrics. Image noise in the liver of 48 typical oncologic 18F-FDG PET/CT studies was analyzed with OSEM3D and PSF harmonized reconstructions. We also evaluated 164 additional 18F-FDG PET/CT reconstructed list-mode images to derive analytic expressions that predict image quality and noise variability. Phantom-to-subject translational analysis was used to derive optimized acquisition and reconstruction protocols. Results: For harmonized quantitation levels, PSF reconstructions yielded decreased noise and lower CRC max variability than regular OSEM3D reconstructions, suggesting they could enable a decreased activity regimen for matched performance. Conclusion: PSF reconstruction with a 7-mm postprocessing filter can provide harmonized quantification performance and acceptable image noise levels with injected activity, duration, and mass settings using a 260 MBq⋅s/kg acquisition parameter at scan time. Similarly, OSEM3D with a 5-mm postprocessing filter can provide similar performance with 401 MBq⋅s/kg.

Optimization of oncological ¹8F-FDG PET/CT imaging based on a multiparameter analysis.

PURPOSE: This paper describes a method to achieve consistent clinical image quality in (18)F-FDG scans accounting for patient habitus, dose regimen, image acquisition, and processing techniques. METHODS: Oncological PET/CT scan data for 58 subjects were evaluated retrospectively to derive analytical curves that predict image quality. Patient noise equivalent count rate and coefficient of variation (CV) were used as metrics in their analysis. Optimized acquisition protocols were identified and prospectively applied to 179 subjects. RESULTS: The adoption of different schemes for three body mass ranges (<60 kg, 60-90 kg, >90 kg) allows improved image quality with both point spread function and ordered-subsets expectation maximization-3D reconstruction methods. The application of this methodology showed that CV improved significantly (p < 0.0001) in clinical practice. CONCLUSIONS: Consistent oncological PET/CT image quality on a high-performance scanner was achieved from an analysis of the relations existing between dose regimen, patient habitus, acquisition, and processing techniques. The proposed methodology may be used by PET/CT centers to develop protocols to standardize PET/CT imaging procedures and achieve better patient management and cost-effective operations.