Retrospective T2 quantification from conventional weighted MRI of the prostate based on deep learning.

Purpose: To develop a deep learning-based method to retrospectively quantify T2 from conventional T1- and T2-weighted images. Methods: Twenty-five subjects were imaged using a multi-echo spin-echo sequence to estimate reference prostate T2 maps. Conventional T1- and T2-weighted images were acquired as the input images. A U-Net based neural network was developed to directly estimate T2 maps from the weighted images using a four-fold cross-validation training strategy. The structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean percentage error (MPE), and Pearson correlation coefficient were calculated to evaluate the quality of network-estimated T2 maps. To explore the potential of this approach in clinical practice, a retrospective T2 quantification was performed on a high-risk prostate cancer cohort (Group 1) and a low-risk active surveillance cohort (Group 2). Tumor and non-tumor T2 values were evaluated by an experienced radiologist based on region of interest (ROI) analysis. Results: The T2 maps generated by the trained network were consistent with the corresponding reference. Prostate tissue structures and contrast were well preserved, with a PSNR of 26.41 ± 1.17 dB, an SSIM of 0.85 ± 0.02, and a Pearson correlation coefficient of 0.86. Quantitative ROI analyses performed on 38 prostate cancer patients revealed estimated T2 values of 80.4 ± 14.4 ms and 106.8 ± 16.3 ms for tumor and non-tumor regions, respectively. ROI measurements showed a significant difference between tumor and non-tumor regions of the estimated T2 maps (P < 0.001). In the two-timepoints active surveillance cohort, patients defined as progressors exhibited lower estimated T2 values of the tumor ROIs at the second time point compared to the first time point. Additionally, the T2 difference between two time points for progressors was significantly greater than that for non-progressors (P = 0.010). Conclusion: A deep learning method was developed to estimate prostate T2 maps retrospectively from clinically acquired T1- and T2-weighted images, which has the potential to improve prostate cancer diagnosis and characterization without requiring extra scans.

A phase 2 clinical trial assessing the efficacy and safety of pembrolizumab and radiotherapy in patients with metastatic triple-negative breast cancer.

BACKGROUND: The current study was conducted to evaluate the efficacy and safety of pembrolizumab-mediated programmed cell death protein 1 inhibition plus radiotherapy (RT) in patients with metastatic triple-negative breast cancer who were unselected for programmed death-ligand 1 expression. METHODS: The current study was a single-arm, Simon 2-stage, phase 2 clinical trial that enrolled a total of 17 patients with a median age of 52 years (range, 37-73 years). An RT dose of 3000 centigrays (cGy) was delivered in 5 daily fractions. Pembrolizumab was administered intravenously at a dose of 200 mg within 3 days of the first RT fraction, and then every 3 weeks ± 3 days until disease progression. The median follow-up was 34.5 weeks (range, 2.1-108.3 weeks). The primary endpoint of the current study was the overall response rate (ORR) at week 13 in patients with unirradiated lesions measured using Response Evaluation Criteria in Solid Tumors (RECIST; version 1.1). Secondary endpoints included safety and progression-free survival. Exploratory objectives were to identify biomarkers predictive of ORR and progression-free survival. RESULTS: The ORR for the entire cohort was 17.6% (3 of 17 patients; 95% CI, 4.7%-44.2%), with 3 complete responses (CRs), 1 case of stable disease, and 13 cases of progressive disease. Eight patients died prior to week 13 due to disease progression. Among the 9 women assessed using RECIST version 1.1 at week 13, 3 (33%) achieved a CR, with a 100% reduction in tumor volume outside of the irradiated portal. The CRs were durable for 18 weeks, 20 weeks, and 108 weeks, respectively. The most common grade 1 to 2 toxicity (assessed according to the National Cancer Institute Common Terminology Criteria for Adverse Events, version 4.0) was dermatitis (29%). Four grade 3 adverse events were attributed to pembrolizumab: fatigue, lymphopenia, and infection. No were no grade 4 adverse events or treatment-related deaths reported. CONCLUSIONS: The combination of pembrolizumab and RT was found to be safe and demonstrated encouraging activity in patients with poor-prognosis, metastatic, triple-negative breast cancer who were unselected for programmed death-ligand 1 expression. Larger clinical trials of checkpoint blockade plus RT with predictive biomarkers of response are needed.