Comparison of reprojected bone SPECT/CT and planar bone scintigraphy for the detection of bone metastases in breast and prostate cancer

Objective The aim of this study was to compare reprojected bone SPECT/CT (RBS) against planar bone scintigraphy (BS) in the detection of bone metastases in breast and prostate cancer patients. Methods Twenty-six breast and 105 prostate cancer patients with high risk for bone metastases underwent Tc-99m-HMDP BS and whole-body SPECT/CT, 1.5-T whole-body diffusion-weighted MRI and F-18-NaF or F-18-PSMA-1007 PET/CT within two prospective clinical trials (NCT01339780 and NCT03537391). Consensus reading of all imaging modalities and follow-up data were used to define the reference standard diagnosis. The SPECT/CT data were reprojected into anterior and posterior views to produce RBS images. Both BS and RBS images were independently double read by two pairs of experienced nuclear medicine physicians. The findings were validated against the reference standard diagnosis and compared between BS and RBS on the patient, region and lesion levels. Results All metastatic patients detected by BS were also detected by RBS. In addition, three metastatic patients were missed by BS but detected by RBS. The average patient-level sensitivity of two readers for metastases was 75% for BS and 87% for RBS, and the corresponding specificity was 79% for BS and 39% for RBS. The average region-level sensitivity of two readers was 64% for BS and 69% for RBS, and the corresponding specificity was 96% for BS and 87% for RBS. Conclusion Whole-body bone SPECT/CT can be reprojected into more familiar anterior and posterior planar images with excellent sensitivity for bone metastases, making additional acquisition of planar BS unnecessary.Peer reviewe

Comparison of different methods for post-therapeutic dosimetry in [177Lu]Lu-PSMA-617 radioligand therapy

Background Dosimetry is of high importance for optimization of patient-individual PSMA-targeted radioligand therapy (PSMA-RLT). The aim of our study was to evaluate and compare the feasibility of different approaches of image-based absorbed dose estimation in terms of accuracy and effort in clinical routine. Methods Whole-body planar images and SPECT/CT images were acquired from 24 patients and 65 cycles at 24h, 48h, and ≥96h after administration of a mean activity of 6.4 GBq [177Lu]Lu-PSMA-617 (range 3–10.9 GBq). Dosimetry was performed by use of the following approaches: 2D planar-based dosimetry, 3D SPECT/CT-based dosimetry, and hybrid dosimetry combining 2D and 3D data. Absorbed doses were calculated according to IDAC 2.1 for the kidneys, the liver, the salivary glands, and bone metastases. Results Mean absorbed doses estimated by 3D dosimetry (the reference method) were 0.54 ± 0.28 Gy/GBq for the kidneys, 0.10 ± 0.05 Gy/GBq for the liver, 0.81 ± 0.34 Gy/GBq for the parotid gland, 0.72 ± 0.39 Gy/GBq for the submandibular gland, and 1.68 ± 1.32 Gy/GBq for bone metastases. Absorbed doses of normal organs estimated by hybrid dosimetry showed small, non-significant differences (median up to 4.0%) to the results of 3D dosimetry. Using 2D dosimetry, in contrast, significant differences (median up to 10.9%) were observed. Regarding bone metastases, small, but significant differences (median up to 7.0%) of absorbed dose were found for both, 2D dosimetry and hybrid dosimetry. Bland-Altman analysis revealed high agreement between hybrid dosimetry and 3D dosimetry for normal organs and bone metastases, but substantial differences between 2D dosimetry and 3D dosimetry. Conclusion Hybrid dosimetry provides high accuracy in estimation of absorbed dose in comparison to 3D dosimetry for all important organs and is therefore feasible for use in individualized PSMA-RLT

Comparison of reprojected bone SPECT/CT and planar bone scintigraphy for the detection of bone metastases in breast and prostate cancer

Objective: The aim of this study was to compare reprojected bone SPECT/CT (RBS) against planar bone scintigraphy (BS) in the detection of bone metastases in breast and prostate cancer patients.Methods: Twenty-six breast and 105 prostate cancer patients with high risk for bone metastases underwent 99mTc-HMDP BS and whole-body SPECT/CT, 1.5-T whole-body diffusion-weighted MRI and 18F-NaF or 18F-PSMA-1007 PET/CT within two prospective clinical trials (NCT01339780 and NCT03537391). Consensus reading of all imaging modalities and follow-up data were used to define the reference standard diagnosis. The SPECT/CT data were reprojected into anterior and posterior views to produce RBS images. Both BS and RBS images were independently double read by two pairs of experienced nuclear medicine physicians. The findings were validated against the reference standard diagnosis and compared between BS and RBS on the patient, region and lesion levels.Results: All metastatic patients detected by BS were also detected by RBS. In addition, three metastatic patients were missed by BS but detected by RBS. The average patient-level sensitivity of two readers for metastases was 75% for BS and 87% for RBS, and the corresponding specificity was 79% for BS and 39% for RBS. The average region-level sensitivity of two readers was 64% for BS and 69% for RBS, and the corresponding specificity was 96% for BS and 87% for RBS.Conclusion: Whole-body bone SPECT/CT can be reprojected into more familiar anterior and posterior planar images with excellent sensitivity for bone metastases, making additional acquisition of planar BS unnecessary.</p

Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation.

Artificial intelligence (AI) will change the face of nuclear medicine and molecular imaging as it will in everyday life. In this review, we focus on the potential applications of AI in the field, both from a physical (radiomics, underlying statistics, image reconstruction and data analysis) and a clinical (neurology, cardiology, oncology) perspective. Challenges for transferability from research to clinical practice are being discussed as is the concept of explainable AI. Finally, we focus on the fields where challenges should be set out to introduce AI in the field of nuclear medicine and molecular imaging in a reliable manner

Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges

Deep learning is a subfield of state-of-the-art artificial intelligence (AI) technology, and multiple deep learning-based AI models have been applied to musculoskeletal diseases. Deep learning has shown the capability to assist clinical diagnosis and prognosis prediction in a spectrum of musculoskeletal disorders, including fracture detection, cartilage and spinal lesions identification, and osteoarthritis severity assessment. Meanwhile, deep learning has also been extensively explored in diverse tumors such as prostate, breast, and lung cancers. Recently, the application of deep learning emerges in bone tumors. A growing number of deep learning models have demonstrated good performance in detection, segmentation, classification, volume calculation, grading, and assessment of tumor necrosis rate in primary and metastatic bone tumors based on both radiological (such as X-ray, CT, MRI, SPECT) and pathological images, implicating a potential for diagnosis assistance and prognosis prediction of deep learning in bone tumors. In this review, we first summarized the workflows of deep learning methods in medical images and the current applications of deep learning-based AI for diagnosis and prognosis prediction in bone tumors. Moreover, the current challenges in the implementation of the deep learning method and future perspectives in this field were extensively discussed

Implementation of a clinical dosimetry workflow to perform personalized dosimetry for internal radiotherapy

La médecine nucléaire est une spécialité médicale qui étudie la physiologie des organes et le métabolisme de divers types de tumeurs. La médecine nucléaire utilise des produits pharmaceutiques liés à un isotope radioactif. La radiothérapie interne vectorisée (RIV) est une spécialité de la médecine nucléaire où le vecteur est dirigé vers des cibles, généralement des tumeurs, et où l'action des rayonnements ionisants vise à détruire les tumeurs. Le suivi et l'optimisation de la RMT nécessitent l'évaluation de l'irradiation délivrée au patient (dosimétrie). Il y a un manque de standardisation en dosimétrie interne. Cette thèse propose une approche standardisée avec des flux de travail descriptifs pour la dosimétrie clinique. Un logiciel appelé OpenDose3D, basé sur 3D-Slicer en tant que module open source mettant en oeuvre les flux de travail proposés, est développé, validé et mis à la disposition du public. Le module a été utilisé en recherche clinique dans le projet MEDIRAD.Nuclear medicine is a medical specialty that studies the physiology of organs and the metabo-lism of various types of tumors. Nuclear medicine uses pharmaceuticals bound to a radioactive isotope. Molecular radiotherapy (MRT) is a specialty of nuclear medicine where the vector is directed to targets, usually tumors, and the action of ionizing radiation is aimed at destroying tumors. The follow-up and optimization of MRT requires the evaluation of the irradiation delivered to the patient (dosimetry). There is a lack of standardization in internal dosimetry. This thesis provides a standardized approach with descriptive clinical dosimetry workflows. A software named OpenDose3D, based in 3D-Slicer and implementing the proposed workflows was developed, validated and was made publicly available as an open source module. The module was used in clinical research within the MEDIRAD project

Personalised advanced 3D dosimetry in peptide receptor radionuclide therapy

Peptide Receptor Radionuclide Therapy is one of the most efficient therapies against Neuro endocrine tumours. In clinical practice, absorbed dose calculations are computed based on the Medical Internal Radiation Dose (MIRD) schema which is not planned or optimised for patient-specific characteristics. This PhD project has aimed to assess the impact that advanced personalised 3D dosimetry can have within a Molecular Radiotherapy (MRT) treatment with an image-based dosimetry component. For this purpose, the impact of image registration algorithms has been studied, comparing rigid and non-rigid schemes. Results showed that nonrigid algorithms performed better than rigid equivalents in aligning images to the same frame of reference. The non-rigid algorithm was then used to investigate a workflow which involved dose maps instead of SPECT images, because such analysis has not previously been reported in the literature. Raydose, a Monte Carlo-based software package, was used to perform 3D personalised dosimetry; the results were compared against the calculations carried out with OLINDA/EXM, a MIRD-based software system. Differences were statistically significant only for kidneys and lesions (p-value<0.005). Finally, a new segmentation method for tumour delineation is described and its performance compared with a manual segmentation performed by expert 2 physicians. JACCARD analysis showed that the two methods do not have a good overlap (mean JACCARD coefficient = 0.29). From visual assessment, the proposed approach obtained better results than the manual segmentation according to the target tissue characteristics. Furthermore, quantitative analysis showed that the manual segmentation significantly overestimates the volume by 3.7 ± 13.3 cc (p-value<0.05), while it significantly underestimates the dose by -2.67 ± 6.8 Gy (p-value<0.05) compared to the proposed method. This study has demonstrated the importance of assessing accurate personalised 3D absorbed dose distribution to lesions and organs at risk. It also has the potentia

Developments in 177Lu-based radiopharmaceutical therapy and dosimetry

177Lu is a radioisotope that has become increasingly popular as a therapeutic agent for treating various conditions, including neuroendocrine tumors and metastatic prostate cancer. 177Lu-tagged radioligands are molecules precisely designed to target and bind to specific receptors or proteins characteristic of targeted cancer. This review paper will present an overview of the available 177Lu-labelled radioligands currently used to treat patients. Based on recurring, active, and completed clinical trials and other available literature, we evaluate current status, interests, and developments in assessing patient-specific dosimetry, which will define the future of this particular treatment modality. In addition, we will discuss the challenges and opportunities of the existing dosimetry standards to measure and calculate the radiation dose delivered to patients, which is essential for ensuring treatments’ safety and efficacy. Finally, this article intends to provide an overview of the current state of 177Lu- tagged radioligand therapy and highlight the areas where further research can improve patient treatment outcomes