Delta radiomic patterns on serial bi-parametric MRI are associated with pathologic upgrading in prostate cancer patients on active surveillance: preliminary findings

Multiparametric MRI to Predict Gleason Score Upgrading and Downgrading at Radical Prostatectomy Compared to Presurgical Biopsy

OBJECTIVE

This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy.

MATERIALS AND METHODS

Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP.

RESULTS

The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PI-RADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001).

CONCLUSION

Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Role of MRI in Active Surveillance of Prostate Cancer

Magnetic resonance imaging (MRI) plays an important role in the management of patients with prostate cancer on active surveillance. In this review, we will explore the incorporation of MRI into active surveillance protocols, detailing its impact on clinical decision-making and patient management and discussing how it aligns with current guidelines and practice patterns. The role of MRI in this patient population continues to evolve over time, and we will discuss some of the recent advancements in the field and highlight potential areas for future research endeavors.

Prostate MRI and artificial intelligence during active surveillance: should we jump on the bandwagon?

OBJECTIVE

To review the components of past and present active surveillance (AS) protocols, provide an overview of the current studies employing artificial intelligence (AI) in AS of prostate cancer, discuss the current challenges of AI in AS, and offer recommendations for future research.

METHODS

Research studies on the topic of MRI-based AI were reviewed to summarize current possibilities and diagnostic accuracies for AI methods in the context of AS. Established guidelines were used to identify possibilities for future refinement using AI.

RESULTS

Preliminary results show the role of AI in a range of diagnostic tasks in AS populations, including the localization, follow-up, and prognostication of prostate cancer. Current evidence is insufficient to support a shift to AI-based AS, with studies being limited by small dataset sizes, heterogeneous inclusion and outcome definitions, or lacking appropriate benchmarks.

CONCLUSION

The AI-based integration of prostate MRI is a direction that promises substantial benefits for AS in the future, but evidence is currently insufficient to support implementation. Studies with standardized inclusion criteria and standardized progression definitions are needed to support this. The increasing inclusion of patients in AS protocols and the incorporation of MRI as a scheduled examination in AS protocols may help to alleviate these challenges in future studies.

CLINICAL RELEVANCE STATEMENT

This manuscript provides an overview of available evidence for the integration of prostate MRI and AI in active surveillance, addressing its potential for clinical optimizations in the context of established guidelines, while highlighting the main challenges for implementation.

KEY POINTS

Active surveillance is currently based on diagnostic tests such as PSA, biopsy, and imaging. Prostate MRI and AI demonstrate promising diagnostic accuracy across a variety of tasks, including the localization, follow-up and risk estimation in active surveillance cohorts. A transition to AI-based active surveillance is not currently realistic; larger studies using standardized inclusion criteria and outcomes are necessary to improve and validate existing evidence.

Delta radiomics: an updated systematic review

BACKGROUND

Radiomics can provide quantitative features from medical imaging that can be correlated with various biological features and diverse clinical endpoints. Delta radiomics, on the other hand, consists in the analysis of feature variation at different acquisition time points, usually before and after therapy. The aim of this study was to provide a systematic review of the different delta radiomics approaches.

METHODS

Eligible articles were searched in Embase, Pubmed, and ScienceDirect using a search string that included free text and/or Medical Subject Headings (MeSH) with 3 key search terms: 'radiomics,' 'texture,' and 'delta.' Studies were analyzed using QUADAS-2 and the RQS tool.

RESULTS

Forty-eight studies were finally included. The studies were divided into preclinical/methodological (5 studies, 10.4%); rectal cancer (6 studies, 12.5%); lung cancer (12 studies, 25%); sarcoma (5 studies, 10.4%); prostate cancer (3 studies, 6.3%), head and neck cancer (6 studies, 12.5%); gastrointestinal malignancies excluding rectum (7 studies, 14.6%) and other disease sites (4 studies, 8.3%). The median RQS of all studies was 25% (mean 21% ± 12%), with 13 studies (30.2%) achieving a quality score < 10% and 22 studies (51.2%) < 25%.

CONCLUSIONS

Delta radiomics shows potential benefit for several clinical endpoints in oncology, such asdifferential diagnosis, prognosis and prediction of treatment response, evaluation of side effects. Nevertheless, the studies included in this systematic review suffer from the bias of overall low methodological rigor, so that the conclusions are currently heterogeneous, not robust and hardly replicable. Further research with prospective and multicenter studies is needed for the clinical validation of delta radiomics approaches.

Radiomic Pipelines for Prostate Cancer in External Beam Radiation Therapy: A Review of Methods and Future Directions

Background: Prostate Cancer (PCa) is asymptomatic at an early stage and often painless, requiring only active surveillance. External Beam Radiotherapy (EBRT) is currently a curative option for localised and locally advanced diseases and a palliative option for metastatic low-volume disease. Although highly effective, especially in a hypofractionation scheme, 17.4% to 39.4% of all patients suffer from cancer recurrence after EBRT. But, radiographic findings also correlate with significant differences in protein expression patterns. In the PCa EBRT workflow, several imaging modalities are available for grading, staging and contouring. Using image data characterisation algorithms (radiomics), one can provide a quantitative analysis of prognostic and predictive treatment outcomes. Methods: This literature review searched for original studies in radiomics for PCa in the context of EBRT. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review includes 73 new studies and analyses datasets, imaging modality, segmentation technique, feature extraction, selection and model building methods. Results: Magnetic Resonance Imaging (MRI) is the preferred imaging modality for radiomic studies in PCa but Computed Tomography (CT), Positron Emission Tomography (PET) and Ultrasound (US) may offer valuable insights on tumour characterisation and treatment response prediction. Conclusions: Most radiomic studies used small, homogeneous and private datasets lacking external validation and variability. Future research should focus on collaborative efforts to create large, multicentric datasets and develop standardised methodologies, ensuring the full potential of radiomics in clinical practice.

A noninvasive method for predicting clinically significant prostate cancer using magnetic resonance imaging combined with PRKY promoter methylation level: a machine learning study

BACKGROUND

Traditional process for clinically significant prostate cancer (csPCA) diagnosis relies on invasive biopsy and may bring pain and complications. Radiomic features of magnetic resonance imaging MRI and methylation of the PRKY promoter were found to be associated with prostate cancer.

METHODS

Fifty-four Patients who underwent prostate biopsy or photoselective vaporization of the prostate (PVP) from 2022 to 2023 were selected for this study, and their clinical data, blood samples and MRI images were obtained before the operation. Methylation level of two PRKY promoter sites, cg05618150 and cg05163709, were tested through bisulfite sequencing PCR (BSP). The PI-RADS score of each patient was estimated and the region of interest (ROI) was delineated by 2 experienced radiologists. After being extracted by a plug-in of 3D-slicer, radiomic features were selected through LASSCO regression and t-test. Selected radiomic features, methylation levels and clinical data were used for model construction through the random forest (RF) algorithm, and the predictive efficiency was analyzed by the area under the receiver operation characteristic (ROC) curve (AUC).

RESULTS

Methylation level of the site, cg05618150, was observed to be associated with prostate cancer, for which the AUC was 0.74. The AUC of T2WI in csPCA prediction was 0.84, which was higher than that of the apparent diffusion coefficient ADC (AUC = 0.81). The model combined with T2WI and clinical data reached an AUC of 0.94. The AUC of the T2WI-clinic-methylation-combined model was 0.97, which was greater than that of the model combined with the PI-RADS score, clinical data and PRKY promoter methylation levels (AUC = 0.86).

CONCLUSIONS

The model combining with radiomic features, clinical data and PRKY promoter methylation levels based on machine learning had high predictive efficiency in csPCA diagnosis.