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Lung cancer is a major cause of death worldwide. As early detection can improve outcome, regular screening is of great interest, especially for certain risk groups. Besides low-dose computed tomography, chest X-ray is a potential option for screening. Convolutional network (CNN) based computer aided diagnosis systems have proven their ability of identifying nodules in radiographies and thus may assist radiologists in clinical practice. Based on segmented pulmonary nodules, we trained a CNN based one-stage detector (RetinaNet) with 257 annotated radiographs and 154 additional radiographs from a public dataset. We compared the performance of the convolutional network with the performance of two radiologists by conducting a reader study with 75 cases. Furthermore, the potential use for screening on patient level and the impact of foreign bodies with respect to false-positive detections was investigated. For nodule location detection, the architecture achieved a performance of 43 true-positives, 26 false-positives and 22 false-negatives. In comparison, performance of the two readers was 42 ± 2 true-positives, 28 ± 0 false-positives and 23 ± 2 false-negatives. For the screening task, we retrieved a ROC AUC value of 0.87 for the reader study test set. We found the trained RetinaNet architecture to be only slightly prone to foreign bodies in terms of misclassifications: out of 59 additional radiographs containing foreign bodies, false-positives in two radiographs were falsely detected due to foreign bodies.

We present a method to generate synthetic thorax radiographs with realistic nodules from CT scans, and a perfect ground truth knowledge. We evaluated the detection performance of nine radiologists and two convolutional neural networks in a reader study. Nodules were artificially inserted into the lung of a CT volume and synthetic radiographs were obtained by forward-projecting the volume. Hence, our framework allowed for a detailed evaluation of CAD systems’ and radiologists’ performance due to the availability of accurate ground-truth labels for nodules from synthetic data. Radiographs for network training (U-Net and RetinaNet) were generated from 855 CT scans of a public dataset. For the reader study, 201 radiographs were generated from 21 nodule-free CT scans with altering nodule positions, sizes and nodule counts of inserted nodules. Average true positive detections by nine radiologists were 248.8 nodules, 51.7 false positive predicted nodules and 121.2 false negative predicted nodules. The best performing CAD system achieved 268 true positives, 66 false positives and 102 false negatives. Corresponding weighted alternative free response operating characteristic figure-of-merits (wAFROC FOM) for the radiologists range from 0.54 to 0.87 compared to a value of 0.81 (CI 0.75–0.87) for the best performing CNN. The CNN did not perform significantly better against the combined average of the 9 readers ( p = 0.49). Paramediastinal nodules accounted for most false positive and false negative detections by readers, which can be explained by the presence of more tissue in this area.

Triple negative breast cancer (TNBC) lacks targeted therapy options. TNBC is enriched in breast cancer stem cells (BCSCs), which play a key role in metastasis, chemoresistance, relapse and mortality. γδ T cells hold great potential in immunotherapy against cancer, and might be an alternative to target TNBC. γδ T cells are commonly observed to infiltrate solid tumors and have an extensive repertoire of tumor sensing, recognizing stress-induced molecules and phosphoantigens (pAgs) on transformed cells. We show that patient derived triple negative BCSCs are efficiently recognized and killed by ex vivo expanded γδ T cells from healthy donors. Orthotopically xenografted BCSCs, however, were refractory to γδ T cell immunotherapy. Mechanistically, we unraveled concerted differentiation and immune escape: xenografted BCSCs lost stemness, expression of γδ T cell ligands, adhesion molecules and pAgs, thereby evading immune recognition by γδ T cells. Indeed, neither pro-migratory engineered γδ T cells, nor anti-PD 1 checkpoint blockade significantly prolonged overall survival of tumor-bearing mice. BCSC immune escape was independent of the immune pressure exerted by the γδ T cells, and could be pharmacologically reverted by Zoledronate or IFNγtreatment. These results pave the way for novel combinatorial immunotherapies for TNBC. Competing Interest Statement The authors have declared no competing interest.