ROLE OF MAGNETIC RESONANCE IMAGING IN NODAL AND METASTATIC STAGING OF PROSTATE CANCER: A SYSTEMATIC REVIEW AND META-ANALYSIS

Objective: To evaluate the usage of MRI in prostate cancer staging, especially in nodal involvement (N-staging) and metastasis (M-staging) of prostate cancer. Methods: This is a systematic review and meta-analysis assessing role of MRI in nodal and metastasis staging of prostate cancer. Search of studies were done through search engine using Pubmed, Cochrane, and EBSCO Host and manual searching. Quality of eligible studies were assessed using a revised version of Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and analyzed in pooled analysis according to nodal involvement or metastasis staging, modality of diagnosis used as the index test and gold standard used using STATA version 13. Results: Total 26 studies corresponding with study’s eligibility criteria were found. Overall, usage of MRI has a sensitivity of 47% (95% CI 35% - 60%; I2 83.08%) and a specificity of 93% (95% CI 89% - 96%, I2 82.21%) in nodal involvement staging of prostate cancer, while using of MRI in M-staging of prostate cancer shows a sensitivity of 94% (95% CI 86% - 97%) and a specificity of 99% (95% CI 97% - 99%). Using lymphotrophic superparamagnetic nanoparticle (LSN) - enhanced MRI gives higher sensitivity than using MRI without LSN for N-staging of prostate cancer. Conclusion: The usage of MRI in prostate cancer staging has a moderate sensitivity and relatively high specificity in detecting lymph node. Moreover, it plays an important role and even can be used as a modality of choice in assisting bone metastatic prostate cancer detection.

Whole-body uptake classification and prostate cancer staging in 68Ga-PSMA-11 PET/CT using dual-tracer learning

Purpose In PSMA-ligand PET/CT imaging, standardized evaluation frameworks and image-derived parameters are increasingly used to support prostate cancer staging. Clinical applicability remains challenging wherever manual measurements of numerous suspected lesions are required. Deep learning methods are promising for automated image analysis, typically requiring extensive expert-annotated image datasets to reach sufficient accuracy. We developed a deep learning method to support image-based staging, investigating the use of training information from two radiotracers. Methods In 173 subjects imaged with 68Ga-PSMA-11 PET/CT, divided into development (121) and test (52) sets, we trained and evaluated a convolutional neural network to both classify sites of elevated tracer uptake as nonsuspicious or suspicious for cancer and assign them an anatomical location. We evaluated training strategies to leverage information from a larger dataset of 18F-FDG PET/CT images and expert annotations, including transfer learning and combined training encoding the tracer type as input to the network. We assessed the agreement between the N and M stage assigned based on the network annotations and expert annotations, according to the PROMISE miTNM framework. Results In the development set, including 18F-FDG training data improved classification performance in four-fold cross validation. In the test set, compared to expert assessment, training with 18F-FDG data and the development set yielded 80.4% average precision [confidence interval (CI): 71.1–87.8] for identification of suspicious uptake sites, 77% (CI: 70.0–83.4) accuracy for anatomical location classification of suspicious findings, 81% agreement for identification of regional lymph node involvement, and 77% agreement for identification of metastatic stage. Conclusion The evaluated algorithm showed good agreement with expert assessment for identification and anatomical location classification of suspicious uptake sites in whole-body 68Ga-PSMA-11 PET/CT. With restricted PSMA-ligand data available, the use of training examples from a different radiotracer improved performance. The investigated methods are promising for enabling efficient assessment of cancer stage and tumor burden.