Objectives
To compare the diagnostic performance of
18
FFDG PET/MRI, MRI, CT, and bone scintigraphy for the detection of bone metastases in the initial staging of primary breast cancer patients.
...Material and methods
A cohort of 154 therapy-naive patients with newly diagnosed, histopathologically proven breast cancer was enrolled in this study prospectively. All patients underwent a whole-body
18
FFDG PET/MRI, computed tomography (CT) scan, and a bone scintigraphy prior to therapy. All datasets were evaluated regarding the presence of bone metastases. McNemar
χ
2
test was performed to compare sensitivity and specificity between the modalities.
Results
Forty-one bone metastases were present in 7/154 patients (4.5%). Both
18
FFDG PET/MRI and MRI alone were able to detect all of the patients with histopathologically proven bone metastases (sensitivity 100%; specificity 100%) and did not miss any of the 41 malignant lesions (sensitivity 100%). CT detected 5/7 patients (sensitivity 71.4%; specificity 98.6%) and 23/41 lesions (sensitivity 56.1%). Bone scintigraphy detected only 2/7 patients (sensitivity 28.6%) and 15/41 lesions (sensitivity 36.6%). Furthermore, CT and scintigraphy led to false-positive findings of bone metastases in 2 patients and in 1 patient, respectively. The sensitivity of PET/MRI and MRI alone was significantly better compared with CT (
p
< 0.01, difference 43.9%) and bone scintigraphy (
p
< 0.01, difference 63.4%).
Conclusion
18
FFDG PET/MRI and MRI are significantly better than CT or bone scintigraphy for the detection of bone metastases in patients with newly diagnosed breast cancer. Both CT and bone scintigraphy show a substantially limited sensitivity in detection of bone metastases.
Key Points
•
18
FFDG PET/MRI and MRI alone are significantly superior to CT and bone scintigraphy for the detection of bone metastases in patients with newly diagnosed breast cancer.
•
Radiation-free whole-body MRI might serve as modality of choice in detection of bone metastases in breast cancer patients.
Age assessment is regularly used in clinical routine by pediatric endocrinologists to determine the physical development or maturity of children and adolescents. Our study investigates whether age ...assessment can be performed using CT scout views from thoracic and abdominal CT scans using a deep neural network. Hence, we retrospectively collected 1949 CT scout views from pediatric patients (acquired between January 2013 and December 2018) to train a deep neural network to predict the chronological age from CT scout views. The network was then evaluated on an independent test set of 502 CT scout views (acquired between January 2019 and July 2020). The trained model showed a mean absolute error of 1.18 ± 1.14 years on the test data set. A one-sided t-test to determine whether the difference between the predicted and actual chronological age was less than 2.0 years was statistically highly significant (p < 0.001). In addition, the correlation coefficient was very high (R = 0.97). In conclusion, the chronological age of pediatric patients can be assessed with high accuracy from CT scout views using a deep neural network.
Our purpose was to investigate differences between PET/MRI and PET/CT in lesion detection and classification in oncologic whole-body examinations and to investigate radiation exposure differences ...between the 2 modalities.
In this observational single-center study, 1,003 oncologic examinations (918 patients; mean age, 57.8 ± 14.4 y) were included. Patients underwent PET/CT and subsequent PET/MRI (149.8 ± 49.7 min after tracer administration). Examinations were reviewed by radiologists and nuclear medicine physicians in consensus. Additional findings, characterization of indeterminate findings on PET/CT, and missed findings on PET/MRI, including their clinical relevance and effective dose of both modalities, were investigated. The McNemar test was used to compare lesion detection between the 2 hybrid imaging modalities (
< 0.001, indicating statistical significance).
Additional information on PET/MRI was reported for 26.3% (264/1,003) of examinations, compared with PET/CT (
< 0.001). Of these, additional malignant findings were detected in 5.3% (53/1,003), leading to a change in TNM staging in 2.9% (29/1,003) due to PET/MRI. Definite lesion classification of indeterminate PET/CT findings was possible in 11.1% (111/1,003) with PET/MRI. In 2.9% (29/1,003), lesions detected on PET/CT were not visible on PET/MRI. Malignant lesions were missed in 1.2% (12/1,003) on PET/MRI, leading to a change in TNM staging in 0.5% (5/1,003). The estimated mean effective dose for whole-body PET/CT amounted to 17.6 ± 8.7 mSv, in comparison to 3.6 ± 1.4 mSv for PET/MRI, resulting in a potential dose reduction of 79.6% (
< 0.001).
PET/MRI facilitates staging comparable to that of PET/CT and improves lesion detectability in selected cancers, potentially helping to promote fast, efficient local and whole-body staging in 1 step, when additional MRI is recommended. Furthermore, younger patients may benefit from the reduced radiation exposure of PET/MRI.
Background
Manual quantification of the metabolic tumor volume (MTV) from whole-body
18
F-FDG PET/CT is time consuming and therefore usually not applied in clinical routine. It has been shown that ...neural networks might assist nuclear medicine physicians in such quantification tasks. However, little is known if such neural networks have to be designed for a specific type of cancer or whether they can be applied to various cancers. Therefore, the aim of this study was to evaluate the accuracy of a neural network in a cancer that was not used for its training.
Methods
Fifty consecutive breast cancer patients that underwent
18
F-FDG PET/CT were included in this retrospective analysis. The PET-Assisted Reporting System (PARS) prototype that uses a neural network trained on lymphoma and lung cancer
18
F-FDG PET/CT data had to detect pathological foci and determine their anatomical location. Consensus reads of two nuclear medicine physicians together with follow-up data served as diagnostic reference standard; 1072
18
F-FDG avid foci were manually segmented. The accuracy of the neural network was evaluated with regard to lesion detection, anatomical position determination, and total tumor volume quantification.
Results
If PERCIST measurable foci were regarded, the neural network displayed high per patient sensitivity and specificity in detecting suspicious
18
F-FDG foci (92%; CI = 79–97% and 98%; CI = 94–99%). If all FDG-avid foci were regarded, the sensitivity degraded (39%; CI = 30–50%). The localization accuracy was high for body part (98%; CI = 95–99%), region (88%; CI = 84–90%), and subregion (79%; CI = 74–84%). There was a high correlation of AI derived and manually segmented MTV (
R
2
= 0.91;
p
< 0.001). AI-derived whole-body MTV (HR = 1.275; CI = 1.208–1.713;
p
< 0.001) was a significant prognosticator for overall survival. AI-derived lymph node MTV (HR = 1.190; CI = 1.022–1.384;
p
= 0.025) and liver MTV (HR = 1.149; CI = 1.001–1.318;
p
= 0.048) were predictive for overall survival in a multivariate analysis.
Conclusion
Although trained on lymphoma and lung cancer, PARS showed good accuracy in the detection of PERCIST measurable lesions. Therefore, the neural network seems not prone to the clever Hans effect. However, the network has poor accuracy if all manually segmented lesions were used as reference standard. Both the whole body and organ-wise MTV were significant prognosticators of overall survival in advanced breast cancer.
Objectives
To reduce the dose of intravenous iodine-based contrast media (ICM) in CT through virtual contrast-enhanced images using generative adversarial networks.
Methods
Dual-energy CTs in the ...arterial phase of 85 patients were randomly split into an 80/20 train/test collective. Four different generative adversarial networks (GANs) based on image pairs, which comprised one image with virtually reduced ICM and the original full ICM CT slice, were trained, testing two input formats (2D and 2.5D) and two reduced ICM dose levels (−50% and −80%). The amount of intravenous ICM was reduced by creating virtual non-contrast series using dual-energy and adding the corresponding percentage of the iodine map. The evaluation was based on different scores (L1 loss, SSIM, PSNR, FID), which evaluate the image quality and similarity. Additionally, a visual Turing test (VTT) with three radiologists was used to assess the similarity and pathological consistency.
Results
The −80% models reach an SSIM of > 98%, PSNR of > 48, L1 of between 7.5 and 8, and an FID of between 1.6 and 1.7. In comparison, the −50% models reach a SSIM of > 99%, PSNR of > 51, L1 of between 6.0 and 6.1, and an FID between 0.8 and 0.95. For the crucial question of pathological consistency, only the 50% ICM reduction networks achieved 100% consistency, which is required for clinical use.
Conclusions
The required amount of ICM for CT can be reduced by 50% while maintaining image quality and diagnostic accuracy using GANs. Further phantom studies and animal experiments are required to confirm these initial results.
Key Points
•
The amount of contrast media required for CT can be reduced by 50% using generative adversarial networks
.
•
Not only the image quality but especially the pathological consistency must be evaluated to assess safety
.
•
A too pronounced contrast media reduction could influence the pathological consistency in our collective at 80%
.
Recently, radiomics has emerged as a non-invasive, imaging-based tissue characterization method in multiple cancer types. One limitation for robust and reproducible analysis lies in the inter-reader ...variability of the tumor annotations, which can potentially cause differences in the extracted feature sets and results. In this study, the diagnostic potential of a rapid and clinically feasible VOI (Volume of Interest)-based approach to radiomics is investigated to assess MR-derived parameters for predicting molecular subtype, hormonal receptor status, Ki67- and HER2-Expression, metastasis of lymph nodes and lymph vessel involvement as well as grading in patients with breast cancer.
A total of 98 treatment-naïve patients (mean 59.7 years, range 28.0-89.4) with BI-RADS 5 and 6 lesions who underwent a dedicated breast MRI prior to therapy were retrospectively included in this study. The imaging protocol comprised dynamic contrast-enhanced T1-weighted imaging and T2-weighted imaging. Tumor annotations were obtained by drawing VOIs around the primary tumor lesions followed by thresholding. From each segmentation, 13.118 quantitative imaging features were extracted and analyzed with machine learning methods. Validation was performed by 5-fold cross-validation with 25 repeats.
Predictions for molecular subtypes obtained AUCs of 0.75 (HER2-enriched), 0.73 (triple-negative), 0.65 (luminal A) and 0.69 (luminal B). Differentiating subtypes from one another was highest for HER2-enriched vs triple-negative (AUC 0.97), followed by luminal B vs triple-negative (0.86). Receptor status predictions for Estrogen Receptor (ER), Progesterone Receptor (PR) and Hormone receptor positivity yielded AUCs of 0.67, 0.69 and 0.69, while Ki67 and HER2 Expressions achieved 0.81 and 0.62. Involvement of the lymph vessels could be predicted with an AUC of 0.8, while lymph node metastasis yielded an AUC of 0.71. Models for grading performed similar with an AUC of 0.71 for Elston-Ellis grading and 0.74 for the histological grading.
Our preliminary results of a rapid approach to VOI-based tumor-annotations for radiomics provides comparable results to current publications with the perks of clinical suitability, enabling a comprehensive non-invasive platform for breast tumor decoding and phenotyping.
For CT pulmonary angiograms, a scout view obtained in anterior-posterior projection is usually used for planning. For bolus tracking the radiographer manually locates a position in the CT scout view ...where the pulmonary trunk will be visible in an axial CT pre-scan. We automate the task of localizing the pulmonary trunk in CT scout views by deep learning methods. In 620 eligible CT scout views of 563 patients between March 2003 and February 2020 the region of the pulmonary trunk as well as an optimal slice ("reference standard") for bolus tracking, in which the pulmonary trunk was clearly visible, was annotated and used to train a U-Net predicting the region of the pulmonary trunk in the CT scout view. The networks' performance was subsequently evaluated on 239 CT scout views from 213 patients and was compared with the annotations of three radiographers. The network was able to localize the region of the pulmonary trunk with high accuracy, yielding an accuracy of 97.5% of localizing a slice in the region of the pulmonary trunk on the validation cohort. On average, the selected position had a distance of 5.3 mm from the reference standard. Compared to radiographers, using a non-inferiority test (one-sided, paired Wilcoxon rank-sum test) the network performed as well as each radiographer (P < 0.001 in all cases). Automated localization of the region of the pulmonary trunk in CT scout views is possible with high accuracy and is non-inferior to three radiographers.
18 F-FDG-PET/MRI in patients with Graves’ orbitopathy Weber, Manuel; Deuschl, Cornelius; Bechrakis, Nikolaos ...
Graefe's archive for clinical and experimental ophthalmology,
10/2021, Letnik:
259, Številka:
10
Journal Article
Recenzirano
Odprti dostop
Purpose
Currently, therapeutic management of patients with Graves’ orbitopathy (GO) relies on clinical assessments and MRI. However, monitoring of inflammation remains difficult since external ...inflammatory signs do not necessarily represent the orbital disease activity. Therefore, we aimed to evaluate the diagnostic value of
18
F-FDG-PET/MRI to assess the inflammation of GO patients.
Methods
Enrolled patients with new onset of GO underwent ophthalmological examinations to evaluate the activity (CAS) and severity of GO (NOSPECS), as well as an
18
F-FDG-PET/MRI (Siemens Biograph mMR) with dual time point imaging (immediately post-injection and 60 min p.i.). A subset of PET parameters including maximum standardized uptake value (SUVmax), metabolic target volume (MTV), and total lesion glycolysis (TLG) were obtained separately per eye and per extraocular eye muscle (EOM). EOM thickness was measured on the co-registered MRI.
Results
Of 14 enrolled patients, three showed mild, seven moderate-to-severe, and four sight-threatening GO. Patients with severe GO showed statistically significant higher TLG than patients with mild GO (
p
= 0.02) and higher MTV than patients with mild (
p
= 0.03) and moderate (
p
= 0.04) GO. Correlation between NOSPECS on one hand and MTV and TLG on the other was significant (
R
2
= 0.49–0.61).
Conclusion
TLG and MTV derived from FDG-PET appear to be good discriminators for severe vs. mild-to-moderate GO and show a significant correlation with NOSPECS. As expected, PET parameters of individual eye muscles were not correlated with associated eye motility, since fibrosis, and not inflammation, is mainly responsible for restricted motility. In conclusion,
18
F-FDG-PET/MRI can be used for assessment of GO inflammation.
In this retrospective study, we aimed to predict the body height and weight of pediatric patients using CT localizers, which are overview scans performed before the acquisition of the CT. We trained ...three commonly used networks (EfficientNetV2-S, ResNet-18, and ResNet-34) on a cohort of 1009 and 1111 CT localizers of pediatric patients with recorded body height and weight (between January 2013 and December 2019) and validated them in an additional cohort of 116 and 127 localizers (acquired in 2020). The best-performing model was then tested in an independent cohort of 203 and 225 CT localizers (acquired between January 2021 and March 2023). In addition, a cohort of 1401 and 1590 localizers from younger adults (acquired between January 2013 and December 2013) was added to the training set to determine if it could improve the overall accuracy. The EfficientNetV2-S using the additional adult cohort performed best with a mean absolute error of 5.58 ± 4.26 cm for height and 4.25 ± 4.28 kg for weight. The relative error was 4.12 ± 4.05% for height and 11.28 ± 12.05% for weight. Our study demonstrated that automated estimation of height and weight in pediatric patients from CT localizers can be performed.
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