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Metastatic lymph nodal number (LNN) is associated with the survival of nasopharyngeal carcinoma (NPC); however, counting multiple nodes is cumbersome.To explore LNN threshold and evaluate its use in risk stratification and induction chemotherapy (IC) indication.Retrospective.A total of 792 radiotherapy-treated NPC patients (N classification: N0 182, N1 438, N2 113, N3 59; training group: 396, validation group: 396; receiving IC: 390).T1-, T2- and postcontrast T1-weighted fast spin echo MRI at 1.5 or 3.0 T.Nomogram with (model B) or without (model A) LNN was constructed to evaluate the 5-year overall (OS), distant metastasis-free (DMFS), and progression-free survival (PFS) for the group as a whole and N1 stage subgroup. High- and low-risk groups were divided (above vs below LNN- or model B-threshold); their response to IC was evaluated among advanced patients in stage III/IV.Maximally selected rank, univariate and multivariable Cox analysis identified the optimal LNN threshold and other variables. Harrell's concordance index (C-index) and 2-fold cross-validation evaluated discriminative ability of models. Matched-pair analysis compared survival outcomes of adding IC or not. A P value 0.05 was considered statistically significant.Median follow-up duration was 62.1 months. LNN ≥ 4 was independently associated with decreased 5-year DMFS, OS, and PFS in entire patients or N1 subgroup. Compared to model A, model B (adding LNN, LNN ≥ 4 vs4) presented superior C-indexes in the training (0.755 vs 0.727) and validation groups (0.676 vs 0.642) for discriminating DMFS. High-risk patients benefited from IC with improved post-IC response and OS, but low-risk patients did not (P = 0.785 and 0.690, respectively).LNN ≥ 4 is an independent risk stratification factor of worse survival in entire or N1 staging NPC patients. LNN ≥ 4 or the associated nomogram has potential to identify high-risk patients requiring IC.4 TECHNICAL EFFICACY: 4.

Chao Luo,1,&ast; Shuqi Li,1,&ast; Qin Zhao,1,&ast; Qiaowen Ou,2 Wenjie Huang,1 Guangying Ruan,1 Shaobo Liang,3 Lizhi Liu,1,4 Yu Zhang,5 Haojiang Li1 1Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, People’s Republic of China; 2Department of Clinical Nutrition, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, Guangdong, People’s Republic of China; 3Department of Radiotherapy, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, People’s Republic of China; 4Department of Radiology, The Third People’s Hospital of Shenzhen, Shenzhen, Guangdong, People’s Republic of China; 5Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Haojiang Li, Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People’s Republic of China, Tel +86-20-87342135, Fax +86-20-87342125, Email lihaoj@sysucc.org.cn Yu Zhang, Department of Pathology, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People’s Republic of China, Email zhangyu@sysucc.org.cnPurpose: Traditional prognostic studies utilized different cut-off values, without evaluating potential information contained in inflammation-related hematological indicators. Using the interpretable machine-learning algorithm RuleFit, this study aimed to explore valuable inflammatory rules reflecting prognosis in nasopharyngeal carcinoma (NPC) patients.Patients and Methods: In total, 1706 biopsy-proven NPC patients treated in two independent hospitals (1320 and 386) between January 2010 and March 2014 were included. RuleFit was used to develop risk-predictive rules using hematological indicators with no distributive difference between the two centers. Time-event-dependent hematological rules were further selected by stepwise multivariate Cox analysis. Combining high-efficiency hematological rules and clinical predictors, a final model was established. Models based on other algorithms (AutoML, Lasso) and clinical predictors were built for comparison, as well as a reported nomogram. Area under the receiver operating characteristic curve (AUROC) and concordance index (C-index) were used to verify the predictive precision of different models. A site-based app was established for convenience.Results: RuleFit identified 22 combined baseline hematological rules, achieving AUROCs of 0.69 and 0.64 in the training and validation cohorts, respectively. By contrast, the AUROCs of the optimal contrast model based on AutoML were 1.00 and 0.58. For overall survival, the final model had a much higher C-index than the base model using TN staging in two cohorts (0.769 vs 0.717, P< 0.001; 0.752 vs 0.688, P< 0.001), and showing great generalizability in training and validation cohorts. The two models based on RuleFit rules performed best, compared with other models. As for other endpoints, the final model showed a similar trend. Kaplan–Meier curve exhibited 22.9% (390/1706) patients were “misclassified” by AJCC staging, but the final model could assess risk classification accurately.Conclusion: The proposed final models based on inflammation-related rules based on RuleFit showed significantly elevated predictive performance.Keywords: machine learning, nomograms, nasopharyngeal carcinoma, prognosis, survival analysis

Objectives Prognoses for nasopharyngeal carcinoma (NPC) between categories T2 and T3 in the Eighth American Joint Committee on Cancer (AJCC) staging system were overlapped. We explored the value of skull base invasion (SBI) subclassification in prognostic stratification and use of induction chemotherapy (IC) to optimize T2/T3 categorization for NPC patients. Methods We retrospectively reviewed 1752 NPC patients from two hospitals. Eight skull base bone structures were evaluated. Survival differences were compared between slight SBI (T3 patients with pterygoid process and/or base of the sphenoid bone invasion only) and severe SBI (T3 patients with other SBIs) with or without IC using random matched-pair analysis. We calculated the prognosis and Harrel concordance index (C-index) for the revised T category and compared IC outcomes for the revised tumor stages. Results Compared to severe SBI, slight SBI showed better 5-year overall survival (OS) (81.5% vs. 92.3%, p = 0.001) and progression-free survival (PFS) (71.5% vs. 83.0%, p = 0.002). Additional IC therapy did not significantly improve OS and PFS in slight SBI. The proposed T category separated OS, PFS, and locoregional recurrence-free survival in T2 and T3 categories with statistical significance. An improved C-index for OS prediction was observed in the proposed T category with combined confounding factors, compared to the AJCC T staging system (0.725 vs. 0.713, p = 0.046). The survival benefits of IC were more obvious in the advanced stage. Conclusions NPC patients with slight SBI were recommended to downstage to T2 category. The adjustment for T category enabled better prognostic stratification and guidance for IC use. Key Points • For nasopharyngeal carcinoma (NPC) patients in T3 category, slight skull base invasion was a significant positive predictor for OS and PFS. • NPC patients with slight SBI might not gain significant survival benefits from induction chemotherapy. • Downstaging slight SBI NPC patients to T2 category would make a more accurate risk stratification, improve the predicting performance in OS, and have a better guidance in the use of IC for patients in advanced stage.

Residual lymphadenopathy and detectable Epstein-Barr virus (EBV) DNA after radiotherapy (RT) are known negative prognostic factors for nasopharyngeal carcinoma (NPC). However, there is a need to distinguish between patients with residual disease that will metastasize and those who will not.To develop a prognostic model to improve the risk stratification of NPC patients after RT.Retrospective.Three hundred eighty-seven NPC patients treated with RT between January 2010 and January 2013.T1-, T2-weighted and enhanced T1-weighted imaging at 1.5 or 3.0 T pretreatment and 3-4 months post-RT.Post-RT central nodal necrosis (CNN) and other nodal characteristics on MRI were assessed by three radiologists independently. EBV DNA was measured by quantitative polymerase chain reaction. The association between these variables and the primary endpoint (5-year distant metastasis-free survival [DMFS], time from the day of diagnosis to any distant metastasis) was analyzed. Nomograms A (pre-/posttreatment EBV-DNA + N stage + post-RT retropharyngeal lymph node [RLN] CNN), B (tumor-node-metastasis [TNM] stage + pretreatment EBV-DNA), and C (TNM stage + post-RT EBV-DNA) were developed.Univariate and multivariate analyses were performed with the Cox regression model. Nomograms were developed based on the Cox regression model and two prognostic models. The concordance index (C-index) and calibration curve were used to evaluate the discriminative ability of the nomograms and TNM stage. P-values 0.05 were considered statistically significant.Post-RT RLN CNN was an independent prognostic factor for 5-year DMFS (hazard ratio, 2.88 [1.48-5.62]). Nomogram A (C-index 0.728 [0.660-0.797]) demonstrated better risk discrimination than nomogram B (0.638 [0.571-0.705]), nomogram C (0.707 [0.636-0.778]), and the TNM stage (0.587 [0.515-0.659]) for 5-year DMFS in NPC.Nomogram A combining pretreatment EBV-DNA and N stage with post-RT EBV-DNA and RLN CNN improved the prognostic risk stratification for DMFS in NPC.4 TECHNICAL EFFICACY: Stage 2.

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

ImportanceHepatitis B surface antigen (HBsAg) reportedly increases the risk of distant metastasis among patients with nasopharyngeal carcinoma (NPC). However, the associated potential interaction and changes in hazard ratios (HRs) between HBsAg and different plasma Epstein-Barr (EBV) DNA levels are unknown. Moreover, the potential HBsAg-positive–associated NPC metastatic mechanism remains unclear.ObjectiveTo investigate the prognostic value and biological associations of HBsAg and plasma EBV DNA levels on distant metastasis in patients with NPC.Design, Setting, and ParticipantsRetrospective cohort study performed at Sun Yat-sen University Cancer Center between January 2010 and January 2013. A total of 792 patients with nonmetastatic NPC were enrolled. The median (range) follow-up time was 62.1 (1.4-83.4) months. Of these patients, 17.8% presented with HBsAg positivity. Cytological experiments were performed to evaluate the role of HBsAg in the invasion and migration of EBV-positive NPC cells. Data analysis was performed from July 2020 to April 2021.Main Outcomes and MeasuresThe primary end point was distant metastasis–free survival. Association rules were used to identify new rules related to distant metastasis. Interaction plots, univariate and multivariate Cox regression analyses, stratification analysis, and quantification using HRs were conducted. Additionally, cell migration and invasion assays, as well as Western blotting, were performed in the cytological validation.ResultsAmong the 792 patients, 576 (72.7%) were male, with a median (IQR) age of 45 (38-53) years. The HBsAg-positive group exhibited a significant interaction and increased risk of distant metastasis when plasma EBV DNA cutoff levels were 1.5 × 1000 copies/mL or greater. The HR was 9.16 (95% CI, 2.46-34.14) when the plasma EBV DNA load reached 6 × 1000 copies/mL, which was higher than that in patients with stage IV disease (HR, 2.01; 95% CI, 1.13-3.56; P = .02). In cytological experiments, HBsAg promoted epithelial-mesenchymal transition by upregulating vimentin and fibronectin in EBV-positive NPC cells in vitro, thereby promoting invasion and migration of EBV-positive NPC cells.Conclusions and RelevanceIn this cohort study, the observed synergistic association between HBsAg and plasma EBV DNA load represented a novel potential mechanism underlying the increased risk of distant metastasis in patients with NPC. Hence, attention should be paid to patients with NPC with HBsAg positivity, especially when the plasma EBV DNA level is 6 × 1000 copies/mL or greater. Consideration of this synergistic association will contribute to more accurate individualized management.

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 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.

The carotid space is an integral part of the parapharyngeal space, with ambiguous prognostic value for patients with nasopharyngeal carcinoma (NPC). This study aimed to investigate the prognostic significance of carotid space involvement (CSI) and propose a treatment strategy.This retrospective study enrolled 792 patients with biopsy-confirmed, non-distant metastatic NPC staged by magnetic resonance imaging before treatment. We used multivariable Cox regression models and Kaplan-Meier methods to assess the association between the variables and survival outcomes. A matched-pair method (1:1) was used to compare the survival differences between the patients with CSI treated with induction chemotherapy (ICT)and that of those who were not.The incidence rate of CSI was 21.7 % (172/792). Multivariate analysis revealed that CSI was not an independent prognostic factor for survival outcomes in the 792 patients with NPC; however, the Chi-square test showed a different distribution of treatment strategies with ICT for patients with and without CSI. After stratification by ICT, CSI was an independent prognostic factor for overall survival (OS) (p = 0.049) in patients without ICT, but not for distant metastasis-free, local recurrence-free, or progression-free survival (p˃0.05). Additionally, ICT improved OS in patients with CSI (hazard ratio, 0.42; p = 0.019). Matched pair analysis showed that patients with CSI gained prolonged OS from ICT compared with the non-ICT group (88.4 % vs 69.4 %, p = 0.028).CSI was an independent negative prognostic factor for OS in patients with NPC without ICT and might be an imaging marker for identifying eligible candidates for ICT.

This study aimed to assess the prognostic value of quantitative cervical nodal necrosis (CNN) burden in N staging risk stratification in patients with nasopharyngeal carcinoma.Univariate and multivariate Cox regression models evaluated the association between lymph node variables based on MRI images and survival. Revisions for the N classification system were proposed and compared to the 8th edition AJCC staging system using Harrell's concordance index (C-index). The survival outcomes of induction chemotherapy plus concurrent chemoradiotherapy (CCRT) and CCRT alone in patients with multiple CNNs were compared.In 1319 patients enrolled, CNN was not an independent prognostic factor for the main survival outcomes, but multiple CNNs (three or more necrotic nodes) were independent prognostic factors for distant metastasis-free survival (DMFS) (adjusted hazard ratio [HR], 2.05; p = 0.020) and progression-free survival (PFS) (HR, 1.78; p = 0.004), surpassing other nodal variables. On upgrading patients with multiple CNNs to revised N3 disease, the proposed N staging widened the differences in DMFS and PFS between N2 and N3 disease. The overall survival of patients with multiple CNNs who received CCRT plus induction chemotherapy was improved compared to that of those who received CCRT alone (76.1% vs. 55.7%; adjusted p = 0.030).Upgrading patients with multiple CNNs to stage N3 may improve prognostication of the current AJCC staging system. Multiple CNNs might be a potential marker for stratifying patients who would benefit from induction chemotherapy.• Quantitatively assessed the prognostic value of CNN burden in patients with NPC. • Upgrading patients with multiple CNNs to stage N3 may improve prognostication. • Multiple CNNs may be used as a stratification marker for induction chemotherapy.

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.