Differential diagnosis of nasopharyngeal carcinoma and nasopharyngeal lymphoma based on DCE-MRI and RESOLVE-DWI

Value of multiparametric magnetic resonance imaging in distinguishing sinonasal lymphoma from sinonasal carcinoma: a case control study

BACKGROUND

The study aimed to evaluate the diagnostic efficacy of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) parameters in distinguishing sinonasal lymphoma from sinonasal carcinoma.

METHODS

Forty-two participants with histologically confirmed sinonasal lymphomas and fifty-two cases of sinonasal carcinoma underwent imaging with a 3.0T MRI scanner. DCE-MRI and DWI were conducted, and various parameters including type of time-intensity curve(TIC), time to peak, peak enhancement, peak contrast enhancement, washout rate, apparent diffusion coefficient (ADC), and relative ADC were measured. Binary logistic regression and receiver operating characteristic (ROC) curve analysis were employed to assess the diagnostic capability of individual and combined indices for differentiating nasal sinus lymphoma from nasal sinus carcinoma.

RESULTS

Sinonasal lymphoma predominantly exhibited type II TIC(n = 20), whereas sinonasal carcinoma predominantly exhibited type III TIC(n = 23). Significant differences were observed in all parameters except washout ratio (p < 0.05), and ADC value emerged as the most reliable diagnostic tool in single parameter. Combined DCE-MRI parameters demonstrated superior diagnostic efficacy compared to individual parameters, with the highest efficiency (area under curve = 0.945) achieved when combining all parameters of DCE-MRI and DWI.

CONCLUSIONS

Multiparametric evaluation involving contrast-enhanced dynamic MRI and DWI holds considerable diagnostic value in distinguishing sinonasal lymphoma from sinonasal carcinoma.

Synthetic MRI and diffusion-weighted imaging for differentiating nasopharyngeal lymphoma from nasopharyngeal carcinoma: combination with morphological features

OBJECTIVES

To investigate the feasibility of synthetic MRI (syMRI), diffusion-weighted imaging (DWI), and their combination with morphological features for differentiating nasopharyngeal lymphoma (NPL) from nasopharyngeal carcinoma (NPC).

METHODS

Sixty-nine patients with nasopharyngeal tumours (NPL, n = 22; NPC, n = 47) who underwent syMRI and DWI were retrospectively enrolled between October 2020 and May 2022. syMRI and DWI quantitative parameters (T1, T2, PD, ADC) and morphological features were obtained. Diagnostic performance was assessed by independent sample t-test, chi-square test, logistic regression analysis, receiver operating characteristic curve (ROC), and DeLong test.

RESULTS

NPL has significantly lower T2, PD, and ADC values compared to NPC (all P < .05), whereas no significant difference was found in T1 value between these two entities (P > .05). The morphological features of tumour type, skull-base involvement, Waldeyer ring involvement, and lymph nodes involvement region were significantly different between NPL and NPC (all P < .05). The syMRI (T2 + PD) model has better diagnostic efficacy, with AUC, sensitivity, specificity, and accuracy of 0.875, 77.27%, 89.36%, and 85.51%. Compared with syMRI model, syMRI + Morph (PD + Waldeyer ring involvement + lymph nodes involvement region), syMRI + DWI (T2 + PD + ADC), and syMRI + DWI + Morph (PD + ADC + skull-base involvement + Waldeyer ring involvement) models can further improve the diagnostic efficiency (all P < .05). Furthermore, syMRI + DWI + Morph model has excellent diagnostic performance, with AUC, sensitivity, specificity, and accuracy of 0.986, 95.47%, 97.87%, and 97.10%, respectively.

CONCLUSION

syMRI and DWI quantitative parameters were helpful in discriminating NPL from NPC. syMRI + DWI + Morph model has the excellent diagnostic efficiency in differentiating these two entities.

ADVANCES IN KNOWLEDGE

syMRI + DWI + morphological feature method can differentiate NPL from NPC with excellent diagnostic performance.

Prediction of pathological complete response in locally advanced head and neck squamous cell carcinoma treated with neoadjuvant chemo-immunotherapy using volumetric multisequence MRI histogram analysis

PURPOSE

This study aimed to develop a multisequence MRI-based volumetric histogram metrics model for predicting pathological complete response (pCR) in advanced head and neck squamous cell carcinoma (HNSCC) patients undergoing neoadjuvant chemo-immunotherapy (NCIT) and compare its predictive performance with AJCC staging and RECIST 1.1 criteria.

METHODS

Twenty-four patients with locally advanced HNSCC from a prospective phase II trial were enrolled for analysis. All patients underwent pre- and post-NCIT MRI examinations from which whole-tumor histogram features were extracted, including T1WI, T2WI, enhanced T1WI (T1Gd), diffusion-weighted imaging (DWI) sequences, and their corresponding apparent diffusion coefficient (ADC) maps. The pathological results divided the patients into pathological complete response (pCR) and non-pCR (N-pCR) groups. Delta features were calculated as the percentage change in histogram features from pre- to post-treatment. After data reduction and feature selection, logistic regression was used to build prediction models. ROC analysis was performed to assess the diagnostic performance.

RESULTS

Eleven of 24 patients achieved pCR. Pre_T2_original_firstorder_Minimum, Post_ADC_original_firstorder_MeanAbsoluteDeviation, and Delta_T1Gd_original_firstorder_Skewness were associated with achieving pCR after NCIT. The Combined_Model demonstrated the best predictive performance (AUC 0.95), outperforming AJCC staging (AUC 0.52) and RECIST 1.1 (AUC 0.72). The Pre_Model (AUC 0.83) or Post-Model (AUC 0.83) had a better predictive ability than AJCC staging.

CONCLUSION

Multisequence MRI-based volumetric histogram analysis can non-invasively predict the pCR status of HNSCC patients undergoing NCIT. The use of histogram features extracted from pre- and post-treatment MRI exhibits promising predictive performance and offers a novel quantitative assessment method for evaluating pCR in HNSCC patients receiving NCIT.

Artificial intelligence for diagnosis and prognosis prediction of natural killer/T cell lymphoma using magnetic resonance imaging

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.

Apparent diffusion coefficient for differentiation between extra-nodal lymphoma and squamous cell carcinoma in the head and neck: a systematic review and meta-analysis

BACKGROUND

Radiological differentiation between extra-nodal lymphoma and squamous cell carcinoma in the head and neck is often difficult due to their similarities.

PURPOSE

To evaluate the diagnostic benefit of apparent diffusion coefficient (ADC) calculated from diffusion-weighted imaging (DWI) in differentiating the two.

MATERIAL AND METHODS

A systematic review was performed by searching the MEDLINE, Scopus, and Embase databases in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Forest plots and the pooled mean difference of ADC values were calculated to describe the relationship between extra-nodal lymphoma and squamous cell carcinoma in the head and neck. Heterogeneity among studies was evaluated using the Cochrane Q test and I2 statistic.

RESULTS

The review identified eight studies with 440 patients (441 lesions) eligible for meta-analysis. Among all studies, the mean ADC values of squamous cell carcinoma was 0.88 × 10-3mm2/s and that of lymphoma was 0.64 × 10-3mm2/s. In the meta-analysis, the ADC value of lymphoma was significantly lower than that of squamous cell carcinoma (pooled mean difference = 0.235, 95% confidence interval [CI] = 0.168-0.302, P <0.0001). The Cochrane Q test (chi-square = 55.7, P <0.0001) and I2 statistic (I2 = 87.4%, 95% CI = 77.4-93.0%) revealed significant heterogeneity.

CONCLUSION

This study highlights the value of quantitative assessment of ADC for objective and reliable differentiation between extra-nodal lymphoma and squamous cell carcinoma in the head and neck. Conclusions should be interpreted with caution due to heterogeneity in the study data.

A collision tumor of nasopharyngeal carcinoma and primary mantle cell lymphoma in the nasopharynx: a case report and review of the literature

BACKGROUND

Nasopharyngeal carcinoma (NPC) is more common in men aged 40 to 59, and radiotherapy is an effective treatment. Nasopharyngeal lymphoma (NPL) is rare, and the coexistence of nasopharyngeal mantle cell lymphoma (MCL) and NPC is even rarer. A collision tumor is a rare type of tumor that refers to two or more different tumors occurring in the same organ. No reports to date have described a collision tumor of NPC and MCL occurring within the same nasopharyngeal mass. We herein report the successful treatment of a unique case of synchronous coexistence of NPC and MCL occurring in the nasopharynx of a Chinese man.

CASE PRESENTATION

A 58-year-old man presented with a 5-month history of swallowing discomfort. Biopsy was performed under nasopharyngeal endoscopy, and histopathology revealed NPC. Magnetic resonance imaging revealed lesions in the nasopharynx, oropharynx, and tonsils, as well as enlarged lymph nodes in the parotid gland, posterior ear, and neck. This may be a synchronous dual primary tumor coexisting with NPC and NPL. Pathology consultation confirmed that the biopsy specimen of the nasopharynx was a collision tumor of NPC and MCL. Positron emission tomography computed tomography (PET-CT) revealed thickening of the posterior wall of the nasopharynx, which was considered NPC with lymphoma. The enlargement of the pharyngeal lymph ring and multiple hypermetabolic lymph nodes were evaluated as lymphoma infiltration. The patient received two courses of R-CHOP chemotherapy (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) followed by head and neck radiotherapy. At the time of this writing, he had remained alive without recurrence for 61 months since the initial treatment and was still undergoing follow-up.

CONCLUSIONS

It is very important to correctly recognize collision tumors. Magnetic resonance imaging helps identify different components of collision tumors. Pathological examination helps to confirm the diagnosis. Histological examination reveals different components, and PET-CT can help determine the extent of the lesion. Dose-adjusted chemotherapy combined with radiotherapy may have promising herapeutic effects, but additional case studies are needed to confirm.

Artificial intelligence aided precise detection of local recurrence on MRI for nasopharyngeal carcinoma: a multicenter cohort study

Background

MRI is the routine examination to surveil the recurrence of nasopharyngeal carcinoma, but it has relatively lower sensitivity than PET/CT. We aimed to find if artificial intelligence (AI) could be competent pre-inspector for MRI radiologists and whether AI-aided MRI could perform better or even equal to PET/CT.

Methods

This multicenter study enrolled 6916 patients from five hospitals between September 2009 and October 2020. A 2.5D convolutional neural network diagnostic model and a nnU-Net contouring model were developed in the training and test cohorts and used to independently predict and visualize the recurrence of patients in the internal and external validation cohorts. We evaluated the area under the ROC curve (AUC) of AI and compared AI with MRI and PET/CT in sensitivity and specificity using the McNemar test. The prospective cohort was randomized into the AI and non-AI groups, and their sensitivity and specificity were compared using the Chi-square test.

Findings

The AI model achieved AUCs of 0.92 and 0.88 in the internal and external validation cohorts, corresponding to the sensitivity of 79.5% and 74.3% and specificity of 91.0% and 92.8%. It had comparable sensitivity to MRI (e.g., 74.3% vs. 74.7%, P = 0.89) but lower sensitivity than PET/CT (77.9% vs. 92.0%, P < 0.0001) at the same individual-specificities. The AI model achieved moderate precision with a median dice similarity coefficient of 0.67. AI-aided MRI improved specificity (92.5% vs. 85.0%, P = 0.034), equaled PET/CT in the internal validation subcohort, and increased sensitivity (81.9% vs. 70.8%, P = 0.021) in the external validation subcohort. In the prospective cohort of 1248 patients, the AI group had higher sensitivity than the non-AI group (78.6% vs. 67.3%, P = 0.23), albeit nonsignificant. In future randomized controlled trials, a sample size of 3943 patients in each arm would be required to demonstrate the statistically significant difference.

Interpretation

The AI model equaled MRI by expert radiologists, and AI-aided MRI by expert radiologists equaled PET/CT. A larger randomized controlled trial is warranted to demonstrate the AI's benefit sufficiently.

Funding

The Sun Yat-sen University Clinical Research 5010 Program (2015020), Guangdong Basic and Applied Basic Research Foundation (2022A1515110356), and Guangzhou Science and Technology Program (2023A04J1788).

Multi-b-values-fitting readout-segmentation of long variable echo-trains diffusion-weighted imaging (RESOLVE DWI) in evaluation of disease activity and curative effect of axial spondyloarthritis (axSpA)

Background

Disease activity is relevant to the treatment and prognosis of axSpA, and methods to quantitatively assess disease activity and efficacy of axSpA are still being explored.

Objective

The purpose of this study was to find an optimal quantitative indicator for evaluating disease activity and curative effect of axSpA, using multi-b-values-fitting RESOLVE DWI.

Methods

The prospective study included 106 patients divided into axSpA group (n=89) and no-axSpA group (n=17) by Assessment of Spondyloarthritis international Society (ASAS) criteria. The axSpA group were divided into active group and inactive group according to ASDAS-CRP. The active group treated with systematic tumour necrosis factor inhibitors (TNFi) was selected as treatment group (n=20). All patients underwent MRI examination of sacroiliac joints (SIJs), including RESOLVE DWI. The ADC values of subchondral bone marrow in SIJs were measured (ADC50,500 was b=50,500s/mm2 fitting, ADC50,700 was b=50,700s/mm2 fitting, and ADC50,500,700 was b=50,500,700s/mm2 fitting). By comparing the ADC values between different groups, a relatively optimal b-values-fitting sequence was obtained, further evaluating curative effect of the treatment group.

Resultd

The ADC values of axSpA group, inactive group and active group SIJs were all higher than those of no-axSpA group. The ADC values of active group SIJs were all higher than those of inactive group. ADC50,500,700 had the largest AUC, relative higher sensitivity and specificity while taking account of the image quality than ADC50,700 and ADC50,500 between different groups. In the treatment group, there was no significant difference in ADC values between pre-treatment and 3 weeks, 3 weeks and 6 weeks, 6 weeks and 12 weeks (all P>0.0083, Bonferroni-corrected threshold), while the decreased ADC values in the interval of 6 weeks or more were statistically significant (all P<0.0083, Bonferroni-corrected threshold).

Conclusion

Multi-b-values-fitting (b=50,500,700s/mm2) RESOLVE DWI has a certain advantage in evaluating disease activity of axSpA. It was worth noting that short-term review (3 weeks or less) of RESOLVE DWI was unsatisfactory and review at 6 weeks or later would help to evaluate curative effect of axSpA.

Glioma grade discrimination with dynamic contrast-enhanced MRI: An accurate analysis based on MRI guided stereotactic biopsy

PURPOSE

To evaluate the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) metrics for glioma grading on a point-to-point basis.

METHODS

Forty patients with treatment-naïve glioma underwent DCE-MR examination and stereotactic biopsy. DCE-derived parameters including endothelial transfer constant (K^trans), volume of extravascular-extracellular space (v_e), fractional plasma volume (f_pv), and reflux transfer rate (k_ep) were measured within ROIs on DCE maps accurately matched with biopsies used for histologic grades diagnosis. Differences in parameters between grades were evaluated by Kruskal-Wallis tests. Diagnostic accuracy of each parameter and their combination was assessed using receiver operating characteristic curve.

RESULTS

Eighty-four independent biopsy samples from 40 patients were analyzed in our study. Significant statistical differences in K^trans and v_e were observed between grades except v_e between grade 2 and 3. K^trans showed good to excellent accuracy in discriminating grade 2 from 3, 3 from 4, and 2 from 4 (area under the curve = 0.802, 0.801 and 0.971, respectively). V_e indicated good accuracy in discriminating grade 3 from 4 and 2 from 4 (AUC = 0.874 and 0.899, respectively). The combined parameter demonstrated fair to excellent accuracy in discriminating grade 2 from 3, 3 from 4, and 2 from 4 (AUC = 0.794, 0.899 and 0.982, respectively).

CONCLUSION

Our study had identified K^trans, v_e and the combination of parameters to be an accurate predictor for grading glioma.

Readout-segmented diffusion weighted imaging of the testis at 3.0 T: comparison with single-shot echo-planar imaging

OBJECTIVE

The current study aimed to explore the feasibility of readout-segmented echo-planar imaging (RS-EPI) of the testis at 3.0 T, by comparing with single-shot echo-planar imaging (SS-EPI) in qualitative image quality and quantitative apparent diffusion coefficient (ADC) values.

METHODS

66 patients undergoing scrotal MRI for various clinical indications were included retrospectively. RS-EPI image quality was rated from 1 (severe distortion or artifact, or nondiagnostic) to 4 (nearly no distortion or artifact, or outstanding). The comparative image quality (RS- vs. SS-EPI) was rated from - 2 (SS-EPI severe or greater conspicuity) to 2 (RS-EPI severe or greater conspicuity). The confidence interval of proportions (CIOP) of comparative image quality and Wilcoxon rank sum test were performed to assess the preferences between RS-EPI and SS-EPI. Paired samples t-test and Bland-Altman analysis were performed to compare the mean ADC values of RS-EPI and SS-EPI. The mean, maximum, and minimum ADC values measured by RS-EPI were compared in normal testicular parenchyma, benign and malignant intratesticular lesions.

RESULTS

The evaluation of RS-EPI image quality showed RS-EPI with the characteristics of slight geometric distortion and susceptibility artifact, and good lesion conspicuity. The assessment of comparative image quality showed SS-EPI with obvious geometric distortion and susceptibility artifact, and RS-EPI preferred in lesion conspicuity. The CIOP ranged from 97 to 100% among three readers, with preferring to RS-EPI improving image quality (P < 0.001). There was a strong correlation and good agreement between mean ADC values measured by RS-EPI and SS-EPI. The mean, maximum and minimum ADC values by RS-EPI were significantly different in normal testicular parenchyma, benign and malignant intratesticular lesions.

CONCLUSION

RS-EPI DWI of the testis improved image quality in geometric distortion, susceptibility artifacts, and lesion conspicuity, and provided highly correlated and consistent mean ADC values when compared to SS-EPI DWI, indicating the feasibility of RS-EPI DWI of testes.

MRI: Evaluating the Application of FOCUS-MUSE Diffusion-Weighted Imaging in the Pancreas in Comparison With FOCUS, MUSE, and Single-Shot DWIs

BACKGROUND

Diffusion-weighted imaging (DWI) is a useful technique to detect pancreatic lesion. In DWIs, field-of-view optimized and constrained undistorted single-shot (FOCUS) can improve the spatial resolution and multiplexed sensitivity-encoding (MUSE) can gain a high signal-to-noise ratio (SNR). Based on the advantage of FOCUS and MUSE, a new DWI sequence-named FOCUS-MUSE DWI (FOCUS combined with MUSE)-was developed to delineate the pancreas.

PURPOSE

To investigate the reliability of FOCUS-MUSE DWI compared to FOCUS, MUSE and single-shot (SS) DWI via the systematical evaluation of the apparent diffusion coefficient (ADC) measurements, SNR and image quality.

STUDY TYPE

Prospective.

SUBJECTS

A total of 33 healthy volunteers and 9 patients with pancreatic lesion.

FIELD STRENGTH/SEQUENCE

A 3.0 T scanner. FOCUS-MUSE DWI, FOCUS DWI, MUSE DWI, SS DWI.

ASSESSMENT

For volunteers, ADC and SNR were measured by two readers in the pancreatic head, body, and tail. For all subjects, the diagnostic image quality score was assessed by three other readers on above four DWIs.

STATISTICAL TESTS

Paired-sample T-test, intraclass correlation (ICC), Bland-Altman method, Friedman test, Dunn-Bonferroni post hoc test and kappa coefficient. A significance level of 0.05 was used.

RESULTS

FOCUS-MUSE DWI had the best intersession repeatability of ADC measurements (head: 59.53, body: 101.64, tail: 42.30) among the four DWIs, and also maintained the significantly highest SNR (reader 1 [head: 19.68 ± 3.23, body: 23.42 ± 5.00, tail: 28.85 ± 4.96], reader 2 [head: 19.93 ± 3.52, body: 23.02 ± 5.69, tail: 29.77 ± 6.33]) except for MUSE DWI. Furthermore, it significantly achieved better image quality in volunteers (median value: 4 score) and 9 patients (most in 4 score).

DATA CONCLUSION

FOCUS-MUSE DWI improved the reliability of pancreatic images with the most stable ADC measurement, best image quality score and sufficient SNR among four DWIs.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Non-invasive evaluation of the pathological and functional characteristics of chronic kidney disease by diffusion kurtosis imaging and intravoxel incoherent motion imaging: comparison with conventional DWI

OBJECTIVE

To explore the diagnostic performance of diffusion kurtosis imaging (DKI) and incoherent intravoxel movement (IVIM) in evaluating the clinical and pathological characteristics in chronic kidney disease (CKD) compared to conventional diffusion-weighted imaging (DWI).

METHODS

Forty-nine CKD patients and 24 healthy volunteers were included in this retrospective study from September 2020 to September 2021. All participants underwent MRI examinations before percutaneous renal biopsy. Coronal T2WI, axial T1WI and T2WI, and DWI (including IVIM and DKI) sequences obtained in one scan. We measured the apparent diffusion coefficient (ADC), true diffusion coefficient (Dt), pseudo-diffusion coefficient (Dp), perfusion fraction (fp), mean kurtosis (MK), and mean diffusivity (MD) values. One-way analysis of variance, correlation analysis, and receiver operating characteristic curve analysis were used in our study.

RESULTS

Cortex and medulla ADC, MK, Dt, fp were significantly different between the healthy volunteers and CKD stages 1-2 (all p < 0.05). All diffusion parameters showed significant differences between CKD stages 1-2 and CKD stages 3-5 (all p < 0.05). Except for the uncorrelation between MDMedulla and vascular lesion score, all other diffusion parameters were low-to-moderately related to clinical and pathological indicators. fpMedulla was the best parameter to differentiate healthy volunteers from CKD stages 1-2. MKCortex was the best parameter to differentiate CKD stages 1-2 from that CKD stages 3-5.

CONCLUSION

Renal cortex and medulla fp, Dt, and MK can provide more valuable information than ADC values for the evaluation of clinical and pathological characteristics of CKD patients, and thus can provide auxiliary diagnosis for fibrosis assessment and clinical management of CKD patients.

ADVANCES IN KNOWLEDGE

IVIM and DKI can provide more diagnostic valuable information for CKD patients than conventional DWI.

Machine Learning Based on MRI DWI Radiomics Features for Prognostic Prediction in Nasopharyngeal Carcinoma

Simple Summary

In the past, radiomics studies of nasopharyngeal carcinoma (NPC) were only based on basic MR sequences. Previous studies have shown that radiomics methods based on T2-weighted imaging and contrast-enhanced T1-weighted imaging have been successfully used to improve the prognosis of patients with nasopharyngeal carcinoma. The purpose of this study was to explore the predictive efficacy of radiomics analyses based on readout-segmented echo-planar diffusion-weighted imaging (RESOLVE-DWI) which quantitatively reflects the diffusion motion of water molecules for prognosis evaluation in nasopharyngeal carcinoma. Several prognostic radiomics models were established by using diffusion-weighted imaging, apparent diffusion coefficient maps, T2-weighted and contrast-enhanced T1-weighted imaging to predict the risk of recurrence or metastasis of nasopharyngeal carcinoma, and the predictive effects of different models were compared. The results show that the model based on MRI DWI can successfully predict the prognosis of patients with nasopharyngeal carcinoma and has higher predictive efficiency than the model based on the conventional sequence, which suggests MRI DWI-radiomics can provide a useful and alternative approach for survival estimation.

Abstract

Purpose: This study aimed to explore the predictive efficacy of radiomics analyses based on readout-segmented echo-planar diffusion-weighted imaging (RESOLVE-DWI) for prognosis evaluation in nasopharyngeal carcinoma in order to provide further information for clinical decision making and intervention. Methods: A total of 154 patients with untreated NPC confirmed by pathological examination were enrolled, and the pretreatment magnetic resonance image (MRI)—including diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) maps, T2-weighted imaging (T2WI), and contrast-enhanced T1-weighted imaging (CE-T1WI)—was collected. The Random Forest (RF) algorithm selected radiomics features and established the machine-learning models. Five models, namely model 1 (DWI + ADC), model 2 (T2WI + CE-T1WI), model 3 (DWI + ADC + T2WI), model 4 (DWI + ADC + CE-T1WI), and model 5 (DWI + ADC + T2WI + CE-T1WI), were constructed. The average area under the curve (AUC) of the validation set was determined in order to compare the predictive efficacy for prognosis evaluation. Results: After adjusting the parameters, the RF machine learning models based on extracted imaging features from different sequence combinations were obtained. The invalidation sets of model 1 (DWI + ADC) yielded the highest average AUC of 0.80 (95% CI: 0.79–0.81). The average AUCs of the model 2, 3, 4, and 5 invalidation sets were 0.72 (95% CI: 0.71–0.74), 0.66 (95% CI: 0.64–0.68), 0.74 (95% CI: 0.73–0.75), and 0.75 (95% CI: 0.74–0.76), respectively. Conclusion: A radiomics model derived from the MRI DWI of patients with nasopharyngeal carcinoma was generated in order to evaluate the risk of recurrence and metastasis. The model based on MRI DWI can provide an alternative approach for survival estimation, and can reveal more information for clinical decision-making and intervention.

Magnetic Resonance Imaging Features on Deep Learning Algorithm for the Diagnosis of Nasopharyngeal Carcinoma

The objective of this research was to investigate the application values of magnetic resonance imaging (MRI) features of the deep learning-based image super-resolution reconstruction algorithm optimized convolutional neural network (OPCNN) algorithm in nasopharyngeal carcinoma (NPC) lesion diagnosis. A total of 54 patients with NPC were selected as research objects. Based on the traditional CNN structure, OPCNN was proposed. Besides, MRI processed by the traditional CNN model and the U-net network model was introduced to be analyzed and compared with its algorithm. The used assessment parameters included volume transfer constant (K^trans), rate constant (K_ep), volume fraction (V_e), and apparent diffusion coefficient (ADC). The results showed that the values of Dice coefficient, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) of the OPCNN algorithm were significantly higher than those of the traditional CNN model and the U-net network model. Meanwhile, the difference was statistically significant (P < 0.05). K^trans, K_ep, and V_e in tumor lesions were significantly higher than those in the healthy side, while the ADC was significantly lower than that in the healthy side (P < 0.05). The sensitivity, specificity, and accuracy of dynamic contrast-enhancement magnetic resonance imaging (DCE-MRI) in the diagnosis of nasopharyngeal carcinoma staging were slightly higher than those in T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI). The diagnostic sensitivity of DCE-MRI was more than 85%, its diagnostic specificity was more than 75%, and its diagnostic accuracy was more than 90%. The AUC area of NPC diagnosed by combination of the three was significantly different from that diagnosed by single T2WI, DWI, and DCE-MRI (P < 0.05). The diagnostic accuracy of MRI based on the OPCNN algorithm for nasopharyngeal carcinoma (93.2%) was significantly higher than that of single MRI (76.4%). In summary, the OPCNN algorithm proposed in this study could improve the quality of MRI images, and the effect was better than the traditional deep learning model, which had the value of clinical promotion. The application value of DCE-MRI in the diagnosis of pathogenic lesions of nasopharyngeal carcinoma was better than conventional MRI. The combined application of T2WI, DWI, and DCE-MRI in the screening of nasopharyngeal carcinoma lesions could greatly improve the diagnostic accuracy of nasopharyngeal carcinoma.

A practical overview of CT and MRI features of developmental, inflammatory, and neoplastic lesions of the sphenoid body and clivus

The sphenoid bone is an unpaired bone that contributes to the formation of the skull base. Despite the enormous progress in transnasal endoscopic visualisation, imaging techniques remain the cornerstones to characterise any pathological condition arising in this area. In the present review, we offer a bird’s-eye view of the developmental, inflammatory, and neoplastic alterations affecting the sphenoid body and clivus, with the aim to propose a practical diagnostic aid for radiologists based on clinico-epidemiological, computed tomography, and magnetic resonance imaging features.

Dynamic contrast-enhanced MRI can quantitatively identify malignant transformation of sinonasal inverted papilloma

OBJECTIVE

To investigate the diagnostic performance of quantitative and semi-quantitative parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) in differentiating sinonasal inverted papilloma (SIP) from SIP with coexisting malignant transformation into squamous cell carcinoma (MT-SIP).

METHODS

This retrospective study included 122 patients with 88 SIP and 34 MT-SIP. Quantitative and semi-quantitative parameters derived from DCE-MRI were compared between SIP and MT-SIP. The multivariate logistic regression analysis was performed to identify independent indicators and construct regression model for distinguishing MT-SIP and SIP. Diagnostic performance of independent indicators and regression model were evaluated using receiver operating coefficient (ROC) analysis and compared using DeLong test.

RESULTS

There were significant differences in maximum slope of increase, contrast-enhancement ratio, bolus arrival time, volume of extravascular extracellular space (Ve), and rate constant (Kep) between SIP and MT-SIP (p < 0.05). There were no significant differences in initial area under the gadolinium curve (p = 0.174) and volume transfer constant (p = 0.105) between two groups. Multivariate analysis results showed that Ve and Kep were identified as the independent indicators for differentiating MT-SIP from SIP (p < 0.001). Areas under the ROC curves (AUCs) for predicting MT-SIP were 0.779 for Ve and 0.766 for Kep. The AUC of the combination of Ve and Kep was 0.831, yielding 83% specificity and 76.5% sensitivity.

CONCLUSION

DCE-MRI can quantitatively differentiate between MT-SIP and SIP. The combination of Ve and Kep yielded an optimal performance for discriminating SIP from its malignant mimics.

ADVANCES IN KNOWLEDGE

DCE-MRI with quantitative and semi-quantitative parameters can provide valuable evidences for quantitatively identifying MT-SIP.

Extramedullary Plasmacytoma of the Sinonasal Cavity: Magnetic Resonance Imaging Characteristics With Readout-Segmented Diffusion-Weighted Imaging and Dual-Energy Computed Tomography Features

PURPOSE

To determine magnetic resonance imaging (MRI) with readout-segmented diffusion-weighted imaging (RESOLVE-DWI) and dual-energy computed tomography (DECT) features of sinonasal extramedullary plasmacytoma (SN-EMP).

METHODS

The MRI and/or DECT of 10 patients with SN-EMP confirmed by pathology were retrospectively reviewed. Apparent diffusion coefficient (ADC) values of RESOLVE-DWI were analyzed in 9 patients. The quantitative parameters derived from DECT, including the iodine concentration (IC), effective atomic number, and the slope (k) of spectral attenuation curve, were measured in 3 patients.

RESULTS

On conventional MRI, typical lesions were well defined (7 of 9), and isointense to the brain on both T1WI and T2WI (9 of 9). Most lesions presented with marked enhancement on contrast-enhanced T1WI without significant necrosis (8 of 9). Notably, multiple flow-void signals were observed in all lesions (9 of 9). On RESOLVE-DWI, the average ADC value was 0.55 × 10-3 mm2/s, and the normalized ADC value was 0.66 ± 0.04. On DECT, the average values of IC, effective atomic number, and slope (k) was 2.7 mg/mL, 8.62, and 3.8, respectively.

CONCLUSIONS

Some typical MRI features (well-defined mass, isointensity to the brain, marked enhancement without obvious cystic changes, multiple flow voids, and a lower ADC value) strongly suggest the diagnosis of SN-EMP. The quantitative parameters derived from RESOLVE-DWI and DECT may provide more information for the diagnosis of SN-EMP.

A CT-based radiomics nomogram for differentiation of squamous cell carcinoma and non-Hodgkin's lymphoma of the palatine tonsil

OBJECTIVES

Accurate preoperative differentiation between squamous cell carcinoma (SCC) and non-Hodgkin's lymphoma (NHL) in the palatine tonsil is crucial because of their different treatment. This study aimed to construct and validate a contrast-enhanced CT (CECT)-based radiomics nomogram for preoperative differentiation of SCC and NHL in the palatine tonsil.

METHODS

This study enrolled 135 patients with a pathological diagnosis of SCC or NHL from two clinical centers, who were divided into training (n = 94; SCC = 50, NHL = 44) and external validation sets (n = 41; SCC = 22, NHL = 19). A radiomics signature was constructed from radiomics features extracted from routine CECT images and a radiomics score (Rad-score) was calculated. A clinical model was established using demographic features and CT findings. The independent clinical factors and Rad-score were combined to construct a radiomics nomogram. Performance of the clinical model, radiomics signature, and nomogram was assessed using receiver operating characteristics analysis and decision curve analysis.

RESULTS

Eleven features were finally selected to construct the radiomics signature. The radiomics nomogram incorporating gender, mean CECT value, and radiomics signature showed better predictive value for differentiating SCC from NHL than the clinical model for training (AUC, 0.919 vs. 0.801, p = 0.004) and validation (AUC, 0.876 vs. 0.703, p = 0.029) sets. Decision curve analysis demonstrated that the radiomics nomogram was more clinically useful than the clinical model.

CONCLUSIONS

A CECT-based radiomics nomogram was constructed incorporating gender, mean CECT value, and radiomics signature. This nomogram showed favorable predictive efficacy for differentiating SCC from NHL in the palatine tonsil, and might be useful for clinical decision-making.

KEY POINTS

• Differential diagnosis between SCC and NHL in the palatine tonsil is difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, gender, and mean contrast-enhanced CT value facilitates differentiation of SCC from NHL with improved diagnostic efficacy.

MR Imaging of Nasopharyngeal Carcinoma

Nasopharyngeal carcinoma is endemic in parts of the world such as southern China and Southeast Asia. It is predominantly an undifferentiated carcinoma with a strong genetic basis and a close association with the Epstein-Barr virus. The ability of MR imaging to depict the boundaries of the primary tumor and its relationship with the complex structures of the skull base makes it the technique of choice for imaging of this disease in the head and neck. This article describes the MR imaging findings pertinent to staging and management and a new role of MR imaging in early cancer detection, in addition to a brief discussion of differential diagnoses.

Multiparametric dual-energy CT to differentiate stage T1 nasopharyngeal carcinoma from benign hyperplasia

Background

Stage T1 nasopharyngeal carcinoma (NPC_T1) and benign hyperplasia (BH) are 2 common causes of nasopharyngeal mucosa/submucosa thickening without specific clinical symptoms. The treatment management of these 2 entities is significantly different. Reliable differentiation between the 2 entities is critical for the treatment decision and prognosis of patients. Therefore, our study aims to explore the optimal energy level of noise-optimized virtual monoenergetic images [VMI (+)] derived from dual-energy computed tomography (DECT) to display NPC_T1 and BH and to explore the clinical value of DECT for differentiating these 2 diseases.

Methods

A total of 91 patients (44 NPC_T1, 47 BH) were enrolled. The demarcation of the lesion margins and overall image quality, noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were evaluated for 40-80 kiloelectron volts (keV) VMIs (+) and polyenergetic images in the contrast-enhanced phase. Image features were assessed in the contrast-enhanced images with optimal visualization of NPC_T1 and BH. The demarcation of NPC_T1 and BH in iodine-water maps was also assessed. The contrast-enhanced images were used to calculate the slope of the spectral Hounsfield unit curve (λ_HU) and normalized iodine concentration (NIC). The nonenhanced phase images were used to calculate the normalized effective atomic number (NZ_eff). The attenuation values on 40-80 keV VMIs (+) in the contrast-enhanced phase were recorded. The diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis.

Results

The 40 keV VMI (+) in the enhanced phase yielded higher demarcation of the lesion margins scores, overall image quality scores, noise, SNR, and CNR values than 50-80 keV VMIs (+) and polyenergetic images. NPC_T1 yielded higher attenuation values on VMI (+) at 40 keV (A₄₀), NIC, λ_HU, and NZ_eff values than BH. The multivariate logistic regression model combining image features (tumor symmetry) with quantitative parameters (A₄₀, NIC, λ_HU, and NZ_eff) yielded the best performance for differentiating the 2 diseases (AUC: 0.963, sensitivity: 89.4%, specificity: 93.2%).

Conclusions

The combination of DECT-derived image features and quantitative parameters contributed to the differentiation between NPC_T1 and BH.

Convolutional Neural Network Intelligent Segmentation Algorithm-Based Magnetic Resonance Imaging in Diagnosis of Nasopharyngeal Carcinoma Foci

The aim of this study was to explore the adoption value of convolutional neural network- (CNN-) based magnetic resonance imaging (MRI) image intelligent segmentation model in the identification of nasopharyngeal carcinoma (NPC) lesions. The multisequence cross convolutional (MSCC) method was used in the complex convolutional network algorithm to establish the intelligent segmentation model two-dimensional (2D) ResUNet for the MRI image of the NPC lesion. Moreover, a multisequence multidimensional fusion segmentation model (MSCC-MDF) was further established. With 45 patients with NPC as the research objects, the Dice coefficient, Hausdorff distance (HD), and percentage of area difference (PAD) were calculated to evaluate the segmentation effect of MRI lesions. The results showed that the 2D-ResUNet model processed by MSCC had the largest Dice coefficient of 0.792 ± 0.045 for segmenting the tumor lesions of NPC, and it also had the smallest HD and PAD, which were 5.94 ± 0.41 mm and 15.96 ± 1.232%, respectively. When batch size = 5, the convergence curve was relatively gentle, and the convergence speed was the best. The largest Dice coefficient of MSCC-MDF model segmenting NPC tumor lesions was 0.896 ± 0.09, and its HD and PAD were the smallest, which were 5.07 ± 0.54 mm and 14.41 ± 1.33%, respectively. Its Dice coefficient was lower than other algorithms (P < 0.05), but HD and PAD were significantly higher than other algorithms (P < 0.05). To sum up, the MSCC-MDF model significantly improved the segmentation performance of MRI lesions in NPC patients, which provided a reference for the diagnosis of NPC.

A three-dimensional spheroid-specific role for Wnt-β-catenin and Eph-ephrin signaling in nasopharyngeal carcinoma cells

One of the greatest unmet needs hindering the successful treatment of nasopharyngeal carcinomas (NPCs) is for representative physiological and cost-effective models. Although Epstein-Barr virus (EBV) infection is consistently present in NPCs, most studies have focused on EBV-negative NPCs. For the first time, we established and analyzed three-dimensional (3D) spheroid models of EBV-positive and EBV-negative NPC cells and compared these to classical two-dimensional (2D) cultures in various aspects of tumor phenotype and drug responses. Compared to 2D monolayers, the 3D spheroids showed significant increases in migration capacity, stemness characteristics, hypoxia and drug resistance. Co-culture with endothelial cells, which mimics essential interactions in the tumor microenvironment, effectively enhanced spheroid dissemination. Furthermore, RNA sequencing revealed significant changes at the transcriptional level in 3D spheroids compared to expression in 2D monolayers. In particular, we identified known (VEGF, AKT and mTOR) and novel (Wnt-β-catenin and Eph-ephrin) cell signaling pathways that are activated in NPC spheroids. Targeting these pathways in 3D spheroids using FDA-approved drugs was effective in monoculture and co-culture. These findings provide the first demonstration of the establishment of EBV-positive and EBV-negative NPC 3D spheroids with features that resemble advanced and metastatic NPCs. Furthermore, we show that NPC spheroids have potential use in identifying new drug targets.

Application of Diffusion Kurtosis Imaging and Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Differentiating Benign and Malignant Head and Neck Lesions

BACKGROUND

Preoperative differentiation of head and neck lesions is important for treatment plan selection.

PURPOSE

To evaluate the diagnostic value of diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating benign from malignant head and neck lesions and subgroups, including lymphoma subgroup (LS), Warthin's tumor subgroup (WS), malignant tumor subgroup (excluding lymphoma) (MTS), and benign tumor subgroup (excluding Warthin's tumor) (BTS).

STUDY TYPE

Retrospective.

POPULATION

Seventy-four patients with 79 head and neck lesions (44 benign, 35 malignant), divided into four subgroups: LS (14), WS (12), MTS (21), and BTS (32).

FIELD STRENGTH/SEQUENCES

A 3.0 T, single-shot echo-planar sequence with 5 b-values for DKI and enhanced T1 high-resolution isotropic volume excitation (eTHRIVE) sequence for DCE-MRI.

ASSESSMENT

The mean diffusivity (MD) and mean kurtosis (MK) derived from DKI and the time-signal intensity curve (TIC), peak time (T_peak ), and washout ratio (WR) based on DCE-MRI were measured. The diagnostic efficiencies of DKI and DCE-MRI, alone and in combination, were calculated and compared. The parameters mentioned above were compared between the four subgroups.

STATISTICAL TEST

Mann-Whitney U test, chi-square test, receiver operating characteristic curve, Delong test, one-way analysis of variance test, and Kruskal-Wallis H test. A P value < 0.05 was considered statistically significant.

RESULTS

The combination of TIC and parameters of DKI and DCE-MRI for differentiating benign and malignant lesions with 94.94% accuracy is superior to DKI or DCE-MRI alone with approximately 75% accuracy. MD, MK, T_peak , and WR showed significant differences among the four subgroups. The accuracy of MD and MK was 91.14% and 92.41% for differentiating BTS from the other three subgroups. WR achieved 100% accuracy for discriminating WS from LS or MTS. MD and MK both differentiated LS from MTS with 97.14% accuracy.

DATA CONCLUSION

A combination of DKI and DCE-MRI can effectively differentiate head and neck lesions with good accuracy.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

The Efficacy of Radiotherapy for Nasopharyngeal Carcinoma under Magnetic Resonance Imaging

This study aimed to analyze the application value of diffusion tensor imaging (DTI) in the diagnosis of nasopharyngeal carcinoma (NC) radiotherapy. In this study, 102 patients with NC were selected as the experimental group (EG), and 58 healthy people examined in hospital were included in a control group (CG). All subjects were required to be examined with routine magnetic resonance imaging (MRI) and DTI before and after the treatment. The fractional anisotropy (FA) of the patients in EG before and after treatment and the CG were recorded. The apparent diffusion coefficients (ADC) of patients in the two groups were measured and recorded before and after the treatment. The recovery rate and adverse events of the patients in EG were observed and recorded after the treatment. The results showed that the FA values of the right cerebellum and left parietal lobe (LPL) of patients after treatment in the EG were much higher than those before treatment and the CG (P < 0.05); the FA values of the right temporal lobe (RTL), right occipital lobe (ROL), and right parietal lobe (RPL) after treatment in the EG were obviously lower than those before the treatment and the CG (P < 0.05); the complete remission rate (CRR) of the EG after treatment was greatly higher than the partial remission rate (PRR) and disease stability rate (DSR) (P < 0.05), and the objective remission rate (ORR) and disease control rate (DCR) were higher than 90%, respectively. The ADC value of the EG before treatment was (0.752 ± 0.021) × 10⁻³ mm²/s, which was visibly lower than that after treatment ((1.365 ± 0.058) × 10⁻³ mm²/s) and that in the CG ((1.856 ± 0.079)) × 10⁻³ mm²/s), showing statistically obvious differences (P < 0.05). The incidence of anemia, oral reactions, hypertension, and gastrointestinal reaction in the EG after treatment was 61.46%, 45.35%, 47.28%, and 39.67%, respectively. In short, the FA value of DTI parameter could clearly indicate the changes in brain area characteristics of NC patients before and after treatment. The RTL, ROL, and RPL of NC patients were damaged after radiotherapy, and the FA value decreased observably, which may be related to brain edema and demyelination changes. The damage of white matter microstructure in each brain area further affected the cognitive function of the patient.

Prognostic and therapeutic evaluation of nasopharyngeal carcinoma by dynamic contrast-enhanced (DCE), diffusion-weighted (DW) magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS)

Nasopharyngeal carcinoma (NPC) is an aggressive head and neck malignancy, and radiotherapy (with or without chemotherapy) is the primary treatment modality. Reliable tumour assessment during the treatment phase, which can portend the efficacy of radiotherapy and early identification of potential treatment failure in radioresistant disease, has been implicit for better cancer management. Technological advancement in the last decade has fostered the development of functional magnetic resonance imaging (fMRI) techniques into a promising tool for diagnostic and therapeutic assessments in head and neck cancer. Apart from conventional morphological assessment, early detection of the physiological environment by fMRI allows a more thorough investigation in monitoring tumour response. This article discusses the relevant fMRI utilities in NPC as an early prognostic and monitoring tool for treatment. Challenges and future developments of fMRI in radiation oncology are also discussed.

Diffusion-Weighted Imaging of the Head and Neck (Including Temporal Bone)

Diffusion techniques provide valuable information when performing head and neck imaging. This information can be used to detect the presence or absence of pathology, refine differential diagnosis, determine the location for biopsy, assess response to treatment, and prognosticate outcomes. For example, when certain technical factors are taken into consideration, diffusion techniques prove indispensable in assessing for residual cholesteatoma following middle ear surgery. In other scenarios, pretreatment apparent diffusion coefficient values may assist in prognosticating outcomes in laryngeal cancer and likelihood of response to radiation therapy. As diffusion techniques continue to advance, so too will its clinical utility.

The biological function and diagnostic value of miR-762 in nasopharyngeal carcinoma

BACKGROUND

Low diagnostic efficiency and high metastasis and recurrence of nasopharyngeal carcinoma (NPC) result in bad survival. A novel diagnostic biomarker is of great importance for the improvement of NPC management. This study aimed to state the biological function and diagnostic values of miR-762 in NPC to provide a novel insight into the detection and therapy of NPC.

METHODS

The expression of miR-762 in NPC and healthy samples was detected by quantitative real-time polymerase chain reaction and its diagnostic value was evaluated by the receiver operating characteristic (ROC) analysis. The functional roles of miR-762 in the proliferation, migration, and invasion of NPC cells were assessed by CCK8 and Transwell assay.

RESULTS

The significant upregulation of miR-762 was observed in NPC serum compared with healthy controls, which was associated with the TNM stage and lymph node metastasis of NPC patients. The ROC curve showed that miR-762 could be a diagnostic biomarker for NPC with high accuracy and specificity. Additionally, miR-762 served as a tumor promoter, which could promote cell proliferation, migration, and invasion of NPC.

CONCLUSION

The upregulation of miR-762 in NPC is associated with the disease progression and diagnosis of NPC. miR-762 might be involved in the tumor progression of NPC, which provides a potential therapeutic target for the treatment and management of NPC.

Simultaneous multi-slice readout-segmentation of long variable echo-trains for accelerated diffusion-weighted imaging of nasopharyngeal carcinoma: A feasibility and optimization study

OBJECTIVES

We aimed to evaluate the feasibility of simultaneous multi-slice readout segmentation of long variable echo-trains (SMS-RESOLVE) for diffusion-weighted imaging (DWI) of nasopharyngeal carcinoma (NPC) and determine the optimal acceleration factor of SMS-RESOLVE DWI compared with conventional RESOLVE DWI.

MATERIALS AND METHODS

One hundred and five patients with histologically confirmed NPC were enrolled. DWIs including conventional RESOLVE DWI, SMS-RESOLVE DWI with acceleration factors of 2 and 3 (2 × SMS-RESOLVE and 3 × SMS-RESOLVE) were acquired at b-values of 0 and 800 s/mm². The subjective image quality, including overall image quality, lesion conspicuity, and lesion distortion scores, and objective image quality, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and contrast in these three protocols, were assessed and compared. The apparent diffusion coefficients (ADCs) of the primary tumor and right masseter muscle and normalized ADCs in the three protocols were measured and compared.

RESULTS

The 2 × SMS-RESOLVE provided comparable image quality and ADCs with a 48.3% reduction in scan time compared with conventional RESOLVE DWI. The 3 × SMS-RESOLVE showed significantly poorer image quality and the highest ADCs with a 56.9% reduction in scan times compared with conventional RESOLVE DWI.

CONCLUSION

The SMS-RESOLVE DWI with an acceleration factor of 2 yielded the best compromise between image quality and scan time reduction and could be an alternative to conventional RESOLVE DWI for nasopharyngeal carcinoma.

Research status and prospects of biomarkers for nasopharyngeal carcinoma in the era of high‑throughput omics (Review)

As a malignant tumor type, nasopharyngeal carcinoma (NPC) is characterized by distinct geographical, ethnic and genetic differences; presenting a major threat to human health in many countries, especially in Southern China. At present, no accurate and effective methods are available for the early diagnosis, efficacious evaluation or prognosis prediction for NPC. As such, a large number of patients have locoregionally advanced NPC at the time of initial diagnosis. Many patients show toxic reactions to overtreatment and have risks of cancer recurrence and distant metastasis owing to insufficient treatment. To solve these clinical problems, high‑throughput '‑omics' technologies are being used to screen and identify specific molecular biomarkers for NPC. Because of the lack of comprehensive descriptions regarding NPC biomarkers, the present study summarized the research progress that has been made in recent years to discover NPC biomarkers, highlighting the existing problems that require exploration. In view of the lack of authoritative reports at present, study design factors that affect the screening of biomarkers are also discussed here and prospects for future research are proposed to provide references for follow‑up studies of NPC biomarkers.

Value of Apparent Diffusion Coefficient Histogram Analysis in the Differential Diagnosis of Nasopharyngeal Lymphoma and Nasopharyngeal Carcinoma Based on Readout-Segmented Diffusion-Weighted Imaging

Background

This study aims to explore the utility of whole-lesion apparent diffusion coefficient (ADC) histogram analysis for differentiating nasopharyngeal lymphoma (NPL) from nasopharyngeal carcinoma (NPC) following readout-segmented echo-planar diffusion-weighted imaging (RESOLVE sequence).

Methods

Thirty-eight patients with NPL and 62 patients with NPC, who received routine head-and-neck MRI and RESOLVE (b-value: 0 and 1,000 s/mm²) examinations, were retrospectively evaluated as derivation cohort (February 2015 to August 2018); another 23 patients were analyzed as validation cohort (September 2018 to December 2019). The RESOLVE data were obtained from the MAGNETOM Skyra 3T MR system (Siemens Healthcare, Erlangen, Germany). Fifteen parameters derived from the whole-lesion histogram analysis (ADC_mean, variance, skewness, kurtosis, ADC₁, ADC₁₀, ADC₂₀, ADC₃₀, ADC₄₀, ADC₅₀, ADC₆₀, ADC₇₀, ADC₈₀, ADC₉₀, and ADC₉₉) were calculated for each patient. Then, statistical analyses were performed between the two groups to determine the statistical significance of each histogram parameter. A receiver operating characteristic curve (ROC) analysis was conducted to assess the diagnostic performance of each histogram parameter for distinguishing NPL from NPC and further tested in the validation cohort; calibration of the selected parameter was tested with Hosmer-Lemeshow test.

Results

NPL exhibited significantly lower ADC_mean, variance, ADC₁, ADC₁₀, ADC₂₀, ADC₃₀, ADC₄₀, ADC₅₀, ADC₆₀, ADC₇₀, ADC₈₀, ADC₉₀ and ADC₉₉, when compared to NPC (all, P < 0.05), while no significant differences were found on skewness and kurtosis. Furthermore, ADC₉₉ revealed the highest diagnostic efficiency, followed by ADC₁₀ and ADC₂₀. Optimal diagnostic performance (AUC = 0.790, sensitivity = 91.9%, and specificity = 63.2%) could be achieved when setting ADC₉₉ = 1,485.0 × 10^-6 mm²/s as the threshold value. The predictive performance was maintained in the validation cohort (AUC = 0.817, sensitivity = 94.6%, and specificity = 56.2%).

Conclusion

Whole-lesion ADC histograms based on RESOLVE are effective in differentiating NPC from NPL.

Can Arterial Spin Labeling Perfusion Imaging be Used to Differentiate Nasopharyngeal Carcinoma From Nasopharyngeal Lymphoma?

BACKGROUND

Differentiating nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoma (NPL) is useful for deciding the appropriate treatment. However, the diagnostic accuracy of current imaging methods is low.

PURPOSE

To explore the feasibility of arterial spin labeling (ASL) perfusion imaging in the qualitative and quantitative differentiation between NPC and NPL to improve the diagnosis of malignancies in the nasopharynx.

STUDY TYPE

Retrospective.

POPULATION

Ninety seven patients: NPC (65 cases) and NPL (32 cases), histologically confirmed.

FIELD STRENGTH/SEQUENCE

3T/3D fast spin echo pseudo-continuous ASL imaging with spiral readout scheme, 3D inverse recovery_- fast spoiled gradient recalled echo brain volume (BRAVO) imaging.

ASSESSMENT

Cerebral blood flow (CBF) images from ASL perfusion imaging were assessed by three radiologists. Each tumor was visually scored based on CBF images. Intratumoral CBF and intramuscular CBF values were obtained from intratumoral and lateral pterygoid muscle areas, respectively. Through dividing intratumoral CBF by intramuscular CBF, normalized CBF (nCBF) was further calculated.

STATISTICAL TESTS

Fleiss's kappa and intraclass correlation coefficients (ICCs) were used to assess interobserver agreement among the three readers. The Mann-Whitney U-test was used to compare visual scoring, and an unpaired t-test was performed to compare CBF value between the NPC and NPL groups. The area under the curve (AUC) value was used to quantify the diagnostic ability of each parameter.

RESULTS

Good interobserver agreements were validated by high Fleiss's kappa and ICC values (all >0.80). NPCs showed significantly higher visual scores than NPLs (P < 0.05). Both intratumoral CBF and nCBF in NPC were significantly higher than those in NPL (both P < 0.05). Intratumoral CBF showed the highest AUC of 0.861 (P < 0.05) in differentiating NPC (n = 65) from NPL (n = 32), while the AUCs of nCBF and visual scoring were 0.847 and 0.753, respectively.

DATA CONCLUSION

For the diagnosis of distinguishing NPC from NPL, ASL perfusion imaging demonstrated high diagnostic efficiency.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Investigation of the feasibility of synthetic MRI in the differential diagnosis of non-keratinising nasopharyngeal carcinoma and benign hyperplasia using different contoured methods for delineation of the region of interest

AIM

To assess the feasibility and preliminary diagnostic performances of relaxation times derived from synthetic magnetic resonance imaging (syMRI) for differentiating nasopharyngeal carcinoma from nasopharyngeal benign lymphoid hyperplasia, and to assess the influence of tissue segmentation method on relaxation estimates.

MATERIALS AND METHODS

Fifty participants with nasopharyngeal carcinoma (NPC) and 40 participants with benign hyperplasia (NPH) who underwent syMRI examination were enrolled prospectively. T1, T2, and proton density (PD) values were obtained from four different regions of interest (ROIs), namely, partial-section, single-section, three-sections, and whole-lesion. The metrics between NPC and NPH or among different ROIs were compared using Student's t-test or one-way ANOVA. The area under curve (AUC) was calculated to assess the performance of metrics obtained from different ROIs to differentiate NPC and NPH.

RESULTS

The T1, T2, and PD values for NPH were significantly higher than those for NPC, regardless of the type of ROI used, except for the PD value obtained from the whole-lesion ROI. The T2 values obtained from the single-section ROI showed the highest diagnostic accuracy in distinguishing NPC from NPH, with an AUC of 0.894, sensitivity of 0.900, and specificity of 0.800. Additionally, the T1, T2, and PD values for nasopharyngeal lesions showed no statistical difference among different kinds of ROI, except for the difference in T1 value between partial-section and other methods.

CONCLUSION

Quantitative analysis of syMRI has the potential to distinguish NPC from NPH. Moreover, different types of ROI showed limited influence on the relaxation time estimation for nasopharyngeal lesions.

The Usefulness of Pretreatment MR-Based Radiomics on Early Response of Neoadjuvant Chemotherapy in Patients With Locally Advanced Nasopharyngeal Carcinoma

The aim of this study was to explore the predictive role of pretreatment MRI-based radiomics on early response of neoadjuvant chemotherapy (NAC) in locoregionally advanced nasopharyngeal carcinoma (NPC) patients. Between January 2016 and December 2016, a total of 108 newly diagnosed NPC patients who were hospitalized in the Cancer Hospital of the University of Chinese Academy of Sciences were reviewed. All patients had complete data of enhanced MR of nasopharynx before treatment, and then received two to three cycles of TP-based NAC. After 2 cycles of NAC, enhanced MR of nasopharynx was conducted again. Compared with the enhanced MR images before treatment, the response after NAC was evaluated. According to the evaluation criteria of RECIST1.1, 108 cases were divided into two groups: 52 cases for the NAC-sensitive group and 56 cases for the NAC-resistance group. ITK-SNAP software was used to manually sketch and segment the region of interest (ROI) of nasopharyngeal tumor on the MR enhanced T1WI sequence image. The parameters were analyzed and extracted by using AI Kit software. ANOVA/MW test, correlation analysis, and LASSO were used to select texture features. We used multivariate logistic regressions to select texture features and establish a predictive model. The ROC curve was used to evaluate the efficiency of the predictive model. A total of 396 texture features were obtained by using feature calculation. After all features were screened, we selected two features including ClusterShade_angle135_offset4 and Correlation_AllDirection_offshe1_SD. Based on these two features, we established a predictive model by using multivariate logistic regression. The AUC of the two features used alone (0.804, 95% CI=0.6020.932; 0.762, 95% CI=0.5560.905) was smaller than the combination of these two features (0.905, 95% CI=0.7240.984, p=0.0005). Moreover, the sensitivity values of the two features used alone and the combined use were 92.9%, 51.7%, and 85.7%, respectively, while the specificity values were 66.7%, 91.7%, and 83.3%, respectively, in the early response of NAC for NPC. The predictive model based on MRI-enhanced sequence imaging could distinguish the sensitivity and resistance to NAC and provide new biomarkers for the early prediction of the curative effect in NPC patients.

High resolution diffusion-weighted imaging with readout segmentation of long variable echo-trains for determining myometrial invasion in endometrial carcinoma

Background

We assessed the image quality of endometrial cancer lesions by readout segmentation of long variable echo-trains (RESOLVE) diffusion-weighted imaging (DWI) compared with that by single-shot echo-planar imaging (SS-EPI) DWI, aimed to explore the value of RESOLVE DWI for determining myometrial invasion and clinical stage in endometrial cancer.

Materials and methods

From April 2017 to March 2018, a total of 30 endometrial cancer patients (mean age 52.8 ± 9.0 years), who had undergone RESOLVE DWI and SS-EPI DWI, were included in the study. The image quality of endometrial carcinoma by two kinds of DWI scanning methods was compared qualitatively and quantitatively. The Spearman rank correlation test was used to assess the correlation of qualitative image quality scores between two readers. The accuracy of two DWI methods in detecting myometrial invasion and staging of endometrial carcinoma was calculated according to postoperative pathological results. The indexes were analyzed including sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV).

Results

The qualitative score of RESOLVE DWI group was superior to SS-EPI DWI group in every aspect of five aspects (all P < 0.001). Interobserver agreement of depiction was good or excellent in two DWI sequences. Signal to noise ratio and contrast to noise ratio values in RESOLVE DWI group were both higher than those in SS-EPI DWI group (P<0.001). No statistical difference of apparent diffusion coefficient value was observed between two DWI groups (P = 0.261). The specificity, accuracy, PPV, and NPV of estimating myometrial invasion by RESOLVE DWI in three cases (intramucosal lesion, <50% superficial invasion and ≥ 50% deep invasion) were all higher than those by SS-EPI DWI for endometrial carcinoma. Especially RESOLVE DWI was valuable in judging <50% superficial invasion (95%CI:0.586, 0.970). No significant difference in accuracy staging was between the two DWI groups (P = 0.125).

Conclusion

RESOLVE DWI can provide higher quality images of endometrial carcinoma than SS-EPI DWI. The high-quality images are helpful for precise assessment of myometrial invasion in endometrial cancer.

Quantitative Analysis of DCE-MRI and RESOLVE-DWI for Differentiating Nasopharyngeal Carcinoma from Nasopharyngeal Lymphoid Hyperplasia

To explore the ability of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) analysis and readout segmentation of long variable echo-trains diffusion weighted imaging (RESOLVE-DWI) to distinguish nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoid hyperplasia (NPLH). Twenty-five patients with NPC and 30 patients with NPLH were evaluated. Three quantitative DCE-MRI parameters (Ktrans, Kep and Ve) and the apparent diffusion coeffcient (ADC) of lesions were calculated. The two independent samples t test or Mann-Whitney U test was used to compare the parameters between NPC and NPLH group. Receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic ability for distinguishing NPC from NPLH. A P value less than 0.05 was considered statistically significant. The difference in Ktrans value between the NPC group and the NPLH group was statistically significant, and the value of the NPC group was larger than that of the NPLH group. There was no statistical difference in Kep and Ve between the two groups. The ADC value of NPC group was smaller than that of NPLH group, and the difference was statistically significant. ROC curve analysis showed that both Ktrans and ADC were effective in diagnosing NPC and the area under the curve (AUC) was 0.773 and 0.704, respectively. In addition, the combination of Ktrans and ADC demonstrated the obviously improved AUC of 0.884. DCE-MRI and RESOLVE-DWI are effective in differentiating NPC from NPLH, especially the combination of the two models.