Auger emitter in combination with Olaparib suppresses tumor growth via promoting antitumor immune responses in pancreatic cancer.

The present study aimed to clarify the hypothesis that auger emitter 125I particles in combination with PARP inhibitor Olaparib could inhibit pancreatic cancer progression by promoting antitumor immune response. Pancreatic cancer cell line (Panc02) and mice subcutaneously inoculated with Panc02 cells were employed for the in vitro and in vivo experiments, respectively, followed by 125I and Olaparib administrations. The apoptosis and CRT exposure of Panc02 cells were detected using flow cytometry assay. QRT-PCR, immunofluorescence, immunohistochemical analysis, and western blot were employed to examine mRNA and protein expression. Experimental results showed that 125I combined with Olaparib induced immunogenic cell death and affected antigen presentation in pancreatic cancer. 125I in combination with Olaparib influenced T cells and dendritic cells by up-regulating CD4, CD8, CD69, Caspase3, CD86, granzyme B, CD80, and type I interferon (IFN)-γ and down-regulating Ki67 in vivo. The combination also activated the cyclic GMP-AMP synthase stimulator of IFN genes (Sting) pathway in Panc02 cells. Moreover, Sting knockdown alleviated the effect of the combination of 125I and Olaparib on pancreatic cancer progression. In summary, 125I in combination with Olaparib inhibited pancreatic cancer progression through promoting antitumor immune responses, which may provide a potential treatment for pancreatic cancer.

Synthetic phase-sensitive inversion-recovery vessel for assessing extramural venous invasion in patients with rectal cancer: imaging quality and added value to T2-wighted imaging.

OBJECTIVES: To evaluate the imaging quality of a synthetic phase-sensitive inversion recovery (SyPSIR) vessel and to add value to T2-weighted imaging (T2WI) for extramural venous invasion (EMVI) detection in patients with rectal cancer. METHODS: Participants in this retrospective study underwent preoperative synthetic MRI between October 2020 and April 2022. SyPSIR image reconstruction was performed with a single inversion time of 10 ms. A junior and a senior radiologist evaluated the imaging quality, including overall imaging quality scores, motion artifact scores, and relative image signal intensity contrast between the tumor and peritumoral vessels (SItumor-vessel), of both T2WI and SyPSIR vessels. Differences in imaging quality between the two methods were assessed using the Wilcoxon signed-rank test and two-sample t-test. EMVI scores were recorded for T2WI and T2WI+SyPSIR vessel. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the diagnostic performance. RESULTS: A total of 106 patients (35 EMVI+ and 71 EMVI-) were evaluated. There were no statistically significant differences in the overall image quality scores, motion artifacts, or SItumor-vessel (p = 0.08-0.93) between the T2WI and SyPSIR vessels. On combining T2WI and SyPSIR vessels, the AUC for pathological EMVI+ diagnoses increased from 0.65 to 0.88 for the junior radiologist and from 0.86 to 0.96 for the senior radiologist. Furthermore, the sensitivity of the analyses by junior and senior radiologists increased from 0.40 to 0.77 and 0.49 to 0.86, respectively. CONCLUSION: A SyPSIR vessel can provide additional information to improve the diagnostic efficiency of pathological EMVI in rectal cancer, which may be beneficial for individualized clinical treatment. KEY POINTS: • SyPSIR vessel and T2WI had similar imaging quality. • EMVI evaluation in SyPSIR vessel has a high inter-observer agreement. • The SyPSIR vessel has the potential to improve the diagnostic efficiency of EMVI detection in rectal cancer.

Synthetic MRI in differentiating benign from metastatic retropharyngeal lymph node: combination with diffusion-weighted imaging.

OBJECTIVES: This study aimed to evaluate the synthetic MRI (syMRI), its combination with diffusion-weighted imaging (DWI), and morphological features for discriminating benign from metastatic retropharyngeal lymph nodes (RLNs). METHODS: Fifty-eight patients with a total of 63 RLNs (21 benign and 42 metastatic) were enrolled. The mean and standard deviation of syMRI-derived relaxometry parameters (T1, T2, PD; T1SD, T2SD, PDSD) were obtained from two different regions of interest (namely, partial-lesion and full-lesion ROI). The parameters derived from benign and metastatic RLNs were compared using Student's t or chi-square tests. Logistic regression analysis was used to construct a multi-parameter model of syMRI, syMRI + DWI, and syMRI + DWI + morphological features. Areas under the curve (AUC) were compared using the DeLong test to determine the best diagnostic approach. RESULTS: Benign RLNs had significantly higher T1, T2, PD, and T1SD values compared with metastatic RLNs in both partial-lesion and full-lesion ROI (all p < 0.05). The T1SD obtained from full-lesion ROI showed the best diagnostic performance among all syMRI-derived single parameters. The AUC of combined syMRI multiple parameters (T1, T2, PD, T1SD) were higher than those of any single parameter from syMRI. The combination of synthetic MRI and DWI can improve the AUC regardless of ROI delineation. Furthermore, the combination of synthetic MRI, DWI-derived quantitative parameters, and morphological features can significantly improve the overall diagnostic performance. CONCLUSIONS: The value of syMRI has been validated in differential diagnosis of benign and metastatic RLNs, and syMRI + DWI + morphological features can further improve the diagnostic efficiency for discriminating these two entities. KEY POINTS: • Synthetic MRI was useful in differential diagnosis of benign and metastatic RLNs. • The combination of syMRI, DWI, and morphological features can significantly improve the diagnostic efficiency.

Deep learning-extracted CT imaging phenotypes predict response to total resection in colorectal cancer.

BACKGROUND: Deep learning surpasses many traditional methods for many vision tasks, allowing the transformation of hierarchical features into more abstract, high-level features. PURPOSE: To evaluate the prognostic value of preoperative computed tomography (CT) image texture features and deep learning self-learning high-throughput features (SHF) on postoperative overall survival in the treatment of patients with colorectal cancer (CRC). MATERIAL AND METHODS: The dataset consisted of 810 enrolled patients with CRC confirmed from 10 November 2011 to 10 February 2018. In contrast, SHF extracted by deep learning with multi-task training mechanism and texture features were extracted from the CT with tumor volume region of interest, respectively, and combined with the Cox proportional hazard (CoxPH) model for initial validation to obtain a RAD score to classify patients into high- and low-risk groups. The SHF stability was further validated in combination with Neural Multi-Task Logistic Regression (N-MTLR) model. The overall recognition ability and accuracy of CoxPH and N-MTLR model were evaluated by C-index and Integrated Brier Score (IBS). RESULTS: SHF had a more significant degree of differentiation than texture features. The result is (SHF vs. texture features: C-index: 0.884 vs. 0.611; IBS: 0.025 vs. 0.073) in the CoxPH model, and (SHF vs. texture features: C-index: 0.861 vs. 0.630; IBS: 0.024 vs. 0.065) in N-MTLR. CONCLUSION: SHF is superior to texture features and has potential application for the preoperative prediction of the individualized treatment of CRC.

External auditory canal and middle ear tumors: characterization by morphology and diffusion features on CT and MRI.

PURPOSE: To explore the value of morphology and diffusion features on CT and MRI in the characterization of external auditory canal and middle ear tumors (EAMETs). METHODS: Forty-seven patients with histologically proved EAMETs (23 benign and 24 malignant) who underwent CT and MRI were retrospectively analyzed in this study. CT and MRI characteristics (including size, shape, signal intensity, border, enhancement degree, and bone changes) and apparent diffusion coefficient (ADC) value were analyzed and compared between benign and malignant EAMETs. Logistic regression, receiver operating characteristic (ROC) curve, and Delong test were performed to assess the diagnostic performance. RESULTS: Compared with benign tumors, the malignant EAMETs are characterized by irregular shape, ill-defined border, invasive bone destruction, and intense enhancement (all p < 0.05). There were no significant differences on the size and signal intensity between benign and malignant tumors. The ADC value of malignant tumors were (879.96 ± 201.15) × 10-6 mm2/s, which was significantly lower than benign ones (p < 0.05). Logistic regression demonstrates the presence of ill-defined margin, invasive bone destruction, and low ADC value (≤ 920.33 × 10-6 mm2/s) have significant relationship with malignant EAMETs. The combination of characterization by morphology and diffusion features on CT and MRI can further improve the diagnostic efficiency when compared with morphology and diffusion features alone (both p < 0.05). CONCLUSION: Some CT and MRI characteristics are helpful in identifying malignant EAMETs from benign ones (especially ill-defined margin, invasive bone destruction, and low ADC value), and the combination of morphology and diffusion features on CT and MRI has best diagnostic efficiency for discriminating these two entities.

Prediction of response to neoadjuvant chemotherapy in advanced gastric cancer: A radiomics nomogram analysis based on CT images and clinicopathological features.

PURPOSE: To investigate the feasibility of predicting the early response to neoadjuvant chemotherapy (NAC) in advanced gastric cancer (AGC) based on CT radiomics nomogram before treatment. MATERIALS AND METHODS: The clinicopathological data and pre-treatment portal venous phase CT images of 180 consecutive AGC patients who received 3 cycles of NAC are retrospectively analyzed. They are randomly divided into training set (n = 120) and validation set (n = 60) and are categorized into effective group (n = 83) and ineffective group (n = 97) according to RECIST 1.1. Clinicopathological features are compared between two groups using Chi-Squared test. CT radiomic features of region of interest (ROI) for gastric tumors are extracted, filtered and minimized to select optimal features and develop radiomics model to predict the response to NAC using Pyradiomics software. Furthermore, a nomogram model is constructed with the radiomic and clinicopathological features via logistic regression analysis. The receiver operating characteristic (ROC) curve analysis is used to evaluate model performance. Additionally, the calibration curve is used to test the agreement between prediction probability of the nomogram and actual clinical findings, and the decision curve analysis (DCA) is performed to assess the clinical usage of the nomogram model. RESULTS: Four optimal radiomic features are selected to construct the radiomics model with the areas under ROC curve (AUC) of 0.754 and 0.743, sensitivity of 0.732 and 0.750, specificity of 0.729 and 0.708 in the training set and validation set, respectively. The nomogram model combining the radiomic feature with 2 clinicopathological features (Lauren type and clinical stage) results in AUCs of 0.841 and 0.838, sensitivity of 0.847 and 0.804, specificity of 0.771 and 0.794 in the training set and validation set, respectively. The calibration curve generates a concordance index of 0.912 indicating good agreement of the prediction results between the nomogram model and the actual clinical observation results. DCA shows that patients can receive higher net benefits within the threshold probability range from 0 to 1.0 in the nomogram model than in the radiomics model. CONCLUSION: CT radiomics nomogram is a potential useful tool to assist predicting the early response to NAC for AGC patients before treatment.

Tuning oxygen vacancies in epitaxial LaInO3 films for ultraviolet photodetection.

LaInO3 (LIO) represents a new, to the best of knowledge, type of perovskite oxides for deep-ultraviolet (DUV) photodetection owing to the wide bandgap nature (∼5.0 eV) and the higher tolerance of defect engineering for tunable carrier transport. Here we fabricate fast-response DUV photodetectors based on epitaxial LIO thin films and demonstrate an effective strategy for balancing the photodetector performance using the oxygen growth pressure as a simple control parameter. Increasing the oxygen pressure is effective to suppress the oxygen vacancy formation in LIO, which is beneficial to suppress the dark current and enhance the response speed. The optimized LIO photodetector achieves a fast rise/fall time of 20 ms/73 ms, a low dark current of 2.0 × 10-12 A, a photo-to-dark current ratio of 1.2 × 103, and a detectivity of 6 × 1012 Jones.

Diagnostic performance of MRI, SPECT, and PET in detecting renal cell carcinoma: a systematic review and meta-analysis.

BACKGROUND: Renal cell carcinoma (RCC) is one of the most common malignancies worldwide. Noninvasive imaging techniques, such as magnetic resonance imaging (MRI), single photon emission computed tomography (SPECT), and positron emission tomography (PET), have been involved in increasing evolution to detect RCC. This meta-analysis aims to compare to compare the performance of MRI, SPECT, and PET in the detection of RCC in humans, and to provide evidence for decision-making in terms of further research and clinical settings. METHODS: Electronic databases including PubMed, Web of Science, Embase, and Cochrane Library were systemically searched. The keywords such as "magnetic resonance imaging", "MRI", "single-photon emission computed tomography", "SPECT", "positron emission tomography", "PET", "renal cell carcinoma" were used for the search. Studies concerning MRI, SPECT, and PET for the detection of RCC were included. Pooled sensitivity, specificity, and the area under the summary receiver operating characteristic (SROC) curve (AUC), etc. were calculated. RESULTS: A total of 44 articles were finally detected for inclusion in this study. The pooled sensitivities of MRI, 18F-FDG PET and 18F-FDG PET/CT were 0.80, 0.83, and 0.89, respectively. Their respective overall specificities were 0.90, 0.86, and 0.88. The pooled sensitivity and specificity of MRI studies at 1.5 T were 0.86 and 0.94, respectively. With respect to prospective PET studies, the pooled sensitivity, specificity and AUC were 0.90, 0.93 and 0.97, respectively. In the detection of primary RCC, PET studies manifested a pooled sensitivity, specificity, and AUC of 0.77, 0.80, and 0.84, respectively. The pooled sensitivity, specificity, and AUC of PET/CT studies in detecting primary RCC were 0.80, 0.85, and 0.89. CONCLUSION: Our study manifests that MRI and PET/CT present better diagnostic value for the detection of RCC in comparison with PET. MRI is superior in the diagnosis of primary RCC.

Differential diagnosis of diffuse sclerotic thyroid papillary carcinoma and Hashimoto's thyroiditis using fine-needle aspiration cytology, BRAFV600E , and ultrasound elastography.

BACKGROUND: The diffuse sclerosing variant of papillary thyroid carcinoma (DSV-PTC) has ultrasound findings that are similar to Hashimoto's thyroiditis (HT), resulting in under-diagnosis. DSV-PTC combined with HT is also common, so early and accurate diagnosis of DSV-PTC using a variety of diagnostic techniques, including FNAC, BRAFV600E mutation detection, and ultrasound elastography, is critical. OBJECTIVE: To assess the diagnostic value of fine-needle aspiration cytology (FNAC) and BRAFV600E detection in combination with ultrasound elastography in the diagnosis of DSV-PTC. METHODS: We performed a retrospective analysis of 40 patients with pathologically confirmed DSV-PTC and 43 patients with HT admitted to our hospital's ultrasound department between January 2015 and December 2020. Preoperative FNAC, BRAFV600E mutation detection, and ultrasound elastography imaging were all performed on all patients. For a definitive diagnosis, the results of these tests were compared to postoperative pathological findings. The diagnostic value of FNAC, BRAFV600E mutation detection, ultrasound elasticity imaging, and their combination for DSV-PTC diagnosis was assessed. RESULTS: The mean elastic strain rate ratio (E1/E2) of the 40 DSV-PTC cases was 5.75 ± 2.14, while that of the 43 HT cases was 2.81 ± 1.20. The receiver operating characteristic (ROC) curve was generated using the average value of E2/E1. The area under the ROC curve was 0.910, and the optimal E2/E1 cut-off value was 4.500. When FNAC, BRAFV600E mutation detection, and ultrasound elasticity imaging detection were combined, the diagnostic sensitivity, specificity, negative predictive value, positive predictive value, and accuracy of DSV-PTC diagnosis were 92.5%, 95.3%, 93.2%, 94.9%, and 94.0%, respectively, which were significantly higher than the single technique (p < 0.05). CONCLUSIONS: The use of FNAC, BRAFV600E mutation detection, and ultrasound elastography in combination is more helpful in establishing an accurate diagnosis of DSV-PTC than using a single diagnostic technique alone.

Can Arterial Spin Labeling Perfusion Imaging be Used to Differentiate Nasopharyngeal Carcinoma From Nasopharyngeal Lymphoma?

BACKGROUND: Differentiating nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoma (NPL) is useful for deciding the appropriate treatment. However, the diagnostic accuracy of current imaging methods is low. PURPOSE: To explore the feasibility of arterial spin labeling (ASL) perfusion imaging in the qualitative and quantitative differentiation between NPC and NPL to improve the diagnosis of malignancies in the nasopharynx. STUDY TYPE: Retrospective. POPULATION: Ninety seven patients: NPC (65 cases) and NPL (32 cases), histologically confirmed. FIELD STRENGTH/SEQUENCE: 3T/3D fast spin echo pseudo-continuous ASL imaging with spiral readout scheme, 3D inverse recovery- fast spoiled gradient recalled echo brain volume (BRAVO) imaging. ASSESSMENT: Cerebral blood flow (CBF) images from ASL perfusion imaging were assessed by three radiologists. Each tumor was visually scored based on CBF images. Intratumoral CBF and intramuscular CBF values were obtained from intratumoral and lateral pterygoid muscle areas, respectively. Through dividing intratumoral CBF by intramuscular CBF, normalized CBF (nCBF) was further calculated. STATISTICAL TESTS: Fleiss's kappa and intraclass correlation coefficients (ICCs) were used to assess interobserver agreement among the three readers. The Mann-Whitney U-test was used to compare visual scoring, and an unpaired t-test was performed to compare CBF value between the NPC and NPL groups. The area under the curve (AUC) value was used to quantify the diagnostic ability of each parameter. RESULTS: Good interobserver agreements were validated by high Fleiss's kappa and ICC values (all >0.80). NPCs showed significantly higher visual scores than NPLs (P < 0.05). Both intratumoral CBF and nCBF in NPC were significantly higher than those in NPL (both P < 0.05). Intratumoral CBF showed the highest AUC of 0.861 (P < 0.05) in differentiating NPC (n = 65) from NPL (n = 32), while the AUCs of nCBF and visual scoring were 0.847 and 0.753, respectively. DATA CONCLUSION: For the diagnosis of distinguishing NPC from NPL, ASL perfusion imaging demonstrated high diagnostic efficiency. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Feasibility and reproducibility of T2 mapping and DWI for identifying malignant lymph nodes in rectal cancer.

OBJECTIVES: To evaluate the diagnostic value and reproducibility of T2 mapping versus apparent diffusion coefficients (ADC) for identifying malignant lymph nodes in patients with non-mucinous rectal adenocarcinoma. METHODS: High-resolution magnetic resonance imaging, diffusion-weighted imaging, and T2 mapping were performed on patients with suspected metastatic lymph nodes in the mesorectum or around the superior rectal artery with a short-axis diameter of 4-10 mm. The T2 and ADC values of pathology-confirmed metastatic versus non-metastatic lymph nodes were compared using the independent-samples t test and receiver operating characteristic curves. Intra- and inter-observer reproducibility were tested. The cutoff value for T2 relaxation time was determined. RESULTS: In total, 67 lymph nodes underwent histological analysis, with 24 in the non-metastatic and 43 in the metastatic groups. Intra- and inter-observer agreements for T2 values were 0.999 and 0.998, respectively, which were higher than the ADC values of 0.924 and 0.844, respectively. The mean T2 and ADC values for metastatic lymph nodes (65 ± 7.8 ms and 1.17 ± 0.16 × 10-3 mm2/s, respectively) were significantly lower than for benign lymph nodes(83 ± 5.7 ms and 1.29 ± 0.15 × 10-3 mm2/s, respectively). T2 values had a higher AUC value of 0.990 than the AUC value for ADC of 0.729. With a cutoff value of 77 ms, sensitivity and specificity for T2 values were 95% and 96%, respectively. CONCLUSIONS: T2 mapping had higher diagnostic efficacy and reproducibility than ADC and may be useful in differentiating metastatic from non-metastatic lymph nodes in rectal cancer. KEY POINTS: • Mean T2 values were significantly shorter for malignant versus benign LNs in patients with non-mucinous rectal adenocarcinoma. • The diagnostic efficacy and reproducibility of T2 values were excellent and superior to ADC values.

Preoperative prediction for lauren type of gastric cancer: A radiomics nomogram analysis based on CT images and clinical features.

PURPOSE: To investigate feasibility of predicting Lauren type of gastric cancer based on CT radiomics nomogram before operation. MATERIALS AND METHODS: The clinical data and pre-treatment CT images of 300 gastric cancer patients with Lauren intestinal or diffuse type confirmed by postoperative pathology were retrospectively analyzed, who were randomly divided into training set and testing set with a ratio of 2:1. Clinical features were compared between the two Lauren types in the training set and testing set, respectively. Gastric tumors on CT images were manually segmented using ITK-SNAP software, and radiomic features of the segmented tumors were extracted, filtered and minimized using the least absolute shrinkage and selection operator (LASSO) regression to select optimal features and develop radiomics signature. A nomogram was constructed with radiomic features and clinical characteristics to predict Lauren type of gastric cancer. Clinical model, radiomics signature model, and the nomogram model were compared using the receiver operating characteristic (ROC) curve analysis with area under the curve (AUC). The calibration curve was used to test the agreement between prediction probability and actual clinical findings, and the decision curve was performed to assess the clinical usage of the nomogram model. RESULTS: In clinical features, Lauren type of gastric cancer relate to age and CT-N stage of patients (all p < 0.05). Radiomics signature was developed with the retained 10 radiomic features. The nomogram was constructed with the 2 clinical features and radiomics signature. Among 3 prediction models, performance of the nomogram was the best in predicting Lauren type of gastric cancer, with the respective AUC, accuracy, sensitivity and specificity of 0.864, 78.0%, 90.0%, 70.0%in the testing set. In addition, the calibration curve showed a good agreement between prediction probability and actual clinical findings (p > 0.05). CONCLUSION: The nomogram combining radiomics signature and clinical features is a useful tool with the increased value to predict Lauren type of gastric cancer.

Chronic pontine strokes: Diffusion tensor imaging of corticospinal tract indicates the prognosis in terms of motor outcome.

OBJECTIVE: To investigate relationship between the diffusion indexes of corticospinal tract (CST) and the neurological motor outcomes in chronic pontine stroke patients. METHODS: Diffusion tensor imaging (DTI) is performed in 27 patients with chronic pontine stroke. Fractional anisotropy (FA) values along the CST area, the track number, and the CST length are measured. Neurological and motor outcomes are evaluated based on Fugl-meyer (FM), National Institutes of Health Stroke Scale (NIHSS), Barthel index (BI), and modified Rankin scale (mRS) scores. The relationships between FA ratios (rFAs) in the CST of stroke subjects and their clinical motor scores are analyzed through Spearman's correlation analysis. Then, diffusion tensor tractography (DTT) is performed to show the injury degree of CST. RESULTS: First, FA values are decreased in the infarct area, cerebral peduncle, posterior limb of the internal capsule, and precentral gyrus compared with those in the contralateral side. The number of CST is decreased in the ipsilateral side of the infarct. Second, rFAs in the cerebral peduncle, posterior limb of the internal capsule, and CST rnum correlate positively with FM scores (r = 0.824, 0.672, 0.651, p < 0.001) and negatively with mRS scores (r = -0.835, -0.604, -0.645, p≤0.001). Third, the injury degree of CST correlates negatively with FM scores (r = -0.627, p < 0.001). CONCLUSIONS: The study demonstrates that rFAs in the cerebral peduncle, posterior limb of the internal capsule, and CST rnum associate with motor outcome, suggesting that DTI may be applicable for outcome evaluation.

A pilot study of radiomics signature based on biparametric MRI for preoperative prediction of extrathyroidal extension in papillary thyroid carcinoma.

OBJECTIVE: To investigate efficiency of radiomics signature to preoperatively predict histological features of aggressive extrathyroidal extension (ETE) in papillary thyroid carcinoma (PTC) with biparametric magnetic resonance imaging findings. MATERIALS AND METHODS: Sixty PTC patients with preoperative MR including T2WI and T2WI-fat-suppression (T2WI-FS) were retrospectively analyzed. Among them, 35 had ETE and 25 did not. Pre-contrast T2WI and T2WI-FS images depicting the largest section of tumor were selected. Tumor regions were manually segmented using ITK-SNAP software and 107 radiomics features were computed from the segmented regions using the open Pyradiomics package. Then, a random forest model was built to do classification in which the datasets were partitioned randomly 10 times to do training and testing with ratio of 1:1. Furthermore, forward greedy feature selection based on feature importance was adopted to reduce model overfitting. Classification accuracy was estimated on the test set using area under ROC curve (AUC). RESULTS: The model using T2WI-FS image features yields much higher performance than the model using T2WI features (AUC = 0.906 vs. 0.760 using 107 features). Among the top 10 important features of T2WI and T2WI-FS, there are 5 common features. After feature selection, the models trained using top 2 features of T2WI and the top 6 features of T2WI-FS achieve AUC 0.845 and 0.928, respectively. Combining features computed from T2WI and T2WI-FS, model performance decreases slightly (AUC = 0.882 based on all features and AUC = 0.913 based on top features after feature selection). Adjusting hyper parameters of the random forest model have negligible influence on the model performance with mean AUC = 0.907 for T2WI-FS images. CONCLUSIONS: Radiomics features based on pre-contrast T2WI and T2WI-FS is helpful to predict aggressive ETE in PTC. Particularly, the model trained using the optimally selected T2WI-FS image features yields the best classification performance. The most important features relate to lesion size and the texture heterogeneity of the tumor region.

Prognostic value of CT radiomics in evaluating lymphovascular invasion in rectal cancer: Diagnostic performance based on different volumes of interest.

OBJECTIVES: This study aims to evaluate diagnostic performance of radiomic analysis using computed tomography (CT) to identify lymphovascular invasion (LVI) in patients diagnosed with rectal cancer and assess diagnostic performance of different lesion segmentations. METHODS: The study is applied to 169 pre-treatment CT images and the clinical features of patients with rectal cancer. Radiomic features are extracted from two different volumes of interest (VOIs) namely, gross tumor volume and peri-tumor tissue volume. The maximum relevance and the minimum redundancy, and the least absolute shrinkage selection operator based logistic regression analyses are performed to select the optimal feature subset on the training cohort. Then, Rad and Rad-clinical combined models for LVI prediction are built and compared. Finally, the models are externally validated. RESULTS: Eighty-three patients had positive LVI on pathology, while 86 had negative LVI. An optimal multi-mode radiology nomogram for LVI estimation is established. The area under the receiver operating characteristic curves of the Rad and Rad-clinical combined model in the peri-tumor VOI group are significantly higher than those in the tumor VOI group (Rad: peri-tumor vs. tumor: 0.85 vs. 0.68; Rad-clinical: peri-tumor vs. tumor: 0.90 vs 0.82) in the validation cohort. Decision curve analysis shows that the peri-tumor-based Rad-clinical combined model has the best performance in identifying LVI than other models. CONCLUSIONS: CT radiomics model based on peri-tumor volumes improves prediction performance of LVI in rectal cancer compared with the model based on tumor volumes.

Identification of potential novel biomarkers to differentiate malignant thyroid nodules with cytological indeterminate.

BACKGROUND: The fine-needle aspiration (FNA) biopsy was broadly applied to clinical diagnostics evaluation for thyroid carcinomas nodule, while companioning with higher uncertainty rate (15~30%) to identify malignancy for cytological indeterminate cases. It is requirement to discover novel molecular biomarkers to differentiate malignant thyroid nodule more precise. METHODS: We employed weighted gene co-expression network analysis (WGCNA) to discover genes significantly associated with malignant histopathology for cytological indeterminate nodules. In addition, identified significantly genes were validated through another independently investigations of thyroid carcinomas patient's samples via cBioportal and Geipa. The key function pathways of significant genes involving were blast through GenClip. RESULTS: Twenty-four signature genes were identified significantly related to thyroid nodules malignancy. Furthermore, five novel genes with missense mutation, FN1 (R534P), PROS1((K200I), (Q571K)), SCEL (T320S), SLC34A2(T688M) and TENM1 (S1131F), were highlighted as potential biomarkers to rule out nodules malignancy. It was identified that the key functional pathways involving in thyroid carcinomas. CONCLUSION: These results will be helpful to better understand the mechanism of thyroid nodules malignant transformation and characterize the potentially biomarkers for thyroid carcinomas early diagnostics.

Preoperative assessment of extrathyroidal extension of papillary thyroid carcinomas by ultrasound and magnetic resonance imaging: a comparative study.

PURPOSE: The purpose of this study was to assess and compare the diagnostic performances of preoperative ultrasonography (US) and magnetic resonance imaging (MRI) in predicting extrathyroidal extension (ETE) in patients with papillary thyroid carcinoma (PTC). MATERIALS AND METHODS: This retrospective study was approved by our institutional review board. Preoperative US and MRI were performed on 225 patients who underwent surgery for PTC between May 2014 and December 2018. The US and MRI features of ETE of each case were retrospectively and independently investigated by two radiologists. The diagnostic performances of US and MRI, including their sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV) for ETE, and their accuracy in predicting ETE were analyzed. RESULTS: Higher sensitivity and NPV in predicting minimal ETE were observed in US (87.5% and 76.2%, respectively) compared with MRI (71.3% and 61.7%, respectively) (p = 0.006 and p = 0.046, respectively). Meanwhile, MRI (85.4%) showed higher sensitivity than US (66.7%) in assessing extensive ETE (p = 0.005). MRI also showed significantly higher specificity and PPV than US in assessing overall ETE (p = 0.025 and p = 0.025, respectively). CONCLUSION: Preoperative US should be used as the first line in predicting minimal ETE, and MRI should be added in extensive ETE assessment. Compared with US, MRI had higher specificity and PPV in detecting the overall ETE of PTC.

Radiomics study for predicting the expression of PD-L1 in non-small cell lung cancer based on CT images and clinicopathologic features.

PURPOSE: To predict programmed death-ligand 1 (PD-L1) expression of tumor cells in non-small cell lung cancer (NSCLC) patients by using a radiomics study based on CT images and clinicopathologic features. MATERIALS AND METHODS: A total of 390 confirmed NSCLC patients who performed chest CT scan and immunohistochemistry (IHC) examination of PD-L1 of lung tumors with clinic data were collected in this retrospective study, which were divided into two cohorts namely, training (n = 260) and validation (n = 130) cohort. Clinicopathologic features were compared between two cohorts. Lung tumors were segmented by using ITK-snap kit on CT images. Total 200 radiomic features in the segmented images were calculated using in-house texture analysis software, then filtered and minimized by least absolute shrinkage and selection operator (LASSO) regression to select optimal radiomic features based on its relevance of PD-L1 expression status in IHC results and develop radiomics signature. Radiomics signature and clinicopathologic risk factors were incorporated to develop prediction model by using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curves were generated and the areas under the curves (AUC) were reckoned to predict PD-L1 expression in both training and validation cohorts. RESULTS: In 200 extracted radiomic features, 9 were selected to develop radiomics signature. In univariate analysis, PD-L1 expression of lung tumors was significantly correlated with radiomics signature, histologic type, and histologic grade (p < 0.05, respectively). However, PD-L1 expression was not correlated with gender, age, tumor location, CEA level, TNM stage, and smoking (p > 0.05). For prediction of PD-L1 expression, the prediction model that combines radiomics signature and clinicopathologic features resulted in AUCs of 0.829 and 0.848 in the training and validation cohort, respectively. CONCLUSION: The prediction model that incorporates the radiomics signature and clinical risk factors has potential to facilitate the individualized prediction of PD-L1 expression in NSCLC patients and identify patients who can benefit from anti-PD-L1 immunotherapy.

Radiomics analysis of multicenter CT images for discriminating mucinous adenocarcinoma from nomucinous adenocarcinoma in rectal cancer and comparison with conventional CT values.

OBJECTIVE: To investigate the value of CT-based radiomics signature for preoperatively discriminating mucinous adenocarcinoma (MA) from nomucinous adenocarcinoma (NMA) in rectal cancer and compare with conventional CT values. METHOD: A total of 225 patients with histologically confirmed MA or NMA of rectal cancer were retrospectively enrolled. Radiomics features were computed from the entire tumor volume segmented from the post-contrast phase CT images. The maximum relevance and minimum redundancy (mRMR) and LASSO regression model were performed to select the best preforming features and build the radiomics models using a training cohort of 155 cases. Then, predictive performance of the models was validated using a validation cohort of 70 cases and receiver operating characteristics (ROC) analysis method. Meanwhile, CT values in post- and pre-contrast phase, as well as their difference (D-values) of tumors in two cohorts were measured by two radiologists. ROC curves were also calculated to assess diagnostic efficacies. RESULTS: One hundred and sixty-three patients were confirmed by pathology as NMA and 62 cases were MA. The radiomics signature comprised 19 selected features and showed good discrimination performance in both the training and validation cohorts. The areas under ROC curves (AUC) are 0.93 (95% confidence interval [CI]: 0.89-0.98) in training cohort and 0.93 (95% CI: 0.87-0.99) in validation cohort, respectively. Three sets of CT values of MA in pre- and post-contrast phase, and their difference (D-value) (31±7.0, 51±12.6 and 20±9.3, respectively) were lower than those of NMA (37±5.6, 69±13.3 and 32±11.7, respectively). Comparing to the radiomics signature, using three sets of conventional CT values yielded relatively low diagnostic performance with AUC of 0.84 (95% CI: 0.78-0.88), 0.75 (95% CI: 0.69-0.81) and 0.78 (95% CI: 0.72-0.83), respectively. CONCLUSION: This study demonstrated that CT radiomics features could be utilized as a noninvasive biomarker to identify MA patients from NMA of rectal cancer preoperatively, which is more accurate than using the conventional CT values.

Radiomics study for differentiating gastric cancer from gastric stromal tumor based on contrast-enhanced CT images.

PURPOSE: To test the feasibility of differentiate gastric cancer from gastric stromal tumor using a radiomics study based on contrast-enhanced CT images. MATERIALS AND METHODS: The contrast-enhanced CT image data of 60 patients with gastric cancer and 40 patients with gastric stromal tumor confirmed by postoperative pathology were retrospectively analyzed. First, CT images were read by two senior radiologists to acquire subjective CT signs model, including perigastric fatty infiltration, perigastric enlarged lymph nodes, the enhancement and growth modes of gastric tumors. Second, the manual segmentation of gastric tumors from the CT images was performed by the two radiologists to extract radiomics features via ITK-SNAP software, and to construct radiomics signature model. Finally, a diagnostic model integrated with subjective CT signs and radiomics signatures was constructed. The diagnostic efficacy of three models in differentiating gastric cancer from gastric stromal tumor was compared by using receiver operating characteristic curves (ROC). RESULTS: There are statistically significant differences between the gastric cancer and gastric stromal tumor in the perigastric enlarged lymph nodes, growth mode and radiomics signature (p < 0.05). The area under ROC curve (AUC), sensitivity and accuracy of subjective CT signs model were the lowest among the three models. While the combined model yields the highest AUC value (0.903), specificity (93.33%) and accuracy (86.00%) among the three models (p = 0.03). CONCLUSION: The diagnostic model integrating subjective CT signs and radiomics signature can improve the diagnostic accuracy of gastric tumors.