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.

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.

A deep learning-based semiautomated workflow for triaging follow-up MR scans in treated nasopharyngeal carcinoma.

It is imperative to optimally utilize virtues and obviate defects of fully automated analysis and expert knowledge in new paradigms of healthcare. We present a deep learning-based semiautomated workflow (RAINMAN) with 12,809 follow-up scans among 2,172 patients with treated nasopharyngeal carcinoma from three centers (ChiCTR.org.cn, Chi-CTR2200056595). A boost of diagnostic performance and reduced workload was observed in RAINMAN compared with the original manual interpretations (internal vs. external: sensitivity, 2.5% [p = 0.500] vs. 3.2% [p = 0.031]; specificity, 2.9% [p < 0.001] vs. 0.3% [p = 0.302]; workload reduction, 79.3% vs. 76.2%). The workflow also yielded a triaging performance of 83.6%, with increases of 1.5% in sensitivity (p = 1.000) and 0.6%-1.3% (all p < 0.05) in specificity compared to three radiologists in the reader study. The semiautomated workflow shows its unique superiority in reducing radiologist's workload by eliminating negative scans while retaining the diagnostic performance of radiologists.

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.

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.

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.

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).

Efficacy and safety of sintilimab plus bevacizumab in metastatic nasopharyngeal carcinoma after failure of platinum-based chemotherapy: an open-label phase 2 study.

Background: There are limited treatment options for patients with metastatic nasopharyngeal carcinoma (mNPC) after failure of platinum-based chemotherapy. In this trial, we assessed the efficacy and safety of sintilimab plus bevacizumab in patients with mNPC where platinum-based chemotherapy has been ineffective. Methods: This was a single-centre, open-label, single-arm, phase 2 trial in Guangzhou, China for patients with mNPC progressed after at least one line of systemic therapy. Eligible patients were between 18 and 75 years old, were histologically confirmed differentiated or undifferentiated non-keratinized NPC, were ineffective after platinum-based chemotherapy, and they had at least one measurable metastatic lesion assessed with Response Evaluation Criteria in Solid Tumors Version 1.1 (RECIST V.1.1) by investigators and unsuitable for local surgery or radiotherapy. Key exclusion criterion was previous treatment with anti-PD-1/PD-L1 antibodies plus anti-VEGF antibodies and high risk of hemorrhage or nasopharyngeal necrosis. Patients were enrolled and received sintilimab (200 mg) plus bevacizumab (7.5 mg/kg) intravenously every 3 weeks. Intention-to-treat population was included in primary endpoint analyses and safety analyses. The primary endpoint was objective response rate (ORR) assessed by investigators following the guidelines of RECIST V1.1. Key secondary endpoints were progression-free survival (PFS), overall survival (OS), duration of response (DOR), and safety. This trial is registered with ClinicalTrials.gov (NCT04872582). Findings: Between July 29, 2021 and August 16, 2022, 33 patients were enrolled. Median age was 46 years (range, 18-64 years), and 63.6% of patients had previously received two or more lines of chemotherapy for metastatic disease. Median follow-up was 7.6 months (range, 4.1-17.5 months). ORR was 54.5% (95% CI, 36.4-71.9%) with 3 complete responses (9.1%) and 15 partial responses (45.5%). Median PFS was 6.8 months (95% CI, 5.2 months to not estimable). Median DOR was 7.2 months (95% CI, 4.4 months to not estimable). Median OS was not reached. The most common potential immune-related adverse event (AE) was Grade 1-2 hypothyroidism (42.4%). Treatment-related grade 3 or 4 AEs occurred in 7 patients (21.2%), including nasal necrosis (3/33), hypertension (1/33), pruritus (1/33), total bilirubin increased (1/33) and anaphylactic shock (1/33). No treatment-related deaths and severe epistaxis occurred. Interpretation: This phase 2 trial showed that sintilimab plus bevacizumab demonstrated promising antitumour activity and manageable toxicities in patients with mNPC after failure of platinum-based chemotherapy. Further trials are warranted, and the detailed mechanisms need to be elucidated. Funding: The Guangdong Basic and Applied Basic Research Foundation, the National Natural Science Foundation of China, the Natural Science Foundation of Guangdong Province, and the Science and Technology Planning Project of International Cooperation of Guangdong Province.

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.

Combining Preoperative Clinical and Imaging Characteristics to Predict MVI in Hepatitis B Virus-Related Combined Hepatocellular Carcinoma and Cholangiocarcinoma.

BACKGROUND: Combined hepatocellular carcinoma and cholangiocarcinoma (cHCC-CCA) is a rare form of primary liver malignancy. Microvascular invasion (MVI) indicates poor postsurgical prognosis in cHCC-CCA. The objective of this study was to investigate preoperative predictors of MVI in hepatitis B virus (HBV) -related cHCC-CCA patients. METHODS: A total of 69 HBV-infected patients with pathologically confirmed cHCC-CCA who underwent hepatectomy were included. Univariate and multivariate analyses were conducted to determine independent risk factors that were then incorporated into the predictive model associated with MVI. Receiver operating characteristic analysis was used to assess the predictive performance of the new model. RESULTS: For the multivariate analysis, γ-glutamyl transpeptidase (OR, 3.69; p = 0.034), multiple nodules (OR, 4.41; p = 0.042) and peritumoral enhancement (OR, 6.16; p = 0.004) were independently associated with MVI. Active replication of HBV indicated by positive HBeAg showed no differences between MVI-positive and MVI-negative patients. The prediction score using the independent predictors achieved an area under the curve of 0.813 (95% CI 0.717-0.908). A significantly lower recurrence-free survival was observed in the high-risk group with a score of ≥1 (p < 0.001). CONCLUSION: γ-glutamyl transpeptidase, peritumoral enhancement and multiple nodules were independent preoperative predictors of MVI in HBV-related cHCC-CCA patients. The established prediction score demonstrated satisfactory performance in predicting MVI pre-operatively and may facilitate prognostic stratification.

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.

CT radiomics-based long-term survival prediction for locally advanced non-small cell lung cancer patients treated with concurrent chemoradiotherapy using features from tumor and tumor organismal environment.

BACKGROUND: Definitive concurrent chemoradiotherapy (CCRT) is the standard treatment for locally advanced non-small cell lung cancer (LANSCLC) patients, but the treatment response and survival outcomes varied among these patients. We aimed to identify pretreatment computed tomography-based radiomics features extracted from tumor and tumor organismal environment (TOE) for long-term survival prediction in these patients treated with CCRT. METHODS: A total of 298 eligible patients were randomly assigned into the training cohort and validation cohort with a ratio 2:1. An integrated feature selection and model training approach using support vector machine combined with genetic algorithm was performed to predict 3-year overall survival (OS). Patients were stratified into the high-risk and low-risk group based on the predicted survival status. Pulmonary function test and blood gas analysis indicators were associated with radiomic features. Dynamic changes of peripheral blood lymphocytes counts before and after CCRT had been documented. RESULTS: Nine features including 5 tumor-related features and 4 pulmonary features were selected in the predictive model. The areas under the receiver operating characteristic curve for the training and validation cohort were 0.965 and 0.869, and were reduced by 0.179 and 0.223 when all pulmonary features were excluded. Based on radiomics-derived stratification, the low-risk group yielded better 3-year OS (68.4% vs. 3.3%, p < 0.001) than the high-risk group. Patients in the low-risk group had better baseline FEV1/FVC% (96.3% vs. 85.9%, p = 0.046), less Grade ≥ 3 lymphopenia during CCRT (63.2% vs. 83.3%, p = 0.031), better recovery of lymphopenia from CCRT (71.4% vs. 27.8%, p < 0.001), lower incidence of Grade ≥ 2 radiation-induced pneumonitis (31.6% vs. 53.3%, p = 0.040), superior tumor remission (84.2% vs. 66.7%, p = 0.003). CONCLUSION: Pretreatment radiomics features from tumor and TOE could boost the long-term survival forecast accuracy in LANSCLC patients, and the predictive results could be utilized as an effective indicator for survival risk stratification. Low-risk patients might benefit more from radical CCRT and further adjuvant immunotherapy. TRIAL REGISTRATION: retrospectively registered.

Magnetic resonance imaging features of intrahepatic extramedullary hematopoiesis: Three case reports.

BACKGROUND: Extramedullary hematopoiesis rarely occurs within the liver alone, and is easily misdiagnosed. The radiological literature on this disease is exclusively case reports. There is a paucity of literature on the role of magnetic resonance imaging (MRI). The most common imaging modalities used are computed tomography and ultrasound. This report aims to provide more data on the appearance of extramedullary hematopoiesis using MRI to help radiologists establish the diagnosis. CASE SUMMARY: Three patients (one male and two females) were incidentally found to have a hepatic mass or nodule, without hepatomegaly or splenomegaly. Laboratory tests including liver function, serum hepatic tumor markers, and hepatitis serologic markers were normal. On MRI scans, all lesions showed lower signal intensity on in-phase images than on out-phase images. One case showed changes in signal intensity on T2 weighted images (WI) and diffusion WI, which shifted from hyperintensity to hypointensity with size enlargement between two rounds of imaging examination. These lesions exhibited different enhancement patterns on dynamic contrast enhancement series. CONCLUSION: The MRI signal change and in-/out-phase image might provide useful information and help radiologists establish the diagnosis of intrahepatic extramedullary hematopoiesis.

Tumor response evaluation by combined modalities of chest magnetic resonance imaging and computed tomography in locally advanced non-small cell lung cancer after concurrent chemoradiotherapy.

PURPOSE: This study aimed to explore the role of a modified criteria for assessing tumor response to concurrent chemoradiotherapy (CCRT) in locally advanced non-small cell lung cancer (LA-NSCLC), using the combined modalities of anatomical and functional MRI and CT. MATERIALS AND METHODS: One hundred and fifty-three patients with LA-NSCLC who underwent CCRT with continuous chest MRI and CT follow-up were analyzed. The tumor response to CCRT was evaluated two months after the completion of CCRT. The RECIST criteria (CT imaging) and modified criteria (combined chest MRI and CT imaging) were compared and validated on follow-up imaging. The chest MRI scan analysis included T1C, T2fs, DWI imaging and the apparent diffusion coefficient values. Progression free survival (PFS) ≥ 18 months was used as a surrogate endpoint of complete response to analyze the accuracy of tumor response assessment. RESULTS: Eight (5.2%) patients were considered to have complete response (CR) by the RECIST criteria while fifty-five (35.9%) were considered to have CR (CT + MRI) by the modified criteria. Using PFS ≥ 18 months as a surrogate for CR, the sensitivity and specificity of the modified criteria were 71.2% and 90.8% (AUC = 0.810, 95%CI 0.735-0.885), but were 9.1% and 97.7%, respectively, for the RECIST criteria (AUC = 0.534, 95%CI 0.441-0.627). The median PFS was 58.4 months for patients with CR (CT + MRI) and 9.7 months for those with non-CR (P < 0.001). Multivariate analysis showed that the tumor response evaluated by the modified criteria [CR (CT + MRI) vs. non-CR] was an independent factor for both PFS (HR 0.182, 95%CI 0.088-0.373, P < 0.001) and overall survival (HR 0.134, 95%CI 0.044-0.410, P < 0.001). CONCLUSIONS: Combined multi-parameter MRI and CT imaging could improve the accuracy of tumor response assessment in LA-NSCLC patients after definitive CCRT, therefore contributed to the risk stratification and survival prediction for clinical practice.

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.

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.

Dual-region radiomics signature: Integrating primary tumor and lymph node computed tomography features improves survival prediction in esophageal squamous cell cancer.

BACKGROUND: Preoperative prognostic biomarkers to guide individualized therapy are still in demand in esophageal squamous cell cancer (ESCC). Some studies reported that radiomic analysis based on CT images has been successfully performed to predict individual survival in EC. The aim of this study was to assess whether combining radiomics features from primary tumor and regional lymph nodes predicts overall survival (OS) better than using single-region features only, and to investigate the incremental value of the dual-region radiomics signature. METHODS: In this retrospective study, three radiomics signatures were built from preoperative enhanced CT in a training cohort (n = 200) using LASSO Cox model. Associations between each signature and survival was assessed on a validation cohort (n = 107). Prediction accuracy for the three signatures was compared. By constructing a clinical nomogram and a radiomics-clinical nomogram, incremental prognostic value of the radiomics signature over clinicopathological factors in OS prediction was assessed in terms of discrimination, calibration, reclassification and clinical usefulness. RESULTS: The dual-region radiomic signature was an independent factor, significantly associated with OS (HR: 1.869, 95% CI: 1.347, 2.592, P = 1.82e-04), which achieved better OS (C-index: 0.611) prediction either than the single-region signature (C-index:0.594-0.604). The resulted dual-region radiomics-clinical nomogram achieved the best discriminative ability in OS prediction (C-index:0.700). Compared with the clinical nomogram, the radiomics-clinical nomogram improved the calibration and classification accuracy for OS prediction with a total net reclassification improvement (NRI) of 26.9% (P=0.008) and integrated discrimination improvement (IDI) of 6.8% (P<0.001). CONCLUSION: The dual-region radiomic signature is an independent prognostic marker and outperforms single-region signature in OS for ESCC patients. Integrating the dual-region radiomics signature and clinicopathological factors improves OS prediction.

An Optimized Integrin α6-Targeted Magnetic Resonance Probe for Molecular Imaging of Hepatocellular Carcinoma in Mice.

INTRODUCTION: Integrin α6 is an attractive diagnostic biomarker for molecular imaging of hepatocellular carcinoma (HCC) as it has an extremely high positive rate (approximately 94%) in clinical early-stage HCC. In this study, based on our previously identified integrin α6-targeted peptide, we developed an optimized integrin α6-targeted magnetic resonance (MR) probe dubbed DOTA(Gd)-ANADYWR for MR imaging of HCC in mice. MATERIALS AND METHODS: The longitudinal (R1) relaxivity of DOTA(Gd)-ANADYWR was measured on a 3.0 T MR system . The specific tumor enhancement of the agent was investigated in four distinct mouse models, including subcutaneous, orthotopic, genetically engineered and chemically induced HCC mice. RESULTS: The R1 relaxivity value of DOTA(Gd)-ANADYWR is 5.11 mM-1s-1 at 3.0 T, which is similar to that of the nonspecific clinical agent Gadoteridol. DOTA(Gd)-ANADYWR generated superior enhanced MR signal in HCC lesions and provided complementary enhancement MR signals to the clinically available hepatobiliary MR contrast agent gadoxetate disodium (Gd-EOB-DTPA). Importantly, DOTA(Gd)-ANADYWR could efficiently visualize small HCC lesion (approximately 1 mm) which was hardly detected by the clinical Gd-EOB-DTPA. CONCLUSION: These findings suggest the potential application of this integrin α6-targeted MR probe for the detection of HCC, particularly for small HCC.

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.