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Radiomics-based prognosis classification for high-risk prostate cancer treated with radiotherapy

Authors :
Ciro Franzese
Luca Cozzi
Marco Badalamenti
Davide Baldaccini
Giuseppe D’Agostino
Antonella Fogliata
Pierina Navarria
Davide Franceschini
Tiziana Comito
Elena Clerici
Giacomo Reggiori
Stefano Tomatis
Marta Scorsetti
Source :
Strahlentherapie und Onkologie. 198:710-718
Publication Year :
2022
Publisher :
Springer Science and Business Media LLC, 2022.

Abstract

Objective:The present study aimed to investigate if CT-based radiomics features could correlate to the risk of metastatic progression in high-risk prostate cancer patients treated with radical RT and long-term androgen deprivation therapy (ADT). Materials and methods:A total of 157 patients were investigated and radiomics features extracted from the contrast-free treatment planning CT series. Three volumes were segmented: the prostate gland only (CTV_p), the prostate gland with seminal vesicles (CTV_psv), and the seminal vesicles only (CTV_sv). The patients were split into two subgroups of 100 and 57 patients for training and validation. Five clinical and 62 radiomics features were included in the analysis. Considering metastases-free survival (MFS) as an endpoint, the predictive model was used to identify the subgroups with favorable or unfavorable prognoses (separated by a threshold selected according to the Youden method). Pure clinical, pure radiomic, and combined predictive models were investigated. Results:With a median follow-up of 30.7 months, the MFS at 1 and 3 years was 97.2% ± 1.5 and 92.1% ± 2.0, respectively. Univariate analysis identified seven potential predictors for MFS in the CTV_p group, 11 in the CTV_psv group, and 9 in the CTV_sv group. After elastic net reduction, these were 4 predictors for MFS in the CTV_p group (positive lymph nodes, Gleason score, H_Skewness, and NGLDM_Contrast), 5 in the CTV_psv group (positive lymph nodes, Gleason score, H_Skewnesss, Shape_Surface, and NGLDM_Contrast), and 6 in the CTV_sv group (positive lymph nodes, Gleason score, H_Kurtosis, GLCM_Correlation, GLRLM_LRHGE, and GLZLM_SZLGE). The patients' group of the training and validation cohorts were stratified into favorable and unfavorable prognosis subgroups. For the combined model, for CTV_p, the mean MFS was 134 ± 14.5 vs. 96.9 ± 22.2 months for the favorable and unfavorable subgroups, respectively, and 136.5 ± 14.6 vs. 70.5 ± 4.3 months for CTV_psv and 150.0 ± 4.2 vs. 91.1 ± 8.6 months for CTV_sv, respectively. Conclusion:Radiomic features were able to predict the risk of metastatic progression in high-risk prostate cancer. Combining the radiomic features and clinical characteristics can classify high-risk patients into favorable and unfavorable prognostic groups.

Details

ISSN :
1439099X and 01797158
Volume :
198
Database :
OpenAIRE
Journal :
Strahlentherapie und Onkologie
Accession number :
edsair.doi.dedup.....1f800dbce14255eab8993f77ebafa2e2
Full Text :
https://doi.org/10.1007/s00066-021-01886-y