Clinical, pathological, and demographic factors associated with development of recurrences after surgical resection in elderly patients with neuroendocrine tumors

Predicting the recurrence risk of pancreatic neuroendocrine neoplasms after radical resection using deep learning radiomics with preoperative computed tomography images

Background

To establish and validate a prediction model for pancreatic neuroendocrine neoplasms (pNENs) recurrence after radical surgery with preoperative computed tomography (CT) images.

Methods

We retrospectively collected data from 74 patients with pathologically confirmed pNENs (internal group: 56 patients, Hospital I; external validation group: 18 patients, Hospital II). Using the internal group, models were trained with CT findings evaluated by radiologists, radiomics, and deep learning radiomics (DLR) to predict 5-year pNEN recurrence. Radiomics and DLR models were established for arterial (A), venous (V), and arterial and venous (A&V) contrast phases. The model with the optimal performance was further combined with clinical information, and all patients were divided into high- and low-risk groups to analyze survival with the Kaplan-Meier method.

Results

In the internal group, the areas under the curves (AUCs) of DLR-A, DLR-V, and DLR-A&V models were 0.80, 0.58, and 0.72, respectively. The corresponding radiomics AUCs were 0.74, 0.68, and 0.70. The AUC of the CT findings model was 0.53. The DLR-A model represented the optimum; added clinical information improved the AUC from 0.80 to 0.83. In the validation group, the AUCs of DLR-A, DLR-V, and DLR-A&V models were 0.77, 0.48, and 0.64, respectively, and those of radiomics-A, radiomics-V, and radiomics-A&V models were 0.56, 0.52, and 0.56, respectively. The AUC of the CT findings model was 0.52. In the validation group, the comparison between the DLR-A and the random models showed a trend of significant difference (P=0.058). Recurrence-free survival differed significantly between high- and low-risk groups (P=0.003).

Conclusions

Using DLR, we successfully established a preoperative recurrence prediction model for pNEN patients after radical surgery. This allows a risk evaluation of pNEN recurrence, optimizing clinical decision-making.

High-Z Metal-Organic Frameworks for X-ray Radiation-Based Cancer Theranostics

X-ray radiation is commonly employed in clinical practice for diagnostic and therapeutic applications. Over the past decade, developments in nanotechnology have led to the use of high-Z elements as the basis for innovative new treatment platforms that enhance the clinical efficacy of X-ray radiation. Nanoscale metal-frameworks (nMOFs) are coordination networks containing organic ligands that have attracted attention as therapeutic platforms in oncology and other areas of medicine. In cancer therapy, X-ray activated, high-Z nMOFs have demonstrated potential as radiosensitizers that increase local radiation dose deposition and generation of reactive oxygen species (ROS). This minireview summarizes current research on high-Z nMOFs in cancer theranostics and discusses factors that may influence future clinical application.

Black phosphorus nanosheets-based platform for targeted chemo-photothermal synergistic cancer therapy

As a new member of two-dimensional (2D) nanomaterials, black phosphorus (BP) has been considered as efficient photothermal therapy (PTT) agents owing to its excellent photothermal efficiency and biodegradability. Herein, a multifunctional nanoplatform based on black phosphorus nanosheets (BP NSs) was developed for chemo-photothermal synergistic cancer therapy. The BP NSs were successfully prepared by a liquid exfoliation technique. Doxorubicin (DOX), as a model drug, was loaded into the cavity of poly (amidoamine) (PAMAM) dendrimer using thin film hydration method. Then, PAMAM@DOX was coated on the surface of BP NSs using an electrostatic adsorption method that combined bath sonication with magnetic stirring. Hyaluronic acid (HA) was also modified onto the BP NS-PAMAM@DOX through electrostatic adsorption. PAMAM and HA layer could effectively isolate BP NSs from water and air to improve physiological stability. BP NSs and BP NS-PAMAM@DOX-HA were characterized by particle size, zeta potential, morphology, UV-vis-NIR absorption spectra, stability, photothermal performance and photothermal stability. This nanosystem exhibited a good pH and near infrared (NIR) dual-responsive drug release property. In addition, the obtained BP NS-PAMAM@D OX-HA nanocomposites possessed excellent PTT efficiency both in vitro and in vivo. The in vitro cell experiments suggested that the targeted BP NS-PAMAM@DOX-HA presented greater cytotoxicity and higher cellular uptake efficiency. Tumor xenograft model was established in BALB/C mice. The therapeutic effect of BP NS-PAMAM@DOX-HA was further augmented under 808 nm laser irradiation, displaying superior antitumor effect in comparison with chemotherapy or PTT alone. Such a biodegradable BP NS-based platform provide new insights for the rational design of PTT-based combinational cancer therapy.