MRI and 11C Acetate PET/CT for Prediction of Regional Lymph Node Metastasis in Newly Diagnosed Prostate Cancer

The predictive value of machine learning and nomograms for lymph node metastasis of prostate cancer: a systematic review and meta-analysis

BACKGROUND

In clinical practice, there are currently a variety of nomograms for predicting lymph node metastasis (LNM) of prostate cancer. At the same time, some scholars have introduced machine learning (ML) into the prediction of LNM of prostate cancer. However, the predictive value of nomograms and ML remains controversial. Based on this situation, this systematic review and meta-analysis was performed to explore the predictive value of various nomograms currently recommended and newly-developed ML models for LNM in prostate cancer patients.

EVIDENCE ACQUISITION

Cochrane, PubMed, Embase, and Web of Science were searched up to November 1, 2022. The risk of bias in the included studies was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST). The concordance index (C-index), sensitivity, and specificity were adopted to evaluate the predictive accuracy of the models.

RESULTS

Thirty-one studies (18,803 patients) were included. Seven kinds of nomograms currently recommended, dominated by Briganti nomogram or MSKCC nomogram, were covered in the included studies. For newly-developed ML models, the C-index for LNM prediction in the training set and validation set was 0.846 [95%CI (0.818, 0.873)] and 0.862 [95%CI (0.819-0.905)] respectively. Most ML models in the training set were based on Logistic Regression (LR), which had a sensitivity of 0.78 [95%CI (0.70, 0.85)] and a specificity of 0.85 [95%CI (0.77, 0.90)] in the training set, and a sensitivity of 0.81 [95%CI (0.67, 0.89)] and a specificity of 0.82 [95%CI (0.75, 0.88)] in the validation set. For the recommended nomograms, the C-index in the validation set was 0.745 [95%CI (0.701, 0.790)] for the Briganti nomogram and 0.714 [95%CI (0.662, 0.765)] for the MSKCC nomogram.

CONCLUSION

The predictive accuracy of ML is superior to existing clinically recommended nomograms, and appropriate updates can be conducted to existing nomograms according to special situations.

A Genomic-Clinicopathologic Nomogram for the Prediction of Lymph Node Invasion in Prostate Cancer

Background

Lymph node status is important for treatment decision making in prostate cancer (PCa). We aimed to develop a genomic-clinicopathologic nomogram for the prediction of lymph node invasion (LNI) in PCa.

Methods

Differentially expressed genes between LNI and non-LNI PCa samples were identified in the Cancer Genome Atlas database. Univariate Cox regression analysis and minimum redundancy maximum relevance were performed for gene selection. The synthetic minority oversampling technique (SMOTE) was conducted to balance the minority group (LNI group). Machine learning models were constructed in the training set and assessed in the validation set. Univariable logistic regression and multivariable logistic regression were applied to build a nomogram. Furthermore, the RNA-sequence data from our center were used to validate the expression levels of hub genes between five matched primary PCa and the corresponding LNI samples.

Results

The 37-gene-based support vector machine (SVM) model had the optimal synthesized performance in the SMOTE-balanced training (area under the curve (AUC): 0.947) and validation (AUC: 0.901) sets. Incorporating the SVM-based risk score and the Gleason grade, the genomic-clinicopathologic nomogram demonstrated good prediction and calibration both in the SMOTE-balanced training (AUC: 0.946) and validation (AUC: 0.910) sets. The dysregulated expression of hub genes between PCa and LNI samples was also validated.

Conclusion

The proposed nomogram combining the 37-gene-based SVM model with the Gleason grade had the potential to preoperatively predict LNI in PCa. Some of the hub genes should be prioritized for functional studies and mechanistic analyses.

Comparison of 68Ga-PSMA-11 PET/CT with 11C-acetate PET/CT in re-staging of prostate cancer relapse

Positron emission tomography (PET) imaging is used to localize recurrent disease in prostate cancer (PCa). The tracer ⁶⁸Ga-PSMA-11 visualizes lesions overexpressing prostate-specific membrane antigen (PSMA), while ¹¹C-acetate visualizes lesions with increased anabolic metabolism. The aim of this study was to compare the performance of PSMA-PET and acetate-PET in re-staging patients with biochemical relapse. Thirty PCa patients with prostate-specific antigen (PSA) relapse after primary curative therapy were prospectively evaluated. PET/CT examinations using ¹¹C-acetate and ⁶⁸Ga-PSMA-11 were performed. Identified lesions were categorized according to anatomical location and PET measurements were correlated with PSA at time of scan. Tumour lesions showed higher semi-quantitative uptake values on PSMA-PET than acetate-PET. PSMA-PET identified more lesions in 11 patients, fewer lesions in eight patients, and identical number of lesions in 11 patients. This study indicates better diagnostic performance of PSMA-PET, particularly in detecting lymph node (81% vs 60%, p = 0.02) and bone metastasis (95% vs 61%, p = 0.0001) compared to acetate-PET. However, 38% of PSMA-expressing metastases appear to be metabolically inactive and 15% of metabolically active metastases lack PSMA expression. Addition of PET with a metabolic tracer, such as ¹¹C-acetate, might be beneficial before making treatment decisions.