Application value of CT radiomic nomogram in predicting T790M mutation of lung adenocarcinoma.

BACKGROUND: The purpose of this study was to develop a radiomic nomogram to predict T790M mutation of lung adenocarcinoma base on non-enhanced CT lung images. METHODS: This retrospective study reviewed demographic data and lung CT images of 215 lung adenocarcinoma patients with T790M gene test results. 215 patients (including 52 positive) were divided into a training set (n = 150, 36 positive) and an independent test set (n = 65, 16 positive). Multivariate logistic regression was used to select demographic data and CT semantic features to build clinical model. We extracted quantitative features from the volume of interest (VOI) of the lesion, and developed the radiomic model with different feature selection algorithms and classifiers. The models were trained by a 5-fold cross validation strategy on the training set and assessed on the test set. ROC was used to estimate the performance of the clinical model, radiomic model, and merged nomogram. RESULTS: Three demographic features (gender, smoking, emphysema) and ten radiomic features (Kruskal-Wallis as selection algorithm, LASSO Logistic Regression as classifier) were determined to build the models. The AUC of the clinical model, radiomic model, and nomogram in the test set were 0.742(95%CI, 0.619-0.843), 0.810(95%CI, 0.696-0.907), 0.841(95%CI, 0.743-0.938), respectively. The predictive efficacy of the nomogram was better than the clinical model (p = 0.042). The nomogram predicted T790M mutation with cutoff value was 0.69 and the score was above 130. CONCLUSION: The nomogram developed in this study is a non-invasive, convenient, and economical method for predicting T790M mutation of lung adenocarcinoma, which has a good prospect for clinical application.

Delta-radiomics models based on multi-phase contrast-enhanced magnetic resonance imaging can preoperatively predict glypican-3-positive hepatocellular carcinoma.

Objectives: The aim of this study is to investigate the value of multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) based on the delta radiomics model for identifying glypican-3 (GPC3)-positive hepatocellular carcinoma (HCC). Methods: One hundred and twenty-six patients with pathologically confirmed HCC (training cohort: n = 88 and validation cohort: n = 38) were retrospectively recruited. Basic information was obtained from medical records. Preoperative multi-phase CE-MRI images were reviewed, and the 3D volumes of interest (VOIs) of the whole tumor were delineated on non-contrast T1-weighted imaging (T1), arterial phase (AP), portal venous phase (PVP), delayed phase (DP), and hepatobiliary phase (HBP). One hundred and seven original radiomics features were extracted from each phase, and delta-radiomics features were calculated. After a two-step feature selection strategy, radiomics models were built using two classification algorithms. A nomogram was constructed by combining the best radiomics model and clinical risk factors. Results: Serum alpha-fetoprotein (AFP) (p = 0.013) was significantly related to GPC3-positive HCC. The optimal radiomics model is composed of eight delta-radiomics features with the AUC of 0.805 and 0.857 in the training and validation cohorts, respectively. The nomogram integrated the radiomics score, and AFP performed excellently (training cohort: AUC = 0.844 and validation cohort: AUC = 0.862). The calibration curve showed good agreement between the nomogram-predicted probabilities and GPC3 actual expression in both training and validation cohorts. Decision curve analysis further demonstrates the clinical practicality of the nomogram. Conclusion: Multi-phase CE-MRI based on the delta-radiomics model can non-invasively predict GPC3-positive HCC and can be a useful method for individualized diagnosis and treatment.

Diffusion-Weighted Imaging as a Quantitative Imaging Biomarker for Predicting Proliferation Rate in Hepatocellular Carcinoma: Developing a Radiomics Nomogram.

PURPOSE: This study aimed to explore the predictive performance of diffusion-weighted imaging with apparent diffusion coefficient map in predicting the proliferation rate of hepatocellular carcinoma and to develop a radiomics-based nomogram. METHODS: This was a single-center retrospective study. A total of 110 patients were enrolled. The sample included 38 patients with low Ki67 expression (Ki67 ≤10%) and 72 with high Ki67 expression (Ki67 >10%) as demonstrated by surgical pathology. Patients were randomly divided into either a training (n = 77) or validation (n = 33) cohort. Diffusion-weighted imaging with apparent diffusion coefficient maps was used to extract radiomic features and the signal intensity values of tumor (SI tumor ), normal liver (SI liver ), and background noise (SI background ) from all samples. Subsequently, the clinical model, radiomic model, and fusion model (with clinical data and radiomic signature) were developed and validated. RESULTS: The area under the curve (AUC) of the clinical model for predicting the Ki67 expression including serum α-fetoprotein level ( P = 0.010), age ( P = 0.015), and signal noise ratio ( P = 0.026) was 0.799 and 0.715 in training and validation cohorts, respectively. The AUC of the radiomic model constructed by 9 selected radiomic features was 0.833 and 0.772 in training and validation cohorts, respectively. The AUC of the fusion model containing serum α-fetoprotein level ( P = 0.011), age ( P = 0.019), and rad score ( P < 0.001) was 0.901 and 0.781 in training and validation cohorts, respectively. CONCLUSIONS: Diffusion-weighted imaging as a quantitative imaging biomarker can predict Ki67 expression level in hepatocellular carcinoma across various models.

A study on quality control using delta data with machine learning technique.

Background: In the big data era, patient-based real-time quality control (PBRTQC), as an emerging quality control (QC) method, is expanding within the clinical laboratory industry. However, the main issue of current PBRTQC methodology is data stability. Our study is aimed to explore a novel protocol for data stability by combining delta data with machine learning (ML) technique to improve the capacity of QC event detection. Methods: A data set of 423,290 laboratory results from Beijing Chao-yang Hospital 2019 patient results were used as a training set (n = 380960, 90%) and internal validation set (n = 42330, 10%). A further 22,460 results from Beijing Long-fu Hospital 2019 patient results were used as a test set. Three-type data (1) Single-type data processed by truncation limits; (2) delta-type data processed by truncation limits and (3)delta-type data processed by Isolated Forest (IF) algorithm were evaluated with accuracy, sensitivity, NPed, etc., and compared with previously published statistical methods. Results: The optimal model was based on Random Forest (RF) algorithm by using delta-type data processed by IF algorithm. The model had a better accuracy (0.99), sensitivity (0.99) specificity (0.99) and AUC (0.99) with the dependent test set, surpassing the critical bias of PBRTQC by over 50%. For the LYMPH#, HGB, and PLT, the cumulative MNPed of MLQC were reduced by 95.43%, 97.39%, and 97.97% respectively when compared to the best of the PBRTQC. Conclusion: Final results indicate that by integrating an innovative ML algorithm with the overall data processing protocol the detection of QC events is improved.

Radiomic analysis based on multi-phase magnetic resonance imaging to predict preoperatively microvascular invasion in hepatocellular carcinoma.

BACKGROUND: The prognosis of hepatocellular carcinoma (HCC) remains poor and relapse occurs in more than half of patients within 2 years after hepatectomy. In terms of recent studies, microvascular invasion (MVI) is one of the potential predictors of recurrence. Accurate preoperative prediction of MVI is potentially beneficial to the optimization of treatment planning. AIM: To develop a radiomic analysis model based on pre-operative magnetic resonance imaging (MRI) data to predict MVI in HCC. METHODS: A total of 113 patients recruited to this study have been diagnosed as having HCC with histological confirmation, among whom 73 were found to have MVI and 40 were not. All the patients received preoperative examination by Gd-enhanced MRI and then curative hepatectomy. We manually delineated the tumor lesion on the largest cross-sectional area of the tumor and the adjacent two images on MRI, namely, the regions of interest. Quantitative analyses included most discriminant factors (MDFs) developed using linear discriminant analysis algorithm and histogram analysis with MaZda software. Independent significant variables of clinical and radiological features and MDFs for the prediction of MVI were estimated and a discriminant model was established by univariate and multivariate logistic regression analysis. Prediction ability of the above-mentioned parameters or model was then evaluated by receiver operating characteristic (ROC) curve analysis. Five-fold cross-validation was also applied via R software. RESULTS: The area under the ROC curve (AUC) of the MDF (0.77-0.85) outperformed that of histogram parameters (0.51-0.74). After multivariate analysis, MDF values of the arterial and portal venous phase, and peritumoral hypointensity in the hepatobiliary phase were identified to be independent predictors of MVI (P < 0.05). The AUC value of the model was 0.939 [95% confidence interval (CI): 0.893-0.984, standard error: 0.023]. The result of internal five-fold cross-validation (AUC: 0.912, 95%CI: 0.841-0.959, standard error: 0.0298) also showed favorable predictive efficacy. CONCLUSION: Noninvasive MRI radiomic model based on MDF values and imaging biomarkers may be useful to make preoperative prediction of MVI in patients with primary HCC.

Traceable machine learning real-time quality control based on patient data.

OBJECTIVES: Patient-based real-time quality control (PBRTQC) has gained attention as an alternative/integrative tool for internal quality control (iQC). However, it is still doubted for its performance and its application in real clinical settings. We aim to generate a newly and easy-to-access patient-based real-time QC by machine learning (ML) traceable to standard reference data with assigned values by National Institute of Metrology of China (NIM), and to compare it with PBRTQC for clinical validity evaluation. METHODS: For five representative biochemistry analytes, 1,195 000 patient testing results each were collected. After data processing, independent training and test sets were divided. Machine learning internal quality control (MLiQC) was set up by Random Forest in ML and was validated by way of both metrology algorithm traceability and 4 PBRTQC methods recommended by IFCC analytical working group. RESULTS: MLiQC were established. As an example of albumin (ALB) at the critical bias, the uncertainty of MLiQC was 0.14%, which was evaluated by standard reference data produced by NIM. Compared with four optimal PBRTQC methods at critical bias, the average of the number of patient samples from a bias introduced until detected (ANPed) of MLiQC averagely decreased from 600 to 20. The median and 95 quantiles of NPeds (MNPed and 95NPed) of MLiQC were superior to all optimal PBRTQCs above 90% for all test items. CONCLUSIONS: MLiQC is highly superior to PBRTQC and well-suited in real settings. The validation of the model from two aspects of algorithm traceability and clinical effectiveness confirms its satisfactory performance.

A multi-model fusion algorithm as a real-time quality control tool for small shift detection.

BACKGROUND: Patient-based real-time quality control (PBRTQC), a complement to traditional QC, may eliminate matrix effect from QC materials, realize real-time monitoring as well as cut costs. However, the accuracy of PBRTQC has not been satisfactory as physicians expect till now. Our aim is to set up a artificial intelligence-based QC for small error detection in real laboratory settings. Taking tPSA as our unique research subject, data extraction, data stimulation, data partition, model construction and evaluation were designed. METHODS: 84241 deidentified results for tPSA were extracted from Laboratory Information System of Aviation General Hospital. The data set was accumulated by way of data simulation. Independent training and test datasets were separated. After three classification models (RF, SVM and DNN) in ML constructed and weighted by information entropy, a multi-model fusion algorithm was generated. Performance of the fusion model was evaluated by comparing with optimal PBRTQC. RESULTS: For 4 PBRTQC methods, MovSO showed overall better performance for 0.2 µg/L bias and optimal MNPed was equal to 200. For the fusion model, MNPeds were less than 12 for all biases, and ACC surpassed MovSO nearly 100 times. Except for 0.01 µg/L bias, ACC was more than 0.9 for the rest of biases. FPR was apparently lower than MovSO, only 0.2% and 0.1%. CONCLUSION: The fusion model shows outstanding performance and reduces incorrect and omitting error detection, adaptable for the real settings.

Multi-Region Radiomic Analysis Based on Multi-Sequence MRI Can Preoperatively Predict Microvascular Invasion in Hepatocellular Carcinoma.

Objectives: Microvascular invasion (MVI) affects the postoperative prognosis in hepatocellular carcinoma (HCC) patients; however, there remains a lack of reliable and effective tools for preoperative prediction of MVI. Radiomics has shown great potential in providing valuable information for tumor pathophysiology. We constructed and validated radiomics models with and without clinico-radiological factors to predict MVI. Methods: One hundred and fifteen patients with pathologically confirmed HCC (training set: n = 80; validation set: n = 35) who underwent preoperative MRI were retrospectively recruited. Radiomics models based on multi-sequence MRI across various regions (including intratumoral and/or peritumoral areas) were built using four classification algorithms. A clinico-radiological model was constructed individually and combined with a radiomics model to generate a fusion model by multivariable logistic regression. Results: Among the radiomics models, the model based on T2WI and arterial phase (T2WI-AP model) in the volume of the liver-HCC interface (VOIinterface) exhibited the best predictive power, with AUCs of 0.866 in the training group and 0.855 in the validation group. The clinico-radiological model exhibited good efficacy (AUC: 0.819 and 0.717, respectively). The fusion model showed excellent predictive ability (AUC: 0.915 and 0.868, respectively), outperforming both the clinico-radiological and the T2WI-AP models in the training and validation sets. Conclusion: The fusion model of multi-region radiomics achieves an enhanced prediction of the individualized risk estimation of MVI in HCC patients. This may be a beneficial tool for clinicians to improve decision-making in personalized medicine.

A highly accurate delta check method using deep learning for detection of sample mix-up in the clinical laboratory.

OBJECTIVES: Delta check (DC) is widely used for detecting sample mix-up. Owing to the inadequate error detection and high false-positive rate, the implementation of DC in real-world settings is labor-intensive and rarely capable of absolute detection of sample mix-ups. The aim of the study was to develop a highly accurate DC method based on designed deep learning to detect sample mix-up. METHODS: A total of 22 routine hematology test items were adopted for the study. The hematology test results, collected from two hospital laboratories, were independently divided into training, validation, and test sets. By selecting six mainstream algorithms, the Deep Belief Network (DBN) was able to learn error-free and artificially (intentionally) mixed sample results. The model's analytical performance was evaluated using training and test sets. The model's clinical validity was evaluated by comparing it with three well-recognized statistical methods. RESULTS: When the accuracy of our model in the training set reached 0.931 at the 22nd epoch, the corresponding accuracy in the validation set was equal to 0.922. The loss values for the training and validation sets showed a similar (change) trend over time. The accuracy in the test set was 0.931 and the area under the receiver operating characteristic curve was 0.977. DBN demonstrated better performance than the three comparator statistical methods. The accuracy of DBN and revised weighted delta check (RwCDI) was 0.931 and 0.909, respectively. DBN performed significantly better than RCV and EDC. Of all test items, the absolute difference of DC yielded higher accuracy than the relative difference for all methods. CONCLUSIONS: The findings indicate that input of a group of hematology test items provides more comprehensive information for the accurate detection of sample mix-up by machine learning (ML) when compared with a single test item input method. The DC method based on DBN demonstrated highly effective sample mix-up identification performance in real-world clinical settings.

Influence of Parent Concrete Properties on Compressive Strength and Chloride Diffusion Coefficient of Concrete with Strengthened Recycled Aggregates.

Parent concrete coming from a wide range of sources can result in considerable differences in the properties of recycled coarse aggregate (RCA). In this study, the RCAs were obtained by crushing the parent concrete with water-to-cement ratios (W/Cparent) of 0.4, 0.5 and 0.6, respectively, and were strengthened by carbonation and nano-silica slurry wrapping methods. It was found that when W/Cparen was 0.3, 0.4 and 0.5, respectively, compared with the mortar in the untreated RCA, the capillary porosity of the mortar in the carbonated RCA decreased by 19%, 16% and 30%, respectively; the compressive strength of concrete containing the carbonated RCA increased by 13%, 11% and 13%, respectively; the chloride diffusion coefficient of RAC (DRAC) containing the nano-SiO2 slurry-treated RCA decreased by 17%, 16% and 11%; and that of RAC containing the carbonated RCA decreased by 21%, 25% and 26%, respectively. Regardless of being strengthened or not, both DRAC and porosity of old mortar in RCAs increased with increasing W/Cparent. For different types of RCAs, DRAC increased obviously with increasing water absorption of RCA. Finally, a theoretical model of DRAC considering the water absorption of RCA was established and verified by experiments, which can be used to predict the DRAC under the influence of different factors, especially the water absorption of RCA.

Niclosamide Induces Cell Cycle Arrest in G1 Phase in Head and Neck Squamous Cell Carcinoma Through Let-7d/CDC34 Axis.

Niclosamide is a traditional anti-tapeworm drug that exhibits potent anti-cancer activity. Our previous study showed that niclosamide induces cell cycle arrest in G1 phase. Nevertheless, the underlying mechanism remains unknown. The following study investigated the molecular mechanism through which niclosamide induced G1 arrest in head and neck squamous cell carcinoma (HNSCC) cell lines. The effect of niclosamide on human HNSCC cell line WSU-HN6 and CNE-2Z were analyzed using IncuCyte ZOOMTM assay, flow cytometry (FCM), real-time PCR and western blot. Luciferase assay was conducted to demonstrate the interaction between let-7d (a let-7 family member which functions as a tumor suppressor by regulating cell cycle) and 3'UTR of CDC34 mRNA. Xenografts tumor model was established to evaluate the niclosamide treatment efficacy in vivo. Briefly, an exposure to niclosamide treatment led to an increased let-7d expression and a decreased expression of cell cycle regulator CDC34, finally leading to G1 phase arrest. Moreover, an overexpression of let-7d induced G1 phase arrest and downregulated CDC34, while the knockdown of let-7d partially rescued the niclosamide-induced G1 phase arrest. Luciferase assay confirmed the direct inhibition of CDC34 through the targeting of let-7d. Furthermore, niclosamide markedly inhibited the xenografts growth through up-regulation of let-7d and down-regulation of CDC34. To sum up, our findings suggest that niclosamide induces cell cycle arrest in G1 phase in HNSCC through let-7d/CDC34 axis, which enriches the anti-cancer mechanism of niclosamide.

Niclosamide acts as a new inhibitor of vasculogenic mimicry in oral cancer through upregulation of miR-124 and downregulation of STAT3.

Tumors require nutrients and oxygen for growth and metastasis. Vasculogenic mimicry (VM) has been found as a new manner of blood supply, which is characterized as the formation of tumor cell-lined vessels instead of endothelial vessels. This is why angiogenesis agents targeted to endothelial cells show a limited efficacy. Up to this point, there is no effective drug reported for inhibiting VM formation. Niclosamide is an oral anti-helminthic drug used to treat human tapeworms. Recent studies have indicated that niclosamide has broad applications for cancer and other diseases. In this study, we found that niclosamide could not only inhibit proliferation and promote apoptosis of oral cancer cells, but also inhibited VM formation in vitro and in vivo through downregulation of the expression of VM-related genes VEGFA, MMP2, ROCK1 and Cdc42. In addition, niclosamide upregulated miR-124 and downregulate phosphorylated (p)-STAT3 expression. Further studies showed that, the stable highly expressing miR-124 cell line HN6-miR-124, such as niclosamide, could downregulate p-STAT3 expression. Moreover, HN6-miR­124 showed lower mobility, invasiveness and VM formation ability than control cells. Taken together, our study suggests that niclosamide functions as a new inhibitor of VM in oral cancer through upregulation of miR-124 and downregulation of STAT3, providing a new and safe potential drug candidate for anti-VM therapy.

Targeting of cell cycle and let-7a/STAT3 pathway by niclosamide inhibits proliferation, migration and invasion in oral squamous cell carcinoma cells.

The low median survival rate of oral squamous cell carcinoma (OSCC) is associated with chemotherapeutic resistance. Niclosamide is an oral anti-helminthic drug, its anti-cancer effect has been reported in recent years. However, the effect of niclosamide on OSCC remains largely unknown. In this study, we, for the first time, investigated the underlying mechanisms from cell cycle arrest and let-7a/STAT3 axis through CCK-8, cell cycle, apoptosis, wound healing, Transwell invasion, generation of stable cell line, real-time PCR, and western blot assays using two OSCC cell lines WSU-HN6 and Tca83. We showed that niclosamide could inhibit OSCC cells proliferation through causing cell cycle arrest in G1 phase and promoting apoptosis, while the cell cycle-related proteins MCM2, MCM7, CDK2 and CDK4 were downregulated and the apoptosis-related proteins p53 and cleaved caspase-3 were upregulated. Furthermore, niclosamide could inhibit migration and invasion of OSCC through upregulation of let-7a expression and downregulation of p-STAT3 expression. What is more, we established the stably expressing let-7a cell line (HN6-let-7a). Like niclosamide, HN6-let-7a could decrease the ability of the cell migration, invasion as well as the expression of p-STAT3. Collectively, our study finds the new mechanisms that niclosamide inhibits OSCC proliferation through causing cell cycle arrest in G1 phase via downregulation of the above cell cycle-related genes; promotes OSCC apoptosis through upregulation of pro-apoptotic genes; decreases migration and invasion of OSCC by let-7a/STAT3 axis, thus providing a preferred therapeutic candidate for OSCC in future.