Ultra-Hypofractionated Re-Irradiation with Anti-PD-1 Immunotherapy for Locoregionally Recurrent (after Radical Chemo-Radiotherapy) Non-Small Cell Lung Cancer.

Large fractions of radiotherapy of 8 Gy (ultra-hypofractionated RT, ultra-hypoRT) promote anti-tumor immune responses that have been clinically substantiated in combination trials with immune checkpoint inhibitors (ICIs). In the current study, we postulated that ultra-hypoRT in combination with ICIs may enhance tumor clearance in NSCLC patients with locoregional relapse after radical chemo-RT. Between 2019 and 2021, eleven patients received re-irradiation with one or two fractions of 8 Gy concurrently with anti-PD1 immunotherapy (nivolumab or pembrolizumab). RT-related toxicities were negligible, while immune-related adverse events enforced immunotherapy interruption in 36% of patients. The overall response rate was 81.8%. Tumor reduction between 80 and 100% was noted in 63.5% of patients. Within a median follow-up of 22 months, the locoregional relapse-free rate was 54.5%, while the projected 2-year disease-specific overall survival was 62%. The results were independent of PD-L1 status. The current report provides encouraging evidence that a relatively low biological dose of RT delivered with 8 Gy fractions is feasible and can be safely combined with anti-PD-1 immunotherapy. Despite the low number of patients, the significant tumor regression achieved and the long-lasting locoregional control and overall progression-free intervals provide a basis to pursue immuno-RT trials with U-hypoRT schemes in this group of NSCLC patients of poor prognosis.

LECANDUS study (LEsion CANdidate Detection in UltraSound Data): evaluation of image analysis algorithms for breast lesion detection in volume ultrasound data.

PURPOSE: This study aims at developing and evaluating a prototype of a lesion candidate detection algorithm for a 3D-US computer-aided diagnosis (CAD) system. METHODS: Additionally, to routine imaging, automated breast volume scans (ABVS) were performed on 63 patients. All ABVS exams were analyzed and annotated before the evaluation with different algorithm blob detectors characterized by different blob-radiuses, voxel-sizes and the quantiles of blob filter responses to find lesion candidates. Lesions found in candidates were compared to the prior annotations. RESULTS: All histologically proven lesions were detected with at least one algorithm. The algorithm with optimal sensitivity detected all cancers (sensitivity = 100 %) with a very low positive predictive value due to a high false-positive rate. CONCLUSIONS: ABVS is a new technology which can be analyzed by a CAD software. Using different algorithms, lesions can be detected with a very high and accurate sensitivity. Further research for feature extraction and lesion classification is needed aiming at reducing the false-positive hits.