On the necessity of specialized knowledge-based models for SBRT prostate treatments plans.

PURPOSE: Test whether a well-grounded KBP model trained on moderately hypo-fractionated prostate treatments can be used to satisfactorily drive the optimization of SBRT prostate treatments. MATERIALS AND METHODS: A KBP model (SBRT-model) was developed, trained and validated using the first forty-seven clinically treated VMAT SBRT prostate plans (42.7 Gy/7fx or 36.25 Gy/5fx). The performance and robustness of this model were compared against a high-quality KBP-model (ST-model) that was already clinically adopted for hypo-fractionated (70 Gy/28fx and 60 Gy/20fx) prostate treatments. The two models were compared in terms of their predictions robustness, and the quality of their outcomes were evaluated against a set of reference clinical SBRT plans. Plan quality was assessed using DVH metrics, blinded clinical ranking, and a dedicated Plan Quality Metric algorithm. RESULTS: The plan libraries of the two models were found to share a high degree of anatomical similarity. The overall quality (APQM%) of the plans obtained both with the ST- and SBRT-models was compatible with that of the original clinical plans, namely (93.7 ± 4.1)% and (91.6 ± 3.9)% vs (92.8.9 ± 3.6)%. Plans obtained with the ST-model showed significantly higher target coverage (PTV V95%): (97.9 ± 0.8)% vs (97.1 ± 0.9)% (p < 0.05). Conversely, plans optimized following the SBRT-model showed a small but not-clinically relevant increase in OAR sparing. ST-model generally provided more reliable predictions than SBRT-model. Two radiation oncologists judged as equivalent the plans based on the KBP prediction, which was also judged better that reference clinical plans. CONCLUSION: A KBP model trained on moderately fractionated prostate treatment plans provided optimal SBRT prostate plans, with similar or larger plan quality than an embryonic SBRT-model based on a limited number of cases.

Characterization of Inclination Analysis for Predicting Onset of Heart Failure from Primary Care Electronic Medical Records.

The aim of this study is to characterize the performance of an inclination analysis for predicting the onset of heart failure (HF) from routinely collected clinical biomarkers extracted from primary care electronic medical records. A balanced dataset of 698 patients (with/without HF), including a minimum of five longitudinal measures of nine biomarkers (body mass index, diastolic and systolic blood pressure, fasting glucose, glycated hemoglobin, low-density and high-density lipoproteins, total cholesterol, and triglycerides) is used. The proposed algorithm achieves an accuracy of 0.89 (sensitivity of 0.89, specificity of 0.90) to predict the inclination of biomarkers (i.e., their trend towards a 'survival' or 'collapse' as defined by an inclination analysis) on a labeled, balanced dataset of 40 patients. Decision trees trained on the predicted inclination of biomarkers have significantly higher recall (0.69 vs. 0.53) and significantly higher negative predictive value (0.60 vs. 0.55) than those trained on the average values computed from the measures of biomarkers available before the onset of the disease, suggesting that an inclination analysis can help identify the onset of HF in the primary care patient population from routinely available clinical data. This exploratory study provides the basis for further investigations of inclination analyses to identify at-risk patients and generate preventive measures (i.e., personalized recommendations to reverse the trend of biomarkers towards collapse).

Impact of patient's habitus on image quality and quantitative metrics in 18F-FDG PET/CT images.

PURPOSE: To study how the quantitative parameters of 18F-FDG PET imaging change with the emission scan duration (ESD) and the body-mass-index (BMI) in phantom and patients on a time-of-flight (TOF)-PET/CT system. METHODS: The image-quality phantom with (b-NEMA-IQ, BMI = 29.2 kg/m2) and without (NEMA-IEC, BMI = 21.4 kg/m2) a 'belt' of water-bags was filled with 18F-FDG activities to obtain nominal standardized uptake values (SUV) of 19, 8 and 5. Patients with BMI ≤ 25 kg/m2 (L-BMI) and BMI > 25 kg/m2 (H-BMI) were enrolled in this study. Phantom and patients underwent list-mode PET acquisition at 120 s/bed-position. Images reconstructed with clinical protocol and different ESD (120, 90, 75, 60, 45, 30 s) were analysed for comparison of maximum SUV (SUVmax), maximum standardized uptake value lean-body-mass corrected (SULmax) and noise. RESULTS: 79 oncologic patients (45 L-BMI, 44 H-BMI) were analysed. From 90 s to 30 s, an increasing variation of SUVmax and SULmax with respect to the reference 120 s time was observed, from 18% to 60% and from 16% to 37% for phantom and patients, respectively. SUVmax values were significantly higher (+50%) in b-NEMA-IQ than NEMA-IQ phantom and in H-BMI (+33%) than L-BMI patients. No significant difference was found in SULmax for the two BMI categories in both phantom and patients. CV values decreased when increasing ESD, being higher in H-BMI patients (0.13-0.25) and b-NEMA-IQ phantom (0.15-0.28) than in L-BMI patients (0.11-0.21) and NEMA-IQ phantom (0.11-0.20). CONCLUSIONS: Reduction of ESD may severely impact on the variations of SUVmax and SULmax in 18F-FDG PET/CT imaging. This study confirms recommendations of using SUL for lesion uptake quantification, being unaffected by BMI variation.

Size-specific dose estimates for pediatric head CT protocols based on the AAPM report TG-293.

PURPOSE: To establish the Size Specific Dose Estimate (SSDE) typical values for pediatric head CT examinations based on the AAPM report TG-293; to provide a new stratification based on the water-equivalent diameter (Dw), given that SSDE is related to the head size. METHODS: 296 Head CT scans of pediatric patients collected using a dose monitoring software were retrospectively analysed. Typical values were derived stratifying data by age in three methods: the first proposed by the European Guidelines on Diagnostic Reference Levels for Pediatric Imaging (RP185), the second by the National Istisan Report 20/22 and a local one related to the clinical protocols (LStrata). For each scan, a self-developed Matlab routine calculated the water-equivalent diameter (Dw) and related SSDE values with the conversion factors fH16and fB16provided by the AAPM reports TG-293 and TG-204, respectively. Eventually, a Dwstratification was introduced starting from a measure of the lateral dimension of the head. RESULTS: SSDE based on TG-204 overestimatesthe dose up to 12%. Four Dwgroups were identified thanks to the good correlation between the head lateral dimension andDw: Dw < 14 cm, 14 ≤ Dw < 16 cm, 16 ≤ Dw< 17 cm, Dw≥ 17 cm. The Dw-stratified dosimetric indices presentgreater variability than those grouped by age because of the large variability of the size of the infant's head. CONCLUSIONS: The variability of the SSDE metric underlines that age-optimized protocols are not when size is considered.