Algorithm for Reducing Overall Biological Detriment Caused by PET/CT: an Age-Based Study

Purpose

This study is to use a simple algorithm based on patient's age to reduce the overall biological detriment associated with PET/CT.

Materials and Methods

A total of 421 consecutive patients (mean age 64 ± 14 years) undergoing PET for various clinical indications were enrolled. For each scan, effective dose (ED in mSv) and additional cancer risk (ACR) were computed both in a reference condition (REF) and after applying an original algorithm (ALGO). The ALGO modified the mean dose of FDG and the PET scan time parameters; indeed, a lower dose and a longer scan time were reported in the younger, while a higher dose and a shorter scan time in the older patients. Moreover, patients were classified by age bracket (18-29, 30-60, and 61-90 years).

Results

The ED was 4.57 ± 0.92 mSv in the REF condition. The ACR were 0.020 ± 0.016 and 0.0187 ± 0.013, respectively, in REF and ALGO. The ACR for the REF and ALGO conditions were significantly reduced in males and females, although it was more evident in the latter gender (all p < 0.0001). Finally, the ACR significantly reduced from the REF condition to ALGO in all three age brackets (all p < 0.0001).

Conclusion

Implementation of ALGO protocols in PET can reduce the overall ACR, mainly in young and female patients.

Vasari Scoring System in Discerning between Different Degrees of Glioma and IDH Status Prediction: A Possible Machine Learning Application?

(1) The aim of our study is to evaluate the capacity of the Visually AcceSAble Rembrandt Images (VASARI) scoring system in discerning between the different degrees of glioma and Isocitrate Dehydrogenase (IDH) status predictions, with a possible application in machine learning. (2) A retrospective study was conducted on 126 patients with gliomas (M/F = 75/51; mean age: 55.30), from which we obtained their histological grade and molecular status. Each patient was analyzed with all 25 features of VASARI, blinded by two residents and three neuroradiologists. The interobserver agreement was assessed. A statistical analysis was conducted to evaluate the distribution of the observations using a box plot and a bar plot. We then performed univariate and multivariate logistic regressions and a Wald test. We also calculated the odds ratios and confidence intervals for each variable and the evaluation matrices with receiver operating characteristic (ROC) curves in order to identify cut-off values that are predictive of a diagnosis. Finally, we did the Pearson correlation test to see if the variables grade and IDH were correlated. (3) An excellent ICC estimate was obtained. For the grade and IDH status prediction, there were statistically significant results by evaluation of the degree of post-contrast impregnation (F4) and the percentage of impregnated area (F5), not impregnated area (F6), and necrotic (F7) tissue. These models showed good performances according to the area under the curve (AUC) values (>70%). (4) Specific MRI features can be used to predict the grade and IDH status of gliomas, with important prognostic implications. The standardization and improvement of these data (aim: AUC > 80%) can be used for programming machine learning software.

Nuclear medicine and radiotherapy in the clinical management of glioblastoma patients

Introduction

The aim of the narrative review was to analyse the applications of nuclear medicine (NM) techniques such as PET/CT with different tracers in combination with radiotherapy for the clinical management of glioblastoma patients.

Materials and methods

Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used.

Results

This paper contains a narrative report and a critical discussion of NM approaches in combination with radiotherapy in glioma patients.

Conclusions

NM can provide the Radiation Oncologist several aids that can be useful in the clinical management of glioblastoma patients. At the same, these results need to be validated in prospective and multicenter trials.