Automated cell classification is an important yet a challenging computer vision task with significant benefits to biomedicine. In recent years, there have been several studies attempted to build an artificial intelligence-based cell classifier using label-free cellular images obtained from an optical microscope. Although these studies showed promising results, such classifiers were not able to reflect the biological diversity of different types of cell. While in terms of malignant cell, it is well-known that intracellular actin filaments are altered substantially. This is thought to be closely related to the abnormal growth features of tumor cells, their ability to invade surrounding tissues and also to metastasize. Therefore, being able to classify different types of cell based on their biological behaviors using automated technique is more advantageous. This article reveals the difference in the actin cytoskeleton structures between breast normal and cancer cells, which may provide new information regarding malignant changes and be used as additional diagnostic marker. Since the features cannot be well detected by human eyes, we proposed the application of convolutional neural network (CNN) in cell classification based on actin-labeled fluorescence microscopy images. The CNN was evaluated on a large number of actin-labeled fluorescence microscopy images of one human normal breast epithelial cell line and two types of human breast cancer cell line with different levels of aggressiveness. The study revealed that the CNN performed better in the cell classification task compared to a human expert.

This study aimed to develop prognosis signatures through a radiomics analysis for patients with nasopharyngeal carcinoma (NPC) by their pretreatment diagnosis magnetic resonance imaging (MRI). A total of 208 radiomics features were extracted for each patient from a database of 303 patients. The patients were split into the training and validation cohorts according to their pretreatment diagnosis date. The radiomics feature analysis consisted of cluster analysis and prognosis model analysis for disease free-survival (DFS), overall survival (OS), distant metastasis-free survival (DMFS) and locoregional recurrence-free survival (LRFS). Additionally, two prognosis models using clinical features only and combined radiomics and clinical features were generated to estimate the incremental prognostic value of radiomics features. Patients were clustered by non-negative matrix factorization (NMF) into two groups. It showed high correspondence with patients’ T stage (p < 0.00001) and overall stage information (p < 0.00001) by chi-squared tests. There were significant differences in DFS (p = 0.0052), OS (p = 0.033), and LRFS (p = 0.037) between the two clustered groups but not in DMFS (p = 0.11) by log-rank tests. Radiomics nomograms that incorporated radiomics and clinical features could estimate DFS with the C-index of 0.751 [0.639, 0.863] and OS with the C-index of 0.845 [0.752, 0.939] in the validation cohort. The nomograms improved the prediction accuracy with the C-index value of 0.029 for DFS and 0.107 for OS compared with clinical features only. The DFS and OS radiomics nomograms developed in our study demonstrated the excellent prognostic estimation for NPC patients with a noninvasive way of MRI. The combination of clinical and radiomics features can provide more information for precise treatment decision.

BackgroundTo analyze the prognostic value of preoperative prognostic nutritional index (PNI) in predicting the survival outcome of hypopharyngeal squamous cell carcinoma (HPSCC) patients receiving radical surgery.MethodsFrom March 2006 to August 2016, 123 eligible HPSCC patients were reviewed. The preoperative PNI was calculated as serum albumin (g/dL) × 10 + total lymphocyte count (mm−3) × 0.005. These biomarkers were measured within 2 weeks prior to surgery. The impact of preoperative PNI on overall survival (OS), progression-free survival (PFS), locoregional recurrence-free survival (LRFS) and distant metastasis-free survival (DMFS) were analyzed using Kaplan–Meier method and Cox proportional hazards model.ResultsMedian value of 52.0 for the PNI was selected as the cutoff point. PNI value was then classified into two groups: high PNI (> 52.0) versus low PNI (≤ 52.0). Multivariate analysis showed that high preoperative PNI was an independent prognostic factor for better OS (P = 0.000), PFS (P = 0.001), LRFS (P = 0.005) and DMFS (P = 0.016).ConclusionsHigh PNI predicts superior survival in HPSCC patients treated with radical surgery. As easily accessible biomarkers, preoperative PNI together with the conventional TNM staging system can be utilized to enhance the accuracy in predicting survival and determining therapy strategies in these patients.