Cutaneous squamous cell carcinoma of the lip successfully treated with Rhenium-188 brachytherapy

Cutaneous squamous cell carcinoma (SCC) is the second most common form of skin cancer. In most cases, non-invasive SCC has a good prognosis and is curable by surgical resection. Nevertheless, a small percentage of patients pose specific management problems due to the technical difficulty of maintaining function and aesthetics because of the size or location of the tumor. An emerging therapeutic approach with high-dose brachytherapy using a nonsealed Rhenium-188 resin, commercially known as Rhenium-SCT®, has shown to be highly effective in non-invasive carcinoma, up to a thickness of 2-3 mm

Impact of SPECT corrections on 3D-dosimetry for TARE

Purpose: Many centers aim to plan liver transarterial radioembolization (TARE) with dosimetry, even without CT-based attenuation correction (AC), or with unoptimized scatter correction (SC) methods. This work investigates the impact of presence vs absence of such corrections, and limited spatial resolution, on 3D dosimetry for TARE. Methods: Three voxelized phantoms were derived from CT images of real patients with different body sizes. Simulations of 99mTc-SPECT projections were performed with the SIMIND code, assuming three activity distributions in the liver: uniform, inside a "liver's segment," or distributing multiple uptaking nodules ("nonuniform liver"), with a tumoral liver/healthy parenchyma ratio of 5:1. Projection data were reconstructed by a commercial workstation, with OSEM protocol not specifically optimized for dosimetry (spatial resolution of 12.6 mm), with/without SC (optimized, or with parameters predefined by the manufacturer; dual energy window), and with/without AC. Activity in voxels was calculated by a relative calibration, assuming identical microspheres and 99mTc-SPECT counts spatial distribution. 3D dose distributions were calculated by convolution with 90Y voxel S-values, assuming permanent trapping of microspheres. Cumulative dose-volume histograms in lesions and healthy parenchyma from different reconstructions were compared with those obtained from the reference biodistribution (the "gold standard," GS), assessing differences for D95%, D70%, and D50% (i.e., minimum value of the absorbed dose to a percentage of the irradiated volume). γ tool analysis with tolerance of 3%/13 mm was used to evaluate the agreement between GS and simulated cases. The influence of deep-breathing was studied, blurring the reference biodistributions with a 3D anisotropic gaussian kernel, and performing the simulations once again. Results: Differences of the dosimetric indicators were noticeable in some cases, always negative for lesions and distributed around zero for parenchyma. Application of AC and SC reduced systematically the differences for lesions by 5%–14% for a liver segment, and by 7%–12% for a nonuniform liver. For parenchyma, the data trend was less clear, but the overall range of variability passed from −10%/40% for a liver segment, and −10%/20% for a nonuniform liver, to −13%/6% in both cases. Applying AC, SC with preset parameters gave similar results to optimized SC, as confirmed by γ tool analysis. Moreover, γ analysis confirmed that solely AC and SC are not sufficient to obtain accurate 3D dose distribution. With breathing, the accuracy worsened severely for all dosimetric indicators, above all for lesions: with AC and optimized SC, −38%/−13% in liver's segment, −61%/−40% in the nonuniform liver. For parenchyma, D50% resulted always less sensitive to breathing and sub-optimal correction methods (difference overall range: −7%/13%). Conclusions: Reconstruction protocol optimization, AC, SC, PVE and respiratory motion corrections should be implemented to obtain the best possible dosimetric accuracy. On the other side, thanks to the relative calibration, D50% inaccuracy for the healthy parenchyma from absence of AC was less than expected, while the optimization of SC was scarcely influent. The relative calibration therefore allows to perform TARE planning, basing on D50% for the healthy parenchyma, even without AC or with suboptimal corrections, rather than rely on nondosimetric methods

Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics

In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds great promise for improving the quality of life of PCa patients, through managing and processing a vast amount of molecular imaging data and beyond, using a multi-omics approach and improving patients' risk-stratification for tailored medicine. Artificial intelligence (AI) and radiomics may allow clinicians to improve the overall efficiency and accuracy of using these "big data" in both the diagnostic and theragnostic field: from technical aspects (such as semi-automatization of tumor segmentation, image reconstruction, and interpretation) to clinical outcomes, improving a deeper understanding of the molecular environment of PCa, refining personalized treatment strategies, and increasing the ability to predict the outcome. This systematic review aims to describe the current literature on AI and radiomics applied to molecular imaging of prostate cancer

The Italian multicentre dosimetric study for lesion dosimetry in 223Ra therapy of bone metastases: Calibration protocol of gamma cameras and patient eligibility criteria

Purpose The aims of this work were to explore patient eligibility criteria for dosimetric studies in 223Ra therapy and evaluate the effects of differences in gamma camera calibration procedures into activity quantification. Methods Calibrations with 223Ra were performed with four gamma cameras (3/8-inch crystal) acquiring planar static images with double-peak (82 and 154 keV, 20% wide) and MEGP collimator. The sensitivity was measured in air by varying activity, source-detector distance, and source diameter. Transmission curves were measured for attenuation/scatter correction with the pseudo-extrapolation number method, varying the experimental setup. 223Ra images of twenty-five patients (69 lesions) were acquired to study the lesions visibility. Univariate ROC analysis was performed considering visible/non visible lesions on 223Ra images as true positive/true negative group, and using as score value the lesion/soft tissue contrast ratio (CR) derived from 99mTc-MDP WB scan. Results Sensitivity was nearly constant varying activity and distance (maximum s.d. = 2%). Partial volume effects were negligible for object area ⩾960 mm2. Transmission curve measurements are affected by experimental setup and source size, leading to activity quantification errors up to 20%. The ROC analysis yielded an AUC of 0.972 and an optimal threshold of CR of 10, corresponding to an accuracy of 92%. Conclusion The minimum calibration protocol requires sensitivity and transmission curve measurements varying the object size, performing a careful procedure standardisation. Lesions with 99mTc-MDP CR higher than 10, not overlapping the GI tract, are generally visible on 223Ra images acquired at 24 h after the administration, and possibly eligible for dosimetric studie

Dosimetry of bone metastases in targeted radionuclide therapy with alpha-emitting (223)Ra-dichloride

Ra-dichloride is an alpha-emitting radiopharmaceutical used in the treatment of bone metastases from castration-resistant prostate cancer. Image-based dosimetric studies remain challenging because the emitted photons are few. The aim of this study was to implement a methodology for in-vivo quantitative planar imaging, and to assess the absorbed dose to lesions using the MIRD approach

Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics

In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds great promise for improving the quality of life of PCa patients, through managing and processing a vast amount of molecular imaging data and beyond, using a multi-omics approach and improving patients' risk-stratification for tailored medicine. Artificial intelligence (AI) and radiomics may allow clinicians to improve the overall efficiency and accuracy of using these "big data" in both the diagnostic and theragnostic field: from technical aspects (such as semi-automatization of tumor segmentation, image reconstruction, and interpretation) to clinical outcomes, improving a deeper understanding of the molecular environment of PCa, refining personalized treatment strategies, and increasing the ability to predict the outcome. This systematic review aims to describe the current literature on AI and radiomics applied to molecular imaging of prostate cancer