Multi‐Site Concordance of Diffusion‐Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness

McGarry, Sean D.; Brehler, Michael; Bukowy, John D.; Lowman, Allison; Bobholz, Samuel; Duenweg, Savannah; Banerjee, Anjishnu; Hurrell, Sarah; Malyarenko, Dariya I.; Chenevert, Thomas L.; Cao, Yue; Li, Yuan; You, Daekeun; Fedorov, Andrey; Bell, Laura C.; Quarles, C. Chad; Prah, Melissa; Schmainda, Kathleen M.; Taouli, Bachir; LoCastro, Eve; Mazaheri, Yousef; Shukla–Dave, Amita; Yankeelov, Thomas E.; Hormuth, David A.; Madhuranthakam, Ananth J.; Hulsey, Keith; Li, Kurt; Huang, Wei; Muzi, Mark; Jacobs, Michael A.; Solaiyappan, Meiyappan; Hectors, Stefanie; Antic, Tatjana; Paner, Gladell P.; Palangmonthip, Watchareepohn; Jacobsohn, Kenneth; Hohenwalter, Mark D.; Duvnjak, Petar; Griffin, Michael O.; See, William A.; Nevalainen, Marja T.; Iczkowski, Kenneth A.; LaViolette, Peter S.

doi:10.1002/jmri.27983

Cited by 13 publications

(13 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Digitized whole-slide images, as well as their respective annotation masks, were co-registered to the T2-weighted image using previously published in-house software and techniques [ 31 , 35 , 41 , 43 , 44 , 45 , 50 , 51 ] ( n = 3 slides/patient). Briefly, slides were flipped and rotated to match their respective MRI slice, and a control-point co-registration was applied using manually fixed points corresponding to their respective position between both modalities.…”

Section: Methodsmentioning

confidence: 99%

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

Duenweg,

Bobholz,

Barrett

et al. 2023

Cancers

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

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

Duenweg,

Bobholz,

Barrett

et al. 2023

Cancers

Self Cite

View full text Add to dashboard Cite

show abstract

“…Digitized whole-mount samples were co-registered to the T2-weighted image using previously published software and techniques [ 25 , 27 , 28 , 33 , 34 , 35 , 36 ]. A control-point co-registration was applied using manually placed analogous points in each modality, specifically along the boundaries of the organ and on clearly identifiable landmarks within the organ.…”

Section: Methodsmentioning

confidence: 99%

“…“RadPath” correlates and integrates radiology and pathology reporting to mitigate discordance between these findings [ 22 ]. Whole-mount tissue alignment to MP-MRI has enabled non-invasive measurement of pathological features [ 23 , 24 , 25 , 26 ]. Recent studies have shown that RadPath techniques can be used to distinguish between Gleason grades, suggesting that MP-MRI features are sensitive to histomorphometric features of prostate cancer [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Diffusion Restriction Comparison between Gleason 4 Fused Glands and Cribriform Glands within Patient Using Whole-Mount Prostate Pathology as Ground Truth

et al. 2022

Self Cite

View full text Add to dashboard Cite

The presence and extent of cribriform patterned Gleason 4 (G4) glands are associated with poor prognosis following radical prostatectomy. This study used whole-mount prostate histology and multiparametric magnetic resonance imaging (MP-MRI) to evaluate diffusion differences in G4 gland morphology. Fourty-eight patients underwent MP-MRI prior to prostatectomy, of whom 22 patients had regions of both G4 cribriform glands and G4 fused glands (G4CG and G4FG, respectively). After surgery, the prostate was sliced using custom, patient-specific 3D-printed slicing jigs modeled according to the T2-weighted MR image, processed, and embedded in paraffin. Whole-mount hematoxylin and eosin-stained slides were annotated by our urologic pathologist and digitally contoured to differentiate the lumen, epithelium, and stroma. Digitized slides were co-registered to the T2-weighted MRI scan. Linear mixed models were fitted to the MP-MRI data to consider the different hierarchical structures at the patient and slide level. We found that Gleason 4 cribriform glands were more diffusion-restricted than fused glands.

show abstract

“…Diffusion images (TR/TE = 4000/69 ms, Matrix-256 × 256, ST = 4 mm) were collected with ten b-values (b = 0, 10,25,50,80,100,200,500,1000,2000). Monoexponential ADC maps were created [41].…”

Section: Prostate Mpmri Datasetmentioning

confidence: 99%

Tumor Connectomics: Mapping the Intra-Tumoral Complex Interaction Network Using Machine Learning

Parekh

Pillai

Macura

et al. 2022

Cancers

Self Cite

View full text Add to dashboard Cite

The high-level relationships that form complex networks within tumors and between surrounding tissue is challenging and not fully understood. To better understand these tumoral networks, we developed a tumor connectomics framework (TCF) based on graph theory with machine learning to model the complex interactions within and around the tumor microenvironment that are detectable on imaging. The TCF characterization model was tested with independent datasets of breast, brain, and prostate lesions with corresponding validation datasets in breast and brain cancer. The TCF network connections were modeled using graph metrics of centrality, average path length (APL), and clustering from multiparametric MRI with IsoSVM. The Matthews Correlation Coefficient (MCC), Area Under the Curve-ROC, and Precision-Recall (AUC-ROC and AUC-PR) were used for statistical analysis. The TCF classified the breast and brain tumor cohorts with an IsoSVM AUC-PR and MCC of 0.86, 0.63 and 0.85, 0.65, respectively. The TCF benign breast lesions had a significantly higher clustering coefficient and degree centrality than malignant TCFs. Grade 2 brain tumors demonstrated higher connectivity compared to Grade 4 tumors with increased degree centrality and clustering coefficients. Gleason 7 prostate lesions had increased betweenness centrality and APL compared to Gleason 6 lesions with AUC-PR and MCC ranging from 0.90 to 0.99 and 0.73 to 0.87, respectively. These TCF findings were similar in the validation breast and brain datasets. In conclusion, we present a new method for tumor characterization and visualization that results in a better understanding of the global and regional connections within the lesion and surrounding tissue.

show abstract

Multi‐Site Concordance of Diffusion‐Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness

Cited by 13 publications

References 35 publications

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

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

Diffusion Restriction Comparison between Gleason 4 Fused Glands and Cribriform Glands within Patient Using Whole-Mount Prostate Pathology as Ground Truth

Tumor Connectomics: Mapping the Intra-Tumoral Complex Interaction Network Using Machine Learning

Contact Info

Product

Resources

About