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Accurate prediction of the prognosis of nasopharyngeal carcinoma (NPC) is important for treatment. Lymph nodes metastasis is an important predictor for distant failure and regional recurrence in patients with NPC. Traditionally, subjective radiological evaluation increases concerns regarding the accuracy and consistency of predictions. Radiomics is an objective and quantitative evaluation algorithm for medical images. This retrospective analysis was conducted based on the data of 729 patients newly diagnosed with NPC without distant metastases to evaluate the performance of radiomics pretreatment using magnetic resonance imaging (MRI)-determined metastatic lymph nodes models to predict NPC prognosis with three delineation methods. Radiomics features were extracted from all lymph nodes (ALN), largest lymph node (LLN), and largest slice of the largest lymph node (LSLN) to generate three radiomics signatures. The radiomics signatures, clinical model, and radiomics-clinic merged models were developed in training cohort for predicting overall survival (OS). The results showed that LSLN signature with clinical factors predicted OS with high accuracy and robustness using pretreatment MR-determined metastatic lymph nodes (C-index [95 % confidence interval]: 0.762[0.760–0.763]), providing a new tool for treatment planning in NPC.

IntroductionAutomatically and accurately delineating the primary nasopharyngeal carcinoma (NPC) tumors in head magnetic resonance imaging (MRI) images is crucial for patient staging and radiotherapy. Inspired by the bilateral symmetry of head and complementary information of different modalities, a multi-modal neural network named BSMM-Net is proposed for NPC segmentation.MethodsFirst, a bilaterally symmetrical patch block (BSP) is used to crop the image and the bilaterally flipped image into patches. BSP can improve the precision of locating NPC lesions and is a simulation of radiologist locating the tumors with the bilateral difference of head in clinical practice. Second, modality-specific and multi-modal fusion features (MSMFFs) are extracted by the proposed MSMFF encoder to fully utilize the complementary information of T1- and T2-weighted MRI. The MSMFFs are then fed into the base decoder to aggregate representative features and precisely delineate the NPC. MSMFF is the output of MSMFF encoder blocks, which consist of six modality-specific networks and one multi-modal fusion network. Except T1 and T2, the other four modalities are generated from T1 and T2 by the BSP and DT modal generate block. Third, the MSMFF decoder with similar structure to the MSMFF encoder is deployed to supervise the encoder during training and assure the validity of the MSMFF from the encoder. Finally, experiments are conducted on the dataset of 7633 samples collected from 745 patients.Results and discussionThe global DICE, precision, recall and IoU of the testing set are 0.82, 0.82, 0.86, and 0.72, respectively. The results show that the proposed model is better than the other state-of-the-art methods for NPC segmentation. In clinical diagnosis, the BSMM-Net can give precise delineation of NPC, which can be used to schedule the radiotherapy.

In locally advanced rectal cancer (LARC), an improved ability to predict prognosis before and after treatment is needed for individualized treatment. We aimed to utilize pre- and post-treatment clinical predictors and baseline magnetic resonance imaging (MRI) radiomic features for establishing prognostic models to predict progression-free survival (PFS) in patients with LARC. Patients with LARC diagnosed between March 2014 and May 2016 were included in this retrospective study. A radiomic signature based on extracted MRI features and clinical prognostic models based on clinical features were constructed in the training cohort to predict 3-year PFS. C-indices were used to evaluate the predictive accuracies of the radiomic signature, clinical prognostic models, and integrated prognostic model (iPostM). In total, 166 consecutive patients were included (110 vs. 56 for training vs. validation). Eleven radiomic features were filtered out to construct the radiomic signature, which was significantly related to PFS. The MRI feature-derived radiomic signature exhibited better prognostic performance than the clinical prognostic models (P = 0.007 vs. 0.077). Then, we proposed an iPostM that combined the radiomic signature with tumor regression grade. The iPostM achieved the highest C-indices in the training and validation cohorts (0.942 and 0.752, respectively), outperforming other models in predicting PFS (all P < 0.05). Decision curve analysis and survival curves of the validation cohort verified that iPostM demonstrated the best performance and facilitated risk stratification. Therefore, iPostM provided the most reliable prognostic prediction for PFS in patients with LARC.

Fast and accurate lesion localization is an important step in medical image analysis. The current supervised deep learning methods have obvious limitations in the application of radiology, as they require a large number of manually annotated images. In response to the above issues, we introduced a deep reinforcement learning (DRL)-based method to locate nasopharyngeal carcinoma lesions in 3D-CT scans. The proposed method uses prior knowledge to guide the agent to reasonably reduce the search space and promote the convergence rate of the model. Furthermore, the multi-scale processing technique is also used to promote the localization of small objects. We trained the proposed model with 3D-CT scans of 50 patients and evaluated it with 3D-CT scans of 30 patients. The experimental results showed that the proposed model has strong robustness, and its accuracy was improved by more than 1 mm on average under the premise of using a smaller dataset compared with the DQN models in recent studies. The proposed model could effectively locate the lesion area of nasopharyngeal carcinoma in 3D-CT scans.

Do nasopharyngeal carcinoma (NPC) patients benefit from induction chemotherapy (IC)? This problem is of great clinical interest; however, it is difficult to obtain an accurate and interpretable model to inform IC decisions for NPC patients. In this study, a time-to-event supervised genetic algorithm was developed to obtain an IC decision-making model for NPC patients. In this algorithm, the fitness function is directly related to the time-to-event, which reflects the IC therapeutic effect for NPC. Then, the optimal models are obtained by stability and validation analysis. The comprehensive clinical model is determined by comprehensive feature analysis using the “or” operation. The overall survival for non-IC vs. IC patients in the potential benefit group was 63.4% vs. 81.5%, with p = 0.020, and the comprehensive clinical model exhibited good generalization ability. However, the benefits of OS according to the current NCCN guidelines are limited (p > 0.05). None of the possible processes of LASSO we tried could obtain the significant models validated in the testing cohort. The proposed method provides an interpretable model construction process, reasonable data grouping strategy, concise experimental design, and convenient clinical application. Moreover, we will develop a toolkit for the treatment decision-making model research to facilitate the use of clinicians and provide technical support for precision medicine.

Abstract Objectives We aimed to reconstitute T2 and T3 stage classification in nasopharyngeal carcinoma (NPC) cases and verify its utility in clinical settings. Materials and Methods We enrolled 792 NPC patients. Cox proportional hazards model was used to compare the effect sizes (hazard ratio [HR]) of the cranial structure invasion on survival and select the structures for up‐staging or downstaging T2 and T3 NPC. The samples were reclassified and the survival curves for T2 and T3 stages were analyzed. The proposed new staging system was validated on an external sample (n = 433). Results Thirteen cranial structures were examined. American Joint Committee on Cancer (AJCC) T3 stage patients with the invasion of the base of the sphenoid (HR = 2.58, 95% CI = 1.16‐5.77) or base of the pterygoid (HR = 2.00, 95% CI = 0.84‐4.77) had significantly lower hazard ratios than T2 stage patients with the invasion of soft tissues in the bilateral parapharyngeal space (HR = 5.26, 95% CI = 2.02‐13.68) and single/bilateral carotid sheath (HR = 7.78, 95% CI = 3.06‐19.76). T3 stage with the invasion of the above‐mentioned bones was reclassified as T2, and T2 stage with the invasion of the above‐mentioned soft‐tissue structures was reclassified as T3. Survival analysis showed a significant difference between the reclassified T2 and T3 stages (P

The effective segmentation of esophagus and esophageal cancer from Computed Tomography (CT) images can meaningfully assist doctors in the diagnosis and treatment of esophageal cancer patients. However, problems such as the small proportion of the esophageal region in CT images and the irregular shape of the esophagus will make the diagnosis difficult. In practical applications, not all esophagus and esophageal cancer morphology can be included in the training set, so the generalization ability of the model is very important. These are the difficulties in segmenting the esophagus and esophageal cancer. Since some adjacent tissues and organs of the esophagus are visually close to the esophagus and esophageal cancer, how to ensure that the network can extract effective distinguishing features has become the focus of research. In this paper, a novel U-Net structure - Channel-attention U-Net is proposed to segment esophagus and esophageal cancer from CT slices. This novel network combines a Channel Attention Module (CAM) that can distinguish the esophagus and surrounding tissues by emphasizing and inhibiting channel feature and Cross-level Feature Fusion Module (CFFM) which is utilized to strengthen the generalization ability of the network by using high-level features to weight low-level features. Because the high-level features represent specific organizational information, and the low-level features represent the characteristics of detailed information such as edges and contours, the network can learn specific detailed features of a definite organization. In addition, to locate the esophageal region better, a 3D semi-automatic method for segmenting esophagus and esophageal cancer is proposed. The proposed network is trained using 46,400 CT pictures as the training set and divides 11,600 CT images from the dataset at a ratio of 0.2 as the validation set. Finally, 7,250 CT images were used as the test set to test the performance of the network. The experimental results show that the IoU value of our network can reach 0.625, the dice value is 0.732 and the Hausdorff distance is 3.193.

Automatic detection of specific anatomical points of interest (POIs) in head magnetic resonance imaging (MRI) is a technical bottleneck in medical image registration and big data analysis for head diseases. A technique for automatically retrieving POIs in head MRI scans is explored in this study. A Haar-like feature of the image is introduced to generate the feature description vector of the POI, and the most appropriate standard vector is selected from multiple marked images. A fixed five-point joint feature description is proposed for solid POI detection, and an adaptive three-point joint feature description is proposed for cavity POI detection. A total of 516 head MRI volumes were used for anatomical point detection. A solid point detection experiment was conducted using the POIs of the right/left internal acoustic pore (RIA/LIA). The POIs of the right/left ascending segment of the internal carotid artery in the posterior cavernous sinus (RAS/LAS) were used in a cavity point detection experiment. The experimental results show that the accuracies of the solid point detection for LIA and RIA are 81.8% and 84.7%, respectively. Those of cavity point detection for LAS and RAS are 66.7% and 76.2%. The performance of the proposed method is better than those of BRIEF and SIFT algorithm. The proposed method can facilitate the marking of anatomical points for doctors, thus providing technical support for head image automatic registration and big data analysis for head diseases.

The effective segmentation and 3-D rendering of the esophagus and esophageal cancer from the computed tomography (CT) images can assist doctors in diagnosing esophageal cancer. Irregular and vague boundary causes great difficulty in the segmentation of esophagus and esophageal cancer. In this paper, U-Net Plus is proposed to segment esophagus and esophageal cancer from a 2-D CT slice. In the new network architecture, two blocks are employed to enhance the feature extraction performance of complex abstract information, which can effectively resolve irregular and vague boundaries. A block is a skip connection operation that is similar to convolution. The architecture is trained through a dataset of 1924 slices from 10 CT scans and tested through 295 slices from 6 CT scans. The training and test datasets are expanded tenfold to simulate the segmentation of the 3-D CT image. Using the new framework, we report a 0.79 ± 0.20 dice value and 5.87 ± 9.91 Hausdorff distance. A semi-automatic scheme is then designed for the 3-D segmentation of the esophagus or esophageal cancer. The 3-D rendering of the esophagus or esophageal cancer is implemented to assist in the diagnosis of esophageal cancer.

ObjectiveShear-wave elastography (SWE) can improve the diagnostic specificity of the B-model ultrasonography (US) in breast cancer. However, whether deep learning-based radiomics signatures based on the B-mode US (B-US-RS) or SWE (SWE-RS) could further improve the diagnostic performance remains to be investigated. We aimed to develop the B-US-RS and SWE-RS and determine their performances in classifying breast masses.Materials and MethodsThis retrospective study included 291 women (mean age ± standard deviation, 40.9 ± 12.3 years) from two centers who had US-visible solid breast masses and underwent biopsy and/or surgical resection between June 2015 and July 2017. B-mode US and SWE images of the 198 masses in 198 patients (training cohort) from center 1 were segmented, respectively, to construct B-US-RS and SWE-RS using the least absolute shrinkage and selection operator regression and tested in an independent validation cohort of 65 masses in 65 patients from center 1 and in an external validation cohort of 28 masses in 28 patients from center 2. The performances of B-US-RS and SWE-RS were assessed using receiver operating characteristic (ROC) analysis and compared with that of radiologist assessment [Breast Imaging Reporting and Data System (BI-RADS)] and quantitative SWE parameters [maximum elasticity (Emax), mean elasticity (Emean), elasticity ratio (Eratio), and elastic modulus standard deviation (ESD)] by using the McNemar test.ResultsThe single best-performing quantitative SWE parameter, Emax, had a higher specificity than BI-RADS assessment in the training and independent validation cohorts (P < 0.001 for both). The areas under the ROC curves (AUCs) of B-US-RS and SWE-RS both were 0.99 (95% CI = 0.99–1.00) in the training cohort, 1.00 (95% CI = 1.00–1.00) in the independent validation cohort, and 1.00 (95% CI = 1.00–1.00) in the external validation cohort. The specificities of B-US-RS and SWE-RS were higher than that of Emax in the training (P < 0.001 for both) and independent validation cohorts (P = 0.02 for both).ConclusionThe B-US-RS and SWE-RS outperformed the quantitative SWE parameters and BI-RADS assessment for classifying breast masses. The integration of the deep learning-based radiomics approach would help improve the classification ability of B-mode US and SWE for breast masses.

Distant metastasis is among the main reasons for treatment failure in nasopharyngeal carcinoma (NPC) patients. To identify patients with a high risk of distant metastasis is important to guide posttreatment surveillance, appropriate time treatments, and prolonging their long-term survival. In this study, we systematically examined the associations between a series of nodal-related characteristics and distant metastasis-free survival (DMFS) by detailed MRI reading and established a nomogram for DMFS in NPC patients. T-stage, age group, Epstein–Barr virus (EBV) level, central nodal necrosis, and nodal number were identified as independent risk factors for distant metastasis and were included into the final nomogram. The calibration plot showed a high agreement between the prediction by the nomogram and actual observations. Our established nomogram achieved a high C-index in predicting distant metastasis in both of the training cohort (0.737) and the validation cohort (0.718). This nomogram incorporated several readily available nodal features from the MR images, and it might be useful for guiding clinical decision and NPC patients' posttreatment surveillance. It also provides cues for how to redefine N-stage. Additional research is needed to confirm our conclusions.

The inflammatory response to chronic injury affects tissue regeneration and has become an important factor influencing the prognosis of patients. In previous stem cell treatments, it was revealed that stem cells not only have the ability for direct differentiation or regeneration in chronic tissue damage but also have a regulatory effect on the immune microenvironment. Stem cells can regulate the immune microenvironment during tissue repair and provide a good “soil” for tissue regeneration. In the current study, the regulation of immune cells by mesenchymal stem cells (MSCs) in the local tissue microenvironment and the tissue damage repair mechanisms are revealed. The application of the concepts of “seed” and “soil” has opened up new research avenues for regenerative medicine. Tissue engineering (TE) technology has been used in multiple tissues and organs using its biomimetic and cellular cell abilities, and scaffolds are now seen as an important part of building seed cell microenvironments. The effect of tissue engineering techniques on stem cell immune regulation is related to the shape and structure of the scaffold, the preinflammatory microenvironment constructed by the implanted scaffold, and the material selection of the scaffold. In the application of scaffold, stem cell technology has important applications in cartilage, bone, heart, and liver and other research fields. In this review, we separately explore the mechanism of MSCs in different tissue and organs through immunoregulation for tissue regeneration and MSC combined with 3D scaffolds to promote MSC immunoregulation to repair damaged tissues.

To examine the morphological changes of the pituitary glands and linear growth of childhood nasopharyngeal carcinoma (NPC) cases who accepted radiotherapy. A total of 90 children (i.e., age less than 18 years) who were diagnosed as NPC at Sun Yat-sen University Cancer Center from January 2009 to January 2016 were identified by reviewing medical records. Two radiologists reviewed and measured the pre-radiation, post-radiation, and the latest available pituitary gland heights independently. Patients' current height information was collected by telephone interviews. We compared the pituitary height differences using paired t-tests and estimated the pituitary height trajectories within each sex by mixed regression models. Height-for-age Z-score was calculated for each patient using the WHO growth reference data for 5–19 years as reference. Most of the included participants were of male sex (75.6%) and over half were diagnosed at stage IV (58.4%). Among the 90 included participants, 89 had one repeated measurement of the pituitary height and 79 had two repeated measurements of the pituitary height. Seventy six of the 89 childhood NPC participants had reduced pituitary heights after radiation and accounted for 85.4% of the whole population. The means of the pituitary heights before and after radiotherapy were 6.4 ± 1.3 mm and 5.6 ± 1.2 mm (P < 0.001), respectively. The mean of height-for-age Z-score for childhood NPC cases was significantly below zero (−0.54, 95% CI = −0.74, −0.34). We concluded that childhood NPC cases had decreased pituitary heights and stunted linear growth after radiotherapy.

Nano biocomposite scaffolds of non-stoichiometric apatite (ns-AP) and poly(ε-caprolactone) (PCL) were prepared by a prototyping controlled process (PCP). The results show that the composite scaffolds with 40 wt% ns-AP contained open and well interconnected pores with a size of 400–500 μm, and exhibited a maximum porosity of 76%. The ns-AP particles were not completely embedded in PCL matrix while exposed on the composite surface, which might be useful for cell attachment and growth. Proliferation of MG63 cells was significantly better on the composite scaffolds with porosity of 76% than that those with porosity of 53%, indicating that the scaffolds with high porosity facilitated cell growth, and could promote cell proliferation. The composite scaffolds were implanted into rabbit thighbone defects to investigate the in vivo biocompatibility and osteogenesis. Radiological and histological examination confirmed that the new bony tissue had grown easily into the entire composite scaffold. The results suggest that the well-interconnected pores in the scaffolds might encourage cell proliferation, and migration to stimulate cell functions, thus enhancing bone formation in the scaffolds. This study shows that bioactive and biocompatible ns-AP/PCL composite scaffolds have potential applications in bone tissue engineering. [ABSTRACT FROM AUTHOR]