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Objectives To develop and validate a radiomics model for evaluating treatment response to immune-checkpoint inhibitor plus chemotherapy (ICI + CT) in patients with advanced esophageal squamous cell carcinoma (ESCC). Methods A total of 64 patients with advance ESCC receiving first-line ICI + CT at two centers between January 2019 and June 2020 were enrolled in this study. Both 2D ROIs and 3D ROIs were segmented. ComBat correction was applied to minimize the potential bias on the results due to different scan protocols. A total of 788 features were extracted and radiomics models were built on corrected/uncorrected 2D and 3D features by using 5-fold cross-validation. The performance of the radiomics models was assessed by its discrimination, calibration and clinical usefulness with independent validation. Results Five features and support vector machine algorithm were selected to build the 2D uncorrected, 2D corrected, 3D uncorrected and 3D corrected radiomics models. The 2D radiomics models significantly outperformed the 3D radiomics models in both primary and validation cohorts. When ComBat correction was used, the performance of 2D models was better (p = 0.0059) in the training cohort, and significantly better (p Conclusions The easy-to-use 2D corrected radiomics model could facilitate noninvasive preselection of ESCC patients who would benefit from ICI + CT.

Summary Background & aim Several studies explored the association of vitamin B6 intake with the risk of cancers. However, it is unclear whether different doses of vitamin B6 have distinct effects on the prognosis of nasopharyngeal carcinoma (NPC) patients. This study investigated the relationship between different doses of B6 intake and the prognosis of NPC patients. Methods This retrospective cohort analysis included 792 newly diagnosed NPC patients with a median follow-up of 62.05 months. Restricted cubic spline and maximally selected rank statistics were performed to determine the cut-off value of vitamin B6 during treatment (VB6DT). Kaplan–Meier method and log-rank tests were performed to analyze survival outcomes. A multivariable Cox proportional hazard model was performed to determine the independent prognostic factors. Results NPC patients were divided into three groups according to the cut-off value of VB6DT: non-users (0 mg/d), VB6DT > 8.6 mg/d, and VB6DT ≤ 8.6 mg/d. Patients with VB6DT > 8.6 mg/d had significantly lower 5-year overall survival (OS) (83.5% vs. 90.8%, p = 0.006), distant metastasis-free survival (DMFS) (83.5% vs. 91.0%, p = 0.004), and progression-free survival (PFS) (73.7% vs. 81.7%, p = 0.011) and slightly but not significantly lower 5-year local recurrence-free survival (LRFS) (87.7% vs. 90.7%, p = 0.214) than the non-users. Patients with VB6DT ≤ 8.6 mg/d had slightly but not significantly better 5-year OS (93.3% vs. 90.8%, p = 0.283) than the non-users, while all other primary endpoints were similar (p > 0.50). Multivariable analyses confirmed that VB6DT > 8.6 mg/d was an independent negative prognostic factor of OS (p = 0.010), DMFS (p = 0.017), and PFS (p = 0.030) but not of LRFS (p = 0.428). Conclusions Excessive VB6DT higher than the cut-off value is an independent negative prognostic factor for NPC patients. Additionally, low dose intake improved OS only slightly but not significantly.

BACKGROUND Soft tissue involvement (STI) indicates poor prognosis in nasopharyngeal carcinoma (NPC). However, only a few studies have systematically assessed this extension using network analysis. PURPOSE To investigate the prognostic value of STI and to propose an improved STI grading system for NPC therapy. STUDY TYPE Retrospective study. POPULATION A total of 1225 consecutive patients with pathologically confirmed NPC treated with intensive-modulated radiotherapy from January 2010 to March 2014 were enrolled from two centers. FIELD STRENGTH/SEQUENCE T1- and T2-weighted imaging and enhanced T1-weighted imaging with fast spin echo sequence at 1.5 or 3.0 T. ASSESSMENT The levator veli palatini and tensor veli palatini involvement were graded "mild," prevertebral muscle involvement, "moderate," medial pterygoid, lateral pterygoid, and the infratemporal fossa involvement, "severe" STI. The above STI sites were evaluated separately by three radiologists using MRI images and graded using network analysis. Overall survival (OS) and progression-free survival (PFS) were assessed. STATISTICAL TESTS Kaplan-Meier method, Cox's proportional hazards model, and concordance index (C-index) were used. RESULTS Five-year OS and PFS rates between mild and moderate groups (90.5% vs. 81.7%, P

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.

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, Hazard ratio was lower for AJCC T3 with bone than AJCC T2 with soft tissue invasion ·The 5‐year OS and PFS were higher for re‐classified T2 than T3 stage NPC patients·Modified staging system for T2 and T3 NPC may result in better management.

BACKGROUND Nodal (N) stage is one of the most important predictors for distant metastasis in nasopharyngeal carcinoma (NPC) patients. It may ignore potentially useful nodal features, such as nodal matting (three or more lymph nodes abutting together with the absence of intervening fat planes). PURPOSE To explore the prognostic value of nodal matting in NPC patients and construct a nomogram with nodal matting for predicting distant metastasis-free survival (DMFS). STUDY TYPE Retrospective. POPULATION In all, 792 NPC patients treated with intensity modulated radiation therapy from 2010 to 2013 were enrolled with 2:1 training (n = 527) and validation (n = 65) cohorts. FIELD STRENGTH/SEQUENCE T1 - and T2 -weighted imaging at 1.5 or 3.0T. ASSESSMENT Nodal matting and other nodal characteristics were assessed with MRI. MR images were evaluated separately by three radiologists. The association between nodal matting and DMFS was analyzed. STATISTICAL TESTS Univariate and multivariate analyses were performed using the Cox proportional hazards regression model. Nomograms were constructed from a multivariate logistic regression model with and without nodal matting. The predictive accuracy and discriminative ability of the nomograms were determined by concordance index (C-index) and calibration curves. The results were validated using bootstrap resampling and validation cohort. RESULTS The incidence of nodal matting was 24.6% (195/792) in all patients. In the training cohort, nodal matting was independently associated with DMFS (hazard ratio [HR] = 1.97 [1.05-3.69], P

Nasopharyngeal carcinoma (NPC) is the most common malignant tumor of the nasopharynx. The delicate nature of the nasopharyngeal structures means that noninvasive magnetic resonance imaging (MRI) is the preferred diagnostic technique for NPC. However, NPC is a typically infiltrative tumor, usually with a small volume, and thus, it remains challenging to discriminate it from tightly connected surrounding tissues. To address this issue, this study proposes a voxel-wise discriminate method for locating and segmenting NPC from normal tissues in MRI sequences. The located NPC is refined to obtain its accurate segmentation results by an original multiviewed collaborative dictionary classification (CODL) model. The proposed CODL reconstructs a latent intact space and equips it with discriminative power for the collective multiview analysis task. Experiments on synthetic data demonstrate that CODL is capable of finding a discriminative space for multiview orthogonal data. We then evaluated the method on real NPC. Experimental results show that CODL could accurately discriminate and localize NPCs of different volumes. This method achieved superior performances in segmenting NPC compared with benchmark methods. Robust segmentation results show that CODL can effectively assist clinicians in locating NPC.

Purpose To develop a predictive model with pre-treatment magnetic resonance imaging (MRI) findings of the structured report template and clinical parameters for poor responses prediction after neoadjuvant chemoradiotherapy (neoCRT) in locally advanced rectal cancers (LARC) patients. Method Patients with clinicopathologically confirmed LARC (training and validation datasets, n = 100 and 71, respectively) were enrolled. Patients’ clinical data were retrospectively collected. MRI findings of the structured report template were analysed. The tumour regression grade (TRG) system as proposed by Mandard et al was used. Poor response was defined as TRG 3–5. Univariate logistic regression analysis and a lasso regression model were performed to select the significant predictive features from the training set. A nomogram was constructed based on a multivariable logistic regression analysis. Calibration, discrimination, and clinical usefulness of the nomogram were assessed. The calibrative and discriminative ability of our model were compared with those of models including the tumour-node-metastasis (TNM) stage and clinical factors. Results The MRI-reported T4b stage, MRI-reported extramural venous invasion (EMVI) positivity, MRI-detected number of positive mesorectal lymph nodes (LNs) > 0, and preoperative oxaliplatin and capecitabine (CAPOX) chemotherapy regimen were incorporated into our nomogram. The nomogram showed good discrimination, with areas under the receiver operating characteristic (ROC) curves of 0·823 and 0·820 in the training and test sets, respectively, and good calibration in both datasets. The decision curve analysis confirmed that the nomogram was clinically useful. The calibrative and discriminative ability of our model were better than those models including the TNM stage and clinical factors. Conclusion A nomogram based on pre-treatment MRI features of the structured report template and clinical risk factors has potential for use as a non-invasive tool to preoperatively predict poor responses in LARC patients after neoCRT.

We aimed to assess the use of automatic machine learning (AutoML) algorithm based on magnetic resonance (MR) image data to assign prediction scores to patients with nasopharyngeal carcinoma (NPC). We also aimed to develop a 4-group classification system for NPC, superior to the current clinical staging system. Between January 2010 and January 2013, 792 patients with recent diagnosis of NPC, who had MR image data, were enrolled in the study. The AutoML algorithm was used and all statistical analyses were based on the 10-fold test. Primary endpoints included the probabilities of overall survival (OS), distant metastasis-free survival (DMFS), and local-region relapse-free survival (LRFS), and their sum was recorded as the final voting score, representative of progression-free survival (PFS) for each patient. The area under the receiver operating characteristic (ROC) curve generated from the MR image data-based model compared with the tumor, node, and metastasis (TNM) system-based model was 0.796 (P=0.008) for OS, 0.752 (P=0.053) for DMFS, and 0.721 (P=0.025) for LRFS. The Kaplan-Meier (KM) test values for II/I, III/II, IV/III groups in our new machine learning-based scoring system were 0.011, 0.010, and P<0.05), whereas the P values of curves obtained from the TNM/AJCC system, between II/I and III/II, were 0.118 and 0.121, respectively, without a significant difference. In conclusion, the AutoML algorithm demonstrated better prognostic performance than the TNM/AJCC system for NPC. The algorithm showed a good potential for clinical application and may aid in improving counseling and facilitate the personalized management of patients with NPC. The clinical application of our new scoring and staging system may significantly improve precision medicine.

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 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.

Background and Objective : An anatomical landmark is biologically meaningful point in medical images and often used for medical image registration. The purpose of this study is to automatically locate anatomical landmarks from 3D medical images. Methods : A two-step automatic location scheme of anatomical landmarks in 3D medical image was designed in this study. In the first step, the full convolutional neural network was used for slice detection from a 3D medical image. In the second step, the scale attention hourglass network was used for landmark location in the detected slice and could overcome the difficulty of similar anatomical structures and different image parameters. This method was implemented and tested on four stable anatomical landmarks in 3D head MRI. Results : A total of 500 and 300 3D head volumes were used for training and testing, respectively. Results showed that the slice detection accuracy reached 85.7% and that the maximum location error was less than one slice. The average accuracy of the four anatomical landmarks in the detected slice reached 87.2%, and the spatial distance was 2.4 ± 2.4, which obtained better performance compared with hourglass network and feature pyramid networks. Conclusions : This method can be useful for locating anatomical landmarks in 3D head MRI and provides technical support for medical image registration and big data analysis.

Background It is difficult to prospectively differentiate between benign (World Health Organization [WHO] I) and nonbenign (WHO II and III) meningiomas. Purpose To evaluate the feasibility of preoperative differentiation between benign and nonbenign meningiomas by using texture analysis from multiparametric MR data. Study type Retrospective. Subjects In all, 184 patients with meningioma (139 benign and 45 nonbenign) were included as the training cohort and 79 patients with meningioma (60 benign and 19 nonbenign) were included as the external validation cohort. Field strength/sequence T1 -weighted, T2 -weighted, and contrast-enhanced T1 -weighted imaging were performed on 1.5 or 3.0T MR systems from two centers. Assessment Tumor segmentation and radiological characteristic (RC) evaluation were performed by experienced radiologists. The texture features were extracted from preprocessed images and combined with RCs, and then the combined features were reduced by using a two-step feature selection. Three single-sequence models and a multiparametric MRI (the combination of single sequences) model were constructed and then evaluated with the external validation cohort. Statistical tests Area under receiver operating characteristic curve (AUC), accuracy (Acc), f1-score (F1), sensitivity (Sen), and specificity (Spec), were calculated to quantify the performance of the models. Results Among the four texture models, the multiparametric MRI model demonstrated the best performance for differentiating between benign and nonbenign meningiomas in both the training and external validation cohorts (AUC 0.91, Acc 89%, F1 0.88, Sen 0.93, and Spec 0.87 in the training cohort; AUC 0.83, Acc 80%, F1 0.77, Sen 0.84, and Spec 0.78 in the validation cohort). Data conclusion Nonbenign meningiomas might be preoperatively differentiated from benign meningiomas by using texture analysis from multiparametric MR data. Level of evidence 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1810-1820.

To investigate the prognostic significance of paranasal sinus invasion in nasopharyngeal carcinoma (NPC) patients.Patients with NPC after intensity-modulated radiotherapy from 2010 to 2013 were identified (n = 1225). Clinical features and magnetic resonance images were analyzed.Paranasal sinus invasion was identified in 182/1225 patients (14.9%). Multivariate analysis showed that paranasal sinus invasion was an independent factor for overall survival, progression-free survival, distant metastasis-free survival, and local recurrence-free survival (P .05 for all). T3 NPC patients with paranasal sinus invasion had a poorer prognosis than those without (P .05), and there was no significant survival difference compared with T4 patients, regardless of involvement of inner structures (P .05 for all). Upgrading NPC with paranasal sinus invasion to T4 disease achieved better predictive abilities.Paranasal sinus invasion is an independent prognostic factor for NPC. It may be appropriate to upgrade the T classification.

This article primarily reviews the applications of three-dimensional printing in cartilage tissue engineering at different anatomical locations and summarizes their strengths and limitations. In addition, we believe that four-dimensional concept and biological microenvironment should not be ignored for functional cartilage regeneration in the future. Finally, we hope the review provide scientist inspiration with constructing anisotropic tissue-engineered organ or tissue.

Background Accurately predicting the risk of death, recurrence, and metastasis of patients with nasopharyngeal carcinoma (NPC) is potentially important for personalized diagnosis and treatment. Survival outcomes of patients vary greatly in distinct stages of NPC. Prognostic models of stratified patients may aid in prognostication. Purpose To explore the prognostic performance of MRI-based radiomics signatures in stratified patients with NPC. Study type Retrospective. Population Seven hundred and seventy-eight patients with NPC (T1-2 stage: 298, T3-4 stage: 480; training cohort: 525, validation cohort: 253). Field strength/sequence Fast-spin echo (FSE) axial T1-weighted images, FSE axial T2-weighted images, contrast-enhanced FSE axial T1-weighted images at 1.5 T or 3.0 T. Assessment Radiomics signatures, clinical nomograms, and radiomics nomograms combining the radiomic score (Radscore) and clinical factors for predicting progression-free survival (PFS) were constructed on T1-2 stage patient cohort (A), T3-4 stage patient cohort (B), and the entire dataset (C). Statistical tests Least absolute shrinkage and selection operator (LASSO) method was applied for radiomics modeling. Harrell's concordance indices (C-index) were employed to evaluate the predictive power of each model. Results Among 4,410 MRI-extracted features, we selected 16, 16, and 14 radiomics features most relevant to PFS for Models A, B, and C, respectively. Only 0, 1, and 4 features were found overlapped between models A/B, A/C, and B/C, respectively. Radiomics signatures constructed on T1-2 stage and T3-4 stage patients yielded C-indices of 0.820 (95% confidence interval [CI]: 0.763-0.877) and 0.726 (0.687-0.765), respectively, which were larger than those on the entire validation cohort (0.675 [0.637-0.713]). Radiomics nomograms combining Radscore and clinical factors achieved significantly better performance than clinical nomograms (P Data conclusion The selected radiomics features and prognostic performance of radiomics signatures differed per the type of NPC patients incorporated into the models. Radiomics models based on pre-stratified tumor stages had better prognostic performance than those on unstratified dataset. Level of evidence 4 Technical Efficacy Stage: 5.

To develop and validate a multiparametric MRI-based radiomics nomogram for pretreatment predicting the axillary sentinel lymph node (SLN) burden in early-stage breast cancer. A total of 230 women with early-stage invasive breast cancer were retrospectively analyzed. A radiomics signature was constructed based on preoperative multiparametric MRI from the training dataset (n = 126) of center 1, then tested in the validation cohort (n = 42) from center 1 and an external test cohort (n = 62) from center 2. Multivariable logistic regression was applied to develop a radiomics nomogram incorporating radiomics signature and predictive clinical and radiological features. The radiomics nomogram’s performance was evaluated by its discrimination, calibration, and clinical use and was compared with MRI-based descriptors of primary breast tumor. The constructed radiomics nomogram incorporating radiomics signature and MRI-determined axillary lymph node (ALN) burden showed a good calibration and outperformed the MRI-determined ALN burden alone for predicting SLN burden (area under the curve [AUC]: 0.82 vs. 0.68 [p < 0.001] in training cohort; 0.81 vs. 0.68 in validation cohort [p = 0.04]; and 0.81 vs. 0.58 [p = 0.001] in test cohort). Compared with the MRI-based breast tumor combined descriptors, the radiomics nomogram achieved a higher AUC in test cohort (0.81 vs. 0.58, p = 0.005) and a comparable AUC in training (0.82 vs. 0.73, p = 0.15) and validation (0.81 vs. 0.65, p = 0.31) cohorts. A multiparametric MRI-based radiomics nomogram can be used for preoperative prediction of the SLN burden in early-stage breast cancer. • Radiomics nomogram incorporating radiomics signature and MRI-determined ALN burden outperforms the MRI-determined ALN burden alone for predicting SLN burden in early-stage breast cancer. • Radiomics nomogram might have a better predictive ability than the MRI-based breast tumor combined descriptors. • Multiparametric MRI-based radiomics nomogram can be used as a non-invasive tool for preoperative predicting of SLN burden in patients with early-stage breast cancer.

To identify the prognosis of parapharyngeal space involvement (PPSI) based on the number of subspaces involved (pre-styloid space, carotid space (CS), areas outside the CS) and explore its significance for current T-staging in patients with nasopharyngeal carcinoma (NPC). PPSI was retrospectively identified in 1224 patients with non-disseminated NPC at two centers on MRI and separated into four invasion patterns: pattern A (only post-styloid space), pattern B (post-styloid space, CS extension), pattern C (post-styloid space, pre-styloid space extension), and pattern D (all spaces). The Kaplan–Meier analysis and multivariate Cox regression models were used. PPSI was diagnosed in 63.4% of cases, with patterns A, B, C, and D in 14.3%, 3.8%, 25.3%, and 18.6% of cases, respectively. No prognostic heterogeneity was observed between pattern B and pattern C (p > 0.05). Thus, the degree of PPSI was based on the number of subspaces involved: grade 0 (none), grade 1 (one), grade 2 (two), and grade 3 (three), which could independently predict overall survival (OS) (p < 0.001). T3 patients with grade 0/1 PPSI (slight-T3) had a better prognosis than those with grade 2/3 PPSI (severe-T3) in terms of OS, locoregional-free survival (LRFS), and progression-free survival (PFS) (all p < 0.001), whose hazard ratios were higher and lower than those with T1 and T2, respectively. Combining the T2 and slight-T3 groups as the proposed T2 provided significant differences in OS, LRFS, and PFS between T2 and T3 (all p < 0.05). The risk of death increased with the number of parapharyngeal subspaces involved. The degree of PPSI is recommended to optimize T3 heterogeneity. • Parapharyngeal space involvement was proposed to differentiate patient risk groups based on the number of involved subspaces: grade 0 (none), grade 1 (one), grade 2 (two), or grade 3 (three). • The degree of parapharyngeal space involvement was an independent negative prognosticator for OS. • The degree of parapharyngeal space involvement may influence T-staging in patients with nasopharyngeal carcinoma.

Objective Shear-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 Methods This 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. Results The 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). Conclusion The 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.

Objectives Weight loss has been validated as a prognostic predictor of nasopharyngeal carcinoma (NPC); however, no global unitary indicator and criteria exist for the definition of weight loss as a prognostic factor. The aim of this study was to determine the most effective indicator for weight loss, evaluate its effect on the prognosis of NPC, and further propose a cutoff value to identify patients in need of nutritional care. Methods This retrospective cohort analysis with a median follow-up of 62.3 mo included 681 newly diagnosed patients with NPC. Principal component analysis was performed to select the best continuous variable including weight loss (kg; value of weight loss [VWL]), percent weight loss (PWL), and body mass index loss (BMIL). Multivariable Cox regression analysis and multiple correspondence analysis were performed to select the best cutoff values by different cutoff methods including the median, receiver operating characteristic curve, and threshold searching. Results PWL was the highest contributor to the prognosis of NPC compared with VWL and BMIL. Cutoff values of PWL (6.3 and 12.3%) were confirmed to be more important and were proposed to differentiate patients into low-, medium-, and high-risk NPC groups, with their 5-y progression-free survival (84.5 versus 77.9%, P = 0.046; 77.9 versus 67.3%, P = 0.046). PWL was an independent adverse prognostic factor (P = 0.002) for NPC. Conclusions PWL is a promising predictor for NPC, and cutoff values could be validated for nutritional risk grading in patients with NPC. These stratified criteria may help accelerate the extensive application of grading nutritional management in NPC therapy.

Background and Purpose The number of metastatic lymph nodes (LNs) has been demonstrated to be an important prognosticator in some head and neck cancers. This study aimed to examine its prognostic value in nasopharyngeal carcinoma (NPC). Materials and Methods Patients with newly-diagnosed, non-distant metastatic NPC from two centers were enrolled in this study. According to the radiologic criteria for involved LNs, the number of positive LNs was assessed on MRI. Univariate and multivariate models were constructed to assess the association between the number of positive LNs and clinical outcomes. Exhaustive method was used to determine the cutoff values of the number of positive LNs. Results In 1224 patients, the number of MRI-positive LNs was an independent risk factor for overall survival (OS), distant metastasis-free survival (DMFS), relapse-free survival (RFS), progression-free survival (PFS) and regional relapse-free survival (RRFS), surpassing other nodal factors. All the patients were divided into no, low (1–4 positive LNs), moderate (5–9 positive LNs) and high (>9 positive LNs) risk groups. The HRs of high-risk group exceeded the HRs of N3 classification for OS, DMFS, RFS, PFS and RRFS. On upgrading patients with more than 9 positive LNs from N2 to N3, the revised N-staging system showed a higher C-index compared to current N-staging system for predicting OS (0.747 vs. 0.741, P = 0.002), RFS (0.655 vs. 0.640, P = 0.015) and PFS (0.674 vs. 0.669, P = 0.035). Conclusions The number of MRI-positive LNs was a predominant independent prognostic factor for NPC patients’ survival. It may be incorporated into the future N-staging system to improve its accuracy in predicting outcomes.

This study aimed to evaluate the value of nodal grouping (NG), defined as the presence of at least three contiguous lymph nodes (LNs) within one LN region, in staging and management of patients with non-metastatic nasopharyngeal carcinoma (NPC). MR images were reviewed to evaluate LN variables, including NG. The Kaplan–Meier method and multivariate Cox regression models evaluated the association between the variables and survival. Harrell’s concordance index (C-index) was used to measure the performance of prognostic models. The outcome of induction chemotherapy (IC) in patients with and without NG was compared using matched-pair analysis. In 1224 patients enrolled, NG was found to be an independent prognostic factor for overall survival (OS), progression-free survival (PFS), distant metastasis-free survival (DMFS), and regional recurrence-free survival. The hazard ratio and 95% confidence interval (CI) of NG for OS (3.86, 2.09–7.12) were higher than those of stage N2 (3.54, 1.89–6.70). On upgrading patients with NG from stages N1 to N2, the revised N staging yielded a higher C-index compared to the American Joint Committee on Cancer system in predicting PFS (0.664 vs. 0.658, p = 0.022) and DMFS (0.699 vs. 0.690, p = 0.005). Results of the matched-pair analysis revealed that for patients with NG in stages N1 and N2, IC was correlated with improved OS (p = 0.022), PFS (p = 0.007), and DMFS (p = 0.021). NG is a significant prognostic factor for patients with NPC. Patients with NG may be upgraded from stages N1 to N2. NG was also a marker for identifying patients who would benefit from IC. • Nodal grouping, defined as the presence of at least three contiguous LNs within one LN region on MRI, was identified as a significant prognostic factor. • In patients with nasopharyngeal carcinoma, nodal grouping may influence lymph node staging. • Nodal grouping was a marker for identifying patients who may benefit from induction chemotherapy.

Background: Neoadjuvant chemoradiotherapy (neoCRT) has been used to promote tumor regression for locally advanced rectal cancers (LARC). However, tumour regression differs from patient to patient and determines the therapeutic strategies. Thus, we aimed to develop a predictive model with pre-treatment magnetic resonance imaging (MRI) findings and clinical parameters for poor response prediction after neoCRT in LARC patients. Methods: Patients with clinicopathologically confirmed LARC (training and validation datasets, n = 100 and 71, respectively) were enrolled. Patients' clinical data were retrospectively collected. All patients underwent pre-neoCRT pelvic MRI. MRI findings were analysed; laboratory examination markers for all patients were collected before treatment. Poor regression was defined as a tumour regression grade (TRG) of 3-5. Univariate logistic regression analysis was performed to select the significant predictive features from the MRI and clinical features for the training set. A lasso regression model was used for further data dimension reduction and feature selection. Based on a multivariable logistic regression analysis, a nomogram was constructed incorporating the MRI signature and selected clinical predictors. Calibration, discrimination, and clinical usefulness of the nomogram were assessed. Findings: Eighty-eight patients (51·5%) showed poor responses (TRG 3-5). The MRI-derived signature was significantly related to tumour regression status and showed predictive performance in both datasets. The nomogram showed good discrimination, with areas under the ROC curves of 0·823 and 0·820 in the training and test sets, respectively, and good calibration in both datasets. The decision curve analysis confirmed the nomogram was clinically useful. Interpretation: A nomogram based on a pre-treatment MRI-derived signature and clinical risk factors has potential for use as a non-invasive tool to preoperatively predict poor responses in LARC patients and guide individualized treatments for an adaptive treatment policy. Funding Statement: The study was funded by the Science and Technology Planning Project of Guangdong Province, China (2016A010101013, 2017B020226004), the Science and Technology Program of Guangzhou, China (201704020060, 201807010057), and grant from the Health and Medical Collaborative Innovation Project of Guangzhou City, China (201803010021). Declaration of Interests: The authors declare that they have no conflicts of interest. Ethics Approval Statement: This study was approved by the institutional ethics committee of our hospital (Sun Yat-sen University Cancer Center) and performed in accordance with the tenets of the Declaration of Helsinki. Written informed consent was provided by each patient before treatment.

Automatic nasopharyngeal carcinoma (NPC) segmentation in magnetic resonance (MR) images remains challenging since NPC is infiltrative and typically has a small or even tiny volume, making it indiscernible from tightly connected surrounding tissues. Recent methods using deep learning models performed unsatisfactorily since the boundary between NPC and its neighbor tissues is difficult to distinguish. In this paper, a novel Convolutional Neural Network (CNN) with recurrent attention modules (RAMs) is proposed to tackle the problem. To enhance the performance of NPC segmentation, the proposed fully automatic NPC segmentation method with recurrent attention exploits the semantic features in higher layers to guide the learning of features in lower layers. Features are fed into RAMs iteratively from the higher layers to the lower ones. The lower layers are updated iteratively by the guidance of higher layers to render with discriminative capability. Our proposed method was validated in a dataset including 596 patients, experimental results demonstrate that our method outperforms state-of-the-art methods.

To explore and evaluate the feasibility of radiomics in stratifying nasopharyngeal carcinoma (NPC) into distinct survival subgroups through multi-modalities MRI.A total of 658 patients (training cohort: 424; validation cohort: 234) with non-metastatic NPC were enrolled in the retrospective analysis. Each slice was considered as a sample and 4863 radiomics features on the tumor region were extracted from T1-weighted, T2-weighted, and contrast-enhanced T1-weighted MRI. Consensus clustering and manual aggregation were performed on the training cohort to generate a baseline model and classification reference used to train a support vector machine classifier. The risk of each patient was defined as the maximum risk among the slices. Each patient in the validation cohort was assigned to the risk model using the trained classifier. Harrell's concordance index (C-index) was used to measure the prognosis performance, and differences between subgroups were compared using the log-rank test.The training cohort was clustered into four groups with distinct survival patterns. Each patient was assigned to one of the four groups according to the estimated risk. Our method gave a performance (C-index = 0.827, p .004 and C-index = 0.814, p .002) better than the T-stage (C-index = 0.815, p = .002 and C-index = 0.803, p = .024), competitive to and more stable than the TNM staging system (C-index = 0.842, p = .003 and C-index = 0.765, p = .050) in the training cohort and the validation cohort.Through investigating a large one-institutional cohort, the quantitative multi-modalities MRI image phenotypes reveal distinct survival subtypes.• Radiomics phenotype of MRI revealed the subtype of nasopharyngeal carcinoma (NPC) patients with distinct survival patterns. • The slice-wise analysis method on MRI helps to stratify patients and provides superior prognostic performance over the TNM staging method. • Risk estimation using the highest risk among slices performed better than using the majority risk in prognosis.

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.

Purpose To establish effective prognostic nomograms using clinical features and detailed magnetic resonance imaging (MRI) findings for primary tumor and regional lymph nodes after intensity-modulated radiotherapy (IMRT) in patients with nasopharyngeal carcinoma. Method The nomogram for overall survival (OS) was based on a retrospective study of 595 patients who underwent IMRT at Sun Yat-sen University Cancer Center from 2010 to 2012. The predictive accuracy and discriminative ability of our nomogram models were determined by concordance index and calibration curve, and were compared with the nomogram models combining clinical features with tumor-node-metastasis (TNM) classification. The results were validated using bootstrap resampling and a cohort study of 241 patients. The same data cohort was used to predict the progress-free survival (PFS) of nasopharyngeal carcinoma with 3:1 training cohort (N = 558) and validation cohort (N = 278). Results The following factors were assembled into our prognostic survival nomogram models: Age, Epstein–Barr virus DNA copy number before treatment (EBV_DNA_CN), tensor veli palatini (TVP) involvement, musculus pterygoideus lateralis (MPL) involvement, prestyloid space (PS) involvement, prevertebral space (PVS) involvement, base of sphenoid bone (BOSB) involvement, paranasal sinus (PNS) involvement, the laterality of Ⅱ (Ⅱ_laterality), the laterality of retropharyngeal lymph node (RPLN_laterality), nodal grouping (NG), extranodal neoplastic spread (ENS), contrast-enhancing rim (CER) and Nodal_number. The calibration curves showed good agreement between nomogram-predicted and actual survival. Our nomogram models for OS and PFS, provided better results than the nomogram models combining clinical features with TNM classification. Results were further confirmed in the validation set. Conclusion Detailed MRI findings of primary tumor and regional lymph nodes can improve the performance of prognostic nomograms for patients with nasopharyngeal carcinoma.

To determine the role of magnetic resonance (MR) imaging and quantitative T2 value measurements in the assessment of diabetic peripheral neuropathy (DPN). Sequential MR imaging, T2 measurement, and quantitative sensory testing of sciatic nerves were performed in streptozotocin-induced diabetic rats (n = 6) and normal control rats (n = 6) over a 7-week follow-up period. Histological assessment was obtained from 48 diabetic rats and 48 control rats once weekly for 7 weeks (n = 6 for each group at each time point). Nerve signal abnormalities were observed, and the T2 values, mechanical withdrawal threshold (MWT), and histological changes were measured and compared between diabetic and control animals. Sciatic nerves in the diabetic rats showed a gradual increase in T2 values beginning at 2 weeks after the induction (P = 0.014), while a decrease in MWT started at 3 weeks after the induction (P = 0.001). Nerve T2 values had a similar time course to sensory functional deficit in diabetic rats. Histologically, sciatic nerves of diabetic rats demonstrated obvious endoneural oedema from 2 to 3 weeks after the induction, followed by progressive axonal degeneration, Schwann cell proliferation, and coexistent disarranged nerve regeneration. Nerve T2 measurement is potentially useful in detecting and monitoring diabetic neuropathy. • Sciatic nerves in diabetic rats showed a gradual increase in T2 values • Nerve T2 values were negatively correlated with sensory function impairment • Longitudinal T2 values can be used to monitor the disease progress • Nerve degeneration contributed mainly to progressive prolongation of nerve T2 values

To investigate the role of MR imaging in neurotmesis combined with surgical repair and Toll-like receptor 4 (TLR4) activation.Forty-eight rats received subepineurial microinjection of the TLR4 agonist lipopolysaccharide (LPS, n = 24) or phosphate buffered saline (PBS, n = 24) immediately after surgical repair of the transected sciatic nerve. Sequential fat-suppressed T2-weighted imaging and quantitative T2 measurements were obtained at 3, 7, 14 and 21 days after surgery, with histologic assessments performed at regular intervals. T2 relaxation times and histological quantification of the distal stumps were measured and compared.The distal stumps of transected nerves treated with LPS or PBS both showed persistent enlargement and hyperintense signal. T2 values of the distal stumps showed a rapid rise to peak level followed by a rapid decline pattern in nerves treated with LPS, while exhibiting a slow rise to peak value followed by a slow decline in nerves treated with PBS. Nerves treated with LPS exhibited more prominent macrophage recruitment, faster myelin debris clearance and more pronounced nerve regeneration.Nerves treated with TLR4 activation had a characteristic pattern of T2 value change over time. Longitudinal T2 measurements can be used to detect the enhanced repair effect associated with TLR4 activation in the surgical repair of neurotmesis.• TLR4 activation had additional beneficial effects on neurotmesis beyond surgical repair. • TLR4 activation had a characteristic time course of T2 values. • T2 measurements can help detect beneficial effects with TLR4 activation.