Evaluation of diagnostic accuracy: multidetector CT image noise correction improves specificity of a Gaussian model-based algorithm used for characterization of incidental adrenal nodules

The current and upcoming era of radiomics in phaeochromocytoma and paraganglioma

The topic of the diagnosis of phaeochromocytomas remains highly relevant because of advances in laboratory diagnostics, genetics, and therapeutic options and also the development of imaging methods. Computed tomography still represents an essential tool in clinical practice, especially in incidentally discovered adrenal masses; it allows morphological evaluation, including size, shape, necrosis, and unenhanced attenuation. More advanced post-processing tools to analyse digital images, such as texture analysis and radiomics, are currently being studied. Radiomic features utilise digital image pixels to calculate parameters and relations undetectable by the human eye. On the other hand, the amount of radiomic data requires massive computer capacity. Radiomics, together with machine learning and artificial intelligence in general, has the potential to improve not only the differential diagnosis but also the prediction of complications and therapy outcomes of phaeochromocytomas in the future. Currently, the potential of radiomics and machine learning does not match expectations and awaits its fulfilment.

Impact of Noise Level on the Accuracy of Automated Measurement of CT Number Linearity on ACR CT and Computational Phantoms

Background

Methods for segmentation, i.e., Full-segmentation (FS) and Segmentation-rotation (SR), are proposed for maintaining Computed Tomography (CT) number linearity. However, their effectiveness has not yet been tested against noise.

Objective

This study aimed to evaluate the influence of noise on the accuracy of CT number linearity of the FS and SR methods on American College of Radiology (ACR) CT and computational phantoms.

Material and Methods

This experimental study utilized two phantoms, ACR CT and computational phantoms. An ACR CT phantom was scanned by a 128-slice CT scanner with various tube currents from 80 to 200 mA to acquire various noises, with other constant parameters. The computational phantom was added by different Gaussian noises between 20 and 120 Hounsfield Units (HU). The CT number linearity was measured by the FS and SR methods, and the accuracy of CT number linearity was computed on two phantoms.

Results

The two methods successfully segmented both phantoms at low noise, i.e., less than 60 HU. However, segmentation and measurement of CT number linearity are not accurate on a computational phantom using the FS method for more than 60-HU noise. The SR method is still accurate up to 120 HU of noise.

Conclusion

The SR method outperformed the FS method to measure the CT number linearity due to its endurance in extreme noise.

Unenhanced Dual-Layer Spectral-Detector CT for Characterizing Indeterminate Adrenal Lesions

Background Unenhanced dual-layer spectral-detector CT may facilitate adrenal lesion characterization; however, no studies have evaluated its incremental diagnostic yield for indeterminate lesions (unenhanced attenuation >10 HU) in comparison to that with conventional unenhanced CT. Purpose To determine whether spectral attenuation analysis improves characterization of lipid-poor adrenal adenomas from nonadenomas compared to that with mean attenuation and histogram analysis of conventional CT images. Materials and Methods This retrospective study included patients with indeterminate adrenal lesions who underwent unenhanced dual-layer spectral-detector CT between March 2018 and June 2020. Mean attenuation on conventional 120-kVp images (HU_conv), histogram-based percentage negative pixels (proportion of all pixels <0 HU) on conventional 120-kVp images, and mean attenuation on virtual monoenergetic images (VMIs) at 40-140 keV were measured for each lesion. The attenuation difference between virtual monoenergetic 140- and 40-keV images (ΔHU; ie, Hounsfield unit at 140 keV - Hounsfield unit at 40 keV) and ΔHU indexed with HU_conv (ΔHU index; ie, ΔHU/HU_conv × 100) were calculated. Conventional and virtual monoenergetic imaging parameters were compared between lipid-poor adenomas and nonadenomas by using the Mann-Whitney U test. Receiver operating characteristic analysis was performed to determine the sensitivity for attaining at least 95% specificity in characterizing adenomas from nonadenomas; sensitivity was compared by using the McNemar test. Results A total of 232 patients (mean age ± standard deviation, 67 years ± 11; 145 men) with 129 lipid-poor adenomas and 103 nonadenomas were evaluated. HU_conv and mean attenuation on VMIs at 40-140 keV were lower and the percentage negative pixels, ΔHU, and ΔHU index higher in lipid-poor adenomas than in nonadenomas (P < .001 for all). Attenuation differences between adenomas and nonadenomas on VMIs were maximal at 40 keV (23 HU at 40 keV vs 5 HU at 140 keV). The highest sensitivities for differentiating adenomas and nonadenomas were achieved for virtual monoenergetic ΔHU index (77% [99 of 129 adenomas]), attenuation on 40-keV images (71% [91 of 129 adenomas]), and ΔHU (67% [87 of 129 adenomas]) compared to HU_conv (35% [45 of 129 adenomas]) and percentage negative pixels (30% [39 of 129 adenomas]) (P < .001 for all; specificity, 95% [98 of 103 nonadenomas]). Conclusion Spectral attenuation analysis enabled differentiation of lipid-poor adenomas from nonadenomas with higher sensitivity than mean attenuation or histogram analysis of conventional CT images. © RSNA, 2021 Online supplemental material is available for this article.

Incidental Adrenal Nodules

Incidentally detected adrenal nodules are common, and prevalence increases with patient age. Although most are benign, it is important for the radiologist to be able to accurately determine which nodules require further testing and which are safely left alone. The American College of Radiology incidental adrenal White Paper provides a structured algorithm based on expert consensus for management of incidental adrenal nodules. If further diagnostic testing is indicated, adrenal computed tomography is the most appropriate test in patients for nodules less than 4 cm. In addition to imaging, biochemical testing and endocrinology referral is warranted to exclude a functioning mass.

Computed Tomography- (CT-) Based Virtual Surgery Planning for Spinal Intervertebral Foraminal Assisted Clinical Treatment

With the development of minimally invasive spine concepts and the introduction of new minimally invasive instruments, minimally invasive spine technology, represented by foraminoscopy, has flourished, and percutaneous foraminoscopy has become one of the most reliable minimally invasive procedures for the treatment of lumbar disc herniation. Percutaneous foraminoscopy is a safe and effective minimally invasive spinal endoscopic surgical technique. It fully protects the paravertebral muscles and soft tissues as well as the posterior column structure of the spine, provides precise treatment of the target nucleus pulposus tissue, with the advantages of less surgical trauma, fewer postoperative complications, and rapid postoperative recovery, and is widely promoted and used in clinical practice. In this paper, we can view the location, morphology, structure, alignment, and adjacency relationships by performing coronary, CT, and diagonal reconstruction along the attachment of the yellow ligaments and performing 3D reconstruction or processing techniques after performing CT scans. This allows clinicians to observe the laminoplasty and the stenosis of the vertebral canal in a more intuitive and overall manner. It has clinical significance for the display of the sublaminar spine as well as the physician's judgment of the disease and the choice of surgery.

Diagnostic Value of Unenhanced CT Attenuation and CT Histogram Analysis in Differential Diagnosis of Adrenal Tumors

Background and Objectives: Our aim was to verify the optimal cut-off value for unenhanced CT attenuation and the percentage of negative voxels in the volume CT histogram analysis of adrenal masses. Materials and Methods: We retrospectively analyzed the CT data of patients who underwent an adrenalectomy in the period 2002-2019. In total, 413 adrenalectomies were performed. Out of these, 233 histologically verified masses (123 adenomas, 58 pheochromocytomas, 18 carcinomas, and 34 metastases) fulfilled the inclusion criteria and were selected for analysis. The mean unenhanced attenuation in Hounsfield units (HU) and the percentage of voxels with attenuation less than 0 HU (negative voxels) were measured in each mass. Results: The mean unenhanced attenuation with a cut-off value of 10 HU reached a sensitivity of 59.4% and a specificity of 99.1% for benign adenomas. The mean unenhanced attenuation with a cut-off value of 15 HU reached a sensitivity of 69.1% and a specificity of 98.2%. For the histogram analysis, a cut-off value of 10% of negative pixels reached a sensitivity of 82.9% and a specificity of 98.2%, whereas a cut-off value of 5% of negative pixels reached a sensitivity of 87.8% and a specificity of 75.5%. The percentage of negative voxels reached a slightly better area under the curve (0.919) than unenhanced attenuation (0.908). Conclusion: Mean unenhanced attenuation with a cut-off value of 10 HU represents a simple tool, and the most specific one, to distinguish adrenal adenomas from non-adenomas. CT histogram analysis with cut-off values of 10% of negative voxels improves sensitivity without any loss of specificity.

Comparison of Histogram-Based Gaussian Analysis With and Without Noise Correction for the Characterization of Indeterminate Adrenal Nodules

OBJECTIVE. The purpose of this study is to determine whether gaussian-based histogram analysis without and with noise correction can characterize indeterminate adrenal nodules (those with attenuation greater than 10 HU on unenhanced CT) as lipid-poor adenomas. MATERIALS AND METHODS. This retrospective study evaluated adrenal nodules larger than 1 cm on unenhanced CT using gaussian analysis without and with noise correction on intralesional ROIs. Two independent readers who were blinded to the final diagnoses evaluated the nodules. The final diagnosis for each nodule was determined on the basis of pathologic findings or accepted imaging criteria. Interreader agreement was assessed using the intraclass correlation coefficient. Algorithm performance was summarized using sensitivity, specificity, and the AUC. RESULTS. Ninety-four adrenal nodules in 85 patients were analyzed; 36 of these were metastases (34 of which were pathologically confirmed), and 58 were presumed adenomas. Interreader agreement was excellent for nodule size, mean attenuation, SD of attenuation, and the gaussian index. Noise-corrected gaussian analysis had significantly higher specificity (81.9% vs 55.6%; p < 0.001) and lower sensitivity (36.2% vs 56.9%; p < 0.001) for identifying adenomas than did the uncorrected gaussian analysis. The AUC of corrected gaussian analysis was 0.72, which is significantly greater than that of uncorrected gaussian analysis (0.51; p ≤ 0.001) and similar to that of mean attenuation (0.77). CONCLUSION. Noise correction is necessary when using a gaussian analysis characterization of indeterminate adrenal nodules on modern unenhanced CT examinations. This method may be able to discriminate between adenomas and nonadenomas.