Seven-month prostate-specific antigen (PSA) is prognostic in patients with prostate cancer initially diagnosed with distant metastases

A semi-automatic deep learning model based on biparametric MRI scanning strategy to predict bone metastases in newly diagnosed prostate cancer patients

Objective

To develop a semi-automatic model integrating radiomics, deep learning, and clinical features for Bone Metastasis (BM) prediction in prostate cancer (PCa) patients using Biparametric MRI (bpMRI) images.

Methods

A retrospective study included 414 PCa patients (BM, n=136; NO-BM, n=278) from two institutions (Center 1, n=318; Center 2, n=96) between January 2016 and December 2022. MRI scans were confirmed with BM status via PET-CT or ECT pre-treatment. Tumor areas on bpMRI images were delineated as tumor's region of interest (ROI) using auto-delineation tumor models, evaluated with Dice similarity coefficient (DSC). Samples were auto-sketched, refined, and used to train the ResNet BM prediction model. Clinical, radiomics, and deep learning data were synthesized into the ResNet-C model, evaluated using receiver operating characteristic (ROC).

Results

The auto-segmentation model achieved a DSC of 0.607. Clinical BM prediction's internal validation had an accuracy (ACC) of 0.650 and area under the curve (AUC) of 0.713; external cohort had an ACC of 0.668 and AUC of 0.757. The deep learning model yielded an ACC of 0.875 and AUC of 0.907 for the internal, and ACC of 0.833 and AUC of 0.862 for the external cohort. The Radiomics model registered an ACC of 0.819 and AUC of 0.852 internally, and ACC of 0.885 and AUC of 0.903 externally. ResNet-C demonstrated the highest ACC of 0.902 and AUC of 0.934 for the internal, and ACC of 0.885 and AUC of 0.903 for the external cohort.

Conclusion

The ResNet-C model, utilizing bpMRI scanning strategy, accurately assesses bone metastasis (BM) status in newly diagnosed prostate cancer (PCa) patients, facilitating precise treatment planning and improving patient prognoses.

Contribution of Serum Biomarkers to Prognostic Assessment in Patients With Oligometastatic Prostate Cancer

BACKGROUND/AIM

The aim of this study was to analyze the prognostic impact of biomarkers, such as serum lactate dehydrogenase (LDH), in patients with oligometastatic castration-resistant prostate cancer, arbitrarily defined as a maximum of five metastatic lesions.

PATIENTS AND METHODS

This was a retrospective single-institution analysis. Overall 34 patients were included, all of whom received first-line docetaxel without ablative local treatment.

RESULTS

Twelve patients (35%) had elevated LDH (≥255 U/l). Their median survival was significantly shorter than that of patients with normal LDH. Due to an interaction with other biomarkers, multivariate Cox regression analysis was performed. The latter showed that serum hemoglobin was the only significant predictor of survival.

CONCLUSION

Correct diagnosis of oligometastatic disease is not trivial, because all radiological modalities are limited by certain thresholds for detection of small metastases. Serum biomarkers may reflect the total burden of malignant disease. However, this relatively small study did not clearly demonstrate that elevation of LDH may be useful for clinical decision-making, e.g. in terms of adding local treatment for all sites of metastatic spread.