Comparing the performance of a deep learning-based lung gross tumour volume segmentation algorithm before and after transfer learning in a new hospital.

Objectives: Radiation therapy for lung cancer requires a gross tumour volume (GTV) to be carefully outlined by a skilled radiation oncologist (RO) to accurately pinpoint high radiation dose to a malignant mass while simultaneously minimizing radiation damage to adjacent normal tissues. This is manually intensive and tedious however, it is feasible to train a deep learning (DL) neural network that could assist ROs to delineate the GTV. However, DL trained on large openly accessible data sets might not perform well when applied to a superficially similar task but in a different clinical setting. In this work, we tested the performance of DL automatic lung GTV segmentation model trained on open-access Dutch data when used on Indian patients from a large public tertiary hospital, and hypothesized that generic DL performance could be improved for a specific local clinical context, by means of modest transfer-learning on a small representative local subset. Methods: X-ray computed tomography (CT) series in a public data set called "NSCLC-Radiomics" from The Cancer Imaging Archive was first used to train a DL-based lung GTV segmentation model (Model 1). Its performance was assessed using a different open access data set (Interobserver1) of Dutch subjects plus a private Indian data set from a local tertiary hospital (Test Set 2). Another Indian data set (Retrain Set 1) was used to fine-tune the former DL model using a transfer learning method. The Indian data sets were taken from CT of a hybrid scanner based in nuclear medicine, but the GTV was drawn by skilled Indian ROs. The final (after fine-tuning) model (Model 2) was then re-evaluated in "Interobserver1" and "Test Set 2." Dice similarity coefficient (DSC), precision, and recall were used as geometric segmentation performance metrics. Results: Model 1 trained exclusively on Dutch scans showed a significant fall in performance when tested on "Test Set 2." However, the DSC of Model 2 recovered by 14 percentage points when evaluated in the same test set. Precision and recall showed a similar rebound of performance after transfer learning, in spite of using a comparatively small sample size. The performance of both models, before and after the fine-tuning, did not significantly change the segmentation performance in "Interobserver1." Conclusions: A large public open-access data set was used to train a generic DL model for lung GTV segmentation, but this did not perform well initially in the Indian clinical context. Using transfer learning methods, it was feasible to efficiently and easily fine-tune the generic model using only a small number of local examples from the Indian hospital. This led to a recovery of some of the geometric segmentation performance, but the tuning did not appear to affect the performance of the model in another open-access data set. Advances in knowledge: Caution is needed when using models trained on large volumes of international data in a local clinical setting, even when that training data set is of good quality. Minor differences in scan acquisition and clinician delineation preferences may result in an apparent drop in performance. However, DL models have the advantage of being efficiently "adapted" from a generic to a locally specific context, with only a small amount of fine-tuning by means of transfer learning on a small local institutional data set.

Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology.

Cancer is a fatal disease and the second most cause of death worldwide. Treatment of cancer is a complex process and requires a multi-modality-based approach. Cancer detection and treatment starts with screening/diagnosis and continues till the patient is alive. Screening/diagnosis of the disease is the beginning of cancer management and continued with the staging of the disease, planning and delivery of treatment, treatment monitoring, and ongoing monitoring and follow-up. Imaging plays an important role in all stages of cancer management. Conventional oncology practice considers that all patients are similar in a disease type, whereas biomarkers subgroup the patients in a disease type which leads to the development of precision oncology. The utilization of the radiomic process has facilitated the advancement of diverse imaging biomarkers that find application in precision oncology. The role of imaging biomarkers and artificial intelligence (AI) in oncology has been investigated by many researchers in the past. The existing literature is suggestive of the increasing role of imaging biomarkers and AI in oncology. However, the stability of radiomic features has also been questioned. The radiomic community has recognized that the instability of radiomic features poses a danger to the global generalization of radiomic-based prediction models. In order to establish radiomic-based imaging biomarkers in oncology, the robustness of radiomic features needs to be established on a priority basis. This is because radiomic models developed in one institution frequently perform poorly in other institutions, most likely due to radiomic feature instability. To generalize radiomic-based prediction models in oncology, a number of initiatives, including Quantitative Imaging Network (QIN), Quantitative Imaging Biomarkers Alliance (QIBA), and Image Biomarker Standardisation Initiative (IBSI), have been launched to stabilize the radiomic features.

A dosimetric comparison of systemic peptide receptor radionuclide therapy and intra-arterial peptide receptor radionuclide therapy in patients with liver dominant gastroenteropancreatic neuroendocrine tumours.

OBJECTIVES: Intra-arterial radionuclide therapy (IART) treatment allows direct delivery of 177 Lu-DOTATATE to the overexpressed somatostatin-positive neuroendocrine liver metastases, which led to higher tumour concentration compared with systemic radionuclide therapy (SRT). The aim was to evaluate and compare the absorbed doses of both IART and SRT to organs and hepatic metastatic sites. METHODS: A total of 48 patients received SRT and IART. In SRT, activity was administered intravenously, whereas in IART, activity was administered directly into hepatic arteries. The sequential whole-body images were acquired at 2, 4, 24, 72 and 160 h. The reconstructed whole-body planar and single-photon emission computed tomography-computed tomography images were processed using the Dosimetry Toolkit for the estimation of normalized cumulated activity in the organs and tumour lesions. The absorbed dose was computed using OLINDA EXM 2.0 software. RESULTS: The median absorbed dose (mGy/MBq) of kidneys and spleen in IART was compared with SRT and found to be decreased by 30.7% ( P  = 0.03) and 37.5% ( P  = 0.08), whereas it was found to be increased by 40% ( P  = 0.26) and 8.1% ( P  = 0.28) in the liver and lungs. The median dose (mGy/MBq) of tumours determined in IART was found to be increased by 62.2% ( P  = 0.04). CONCLUSION: IART with 177 Lu-DOTATATE significantly increases tumour dose while reducing overall systemic toxicity in comparison to SRT treatment. After considering the maximum tolerance limit of kidneys in peptide receptor radionuclide therapy, the number of treatment cycles and injected activity can be optimized further with IART for better response and survival.

Systematic review and meta-analysis of prediction models used in cervical cancer.

BACKGROUND: Cervical cancer is one of the most common cancers in women with an incidence of around 6.5 % of all the cancer in women worldwide. Early detection and adequate treatment according to staging improve the patient's life expectancy. Outcome prediction models might aid treatment decisions, but a systematic review on prediction models for cervical cancer patients is not available. DESIGN: We performed a systematic review for prediction models in cervical cancer following PRISMA guidelines. Key features that were used for model training and validation, the endpoints were extracted from the article and data were analyzed. Selected articles were grouped based on prediction endpoints i.e. Group1: Overall survival, Group2: progression-free survival; Group3: recurrence or distant metastasis; Group4: treatment response; Group5: toxicity or quality of life. We developed a scoring system to evaluate the manuscript. As per our criteria, studies were divided into four groups based on scores obtained in our scoring system, the Most significant study (Score > 60 %); Significant study (60 % > Score > 50 %); Moderately Significant study (50 % > Score > 40 %); least significant study (score < 40 %). A meta-analysis was performed for all the groups separately. RESULTS: The first line of search selected 1358 articles and finally 39 articles were selected as eligible for inclusion in the review. As per our assessment criteria, 16, 13 and 10 studies were found to be the most significant, significant and moderately significant respectively. The intra-group pooled correlation coefficient for Group1, Group2, Group3, Group4, and Group5 were 0.76 [0.72, 0.79], 0.80 [0.73, 0.86], 0.87 [0.83, 0.90], 0.85 [0.77, 0.90], 0.88 [0.85, 0.90] respectively. All the models were found to be good (prediction accuracy [c-index/AUC/R2] >0.7) in endpoint prediction. CONCLUSIONS: Prediction models of cervical cancer toxicity, local or distant recurrence and survival prediction show promising results with reasonable prediction accuracy [c-index/AUC/R2 > 0.7]. These models should also be validated on external data and evaluated in prospective clinical studies.

Clinical Concept-Based Radiology Reports Classification Pipeline for Lung Carcinoma.

Rising incidence and mortality of cancer have led to an incremental amount of research in the field. To learn from preexisting data, it has become important to capture maximum information related to disease type, stage, treatment, and outcomes. Medical imaging reports are rich in this kind of information but are only present as free text. The extraction of information from such unstructured text reports is labor-intensive. The use of Natural Language Processing (NLP) tools to extract information from radiology reports can make it less time-consuming as well as more effective. In this study, we have developed and compared different models for the classification of lung carcinoma reports using clinical concepts. This study was approved by the institutional ethics committee as a retrospective study with a waiver of informed consent. A clinical concept-based classification pipeline for lung carcinoma radiology reports was developed using rule-based as well as machine learning models and compared. The machine learning models used were XGBoost and two more deep learning model architectures with bidirectional long short-term neural networks. A corpus consisting of 1700 radiology reports including computed tomography (CT) and positron emission tomography/computed tomography (PET/CT) reports were used for development and testing. Five hundred one radiology reports from MIMIC-III Clinical Database version 1.4 was used for external validation. The pipeline achieved an overall F1 score of 0.94 on the internal set and 0.74 on external validation with the rule-based algorithm using expert input giving the best performance. Among the machine learning models, the Bi-LSTM_dropout model performed better than the ML model using XGBoost and the Bi-LSTM_simple model on internal set, whereas on external validation, the Bi-LSTM_simple model performed relatively better than other 2. This pipeline can be used for clinical concept-based classification of radiology reports related to lung carcinoma from a huge corpus and also for automated annotation of these reports.

Bone metastases in prostate cancer - Gallium-68-labeled prostate-specific membrane antigen or Fluorine 18 sodium fluoride PET/computed tomography - the better tracer?

OBJECTIVE: The objective was to assess the roles of 68Ga-PSMA PET/CT and 18F-NaF PET/CT in evaluation of skeletal metastatic lesions in prostate cancer. METHODS: Two hundred consecutive prostate cancer patients who had undergone 68Ga-PSMA PET/CT and 18F-NaF PET/CT at baseline evaluation (n = 80) and following suspected recurrence or disease progression (restaging) (n = 120) were analyzed retrospectively. RESULTS: PSMA and NAF scans were positive for skeletal metastatic lesions in 67% (134 patients) and negative in 33% (66 patients). The scans were concordant in 80% (160 patients: 66 negative and 94 positive) and discordant in 20% (40 patients). Among 40 discordant results, 14 were baseline and 26 were restaging studies. PSMA detected more number of lesions in 11 (nine baseline and two restaging). These were true positive marrow or lytic metastatic lesions. NaF revealed more number of lesions in 29 (5 initial and 24 restaging). These were false positive on follow-up imaging. No statistical difference (P value = 0.7 by McNemar test) between the two scans for identifying absence or presence of at least one skeletal lesion was noted at baseline staging. CONCLUSION: Though, both 18F-NaF and 68Ga-PSMA are excellent tracers for evaluation of skeletal metastases in prostate cancer, there is a distinct advantage of 68Ga-PSMA PET/CT due to detection of additional skeletal lesions and absence of false positive lesions. In addition, absence of PSMA avidity in healed metastases in the restaging setting opens up new avenue for assessment of response of skeletal metastases.

Radiomics: a quantitative imaging biomarker in precision oncology.

Cancer treatment is heading towards precision medicine driven by genetic and biochemical markers. Various genetic and biochemical markers are utilized to render personalized treatment in cancer. In the last decade, noninvasive imaging biomarkers have also been developed to assist personalized decision support systems in oncology. The imaging biomarkers i.e., radiomics is being researched to develop specific digital phenotype of tumor in cancer. Radiomics is a process to extract high throughput data from medical images by using advanced mathematical and statistical algorithms. The radiomics process involves various steps i.e., image generation, segmentation of region of interest (e.g. a tumor), image preprocessing, radiomic feature extraction, feature analysis and selection and finally prediction model development. Radiomics process explores the heterogeneity, irregularity and size parameters of the tumor to calculate thousands of advanced features. Our study investigates the role of radiomics in precision oncology. Radiomics research has witnessed a rapid growth in the last decade with several studies published that show the potential of radiomics in diagnosis and treatment outcome prediction in oncology. Several radiomics based prediction models have been developed and reported in the literature to predict various prediction endpoints i.e., overall survival, progression-free survival and recurrence in various cancer i.e., brain tumor, head and neck cancer, lung cancer and several other cancer types. Radiomics based digital phenotypes have shown promising results in diagnosis and treatment outcome prediction in oncology. In the coming years, radiomics is going to play a significant role in precision oncology.

Dosimetry in Lu-177-PSMA-617 prostate-specific membrane antigen targeted radioligand therapy: a systematic review.

BACKGROUND: 177Lu-prostate-specific membrane antigen (PSMA) gained popularity as a choice of agent in the treatment of patients with advanced prostate cancer or metastatic castration-resistant stage of prostate carcinoma (mCRPC) diseases. However, this treatment may cause fatal effects, probably due to unintended irradiation of normal organs. We performed an extensive systematic review to assess the organs at risk and the absorbed dose received by tumor lesions in 177Lu-PSMA therapy. DESIGN: In this review, published peer-reviewed articles that cover clinical dosimetry in patients following peptide radionuclide ligand therapy using 177Lu-PSMA have been included. Two senior researchers independently checked the articles for inclusion. A systematic search in the database was made using PubMed, Publons and DOAJ. All selected articles were categorized into three groups: (1) clinical studies with the technical description of dosimetry in 177Lu-PSMA therapy (2) organ dosimetry in 177Lu-PSMA therapy or (3) tumor dosimetry in 177Lu-PSMA therapy. RESULT: In total, 182 citations were identified on PSMA therapy and 17 original articles on 177Lu-PSMA dosimetry were recognized as eligible for review. The median absorbed dose per unit of administered activity for kidneys, salivary, liver, spleen, lacrimal and bone marrow was 0.55, 0.81, 0.1, 0.1, 2.26 and 0.03 Gy/GBq, respectively. The median absorbed dose per unit of activity for tumor lesions was found in a range of 2.71-10.94 Gy/GBq. CONCLUSION: 177Lu-PSMA systemic radiation therapy (SRT) is a well-tolerated and reliable treatment option against the management of the mCRPC stage of prostate carcinoma. Lacrimal glands and salivary glands are the major critical organs in 177Lu-PSMA SRT. Besides, tumors receive 3-6 times higher absorbed doses compared to organs at risk.

Analysis of absorbed dose in radioimmunotherapy with 177Lu-trastuzumab using two different imaging scenarios: a pilot study.

OBJECTIVES: Internal organ dosimetry is an important procedure to demonstrate the reliable application of 177Lu-trastuzumab radioimmunotherapy for human epidermal growth factor receptor-positive metastatic breast cancers. We are reporting the first human dosimetry study for 177Lu-trastuzumab. Another objective of our study was to calculate and compare the absorbed doses for normal organs and tumor lesions in patients before radioimmunotherapy with 177Lu-trastuzumab using two different imaging scenarios. METHODS: Eleven patients (48.27 ± 8.95 years) with a history of metastatic breast cancer were included in the study. Postadministration of 177Lu-trastuzumab (351.09 ± 23.89 MBq/2 mg), acquisition was performed using planar and hybrid imaging scenarios at 4, 24, 72 and 168 h. Single-photon emission computed tomography/computed tomography imaging was performed at 72 h postinjection. Acquired images were processed using Dosimetry Toolkit software for the estimation of normalized cumulated activity in organs and tumor lesions. OLINDA/EXM 2.0 software was used for absorbed dose calculation in both scenarios. RESULTS: Significant difference in normalized cumulated activity and the absorbed dose is noted between two imaging scenarios for the organs and tumor lesions (P < 0.05). Mean absorbed dose (mGy/MBq) estimated from heart, lungs, liver, spleen, kidney, adrenal, pancreas and colon using planar and hybrid scenarios were 0.81 ± 0.19 and 0.63 ± 0.17; 0.75 ± 0.13 and 0.32 ± 0.06; 1.26 ± 0.25 and 1.01 ± 0.17; 0.68 ± 0.22 and 0.53 ± 0.16; 0.91 ± 0.3 and 0.69 ± 0.24; 0.18 ± 0.04 and 0.11 ± 0.02; 0.25 ± 0.22 and 0.09 ± 0.02 and 0.75 ± 0.61 and 0.44 ± 0.28, respectively. CONCLUSIONS: On the basis of our dosimetric evaluation, we concluded that radioimmunotherapy with 177Lu-trastuzumab is well tolerated to be implemented in routine clinical practice against HER2 positive metastatic breast cancer. Liver is the main critical organ at risk. Hybrid scenario demonstrated significantly lower absorbed doses in organs and tumors compared to the multiplanar method.

Emerging role of artificial intelligence in nuclear medicine.

The role of artificial intelligence is increasing in all branches of medicine. The emerging role of artificial intelligence applications in nuclear medicine is going to improve the nuclear medicine clinical workflow in the coming years. Initial research outcomes are suggestive of increasing role of artificial intelligence in nuclear medicine workflow, particularly where selective automation tasks are of concern. Artificial intelligence-assisted planning, dosimetry and procedure execution appear to be areas for rapid and significant development. The role of artificial intelligence in more directly imaging-related tasks, such as dose optimization, image corrections and image reconstruction, have been particularly strong points of artificial intelligence research in nuclear medicine. Natural Language Processing (NLP)-based text processing task is another area of interest of artificial intelligence implementation in nuclear medicine.

Normal Skeletal Standardized Uptake Values Obtained from Quantitative Single-Photon Emission Computed Tomography/Computed Tomography: Time-Dependent Study on Breast Cancer Patients.

AIM: To estimate the standard uptake values (SUVs) of Tc-99m methylene-diphosphonate (Tc-99m MDP) from normal skeletal sites in breast cancer patients using quantitative single-photon emission computed tomography (SPECT). MATERIALS AND METHODS: A total of 60 breast cancer patients who underwent Tc-99m MDP SPECT/CT study at different postinjection acquisition times were included in this study. Based on postinjection acquisition time, patients were divided into four study groups (n_15 each), i.e. Ist (2 h), IInd (3 h), IIIrd (4 h), and IVth (5 h). Image quantification (SUVmax and SUVmean) was performed using Q.Metrix software. Delineation of volume of interest was shaped around different bones of the skeletal system. RESULTS: The highest normal SUVmax and SUVmean values were observed in lumber and thoracic vertebra (8.89 ± 2.26 and 2.89 ± 0.58) for Group I and in pelvis and thoracic (9.6 ± 1.32 and 3.04 ± 0.64), (10.93 ± 3.91 and 3.65 ± 0.97), (11.33 ± 2.67 and 3.65 ± 0.22) for Group II, III and IV, respectively. Lowest normal SUVmax and SUVmean values were observed in humerus and ribs (3.22 ± 0.67 and 0.97 ± 0.18), (5.16 ± 1.82 and 1.18 ± 0.16) for Group I, IV, and in humerus (3.17 ± 0.58 and 0.85 ± 0.26), (3.98 ± 1.12 and 1.04 ± 0.28) for Group II and III, respectively. Significant difference (P < 0.05) noted in SUVmax for sternum, cervical, humerus, ribs, and pelvis with respect to time. However, significant difference (P < 0.05) noted in SUVmean for all skeletal sites with respect to time. CONCLUSIONS: Our study shows variability in normal SUV values for different skeletal sites in breast cancer patients. Vertebral bodies and pelvis contribute highest SUV values. Time dependency of SUVs emphasizes the usefulness of routinely acquired images at the same time after Tc-99m MDP injection, especially in follow-up studies.

Performance characteristic evaluation of a bismuth germanate-based high-sensitivity 5-ring discovery image quality positron emission tomography/computed tomography system as per National Electrical Manufacturers Association NU 2-2012.

National Electrical Manufacturers Association (NEMA) provides guidelines to assess the performance of Positron Emission Tomography (PET). A PET/CT scanner, Discovery IQ, GE Medical systems, Milwaukee, USA was installed in our department which has high a sensitivity PET component. We have performed the NEMA NU-2 2012 quality control tests to evaluate this system on site before clinical use. Performance measurements of the PET scanner were made using the NEMA NU2-2012 procedures for spatial resolution, scatter fraction, sensitivity, count rate loss and random coincidence estimation, Noise Equivalent Count Rate (NECR) and image quality. As per NU2 2012, spatial resolution was measured at 1 cm, 10 cm and 20 cm vertically from the centre and at each of these points resolution was measured at tangential, radial and axial directions. Sensitivity was measured at centre and 10 cm off center vertically from the center. The system sensitivity is reported as an average of the two measured values. Scatter fraction and NECR measurements, Image quality test was also performed. The tangential, radial and axial FWHM were 4.99 mm, 4.20 mm and 4.79 mm at 1 cm off centre, 5.49 mm, 4.69 mm and 4.81 mm at 10 cm off centre and 7.99 mm, 5.07 mm and 4.95 mm at 20 cm off centre respectively. The absolute sensitivity of this scanner was found to be 20.1 cps/kBq. The scatter fraction calculated from the decay method was 37.94% and NECR was 125 kcps. The peak NECR was achieved at activity concentration of 8.7 KBq/ml and the count loss below the peak NECR was found to be 0.68%. Image quality test for, contrast recovery, background variability and lung error residual mean met all specifications. Overall PET performance of Discovery IQ whole-body scanner was satisfactory and the scanner met all the performance specifications required by NEMA 2012.

Software development for hepatopulmonary shunt estimation by gamma camera method in transarterial radioembolization.

Trans-arterial radioembolization (TARE) is an established treatment for inoperable hepatocellular carcinoma and liver metastases from Carcinoma of gastrointestinal tract. Radiation-induced toxicity to the lung parenchyma is the dose-limiting factor in TARE. Pretreatment hepatopulmonary shunt (HPS) is estimated by gamma camera method by transarterial administration of 370MBq 99mTc macro aggregated albumin. We have developed HPS software on XELERIS-1.123 workstation, GE medical systems, Milwaukee, USA, for accurate calculation of HPS. This software has also been tested on a higher version of XELERIS workstation, and it has been found to work well in all versions.

Reduction of Radiation Exposure to Patients and Professionals by Reducing the Administered Activity of 18F-Fluorodeoxyglucose in a Positron-emission Tomography/Computed Tomography Study.

AIM: With increased clinical indications for positron-emission tomography/computed tomography (PET/CT) and repeated PET/CT scans, there is a need to reduce the radiation burden to the patient, professionals as well as public. This requires a redefining of the workflow and the 18-F-fluorodeoxyglucose (18F-FDG) administered activity. The objective of our study is to observe the impact of strike out reduction of administered activity on the radiation exposure to personnel and public, as well as the absorbed dose to the patient with no compromise on image quality by increasing the image acquisition time. MATERIALS AND METHODS: Nineteen patients evaluated in this study (11 males, 8 females) were put into two groups, namely, A and B. Patients in Group A (n = 10) were administered with 18F-FDG equivalent to the recommended dose (7-8 MBq/kg body weight) whereas patients in Group B (n = 9) were administered with 18F-FDG equivalent to half the recommended dose (3-4MBq/kg body weight). The exposure rates from the patients at the body surface and 100 cm distance were measured immediately and 1 h postinjection. RESULTS: The average surface dose rate and 100 cm dose rate of the adult patients immediately postinjection for patients of Group A were 0.94 ± 0.19 mSv/h and 0.057 ± 0.007 mSv/h, and for Group B were 0.34 ± 0.24 mSv/h and 0.031 ± 0.01 mSv/h. CONCLUSION: This study suggests that reduction in injected 18F-FDG activity reduces the radiation exposure rate from the patient, absorbed dose to the patient with reportable image quality.

A Novel 141Ce-Based Flood Field Phantom: Assessment of Suitability for Daily Uniformity Testing in a Clinical Nuclear Medicine Department.

Daily quality control testing of a γ-camera is of the utmost importance in assessing whether the camera is suitable for clinical use. The aim of our study was to assess the suitability of a fillable 141Ce-based flood field phantom developed in-house for daily quality control testing of γ-cameras. Methods: Daily uniformity testing was performed for 113 d using the fillable 141Ce phantom and a commercially available sheet-type 57Co phantom, and the results were compared. Results: The average integral uniformity obtained by the 141Ce and 57Co phantoms was 3.24% and 2.72%, respectively, for detector 1 and 3.31% and 2.78%, respectively, for detector 2. Conclusion: The 141Ce phantom we developed is a suitable alternative to the commercially available 57Co phantom.

Impact of the activity calculation method used in transarterial radioembolization: a dosimetric comparison between 90Y-SIRSphere and 90Y-TheraSphere therapy.

PURPOSE: Transarterial radioembolization is used to treat primary and secondary liver malignancies. Two commercially available drugs are utilized for the purpose. The aim of our study is to compare the radiation dose delivered to the tumor by these drugs. MATERIALS AND METHODS: This study included 86 patients (M : F - 7.6 : 1, median age=50.5 years), 46 patients were treated by Y-TheraSphere and 42 patients were treated by Y-SIRSphere. Activity administered in Y-TheraSphere and Y-SIRSphere was calculated using a modified partition model and a modified body surface area model, respectively. The radiation dose delivered by two drugs was calculated and compared in our study. RESULT: Activity administered in Y-TheraSphere was significantly higher than that of Y-SIRSphere. Hence, the radiation dose delivered to the tumor by Y-SIRSphere was significantly lower (58.4%) than that of Y-TheraSphere (P=0.000). CONCLUSION: As the radiation dose delivered by Y-SIRSphere was lower than Y-TheraSphere, we believe that the formula for Y-SIRSphere activity calculation needs to be modified so that the optimal dose can be delivered to the tumor.

A rare cause of tube arcing artifact seen in computed tomography image of a positron emission tomography/computed tomography scanner.

Tube arcing artifact is known to be caused by a temporary short circuit in the X-ray tube causing momentary loss of X-ray output. It is seen as near-parallel and an equidistant streak pattern on transaxial computed tomography (CT) images and as a "horizontal" hypodense band on the coronal and sagittal CT images. This artifact can be a random occurrence and was caused in this particular case due to voltage fluctuations in the high-voltage supply transformer supplying the rotor of the anode in the X-ray tube. This problem was initially corrected by reducing the tube voltage to 120 kV from the original 140 kV and, subsequently, replacing the faulty transformer. This kind of artifact, which is a very rare situation, can affect the image quality, and could also be an early sign of equipment failure. To the authors' knowledge, such an artifact has not been reported till date in a clinical scenario. Hence, we would like to report a rare situation of tube arcing artifact along with a unique remedy.

18-Month Performance Assessment of Gemini TF 16 PET/CT System in a High-Volume Department.

UNLABELLED: Acceptance testing is a set of quality control tests performed to verify various manufacturer-specified parameters before a newly installed PET/CT system can be accepted for clinical use. A new PET/CT system, Gemini TF 16, installed in our department in September 2012 has a PET component capable of time-of-flight imaging using lutetium-yttrium-oxyorthosilicate crystals and operates in 3-dimensional mode. Our aim was to evaluate the system before acceptance and observe the consistency of its performance during high-volume work for 18 mo after installation (we perform an average of 30 PET/CT scans daily). METHODS: We performed NEMA (National Electrical Manufacturers Association) NU-2 2007 acceptance testing on the Gemini TF 16; continuously evaluated its gain calibration, timing resolution, and energy resolution during the subsequent 18 mo; and analyzed the results. RESULTS: The system passed the acceptance testing and showed few fluctuations in energy and timing resolutions during the observation period. CONCLUSION: The Gemini TF 16 whole-body PET/CT system performed excellently during the 18-mo study period despite the high volume of work.

Designing of High-Volume PET/CT Facility with Optimal Reduction of Radiation Exposure to the Staff: Implementation and Optimization in a Tertiary Health Care Facility in India.

Positron emission tomography (PET) has been in use for a few decades but with its fusion with computed tomography (CT) in 2001, the new PET/CT integrated system has become very popular and is now a key influential modality for patient management in oncology. However, along with its growing popularity, a growing concern of radiation safety among the radiation professionals has become evident. We have judiciously developed a PET/CT facility with optimal shielding, along with an efficient workflow to perform high volume procedures and minimize the radiation exposure to the staff and the general public by reducing unnecessary patient proximity to the staff and general public.