Predictive value of magnetic resonance imaging diffusion parameters using artificial intelligence in low-and intermediate-risk prostate cancer patients treated with stereotactic ablative radiotherapy: A pilot study

INTRODUCTION

To investigate the predictive value of the pre-treatment diffusion parameters of diffusion-weighted magnetic resonance imaging (DW-MRI) using artificial intelligence (AI) for prostate-specific antigen (PSA) response in patients with low- and intermediate-risk prostate cancer (PCa) treated with stereotactic ablative radiotherapy (SABR).

METHODS

Retrospective evaluation was performed for 30 patients using pre-treatment multi-parametric MR image datasets between 2017 and 2021. MR-based mean- and minimum apparent diffusion coefficients (ADCmean, ADCmin) were calculated for the intraprostatic dominant lesion. Therapeutic response was assessed using PSA levels. Predictive performance was assessed by the receiver operating characteristic (ROC) analysis. Statistics performed with a significance level of p ≤ 0.05.

RESULTS

No biochemical relapse was detected after a median follow-up of twenty-three months (range: 3-50), with a median PSA of 0.01 ng/ml (range: 0.006-2.8) at the last examination. Significant differences were observed between the pre-treatment ADCmean, ADCmin parameters, and the group averages of patients with low and high 1-year-PSA measurements (p < 0.0001, p < 0.0001). In prediction, the random forest (RF) model outperformed the decision tree (DT) and support vector machine (SVM) models by yielding area under the curves (AUC), with 0.722, 0.685, and 0.5, respectively.

CONCLUSION

Our findings suggest that pre-treatment MR diffusion data may predict therapeutic response using the novel approach of machine learning in PCa patients treated with SABR.

IMPLICATIONS FOR PRACTICE

Clinicians shall measure and implement the evaluation of the suggested parameters (ADCmin, ADCmean) to provide the most accurate therapy for the patient.

A scoping review of person-centred care strategies used in diagnostic Nuclear Medicine

INTRODUCTION

Person-centred care (PCC) emphasises the need for the health care professional to prioritise individual patient needs, thereby fostering a collaborative and emphatic environment that empowers patients to actively participate in their own care. This article will explore the purpose of PCC in Nuclear Medicine (NM), while discussing strategies that may be used to implement PCC during diagnostic NM examinations performed on adult patients.

METHODS

The scoping review was conducted in accordance with the Joanna Briggs Institute methodology. The search was performed on PubMed, Embase and Cinhal in June 2023 and included studies in English, Spanish, Portuguese and Italian. The research equation combined keywords and Medical Subject Heading terms (MeSH) related to person-centred care (PCC), for all types of nuclear medicine diagnostic examinations performed. Three independent review authors screened all abstracts and titles, and all eligible full-text publications were included in this scoping review.

RESULTS

Fifty-three articles, published between 1993 and 2022, met the inclusion criteria for this scoping review. Seven articles were published in 2015 while 56.6 % of all included studies were performed in Europe. Most studies (n = 39/53) focused on the patients only, with the identified patient benefits being: improve patient experience (67.9 %), increase patient comfort (13.2 %), increase patient knowledge (5.7 %), reduction of patient anxiety (9.4 %) and reduction of waiting/scan time (3.8 %).

CONCLUSION

The scoping review identified a lack of research investigating the use of person-centred care strategies in NM. Future research will focus on using an international survey to explore this topic in nuclear medicine departments overseas.

IMPLICATIONS FOR PRACTICE

By applying PCC principles, the NM professional can improve the patient care pathway and increase patient satisfaction, leading to enhanced clinical outcomes.

Treatment of corn ethanol distillery wastewater using two-stage anaerobic digestion

In this study the mesophilic two-stage anaerobic digestion (AD) of corn bioethanol distillery wastewater is investigated in laboratory-scale reactors. Two-stage AD technology separates the different sub-processes of the AD in two distinct reactors, enabling the use of optimal conditions for the different microbial consortia involved in the different process phases, and thus allowing for higher applicable organic loading rates (OLRs), shorter hydraulic retention times (HRTs) and better conversion rates of the organic matter, as well as higher methane content of the produced biogas. In our experiments the reactors have been operated in semi-continuous phase-separated mode. A specific methane production of 1,092 mL/(L·d) has been reached at an OLR of 6.5 g TCOD/(L·d) (TCOD: total chemical oxygen demand) and a total HRT of 21 days (5.7 days in the first-stage, and 15.3 days in the second-stage reactor). Nonetheless the methane concentration in the second-stage reactor was very high (78.9%); the two-stage AD outperformed the reference single-stage AD (conducted at the same reactor loading rate and retention time) by only a small margin in terms of volumetric methane production rate. This makes questionable whether the higher methane content of the biogas counterbalances the added complexity of the two-stage digestion.