The Effect of Image Resampling on the Performance of Radiomics-Based Artificial Intelligence in Multicenter Prostate MRI

BACKGROUND

Single center MRI radiomics models are sensitive to data heterogeneity, limiting the diagnostic capabilities of current prostate cancer (PCa) radiomics models.

PURPOSE

To study the impact of image resampling on the diagnostic performance of radiomics in a multicenter prostate MRI setting.

STUDY TYPE

Retrospective.

POPULATION

Nine hundred thirty patients (nine centers, two vendors) with 737 eligible PCa lesions, randomly split into training (70%, N = 500), validation (10%, N = 89), and a held-out test set (20%, N = 148).

FIELD STRENGTH/SEQUENCE

1.5T and 3T scanners/T2-weighted imaging (T2W), diffusion-weighted imaging (DWI), and apparent diffusion coefficient maps.

ASSESSMENT

A total of 48 normalized radiomics datasets were created using various resampling methods, including different target resolutions (T2W: 0.35, 0.5, and 0.8 mm; DWI: 1.37, 2, and 2.5 mm), dimensionalities (2D/3D) and interpolation techniques (nearest neighbor, linear, Bspline and Blackman windowed-sinc). Each of the datasets was used to train a radiomics model to detect clinically relevant PCa (International Society of Urological Pathology grade ≥ 2). Baseline models were constructed using 2D and 3D datasets without image resampling. The resampling configurations with highest validation performance were evaluated in the test dataset and compared to the baseline models.

STATISTICAL TESTS

Area under the curve (AUC), DeLong test. The significance level used was 0.05.

RESULTS

The best 2D resampling model (T2W: Bspline and 0.5 mm resolution, DWI: nearest neighbor and 2 mm resolution) significantly outperformed the 2D baseline (AUC: 0.77 vs. 0.64). The best 3D resampling model (T2W: linear and 0.8 mm resolution, DWI: nearest neighbor and 2.5 mm resolution) significantly outperformed the 3D baseline (AUC: 0.79 vs. 0.67).

DATA CONCLUSION

Image resampling has a significant effect on the performance of multicenter radiomics artificial intelligence in prostate MRI. The recommended 2D resampling configuration is isotropic resampling with T2W at 0.5 mm (Bspline interpolation) and DWI at 2 mm (nearest neighbor interpolation). For the 3D radiomics, this work recommends isotropic resampling with T2W at 0.8 mm (linear interpolation) and DWI at 2.5 mm (nearest neighbor interpolation).

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Single-center versus multi-center biparametric MRI radiomics approach for clinically significant peripheral zone prostate cancer

Objectives

To investigate a previously developed radiomics-based biparametric magnetic resonance imaging (bpMRI) approach for discrimination of clinically significant peripheral zone prostate cancer (PZ csPCa) using multi-center, multi-vendor (McMv) and single-center, single-vendor (ScSv) datasets.

Methods

This study’s starting point was a previously developed ScSv algorithm for PZ csPCa whose performance was demonstrated in a single-center dataset. A McMv dataset was collected, and 262 PZ PCa lesions (9 centers, 2 vendors) were selected to identically develop a multi-center algorithm. The single-center algorithm was then applied to the multi-center dataset (single–multi-validation), and the McMv algorithm was applied to both the multi-center dataset (multi–multi-validation) and the previously used single-center dataset (multi–single-validation). The areas under the curve (AUCs) of the validations were compared using bootstrapping.

Results

Previously the single–single validation achieved an AUC of 0.82 (95% CI 0.71–0.92), a significant performance reduction of 27.2% compared to the single–multi-validation AUC of 0.59 (95% CI 0.51–0.68). The new multi-center model achieved a multi–multi-validation AUC of 0.75 (95% CI 0.64–0.84). Compared to the multi–single-validation AUC of 0.66 (95% CI 0.56–0.75), the performance did not decrease significantly (p value: 0.114). Bootstrapped comparison showed similar single-center performances and a significantly different multi-center performance (p values: 0.03, 0.012).

Conclusions

A single-center trained radiomics-based bpMRI model does not generalize to multi-center data. Multi-center trained radiomics-based bpMRI models do generalize, have equal single-center performance and perform better on multi-center data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-021-01099-y.

[Normograms of spirometric values for the city of Buenos Aires]

There is a large variability between the different normograms of spirometric values, so that we designed our normogram for Buenos Aires and Gran Buenos Aires. We performed forced spirometry, under American Thoracic Society standardization, in 237 normal subjects (105 females) between 18 to 86 years old, and 144 to 194 cm. We measured Forced Vital Capacity (FVC), forced Expiratory Volume in one second (FEV1), and forced expiratory flow during the middle half of the Forced vital capacity (FEF25-75), in previously calibrated by the explosive decompressor spirometers. Linear regression using height and age was used for each measured value for each sex. The values obtained were in normal distribution, so that we determined the Low Limit of Normality calculating the 95% confidence interval to one tail, and this should replace the common method of the fixed percent of each value to determine the lower limit of normality for a predicted value.

[Not Available]

In the 1820s, some years after smallpox vaccination had been introduced by most German states, physicians were faced with a seemingly new disease that they called "false smallpox" or "varioloid". The paper deals with two epidemics of "varioloid" that spread in Würzburg between 1825 and 1829. The reports of eyewitnesses, case histories, and patients records from the Julius Hospital in Würzburg are analysed with respect to the state of vaccination in this region, and in consideration of the personal, practical and scientific interests of the physicians involved. Special points of discussion are Schoenleins concept of epidemic disease, the role of contagionism, and the ambiguity of historical diagnosis.