Correlation between fibroblast growth factor receptor mutation, programmed death ligand-1 expression and survival in urinary bladder cancer based on real-world data

Background: Programmed cell death (PD)-1/PD-ligand 1 (PD-L1) inhibitors have made a breakthrough in the therapy of advanced urothelial bladder cancer (UBC). The impact of Fibroblast Growth Factor Receptor 3 (FGFR3) mutation on the effectiveness of PD-L1 treatment remains still unclear. Objective: Our study aimed to investigate the frequency of FGFR mutations at different tumor stages, and their relation to PD-L1 status and survival.Methods: 310 patients with urothelial bladder cancer and subsequent radical cystectomy were included in a retrospective study over a 10-year study period at the University of Szeged, Hungary. FGFR3 mutations from the most infiltrative areas of the tumor were analyzed by targeted next-generation sequencing and PD-L1 (28-8 DAKO) tests (tumor positive score -TPS and combined positives score–CPS). In T0 cases FGFR3 mutations were analyzed from the earlier resection samples. Survival and oncological treatment data were collected from the National Health Insurance Fund (NHIF). Neoadjuvant, adjuvant and palliative conventional chemotherapies were allowed; immunotherapies were not. The relationship between the covariates was tested using chi-square tests, and survival analysis was performed using the Kaplan-Meier model and Cox proportional hazards regression.Results: PD-L1 and FGFR could be tested successfully in 215 of the 310 UBC samples [pT0cyst 19 (8.8%); St.0-I 43 (20%); St.II 41 (19%); St.III-IV 112 (52%)]. Significant pairwise dependency was found between tumor stage, FGFR3 mutation status and PD-L1 expression (p < 0.01). Samples with FGFR mutation were more common in less advanced stages and were also less likely to demonstrate PD-L1 expression. The effect of all investigated factors on survival was found to correlate with tumor stage.Conclusion: FGFR alteration frequency varied between the different stages of cancer. Higher positivity rates were observed at early stages, but lower levels of PD-L1 expression were detected in patients with FGFR mutations across at all stages of the disease

Prostate-specific membrane antigen-based imaging for stereotactic irradiation of low-volume progressive prostate cancer: a single-center experience

IntroductionProstate-specific membrane antigen (PSMA) is a transmembrane protein that may be expressed on the surface of prostate cancer (PC) cells. It enables a more sensitive and specific diagnosis PC, compared to conventional anatomical imaging.AimThe integration of PSMA-based imaging in the personalized radiotherapy of PC patients and the evaluation of its impact on target volume definition if stereotactic body radiotherapy (SBRT) is planned for locally recurrent or oligometastatic disease.Patients and methodsThe data from 363 examinations were analyzed retrospectively. Inclusion criteria were histologically verified PC and clinical data suggesting local recurrence or distant metastasis. Whole-body 99mTc-PSMA-I&S single-photon emission computed tomography (SPECT)/CT or 18F-JK-PSMA-7 positron emission tomography/computer tomography (PET/CT) was carried out, and the evaluation of the scans and biological tumor volume contouring was performed at the Department of Nuclear Medicine. The target volume delineation on topometric CT (TCT) scan was performed at the Department of Oncotherapy. The comparison of the two volumes was performed by image fusion and registration.ResultsFrom 363 PSMA isotope-based examinations, 84 lesions of 64 patients were treated with SBRT. In 50 patients, 70 lesions were examined for intermodality comparison. The target volume defined by the PSMA density was significantly smaller than the tumor size defined by the TCT scan: GTVCT (gross tumor volume on the TCT), 27.58 ± 46.07 cm3; BTVPSMA (biological target volume on the PSMA-based examination), 16.14 ± 29.87 cm3. During geometrical analyses, the Dice similarity coefficient (DSC) was 0.56 ± 0.20 (0.07–0.85). Prostate-specific antigen (PSA) control was performed to evaluate the response: mean pre-radiotherapy (pre-RT) PSA was 16.98 ng/ml ( ± SD: 33.81), and post-RT PSA at 3 months after SBRT was 11.19 ng/ml ( ± SD: 32.85). Three-month post-therapy PSMA-based imaging was performed in 14 cases, in which we observed a decrease or cessation of isotope uptake. Conventional imaging control was performed in 42 cases (65.6% of all cases): 22 (52.4%) complete remissions, 14 (33.3%) partial remissions, four (9.5%) stable diseases, and two (4.8%) progressive diseases were described.ConclusionPSMA-based imaging is a promising diagnostic method for specifying the stage and detecting the low-volume progression. Our results suggest that PSMA-based hybrid imaging can influence treatment decisions and target volume delineation for SBRT

Comprehensive deep learning-based framework for automatic organs-at-risk segmentation in head-and-neck and pelvis for MR-guided radiation therapy planning

Introduction: The excellent soft-tissue contrast of magnetic resonance imaging (MRI) is appealing for delineation of organs-at-risk (OARs) as it is required for radiation therapy planning (RTP). In the last decade there has been an increasing interest in using deep-learning (DL) techniques to shorten the labor-intensive manual work and increase reproducibility. This paper focuses on the automatic segmentation of 27 head-and-neck and 10 male pelvis OARs with deep-learning methods based on T2-weighted MR images.Method: The proposed method uses 2D U-Nets for localization and 3D U-Net for segmentation of the various structures. The models were trained using public and private datasets and evaluated on private datasets only.Results and discussion: Evaluation with ground-truth contours demonstrated that the proposed method can accurately segment the majority of OARs and indicated similar or superior performance to state-of-the-art models. Furthermore, the auto-contours were visually rated by clinicians using Likert score and on average, 81% of them was found clinically acceptable