Absolute Quantification in Diagnostic SPECT/CT: The Phantom Premise

Establishment and validation of novel predictive models to predict bone metastasis in newly diagnosed prostate adenocarcinoma based on single-photon emission computed tomography radiomics

PURPOSE

To establish and validate novel predictive models for predicting bone metastasis (BM) in newly diagnosed prostate adenocarcinoma (PCa) via single-photon emission computed tomography radiomics.

METHOD

In a retrospective review of the clinical single-photon emission computed tomography (SPECT) database, 176 patients (training set: n = 140; validation set: n = 36) who underwent SPECT/CT imaging and were histologically confirmed to have newly diagnosed PCa from June 2016 to June 2022 were enrolled. Radiomic features were extracted from the region of interest (ROI) in a targeted lesion in each patient. Clinical features, including age, total prostate-specific antigen (t-PSA), and Gleason grades, were included. Statistical tests were then employed to eliminate irrelevant and redundant features. Finally, four types of optimized models were constructed for the prediction. Furthermore, fivefold cross-validation was applied to obtain sensitivity, specificity, accuracy, and area under the curve (AUC) for performance evaluation. The clinical usefulness of the multivariate models was estimated through decision curve analysis (DCA).

RESULTS

A radiomics signature consisting of 27 selected features which were obtained by radiomics' LASSO treatment was significantly correlated with bone status (P < 0.01 for both training and validation sets). Collectively, the models showed good predictive efficiency. The AUC values ranged from 0.87 to 0.98 in four models. The AUC values of the human experts were 0.655 and 0.872 in the training and validation groups, respectively. Most radiomic models showed better diagnostic accuracy than human experts in the training and validation groups. DCA also demonstrated the superiority of the radiomics models compared to human experts.

CONCLUSION

Radiomics models are superior to humans in differentiating between benign bone and prostate cancer bone metastases; it can be used to facilitate personalized prediction of BM in newly diagnosed PCa patients.

Brachytherapy and 3D printing for skin cancer: A review paper

Brachytherapy is a type of radiation therapy, in which a radiation source is placed directly or close to a tumor. It is commonly used to treat skin cancer, and enables precise irradiation treatment of affected area (planning target volume - PTV) while minimizing exposure dose to surrounding healthy tissue (organs at risk - OARs). Recently, the use of 3D printing has begun revolutionizing brachytherapy, as it allows manufacturing of custom-designed applicators for unique shape of skin topography, tumor, and surrounding tissues. Outcome of the combination of 3D printing and brachytherapy has several advantages over traditional treatment planning methods. Some of the advantages are intuitive, whereas others can be concluded from a literature overview as follows: 1) Possibility of developing patient-specific applicators that precisely match the shape of tumor area; 2) Reduction of the time required for applicator production, especially when custom-made devices are needed; 3) Reduction of manufacturing costs; 4) Treatment procedures improvement; 5) Improvement of safety measures accelerated by the development of smart materials (e.g., polymer filaments with admixture of heavy elements); 6) Possibility of nearly instant adjustment into tumor treatment (applicators can be changed as the tumor is changing its shape); and 7) Applicators designed to securely fit to treatment area to hold radioactive source always in the same place for each fraction. Consequently, tumor-provided dose is accurate and leads to effective treatment. In this review paper, we investigated the current state-of-the-art of the application of 3D printing in brachytherapy. A number of existing reports were chosen and reviewed in terms of printing technology, materials used, treatment effectiveness, and fabrication protocols. Furthermore, the development of future directions that should be considered by collaborative teams bridging different fields of science, such as medicine, physics, chemistry, and material science were summarized. With the indicated topics, we hope to stimulate the innovative progress of 3D printing technology in brachytherapy.

3D printed non-uniform anthropomorphic phantoms for quantitative SPECT

BACKGROUND

A 3D printing grid-based method was developed to construct anthropomorphic phantoms with non-uniform activity distributions, to be used for evaluation of quantitative SPECT images. The aims were to characterize the grid-based method and to evaluate its capability to provide realistically shaped phantoms with non-uniform activity distributions.

METHODS

Characterization of the grid structures was performed by printing grid-filled spheres. Evaluation was performed by micro-CT imaging to investigate the printing accuracy and by studying the modulation contrast ([Formula: see text]) in SPECT images for 177Lu and 99mTc as a function of the grid fillable-volume fraction (FVF) determined from weighing. The grid-based technique was applied for the construction of two kidney phantoms and two thyroid phantoms, designed using templates from the XCAT digital phantoms. The kidneys were constructed with a hollow outer container shaped as cortex, an inner grid-based structure representing medulla and a solid section representing pelvis. The thyroids consisted of two lobes printed as grid-based structures, with void hot spots within the lobes. The phantoms were filled with solutions of 177Lu (kidneys) or 99mTc (thyroids) and imaged with SPECT. For verification, Monte Carlo simulations of SPECT imaging were performed for activity distributions corresponding to those of the printed phantoms. Measured and simulated SPECT images were compared qualitatively and quantitatively.

RESULTS

Micro-CT images showed that printing inaccuracies were mainly uniform across the grid. The relationships between the FVF from weighing and [Formula: see text] were found to be linear (r = 0.9995 and r = 0.9993 for 177Lu and 99mTc, respectively). The FVF-deviations from the design were up to 15% for thyroids and 4% for kidneys, mainly related to possibilities of cleaning after printing. Measured and simulated SPECT images of kidneys and thyroids exhibited similar activity distributions and quantitative comparisons agreed well, thus verifying the grid-based method.

CONCLUSIONS

We find the grid-based technique useful for the provision of 3D printed, realistically shaped, phantoms with non-uniform activity distributions, which can be used for evaluation of different quantitative methods in SPECT imaging.

Association between body mass index (BMI) and [123I]Ioflupane (DaTSCAN) availabilities in patients with parkinsonism using single-photon emission computed tomography-computed tomography (SPECT-CT)

AIM

[123I]Ioflupane (DaTSCAN) has a high binding affinity to the dopamine (DA) transporter (DaT) and tenfold less affinity to serotonin (5-HT) transporter (SERT). Both neurotransmitters are considered to contribute to body weight regulation. This study assesses the association between body mass index (BMI) and DaTSCAN availability in brain.

METHOD

Scans from 74 consecutive patients who had undergone DaTSCAN single-photon emission computed tomography-computed tomography (SPECT-CT) were used to obtain semi- and absolute quantitative data in several volumes of interest (VOIs). Relative semi-quantitative specific binding ratios (SBRs) from Chang attenuated SPECT were obtained from GE DaTQUANT. Absolute normalised concentration (NC) was calculated from attenuation/scatter corrected SPECT-CT images, using an adapted version of the EARL Ltd (European Association of Nuclear Medicine (EANM) Research 4 Life) template. Scans were subdivided into either degenerative parkinsonism (abnormal = 49), borderline (n = 14) or scan without evidence of dopaminergic deficit (SWEDD = 11) using visual assessment and SBR values by two nuclear medicine consultants.

RESULTS

SBRs did not correlate with BMI. However, NC values correlated negatively in the entire cohort, with the strongest correlation in the frontal (r = - 0.649. p = 0.000), occipital (r = - 0.555, p = 0.000) regions and pons (r = - 0.555, p = 0.000). In the abnormal (n = 49) and SWEDD group (n = 11), NC of the frontal region was the most correlated with BMI (r = - 0.570, p = 0.000; r = - 0.813, p = 0.002, respectively). In the borderline group (n = 14), the left posterior putamen displayed the strongest correlation (r = - 0.765, p = 0.001).

CONCLUSION

Absolute NC values demonstrate a strong inverse correlation with BMI, strongest in the extrastriatal regions. Due to the predominately non-overlapping distribution of DaT and SERT, this study suggests greater involvement of SERT in obesity with possible interplay with DA transmission.

Eliminating the need for manual segmentation to determine size and volume from MRI. A proof of concept on segmenting the lateral ventricles

Manual segmentation, which is tedious, time-consuming, and operator-dependent, is currently used as the gold standard to validate automatic and semiautomatic methods that quantify geometries from 2D and 3D MR images. This study examines the accuracy of manual segmentation and generalizes a strategy to eliminate its use. Trained individuals manually measured MR lateral ventricles images of normal and hydrocephalus infants from 1 month to 9.5 years of age. We created 3D-printed models of the lateral ventricles from the MRI studies and accurately estimated their volume by water displacement. MRI phantoms were made from the 3D models and images obtained. Using a previously developed artificial intelligence (AI) algorithm that employs four features extracted from the images, we estimated the ventricular volume of the phantom images. The algorithm was certified when discrepancies between the volumes-gold standards-yielded by the water displacement device and those measured by the automation were smaller than 2%. Then, we compared volumes after manual segmentation with those obtained with the certified automation. As determined by manual segmentation, lateral ventricular volume yielded an inter and intra-operator variation up to 50% and 48%, respectively, while manually segmenting saggital images generated errors up to 71%. These errors were determined by direct comparisons with the volumes yielded by the certified automation. The errors induced by manual segmentation are large enough to adversely affect decisions that may lead to less-than-optimal treatment; therefore, we suggest avoiding manual segmentation whenever possible.

Synthesis and Bioevaluation of Novel Technetium-99m-Labeled Complexes with Norfloxacin HYNIC Derivatives for Bacterial Infection Imaging

To seek a novel ^99mTc-labeled quinolone derivative for bacterial infection SPECT imaging that aims to lower nontarget organ uptake, a novel norfloxacin 6-hydrazinoicotinamide (HYNIC) derivative (HYNICNF) was designed and synthesized. It was radiolabeled with different coligands, such as tricine, trisodium triphenylphosphine-3,3',3″-trisulfonate (TPPTS), sodium triphenylphosphine-3-monosulfonate (TPPMS), and ethylenediamine-N,N'-diacetic acid (EDDA), to obtain three ^99mTc-labeled norfloxacin HYNIC complexes, namely, [^99mTc]Tc-tricine-TPPTS-HYNICNF, [^99mTc]Tc-tricine-TPPMS-HYNICNF, and [^99mTc]Tc-EDDA-HYNICNF. These complexes were purified (RCP > 95%) and evaluated in vitro and in vivo for targeting bacteria. All three complexes are hydrophilic, maintain good stability, and specifically bind Staphylococcus aureus in vitro. The biodistribution in mice with bacterial infection demonstrated that [^99mTc]Tc-EDDA-HYNICNF showed a higher abscess uptake and lower nontarget organ uptake and was able to distinguish bacterial infection and sterile inflammation. Single photon emission computed tomography (SPECT) image study in bacterial infection mice showed there was a visible accumulation in the infection site, suggesting that [^99mTc]Tc-EDDA-HYNICNF is a potential radiotracer for bacterial infection imaging.