The concordance between the volume hotspot and the grade hotspot: a 3-D reconstructive model using the pathology outputs from the PROMIS trial

ImaGene: a web-based software platform for tumor radiogenomic evaluation and reporting

Summary

Radiographic imaging techniques provide insight into the imaging features of tumor regions of interest, while immunohistochemistry and sequencing techniques performed on biopsy samples yield omics data. Relationships between tumor genotype and phenotype can be identified from these data through traditional correlation analyses and artificial intelligence (AI) models. However, the radiogenomics community lacks a unified software platform with which to conduct such analyses in a reproducible manner. To address this gap, we developed ImaGene, a web-based platform that takes tumor omics and imaging datasets as inputs, performs correlation analysis between them, and constructs AI models. ImaGene has several modifiable configuration parameters and produces a report displaying model diagnostics. To demonstrate the utility of ImaGene, we utilized data for invasive breast carcinoma (IBC) and head and neck squamous cell carcinoma (HNSCC) and identified potential associations between imaging features and nine genes (WT1, LGI3, SP7, DSG1, ORM1, CLDN10, CST1, SMTNL2, and SLC22A31) for IBC and eight genes (NR0B1, PLA2G2A, MAL, CLDN16, PRDM14, VRTN, LRRN1, and MECOM) for HNSCC. ImaGene has the potential to become a standard platform for radiogenomic tumor analyses due to its ease of use, flexibility, and reproducibility, playing a central role in the establishment of an emerging radiogenomic knowledge base.

Availability and implementation

www.ImaGene.pgxguide.org, https://github.com/skr1/Imagene.git.

Supplementary information

Supplementary data are available at https://github.com/skr1/Imagene.git.

Determination of Gleason score discrepancy for risk stratification in magnetic resonance-ultrasound fusion prostate biopsy

BACKGROUND

Magnetic resonance imaging (MRI)-ultrasound (US) fusion biopsy remains challenging and highlights the need towards standardization.

PURPOSE

To characterize the clinical and MRI features of clinically significant prostate cancer (csPCa) with discrepant Gleason score (GS) in MRI-US fusion biopsy.

MATERIAL AND METHODS

A total of 400 consecutive patients with suspected cancer lesions who underwent MRI-US fusion biopsy and subsequent prostatectomy were included. In the comparison of biopsy GS with pathology GS, matched lesions were defined as a GS, and discrepant lesions were defined as an upgrade of the GS. Descriptive statistics were used to define clinical characteristics, including age, prostate-specific antigen (PSA), PSA density, and maximal cancer core length (MCCL). Differences between lesions with matched and discrepant GS were determined considering the location and PI-RADS v2 score. A paired comparison of the volumes between the two groups was performed.

RESULTS

There were 130 lesions with discrepant GS in 124 patients. There was no significant difference in the age, PSA, and PSA density between the two groups, except for the MCCL (P = 0.028). The lesions were distributed in the peripheral (n = 88) and transition (n = 42) zones; 33, 50, and 47 lesions were at the apex, mid-gland, and base levels, respectively. PI-RADS scores were as follows: 2 (n = 5), 3 (n = 8), 4 (n = 68), and 5 (n = 39). In comparison with matched lesions, discrepant lesions had significantly smaller multiparametric MRI-measured cancer volumes (P < 0.05).

CONCLUSION

Knowledge of discrepant GS in MRI-US fusion biopsy is important, and a careful approach is needed to reduce this discrepancy.

Accurate validation of ultrasound imaging of prostate cancer: a review of challenges in registration of imaging and histopathology

As the development of modalities for prostate cancer (PCa) imaging advances, the challenge of accurate registration between images and histopathologic ground truth becomes more pressing. Localization of PCa, rather than detection, requires a pixel-to-pixel validation of imaging based on histopathology after radical prostatectomy. Such a registration procedure is challenging for ultrasound modalities; not only the deformations of the prostate after resection have to be taken into account, but also the deformation due to the employed transrectal probe and the mismatch in orientation between imaging planes and pathology slices. In this work, we review the latest techniques to facilitate accurate validation of PCa localization in ultrasound imaging studies and extrapolate a general strategy for implementation of a registration procedure.