Developing and validating ultrasound-based radiomics models for predicting high-risk endometrial cancer.

OBJECTIVES: The primary aim of this study was to develop and validate radiomics models, applied to ultrasound images, capable of differentiating from other cancers high-risk endometrial cancer, as defined jointly by the European Society for Medical Oncology, European Society of Gynaecological Oncology and European Society for Radiotherapy & Oncology (ESMO-ESGO-ESTRO) in 2016. The secondary aim was to develop and validate radiomics models for differentiating low-risk endometrial cancer from other endometrial cancers. METHODS: This was a multicenter, retrospective, observational study. From two participating centers, we identified consecutive patients with histologically confirmed diagnosis of endometrial cancer who had undergone preoperative ultrasound examination by an experienced examiner between 2016 and 2019. Patients recruited in Center 1 (Rome) were included as the training set and patients enrolled in Center 2 (Milan) formed the external validation set. Radiomics analysis (extraction of a high number of quantitative features from medical images) was applied to the ultrasound images. Clinical (including preoperative biopsy), ultrasound and radiomics features that were statistically significantly different in the high-risk group vs the other groups and in the low-risk group vs the other groups on univariate analysis in the training set were considered for multivariate analysis and for developing ultrasound-based machine-learning risk-prediction models. For discriminating between the high-risk group and the other groups, a random forest model from the radiomics features (radiomics model), a binary logistic regression model from clinical and ultrasound features (clinical-ultrasound model) and another binary logistic regression model from clinical, ultrasound and previously selected radiomics features (mixed model) were created. Similar models were created for discriminating between the low-risk group and the other groups. The models developed in the training set were tested in the validation set. The performance of the models in discriminating between the high-risk group and the other groups, and between the low-risk group and the other risk groups for both validation and training sets was compared. RESULTS: The training set comprised 396 patients and the validation set 102 patients. In the validation set, for predicting high-risk endometrial cancer, the radiomics model had an area under the receiver-operating-characteristics curve (AUC) of 0.80, sensitivity of 58.7% and specificity of 85.7% (using the optimal risk cut-off of 0.41); the clinical-ultrasound model had an AUC of 0.90, sensitivity of 80.4% and specificity of 83.9% (using the optimal cut-off of 0.32); and the mixed model had an AUC of 0.88, sensitivity of 67.3% and specificity of 91.0% (using the optimal cut-off of 0.42). For the prediction of low-risk endometrial cancer, the radiomics model had an AUC of 0.71, sensitivity of 65.0% and specificity of 64.5% (using the optimal cut-off of 0.38); the clinical-ultrasound model had an AUC of 0.85, sensitivity of 70.0% and specificity of 80.6% (using the optimal cut-off of 0.46); and the mixed model had an AUC of 0.85, sensitivity of 87.5% and specificity of 72.5% (using the optimal cut-off of 0.36). CONCLUSIONS: Radiomics seems to have some ability to discriminate between low-risk endometrial cancer and other endometrial cancers and better ability to discriminate between high-risk endometrial cancer and other endometrial cancers. However, the addition of radiomics features to the clinical-ultrasound models did not result in any notable increase in performance. Other efficacy studies and further effectiveness studies are needed to validate the performance of the models. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

A cost-effectiveness analysis of an integrated clinical-radiogenomic screening program for the identification of BRCA 1/2 carriers (e-PROBE study).

Current approach to identify BRCA 1/2 carriers in the general population is ineffective as most of the carriers remain undiagnosed. Radiomics is an emerging tool for large scale quantitative analysis of features from standard diagnostic imaging and has been applied also to identify gene mutational status. The objective of this study was to evaluate the clinical and economic impact of integrating a radiogenomics model with clinical and family history data in identifying BRCA mutation carriers in the general population. This cost-effective analysis compares three different approaches to women selection for BRCA testing: established clinical criteria/family history (model 1); established clinical criteria/family history and the currently available radiogenomic model (49% sensitivity and 87% specificity) based on ultrasound images (model 2); same approach used in model 2 but simulating an improvement of the performances of the radiogenomic model (80% sensitivity and 95% specificity) (model 3). All models were trained with literature data. Direct costs were calculated according to the rates currently used in Italy. The analysis was performed simulating different scenarios on the generation of 18-year-old girls in Italy (274,000 people). The main outcome was to identify the most effective model comparing the number of years of BRCA-cancer healthy life expectancy (HLYs). An incremental cost-effectiveness ratio (ICER) was also derived to determine the cost in order to increase BRCA carriers-healthy life span by 1 year. Compared to model 1, model 2 increases the detection rate of BRCA carriers by 41.8%, reduces the rate of BRCA-related cancers by 23.7%, generating over a 62-year observation period a cost increase by 2.51 €/Year/Person. Moreover, model 3 further increases BRCA carriers detection (+ 68.3%) and decrease in BRCA-related cancers (- 38.4%) is observed compared to model 1. Model 3 increases costs by 0.7 €/Year/Person. After one generation, the estimated ICER in the general population amounts to about 3800€ and 653€ in model 2 and model 3 respectively. Model 2 has a massive effect after only one generation in detecting carriers in the general population with only a small cost increment. The clinical impact is limited mainly due to the current low acceptance rate of risk-reducing surgeries. Further multicentric studies are required before implementing the integrated clinical-radiogenomic model in clinical practice.

On dose cube pixel spacing pre-processing for features extraction stability in dosiomic studies.

PURPOSE: Dosiomics allows to parameterize regions of interest (ROIs) and to produce quantitative dose features encoding the spatial and statistical distribution of radiotherapy dose. The stability of dosiomics features extraction on dose cube pixel spacing variation has been investigated in this study. MATERIAL AND METHODS: Based on 17 clinical delivered dose distributions (Pn), dataset has been generated considering all the possible combinations of four dose grid resolutions and two calculation algorithms. Each dose voxel cube has been post-processed considering 4 different dose cube pixel spacing values: 1x1x1, 2x2x2, 3x3x3 mm3 and the one equal to the planning CT. Dosiomics features extraction has been performed from four different ROIs. The stability of each extracted dosiomic feature has been analyzed in terms of coefficient of variation (CV) intraclass correlation coefficient (ICC). RESULTS: The highest CV mean values were observed for PTV ROI and for the grey level size zone matrix features family. On the other hand, the lowest CV mean values have been found for RING ROI for the grey level co-occurrence matrix features family. P3 showed the highest percentage of CV >1 (1.14%) followed by P15 (0.41%), P1 (0.29%) and P13 (0.19%). ICC analysis leads to identify features with an ICC >0.95 that could be considered stable to use in dosiomic studies when different dose cube pixel spacing are considered, especially the features in common among the seventeen plans. CONCLUSION: Considering the observed variability, dosiomic studies should always provide a report not only on grid resolution and algorithm dose calculation, but also on dose cube pixel spacing.

Process mining to optimize palliative patient flow in a high-volume radiotherapy department.

INTRODUCTION: In radiotherapy, palliative patients are often suboptimal managed and patients experience long waiting times. Event-logs (recorded local files) of palliative patients, could provide a continuative decision-making system by means of shared guidelines to improve patient flow. Based on an event-log analysis, we aimed to accurately understand how to successively optimize patient flow in palliative care. METHODS: A process mining methodology was applied on palliative patient flow in a high-volume radiotherapy department. Five hundred palliative radiation treatment plans of patients with bone and brain metastases were included in the study, corresponding to 290 patients treated in our department in 2018. Event-logs and the relative attributes were extracted and organized. A process discovery algorithm was applied to describe the real process model, which produced the event-log. Finally, conformance checking was performed to analyze how the acquired event-log database works in a predefined theoretical process model. RESULTS: Based on the process discovery algorithm, 53 (10%) plans had a dose prescription of 8 Gy, 249 (49.8%) plans had a dose prescription of 20 Gy and 159 (31.8%) plans had a dose prescription of 30 Gy. The remaining 39 (7.8%) plans had different dose prescriptions. Considering a median value, conformance checking demonstrated that event-logs work in the theoretical model. CONCLUSIONS: The obtained results partially validate and support the palliative patient care guideline implemented in our department. Process mining can be used to provide new insights, which facilitate the improvement of existing palliative patient care flows.

Stability of dosomics features extraction on grid resolution and algorithm for radiotherapy dose calculation.

PURPOSE: Dosomics is a novel texture analysis method to parameterize regions of interest and to produce dose features that encode the spatial and statistical distribution of radiotherapy dose at higher resolution than organ-level dose-volume histograms. This study investigates the stability of dosomics features extraction, as their variation due to changes of grid resolution and algorithm dose calculation. MATERIAL AND METHODS: Dataset has been generated considering all the possible combinations of four grid resolutions and two algorithms dose calculation of 18 clinical delivered dose distributions, leading to a 144 3D dose distributions dataset. Dosomics features extraction has been performed with an in-house developed software. A total number of 214 dosomics features has been extracted from four different region of interest: PTV, the two closest OARs and a RING structure. Reproducibility and stability of each extracted dosomic feature (Rfe, Sfe), have been analyzed in terms of intraclass correlation coefficient (ICC) and coefficient of variation. RESULTS: Dosomics features extraction was found reproducible (ICC > 0.99). Dosomic features, across the combination of grid resolutions and algorithms dose calculation, are more stable in the RING for all the considered feature's families. Sfe is higher in OARs, in particular for GLSZM features' families. Highest Sfe have been found in the PTV, in particular in the GLCM features' family. CONCLUSION: Stability and reproducibility of dosomics features have been evaluated for a representative clinical dose distribution case mix. These results suggest that, in terms of stability, dosomic studies should always perform a reporting of grid resolution and algorithm dose calculation.