The potential of prostate gland radiomic features in identifying the Gleason score.
Comput Biol Med
; 144: 105318, 2022 05.
Article
en En
| MEDLINE
| ID: mdl-35245698
ABSTRACT
BACKGROUND:
Gleason score (GS) is one of the most critical predictors of diagnosing prostate cancer (PCa). The prostate gland, including both lesions and their microenvironment, may contain more comprehensive information about the PCa. We aimed to investigate the potential of prostate gland radiomic features in identifying Gleason scores (GS) < 7, = 7, and >7.METHODS:
We retrospectively examined preoperative magnetic resonance imaging (MRI) results, clinical data, and postoperative pathological findings from 489 PCa patients. The three-dimensional (3D) and two-dimensional (2D) radiomic features were extracted from the manually segmented 3D prostate gland and its maximum 2D layer on MRI, respectively. Significant features were selected, and sequence signatures were then developed via multi-class linear regression (MLR) accordingly. Subsequently, 2D and 3D radiomic models were constructed by applying MLR to the combination of the sequence signatures, respectively. The stability of the significant features was discussed by their average ranking in the other 30 random cohorts. Based on our distance matrix algorithm, we generated different regions of interest to simulate the manual segmentation biases and discuss the model's tolerance to them.RESULTS:
Our 2D model reached a C-index of 0.728 and an average area under the receiver operating characteristic curve of 0.794 in the validation cohort. The corresponding key features were stable, with an average ranking of the top 8.352% in 30 random cohorts, and the model could tolerate a segmentation boundary deviation of 2 mm without significant performance degradation.CONCLUSION:
2D prostate-gland-MRI-based radiomic features showed stable potential in identifying GS.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Próstata
/
Neoplasias de la Próstata
Tipo de estudio:
Guideline
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
/
Male
Idioma:
En
Revista:
Comput Biol Med
Año:
2022
Tipo del documento:
Article