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Background: Lymph node characteristics markedly affect nasopharyngeal carcinoma (NPC) prognosis. Matted node (MN), an important characteristic for lymph node, lacks explored MRI‐based prognostic implications. Purpose: Investigate MRI‐determined MNs' prognostic value in NPC, including 5‐year overall survival (OS), distant metastasis‐free survival (DMFS), local recurrence‐free survival (LRFS), progression‐free survival (PFS), and its role in induction chemotherapy (IC). Study Type: Retrospective cohort survival study. Population: Seven hundred ninety‐two patients with non‐metastatic NPC (female: 27.3%, >45‐year old: 50.1%) confirmed by biopsy. Field Strength/Sequence: 5‐T/3.0‐T, T1‐, T2‐ and post‐contrast T1‐weighted fast spin echo sequences acquired. Assessment: MNs were defined as ≥3 nodes abutting with intervening fat plane replaced by extracapsular nodal spread (ENS). Patients were observed every 3 months for 2 years and every 6 months for 5 years using MRI. Follow‐up extended from treatment initiation to death or final follow‐up. MNs were evaluated by three radiologists with inter‐reader reliability calculated. A 1:1 matched‐pair method compared survival differences between MN‐positive patients with or without IC. Primary endpoints (OS, DMFS, LRFS, PFS) were calculated from therapy initiation to respective event. Statistical Tests: Kappa values assessed inter‐reader reliability. Correlation between MN, ENS, and LNN was studied through Spearman's correlation coefficient. Clinical characteristics were calculated via Fisher's exact, Chi‐squared, and Student's t‐test. Kaplan–Meier curves and log‐rank tests analyzed all time‐to‐event data. Confounding factors were included in Multivariable Cox proportional hazard models to identify independent prognostic factors. P‐values <0.05 were considered statistically significant. Results: MNs incidence was 24.6%. MNs independently associated with decreased 5‐year OS, DMFS, and PFS; not LRFS (P = 0.252). MN‐positive patients gained significant survival benefit from IC in 5‐year OS (88.4% vs. 66.0%) and PFS (76.4% vs. 53.5%), but not DMFS (83.1% vs. 69.9%, P = 0.145) or LRFS (89.9% vs. 77.8%, P = 0.140). Data Conclusion: MNs may independently stratify NPC risk and offer survival benefit from IC. Evidence Level: 3 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Background: Image registration technology has become an important medical image preprocessing step with the wide application of computer‐aided diagnosis technology in various medical image analysis tasks. Purpose: We propose a multiscale feature fusion registration based on deep learning to achieve the accurate registration and fusion of head magnetic resonance imaging (MRI) and solve the problem that general registration methods cannot handle the complex spatial information and position information of head MRI. Methods: Our proposed multiscale feature fusion registration network consists of three sequentially trained modules. The first is an affine registration module that implements affine transformation; the second is to realize non‐rigid transformation, a deformable registration module composed of top‐down and bottom‐up feature fusion subnetworks in parallel; and the third is a deformable registration module that also realizes non‐rigid transformation and is composed of two feature fusion subnetworks in series. The network decomposes the deformation field of large displacement into multiple deformation fields of small displacement by multiscale registration and registration, which reduces the difficulty of registration. Moreover, multiscale information in head MRI is learned in a targeted manner, which improves the registration accuracy, by connecting the two feature fusion subnetworks. Results: We used 29 3D head MRIs for training and seven volumes for testing and calculated the values of the registration evaluation metrics for the new algorithm to register anterior and posterior lateral pterygoid muscles. The Dice similarity coefficient was 0.745 ± 0.021, the Hausdorff distance was 3.441 ± 0.935 mm, the Average surface distance was 0.738 ± 0.098 mm, and the Standard deviation of the Jacobian matrix was 0.425 ± 0.043. Our new algorithm achieved a higher registration accuracy compared with state‐of‐the‐art registration methods. Conclusions: Our proposed multiscale feature fusion registration network can realize end‐to‐end deformable registration of 3D head MRI, which can effectively cope with the characteristics of large deformation displacement and the rich details of head images and provide reliable technical support for the diagnosis and analysis of head diseases. [ABSTRACT FROM AUTHOR]

Background: Metastatic lymph nodal number (LNN) is associated with the survival of nasopharyngeal carcinoma (NPC); however, counting multiple nodes is cumbersome. Purpose: To explore LNN threshold and evaluate its use in risk stratification and induction chemotherapy (IC) indication. Study Type: Retrospective. Population: 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). Field Strength/Sequence: T1‐, T2‐ and postcontrast T1‐weighted fast spin echo MRI at 1.5 or 3.0 T. Assessment: 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. Statistical Tests: 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. Results: 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 vs <4) 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). Conclusions: 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. Evidence Level: 4 Technical Efficacy: 4 [ABSTRACT FROM AUTHOR]

Purpose: 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. [ABSTRACT FROM AUTHOR]

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 < 0.05 for all). 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 [ABSTRACT FROM AUTHOR]

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 < 0.05 and 82.9% vs. 72.5%, P < 0.05, respectively) and between moderate and severe groups (81.7% vs. 70.4%, P < 0.05 and 72.5% vs. 61.2%, P < 0.05, respectively) revealed significant differences. The C-index of the nomogram with STI grading was higher compared with current T-classification (OS 0.641 vs. 0.604, P < 0.05 and PFS 0.605 vs. 0.581, P < 0.05, respectively). Significant OS differences were observed between patients with severe STI who underwent induction chemotherapy (IC) and those who did not (84.5% vs. 70.7%, P < 0.05).Data Conclusion: STI grading was an independent prognostic factor for OS and PFS in NPC patients and it may be help to improve the accuracy in predicting survival outcomes. Patients with severe STI might benefit from IC to improve OS.Level Of Evidence: 4 TECHNICAL EFFICACY: Stage 2. [ABSTRACT FROM AUTHOR]

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. 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. 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). 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. [ABSTRACT FROM AUTHOR]

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 < 0.05). N1 patients with nodal matting had worse DMFS than N1 patients without (P < 0.05). However, no significant difference was observed when comparing N1 patients with nodal matting to N2 patients (P = 0.464). The C‐index of the nomogram with nodal matting was higher than the nomogram without (0.717 vs. 0.699, P = 0.084). Data Conclusion: Nodal matting was an independent prognostic factor for DMFS in NPC patients. It may help to select patients at high risk of distant metastasis. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To compare the oncological outcomes between microwave ablation (MWA) and surgical resection (SR) in patients with ovarian cancer liver metastasis (OCLM). In this retrospective study, a total of 29 female patients (mean age, 47.8 ± 12.9 years; range, 21–65 years) diagnosed with forty-three OCLM nodules between September 2008 and July 2016 were included. All patients with ovarian cancer received chemotherapy and cytoreductive surgery (CRS). Fifteen patients with 22 nodules underwent MWA, and 14 patients with 21 nodules underwent SR. Overall survival (OS), local tumor recurrence-free survival (LTRS), and operation-related parameters were compared between the two groups. Multivariate analyses were performed on clinicopathological variables to identify factors affecting OS and LTRS. The median follow-up time was 70.2 months (range, 12.1–107.2 months). Fourteen patients died during this period. The 1-, 3-, and 5-year OS and LTRS rates after MWA were comparable to those after SR (p =.198 and p =.889, respectively). Compared with the SR group, the MWA group had a shorter surgical time (p <.001), less estimated blood loss (p <.001), shorter postoperative hospitalization (p <.001) and fewer costs (p =.015). The multivariate analysis showed that old age (p =.001) was a predictor of poor OS and that intrahepatic tumor size (p =.005) and intrahepatic tumor number (p =.001) were predictors of poor LTRS. Percutaneous MWA had comparable oncologic outcomes with those of SR and could be a safe and effective treatment for OCLM. [ABSTRACT FROM AUTHOR]

• The SeqSeg addresses the problem of background dominance to achieve accurate NPC detection and segmentation. • Intelligent deep Q-learning-based nasopharyngeal carcinoma detection model is proposed. • Experiments on a large dataset from multi-device and multi-center demonstrate the reliability of the proposed method. • Efficient reward function and exploration strategy boost NPC detectio performance. • Recurrent attention and dilated border weighted loss function foster NPC segmentation. [Display omitted] Reliable nasopharyngeal carcinoma (NPC) segmentation plays an important role in radiotherapy planning. However, recent deep learning methods fail to achieve satisfactory NPC segmentation in magnetic resonance (MR) images, since NPC is infiltrative and typically has a small or even tiny volume with indistinguishable border, making it indiscernible from tightly connected surrounding tissues from immense and complex backgrounds. To address such background dominance problems, this paper proposes a sequential method (SeqSeg) to achieve accurate NPC segmentation. Specifically, the proposed SeqSeg is devoted to solving the problem at two scales: the instance level and feature level. At the instance level, SeqSeg is forced to focus attention on the tumor and its surrounding tissue through the deep Q-learning (DQL)-based NPC detection model by prelocating the tumor and reducing the scale of the segmentation background. Next, at the feature level, SeqSeg uses high-level semantic features in deeper layers to guide feature learning in shallower layers, thus directing the channel-wise and region-wise attention to mine tumor-related features to perform accurate segmentation. The performance of our proposed method is evaluated by extensive experiments on the large NPC dataset containing 1101 patients. The experimental results demonstrated that the proposed SeqSeg not only outperforms several state-of-the-art methods but also achieves better performance in multi-device and multi-center datasets. [ABSTRACT FROM AUTHOR]

Background: We aimed to establish the most suitable threshold for objective response (OR) in the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 in patients with nasopharyngeal carcinoma (NPC)., Methods: According to RECIST 1.1, we retrospectively evaluated MR images of NPC lesions in patients before and after induction chemotherapy (IC). Restricted cubic spline and maximally selected rank statistics were used to determine the cut-off value. Survival rates and differences between groups were compared with Kaplan-Meier curves and log-rank tests., Results: Of 1126 patients, 365 cases who received IC treatment were suitable for RECIST 1.1 evaluation. The 20% cut-off value maximized between-group differences according to maximally selected rank statistics. No difference in distant metastasis-free survival between OR and non-response groups was shown using the primary threshold of OR (30%), while it differed when 20% was employed., Conclusions: With an optimal cut-off value of 20%, RECIST may assist clinicians to accurately evaluate disease response in NPC patients., (© 2024 Wiley Periodicals LLC.)

Automatically delineating colorectal cancers with fuzzy boundaries from 3D images is a challenging task, but the problem of fuzzy boundary delineation in existing deep learning-based methods have not been investigated in depth. Here, an encoder-decoder-based U-shaped network (U-Net) based on top-down deep supervision (TdDS) was designed to accurately and automatically delineate the fuzzy boundaries of colorectal cancer. TdDS refines the semantic targets of the upper and lower stages by mapping ground truths that are more consistent with the stage properties than upsampling deep supervision. This stage-specific approach can guide the model to learn a coarse-to-fine delineation process and improve the delineation accuracy of fuzzy boundaries by gradually shrinking the boundaries. Experimental results showed that TdDS is more customizable and plays a role similar to the attentional mechanism, and it can further improve the capability of the model to delineate colorectal cancer contours. A total of 103, 12, and 29 3D pelvic magnetic resonance imaging volumes were used for training, validation, and testing, respectively. The comparative results indicate that the proposed method exhibits the best comprehensive performance, with a dice similarity coefficient (DSC) of 0.805 ± 0.053 and a hausdorff distance (HD) of 9.28 ± 5.14 voxels. In the delineation performance analysis section also showed that 44.49% of the delineation results are satisfactory and do not require revisions. This study can provide new technical support for the delineation of 3D colorectal cancer. Our method is open source, and the code is available athttps://github.com/odindis/TdDS/tree/main., (© 2024 Institute of Physics and Engineering in Medicine.)

Background: Tumor invasion risk (TIR) is an important prognostic factor in nasopharyngeal carcinoma (NPC). We propose a novel prognostic analytic method for NPC based on a voxelwise analysis of TIR in a coordinate system of the nasopharynx., Methods: A stable nasopharynx coordinate system was constructed based on anatomical landmarks to obtain an accurate TIR profile for NPC. The coordinate system was validated by image registration of the lateral pterygoid muscle (LPM). The tumors were registered to the coordinate system through shift, scale, and rotation transformations. The voxelwise TIR map for NPC was obtained by superposition of all registered and mirrored tumor regions of interest. The minimum risk (MinR) point of the tumor region was used as an independent prognostic factor for NPC. The cutoff value was calculated with density plot and validated with restricted cubic splines (RCSs), and then the patients were divided into 2 groups for overall survival (OS) analysis., Results: The first voxelwise TIR map of NPC was obtained based on 778 patients. The OS of patients with a low TIR was 76.8% and was 92.6% for patients with a high TIR [P<0.001; hazard ratio (HR) =1/0.45; 95% CI: 0.27-0.77; adjusted P=0.004]. Thus, patients with a low TIR had a poor prognosis, whereas patients with a high TIR had a good prognosis. The MinR may be better at grading the prognosis of patients compared to the American Joint Committee on Cancer (AJCC) staging or tumor/node (T/N) classification systems., Conclusions: The voxelwise TIR map provides a new method for the prognostic analysis of NPC. Potential clinical applications of voxelwise TIR mapping are clinical target volume (CTV) delineation and dose-painting for NPC., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-22-744/coif). The authors have no conflicts of interest to declare., (2023 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Introduction: Automatically 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., Methods: First, 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 Discussion: The 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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer ZH declared a shared parent affiliation with the authors HL, GR, and LL to the handling editor at the time of review., (Copyright © 2023 Zhou, Li, Chen, Yang, Ruan, Liu and Chen.)

Background: Patients with nasopharyngeal carcinoma (NPC) who have hepatitis B virus (HBV) infection tend to be treated with induction chemotherapy (IC) due to a higher metastasis rate. However, additional IC may lead to immunosuppression and can negatively affect the prognosis. We evaluated whether receiving IC improved the prognosis of patients with NPC co-infected with HBV, on the basis of concurrent chemoradiotherapy (CCRT)., Methods: This large-scale retrospective cohort study included data of patients with pathologically confirmed NPC that were collected from two hospitals between January 2010 and March 2014. Patients were followed-up every 3 months during the first 2 years and once every 6 months thereafter. Univariate analysis identified confounding factors associated with prognosis. Stage-based subgroup analyses and 1:1 random-matched pair analyses were performed to compare the survival differences between patients treated with IC + CCRT and those treated with CCRT alone., Results: Among the 1,076 enrolled patients, 16.6% were hepatitis B surface antigen (HBsAg)-positive. Among HBsAg-positive patients with stage II/III/IV NPC, distant metastasis-free survival (DMFS) (79.3% vs. 89.9%; P=0.045) and progression-free survival (PFS) (70.6% vs. 83.7%; P=0.025) were lower in patients who received IC + CCRT than in those who received CCRT alone. After adjusting for confounding factors, IC + CCRT was validated as a negative prognosticator for DMFS and PFS, while matched-pair analysis with HBsAg-negative patients showed a better overall survival (OS) for IC + CCRT (88.4% vs. 82.6%; P=0.04)., Conclusions: Compared with CCRT alone, IC + CCRT negatively affects DMFS and PFS in patients with NPC with chronic HBV infection. We advocate withholding IC but administering stronger initial treatment in NPC patients complicated with HBV infection., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-22-33/coif). The authors have no conflicts of interest to declare., (2022 Annals of Translational Medicine. All rights reserved.)