Keeping your best options open with AI-based treatment planning in prostate and cervix brachytherapy.

PURPOSE: Without a clear definition of an optimal treatment plan, no optimization model can be perfect. Therefore, instead of automatically finding a single "optimal" plan, finding multiple, yet different near-optimal plans, can be an insightful approach to support radiation oncologists in finding the plan they are looking for. METHODS AND MATERIALS: BRIGHT is a flexible AI-based optimization method for brachytherapy treatment planning that has already been shown capable of finding high-quality plans that trade-off target volume coverage and healthy tissue sparing. We leverage the flexibility of BRIGHT to find plans with similar dose-volume criteria, yet different dose distributions. We further describe extensions that facilitate fast plan adaptation should planning aims need to be adjusted, and straightforwardly allow incorporating hospital-specific aims besides standard protocols. RESULTS: Results are obtained for prostate (n = 12) and cervix brachytherapy (n = 36). We demonstrate the possible differences in dose distribution for optimized plans with equal dose-volume criteria. We furthermore demonstrate that adding hospital-specific aims enables adhering to hospital-specific practice while still being able to automatically create cervix plans that more often satisfy the EMBRACE-II protocol than clinical practice. Finally, we illustrate the feasibility of fast plan adaptation. CONCLUSIONS: Methods such as BRIGHT enable new ways to construct high-quality treatment plans for brachytherapy while offering new insights by making explicit the options one has. In particular, it becomes possible to present to radiation oncologists a manageable set of alternative plans that, from an optimization perspective are equally good, yet differ in terms of coverage-sparing trade-offs and shape of the dose distribution.

Practical use of visual medial temporal lobe atrophy cut-off scores in Alzheimer's disease: Validation in a large memory clinic population.

OBJECTIVE: To provide age-specific medial temporal lobe atrophy (MTA) cut-off scores for routine clinical practice as marker for Alzheimer's disease (AD). METHODS: Patients with AD (n = 832, mean age 81.8 years) were compared with patients with subjective cognitive impairment (n = 333, mean age 71.8 years) in a large single-centre memory clinic. Mean of right and left MTA scores was determined with visual rating (Scheltens scale) using CT (0, no atrophy to 4, severe atrophy). Relationships between age and MTA scores were analysed with regression analysis. For various MTA cut-off scores, decade-specific sensitivity and specificity and area under the curve (AUC) values, computed with receiver operator characteristic curves, were determined. RESULTS: MTA strongly increased with age in both groups to a similar degree. Optimal MTA cut-off values for the age ranges <65, 65-74, 75-84 and ≥85 were: ≥1.0, ≥1.5, ≥ 2.0 and ≥2.0. Corresponding values of sensitivity and specificity were 83.3% and 86.4%; 73.7% and 84.6%; 73.7% and 76.2%; and 84.0% and 62.5%. CONCLUSION: From this large unique memory clinic cohort we suggest decade-specific MTA cut-off scores for clinical use. After age 85 years, however, the practical usefulness of the MTA cut-off is limited. KEY POINTS: • We suggest decade-specific MTA cut-off scores for AD. • MTA cut-off after the age of 85 years has limited use. • CT is feasible and accurate for visual MTA rating.

Low Prevalence of Mixed Dementia in a Cohort of 2,000 Elderly Patients in a Memory Clinic Setting.

BACKGROUND: It is generally assumed that with increasing age, pathology in clinically diagnosed Alzheimer's disease (AD) becomes more mixed, i.e., co-existence of amyloid plaques and cerebrovascular pathology. OBJECTIVE: To test the hypothesis of increasing prevalence of mixed dementia in late-onset clinically diagnosed Alzheimer's disease (AD) in a single-center memory clinic population. METHODS: Patients included had diagnoses of AD (n = 832), subjective cognitive impairment (SCI, n = 333), mild cognitive impairment (MCI, n = 492), vascular dementia (VaD, n = 57), other dementia (n = 53), or other diagnosis (n = 233). Prevalence of severe white matter lesions (WML) was defined as a score of 2 or higher on the Fazekas-scale on brain computed tomography to classify AD patients as having mixed dementia. We examined the effect of age on WML using multiple linear regression analysis, and AD patients were compared to SCI to determine the effect of disease on WML. RESULTS: Prevalence of severe WML was 33.6% in AD patients (mixed dementia), 11.4% in SCI, 22.7% in MCI, 75.4% in VaD, 3.8% in other dementia, and 15.5% in other diagnosis. With increasing age there was a significant and similar increase of WML scores in SCI, MCI, AD, other dementia, and other diagnosis, indicating no effect modification by AD. The difference between AD patients and SCI averaged 0.16 on the WML score and difference in percentage severe WML between AD and SCI patients was 15% across all ages. CONCLUSION: We found a low prevalence of mixed dementia. Furthermore, severe WML in AD was largely explained by age rather than effect of disease.