Radiomic signatures reveal multiscale intratumor heterogeneity associated with tissue tolerance and survival in re-irradiated nasopharyngeal carcinoma: a multicenter study.

BACKGROUND: Post-radiation nasopharyngeal necrosis (PRNN) is a severe adverse event following re-radiotherapy for patients with locally recurrent nasopharyngeal carcinoma (LRNPC) and associated with decreased survival. Biological heterogeneity in recurrent tumors contributes to the different risks of PRNN. Radiomics can be used to mine high-throughput non-invasive image features to predict clinical outcomes and capture underlying biological functions. We aimed to develop a radiogenomic signature for the pre-treatment prediction of PRNN to guide re-radiotherapy in patients with LRNPC. METHODS: This multicenter study included 761 re-irradiated patients with LRNPC at four centers in NPC endemic area and divided them into training, internal validation, and external validation cohorts. We built a machine learning (random forest) radiomic signature based on the pre-treatment multiparametric magnetic resonance images for predicting PRNN following re-radiotherapy. We comprehensively assessed the performance of the radiomic signature. Transcriptomic sequencing and gene set enrichment analyses were conducted to identify the associated biological processes. RESULTS: The radiomic signature showed discrimination of 1-year PRNN in the training, internal validation, and external validation cohorts (area under the curve (AUC) 0.713-0.756). Stratified by a cutoff score of 0.735, patients with high-risk signature had higher incidences of PRNN than patients with low-risk signature (1-year PRNN rates 42.2-62.5% vs. 16.3-18.8%, P < 0.001). The signature significantly outperformed the clinical model (P < 0.05) and was generalizable across different centers, imaging parameters, and patient subgroups. The radiomic signature had prognostic value concerning its correlation with PRNN-related deaths (hazard ratio (HR) 3.07-6.75, P < 0.001) and all causes of deaths (HR 1.53-2.30, P < 0.01). Radiogenomics analyses revealed associations between the radiomic signature and signaling pathways involved in tissue fibrosis and vascularity. CONCLUSIONS: We present a radiomic signature for the individualized risk assessment of PRNN following re-radiotherapy, which may serve as a noninvasive radio-biomarker of radiation injury-associated processes and a useful clinical tool to personalize treatment recommendations for patients with LANPC.

Benefit of [18F] FDG PET/CT in the diagnosis and salvage treatment of recurrent nasopharyngeal carcinoma.

PURPOSE: To compare PET/CT, MRI and ultrasonography in detecting recurrence of nasopharyngeal carcinoma and identify their benefit in staging, contouring and overall survival (OS). METHODS: Cohort A included 1453 patients with or without histopathology-confirmed local recurrence, while cohort B consisted of 316 patients with 606 histopathology-confirmed lymph nodes to compare the sensitivities and specificities of PET/CT, MRI and ultrasonography using McNemar test. Cohorts C and D consisted of 273 patients from cohort A and 267 patients from cohort B, respectively, to compare the distribution of PET/CT-based and MRI-based rT-stage and rN-stage and the accuracy of rN-stage using McNemar test. Cohort E included 30 random patients from cohort A to evaluate the changes in contouring with or without PET/CT by related-samples T test or Wilcoxon rank test. The OS of 61 rT3-4N0M0 patients staged by PET/CT plus MRI (cohort F) and 67 MRI-staged rT3-4N0M0 patients (cohort G) who underwent similar salvage treatment were compared by log-rank test and Cox regression. RESULTS: PET/CT had similar specificity to MRI but higher sensitivity (93.9% vs. 79.3%, P < 0.001) in detecting local recurrence. PET/CT, MRI and ultrasonography had comparable specificities, but PET/CT had greater sensitivity than MRI (90.9% vs. 67.6%, P < 0.001) and similar sensitivity to ultrasonography in diagnosing lymph nodes. According to PET/CT, more patients were staged rT3-4 (82.8% vs. 68.1%, P < 0.001) or rN + (89.9% vs. 69.3%, P < 0.001), and the rN-stage was more accurate (90.6% vs. 73.8%, P < 0.001). Accordingly, the contours of local recurrence were more precise (median Dice similarity coefficient 0.41 vs. 0.62, P < 0.001) when aided by PET/CT plus MRI. Patients staged by PET/CT plus MRI had a higher 3-year OS than patients staged by MRI alone (85.5% vs. 60.4%, P = 0.006; adjusted HR = 0.34, P = 0.005). CONCLUSION: PET/CT more accurately detected and staged recurrence of nasopharyngeal carcinoma and accordingly complemented MRI, providing benefit in contouring and OS.

Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence-Free Survival in Hepatocellular Carcinoma.

BACKGROUND: The metastatic vascular patterns of hepatocellular carcinoma (HCC) are mainly microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC). However, most existing VETC-related radiological studies still focus on the prediction of VETC status. PURPOSE: This study aimed to build and compare VETC-MVI related models (clinical, radiomics, and deep learning) associated with recurrence-free survival of HCC patients. STUDY TYPE: Retrospective. POPULATION: 398 HCC patients (349 male, 49 female; median age 51.7 years, and age range: 22-80 years) who underwent resection from five hospitals in China. The patients were randomly divided into training cohort (n = 358) and test cohort (n = 40). FIELD STRENGTH/SEQUENCE: 3-T, pre-contrast T1-weighted imaging spoiled gradient recalled echo (T1WI SPGR), T2-weighted imaging fast spin echo (T2WI FSE), and contrast enhanced arterial phase (AP), delay phase (DP). ASSESSMENT: Two radiologists performed the segmentation of HCC on T1WI, T2WI, AP, and DP images, from which radiomic features were extracted. The RFS related clinical characteristics (VETC, MVI, Barcelona stage, tumor maximum diameter, and alpha fetoprotein) and radiomic features were used to build the clinical model, clinical-radiomic (CR) nomogram, deep learning model. The follow-up process was done 1 month after resection, and every 3 months subsequently. The RFS was defined as the date of resection to the date of recurrence confirmed by radiology or the last follow-up. Patients were followed up until December 31, 2022. STATISTICAL TESTS: Univariate COX regression, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier curves, log-rank test, C-index, and area under the curve (AUC). P < 0.05 was considered statistically significant. RESULTS: The C-index of deep learning model achieved 0.830 in test cohort compared with CR nomogram (0.731), radiomic signature (0.707), and clinical model (0.702). The average RFS of the overall patients was 26.77 months (range 1-80 months). DATA CONCLUSION: MR deep learning model based on VETC and MVI provides a potential tool for survival assessment. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Does dual-layer spectral detector CT provide added value in predicting spread through air spaces in lung adenocarcinoma? A preliminary study.

OBJECTIVES: To examine the predictive value of dual-layer spectral detector CT (DLCT) for spread through air spaces (STAS) in clinical lung adenocarcinoma. METHODS: A total of 225 lung adenocarcinoma cases were retrospectively reviewed for demographic, clinical, pathological, traditional CT, and spectral parameters. Multivariable logistic regression analysis was carried out based on three logistic models, including a model using traditional CT features (traditional model), a model using spectral parameters (spectral model), and an integrated model combining traditional CT and spectral parameters (integrated model). Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were performed to assess these models. RESULTS: Univariable analysis showed significant differences between the STAS and non-STAS groups in traditional CT features, including nodule density (p < 0.001), pleural indentation types (p = 0.006), air-bronchogram sign (p = 0.031), the presence of spiculation (p < 0.001), long-axis diameter of the entire nodule (LD) (p < 0.001), and consolidation/tumor ratio (CTR) (p < 0.001). Multivariable analysis revealed that LD > 20 mm (odds ratio [OR] = 2.271, p = 0.025) and CTR (OR = 24.208, p < 0.001) were independent predictors in the traditional model, while electronic density (ED) in the venous phase was an independent predictor in the spectral (OR = 1.062, p < 0.001) and integrated (OR = 1.055, p < 0.001) models. The area under the curve (AUC) for the integrated model (0.84) was the highest (spectral model, 0.83; traditional model, 0.80), and the difference between the integrated and traditional models was statistically significant (p = 0.015). DCA showed that the integrated model had superior clinical value versus the traditional model. CONCLUSIONS: DLCT has added value for STAS prediction in lung adenocarcinoma. CLINICAL RELEVANCE STATEMENT: Spectral CT has added value for spread through air spaces prediction in lung adenocarcinoma so may impact treatment planning in the future. KEY POINTS: • Electronic density may be a potential spectral index for predicting spread through air spaces in lung adenocarcinoma. • A combination of spectral and traditional CT features enhances the performance of traditional CT for predicting spread through air spaces.

Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma.

OBJECTIVES: To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of lung adenocarcinoma (LUAD) and compared with manual measurements. METHODS: A total of 542 patients with clinical stage 0-I peripheral LUAD and with preoperative CT data of 1-mm slice thickness were included. Maximal solid size on axial image (MSSA) was evaluated by two chest radiologists. MSSA, volume of solid component (SV), and mass of solid component (SM) were evaluated by DL. Consolidation-to-tumor ratios (CTRs) were calculated. For ground glass nodules (GGNs), solid parts were extracted with different density level thresholds. The prognosis prediction efficacy of DL was compared with that of manual measurements. Multivariate Cox proportional hazards model was used to find independent risk factors. RESULTS: The prognosis prediction efficacy of T-staging (TS) measured by radiologists was inferior to that of DL. For GGNs, MSSA-based CTR measured by radiologists (RMSSA%) could not stratify RFS and OS risk, whereas measured by DL using 0HU (2D-AIMSSA0HU%) could by using different cutoffs. SM and SV measured by DL using 0 HU (AISM0HU% and AISV0HU%) could effectively stratify the survival risk regardless of different cutoffs and were superior to 2D-AIMSSA0HU%. AISM0HU% and AISV0HU% were independent risk factors. CONCLUSION: DL algorithm can replace human for more accurate T-staging of LUAD. For GGNs, 2D-AIMSSA0HU% could predict prognosis rather than RMSSA%. The prediction efficacy of AISM0HU% and AISV0HU% was more accurate than of 2D-AIMSSA0HU% and both were independent risk factors. CLINICAL RELEVANCE STATEMENT: Deep learning algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma. KEY POINTS: • Deep learning (DL) algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma (LUAD). • For GGNs, maximal solid size on axial image (MSSA)-based consolidation-to-tumor ratio (CTR) measured by DL using 0 HU could stratify survival risk than that measured by radiologists. • The prediction efficacy of mass- and volume-based CTRs measured by DL using 0 HU was more accurate than of MSSA-based CTR and both were independent risk factors.

Dynamic alteration in myocardium creatine during acute infarction using MR CEST imaging.

Creatine (Cr) is an essential metabolite in the creatine kinase reaction, which plays a critical role in maintaining normal cardiac function. Chemical exchange saturation transfer (CEST) MRI offers a novel way to map myocardium Cr. This study aims to investigate the dynamic alteration in myocardium Cr during acute infarction using CEST MRI, which may facilitate understanding of the heart remodeling mechanism at the molecular level. Seven adult Bama pigs underwent cardiac cine, Cr CEST, and late gadolinium-enhanced (LGE) T1 -weighted (T1 w) imaging three and 14 days after myocardial infarction induction on a 3 T scanner. Cardiac structural and functional indices, including myocardium mass (MM), end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), and ejection fraction (EF), were measured from cines. Infarct angle was determined from LGE T1 w images, based on which myocardium was classified into infarct, adjacent, and remote regions. Cr-weighted CEST signal was quantified from a three-pool Lorentzian fitting model and measured within each region and the entire myocardium. Student's t-test was conducted to evaluate any significant differences in measurements between the two time points. Correlation was assessed with Pearson correlation. P values less than 0.05 were considered statistically significant. Over the studied period, MM, EDV, and ESV did not alter significantly (P > 0.05), whereas significant increases of SV and EF and decrease of infarct angle were observed (P < 0.05). Meanwhile, the Cr-weighted CEST signal elevated significantly on Day 14 compared with Day 3 in the infarct (10.00 ± 1.28% versus 6.91 ± 1.54%, P < 0.01), adjacent (11.17 ± 2.00% versus 8.01 ± 1.58%, P = 0.01), and entire myocardium (11.03 ± 1.36% versus 8.19 ± 1.28%, P < 0.01). Moderate negative correlations were shown between the infarct angle and Cr-weighted CEST signals in the infarct (r = -0.80, P < 0.001), adjacent (r = -0.58, P = 0.03), and entire myocardium (r = -0.76, P < 0.01). In conclusion, the dynamic increase of myocardium Cr during acute infarction may interact with cardiac structural and functional recovery. The study provides supplementary insights into the heart remodeling process from the metabolic viewpoint.

Computed tomography radiomics to predict EBER positivity in Epstein-Barr virus-associated gastric adenocarcinomas: a retrospective study.

BACKGROUND: The relevance of Epstein-Barr virus (EBV) in gastric carcinoma has been represented by the existence of EBV-encoded small RNA (EBER) in the tumor cells and has prognostic significance in gastric cancer, while gastric adenocarcinoma represents the most frequently occurring gastric malignancy. PURPOSE: To observe the capacity of radiomic features extracted from contrast-enhanced computed tomography (CE-CT) images to differentiate EBER-positive gastric adenocarcinoma from EBER-negative ones. MATERIAL AND METHODS: A total of 54 patients with gastric adenocarcinoma (EBER-positive: 27, EBER-negative: 27) were retrospectively examined. Radiomic imaging features were extracted from all regions of interest (ROI) delineated by two experienced radiologists on late arterial phase CT images. We distinguished related radiomic features through the two-tailed t test and applied them to construct a decision tree model to evaluate whether EBER in situ hybridization positive had appeared. RESULTS: Nine radiomics features were significantly related to EBER in situ hybridization status (P < 0.05), four of which were used to build the decision tree through backward elimination: Correlation_ AllDirection_offset7, Correlation_ angle135_offset7, RunLengthNonuniformity_ AllDirection_offset1_SD, and HighGreyLevelRunEmphasis_ AllDiretion_offset1_SD. The decision tree model consisted of seven decision nodes and six terminal nodes, three of which demonstrated positive EBER in situ hybridization. The specificity, sensitivity, and accuracy of the model were 84%, 80%, and 81.7%, respectively. The area under the curve of the decision tree model was 0.87. CONCLUSION: Radiomics based on CE-CT could be applied to predict EBER in situ hybridization status preoperatively in patients with gastric adenocarcinoma.

Comparison of new magnetic resonance imaging grading system with conventional endoscopy for the early detection of nasopharyngeal carcinoma.

BACKGROUND: Although stratifying individuals with respect to nasopharyngeal carcinoma (NPC) risk with Epstein-Barr virus-based markers is possible, the performance of diagnostic methods for detecting lesions among screen-positive individuals is poorly understood. METHODS: The authors prospectively evaluated 882 participants aged 30 to 70 years who were enrolled between October 2014 and November 2018 in an ongoing, population-based NPC screening program and had an elevated NPC risk. Participants were offered endoscopy and magnetic resonance imaging (MRI), and lesions were identified either by biopsy at a follow-up endoscopy or further contact and linkage to the local cancer registry through December 31, 2019. The diagnostic performance characteristics of endoscopy and MRI for NPC detection were investigated. RESULTS: Eighteen of 28 identified NPC cases were detected by both methods, 1 was detected by endoscopy alone, and 9 were detected by MRI alone. MRI had significantly higher sensitivity than endoscopy for NPC detection overall (96.4% vs 67.9%; Pdifference = .021) and for early-stage NPC (95.2% vs 57.1%; P = .021). The sensitivity of endoscopy was suggestively lower among participants who had previously been screened in comparison with those undergoing an initial screening (50.0% vs 81.2%; P = .11). The authors observed a higher overall referral rate by MRI versus endoscopy (17.3% vs 9.1%; P < .001). Cases missed by endoscopy had early-stage disease and were more commonly observed for tumors originating from the pharyngeal recess. CONCLUSIONS: MRI was more sensitive than endoscopy for NPC detection in the context of population screening but required the referral of a higher proportion of screen-positive individuals. The sensitivity of endoscopy was particularly low for individuals who had previously been screened.

Percent change in apparent diffusion coefficient and plasma EBV DNA after induction chemotherapy identifies distinct prognostic response phenotypes in advanced nasopharyngeal carcinoma.

BACKGROUND: To evaluate the prognostic value of the apparent diffusion coefficient (ADC) derived from diffusion-weighted magnetic resonance imaging (MRI) and monitor the early treatment response to induction chemotherapy (IC) with plasma EBV DNA in locoregionally advanced nasopharyngeal carcinoma (LA-NPC). RESULTS: A total of 307 stage III-IVb NPC patients were prospectively enrolled. All patients underwent MRI examinations to calculate ADC and plasma EBV DNA measurements pretreatment and post-IC. The participants' ADC value of 92.5% (284/307) increased post-IC. A higher percent change in ADC value (ΔADC%high group) post-IC was associated with a higher 5-year OS rate (90.7% vs 74.9%, p < 0.001) than those in the ΔADC%low group. Interestingly, ΔADC% was closely related to the response measured by RECIST 1.1 (p < 0.001) and plasma EBV DNA level (p = 0.037). The AUC significantly increased when post-IC plasma EBV DNA was added to ΔADC% to predict treatment failure. Thus, based on ΔADC% and plasma EBV DNA, we further divided the participants into three new prognostic response phenotypes (early response, intermediate response, and no response) that correlated with disparate risks of death (p = 0.001), disease progression (p < 0.001), distant metastasis (p < 0.001), and locoregional relapse (p < 0.001). CONCLUSION: The percentage change in ADC post-IC is indicative of treatment response and clinical outcome. ΔADC% and plasma EBV DNA-based response phenotypes may provide potential utility for early termination of treatment and allow guiding risk-adapted therapeutic strategies for LA-NPC.

Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma.

Background Nasopharyngeal carcinoma (NPC) may be cured with radiation therapy. Tumor proximity to critical structures demands accuracy in tumor delineation to avoid toxicities from radiation therapy; however, tumor target contouring for head and neck radiation therapy is labor intensive and highly variable among radiation oncologists. Purpose To construct and validate an artificial intelligence (AI) contouring tool to automate primary gross tumor volume (GTV) contouring in patients with NPC. Materials and Methods In this retrospective study, MRI data sets covering the nasopharynx from 1021 patients (median age, 47 years; 751 male, 270 female) with NPC between September 2016 and September 2017 were collected and divided into training, validation, and testing cohorts of 715, 103, and 203 patients, respectively. GTV contours were delineated for 1021 patients and were defined by consensus of two experts. A three-dimensional convolutional neural network was applied to 818 training and validation MRI data sets to construct the AI tool, which was tested in 203 independent MRI data sets. Next, the AI tool was compared against eight qualified radiation oncologists in a multicenter evaluation by using a random sample of 20 test MRI examinations. The Wilcoxon matched-pairs signed rank test was used to compare the difference of Dice similarity coefficient (DSC) of pre- versus post-AI assistance. Results The AI-generated contours demonstrated a high level of accuracy when compared with ground truth contours at testing in 203 patients (DSC, 0.79; 2.0-mm difference in average surface distance). In multicenter evaluation, AI assistance improved contouring accuracy (five of eight oncologists had a higher median DSC after AI assistance; average median DSC, 0.74 vs 0.78; P < .001), reduced intra- and interobserver variation (by 36.4% and 54.5%, respectively), and reduced contouring time (by 39.4%). Conclusion The AI contouring tool improved primary gross tumor contouring accuracy of nasopharyngeal carcinoma, which could have a positive impact on tumor control and patient survival. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Chang in this issue.

The primary lesion apparent diffusion coefficient is a prognostic factor for locoregionally advanced nasopharyngeal carcinoma: a retrospective study.

BACKGROUND: To explore prognostic value of the pre-treatment primary lesion apparent diffusion coefficient (ADC) in locoregionally advanced nasopharyngeal carcinoma (LA-NPC). METHODS: A total of 843 patients with newly diagnosed LA-NPC were enrolled from January 2011 to April 2014 and divided into two groups based on ADC values: the low-ADC group and high-ADC group. The 3-year local relapse-free survival (LRFS), distant metastasis free survival (DMFS), disease-free survival (DFS) and overall survival (OS) rates between two groups were compared using Kaplan-Meier curve, and Cox regression analyses were performed to test prognostic value of the pretreatment ADC in LA-NPC. RESULTS: The cut-off value of the pretreatment ADC for predicting local relapse was 784.5 × 10- 6 mm2/s (AUC [area under curve] = 0.604; sensitivity = 0.640; specificity = 0.574), thus patients were divided into low-ADC (< 784.5 × 10- 6; n = 473) group and high-ADC (≥784.5 × 10- 6; n = 370) group. The low-ADC group had significantly higher 3-year LRFS rate and DFS rate than the high-ADC group (LRFS: 96.2% vs. 91.4%, P = 0.003; DFS: 81.4% vs. 73.0%, P = 0.0056). Multivariate analysis showed that the pretreatment ADC is an independent prognostic factor for LRFS (HR, 2.04; 95% CI, 1.13-3.66; P = 0.017) and DFS (HR, 1.41; 95% CI, 1.04-1.89; P = 0.024). CONCLUSIONS: The pretreatment ADC of the primary lesion is an independent prognostic factor for LRFS and DFS in LA-NPC patients.

Advanced-Stage Nasopharyngeal Carcinoma: Restaging System after Neoadjuvant Chemotherapy on the Basis of MR Imaging Determines Survival.

Purpose To evaluate the prognostic value of the restaging system after neoadjuvant chemotherapy (NACT) in patients with advanced-stage nasopharyngeal carcinoma (NPC). Materials and Methods This study was approved by the clinical research committee and a written informed consent was required before enrolling in the study. Prospectively enrolled were 412 consecutive patients with stage III-IVb NPC treated with NACT followed by concurrent chemotherapy and radiation therapy. Patients were staged before NACT and restaged after NACT. The progression-free survival (PFS) and distant metastasis-free survival (DMFS) were calculated with the Kaplan-Meier method, and differences were compared by using the log-rank test. Results Post-NACT T classification (PFS, P = .001) and N classification (PFS, P < .001; DMFS, P = .001) resulted in better survival curve separations than pre-NACT T classification and N classification. Patients downstaged from N2-N3 to N0-N1 disease had a better prognosis than did patients who continued to have N2-N3 diseases (3-year PFS, 83.8% vs 66.6%, P = .001; 3-year DMFS, 88.0% vs 78.4%, P = .026). Multivariate analysis revealed that post-NACT T classification (hazard ratio [HR] = 1.67; 95% confidence interval [CI]: 1.18, 2.36; P = .003) and post-NACT N classification (HR = 1.54; 95% CI: 1.17, 2.03; P = .002) were independent prognostic factors for PFS; also, post-NACT N classification (HR = 1.48; 95% CI: 1.05, 2.07; P = .025) was an independent prognostic factor for DMFS. Multivariate analysis in patients with N2-N3 disease demonstrated that the N downstaging effects of NACT was the only independent prognostic factor for PFS (HR = 0.48; 95% CI: 0.29, 0.81; P = .006) and DMFS (HR = 0.52; 95% CI: 0.28, 0.97; P = .039). Conclusion The post-NACT stage is more representative of prognosis than the pre-NACT stage in advanced-stage NPC patients, which suggests that major clinical decisions should be based on the post-NACT stage. © RSNA, 2016 Online supplemental material is available for this article.

Nasopharyngeal carcinoma patients with retropharyngeal lymph node metastases: a minimum axial diameter of 6 mm is a more accurate prognostic predictor than 5 mm.

OBJECTIVE: The criteria for the diagnosis of metastatic retropharyngeal lymph nodes (RLNs) have not yet been resolved and are not included in the current edition of the American Joint Committee on Cancer (AJCC) staging system (seventh edition) for the staging of nasopharyngeal carcinoma (NPC). The aim of this study was to use MRI to identify an RLN size criterion that can accurately predict prognosis in patients with NPC. MATERIALS AND METHODS: Eight hundred seventeen patients with newly diagnosed localized NPC were identified. All of the patients underwent MRI before treatment with definitive radiation therapy. All the MRI studies and medical records were reviewed retrospectively. Overall survival (OS), distant metastasis-free survival (DMFS), and local relapse-free survival (LRFS) were assessed using SPSS software (version 17.0). RESULTS: RLN size cutoffs of ≥ 5 mm and of ≥ 6 mm were used. There was no significant difference in OS (p = 0.216), DMFS (p = 0.081), or LRFS (p = 0.067) in patients with RLNs ≥ 5 mm and in those with RLNs < 5 mm. When 6 mm was used as a size cutoff, significant differences in OS (p = 0.000) and DMFS (p = 0.001) were identified; there was no significant difference observed for LRFS (p = 0.380). CONCLUSION: A minimum axial RLN diameter of 6 mm was a more accurate prognostic predictor in NPC patients with RLN metastases than 5 mm.

Nasopharyngeal carcinoma with paranasal sinus invasion: the prognostic significance and the evidence-based study basis of its T-staging category according to the AJCC staging system.

BACKGROUND: To evaluate the prognostic significance of paranasal sinus invasion for patients with NPC and to provide empirical proofs for the T-staging category of paranasal sinus invasion according to the AJCC staging system for nasopharyngeal carcinoma. METHODS: The clinical records and imaging studies of 770 consecutive patients with newly diagnosed, untreated, and nondisseminated NPC were reviewed retrospectively. The overall survival, distant metastasis-free survival, and local relapse-free survival of these patients were analyzed using the Kaplan-Meier method, and the differences were compared using the log-rank test. RESULTS: The incidence of paranasal sinus invasion was 23.6%, with the rate of incidence of sphenoid sinus invasion being the highest. By multivariate analysis, paranasal sinus invasion was shown to be an independent prognostic factor for overall survival, distant metastasis-free survival, and local relapse-free survival (p < 0.05 for all). No significant differences in overall survival, distant metastasis-free survival, and local relapse-free survival were observed between patients with sphenoid sinus invasion alone and those with maxillary sinus and ethmoid sinus invasion (p = 0.87, p = 0.80, and p = 0.37, respectively). The overall survival, distant metastasis-free survival, and local relapse-free survival for patients with stage T3 disease with paranasal sinus invasion were similar to the survival rates for patients with stage T3 disease without paranasal sinus invasion (p = 0.22, p = 0.15, and p = 0.93, respectively). However, the rates of overall survival and local relapse-free survival were better for patients with stage T3 disease with paranasal sinus invasion than for patients with stage T4 disease (p < 0.01, and p = 0.03, respectively). CONCLUSIONS: Paranasal sinus invasion is an independent negative prognostic factor for NPC, regardless of which sinus is involved. Our results confirm that it is scientific and reasonable for the AJCC staging system for nasopharyngeal carcinoma to define paranasal sinus invasion as stage T3 disease.

Simple measurements on diffusion-weighted MR imaging for assessment of complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

PURPOSE: To determine diagnostic performance of simple measurements on diffusion-weighted MR imaging (DWI) for assessment of complete tumour response (CR) after neoadjuvant chemoradiotherapy (CRT) in patients with locally advanced rectal cancer (LARC) by signal intensity (SI) and apparent diffusion coefficient (ADC) measurements. MATERIALS AND METHODS: Sixty-five patients with LARC who underwent neoadjuvant CRT and subsequent surgery were included. Patients underwent pre-CRT and post-CRT 3.0 T MRI. Regions of interest of the highest brightness SI were included in the tumour volume on post-CRT DWI to calculate the SIlesion, rSI, ADClesion and rADC; diagnostic performance was compared by using the receiver operating characteristic (ROC) curves. In order to validate the accuracy and reproducibility of the current strategy, the same procedure was reproduced in 80 patients with LARC at 1.5 T MRI. RESULTS: Areas under the ROC curve for identification of a CR, based on SIlesion, rSI, ADClesion, and rADC, respectively, were 0.86, 0.94, 0.66, and 0.71 at 3.0 T MRI, and 0.92, 0.91, 0.64, and 0.61 at 1.5 T MRI. CONCLUSION: Post-CRT DWI SIlesion and rSI provided high diagnostic performance in assessing CR and were significantly more accurate than ADClesion, and rADC at 3.0 T MRI and 1.5 T MRI. KEY POINTS: • Signal intensity (SI lesion ) and rSI are accurate for assessment of complete response. • rSI seems to be superior to SI lesion at 3.0 T MRI. • ADC or rADC measurements are not accurate for assessment of complete response.

A modified method for locating parapharyngeal space neoplasms on magnetic resonance images: implications for differential diagnosis.

The parapharyngeal space (PPS) is an inverted pyramid-shaped deep space in the head and neck region, and a variety of tumors, such as salivary gland tumors, neurogenic tumors, nasopharyngeal carcinomas with parapharyngeal invasion, and lymphomas, can be found in this space. The differential diagnosis of PPS tumors remains challenging for radiologists. This study aimed to develop and test a modified method for locating PPS tumors on magnetic resonance (MR) images to improve preoperative differential diagnosis. The new protocol divided the PPS into three compartments: a prestyloid compartment, the carotid sheath, and the areas outside the carotid sheath. PPS tumors were located in these compartments according to the displacements of the tensor veli palatini muscle and the styloid process, with or without blood vessel separations and medial pterygoid invasion. This protocol, as well as a more conventional protocol that is based on displacements of the internal carotid artery (ICA), was used to assess MR images captured from a series of 58 PPS tumors. The consequent distributions of PPS tumor locations determined by both methods were compared. Of all 58 tumors, our new method determined that 57 could be assigned to precise PPS compartments. Nearly all (13/14; 93%) tumors that were located in the pre-styloid compartment were salivary gland tumors. All 15 tumors within the carotid sheath were neurogenic tumors. The vast majority (18/20; 90%) of trans-spatial lesions were malignancies. However, according to the ICA-based method, 28 tumors were located in the pre-styloid compartment, and 24 were located in the post-styloid compartment, leaving 6 tumors that were difficult to locate. Lesions located in both the pre-styloid and the post-styloid compartments comprised various types of tumors. Compared with the conventional ICA-based method, our new method can help radiologists to narrow the differential diagnosis of PPS tumors to specific compartments.

Machine learning-based identification of contrast-enhancement phase of computed tomography scans.

Contrast-enhanced computed tomography scans (CECT) are routinely used in the evaluation of different clinical scenarios, including the detection and characterization of hepatocellular carcinoma (HCC). Quantitative medical image analysis has been an exponentially growing scientific field. A number of studies reported on the effects of variations in the contrast enhancement phase on the reproducibility of quantitative imaging features extracted from CT scans. The identification and labeling of phase enhancement is a time-consuming task, with a current need for an accurate automated labeling algorithm to identify the enhancement phase of CT scans. In this study, we investigated the ability of machine learning algorithms to label the phases in a dataset of 59 HCC patients scanned with a dynamic contrast-enhanced CT protocol. The ground truth labels were provided by expert radiologists. Regions of interest were defined within the aorta, the portal vein, and the liver. Mean density values were extracted from those regions of interest and used for machine learning modeling. Models were evaluated using accuracy, the area under the curve (AUC), and Matthew's correlation coefficient (MCC). We tested the algorithms on an external dataset (76 patients). Our results indicate that several supervised learning algorithms (logistic regression, random forest, etc.) performed similarly, and our developed algorithms can accurately classify the phase of contrast enhancement.

Radiomic signatures associated with tumor immune heterogeneity predict survival in locally recurrent nasopharyngeal carcinoma.

BACKGROUND: The prognostic value of traditional clinical indicators for locally recurrent nasopharyngeal carcinoma (lrNPC) is limited due to their inability to reflect intratumor heterogeneity. We aimed to develop a radiomic signature to reveal tumor immune heterogeneity and predict survival in lrNPC. METHODS: This multicenter, retrospective study included 921 patients with lrNPC. A machine learning signature and nomogram based on pretreatment MRI features were developed for predicting overall survival (OS) in a training cohort and validated in two independent cohorts. A clinical nomogram and an integrated nomogram were constructed for comparison. Nomogram performance was evaluated by concordance index (C-index) and receiver operating characteristic curve analysis. Accordingly, patients were classified into risk groups. The biological characteristics and immune infiltration of the signature were explored by RNA sequencing (RNA-seq) analysis. RESULTS: The machine learning signature and nomogram demonstrated comparable prognostic ability to a clinical nomogram, achieving C-indexes of 0.729, 0.718, and 0.731 in the training, internal, and external validation cohorts, respectively. Integration of the signature and clinical variables significantly improved the predictive performance. The proposed signature effectively distinguished patients between risk groups with significantly distinct OS rates. Subgroup analysis indicated the recommendation of local salvage treatments for low-risk patients. Exploratory RNA-seq analysis revealed differences in interferon response and lymphocyte infiltration between risk groups. CONCLUSIONS: An MRI-based radiomic signature predicted OS more accurately. The proposed signature associated with tumor immune heterogeneity may serve as a valuable tool to facilitate prognostic stratification and guide individualized management for lrNPC patients.

Abnormal regional homogeneity of resting-state brain activity in patients with HBV-related cirrhosis without overt hepatic encephalopathy.

BACKGROUND: Many studies have reported that cognitive deficits exist in cirrhotic patients without overt hepatic encephalopathy (OHE). However, the neurobiological mechanisms underlying these deficits are still not fully understood. AIM: To investigate regional activity abnormalities in patients with hepatitis B virus-related cirrhosis (HBV-RC) without OHE using resting-state functional MRI (Rs-fMRI), and to examine the relationship between regional activity abnormalities and impaired cognition. METHODS: A newly reported regional homogeneity (ReHo) approach was used to compare the local synchronization of Rs-fMRI signals in 32 patients with HBV-RC without OHE and 32 well-matched healthy controls. Cognition was measured in all patients using psychometric hepatic encephalopathy score (PHES) tests, and the relationship between ReHo variation and PHES was analysed. RESULTS: Relative to healthy controls, the cirrhosis group showed high ReHo in the prefrontal cortex, and widespread low ReHo in visual association areas (left lingual gyrus, middle temporal gyrus and right middle occipital gyrus), motor association areas (bilateral precentral gyrus and paracentral lobule) and the bilateral precuneus. Correlation analysis of the mean ReHo values in different brain areas and PHES in cirrhotic patients revealed a significantly positive correlation in the left lingual gyrus (r = 0.352; P = 0.048), right middle occipital gyrus (r = 0.453; P = 0.009) and bilateral precentral gyrus (left: r = 0.436, P = 0.013; right: r = 0.582, P < 0.001), paracentral lobule (r = 0.485; P = 0.005) and precuneus (r = 0.468; P = 0.007). CONCLUSIONS: Our results provide information on the pathophysiological mechanisms underlying cognitive alterations in cirrhotic patients and demonstrate the feasibility of using Rs-fMRI with ReHo analysis as a noninvasive modality with which to detect the progression of cognitive changes in cirrhotic patients.

CT and MRI of radiation-induced sarcomas of the head and neck following radiotherapy for nasopharyngeal carcinoma.

AIM: To investigate the radiological findings of head and neck radiation-induced sarcomas (RISs) following radiotherapy for nasopharyngeal carcinoma (NPC). MATERIALS AND METHODS: Fifty-nine patients with RISs were identified. Imaging characteristics on computed tomography (CT) and magnetic resonance imaging (MRI), including lesion location, extent, size, margin, internal architecture, pattern, and degree of enhancement, together with patient characteristics at NPC diagnosis and latency periods, were reviewed. RESULTS: The study included 20 women and 39 men, with a median age of 49 years (range 30-71 years). The median latency was 9 years (range 3-37 years). The median radiation dose at the site of RIS was 66 Gy (range 44-78 Gy). The most common histological RIS types were fibrosarcoma (44.1%) and osteosarcoma (30.5%). The most common RIS sites were the paranasal sinuses and the nasal cavity (39%), the neck (16.9%), and the mandible (15.3%). The mean size was 5.1 cm (range 1.2-8.6 cm). Overall, 78% of lesions extended to adjacent spaces and 66.1% were accompanied by bone destruction. Heterogeneous density/signal intensity before and after enhancement was seen in all lesions on imaging. Marked lesion enhancement was noted in 49 cases (76.3%). CONCLUSIONS: The radiologist should be aware of the different sites at which RISs occur and the radiological appearance of the wide variety of RIS subtypes. Careful imaging follow-up is necessary for early detection of RISs in patients with NPC after radiotherapy.