Thyroid Nodule Detection and Region Estimation in Ultrasound Images: A Comparison between Physicians and an Automated Decision Support System Approach.

BACKGROUND: Thyroid nodules are very common. In most cases, they are benign, but they can be malignant in a low percentage of cases. The accurate assessment of these nodules is critical to choosing the next diagnostic steps and potential treatment. Ultrasound (US) imaging, the primary modality for assessing these nodules, can lack objectivity due to varying expertise among physicians. This leads to observer variability, potentially affecting patient outcomes. PURPOSE: This study aims to assess the potential of a Decision Support System (DSS) in reducing these variabilities for thyroid nodule detection and region estimation using US images, particularly in lesser experienced physicians. METHODS: Three physicians with varying levels of experience evaluated thyroid nodules on US images, focusing on nodule detection and estimating cystic and solid regions. The outcomes were compared to those obtained from a DSS for comparison. Metrics such as classification match percentage and variance percentage were used to quantify differences. RESULTS: Notable disparities exist between physician evaluations and the DSS assessments: the overall classification match percentage was just 19.2%. Individually, Physicians 1, 2, and 3 had match percentages of 57.6%, 42.3%, and 46.1% with the DSS, respectively. Variances in assessments highlight the subjectivity and observer variability based on physician experience levels. CONCLUSIONS: The evident variability among physician evaluations underscores the need for supplementary decision-making tools. Given its consistency, the CAD offers potential as a reliable "second opinion" tool, minimizing human-induced variabilities in the critical diagnostic process of thyroid nodules using US images. Future integration of such systems could bolster diagnostic precision and improve patient outcomes.

Optimization of SPECT/CT imaging protocols for quantitative and qualitative 99mTc SPECT.

BACKGROUND: The introduction of hybrid SPECT/CT devices enables quantitative imaging in SPECT, providing a methodological setup for quantitation using SPECT tracers comparable to PET/CT. We evaluated a specific quantitative reconstruction algorithm for SPECT data using a 99mTc-filled NEMA phantom. Quantitative and qualitative image parameters were evaluated for different parametrizations of the acquisition and reconstruction protocol to identify an optimized quantitative protocol. RESULTS: The reconstructed activity concentration (ACrec) and the signal-to-noise ratio (SNR) of all examined protocols (n = 16) were significantly affected by the parametrization of the weighting factor k used in scatter correction, the total number of iterations and the sphere volume (all, p < 0.0001). The two examined SPECT acquisition protocols (with 60 or 120 projections) had a minor impact on the ACrec and no significant impact on the SNR. In comparison to the known AC, the use of default scatter correction (k = 0.47) or object-specific scatter correction (k = 0.18) resulted in an underestimation of ACrec in the largest sphere volume (26.5 ml) by - 13.9 kBq/ml (- 16.3%) and - 7.1 kBq/ml (- 8.4%), respectively. An increase in total iterations leads to an increase in estimated AC and a decrease in SNR. The mean difference between ACrec and known AC decreased with an increasing number of total iterations (e.g., for 20 iterations (2 iterations/10 subsets) = - 14.6 kBq/ml (- 17.1%), 240 iterations (24i/10s) = - 8.0 kBq/ml (- 9.4%), p < 0.0001). In parallel, the mean SNR decreased significantly from 2i/10s to 24i/10s by 76% (p < 0.0001). CONCLUSION: Quantitative SPECT imaging is feasible with the used reconstruction algorithm and hybrid SPECT/CT, and its consistent implementation in diagnostics may provide perspectives for quantification in routine clinical practice (e.g., assessment of bone metabolism). When combining quantitative analysis and diagnostic imaging, we recommend using two different reconstruction protocols with task-specific optimized setups (quantitative vs. qualitative reconstruction). Furthermore, individual scatter correction significantly improves both quantitative and qualitative results.

Quantitative imaging of bone remodeling in patients with a unicompartmental joint unloading knee implant (ATLAS Knee System)-effect of metal artifacts on a SPECT-CT-based quantification.

BACKGROUND: SPECT-CT using radiolabeled phosphonates is considered a standard for assessing bone metabolism (e.g., in patients with osteoarthritis of knee joints). However, SPECT can be influenced by metal artifacts in CT caused by endoprostheses affecting attenuation correction. The current study examined the effects of metal artifacts in CT of a specific endoprosthesis design on quantitative hybrid SPECT-CT imaging. The implant was positioned inside a phantom homogenously filled with activity (955 MBq 99mTc). CT imaging was performed for different X-ray tube currents (I = 10, 40, 125 mA) and table pitches (p = 0.562 and 1.375). X-ray tube voltage (U = 120 kVp) and primary collimation (16 × 0.625 mm) were kept constant for all scans. The CT reconstruction was performed with five different reconstruction kernels (slice thickness, 1.25 mm and 3.75 mm, each 512 × 512 matrix). Effects from metal artifacts were analyzed for different CT scans and reconstruction protocols. ROI analysis of CT and SPECT data was performed for two slice positions/volumes representing the typical locations for target structures relative to the prosthesis (e.g., femur and tibia). A reference region (homogenous activity concentration without influence from metal artifacts) was analyzed for comparison. RESULTS: Significant effects caused by CT metal artifacts on attenuation-corrected SPECT were observed for the different slice positions, reconstructed slice thicknesses of CT data, and pitch and CT-reconstruction kernels used (all, p < 0.0001). Based on the optimization, a set of three protocols was identified minimizing the effect of CT metal artifacts on SPECT data. Regarding the reference region, the activity concentration in the anatomically correlated volume was underestimated by 8.9-10.1%. A slight inhomogeneity of the reconstructed activity concentration was detected inside the regions with a median up to 0.81% (p < 0.0001). Using an X-ray tube current of 40 mA showed the best result, balancing quantification and CT exposure. CONCLUSION: The results of this study demonstrate the need for the evaluation of SPECT-CT protocols in prosthesis imaging. Phantom experiments demonstrated the possibility for quantitative SPECT-CT of bone turnover in a specific prosthesis design. Meanwhile, a systematic bias caused by metal implants on quantitative SPECT data has to be considered.

Ultrasound Assessment of Autonomous Thyroid Nodules before and after Radioiodine Therapy Using Thyroid Imaging Reporting and Data System (TIRADS).

The Thyroid Imaging and Reporting System (TIRADS) allows a sonographic assessment of the malignancy risk of thyroid nodules (TNs). To date, there is a lack of systematic data about the change in ultrasound (US) features after therapeutic interventions. The aim of this study was to characterize the changes in autonomously functioning thyroid nodules (AFTNs) after radioiodine therapy (RIT) by using TIRADS. We retrospectively assessed data from 68 patients with AFTNs treated with RIT between 2016 and 2018 who had available first and second follow-up US imaging. Before RIT, 69.1% of the AFTNs were classified as low-risk TNs when applying Kwak TIRADS (EU-TIRADS 52.9%), 22.1% were intermediate-risk TNs (EU-TIRADS 19.1%), and 8.8% were high-risk TNs (EU-TIRADS 27.9%). Twelve months after RIT, 22.1% of the AFTNs showed features of high-risk TNs according to Kwak TIRADS (EU-TIRADS 45.6%). The proportion of intermediate TNs also increased to 36.8% (EU-TIRADS 29.4%), and 41.2% were low-risk TNs (EU-TIRADS 25%). A significant percentage of AFTNs presented with features suspicious for malignancy according to TIRADS before RIT, and this number increased significantly after therapy. Therefore, before thyroid US, thorough anamnesis regarding prior radioiodine treatment is necessary to prevent unneeded diagnostic procedures.

Analysis of Bone Scans in Various Tumor Entities Using a Deep-Learning-Based Artificial Neural Network Algorithm-Evaluation of Diagnostic Performance.

The bone scan index (BSI), initially introduced for metastatic prostate cancer, quantifies the osseous tumor load from planar bone scans. Following the basic idea of radiomics, this method incorporates specific deep-learning techniques (artificial neural network) in its development to provide automatic calculation, feature extraction, and diagnostic support. As its performance in tumor entities, not including prostate cancer, remains unclear, our aim was to obtain more data about this aspect. The results of BSI evaluation of bone scans from 951 consecutive patients with different tumors were retrospectively compared to clinical reports (bone metastases, yes/no). Statistical analysis included entity-specific receiver operating characteristics to determine optimized BSI cut-off values. In addition to prostate cancer (cut-off = 0.27%, sensitivity (SN) = 87%, specificity (SP) = 99%), the algorithm used provided comparable results for breast cancer (cut-off 0.18%, SN = 83%, SP = 87%) and colorectal cancer (cut-off = 0.10%, SN = 100%, SP = 90%). Worse performance was observed for lung cancer (cut-off = 0.06%, SN = 63%, SP = 70%) and renal cell carcinoma (cut-off = 0.30%, SN = 75%, SP = 84%). The algorithm did not perform satisfactorily in melanoma (SN = 60%). For most entities, a high negative predictive value (NPV ≥ 87.5%, melanoma 80%) was determined, whereas positive predictive value (PPV) was clinically not applicable. Automatically determined BSI showed good sensitivity and specificity in prostate cancer and various other entities. Particularly, the high NPV encourages applying BSI as a tool for computer-aided diagnostic in various tumor entities.

Abstract ID: 247 Patient specific scatter reduction in SIRT gamma camera images.

INTRODUCTION: Clinical Bremsstrahlung imaging with gamma cameras or SPECT scanners, for example used in selective internal radiation therapy (SIRT) [1], suffers from low contrast due to a continuous spectrum and a high amount of scatter. Information about the scattering of the radionuclide within the patient's body can be obtained from Monte-Carlo simulations and subsequently being used to improve image quality. METHODS: An MAA-acquisition (CT+SPECT) of a HCC patient with a unifocal uptake in the right liver lobe is segmented (lesion) and loaded into the MC simulation framework GATE [2]. The voxelized lesion is used as Y90 source (1.5 GBq, 'fastY90' [3] is used yielding a speedup of 2.3×), the CT dataset is used for attenuation by converting HUs into corresponding materials. A mini gamma camera (Crystal Photonics, Germany) with a LEHR collimator and 4 × 4 cm2 detector size is positioned close to liver on the patient's skin, pointing towards the lesion. A simulation (60 s acquisition time) is performed on a cluster with 512 cores (2.2-2.5 Ghz each). The total number of counts, the energy spectrum and the order of scattered particles within each geometric volume are obtained from the ROOT output. Particles that have not scattered at all are defined as primary events. Scattering is only calculated within the phantom. RESULTS: The simulation took 172 min on the cluster with input data voxel size of 1 × 1 × 3.75 mm3. The number of emitted particles is 7.6 M with 600 k detected counts (∼8% ratio). 13.3% of the detected particles are primary events. Additionally, scatter of multiple orders has been observed (41%, 24% and 13% and 8% for the first four orders). CONCLUSION: The energy-spectrum of the simulated 2D gamma camera image can be analyzed and used to correct the actual image acquired of that specific patient to achieve improved image quality and subsequently also dosimetry.