Is it possible to detect cribriform adverse pathology in prostate cancer with magnetic resonance imaging machine learning-based radiomics?
Abstract
Rationale and objectives: Cribriform patterns are accepted as aggressive variants of prostate cancer. These adverse pathologies are closely associated with early biochemical recurrence, metastasis, castration resistance, and poor disease-related survival. A few publications exist to diagnose these two adverse pathologies with multiparametric magnetic resonance imaging (mpMRI). Most of these publications are retrospective and are not studies that have made a difference in diagnosing adverse pathology. It is also known that fusion biopsies taken from lesions detected in mpMRI are insufficient to detect these adverse pathologies. Our study aims to diagnose this adverse pathology using machine learning-based radiomics data from MR images. Materials and methods: A total of 88 patients who had pathology results indicating the presence of cribriform pattern and prostate adenocarcinoma underwent preoperative MRI examinations and radical prostatectomy. Manual slice-by-slice 3D volumetric segmentation was performed on all axial images. Data processing and machine learning analysis were conducted using Python 3.9.12 (Jupyter Notebook, Pycaret Library). Results: Two radiologists, SE and MAG, with 7 and 8 years of post-graduate experience, respectively, evaluated the images using the 3D-Slicer software without knowledge of the histopathological findings. One hundred seventeen radiomic tissue features were extracted from T1 weighted (T1W) and apparent diffusion coefficient (ADC) sequences for each patient. The interobserver agreement for these features was analyzed using the intraclass correlation coefficient (ICC). Features with excellent interobserver agreement (ICC > 0.90) were further analyzed for collinearity between predictors using Pearson’s correlation. Variables showing a very high correlation (r ≥ ±0.80) were disregarded. The selected features for T1W and ADC images were First-order maximum, First-order skewness, First-order 10th percentile for ADC, and Gray level size zone matrix, Large area low gray level emphasis for T1W.As a result of the classification of PyCaret, the three best models were found. A single model was obtained by blending these three models. AUC, accuracy, recall, precision, and F1 scores were 0.79, 0.77, 0.85, 0.82, and 0.83, respectively. Conclusion: ML-based MRI radiomics of prostate cancer can predict the cribriform pattern. This prognostic factor cannot be determined through qualitative radiological evaluation and may be overlooked in preoperative histopathological specimens.
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