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Objective: The prognostic stratification for oral tongue squamous cell carcinoma (OTSCC) is heavily based on postoperative pathological depth of invasion (pDOI). This study aims to propose a preoperative MR T-staging system based on tumor size for non-pT4 OTSCC., Methods: Retrospectively, 280 patients with biopsy-confirmed, non-metastatic, pT1-3 OTSCC, treated between January 2010 and December 2017, were evaluated. Multiple MR sequences, including axial T2-weighted imaging (WI), unenhanced T1WI, and axial, fat-suppressed coronal, and sagittal contrast-enhanced (CE) T1WI, were utilized to measure radiological depth of invasion (rDOI), tumor thickness, and largest diameter. Intra-class correlation (ICC) and univariate and multivariate analyses were used to evaluate measurement reproducibility, and factors' significance, respectively. Cutoff values were established using an exhaustive method., Results: Intra-observer (ICC = 0.81-0.94) and inter-observer (ICC = 0.79-0.90) reliability were excellent for rDOI measurements, and all measurements were significantly associated with overall survival (OS) (all p < .001). Measuring the rDOI on axial CE-T1WI with cutoffs of 8 mm and 12 mm yielded an optimal MR T-staging system for rT1-3 disease (5-year OS of rT1 vs rT2 vs rT3: 94.0% vs 72.8% vs 57.5%). Using multivariate analyses, the proposed T-staging exhibited increasingly worse OS (hazard ratio of rT2 and rT3 versus rT1, 3.56 [1.35-9.6], p = .011; 4.33 [1.59-11.74], p = .004; respectively), which outperformed pathological T-staging based on nonoverlapping Kaplan-Meier curves and improved C-index (0.682 vs. 0.639, p < .001)., Conclusions: rDOI is a critical predictor of OTSCC mortality and facilitates preoperative prognostic stratification, which should be considered in future oral subsite MR T-staging., Clinical Relevance Statement: Utilizing axial CE-T1WI, an MR T-staging system for non-pT4 OTSCC was developed by employing rDOI measurement with optimal thresholds of 8 mm and 12 mm, which is comparable with pathological staging and merits consideration in future preoperative oral subsite planning., Key Points: • Tumor morphology, measuring sequences, and observers could impact MR-derived measurements and compromise the consistency with histology. • MR-derived measurements, including radiological depth of invasion (rDOI), tumor thickness, and largest diameter, have a prognostic impact on OS (all p < .001). • rDOI with cutoffs of 8 mm and 12 mm on axial CE-T1WI is an optimal predictor of OS and could facilitate risk stratification in non-pT4 OTSCC disease., (© 2024. The Author(s).)

Background: The number of metastatic lymph nodes (MLNs) is crucial for the survival of nasopharyngeal carcinoma (NPC), but manual counting is laborious. This study aims to explore the feasibility and prognostic value of automatic MLNs segmentation and counting., Methods: We retrospectively enrolled 980 newly diagnosed patients in the primary cohort and 224 patients from two external cohorts. We utilized the nnUnet model for automatic MLNs segmentation on multimodal magnetic resonance imaging. MLNs counting methods, including manual delineation-assisted counting (MDAC) and fully automatic lymph node counting system (AMLNC), were compared with manual evaluation (Gold standard)., Results: In the internal validation group, the MLNs segmentation results showed acceptable agreement with manual delineation, with a mean Dice coefficient of 0.771. The consistency among three counting methods was as follows 0.778 (Gold vs. AMLNC), 0.638 (Gold vs. MDAC), and 0.739 (AMLNC vs. MDAC). MLNs numbers were categorized into three-category variable (1-4, 5-9, > 9) and two-category variable (<4, ≥ 4) based on the gold standard and AMLNC. These categorical variables demonstrated acceptable discriminating abilities for 5-year overall survival (OS), progression-free, and distant metastasis-free survival. Compared with base prediction model, the model incorporating two-category AMLNC-counting numbers showed improved C-indexes for 5-year OS prediction (0.658 vs. 0.675, P = 0.045). All results have been successfully validated in the external cohort., Conclusions: The AMLNC system offers a time- and labor-saving approach for fully automatic MLNs segmentation and counting in NPC. MLNs counting using AMLNC demonstrated non-inferior performance in survival discrimination compared to manual detection., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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., (© 2023 International Society for Magnetic Resonance in Medicine.)

Accurate diagnosis and prognosis prediction are conducive to early intervention and improvement of medical care for natural killer/T cell lymphoma (NKTCL). Artificial intelligence (AI)-based systems are developed based on nasopharynx magnetic resonance imaging. The diagnostic systems achieve areas under the curve of 0.905-0.960 in detecting malignant nasopharyngeal lesions and distinguishing NKTCL from nasopharyngeal carcinoma in independent validation datasets. In comparison to human radiologists, the diagnostic systems show higher accuracies than resident radiologists and comparable ones to senior radiologists. The prognostic system shows promising performance in predicting survival outcomes of NKTCL and outperforms several clinical models. For patients with early-stage NKTCL, only the high-risk group benefits from early radiotherapy (hazard ratio = 0.414 vs. late radiotherapy; 95% confidence interval, 0.190-0.900, p = 0.022), while progression-free survival does not differ in the low-risk group. In conclusion, AI-based systems show potential in assisting accurate diagnosis and prognosis prediction and may contribute to therapeutic optimization for NKTCL., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: Declaration of Competing Interest We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted., (Copyright © 2022. Published by Elsevier B.V.)

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., Competing Interests: Declaration of Competing Interest None., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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]

Objectives: 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. Methods: 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. Results: 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). Conclusions: 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. Key Points: • 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. [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]

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]

• Rulefit enables the utilization of logical associations behind structured MRI report for nasopharyngeal carcinoma in the forms of prognostic rules. • The Rules model using Rulefit achieved a better accuracy and generalizability than common models for the prediction of overall survival among advanced nasopharyngeal carcinoma, with the highest C-index of 0.711 in external testing cohort. • Significant survival benefit was achieved in high-risk group stratified by Rules model with additional induction chemotherapy treatment. Structured MRI report facilitate prognostic prediction for nasopharyngeal carcinoma (NPC). However, the intrinsic association among structured variables is not fully utilised. This study aimed to investigate the performance of a Rulefit-based model in feature integration behind structured MRI report and prognostic prediction in advanced NPC. We retrospectively enrolled 1207 patients diagnosed with non-metastatic advanced NPC from two centres, and divided into training (N = 544), internal testing (N = 367), and external testing (N = 296) cohorts. Machine learning algorithms including multivariate analysis, deep learning, Lasso, and Rulefit were used to establish corresponding prognostic models. The concordance indices (C- indices) of three clinical and six combined models with different algorithms for overall survival (OS) prediction were compared. Survival benefits of induction chemotherapy (IC) were calculated among risk groups stratified by different models. A website was established for individualised survival visualisation. Incorporating structured variables into Stage model significantly improved the prognostic prediction performance. Six prognostic rules with structured variables were identified by Rulefit. OS prediction of Rules model was comparable to Lasso model in internal testing cohort (C-index: 0.720 vs. 0.713, P = 0.100) and achieved the highest C-index of 0.711 in external testing cohort, indicating better generalisability. The Rules model stratified patients into risk groups with significant 5-year OS differences in each cohort, and revealed significant survival benefits from additional IC in high-risk group. The Rulefit-based Rules model, with the revelation of intrinsic associations behind structured variables, is promising in risk stratification and guiding individualised IC treatment for advanced NPC. [ABSTRACT FROM AUTHOR]