Polyphyllin D punctures hypertrophic lysosomes to reverse drug resistance of hepatocellular carcinoma by targeting acid sphingomyelinase.

Hypertrophic lysosomes are critical for tumor progression and drug resistance; however, effective and specific lysosome-targeting compounds for cancer therapy are lacking. Here we conducted a lysosomotropic pharmacophore-based in silico screen in a natural product library (2,212 compounds), and identified polyphyllin D (PD) as a novel lysosome-targeted compound. PD treatment was found to cause lysosomal damage, as evidenced by the blockade of autophagic flux, loss of lysophagy, and the release of lysosomal contents, thus exhibiting anticancer effects on hepatocellular carcinoma (HCC) cell both in vitro and in vivo. Closer mechanistic examination revealed that PD suppressed the activity of acid sphingomyelinase (SMPD1), a lysosomal phosphodieserase that catalyzes the hydrolysis of sphingomyelin to produce ceramide and phosphocholine, by directly occupying its surface groove, with Trp148 in SMPD1 acting as a major binding residue; this suppression of SMPD1 activity irreversibly triggers lysosomal injury and initiates lysosome-dependent cell death. Furthermore, PD-enhanced lysosomal membrane permeabilization to release sorafenib, augmenting the anticancer effect of sorafenib both in vivo and in vitro. Overall, our study suggests that PD can potentially be further developed as a novel autophagy inhibitor, and a combination of PD with classical chemotherapeutic anticancer drugs could represent a novel therapeutic strategy for HCC intervention.

Image-based artificial intelligence for the prediction of pathological complete response to neoadjuvant chemoradiotherapy in patients with rectal cancer: a systematic review and meta-analysis.

OBJECTIVE: Artificial intelligence (AI) holds enormous potential for noninvasively identifying patients with rectal cancer who could achieve pathological complete response (pCR) following neoadjuvant chemoradiotherapy (nCRT). We aimed to conduct a meta-analysis to summarize the diagnostic performance of image-based AI models for predicting pCR to nCRT in patients with rectal cancer. METHODS: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A literature search of PubMed, Embase, Cochrane Library, and Web of Science was performed from inception to July 29, 2023. Studies that developed or utilized AI models for predicting pCR to nCRT in rectal cancer from medical images were included. The Quality Assessment of Diagnostic Accuracy Studies-AI was used to appraise the methodological quality of the studies. The bivariate random-effects model was used to summarize the individual sensitivities, specificities, and areas-under-the-curve (AUCs). Subgroup and meta-regression analyses were conducted to identify potential sources of heterogeneity. Protocol for this study was registered with PROSPERO (CRD42022382374). RESULTS: Thirty-four studies (9933 patients) were identified. Pooled estimates of sensitivity, specificity, and AUC of AI models for pCR prediction were 82% (95% CI: 76-87%), 84% (95% CI: 79-88%), and 90% (95% CI: 87-92%), respectively. Higher specificity was seen for the Asian population, low risk of bias, and deep-learning, compared with the non-Asian population, high risk of bias, and radiomics (all P < 0.05). Single-center had a higher sensitivity than multi-center (P = 0.001). The retrospective design had lower sensitivity (P = 0.012) but higher specificity (P < 0.001) than the prospective design. MRI showed higher sensitivity (P = 0.001) but lower specificity (P = 0.044) than non-MRI. The sensitivity and specificity of internal validation were higher than those of external validation (both P = 0.005). CONCLUSIONS: Image-based AI models exhibited favorable performance for predicting pCR to nCRT in rectal cancer. However, further clinical trials are warranted to verify the findings.

An interpretable machine learning model based on contrast-enhanced CT parameters for predicting treatment response to conventional transarterial chemoembolization in patients with hepatocellular carcinoma.

OBJECTIVE: To explore the potential of pre-therapy computed tomography (CT) parameters in predicting the treatment response to initial conventional TACE (cTACE) in intermediate-stage hepatocellular carcinoma (HCC) and develop an interpretable machine learning model. METHODS: This retrospective study included 367 patients with intermediate-stage HCC who received cTACE as first-line therapy from three centers. We measured the mean attenuation values of target lesions on multi-phase contrast-enhanced CT and further calculated three CT parameters, including arterial (AER), portal venous (PER), and arterial portal venous (APR) enhancement ratios. We used logistic regression analysis to select discriminative features and trained three machine learning models via 5-fold cross-validation. The performance in predicting treatment response was evaluated in terms of discrimination, calibration, and clinical utility. Afterward, a Shapley additive explanation (SHAP) algorithm was leveraged to interpret the outputs of the best-performing model. RESULTS: The mean diameter, ECOG performance status, and cirrhosis were the important clinical predictors of cTACE treatment response, by multiple logistic regression. Adding the CT parameters to clinical variables showed significant improvement in performance (net reclassification index, 0.318, P < 0.001). The Random Forest model (hereafter, RF-combined model) integrating CT parameters and clinical variables demonstrated the highest performance on external validation dataset (AUC of 0.800). The decision curve analysis illustrated the optimal clinical benefits of RF-combined model. This model could successfully stratify patients into responders and non-responders with distinct survival (P = 0.001). CONCLUSION: The RF-combined model can serve as a robust and interpretable tool to identify the appropriate crowd for cTACE sessions, sparing patients from receiving ineffective and unnecessary treatments.

Deep learning nomogram based on Gd-EOB-DTPA MRI for predicting early recurrence in hepatocellular carcinoma after hepatectomy.

OBJECTIVES: The accurate prediction of post-hepatectomy early recurrence in patients with hepatocellular carcinoma (HCC) is crucial for decision-making regarding postoperative adjuvant treatment and monitoring. We aimed to explore the feasibility of deep learning (DL) features derived from gadoxetate disodium (Gd-EOB-DTPA) MRI, qualitative features, and clinical variables for predicting early recurrence. METHODS: In this bicentric study, 285 patients with HCC who underwent Gd-EOB-DTPA MRI before resection were divided into training (n = 195) and validation (n = 90) sets. DL features were extracted from contrast-enhanced MRI images using VGGNet-19. Three feature selection methods and five classification methods were combined for DL signature construction. Subsequently, an mp-MR DL signature fused with multiphase DL signatures of contrast-enhanced images was constructed. Univariate and multivariate logistic regression analyses were used to identify early recurrence risk factors including mp-MR DL signature, microvascular invasion (MVI), and tumor number. A DL nomogram was built by incorporating deep features and significant clinical variables to achieve early recurrence prediction. RESULTS: MVI (p = 0.039), tumor number (p = 0.001), and mp-MR DL signature (p < 0.001) were independent risk factors for early recurrence. The DL nomogram outperformed the clinical nomogram in the training set (AUC: 0.949 vs. 0.751; p < 0.001) and validation set (AUC: 0.909 vs. 0.715; p = 0.002). Excellent DL nomogram calibration was achieved in both training and validation sets. Decision curve analysis confirmed the clinical usefulness of DL nomogram. CONCLUSION: The proposed DL nomogram was superior to the clinical nomogram in predicting early recurrence for HCC patients after hepatectomy. KEY POINTS: • Deep learning signature based on Gd-EOB-DTPA MRI was the predominant independent predictor of early recurrence for hepatocellular carcinoma (HCC) after hepatectomy. • Deep learning nomogram based on clinical factors and Gd-EOB-DTPA MRI features is promising for predicting early recurrence of HCC. • Deep learning nomogram outperformed the conventional clinical nomogram in predicting early recurrence.

Turning gray selenium and sublimed sulfur into a nanocomposite to accelerate tissue regeneration by isothermal recrystallization.

BACKGROUND: Globally, millions of patients suffer from regenerative deficiencies, such as refractory wound healing, which is characterized by excessive inflammation and abnormal angiogenesis. Growth factors and stem cells are currently employed to accelerate tissue repair and regeneration; however, they are complex and costly. Thus, the exploration of new regeneration accelerators is of considerable medical interest. This study developed a plain nanoparticle that accelerates tissue regeneration with the involvement of angiogenesis and inflammatory regulation. METHODS: Grey selenium and sublimed sulphur were thermalized in PEG-200 and isothermally recrystallised to composite nanoparticles (Nano-Se@S). The tissue regeneration accelerating activities of Nano-Se@S were evaluated in mice, zebrafish, chick embryos, and human cells. Transcriptomic analysis was performed to investigate the potential mechanisms involved during tissue regeneration. RESULTS: Through the cooperation of sulphur, which is inert to tissue regeneration, Nano-Se@S demonstrated improved tissue regeneration acceleration activity compared to Nano-Se. Transcriptome analysis revealed that Nano-Se@S improved biosynthesis and ROS scavenging but suppressed inflammation. The ROS scavenging and angiogenesis-promoting activities of Nano-Se@S were further confirmed in transgenic zebrafish and chick embryos. Interestingly, we found that Nano-Se@S recruits leukocytes to the wound surface at the early stage of regeneration, which contributes to sterilization during regeneration. CONCLUSION: Our study highlights Nano-Se@S as a tissue regeneration accelerator, and Nano-Se@S may provide new inspiration for therapeutics for regenerative-deficient diseases.

Thy-Wise: An interpretable machine learning model for the evaluation of thyroid nodules.

Current risk stratification systems for thyroid nodules suffer from low specificity and high biopsy rates. Recently, machine learning (ML) is introduced to assist thyroid nodule diagnosis but lacks interpretability. Here, we developed and validated ML models on 3965 thyroid nodules, as compared to the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS). Subsequently, a SHapley Additive exPlanation (SHAP) algorithm was leveraged to interpret the results of the best-performing ML model. Clinical characteristics including thyroid-function tests were collected from medical records. Five ACR TI-RADS ultrasonography (US) categories plus nodule size were assessed by experienced radiologists. Random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGBoost) were used to build US-only and US-clinical ML models. The ML models and ACR TI-RADS were compared in terms of diagnostic performance and unnecessary biopsy rate. Among the ML models, the US-only RF model (hereafter, Thy-Wise) achieved the optimal performance. Compared to ACR TI-RADS, Thy-Wise showed higher accuracy (82.4% vs 74.8% for the internal validation; 82.1% vs 73.4% for external validation) and specificity (78.7% vs 68.3% for internal validation; 78.5% vs 66.9% for external validation) while maintaining sensitivity (91.7% vs 91.2% for internal validation; 91.9% vs 91.1% for external validation), as well as reduced unnecessary biopsies (15.3% vs 32.3% for internal validation; 15.7% vs 47.3% for external validation). The SHAP-based interpretation of Thy-Wise enables clinicians to better understand the reasoning behind the diagnosis, which may facilitate the clinical translation of this model.

Carbonic anhydrase IX stratifies patient prognosis and identifies nodal status in animal models of nasopharyngeal carcinoma using a targeted imaging strategy.

PURPOSE: Accurate identification of nodal status enables adequate neck irradiation for nasopharyngeal carcinoma (NPC). However, most conventional techniques are unable to pick up occult metastases, leading to underestimation of tumor extensions. Here we investigate the clinical significance of carbonic anhydrase IX (CAIX) in human NPC samples, and develop a CAIX-targeted imaging strategy to identify occult lymph node metastases (LNMs) and extranodal extension (ENE) in animal studies. METHODS: A total of 211 NPC samples are performed CAIX staining, and clinical outcomes are analyzed. The metastatic murine models are generated by foot pad injection of NPC cells, and a CAIX-targeted imaging agent (CAIX-800) is intravenously administered. We adopt fluorescence molecular tomography and ultrasonography (US)-guided spectroscopic photoacoustic (sPA) imaging to perform in vivo studies. Histological and immunohistochemical characterization are carried out via node-by-node analysis. RESULTS: For clinical samples, 90.1% (91/101) primary tumors, 73.3% (66/90) metastases, and 100% (20/20) local recurrences are CAIX positive. In metastases group, 84.7% (61/72) nodal metastases and 22.2% (4/18) organ metastases are CAIX positive. CAIX expression in primary tumors is significantly associated with NPC stage and prognosis. For animal studies, CAIX-800-based fluorescence imaging achieves 81.3% sensitivity and 93.8% specificity in detecting occult LNMs in vivo, with a minimum detectable diameter of 1.7 mm. Coupled with CAIX-800, US-guided sPA imaging could not only detect subcapsular deposits of metastatic cancer cells 2 weeks earlier than conventional techniques, but also successfully track pathological ENE. CONCLUSION: CAIX remarkably expresses in human NPCs and stratifies patient prognosis. In preclinical studies, CAIX-800-based imaging successfully identifies occult LNMs and tracks early stage of pathological ENE. This attractive method shows potential in clinic, allowing medical workers to longitudinally monitor nodal status and helping to reduce unnecessary nodal biopsy for patients with NPC. The schematic diagram for the study. CAIX, carbonic anhydrase IX; NPC, nasopharyngeal carcinoma; US, ultrasonography; sPA, spectroscopic photoacoustic.

ABI2-mediated MEOX2/KLF4-NANOG axis promotes liver cancer stem cell and drives tumour recurrence.

Tumour recurrence and drug resistance in hepatocellular carcinoma remain challenging. Cancer stem cells (CSCs) are responsible for tumour initiation because of their stemness characteristics. CSCs accounting for drug resistance and tumour relapse are promising therapeutic targets. We report that Abelson interactor 2 (ABI2) is a novel therapeutic target of HCC CSCs. First, ABI2 was upregulated in HCC tissues compared with liver tissues and was associated with tumour size, pathological grade, liver cirrhosis, worse prognosis and a high recurrence rate. Functional studies illustrate that ABI2 knockdown suppresses cell growth, migration, invasion and sorafenib resistance in vitro. Furthermore, ABI2 knockdown inhibited HCC sphere formation and decreased the CD24+ , CD133+ and CD326+ CSCs populations, suggesting the suppression of HCC stemness characteristics. A tumour xenograft model and limiting dilution assay demonstrated the inhibition of tumorigenicity and tumour initiation. Moreover, molecular mechanism studies showed that ABI2 recruits and directly interacts with the transcription factor MEOX2, which binds to the KLF4 and NANOG promoter regions to activate their transcription. Furthermore, overexpression of MEOX2 restored HCC malignant behaviour and the CSC population. The ABI2-mediated transcriptional axis MEOX2/KLF4-NANOG promotes HCC growth, metastasis and sorafenib resistance by maintaining the CSC population, suggesting that ABI2 is a promising CSC target in HCC treatment.

Radiomics in precision medicine for gastric cancer: opportunities and challenges.

OBJECTIVES: Radiomic features derived from routine medical images show great potential for personalized medicine in gastric cancer (GC). We aimed to evaluate the current status and quality of radiomic research as well as its potential for identifying biomarkers to predict therapy response and prognosis in patients with GC. METHODS: We performed a systematic search of the PubMed and Embase databases for articles published from inception through July 10, 2021. The phase classification criteria for image mining studies and the radiomics quality scoring (RQS) tool were applied to evaluate scientific and reporting quality. RESULTS: Twenty-five studies consisting of 10,432 patients were included. 96% of studies extracted radiomic features from CT images. Association between radiomic signature and therapy response was evaluated in seven (28%) studies; association with survival was evaluated in 17 (68%) studies; one (4%) study analyzed both. All results of the included studies showed significant associations. Based on the phase classification criteria for image mining studies, 18 (72%) studies were classified as phase II, with two, four, and one studies as discovery science, phase 0 and phase I, respectively. The median RQS score for the radiomic studies was 44.4% (range, 0 to 55.6%). There was extensive heterogeneity in the study population, tumor stage, treatment protocol, and radiomic workflow amongst the studies. CONCLUSIONS: Although radiomic research in GC is highly heterogeneous and of relatively low quality, it holds promise for predicting therapy response and prognosis. Efforts towards standardization and collaboration are needed to utilize radiomics for clinical application. KEY POINTS: • Radiomics application of gastric cancer is increasingly being reported, particularly in predicting therapy response and survival. • Although radiomics research in gastric cancer is highly heterogeneous and relatively low quality, it holds promise for predicting clinical outcomes. • Standardized imaging protocols and radiomic workflow are needed to facilitate radiomics into clinical use.

Radiogenomic association between the T2-FLAIR mismatch sign and IDH mutation status in adult patients with lower-grade gliomas: an updated systematic review and meta-analysis.

OBJECTIVES: To reveal a radiogenomic correlation between the presence of the T2-fluid-attenuated inversion recovery resection (T2-FLAIR) mismatch sign on MR images and isocitrate dehydrogenase (IDH) mutation status in adult patients with lower-grade gliomas (LGGs). METHODS: A web-based systemic search for eligible literature up to April 13, 2021, was conducted on PubMed, Embase, and the Cochrane Library databases by two independent reviewers. This study was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. We included studies evaluating the accuracy of the T2-FLAIR mismatch sign in diagnosing the IDH mutation in adult patients with LGGs. The T2-FLAIR mismatch sign was defined as a T2-hyperintense lesion that is hypointense on FLAIR except for a hyperintense rim. RESULTS: Fourteen studies (n = 1986) were finally identified. The mean age of patients in the included studies ranged from 38.5 to 56 years. The pooled area under the curve (AUC), sensitivity, and specificity were obtained for each molecular profile: IDHmut-Codel: 0.46 (95% confidence interval [CI]: 0.42-0.50), 1% (95%CI: 0-7%), and 69% (95%CI: 62-75%), respectively; IDHmut-Noncodel: 0.75 (95%CI: 0.71-0.79), 42% (95%CI: 34-50%), and 99% (95%CI: 96-100%), respectively; IDH-Mutation regardless of 1p/19q codeletion status: 0.77 (95%CI: 0.73-0.80), 29% (95%CI: 21-40%), and 99% (95%CI: 92-100%), respectively. CONCLUSIONS: The T2-FLAIR mismatch sign was an insensitive but highly specific marker for IDHmut-Noncodel and IDH-Mutation LGGs, whereas it was not a useful marker for IDHmut-Codel LGGs. The findings might identify the T2-FLAIR mismatch sign as a non-invasive imaging biomarker for the selection of patients with IDH-mutant LGGs. KEY POINTS: • The T2-FLAIR mismatch sign was not a sensitive sign for IDH mutation in LGGs. • The T2-FLAIR mismatch sign was related to IDHmut-Noncodel with a specificity of 99%. • The pooled specificity (69%) of the T2-FLAIR mismatch sign for IDHmut-Codel was low.

Current status and quality of radiomic studies for predicting immunotherapy response and outcome in patients with non-small cell lung cancer: a systematic review and meta-analysis.

PURPOSE: Prediction of immunotherapy response and outcome in patients with non-small cell lung cancer (NSCLC) is challenging due to intratumoral heterogeneity and lack of robust biomarkers. The aim of this study was to systematically evaluate the methodological quality of radiomic studies for predicting immunotherapy response or outcome in patients with NSCLC. METHODS: We systematically searched for eligible studies in the PubMed and Web of Science datasets up to April 1, 2021. The methodological quality of included studies was evaluated using the phase classification criteria for image mining studies and the radiomics quality scoring (RQS) tool. A meta-analysis of studies regarding the prediction of immunotherapy response and outcome in patients with NSCLC was performed. RESULTS: Fifteen studies were identified with sample sizes ranging from 30 to 228. Seven studies were classified as phase II, and the remaining as discovery science (n = 2), phase 0 (n = 4), phase I (n = 1), and phase III (n = 1). The mean RQS score of all studies was 29.6%, varying from 0 to 68.1%. The pooled diagnostic odds ratio for predicting immunotherapy response in NSCLC using radiomics was 14.99 (95% confidence interval [CI] 8.66-25.95). In addition, radiomics could divide patients into high- and low-risk group with significantly different overall survival (pooled hazard ratio [HR]: 1.96, 95%CI 1.61-2.40, p < 0.001) and progression-free survival (pooled HR: 2.39, 95%CI 1.69-3.38, p < 0.001). CONCLUSIONS: Radiomics has potential to noninvasively predict immunotherapy response and outcome in patients with NSCLC. However, it has not yet been implemented as a clinical decision-making tool. Further external validation and evaluation within clinical pathway can facilitate personalized treatment for patients with NSCLC.

Deep Learning With 3D Convolutional Neural Network for Noninvasive Prediction of Microvascular Invasion in Hepatocellular Carcinoma.

BACKGROUND: Microvascular invasion (MVI) is a critical prognostic factor of hepatocellular carcinoma (HCC). However, it could only be obtained by postoperative histological examination. PURPOSE: To develop an end-to-end deep-learning models based on MRI images for preoperative prediction of MVI in HCC patients who underwent surgical resection. STUDY TYPE: Retrospective. POPULATION: Two hundred and thirty-seven patients with histologically confirmed HCC. FIELD STRENGTH: 1.5 T and 3.0 T. SEQUENCE: Axial T2 -weighted (T2 -w) with turbo spin echo sequence, T2 -Spectral Presaturation with Inversion Recovery (T2 -SPIR), and dynamic contrast-enhanced (DCE) imaging with fat suppressed enhanced T1 high-resolution isotropic volume examination. ASSESSMENT: The patients were randomly divided into training (N = 158) and validation (N = 79) sets. Data augmentation by random rotation was performed on the training set and the sample size increased to 1940 for each MR sequence. A three-dimensional convolutional neural network (3D CNN) was used to develop four deep-learning models, including three single-layer models based on single-sequence, and fusion model combining three sequences. MVI status was obtained from the postoperative pathology reports. STATISTICAL TESTS: The dice similarity coefficient (DSC) and Hausdorff distance (HD) were applied to assess the similarity and reproducibility between the manual segmentations of tumor from two radiologists. Receiver operating characteristic curve analysis was used to evaluate model performance. MVI was identified in 92 (38.8%) patients. Good reproducibility with interobserver DSCs of 0.90, 0.89, and 0.89 and HDs of 4.09, 3.67, and 3.60 was observed for PVP, T2 WI, and T2 -SPIR, respectively. The fusion model achieved an area under the curve (AUC) of 0.81, sensitivity of 69%, and specificity of 79% in the training set and 0.72, sensitivity of 55%, and specificity of 81% in the validation set. DATA CONCLUSION: 3D CNN model may serve as a noninvasive tool to predict MVI in HCC, whereas its accuracy needs to be enhanced with larger cohort. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

MRI-Based Deep-Learning Model for Distant Metastasis-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma.

BACKGROUND: Distant metastasis is the primary cause of treatment failure in locoregionally advanced nasopharyngeal carcinoma (LANPC). PURPOSE: To develop a model to evaluate distant metastasis-free survival (DMFS) in LANPC and to explore the value of additional chemotherapy to concurrent chemoradiotherapy (CCRT) for different risk groups. STUDY TYPE: Retrospective. POPULATION: In all, 233 patients with biopsy-confirmed nasopharyngeal carcinoma (NPC) from two hospitals. FIELD STRENGTH: 1.5T and 3T. SEQUENCE: Axial T2 -weighted (T2 -w) and contrast-enhanced T1 -weighted (CET1 -w) images. ASSESSMENT: Deep learning was used to build a model based on MRI images (including axial T2 -w and CET1 -w images) and clinical variables. Hospital 1 patients were randomly divided into training (n = 169) and validation (n = 19) cohorts; Hospital 2 patients were assigned to a testing cohort (n = 45). LANPC patients were divided into low- and high-risk groups according to their DMFS (P < 0.05). Kaplan-Meier survival analysis was performed to compare the DMFS of different risk groups and subgroup analysis was performed to compare patients treated with CCRT alone and treated with additional chemotherapy to CCRT in different risk groups, respectively. STATISTICAL TESTS: Univariate analysis was performed to identify significant clinical variables. The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the model performance. RESULTS: Our deep-learning model integrating the deep-learning signature, node (N) stage (from TNM staging), plasma Epstein-Barr virus (EBV)-DNA, and treatment regimens yielded an AUC of 0.796 (95% confidence interval [CI]: 0.729-0.863), 0.795 (95% CI: 0.540-1.000), and 0.808 (95% CI: 0.654-0.962) in the training, internal validation, and external testing cohorts, respectively. Low-risk patients treated with CCRT alone had longer DMFS than patients treated with additional chemotherapy to CCRT (P < 0.05). DATA CONCLUSION: The proposed deep-learning model, based on MRI features and clinical variates, facilitated the prediction of DMFS in LANPC patients. LEVEL OF EVIDENCE: 3. TECHNICAL EFFICACY STAGE: 4.

Development and validation of a Web-based malignancy risk-stratification system of thyroid nodules.

OBJECTIVES: Previous publications on risk-stratification systems for malignant thyroid nodules were based on conventional ultrasound only. We aimed to develop a practical and simplified prediction model for categorizing the malignancy risk of thyroid nodules based on clinical data, biochemical data, conventional ultrasound and real-time elastography. DESIGN: Retrospective cohort study. PATIENTS: A total of 2818 patients (1890 female, mean age, 45.5 ± 13.2 years) with 2850 thyroid nodules were retrospectively evaluated between April 2011 and October 2016. 26.8% nodules were malignant. MEASUREMENTS: We used a randomly divided sample of 80% of the nodules to perform a multivariate logistic regression analysis. Cut-points were determined to create a risk-stratification scoring system. Patients were classified as having low, moderate and high probability of malignancy according to their scores. We validated the models to the remaining 20% of the nodules. The area under the curve (AUC) was used to evaluate the discrimination ability of the systems. RESULTS: Ten variables were selected as predictors of malignancy. The point-based scoring systems with and without elasticity score achieved similar AUCs of 0.916 (95% confidence interval [CI]: 0.885-0.948) and 0.906 (95% CI: 0.872-0.941) when validated. Malignancy risk was segmented from 0% to 100.0% and was positively associated with an increase in risk scores. We then developed a Web-based risk-stratification system of thyroid nodules (http: thynodscore.com). CONCLUSION: A simple and reliable Web-based risk-stratification system could be practically used in stratifying the risk of malignancy in thyroid nodules.

Novel coronavirus disease 2019 (COVID-19): relationship between chest CT scores and laboratory parameters.

PURPOSE: To quantify the severity of 2019 novel coronavirus disease (COVID-19) on chest CT and to determine its relationship with laboratory parameters. METHODS: Patients with real-time fluorescence polymerase chain reaction (RT-PCR)-confirmed COVID-19 between January 01 and February 18, 2020, were included in this study. Laboratory parameters were retrospectively collected from medical records. Severity of lung changes on chest CT of early, progressive, peak, and absorption stages was scored according to the percentage of lung involvement (5 lobes, scores 1-5 for each lobe, range 0-20). Relationship between CT scores and laboratory parameters was evaluated by the Spearman rank correlation. The Bonferroni correction adjusted significance level was at 0.05/4 = 0.0125. RESULTS: A total of 84 patients (mean age, 47.8 ± 12.0 years [standard deviation]; age range, 24-80 years) were evaluated. The patients underwent a total of 339 chest CT scans with a median interval of 4 days (interquartile range, 3-5 days). Median chest CT scores peaked at 4 days after the beginning of treatment and then declined. CT score of the early stage was correlated with neutrophil count (r = 0.531, P = 0.011). CT score of the progressive stage was correlated with neutrophil count (r = 0.502, P < 0.001), white blood cell count (r = 0.414, P = 0.001), C-reactive protein (r = 0.511, P < 0.001), procalcitonin (r = 0.423, P = 0.004), and lactose dehydrogenase (r = 0.369, P = 0.010). However, CT scores of the peak and absorption stages were not correlated with any parameter (P > 0.0125). No sex difference occurred regarding CT score (P > 0.05). CONCLUSION: Severity of lung abnormalities quantified on chest CT might correlate with laboratory parameters in the early and progressive stages. However, larger cohort studies are necessary.

In vivo three-dimensional evaluation of tumour hypoxia in nasopharyngeal carcinomas using FMT-CT and MSOT.

PURPOSE: Accurate evaluation of hypoxia is particularly important in patients with nasopharyngeal carcinoma (NPC) undergoing radiotherapy. The aim of this study was to propose a novel imaging strategy for quantitative three-dimensional (3D) evaluation of hypoxia in a small animal model of NPC. METHODS: A carbonic anhydrase IX (CAIX)-specific molecular probe (CAIX-800) was developed for imaging of hypoxia. Mouse models of subcutaneous, orthotopic, and spontaneous lymph node metastasis from NPC (5 mice per group) were established to assess the imaging strategy. A multi-modality imaging method that consisted of a hybrid combination of fluorescence molecular tomography-computed tomography (FMT-CT) and multispectral optoacoustic tomography (MSOT) was used for 3D quantitative evaluation of tumour hypoxia. Magnetic resonance imaging, histological examination, and immunohistochemical analysis were used as references for comparison and validation. RESULTS: In the early stage of NPC (2 weeks after implantation), FMT-CT enabled precise 3D localisation of the hypoxia biomarker with high sensitivity. At the advanced stage (6 weeks after implantation), MSOT allowed multispectral analysis of the biomarker and haemoglobin molecules with high resolution. The combination of high sensitivity and high resolution from FMT-CT and MSOT could not only detect hypoxia in small-sized NPCs but also visualise the heterogeneity of hypoxia in 3D. CONCLUSIONS: Integration of FMT-CT and MSOT could allow comprehensive and quantifiable evaluation of hypoxia in NPC. These findings may potentially benefit patients with NPC undergoing radiotherapy in the future. Graphical abstract A novel multimodality imaging strategy for three-dimensional evaluation of tumour hypoxia in an orthotopic model of nasopharyngeal carcinoma.

N-Aryl Amides as Chemical Exchange Saturation Transfer Magnetic Resonance Imaging Contrast Agents.

Chemical exchange saturation transfer (CEST) MRI has recently emerged as a versatile molecular imaging approach in which diamagnetic compounds can be utilized to generate an MRI signal. To expand the scope of CEST MRI applications, herein, we systematically investigated the CEST properties of N-aryl amides with different N-aromatic substitution, revealing their chemical shifts (4.6-5.8 ppm) and exchange rates (up to thousands s-1 ) are favorable to be used as CEST agents as compared to alkyl amides. As the first proof-of-concept study, we used CEST MRI to detect the enzymatic metabolism of the drug acebutolol directly by its intrinsic CEST signal without any chemical labeling. Our study implies that N-aryl amides may enable the label-free CEST MRI detection of the metabolism of many N-aryl amide-containing drugs and a variety of enzymes that act on N-aryl amides, greatly expanding the scope of CEST MR molecular imaging.

Machine-learning based MRI radiomics models for early detection of radiation-induced brain injury in nasopharyngeal carcinoma.

BACKGROUND: Early radiation-induced temporal lobe injury (RTLI) diagnosis in nasopharyngeal carcinoma (NPC) is clinically challenging, and prediction models of RTLI are lacking. Hence, we aimed to develop radiomic models for early detection of RTLI. METHODS: We retrospectively included a total of 242 NPC patients who underwent regular follow-up magnetic resonance imaging (MRI) examinations, including contrast-enhanced T1-weighted and T2-weighted imaging. For each MRI sequence, four non-texture and 10,320 texture features were extracted from medial temporal lobe, gray matter, and white matter, respectively. The relief and 0.632 + bootstrap algorithms were applied for initial and subsequent feature selection, respectively. Random forest method was used to construct the prediction model. Three models, 1, 2 and 3, were developed for predicting the results of the last three follow-up MRI scans at different times before RTLI onset, respectively. The area under the curve (AUC) was used to evaluate the performance of models. RESULTS: Of the 242 patients, 171 (70.7%) were men, and the mean age of all the patients was 48.5 ± 10.4 years. The median follow-up and latency from radiotherapy until RTLI were 46 and 41 months, respectively. In the testing cohort, models 1, 2, and 3, with 20 texture features derived from the medial temporal lobe, yielded mean AUCs of 0.830 (95% CI: 0.823-0.837), 0.773 (95% CI: 0.763-0.782), and 0.716 (95% CI: 0.699-0.733), respectively. CONCLUSION: The three developed radiomic models can dynamically predict RTLI in advance, enabling early detection and allowing clinicians to take preventive measures to stop or slow down the deterioration of RTLI.

Diffusion Tensor Imaging for Quantitative Assessment of Anterior Cruciate Ligament Injury Grades and Graft.

BACKGROUND: As the need for quantitative assessment of anterior cruciate ligament (ACL) injuries and ACL graft increases, diffusion tensor imaging (DTI) becomes a more valuable measuring tool. However, DTI changes in differing injury grades of ACL and longitudinal graft remain unclear. PURPOSE: To investigate the diagnostic performance of DTI in quantitatively assessing ACL injury severity and the development of ACL grafts within 6 months of surgery. STUDY TYPE: A cohort study. SUBJECTS: Thirty-five patients diagnosed with grades I-IV ACL injuries and 20 volunteers as controls were recruited. FIELD STRENGTH/SEQUENCE: T1 -weighted, T2 -weighted, proton density (PD)-weighted, and DTI at 3.0T MRI. ASSESSMENT: ACL injury grades in arthroscopic images and DTI quantitative data were evaluated from July 2016 to July 2018. STATISTICAL TESTS: Chi-square test, analysis of variance, Spearman correlation analysis, and receiver operator characteristic (ROC) curves. RESULTS: Both fractional anisotropy (FA) (r = -0.898, P < 0.05) and apparent diffusion coefficient (ADC) (r = 0.851, P < 0.05) were significantly correlated with the severity of ACL injuries. The area under the curve (AUC) values for differentiation between low- and high-grade ACL injuries with FA and ADC were 0.973 and 0.963, respectively. Although there were no significant differences in FA (P > 0.05) and ADC (P > 0.05) between grades I and II ACL injuries or in ADC (P > 0.05) between grades III and IV, there were significant differences in FA and ADC between two grades (P < 0.05). There were significant differences in FA (P < 0.05) and ADC (P < 0.05) between normal ACL and 3-month graft postoperation, as well as in ADC values between 3-month and 6-month graft postoperation (P < 0.05). DATA CONCLUSION: DTI could be used to quantitatively evaluate the ACL injury grades and the development of ACL grafts. The diagnostic efficiency of FA values was higher than that of ADC values. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 3.

Prognostic value of the radiomics-based model in progression-free survival of hypopharyngeal cancer treated with chemoradiation.

PURPOSE: To develop a radiomics-based model to stratify the risk of early progression (local/regional recurrence or metastasis) among patients with hypopharyngeal cancer undergoing chemoradiotherapy and modify their pretreatment plans. MATERIALS AND METHODS: We randomly assigned 113 patients into two cohorts: training (n = 80) and validation (n = 33). The radiomic significant features were selected in the training cohort using least absolute shrinkage and selection operator and Akaike information criterion methods, and they were used to build the radiomic model. The concordance index (C-index) was applied to evaluate the model's prognostic performance. A Kaplan-Meier analysis and the log-rank test were used to assess risk stratification ability of models in predicting progression. A nomogram was plotted to predict individual risk of progression. RESULTS: Composed of four significant features, the radiomic model showed good performance in stratifying patients into high- and low-risk groups of progression in both the training and validation cohorts (log-rank test, p = 0.00016, p = 0.0063, respectively). Peripheral invasion and metastasis were selected as significant clinical variables. The combined radiomic-clinical model showed good discriminative performance, with C-indices 0.804 (95% confidence interval (CI), 0.688-0.920) and 0.756 (95% CI, 0.605-0.907) in the training and validation cohorts, respectively. The median progression-free survival (PFS) in the high-risk group was significantly shorter than that in the low-risk group in the training (median PFS, 9.5 m and 19.0 m, respectively; p [log-rank] < 0.0001) and validation (median PFS, 11.3 m and 22.5 m, respectively; p [log-rank] = 0.0063) cohorts. CONCLUSIONS: A radiomics-based model was established to predict the risk of progression in hypopharyngeal cancer with chemoradiotherapy. KEY POINTS: • Clinical information showed limited performance in stratifying the risk of progression among patients with hypopharyngeal cancer. • Imaging features extracted from CECT and NCCT images were independent predictors of PFS. • We combined significant features and valuable clinical variables to establish a nomogram to predict individual risk of progression.