Predictors for late genitourinary toxicity in men receiving radiotherapy for high-risk prostate cancer using planned and accumulated dose

Comparative efficacy of radical prostatectomy and radiotherapy in the treatment of high-risk prostate cancer

BACKGROUND

Both radical prostatectomy and radiation therapy are effective in controlling the condition of patients with hormone-resistant prostate cancer (HRPCa). However, there is limited research on the prognosis and quality of life of HRPCa patients after different treatment modalities.

OBJECTIVE

To explore the efficacy of radical prostatectomy (RP) and radiotherapy (RT), when treating high-risk prostate cancer (HRPCa).

METHODS

Overall 103 HRPCa patients were included and were divided into RP group and RT group according to different treatment methods. The propensity score matching method (PSM) was used to balance the baseline data of the two groups and match 34 patients in each group. The prognosis, quality of life, and basic efficacy of patients were compared.

RESULTS

After intervention, the disease-free survival rate of the RT group was higher than that of the RP group (79.41% vs. 55.88%, p= 0.038). Quality of life scores between the two treatment methods had no difference before intervention (p> 0.05), but higher in RT group than that of the RP group after intervention (p< 0.05). After treatment, there was no statistically significant difference in total effective rate of treatment between two groups (44.12% vs. 58.82%, p> 0.05), but the disease control rate was significantly higher in RT group (94.12% vs. 76.47%, p= 0.040).

CONCLUSION

Radical radiotherapy is effective in the clinical treatment of HRPCa patients, with a higher disease-free survival rate and improved quality of life after treatment, and is worth promoting.

Artificial intelligence-based automated segmentation and radiotherapy dose mapping for thoracic normal tissues

Background and purpose

Objective assessment of delivered radiotherapy (RT) to thoracic organs requires fast and accurate deformable dose mapping. The aim of this study was to implement and evaluate an artificial intelligence (AI) deformable image registration (DIR) and organ segmentation-based AI dose mapping (AIDA) applied to the esophagus and the heart.

Materials and methods

AIDA metrics were calculated for 72 locally advanced non-small cell lung cancer patients treated with concurrent chemo-RT to 60 Gy in 2 Gy fractions in an automated pipeline. The pipeline steps were: (i) automated rigid alignment and cropping of planning CT to week 1 and week 2 cone-beam CT (CBCT) field-of-views, (ii) AI segmentation on CBCTs, and (iii) AI-DIR-based dose mapping to compute dose metrics. AIDA dose metrics were compared to the planned dose and manual contour dose mapping (manual DA).

Results

AIDA required ∼2 min/patient. Esophagus and heart segmentations were generated with a mean Dice similarity coefficient (DSC) of 0.80±0.15 and 0.94±0.05, a Hausdorff distance at 95th percentile (HD95) of 3.9±3.4 mm and 14.1±8.3 mm, respectively. AIDA heart dose was significantly lower than the planned heart dose (p = 0.04). Larger dose deviations (>=1Gy) were more frequently observed between AIDA and the planned dose (N = 26) than with manual DA (N = 6).

Conclusions

Rapid estimation of RT dose to thoracic tissues from CBCT is feasible with AIDA. AIDA-derived metrics and segmentations were similar to manual DA, thus motivating the use of AIDA for RT applications.

Application of machine learning approaches in predicting clinical outcomes in older adults - a systematic review and meta-analysis

BACKGROUND

Machine learning-based prediction models have the potential to have a considerable positive impact on geriatric care.

DESIGN

Systematic review and meta-analyses.

PARTICIPANTS

Older adults (≥ 65 years) in any setting.

INTERVENTION

Machine learning models for predicting clinical outcomes in older adults were evaluated. A random-effects meta-analysis was conducted in two grouped cohorts, where the predictive models were compared based on their performance in predicting mortality i) under and including 6 months ii) over 6 months.

OUTCOME MEASURES

Studies were grouped into two groups by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric.

RESULTS

Thirty-seven studies that satisfied the systematic review criteria were appraised, and eight studies predicting a mortality outcome were included in the meta-analyses. We could only pool studies by mortality as there were inconsistent definitions and sparse data to pool studies for other clinical outcomes. The area under the receiver operating characteristic curve from the meta-analysis yielded a summary estimate of 0.80 (95% CI: 0.76 - 0.84) for mortality within 6 months and 0.81 (95% CI: 0.76 - 0.86) for mortality over 6 months, signifying good discriminatory power.

CONCLUSION

The meta-analysis indicates that machine learning models display good discriminatory power in predicting mortality. However, more large-scale validation studies are necessary. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; there should be an effort to integrate these models into a larger research setting to predict various clinical outcomes.