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. 2023 Sep 6;15(18):4437.
doi: 10.3390/cancers15184437.

T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence

Savannah R Duenweg  1 , Samuel A Bobholz  2 , Michael J Barrett  2 , Allison K Lowman  2 , Aleksandra Winiarz  1 , Biprojit Nath  1 , Margaret Stebbins  1 , John Bukowy  3 , Kenneth A Iczkowski  4 , Kenneth M Jacobsohn  5 , Stephanie Vincent-Sheldon  2 , Peter S LaViolette  1   2   6
Affiliations

T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence

Savannah R Duenweg et al. Cancers (Basel). .

Abstract

Prostate cancer (PCa) is the most diagnosed non-cutaneous cancer in men. Despite therapies such as radical prostatectomy, which is considered curative, distant metastases may form, resulting in biochemical recurrence (BCR). This study used radiomic features calculated from multi-parametric magnetic resonance imaging (MP-MRI) to evaluate their ability to predict BCR and PCa presence. Data from a total of 279 patients, of which 46 experienced BCR, undergoing MP-MRI prior to surgery were assessed for this study. After surgery, the prostate was sectioned using patient-specific 3D-printed slicing jigs modeled using the T2-weighted imaging (T2WI). Sectioned tissue was stained, digitized, and annotated by a GU-fellowship trained pathologist for cancer presence. Digitized slides and annotations were co-registered to the T2WI and radiomic features were calculated across the whole prostate and cancerous lesions. A tree regression model was fitted to assess the ability of radiomic features to predict BCR, and a tree classification model was fitted with the same radiomic features to classify regions of cancer. We found that 10 radiomic features predicted eventual BCR with an AUC of 0.97 and classified cancer at an accuracy of 89.9%. This study showcases the application of a radiomic feature-based tool to screen for the presence of prostate cancer and assess patient prognosis, as determined by biochemical recurrence.

Keywords: Gleason pattern; biochemical recurrence; mp-MRI; prostate cancer; radiomic features.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Tissue processing and MRI co-registration process. Slicing jigs (bottom, left) are 3D modeled to section tissue in line with the slice thickness and orientation of each patient’s T2-weighted image (left). Slides are digitized and annotated by Gleason pattern (legend, (right)) and aligned to the corresponding slice on the T2WI (bottom, middle).
Figure 2
Figure 2
Results from the two tested models. The ROC curve presented for the biochemical recurrence regression model (A) has an AUC = 0.97. The cancer classification model confusion matrix (B) is displayed with normalized classification rates (accuracy = 89.9%). Confusion matrix legend: blue = true positive, red = false positive.
Figure 3
Figure 3
T2-weighted images from the withheld test set were down-sampled by a factor of two and noise was added to each image (A). Cancer classification performance is displayed as normalized values. Applying our BCR model to these noisy images had an AUC = 0.96 (B). The cancer classification model confusion matrix (C) is displayed with normalized classification rates (accuracy = 92.8%).
See this image and copyright information in PMC

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