An In-House 3D Voxel Dosimetric Tool to Compare Predictive and Post- Treatment Dosimetry in 90Y Radioembolization: A Proof of Concept.

AIM: The aim of this study was to implement an in-house dosimetric tool to assess tumour- absorbed doses in pre and post-dosimetry for 90Y radioembolization with resin spheres. MATERIALS AND METHODS: To perform dosimetric calculations we set up a dosimetric procedure and developed homemade software to calculate tumour absorbed dose and dose volume histograms (DVHs). The method is based on a simplified voxel dosimetry for an estimated 3D absorbed dose and it can be applied to both 99mTc-MAA SPECT/CT and 90Y PET/CT acquisitions for pre and post-dosimetry. We tested the software performance in a retrospective study using the data of 22 patients with hepatocellular carcinoma who underwent radioembolization with 90Y resin spheres in the period 2016-2021. The software calculates tumour doses (mean, minimum and maximum doses) from voxel counts and dose-volume histograms (DVH_spect, DVH_pet) for both 99mTc-MAA SPECT/CT and 90Y PET/CT imaging. DVH_spect and DVH_pet data were analyzed and compared with the aim to assess an agreement between them. Concordance between dosimetric data were evaluated with the Wilcoxon Signed Ranked test, descriptive statistical analysis and Pearson correlation coefficient. RESULTS: The mean administrated activity was 1313 MBq (range 444 MBq - 2200 MBq). Tumour volumes ranged from 75 mL to 1012 mL. The mean absorbed dose for tumour volume was 161 ± 66 Gy (Dm_spect) and 173 ± 79 Gy (Dm_pet). From Wilcoxon Signed Rank Test the differences between the dosimetric data extrapolated from DVH_spect and DVH_pet results were not significant with α = 0.05 (two-sided test). A good linear correlation was found between 99mTc-MAA and 90Y dosimetric data (Pearson correlation coefficient 0.887 p < 0.001). Generally, DVHs calculated on 99mTc-MAA SPECT/CT and 90Y PET/CT gave comparable results, some discrepancies were observed particularly with those patients where SPECT and PET imaging presented a visual mismatching. CONCLUSION: A simplified 3D dosimetry methodology was implemented and tested retrospectively on patient data treated with 90Y resin spheres. Even if the clinical feasibility of our approach has to be further validated on an extended patient cohort, the preliminary results of our study highlight the potential of the implemented dosimetric tool for tumour dose assessment.

Machine Learning Model to Predict Diagnosis of Mild Cognitive Impairment by Using Radiomic and Amyloid Brain PET.

PURPOSE: The study aimed to develop a deep learning model for predicting amnestic mild cognitive impairment (aMCI) diagnosis using radiomic features and amyloid brain PET. PATIENTS AND METHODS: Subjects (n = 328) from the Alzheimer's Disease Neuroimaging Initiative database and the EudraCT 2015-001184-39 trial (159 males, 169 females), with a mean age of 72 ± 7.4 years, underwent PET/CT with 18 F-florbetaben. The study cohort consisted of normal controls (n = 149) and subjects with aMCI (n = 179). Thirteen gray-level run-length matrix radiomic features and amyloid loads were extracted from 27 cortical brain areas. The least absolute shrinkage and selection operator regression was used to select features with the highest predictive value. A feed-forward neural multilayer network was trained, validated, and tested on 70%, 15%, and 15% of the sample, respectively. Accuracy, precision, F1-score, and area under the curve were used to assess model performance. SUV performance in predicting the diagnosis of aMCI was also assessed and compared with that obtained from the machine learning model. RESULTS: The machine learning model achieved an area under the receiver operating characteristic curve of 90% (95% confidence interval, 89.4-90.4) on the test set, with 80% and 78% for accuracy and F1-score, respectively. The deep learning model outperformed SUV performance (area under the curve, 71%; 95% confidence interval, 69.7-71.4; 57% accuracy, 48% F1-score). CONCLUSIONS: Using radiomic and amyloid PET load, the machine learning model identified MCI subjects with 84% specificity at 81% sensitivity. These findings show that a deep learning algorithm based on radiomic data and amyloid load obtained from brain PET images improves the prediction of MCI diagnosis compared with SUV alone.

Methodological Aspects and the Prognostic Value of Metabolic Tumor Volume assessed with 18F-FDG PET/CT in Lymphomas.

Although metabolic tumor volume (MTV) assessed with pretreatment 18F-FDG PET/CT has shown significant prognostic value across many lymphoma types, it is still not used in clinical practice due to technical concerns and the lack of standardisation. Numerous studies on the prognostic value of MTV in lymphomas have been published in recent years, but there is still no full agreement on the best methodology for MTV calculation. In this paper, we reviewed the methodological aspects of MTV assessment and reported recent works about its impact on outcome in lymphomas, with a focus on Hodgkin lymphoma (HL) and diffuse large B cell lymphoma (DLBCL).

Impact of Tracer Retention Levels on Visual Analysis of Cerebral [18F]- Florbetaben Pet Images.

BACKGROUND: To compare visual and semi-quantitative analysis of brain [18F]Florbetaben PET images in Mild Cognitive Impairment (MCI) patients and relate this finding to the degree of ß-amyloid burden. METHODS: A sample of 71 amnestic MCI patients (age 74 ± 7.3 years, Mini Mental State Examination 24.2 ± 5.3) underwent cerebral [18F]Florbetaben PET/CT. Images were visually scored as positive or negative independently by three certified readers blinded to clinical and neuropsychological assessment. Amyloid positivity was also assessed by semiquantitative approach by means of a previously published threshold (SUVr ≥ 1.3). Fleiss kappa coefficient was used to compare visual analysis (after consensus among readers) and semi-quantitative analysis. Statistical significance was taken at P<0.05. RESULTS: After the consensus reading, 43/71 (60.6%) patients were considered positive. Cases that were interpreted as visually positive had higher SUVr than visually negative patients (1.48 ± 0.19 vs 1.11 ± 0.09) (P<0.05). Agreement between visual analysis and semi-quantitative analysis was excellent (k=0.86, P<0.05). Disagreement occurred in 7/71 patients (9.9%) (6 false positives and 1 false negative). Agreement between the two analyses was 90.0% (18/20) for SUVr < 1.1, 83% (24/29) for SUVr between 1.1 and 1.5, and 100% (22/22) for SUVr > 1.5 indicating lowest agreement for the group with intermediate amyloid burden. CONCLUSION: Inter-rater agreement of visual analysis of amyloid PET images is high. Agreement between visual analysis and SUVr semi-quantitative analysis decreases in the range of 1.1

Amyloid burden identifies neuropsychological phenotypes at increased risk of progression to Alzheimer's disease in mild cognitive impairment patients.

PURPOSE: The extent of amyloid burden associated with cognitive impairment in amnestic mild cognitive impairment is unknown. The primary aim of the study was to determine the extent to which amyloid burden is associated to the cognitive impairment. The secondary objective was to test the relationship between amyloid accumulation and memory or cognitive impairment. MATERIALS AND METHODS: In this prospective study 66 participants with amnestic mild cognitive impairment underwent clinical, neuropsychological and PET amyloid imaging tests. Composite scores assessing memory and non-memory domains were used to identify two clinical classes of neuropsychological phenotypes expressing different degree of cognitive impairment. Detection of amyloid status and definition of optimal amyloid ± cutoff for discrimination relied on unsupervised k-means clustering method. RESULTS: Threshold for identifying low and high amyloid retention groups was of SUVr = 1.3. Aß + participants showed poorer global cognitive and episodic memory performance than subjects with low amyloid deposition. Aß positivity significantly identified individuals with episodic memory impairment with a sensitivity and specificity of 80 and 79%, (χ2 = 21.48; P < 0.00001). Positive and negative predictive values were 82 and 76%, respectively. Amyloid deposition increased linearly as function of memory impairment with a rate of 0.13/ point of composite memory score (R = -44, P = 0.0003). CONCLUSION: The amyloid burden of SUVr = 1.3 allows early identification of subjects with episodic memory impairment which might predict progression from MCI to Alzheimer's disease. TRIAL REGISTRATION: EudraCT 2015-001184-39.