Since the Bosniak cysts classification is highly reader-dependent, automated tools based on radiomics could help in the diagnosis of the lesion. This study is an initial step in the search for radiomic features that may be good classifiers of benign–malignant Bosniak cysts in machine learning models. A CCR phantom was used through five CT scanners. Registration was performed with ARIA software, while Quibim Precision was used for feature extraction. R software was used for the statistical analysis. Robust radiomic features based on repeatability and reproducibility criteria were chosen. Excellent correlation criteria between different radiologists during lesion segmentation were imposed. With the selected features, their classification ability in benignity–malignity terms was assessed. From the phantom study, 25.3% of the features were robust. For the study of inter-observer correlation (ICC) in the segmentation of cystic masses, 82 subjects were prospectively selected, finding 48.4% of the features as excellent regarding concordance. Comparing both datasets, 12 features were established as repeatable, reproducible, and useful for the classification of Bosniak cysts and could serve as initial candidates for the elaboration of a classification model. With those features, the Linear Discriminant Analysis model classified the Bosniak cysts in terms of benignity or malignancy with 88.2% accuracy.

Background Since it was first approved in Europe in 2016, the gallium-68 (68Ga) radiopharmaceutical [68Ga]Ga-DOTA-TOC has been widely used for imaging of somatostatin receptor (SSTR) positive tumours using positron emission tomography–computed tomography (PET/CT). Significant patient benefits have been reported, so its use is rapidly increasing. However, few studies have been published regarding occupational doses to nuclear medicine personnel handling this radiopharmaceutical, despite its manual usage at low distances from the skin and the beta-emission decay scheme, which may result in an increased absorbed dose to their hands. In this context, this study aims to analyse the occupational exposure during the administration of [68Ga]Ga-DOTA-TOC for PET/CT imaging. For this purpose, extremity, eye lens and whole-body dosimetry in terms of Hp(0.07), Hp(3) and Hp(10), respectively, was conducted on six workers with both thermoluminescent dosimeters, and personal electronic dosimeters. Results The non-dominant hand is more exposed to radiation than the dominant hand, with the thumb and the index fingertip being the most exposed sites on this hand. Qualitative analysis showed that when no shielding is used during injection, doses increase significantly more in the dominant than in the non-dominant hand, so the use of shielding is strongly recommended. While wrist dosimeters may significantly underestimate doses to the hands, placing a ring dosimeter at the base of the ring or middle finger of the non-dominant hand may give a valuable estimation of maximum doses to the hands if at least a correction factor of 5 is applied. Personal equivalent doses for the eyes did not result in measurable values (i.e., above the lowest detection limit) for almost all workers. The extrapolated annual dose estimations showed that there is compliance with the annual dose limits during management of [68Ga]Ga-DOTA-TOC for diagnostics with PET in the hospital included in this study. Conclusions Imaging with [68Ga]Ga-DOTA-TOC is a safe process for the workers performing the administration of the radiopharmaceutical, including intravenous injection to the patient and the pre- and post-activity control, as it is highly unlikely that annual dose limits will be exceeded if good working practices and shielding are used.

Background Selective internal radiation therapy (SIRT) with 90Y radioembolization aims to selectively irradiate liver tumors by administering radioactive microspheres under the theragnostic assumption that the pre-therapy injection of 99mTc labeled macro-aggregated albumin (99mTc-MAA) provides an estimation of the 90Y microspheres biodistribution, which is not always the case. Due to the growing interest in theragnostic dosimetry for personalized radionuclide therapy, a robust relationship between the delivered and pre-treatment radiation doses is required. In this work, we aim to investigate the predictive value of absorbed dose metrics calculated from 99mTc-MAA (simulation) compared to those obtained from 90Y post-therapy SPECT/CT.Results A total of 79 patients were analyzed. Pre- and post-therapy 3D-voxel dosimetry was calculated on 99mTc-MAA and 90Y SPECT/CT, respectively, based on Local Deposition Method (LDM). Mean absorbed dose, tumor-to-normal ratio, and dose distribution in terms of dose-volume histogram (DVH) metrics were obtained and compared for each volume of interest (VOIs). Mann-Whitney U-test and Pearson’s correlation coefficient was used to assess the correlation between both methods. The effect of the tumoral liver volume on the absorbed dose metrics was also investigated. Strong correlation was found between simulation and therapy mean absorbed doses for all VOIs, although simulation tended to overestimate tumor absorbed doses by 26%. DVH metrics showed good correlation too, but significant differences were found for several metrics, mostly on non-tumoral liver. It was observed that the tumoral liver volume does not significantly affect the differences between simulation and therapy absorbed dose metrics.Conclusion This study supports the strong correlation between absorbed dose metrics from simulation and therapy dosimetry based on 90Y SPECT/CT, highlighting the predictive ability of 99mTc-MAA, not only in terms of mean absorbed dose but also of the dose distribution.

Background The use of lutetium-177 (177Lu)-based radiopharmaceuticals in peptide receptor nuclear therapy is increasing, but so is the number of nuclear medicine workers exposed to higher levels of radiation. In recent years, [177Lu]Lu-DOTA-TATE has begun to be widely used for the treatment of neuroendocrine tumours. However, there are few studies evaluating the occupational radiation exposure during its administration, and there are still some challenges that can result in higher doses to the staff, such as a lack of trained personnel or fully standardised procedures. In response, this study aims to provide a comprehensive analysis of occupational doses to the staff involved in the administration of [177Lu]Lu-DOTA-TATE. Results A total of 32 administrations of [177Lu]Lu-DOTA-TATE (7.4 GBq/session) carried out by a physician and a nurse, were studied. In total, two physicians and four nurses were independently monitored with cumulative (passive) and/or real-time (active) dosemeters. Extremity, eye lens and whole-body doses were evaluated in terms of the dosimetric quantities Hp(0.07), Hp(3) and Hp(10), respectively. It was obtained that lead aprons reduced dose rates and effective doses by 71% and 69% for the physician, respectively, and by 56% and 68% for the nurse. On average, normalised Hp(10) showed lower values with active (0.65 ± 0.18 µSv/GBq) than with passive (11.6 ± 2.9 µSv/GBq) dosemeters, so further measurements are needed. For physicians, the median of the maximum normalised Hp(0.07) values was 41.5 µSv/GBq on the non-dominant hand and 45.2 µSv/GBq on the dominant hand. For nurses 15.4 µSv/GBq on the non-dominant and 13.9 µSv/GBq on the dominant hand. The ratio between the maximum dose measured on the hand and the dose measured on the base of the middle/ring finger of the non-dominant hand resulted in a factor of 5/6 for the physicians and 3/4 for the nurses. Finally, maximum normalised Hp(3) doses resulted in 2.02 µSv/GBq for physicians and 1.76 µSv/GBq for nurses. Conclusions If appropriate safety measures are taken, the administration of [177Lu]Lu-DOTA-TATE is a safe procedure for workers. However, regular monitoring is recommended to ensure that the annual dose limits are not exceeded.

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Background Selective internal radiation therapy with 90Y radioembolization aims to selectively irradiate liver tumours by administering radioactive microspheres under the theragnostic assumption that the pre-therapy injection of 99mTc labelled macroaggregated albumin (99mTc-MAA) provides an estimation of the 90Y microspheres biodistribution, which is not always the case. Due to the growing interest in theragnostic dosimetry for personalized radionuclide therapy, a robust relationship between the delivered and pre-treatment radiation absorbed doses is required. In this work, we aim to investigate the predictive value of absorbed dose metrics calculated from 99mTc-MAA (simulation) compared to those obtained from 90Y post-therapy SPECT/CT. Results A total of 79 patients were analysed. Pre- and post-therapy 3D-voxel dosimetry was calculated on 99mTc-MAA and 90Y SPECT/CT, respectively, based on Local Deposition Method. Mean absorbed dose, tumour-to-normal ratio, and absorbed dose distribution in terms of dose-volume histogram (DVH) metrics were obtained and compared for each volume of interest (VOI). Mann–Whitney U-test and Pearson’s correlation coefficient were used to assess the correlation between both methods. The effect of the tumoral liver volume on the absorbed dose metrics was also investigated. Strong correlation was found between simulation and therapy mean absorbed doses for all VOIs, although simulation tended to overestimate tumour absorbed doses by 26%. DVH metrics showed good correlation too, but significant differences were found for several metrics, mostly on non-tumoral liver. It was observed that the tumoral liver volume does not significantly affect the differences between simulation and therapy absorbed dose metrics. Conclusion This study supports the strong correlation between absorbed dose metrics from simulation and therapy dosimetry based on 90Y SPECT/CT, highlighting the predictive ability of 99mTc-MAA, not only in terms of mean absorbed dose but also of the dose distribution.

Purpose: Population-based screening programs for the early detection of breast cancer have significantly reduced mortality in women, but they are resource intensive in terms of time, cost, and workload and still have limitations mainly due to the use of 2D imaging techniques, which may cause overlapping of tissues, and interobserver variability. Artificial intelligence (AI) systems may be a valuable tool to assist radiologist when reading and classifying mammograms based on the malignancy of the detected lesions. However, there are several factors that can influence the outcome of a mammogram and thus also the detection capability of an AI system. The aim of our work is to analyze the robustness of the diagnostic ability of an AI system designed for breast cancer detection.Approach: Mammograms from a population-based screening program were scored with the AI system. The sensitivity and specificity by means of the area under the receiver operating characteristic (ROC) curve were obtained as a function of the mammography unit manufacturer, demographic characteristics, and several factors that may affect the image quality (age, breast thickness and density, compression applied, beam quality, and delivered dose). Results:The area under the curve (AUC) from the scoring ROC curve was 0.92 (95% confidence interval = 0.89 − 0.95). It showed no dependence with any of the parameters considered, as the differences in the AUC for different interval values were not statistically significant. Conclusion:The results suggest that the AI system analyzed in our work has a robust diagnostic capability, and that its accuracy is independent of the studied parameters.

Entrevista con Mercedes Riveira-Martín