Boundary delineation in transrectal ultrasound images for region of interest of prostate.

Accurate and robust prostate segmentation in transrectal ultrasound (TRUS) images is of great interest for ultrasound-guided brachytherapy for prostate cancer. However, the current practice of manual segmentation is difficult, time-consuming, and prone to errors. To overcome these challenges, we developed an accurate prostate segmentation framework (A-ProSeg) for TRUS images. The proposed segmentation method includes three innovation steps: (1) acquiring the sequence of vertices by using an improved polygonal segment-based method with a small number of radiologist-defined seed points as prior points; (2) establishing an optimal machine learning-based method by using the improved evolutionary neural network; and (3) obtaining smooth contours of the prostate region of interest using the optimized machine learning-based method. The proposed method was evaluated on 266 patients who underwent prostate cancer brachytherapy. The proposed method achieved a high performance against the ground truth with a Dice similarity coefficient of 96.2% ± 2.4%, a Jaccard similarity coefficient of 94.4% ± 3.3%, and an accuracy of 95.7% ± 2.7%; these values are all higher than those obtained using state-of-the-art methods. A sensitivity evaluation on different noise levels demonstrated that our method achieved high robustness against changes in image quality. Meanwhile, an ablation study was performed, and the significance of all the key components of the proposed method was demonstrated.

A Robust and Explainable Structure-Based Algorithm for Detecting the Organ Boundary From Ultrasound Multi-Datasets.

Detecting the organ boundary in an ultrasound image is challenging because of the poor contrast of ultrasound images and the existence of imaging artifacts. In this study, we developed a coarse-to-refinement architecture for multi-organ ultrasound segmentation. First, we integrated the principal curve-based projection stage into an improved neutrosophic mean shift-based algorithm to acquire the data sequence, for which we utilized a limited amount of prior seed point information as the approximate initialization. Second, a distribution-based evolution technique was designed to aid in the identification of a suitable learning network. Then, utilizing the data sequence as the input of the learning network, we achieved the optimal learning network after learning network training. Finally, a scaled exponential linear unit-based interpretable mathematical model of the organ boundary was expressed via the parameters of a fraction-based learning network. The experimental outcomes indicated that our algorithm 1) achieved more satisfactory segmentation outcomes than state-of-the-art algorithms, with a Dice score coefficient value of 96.68 ± 2.2%, a Jaccard index value of 95.65 ± 2.16%, and an accuracy of 96.54 ± 1.82% and 2) discovered missing or blurry areas.

Dysfunction of natural killer cells mediated by PD-1 and Tim-3 pathway in anaplastic thyroid cancer.

The survival rate of anaplastic thyroid cancer (ATC) remains about 7% to 14%. The natural killer (NK) cells are a critical component of antitumor immunity, and their composition and function in thyroid cancer patients are investigated in this study. In healthy controls and early stage thyroid cancer patients, >90% of circulating NK cells were CD56loCD16hi and fewer than 10% were CD56hiCD16hi/lo. However, the frequency of the CD56hiCD16hi/lo NK subset was significantly higher in more advanced thyroid cancer patients and further increased in ATC patients. Two members of the inhibitory KIR family, CD158a and CD158b, was significantly higher in CD56hiCD16hi/lo NK cells than in CD56loCD16hi NK cells, while NKG2D, an activator of NK cells, was significantly lower in CD56hiCD16hi/lo NK cells than in CD56loCD16hi NK cells. We also found that the CD56hiCD16hi/lo NK cells presented higher PD-1, higher Tim-3, and lower cytotoxicity against the human ATC cell line CAL-62, than the CD56loCD16hi NK cells. The expression of exhaustion markers and reduction in cytotoxicity was further exacerbated in more advanced thyroid cancer patients and in ATC patients. Interestingly, PD-1 and Tim-3 blockade was effective at reinvigorating both the more impaired CD56hiCD16hi/lo NK cells and the less impaired CD56loCD16hi NK cells from ATC patients. Together, our study identified a dysfunction of NK cells in more advanced thyroid cancer patients and ATC patients, and presented actionable targets for future development of immunotherapies in thyroid cancers.