Histogram analysis of quantitative parameters from synthetic MRI: correlations with prognostic factors in nasopharyngeal carcinoma.

OBJECTIVES: To evaluate the correlation between histogram parameters derived from synthetic magnetic resonance imaging (SyMRI) and prognostically relevant factors in nasopharyngeal carcinoma (NPC). METHODS: Fifty-nine consecutive NPC patients were prospectively enrolled. Quantitative parameters (T1, T2, and proton density (PD)) were obtained by outlining the three-dimensional volume of interest (VOI) of all lesions. Then, histogram analysis of these quantitative parameters was performed and the correlations with prognostically relevant factors were assessed. By choosing appropriate cutoff, we divided the sample into two groups. Independent-samples t test/Mann-Whitney U test was used and ROC curve analysis was further processed. RESULTS: Histogram parameters of the T1, T2, and PD maps were positively correlated with the Ki-67 expression levels, and PD_mean was the most representative parameter (AUC: 0.861). The PD map exhibited good performance in differentiating epidermal growth factor receptor (EGFR) expression levels (AUC: 0.706~0.732) and histological type (AUC: 0.650~0.660). T2_minimum was highest correlated with Epstein-Barr virus (EBV) DNA levels (r = - 0.419), and PD_75th percentile exhibited the highest performance in distinguishing positive and negative EBV DNA groups (AUC: 0.721). T1_minimum was statistically correlated with EA-IgA expression (r = - 0.313). Additionally, several histogram parameters were negatively correlated with tumor stage (T stage: r = - 0.259 ~ - 0.301; N stage: r = - 0.348 ~ - 0.456; clinical stage: r = - 0.419). CONCLUSIONS: Histogram parameters of SyMRI could reflect tissue intrinsic characteristics and showed potential value in assessing the Ki-67 and EGFR expression levels, histological type, EBV DNA level, EA-IgA, and tumor stage. KEY POINTS: • SyMRI combined with histogram analysis may help clinicians to assess different prognostic factor statuses in nasopharyngeal carcinoma. • The PD map exhibited good discriminating performance in the Ki-67 and EGFR expression levels. • Histogram parameters of SyMRI were negatively correlated with EBV-related blood biomarkers and TNM stage.

Pretreatment synthetic magnetic resonance imaging predicts disease progression in nonmetastatic nasopharyngeal carcinoma after intensity modulation radiation therapy.

BACKGROUND: To investigate the potential of synthetic MRI (SyMRI) in the prognostic assessment of patients with nonmetastatic nasopharyngeal carcinoma (NPC), and the predictive value when combined with diffusion-weighted imaging (DWI) as well as clinical factors. METHODS: Fifty-three NPC patients who underwent SyMRI were prospectively included. 10th Percentile, Mean, Kurtosis, and Skewness of T1, T2, and PD maps and ADC value were obtained from the primary tumor. Cox regression analysis was used for analyzing the association between SyMRI and DWI parameters and progression-free survival (PFS), and then age, sex, staging, and treatment as confounding factors were also included. C-index was obtained by bootstrap. Moreover, significant parameters were used to construct models in predicting 3-year disease progression. ROC curves and leave-one-out cross-validation were used to evaluate the performance and stability. RESULTS: Disease progression occurred in 16 (30.2%) patients at a follow-up of 39.6 (3.5, 48.2) months. T1_Kurtosis, T1_Skewness, T2_10th, PD_Mean, and ADC were correlated with PFS, and T1_Kurtosis (HR: 1.093) and ADC (HR: 1.009) were independent predictors of PFS. The C-index of SyMRI and SyMRI + DWI + Clinic models was 0.687 and 0.779. Moreover, the SyMRI + DWI + Clinic model predicted 3-year disease progression better than DWI or Clinic model (p ≤ 0.008). Interestingly, there was no significant difference between the SyMRI model (AUC: 0.748) and SyMRI + DWI + Clinic model (AUC: 0.846, p = 0.092). CONCLUSION: SyMRI combined with histogram analysis could predict disease progression in NPC patients, and SyMRI + DWI + Clinic model further improved the predictive performance.

Histogram analysis of synthetic magnetic resonance imaging: Correlations with histopathological factors in head and neck squamous cell carcinoma.

PURPOSE: To analyse the association between histogram parameters derived from synthetic MRI (SyMRI) and different histopathological factors in head and neck squamous cell carcinoma (HNSCC). METHOD: Sixty-one patients with histologically proven primary HNSCC were prospectively enrolled. The correlations between histogram parameters of SyMRI (T1, T2 and proton density (PD) maps) and histopathological factors were analysed using Spearman analysis. The Mann-Whitney U test or Student's t test was utilized to differentiate histological grades and human papillomavirus (HPV) status. The ROC curves and leave-one-out cross-validation (LOOCV) were used to evaluate the differentiation performance. Bootstrapping was applied to avoid overfitting. RESULTS: Several histogram parameters were associated with histological grade: T1 map (r = 0.291) and PD map (r = 0.294 - 0.382/-0.343), and PD_75th Percentile showed the highest differentiation performance (AUC: 0.721 (ROC) and 0.719 (LOOCV)). Moderately negative correlations were found between p16 status and the histogram parameters: T1 map (r = -0.587 - -0.390), T2 map (r = -0.649 - -0.357) and PD map (r = -0.537 - -0.338). In differentiating HPV infection, Entropy was the most discriminative parameter in each map and T2_Entropy showed the highest diagnostic performance (AUC: 0.851 [ROC] and 0.851 [LOOCV]). Additionally, several histogram parameters were correlated with Ki-67 (r = -0.379/-0.397), epidermal growth factor receptor (EGFR) (r = 0.318/0.322) status and p53 (r = 0.452 - 0.665/-0.607) status. CONCLUSIONS: Histogram parameters derived from SyMRI may serve as a potential biomarker for discriminating relevant histopathological features, including histological differentiation grade, HPV infection, Ki-67, EGFR and p53 statuses.

The utility of texture analysis based on quantitative synthetic magnetic resonance imaging in nasopharyngeal carcinoma: a preliminary study.

BACKGROUND: Magnetic resonance imaging (MRI) is commonly used for the diagnosis of nasopharyngeal carcinoma (NPC) and occipital clivus (OC) invasion, but a proportion of lesions may be missed using non-enhanced MRI. The purpose of this study is to investigate the diagnostic performance of synthetic magnetic resonance imaging (SyMRI) in differentiating NPC from nasopharyngeal hyperplasia (NPH), as well as evaluating OC invasion. METHODS: Fifty-nine patients with NPC and 48 volunteers who underwent SyMRI examination were prospectively enrolled. Eighteen first-order features were extracted from VOIs (primary tumours, benign mucosa, and OC). Statistical comparisons were conducted between groups using the independent-samples t-test and the Mann-Whitney U test to select significant parameters. Multiple diagnostic models were then constructed using multivariate logistic analysis. The diagnostic performance of the models was calculated by receiver operating characteristics (ROC) curve analysis and compared using the DeLong test. Bootstrap and 5-folds cross-validation were applied to avoid overfitting. RESULTS: The T1, T2 and PD map-derived models had excellent diagnostic performance in the discrimination between NPC and NPH in volunteers, with area under the curves (AUCs) of 0.975, 0.972 and 0.986, respectively. Besides, SyMRI models also showed excellent performance in distinguishing OC invasion from non-invasion (AUC: 0.913-0.997). Notably, the T1 map-derived model showed the highest diagnostic performance with an AUC, sensitivity, specificity, and accuracy of 0.997, 96.9%, 97.9% and 97.5%, respectively. By using 5-folds cross-validation, the bias-corrected AUCs were 0.965-0.984 in discriminating NPC from NPH and 0.889-0.975 in discriminating OC invasion from OC non-invasion. CONCLUSIONS: SyMRI combined with first-order parameters showed excellent performance in differentiating NPC from NPH, as well as discriminating OC invasion from non-invasion.

MRI-based radiomics for pretreatment prediction of response to concurrent chemoradiotherapy in locally advanced cervical squamous cell cancer.

PURPOSE: To investigate the value of magnetic resonance imaging (MRI)-based radiomics in predicting the treatment response to concurrent chemoradiotherapy (CCRT) in patients with locally advanced cervical squamous cell cancer (LACSC). METHODS: In total, 198 patients (training: n = 138; testing: n = 60) with LACSC treated with CCRT between January 2014 and December 2019 were retrospectively enrolled in this study. Responses were evaluated by MRI and clinical data performed at one month after completion of CCRT according to RECIST standards, and patients were divided into the residual group and nonresidual group. Overall, 200 radiomics features were extracted from T2-weighted imaging and apparent diffusion coefficient maps. The radiomics score (Rad-score) was constructed with a feature selection strategy. Logistic regression analysis was used for multivariate analysis of radiomics features and clinical variables. The performance of all models was assessed using receiver operating characteristic curves. RESULTS: Among the clinical variables, tumor grade and FIGO stage were independent risk factors, and the areas under the curve (AUCs) of the clinical model were 0.741 and 0.749 in the training and testing groups. The Rad-score, consisting of 4 radiomics features selected from 200 radiomics features, showed good predictive performance with an AUC of 0.819 in the training group and 0.776 in the testing group, which were higher than the clinical model, but the difference was not statistically significant. The combined model constructed with tumor grade, FIGO stage, and Rad-score achieved the best performance, with an AUC of 0.857 in the training group and 0.842 in the testing group, which were significantly higher than the clinical model. CONCLUSION: MRI-based radiomics features could be used as a noninvasive biomarker to improve the ability to predict the treatment response to CCRT in patients with LACSC.

Splenomegaly during oxaliplatin-based chemotherapy: impact on blood parameters and anti-neoplastic treatment.

Background: Oxaliplatin induces splenomegaly, which can cause blood sequestration and relevant cytopenia, leading to further dose reductions and schedule modifications of chemotherapy. Here, we aimed to explore the changes of spleen volume induced by oxaliplatin as well as its impact on blood parameters and anti-neoplastic treatment in patients with colon cancer. Methods: We conducted a retrospective analysis of patients with resectable stage II-IV colon cancer who were treated with oxaliplatin and capecitabine at our institution from January 2016 to December 2017. Laboratory tests and computed tomographic (CT) data before, during and up to 18 months after the end of chemotherapy were extracted. Spleen volume was determined by volumetric measurements. Splenomegaly was defined as an over 30% increase in spleen volume from baseline. Results: Out of 144 patients, 102 patients (70.8%) developed splenomegaly, and 75 (73.5%) recovered at 18 months after the end of chemotherapy. Higher cumulative dose intensity of oxaliplatin (P=0.001) and higher baseline platelet count (P=0.045) were risk factors associated with splenomegaly. Compared to those without splenomegaly, splenomegaly patients experienced more severe reduction in platelet (P<0.001), erythrocyte (P=0.010), neutrophil (P=0.002) and lymphocyte (P=0.006) counts and were more likely to undergo dose reductions of oxaliplatin (21.6% vs. 7.1%, P=0.038) as well as interruptions of chemotherapy (18.6% vs. 4.8%, P=0.040) due to thrombocytopenia. Conclusions: Splenomegaly was a common and partly irreversible side effect of oxaliplatin-based therapy in patients with colon cancer. It is correlated with more severe pancytopenia and might exert a negative impact on the efficacy of anti-neoplastic treatment.

Arterial spin labeling and diffusion-weighted imaging for identification of retropharyngeal lymph nodes in patients with nasopharyngeal carcinoma.

BACKGROUND: To evaluate the parameters derived from arterial spin labeling (ASL) and multi-b-value diffusion-weighted imaging (DWI) for differentiating retropharyngeal lymph nodes (RLNs) in patients with nasopharyngeal carcinoma (NPC). METHODS: This prospective study included 50 newly diagnosed NPC and 23 healthy control (HC) participants. RLNs of NPC were diagnosed according to the follow-up MRI after radiotherapy. Parameters derived from ASL and multi-b-value DWI, and RLNs axial size on pre-treatment MRI among groups were compared. Receiver operating characteristic curve (ROC) was used to analyze the diagnostic efficiency. RESULTS: A total of 133 RLNs were collected and divided into a metastatic group (n = 71) and two non-metastatic groups (n = 62, including 29 nodes from NPC and 33 nodes from HC). The axial size, blood flow (BF), and apparent diffusion coefficient (ADC) of RLNs were significantly different between the metastasis and the non-metastasis group. For NPC patients with a short axis < 5 mm or < 6 mm, or long axis < 7 mm, if BF > 54 mL/min/100 g or ADC ≤ 0.95 × 10-3 mm2/s, the RLNs were still considered metastatic. Compared with the index alone, a combination of size and functional parameters could improve the accuracy significantly, except the long axis combined with ADC; especially, combined size with BF exhibited better performance with an accuracy of 91.00-92.00%. CONCLUSIONS: ASL and multi-b-value DWI could help determine the N stage of NPC, while the BF combination with RLNs size may significantly improve the diagnostic efficiency.

Whole-lesion apparent diffusion coefficient (ADC) histogram as a quantitative biomarker to preoperatively differentiate stage IA endometrial carcinoma from benign endometrial lesions.

BACKGROUND: To assess the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in differentiating stage IA endometrial carcinoma (EC) from benign endometrial lesions (BELs) and characterizing histopathologic features of stage IA EC preoperatively. METHODS: One hundred and six BEL and 126 stage IA EC patients were retrospectively enrolled. Eighteen volumetric histogram parameters were extracted from the ADC map of each lesion. The Mann-Whitney U or Student's t-test was used to compare the differences between the two groups. Models based on clinical parameters and histogram features were established using multivariate logistic regression. Receiver operating characteristic (ROC) analysis and calibration curves were used to assess the models. RESULTS: Stage IA EC showed lower ADC10th, ADC90th, ADCmin, ADCmax, ADCmean, ADCmedian, interquartile range, mean absolute deviation, robust mean absolute deviation (rMAD), root mean squared, energy, total energy, entropy, variance, and higher skewness, kurtosis and uniformity than BELs (all p < 0.05). ADCmedian yielded the highest area under the ROC curve (AUC) of 0.928 (95% confidence interval [CI] 0.895-0.960; cut-off value = 1.161 × 10-3 mm2/s) for differentiating stage IA EC from BELs. Moreover, multivariate analysis demonstrated that ADC-score (ADC10th + skewness + rMAD + total energy) was the only significant independent predictor (OR = 2.641, 95% CI 2.045-3.411; p < 0.001) for stage IA EC when considering clinical parameters. This ADC histogram model (ADC-score) achieved an AUC of 0.941 and a bias-corrected AUC of 0.937 after bootstrap resampling. The model performed well for both premenopausal (accuracy = 0.871) and postmenopausal (accuracy = 0.905) patients. Besides, ADCmin and ADC10th were significantly lower in Grade 3 than in Grade 1/2 stage IA EC (p = 0.022 and 0.047). At the same time, no correlation was found between ADC histogram parameters and the expression of Ki-67 in stage IA EC (all p > 0.05). CONCLUSIONS: Whole-lesion ADC histogram analysis could serve as an imaging biomarker for differentiating stage IA EC from BELs and assisting in tumor grading of stage IA EC, thus facilitating personalized clinical management for premenopausal and postmenopausal patients.

MRI-based radiomics value for predicting the survival of patients with locally advanced cervical squamous cell cancer treated with concurrent chemoradiotherapy.

BACKGROUND: To investigate the magnetic resonance imaging (MRI)-based radiomics value in predicting the survival of patients with locally advanced cervical squamous cell cancer (LACSC) treated with concurrent chemoradiotherapy (CCRT). METHODS: A total of 185 patients (training group: n = 128; testing group: n = 57) with LACSC treated with CCRT between January 2014 and December 2018 were retrospectively enrolled in this study. A total of 400 radiomics features were extracted from T2-weighted imaging, apparent diffusion coefficient map, arterial- and delayed-phase contrast-enhanced MRI. Univariate Cox regression and least absolute shrinkage and selection operator Cox regression was applied to select radiomics features and clinical characteristics that could independently predict progression-free survival (PFS) and overall survival (OS). The predictive capability of the prediction model was evaluated using Harrell's C-index. Nomograms and calibration curves were then generated. Survival curves were generated using the Kaplan-Meier method, and the log-rank test was used for comparison. RESULTS: The radiomics score achieved significantly better predictive performance for the estimation of PFS (C-index, 0.764 for training and 0.762 for testing) and OS (C-index, 0.793 for training and 0.750 for testing), compared with the 2018 FIGO staging system (C-index for PFS, 0.657 for training and 0.677 for testing; C-index for OS, 0.665 for training and 0.633 for testing) and clinical-predicting model (C-index for PFS, 0.731 for training and 0.725 for testing; C-index for OS, 0.708 for training and 0.693 for testing) (P < 0.05). The combined model constructed with T stage, lymph node metastasis position, and radiomics score achieved the best performance for the estimation of PFS (C-index, 0.792 for training and 0.809 for testing) and OS (C-index, 0.822 for training and 0.785 for testing), which were significantly higher than those of the radiomics score (P < 0.05). CONCLUSIONS: The MRI-based radiomics score could provide effective information in predicting the PFS and OS in patients with LACSC treated with CCRT. The combined model (including MRI-based radiomics score and clinical characteristics) showed the best prediction performance.

Multimodal MRI-Based Radiomics-Clinical Model for Preoperatively Differentiating Concurrent Endometrial Carcinoma From Atypical Endometrial Hyperplasia.

Objectives: To develop and validate a radiomics model based on multimodal MRI combining clinical information for preoperative distinguishing concurrent endometrial carcinoma (CEC) from atypical endometrial hyperplasia (AEH). Materials and Methods: A total of 122 patients (78 AEH and 44 CEC) who underwent preoperative MRI were enrolled in this retrospective study. Radiomics features were extracted based on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. After feature reduction by minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithm, single-modal and multimodal radiomics signatures, clinical model, and radiomics-clinical model were constructed using logistic regression. Receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis were used to assess the models. Results: The combined radiomics signature of T2WI, DWI, and ADC maps showed better discrimination ability than either alone. The radiomics-clinical model consisting of multimodal radiomics features, endometrial thickness >11mm, and nulliparity status achieved the highest area under the ROC curve (AUC) of 0.932 (95% confidential interval [CI]: 0.880-0.984), bootstrap corrected AUC of 0.922 in the training set, and AUC of 0.942 (95% CI: 0.852-1.000) in the validation set. Subgroup analysis further revealed that this model performed well for patients with preoperative endometrial biopsy consistent and inconsistent with postoperative pathologic data (consistent group, F1-score = 0.865; inconsistent group, F1-score = 0.900). Conclusions: The radiomics model, which incorporates multimodal MRI and clinical information, might be used to preoperatively differentiate CEC from AEH, especially for patients with under- or over-estimated preoperative endometrial biopsy.

The Value of Whole-Tumor Texture Analysis of ADC in Predicting the Early Recurrence of Locally Advanced Cervical Squamous Cell Cancer Treated With Concurrent Chemoradiotherapy.

Objectives: To investigate the value of whole-tumor texture analysis of apparent diffusion coefficient (ADC) map in predicting the early recurrence of patients with locally advanced cervical squamous cell cancer (LACSC) treated with concurrent chemoradiotherapy (CCRT) and establish a combined prediction model including clinical variables and first-order texture features. Methods: In total, 219 patients (training: n = 153; testing: n = 66) with stage IIB-IVA LACSC treated by CCRT between January 2014 and December 2019 were retrospectively enrolled in this study. Clinical variables and 22 first-order texture features extracted from ADC map were collected. The Mann-Whitney U test or independent sample t test, chi-square test or Fisher's exact were used to analyze statistically significant parameters, logistic regression analysis was used for multivariate analysis, and receiver operating characteristic analysis was used to compare the diagnostic performance. Results: In the clinical variables, T stage and lymph node metastasis (LNM) were independent risk factors, and the areas under the curve (AUCs) of the clinical model were 0.697 and 0.667 in the training and testing cohorts, the sensitivity and specificity were 48.8% and 85.5% in the training cohort, and 84.1% and 51.1% in the testing cohort, respectively. In the first-order texture features, mean absolute deviation (MAD) was the independent protective factor, with an AUC of 0.756 in the training cohort and 0.783 in the testing cohort. The sensitivity and specificity were 95.3% and 52.7% in the training cohort and 94.7% and 53.2% in the testing cohort, respectively. The combined model (MAD, T stage, and LNM) was established, it exhibited the highest AUC of 0.804 in the training cohort and 0.821 in the testing cohort, which was significantly higher than the AUC of the clinical prediction model. The sensitivity and specificity were 67.4% and 85.5% in the training cohort and 94.7% and 70.2% in the testing cohort, respectively. Conclusions: The first-order texture features of the ADC map could be used along with clinical predictive biomarkers to predict early recurrence in patients with LACSC treated by CCRT.

Added-value of texture analysis of ADC in predicting the survival of patients with 2018 FIGO stage IIICr cervical cancer treated by concurrent chemoradiotherapy.

PURPOSE: To investigate the value of texture analysis of ADC in predicting the survival of patients with 2018 International Federation of Gynecology and Obstetrics (FIGO) stage IIICr cervical squamous cell cancer (CSCC) treated with concurrent chemoradiotherapy (CCRT). METHODS: A total of 91 patients with stage IIICr CSCC treated by CCRT between January 2014 and December 2018 were retrospectivelyenrolled in this study. Clinical variables and 21 first-order texture features extracted from ADC maps were collected. Univariate and multivariate Cox hazard regression analyses were performed to evaluate these parameters in predicting progression-free survival (PFS) and overall survival (OS). The independent variables were combined to build a prediction model and compared with the 2018 FIGO staging system. Survival curves were generated using the Kaplan-Meier method, and the log-rank test was used for comparison. RESULTS: Mean Absolute Deviation (MAD), T stage, and the number of lymph node metastasis (LNM) were independently associated with PFS, while MAD, energy, T stage, number of LNM, and tumor grade were independently associated with OS. The C-index values of the combined models for PFS and OS, which were respectively 0.750 and 0.832, were significantly higher compared to 2018 FIGO staging system values of 0.629 and 0.630, respectively (P < 0.05). CONCLUSIONS: The texture analysis of the ADC maps could be used along with clinical prognostic biomarkers to predict PFS and OS in patients with stage IIICr CSCC treated by CCRT.

Added-value of dynamic contrast-enhanced MRI on prediction of tumor recurrence in locally advanced cervical cancer treated with chemoradiotherapy.

OBJECTIVES: To evaluate whether the DCE-MRI derived parameters integrated into clinical and conventional imaging variables may improve the prediction of tumor recurrence for locally advanced cervical cancer (LACC) patients following concurrent chemoradiotherapy (CCRT). METHODS: Between March 2014 and November 2019, 79 consecutive LACC patients who underwent pelvic MRI examinations with DCE-MRI sequence before treatment were prospectively enrolled. The primary outcome was disease-free survival (DFS). DCE-MRI derived parameters, conventional imaging, and clinical factors were collected. Univariate and multivariate Cox hazard regression analyses were performed to evaluate these parameters in the prediction of DFS. The independent and prognostic interested variables were combined to build a prediction model compared with the clinical International Federation of Gynecological (FIGO) staging system. RESULTS: Lymph node metastasis (LNM) and the mean value of ve (ve_mean) were independently associated with tumor recurrence (all p < 0.05). The prediction model based on T stage, LNM, and ve_mean demonstrated a moderate predictive capability in identifying LACC patients with a high risk of tumor recurrence; the model was more accurate than the FIGO staging system alone (c-index: 0.735 vs. 0.661) and the combination of ve_mean and the FIGO staging system (c-index: 0.735 vs. 0.688). Moreover, patients were grouped into low-, medial-, and high-risk levels based on the advanced T stage, positive LNM, and ve_mean < 0.361, with which the 2-year DFS was significantly stratified (p < 0.001). CONCLUSIONS: The ve_mean from DCE-MRI could be used as a useful biomarker to predict DFS in LACC patients treated with CCRT as an assistant of LNM and T stage. KEY POINTS: Lower ve_mean is an independent predictor of poor prognosis for disease-free survival in locally advanced cervical cancer patients treated with concurrent chemoradiotherapy (hazard ratio [HR]: 0.016, p<0.023). A combined prediction model based on advanced T stage, LNM, and ve_mean performed better than the FIGO staging system alone.

Different Primary Sites of Hypopharyngeal Cancer Have Different Lymph Node Metastasis Patterns: A Retrospective Analysis From Multi-Center Data.

BACKGROUND: Most hypopharyngeal cancers (HPCs) develop lymph node metastasis (LNM) at initial diagnosis. Understanding the pattern of LNM in HPC could help both surgeons and radiologists make decisions in the management of cervical lymph nodes. METHODS: A total of 244 newly diagnosed HPC patients between January 2010 and December 2018 were recruited from three specialized cancer hospitals in mainland China. All patients received pre-treatment magnetic resonance imaging (MRI), and definitive radiotherapy with or without concurrent chemotherapy. We reassessed the features of the primary tumor (tumor size, primary location, and extent of invasion) and the involvement of lymph nodes at each level. According to the incidence of LNM, these levels were sequenced and sorted into drainage stations. Univariate and multivariate analyses were used to determine the risk factors for bilateral and regional lymph node metastasis. RESULTS: The cohort consisted of 195 piriform sinus cancers (PSC), 47 posterior wall cancers (PWC), and 2 post-cricoid cancers (PCC). A total of 176 patients (72.1%) presented with MRI-detectable LNMs. The overall LNM rates for level II-VI and retropharyngeal lymph nodes (RPLNs) were 59.0%, 52.9%, 14.3%, 1.6%, 2.9%, and 16.4%, respectively. Based on the prevalence of LNM at each level, we hypothesize that the lymphatic drainage of PSC was carried out in sequence along three stations: Level II and III (61.0% and 55.4%), Level IV and RPLN (15.9% and 11.3%), and Level V and VI (1.5% and 3.1%). For PWCs, lymphatic drainage is carried out at two stations: Level II, III, and RPLN (48.9%, 40.4%, and 34.0%) and Level IV-VI (6.4%, 0%, and 2.1%). According to univariate and multivariate analyses, posterior wall invasion was significantly correlated with bilateral LNM (P = 0.030, HR = 2.853 95%CI, 1.110-7.338) and RPLN metastasis (P = 0.017, HR = 2.880 95%CI, 1.209-6.862). However, pyriform sinus invasion was less likely to present with bilateral LNM (P = 0.027, HR = 0.311, 95%CI, 0.111-0.875) and RPLN metastasis (P = 0.028, HR = 0.346, 95%CI, 0.134-0.891). CONCLUSIONS AND RELEVANCE: The primary tumor site and extent of invasion are related to the pattern of lymph node metastasis. That is, the metastasis would drainage station by station along different directions.

Retropharyngeal Lymph Node Metastasis Diagnosed by Magnetic Resonance Imaging in Hypopharyngeal Carcinoma: A Retrospective Analysis From Chinese Multi-Center Data.

BACKGROUND: To assess the prevalence, risk factors and prognostic significance of retropharyngeal lymph node (RPLN) metastasis diagnosed by magnetic resonance imaging (MRI) in patients with hypopharyngeal squamous cell carcinoma (HPSCC). METHODS: 259 patients from three cancer institutions in China from Jan 2010 to Dec 2018 were analyzed, retrospectively. All the patients had been given pre-treatment magnetic resonance imaging (MRI) of head and neck and were then treated with definitive radiotherapy with or without chemotherapy. Pretreatment diagnostic MRIs were reviewed by a dedicated head and neck radiologist, for the presence or absence of radiographically positive RPLN, cervical LN and tumor invasion.Demographic variables were analysed by descriptive statistics using SPSS 20.0. Predictors of the presence of RPLN and its prognostic significance were examined. RESULTS: RPLN metastasis was discovered in 44 patients (17%). Logistic analysis showed that posterior pharyngeal wall (PPW) primary tumor; PPW invasion; N2-3; multiple cervical lymph node (LN) involvement (>2 LNs) were associated with RPLN metastasis, with metastasis rates 37%, 30%, 31% and 33% respectively. Patients with RPLN metastasis had a significantly reduced 5-year overall survival (OS) and disease-free survival (DFS) compared to the non-RPLN metastasis group (OS 28% vs. 48%, p=0.001; DFS 25% vs. 41%, p=0.040). CONCLUSIONS: RPLN metastasis was not uncommon in HPSCC patients. Risk factors were: PPW primary tumor, PPW invasion and cervical LN status. RPLN metastasis is a poor prognosticator for survival.

Whole-tumor texture model based on diffusion kurtosis imaging for assessing cervical cancer: a preliminary study.

OBJECTIVES: To evaluate the diagnostic potential of diffusion kurtosis imaging (DKI) functional maps with whole-tumor texture analysis in differentiating cervical cancer (CC) subtype and grade. METHODS: Seventy-six patients with CC were enrolled. First-order texture features of the whole tumor were extracted from DKI and DWI functional maps, including apparent kurtosis coefficient averaged over all directions (MK), kurtosis along the axial direction (Ka), kurtosis along the radial direction (Kr), mean diffusivity (MD), fractional anisotropy (FA), and ADC maps, respectively. The Mann-Whitney U test and ROC curve were used to select the most representative texture features. Models based on each individual and combined functional maps were established using multivariate logistic regression analysis. Conventional parameters-the average values of ADC and DKI parameters derived from the conventional ROI method-were also evaluated. RESULTS: The combined model based on Ka, Kr, MD, and FA maps yielded the best diagnostic performance in discrimination of cervical squamous cell cancer (SCC) and cervical adenocarcinoma (CAC) with the highest AUC (0.932). Among individual functional map derived models, Kr map-derived model showed the best performance when differentiating tumor subtypes (AUC = 0.828). MK_90th percentile was useful for distinguishing high-grade and low-grade in SCC tumors with an AUC of 0.701. The average values of MD, FA, and ADC were significantly different between SCC and CAC, but no conventional parameters were useful for tumor grading. CONCLUSIONS: The whole-tumor texture analysis applied to DKI functional maps can be used for differential diagnosis of cervical cancer subtypes and grading SCC. KEY POINTS: • The whole-tumor texture analysis applied to DKI functional maps allows accurate differential diagnosis of CC subtype and grade. • The combined model derived from multiple functional maps performs significantly better than the single models when differentiating tumor subtypes. • MK_90th percentile was useful for distinguishing poorly and well-/moderately differentiated SCC tumors with an AUC of 0.701.

Feasibility of intravoxel incoherent motion diffusion-weighted imaging in distinguishing adenocarcinoma originated from uterine corpus or cervix.

PURPOSE: To prospectively assess the incremental value of intravoxel incoherent motion (IVIM) DWI in determining whether the adenocarcinoma originated from the uterine corpus or cervix. METHODS: Eighty consecutive uterine adenocarcinomas from the cervix or endometrium confirmed by histopathology underwent IVIM DWI acquisition on a 3.0T MR scanner before treatment. Five morphologic features were analyzed using Fisher exact test; IVIM DWI-derived parameters, including apparent diffusion coefficient (ADC), true coefficient diffusivity (D), perfusion-related diffusivity (D*), and perfusion fraction (f) were compared using two-sample independent t-test or Mann-Whitney U test. Logistic regression analysis was used to develop different diagnosis model. The ROCs of these variables and diagnostic models were compared to evaluate the diagnostic efficiency. RESULTS: Among single morphologic features, tumor location yielded the highest AUC of 0.891 in distinguishing endometrial adenocarcinoma (EAC) from cervical adenocarcinoma (CAC). Among single IVIM DWI-derived parameters, f values showed the best diagnostic performance (AUC: 0.837) at the optimal cut-off value of 0.261. Additionally, the combined diagnostic model, which consisted of tumor location, ADC and f showed the largest AUC of 0.967 with the highest sensitivity of 88.14%, highest specificity of 100.00%, and highest accuracy of 91.25%. CONCLUSION: IVIM DWI-derived parameters add additional diagnostic value to conventional morphologic features. A combined diagnosis model is a promising imaging tool for predicting the origin of uterine adenocarcinoma, further contributing to therapeutic decision-making.

Investigating the value of arterial spin labeling and intravoxel incoherent motion imaging on diagnosing nasopharyngeal carcinoma in T1 stage.

BACKGROUND: To investigate the diagnostic value of arterial spin labeling (ASL) and intravoxel incoherent motion (IVIM) imaging in distinguishing nasopharyngeal carcinoma (NPC) in T1 stage from healthy controls (HC). METHODS: Forty-five newly diagnosed NPC patients in the T1 stage and thirty-one healthy volunteers who underwent MR examinations for both 3D pseudo-continuous ASL (pCASL) and IVIM were enrolled in this study. The Mann-Whitney test was used to compare the mean values of blood flow (BF) derived from pCASL and IVIM derived parameters, including apparent diffusion coefficient (ADC), pure molecular diffusion (D), pseudo-diffusion coefficient (D*) and perfusion fraction (f) between NPC tumor and benign nasopharyngeal mucosa of HC. Receiver Operating Characteristic (ROC) was performed to determine diagnostic cutoff and efficiency. The correlation coefficients among parameters were investigated using Spearman's test. RESULTS: The NPC in the T1 stage showed higher mean BF, lower ADC, D, and f compared to benign nasopharyngeal mucosa (P < 0.001) with the area under curve of ROC of 0.742-0.996 (highest by BF). BF cutoff was set at > 36 mL/100 g/min; the corresponding sensitivity, specificity, and accuracy in differentiating NPC stage T1 from benign nasopharyngeal mucosa were 95.56% (43/45), 100% (31/31) and 97.37% (74/76), respectively. BF demonstrated moderate negative correlation with D* on HC (ρ [Spearman correlation coefficients] = - 0.426, P = 0.017). CONCLUSIONS: ASL and IVIM could reflect the difference in perfusion and diffusion between tumor and benign nasopharyngeal mucosa, indicating a potential for accessing early diagnosis of NPC. Notably, BF, with a specificity of 100%, demonstrated better performance compared to IVIM in distinguishing malignant lesions from healthy tissue.

Differentiation of endometrial adenocarcinoma from adenocarcinoma of cervix using kinetic parameters derived from DCE-MRI.

PURPOSE: This prospective study aimed to investigate the value of kinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating uterine endometrioid adenocarcinoma (EAC) from adenocarcinoma of cervix (AdC). METHODS: Seventy-five newly diagnosed patients with distinctive pathology underwent DCE-MRI. Observers independently calculated the tumor diameters and DCE-MRI parameters using both population and individual-based arterial input function (AIF). Inter-observer consistency was evaluated, and a comparative analysis between EAC (n = 47) and AdC (n = 28) was performed. Regression analysis was used to select parameters that best distinguished EAC from AdC, and to generate predictive models. Receiver operating characteristic curve (ROC) was applied to calculate the diagnostic efficiency of single parameter and the predictive models. RESULTS: Inter-observer consistency was excellent (intra-class correlation [ICC] = 0.902-0.981), especially when calculated via population AIF with relatively higher ICC and smaller SD on Bland-Altman plot. Tumor diameters were not correlated with tumor types. All the DCE-MRI parameters were lower in EAC compared to AdC, except Kep by population AIF and TTP by both sets of AIFs. The statistical parameters were Ve, Maxslop, and Maxconc by population AIF, and Maxslop and Ktrans by individual AIF included in the predictive models, respectively. The two predictive models with combined parameters showed improved diagnostic efficiency in differentiating these two diseases compared with a single parameter. CONCLUSION: DCE-MRI can quantitatively evaluate the perfusion difference between EAC and AdC, thus improving the identification of uterine adenocarcinoma with uncertain biopsy pathology.