Evaluating the added value of synthetic magnetic resonance imaging in predicting sentinel lymph node status in breast cancer.

Background: The noninvasive prediction of sentinel lymph node (SLN) metastasis using quantitative magnetic resonance imaging (MRI), particularly with synthetic MRI (syMRI), is an emerging field. This study aimed to explore the potential added benefits of syMRI over conventional MRI and diffusion-weighted imaging (DWI) in predicting metastases in SLNs. Methods: This retrospective study consecutively enrolled 101 patients who were diagnosed with breast cancer (BC) and underwent SLN biopsy from December 2022 to October 2023 at the Affiliated Hospital of Jiangnan University. These patients underwent preoperative MRI including conventional MRI, DWI, and syMRI and were categorized into two groups according to the postoperative pathological results: those with and without metastatic SLNs. MRI morphological features, DWI, and syMRI-derived quantitative parameters of breast tumors were statistically compared between these two groups. Binary logistic regression was used to separately develop predictive models for determining the presence of SLN involvement, with variables that exhibited significant differences being incorporated. The performance of each model was evaluated through receiver operating characteristic (ROC) curve analysis, including the area under the curve (AUC), sensitivity, and specificity. Results: Compared to the group of 54 patients with BC but no metastatic SLNs, the group of 47 patients with BC and metastatic SLNs had a significantly larger maximum axis diameter [metastatic SLNs: median 2.40 cm, interquartile range (IQR) 1.50-3.00 cm; no metastatic SLNs: median 1.80 cm, IQR 1.37-2.50 cm; P=0.03], a higher proton density (PD) (78.44±11.92 vs. 69.20±10.63 pu; P<0.001), and a lower apparent diffusion coefficient (ADC) (metastatic SLNs: median 0.91×10-3 mm2/s, IQR 0.79-1.01 mm2/s; no metastatic SLNs: median 1.02×10-3 mm2/s, IQR 0.92-1.12 mm2/s; P=0.001). Moreover, the prediction model with maximum axis diameter and ADC yielded an AUC of 0.71 [95% confidence interval (CI): 0.618-0.802], with a sensitivity of 78.72% and a specificity of 51.85%; After addition of syMRI-derived PD to the prediction model, the AUC increased significantly to 0.86 (AUC: 0.86 vs. 0.71; 95% CI: 0.778-0.922; P=0.002), with a sensitivity of 80.85% and a specificity of 81.50%. Conclusions: Combined with conventional MRI and DWI, syMRI can offer additional value in enhancing the predictive performance of determining SLN status before surgery in patients with BC.

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.

A nomogram for predicting postoperative complications based on tumor spectral CT parameters and visceral fat area in gastric cancer patients.

PURPOSE: To construct a nomogram combining tumor spectral CT parameters and visceral fat area (VFA) to predict postoperative complications (POCs) in patients with gastric cancer (GC). METHOD: This retrospective study included 101 GC patients who underwent preoperative abdominal spectral CT scan and were divided into two groups (37 with POCs and 64 without POCs) according to the Clavien-Dindo classification standard. Logistic regression was used to establish spectral, VFA, and combined models for predicting POCs. The combined prediction model was presented as a nomogram, and the diagnostic performance of each model was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: The AUCs of the VFA and spectral model were 0.71 (95% CI: 0.62-0.80) and 0.81 (95% CI: 0.72-0.88), respectively. VFA, the slope of spectral curve (λ) in venous phase (λ-VP) and tumor Hounsfield units on monoenergetic images 40 keV in VP (MonoE40keV-VP) were independent predictors of POCs in GC. The nomogram yielded an AUC of 0.89 (95% CI: 0.81-0.94). The combined model was superior to the VFA or spectral models by comparing their AUCs (P = 0.000 and 0.022). CONCLUSIONS: The nomogram based on two tumor spectral parameters (λ-VP, MonoE40keV-VP) and VFA could serve as a convenient tool for predicting the POCs of GC patients.

Predictive modeling based on tumor spectral CT parameters and clinical features for postoperative complications in patients undergoing colon resection for cancer.

BACKGROUND: Colon cancer is a particularly prevalent malignancy that produces postoperative complications (POCs). However, limited imaging modality exists on the accurate diagnosis of POCs. The purpose of this study was therefore to construct a model combining tumor spectral CT parameters and clinical features to predict POCs before surgery in colon cancer. METHODS: This retrospective study included 85 patients who had preoperative abdominal spectral CT scans and underwent radical colon cancer resection at our institution. The patients were divided into two groups based on the absence (no complication/grade I) or presence (grades II-V) of POCs according to the Clavien-Dindo grading system. The visceral fat areas (VFA) of patients were semi-automatically outlined and calculated on L3-level CT images using ImageJ software. Clinical features and tumor spectral CT parameters were statistically compared between the two groups. A combined model of spectral CT parameters and clinical features was established by stepwise regression to predict POCs in colon cancer. The diagnostic performance of the model was evaluated using the receiver operating characteristic (ROC) curve, including area under the curve (AUC), sensitivity, and specificity. RESULTS: Twenty-seven patients with POCs and 58 patients without POCs were included in this study. MonoE40keV-VP and VFA were independent predictors of POCs. The combined model based on predictors yielded an AUC of 0.84 (95% CI: 0.74-0.91), with a sensitivity of 77.8% and specificity of 87.9%. CONCLUSIONS: The model combining MonoE40keV-VP and VFA can predict POCs before surgery in colon cancer and provide a basis for individualized management plans. CRITICAL RELEVANCE STATEMENT: The model combining MonoE40keV-VP and visceral fat area can predict postoperative complications before surgery in colon cancer and provide a basis for individualized management plans. KEY POINTS: • Visceral fat area and MonoE40keV-VP were independent predictors of postoperative complications in colon cancer. • The combined model yielded a high AUC, sensitivity, and specificity in predicting postoperative complications. • The combined model was superior to the single visceral fat area or MonoE40keV-VP in predicting postoperative complications.

Immunotherapy Efficacy Prediction for Non-Small Cell Lung Cancer Using Multi-View Adaptive Weighted Graph Convolutional Networks.

Immunotherapy is an effective way to treat non-small cell lung cancer (NSCLC). The efficacy of immunotherapy differs from person to person and may cause side effects, making it important to predict the efficacy of immunotherapy before surgery. Radiomics based on machine learning has been successfully used to predict the efficacy of NSCLC immunotherapy. However, most studies only considered the radiomic features of the individual patient, ignoring the inter-patient correlations. Besides, they usually concatenated different features as the input of a single-view model, failing to consider the complex correlation among features of multiple types. To this end, we propose a multi-view adaptive weighted graph convolutional network (MVAW-GCN) for the prediction of NSCLC immunotherapy efficacy. Specifically, we group the radiomic features into several views according to the type of the fitered images they extracted from. We construct a graph in each view based on the radiomic features and phenotypic information. An attention mechanism is introduced to automatically assign weights to each view. Considering the view-shared and view-specific knowledge of radiomic features, we propose separable graph convolution that decomposes the output of the last convolution layer into two components, i.e., the view-shared and view-specific outputs. We maximize the consistency and enhance the diversity among different views in the learning procedure. The proposed MVAW-GCN is evaluated on 107 NSCLC patients, including 52 patients with valid efficacy and 55 patients with invalid efficacy. Our method achieved an accuracy of 77.27% and an area under the curve (AUC) of 0.7780, indicating its effectiveness in NSCLC immunotherapy efficacy prediction.

Combination of transcatheter arterial chemoembolization and anti-PD-L1 liposomes therapy suppressed hepatocellular carcinoma progression in mice.

BACKGROUND: Hepatocellular carcinoma (HCC) is a serious life-threatened tumor with high morbidity and mortality. This study aimed to study the effects of combination TACE and anti-PD-L1 liposome drug in treating HCC in mice models. METHODS: We constructed the liposome drug with phosphatidylcholine and cholesterol and mannitol, etc. Besides, the HCC mice model was established through abdominal subcutaneous injection HepG2 cancer cells in mice, then the PE-10 polyethylene catheter was used for TACE therapy. The mice were separately received transcatheter arterial chemoembolization treatment, avelumab liposome drug therapy, and TACE combined with avelumab liposome drug therapy. Flow cytometry was used to analyze cell apoptosis. Western blot, Immunofluorescence staining, real-time PCR were performed to detect protein and gene expressions. RESULTS: The liposomes drug was successfully constructed with a diameter of (125.5 ± 15.3) nm. After the mice received TACE and (or) immunotherapy, the combined liposome drug therapy significantly reduced the volume of hepatic carcinoma tissues, besides, the apoptotic rate of hepatic carcinoma cells in the combined liposome drug treatment group was increased obviously compared with other groups. Moreover, the protein TGFßR2 located in the cellular membrane was obviously down-regulated in the combined liposome drug therapy, while the expression of SMAD7 and PTPN14 was up-regulated in the treatment groups compared with the mice without treatment, besides, the protein PTPN14 was mainly located in the nucleus. Additionally, the mRNA expression of genes SNAI1 and Vimentin was significantly down-regulated in the combined liposome drug therapy. CONCLUSION: Combination of transcatheter arterial chemoembolization and anti-PD-L1 liposome drug therapy significantly suppressed hepatocellular carcinoma proliferation and metastasis in mice models.

Immune-induced pneumonia in patients with advanced solid tumors treated with immunotherapy: a real-world assessment.

Aim: To investigate the computed tomography (CT) and clinical characteristics of immunotherapy-induced pneumonitis (IIP) in patients with advanced solid tumors. Patients & methods: CT and clinical data of 254 patients with advanced solid tumors treated with immune checkpoint inhibitors in our hospital were collected retrospectively. Results: The incidences of IIP in patients with non-small-cell lung cancer, lymphoma and gastrointestinal tumors were 19% (19/100), 9.8% (6/61) and 6.2% (4/65), respectively. The median onset time for all 31 IIP patients was 44 days (interquartile range: 24-65). Most IIP patients (21/31) had grade 1-2 disease. Multifocal ground-glass opacities (seen in 21/31 patients) were the main CT findings of IIP. Conclusion: Patients should be alerted to the risk of IIP, an adverse reaction that has a relatively low incidence but which is sometimes life-threatening.

The study aimed to investigate the clinical and computed tomography (CT) features of immunotherapy-induced pneumonitis (IIP) in patients with advanced solid tumors. To describe these characteristics, clinical and CT information of 254 patients with advanced solid tumors who were treated with drugs called immune checkpoint inhibitors were collected. The incidences of IIP in patients with non-small-cell lung cancer, lymphoma and gastrointestinal tumors were 19% (19/100), 9.8% (6/61) and 6.2% (4/65), respectively. The median time taken to develop IIP for all 31 IIP patients was 44 days. Most IIP patients had mild or moderate (grade 1­2) disease. The main CT findings of IIP were abnormalities called multifocal ground-glass opacities (21/31). Most IIP patients can recover well after glucocorticoid discontinuation. This real-world study was done to raise physicians' awareness of the possible development of IIP, an adverse reaction with a relatively low incidence but which is sometimes life-threatening, to highlight the variety of CT manifestations, and to provide advice on regulating the timing and method of glucocorticoid therapy.

CT and MR imaging features of mixed neuroendocrine-non-neuroendocrine neoplasm of the pancreas compared with pancreatic ductal adenocarcinoma and neuroendocrine tumor.

OBJECTIVE: This study aimed to assess the computed tomography (CT) and magnetic resonance imaging (MRI) features of pancreatic mixed neuroendocrine-non-neuroendocrine neoplasm (MiNEN) and compare them with those of pancreatic ductal adenocarcinoma (PDAC) and neuroendocrine tumor (NET). METHODS: Twelve patients with pancreatic MiNEN, 24 patients with PDAC, and 24 patients with NET, who underwent both contrast-enhanced CT and MRI, were included. Clinical data and the key imaging features were retrospectively evaluated by two independent readers and compared between MiNEN and PDAC or NET. Univariate and multivariable logistic regression analyses were performed to obtain predictors for pancreatic MiNEN. RESULTS: Patients with pancreatic MiNEN more frequently presented with large size and heterogeneous and cystic components compared with PDAC (p < 0.031) and ill-defined irregular margins, progressive enhancement, and adjacent organ involvement compared with NET (p < 0.036). However, vascular invasion was less commonly seen in MiNEN than PDAC (p = 0.010). Moderate enhancement was observed more frequently in MiNEN than in PDAC or NET (p < 0.001). Multivariate logistic analyses demonstrated that moderate enhancement and ill-defined irregular margin were the most valuable features for the prediction of pancreatic MiNEN (p ≤ 0.044). The combination of the two features resulted in a specificity of 93.8%, sensitivity of 83.3%, and accuracy of 91.7%. CONCLUSIONS: We have mainly described the radiological findings of pancreatic MiNEN with ill-defined irregular margin and moderate enhancement compared with PDAC and NET. The combination of imaging features could improve diagnostic efficiency and help in the selection of the correct treatment method.

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.

Texture analysis of myometrium-derived T2WI in the evaluation of placenta increta: An observational retrospective study.

INTRODUCTION: MRI has demonstrated its potential in the diagnosis of placenta percreta. Texture analysis is a novel technique to quantify tissue heterogeneity. The study aimed to evaluate the feasibility of using texture analysis based on myometrium-derived T2WI to differentiate placenta accreta from increta. METHODS: Participants with MRI and clinical or histopathological diagnosis of placenta increta were retrospectively enrolled. Texture analysis of T2WI was implemented on normal myometrium and placenta increta by MaZda software. With the Fisher discriminant method, parameter selection and reduction were done automatically. Multivariate analysis was used for the comparison of response variables between two groups. The contours of multivariable average vectors were compared using profile analysis. Two-step clustering was performed to assess the importance of parameters. RESULTS: There were a total of 23 participants (median age 29 years, range 22-43 years). The pixel intensity distribution was narrow and wide in two first-order histograms taken from normal myometrium and placenta increta, respectively. Multivariate analysis showed nine second-order parameters derived from the histogram were statistically significant (P < 0.05). The results of two-step clustering indicated that three second-order parameters (Mean, Percentile 90%, and Percentile 99%) were important (predictor importance > 0.8). Multivariate analysis of three second-order parameters further showed they were different between normal myometrium and placenta increta. DISCUSSION: Texture analysis based on myometrium-derived T2WI may be a useful add-on to MRI in diagnosing placenta increta. TRIAL REGISTRATION: Registration number: ChiCTR2000038604 and name of registry: Evaluation of diagnostic accuracy of MRI multi-parameter imaging combined with texture analysis for placenta accreta spectrum disorders (PAD).

Reduced-Dose Full-Body CT in Lymphoma Follow-up: A Pilot Study.

BACKGROUND: How to reduce the radiation dose received from full-body CT scans during the follow-up of lymphoma patients is a concern. OBJECTIVE: The aim of the study was to investigate the image quality and radiation dose of reduced-dose full-body computerized tomography (CT) in lymphoma patients during the follow-up. METHODS: 121 patients were included and divided into conventional CT group (group 1, 120-kVp, n = 61) or reduced-dose CT group (group 2, 100-kVp combined dual-energy CT (DECT), n = 60). 140-kVp polychromatic images and 70-keV monochromatic images were reconstructed from DECT. The abdominal virtual non-enhanced (VNE) images were reconstructed from monochromatic images. Two radiologists rated the overall image quality with a five-point scale and graded the depiction of lesions using a four-point scale. The objective image quality was evaluated using image noise, signal-to-noise ratio, and contrast-to-noise ratio. The radiation dose and image quality were compared between the groups. RESULTS: The comparable subjective image quality was observed between 70-keV and 120-kVp images in the neck, while 120-kVp images showed better objective image quality. 70-keV images showed better objective image quality in the chest. While the subjective image quality of abdominal VNE images was inferior to that of true non-enhanced images, the improved objective image quality was observed in VNE images. In the abdominal arterial phase, similar subjective image quality was observed between the groups. Abdominal 70-keV images in the arterial phase showed improved objective image quality. Similar image quality was obtained in the abdominal venous phase between the groups. The effective radiation dose in group 2 showed a significant reduction. CONCLUSION: The application of reduced-dose full-body CT can significantly reduce the radiation dose for lymphoma patients during the follow-up while maintaining or improving the image quality.

Assessing Diagnostic Value of Combining Ultrasound and MRI in Extrathyroidal Extension of Papillary Thyroid Carcinoma.

Purpose: To explore the separate diagnostic value of preoperative ultrasound (US), magnetic resonance imaging (MRI), and the combination of US and MRI in extrathyroidal extension (ETE) of papillary thyroid carcinoma (PTC). Materials and Methods: This retrospective study was approved by the Affiliated People's Hospital of Jiangsu University review board. A total of 158 PTC patients with ETE received preoperative US and MRI examination and underwent surgery between May 2014 and December 2018 in Affiliated People's Hospital of Jiangsu University. For each case, the US and MRI features of ETE were retrospectively and independently investigated by two radiologists. The clinical assessment for each case was implemented, respectively, using US imaging only, MRI only, and a combination of both modalities at three different time points with one-month intervals. Results: The diagnostic accuracies of US, MRI, and the combined set for T3 (minimal ETE) were 91.7% (88/96), 74.0% (71/96), and 97.9% (94/96), respectively, indicating a significantly different performance (P < 0.001). The diagnostic accuracies for T4 (extensive ETE) were 62.9% (39/62), 87.1% (54/62), and 93.5% (58/62), respectively. The difference between the three methods for T4 was statistically significant (P = 0.000). The diagnostic accuracies for overall ETE were 80.4% (127/158), 79.1% (125/158), and 96.2% (152/158), respectively. The difference between the three methods for ETE was statistically significant (P = 0.001). Conclusion: This study suggests that ETE can be predicted most accurately by the combination of preoperative US and MRI.

Native T1-mapping and diffusion-weighted imaging (DWI) can be used to identify lung cancer pathological types and their correlation with Ki-67 expression.

Background: This study aimed to explore the value of native T1-mapping and diffusion-weighted imaging (DWI) in differentiating the pathological types and degree of tumor differentiation of lung cancer and their correlation with Ki-67 protein expression. Methods: A total of 78 consecutive lung cancer patients who received chest magnetic resonance imaging (MRI) scans between May 2020 and June 2021 were enrolled in this study. Two radiologists independently analyzed the apparent diffusion coefficient (ADC) and T1 values for each lesion. The intraclass correlation coefficient (ICC) and Bland-Altman plots were generated to assess interobserver agreement of the T1 and ADC mean values in lesions. The difference in ADC and T1 values among different pathological types, as well as between high- and low-differentiated lung cancers were analyzed, and diagnostic efficacy was evaluated by receiver operating characteristic (ROC) curve analysis. The correlation between ADC value, T1 value, and Ki-67 protein expression index was determined. Results: The ADC and T1 values showed excellent interobserver agreement (ICC 0.820, 0.942, respectively). There was a significant difference in ADC values between small cell carcinoma and squamous carcinoma (P<0.05), and between small cell carcinoma and adenocarcinoma (P<0.05), but not between squamous carcinoma and adenocarcinoma (P>0.05). A significant difference in T1 values was observed between small cell carcinoma (P<0.05) and adenocarcinoma, and between squamous carcinoma (P<0.05) and adenocarcinoma, but not between squamous carcinoma and small cell carcinoma (P>0.05). There were statistically significant differences in ADC and T1 values between the moderately and highly differentiated group and the poorly differentiated group (P<0.05). ROC curve analysis showed that the T1 combined with ADC value had high diagnostic value for the degree of differentiation of the tumor [area under the curve (AUC) =0.912]. Pearson correlation analysis showed a significant positive correlation between T1 value and Ki-67 index (r=0.66, P<0.001) and a significant negative correlation between ADC value and Ki-67 index (r=-0.45, P<0.01). Conclusions: T1 and ADC values can be used to distinguish the pathological type and differentiation degree of lung cancer.

Can 3D pseudo-continuous arterial spin labeling perfusion imaging be applied to predict early response to chemoradiotherapy in patients with advanced nasopharyngeal carcinoma?

BACKGROUND AND PURPOSE: Chemoradiotherapy (CRT) has been widely applied in patients with advanced nasopharyngeal carcinoma (ANPC). However, limited imaging modality exists on the evaluation of early response to CRT. The purpose of this study was therefore to investigate whether 3D pseudo-continuous arterial spin labeling (3D pCASL) perfusion imaging could predict early response to CRT in ANPC patients. MATERIALS AND METHODS: Seventy ANPC patients who received CRT underwent pre-treatment MRI including 3D pCASL perfusion measurements, and were categorized into response group (RG) and no-response group (NRG) according to RECIST 1.1. Pre-treatment 3D pCASL derived cerebral blood flow (CBF) values in tumors were compared between RG and NRG patients. Receiver-operating characteristic (ROC) analysis was performed to determine the optimal diagnostic cutoff value for CBF in predicting tumor response to CRT. Clinicopathological variables were also analyzed by using univariate and binary logistic regression. The corresponding obtained variables with statistical significance were further applied to create a nomogram in which the bootstrap resampling method was used for calibration. RESULTS: Forty-eight patients in RG had significantly higher pre-treatment CBF values in tumors compared with 22 patients in NRG (P < 0.001). CBF showed the high area under the ROC curve (AUC = 0.843) in distinguishing RG from NRG patients. The corresponding cutoff value for CBF was 103.68 ml/100 g/min, with respective accuracy, sensitivity and specificity of 82.86%, 87.50% and 72.73%. The nomogram was generated by binary logistic regression results, incorporating three variables: CBF value, clinical stage and pathological type. The AUC, accuracy, sensitivity and specificity of the nomogram was respectively 0.893, 84.28%, 81.25% and 90.91% in predicting tumor response to CRT. Moreover, as shown in the calibration curve, a strong agreement was observed between nomogram prediction probability and actual clinical findings (P = 0.309). CONCLUSIONS: 3D pCASL derived CBF in tumor could act as a noninvasive effective biomarker to predict tumor response to CRT in ANPC patients before clinical treatment. Furthermore, the nomogram combining CBF and clinicopathological variables could serve as a novel clinical analysis tool for treatment response prediction.

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.

Immunotherapy-induced pneumonitis in non-small cell lung cancer patients: current concern in treatment with immune-check-point inhibitors.

Purpose Immune-related adverse events (IrAEs) are auto-immune reactions associated with immune checkpoint inhibitor-based therapy (ICI). To date, little is known about immunotherapy-induced pneumonitis (IIP). In this study, we investigated the clinical and CT features of IIP in non-small cell lung cancer (NSCLC) patients treated with ICI. Methods CT images and clinical data of 98 NSCLC patients in our hospital were retrospectively analyzed after ICI therapy, and the incidence, onset time, CT findings, grade, treatment and prognosis of IIP were recorded. Results Nineteen patients developed IIP, which occurred 42∼210 days after ICI therapy, and the median time was 97 days. The CT findings for IIP showed multifocal ground-glass opacity (GGO) in 5 cases, patchy shadows in 6 cases, mixed distribution of patchy and strip-like shadows in 4 cases, and patchy shadows with honeycomb lung in 4 cases. The mean age and proportions of smokers, CD3+ and CD4+ of T lymphocyte subset in patients with IIP were significantly higher than those in patients without IIP (all p < 0.05). Among 19 patients with IIP, there were 10 patients with grade 1 ~ 2 and 9 patients with grade 3 ~ 4; 13 patients received hormone therapy, 12 of them were improved or stable, and 1 patient got worse after hormone therapy. No deaths from IIP were found. Conclusion IIP is a relatively rare but serious adverse event, and it is sensitive to hormone therapy. Its CT manifestations are diverse, and timely detection and treatment are the keys to reduce IIP.

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.

Can Diffusion-Weighted MR Imaging Be Used as a Tool to Predict Extrathyroidal Extension in Papillary Thyroid Carcinoma?

RATIONALE AND OBJECTIVES: To evaluate whether quantitative diffusion-weighted MR imaging (DWI) with multi-b values can be used as a tool to predict the aggressiveness by using the histological feature of extrathyroidal extension (ETE) in papillary thyroid carcinoma (PTC). MATERIALS AND METHODS: 238 patients were pathologically confirmed as PTCs underwent preoperative multi-b value DWI (300, 500, and 800 s/mm2) between January 2015 and December 2017. The patients were divided into three groups according to the degree of ETE: without ETE, minimal ETE, and extensive ETE. The apparent diffusion coefficients (ADCs) were evaluated for three different b values (b = 300 s/mm2, b = 500 s/mm2, and b = 800 s/mm2). The ADC values of the groups with and without ETE, minimal and extensive ETE were compared. The diagnostic relevance of the ADC values in terms of predicting ETE was compared using a receiver operating characteristic analysis. Differences between the areas under the curves (AUCs) were compared by using a Delong test. RESULTS: PTCs with ETE had significantly lower ADC300, ADC500 and ADC800 values than PTCs without ETE (p = 0.001, p < 0.001, and p < 0.001, respectively). The AUC of the mean ADC500 value (0.905) was higher than that of the ADC300 and ADC800 values (0.607 and 0.770, respectively) in differentiating ETE from without ETE (p < 0.001). The cut-off value of ADC500 to discriminate PTCs with and without ETE was determined at 1.407 × 10-3 mm2/s, with sensitivity of 80.7%, specificity of 86.7%, and an AUC of 0.905. CONCLUSION: The ADC value can be demonstrated an effective tool for evaluating the aggressiveness with the histological feature of ETE in PTC. In particular, ADC value at b = 500 s/mm2 showed the best performance for noninvasive preoperative evaluation of ETE.

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.

Iodine-125 seed implantation treatment of lung metastases < 2.5 cm using 5-ml syringe as a guide.

PURPOSE: Small lung metastases change their location with respiration, making difficult to localize, therefore, increasing the number of punctures. Accurate puncture can reduce trauma to lung tissue and accelerate patient's recovery. The aim of the study was to present our experience with the technique of using local anesthesia 5-ml syringe as a guide for computerized tomography-guided iodine-125 seed implantation (CT-ISI). MATERIAL AND METHODS: This was a retrospective study, including patients with small metastatic tumors in the lung, treated with CT-ISI between December 2013 and March 2018 at the Affiliated Hospital of the University. The patients were divided according to whether a 5-ml syringe was used as a guide during CT-ISI or not. The final follow-up was on March 31st, 2018. Implantation success and complications were examined. RESULTS: Nineteen patients were included. A total of 840 seeds were used, with 44.2 ±33.6 (range, 10-160) seeds per patient. The mean D90 for CT-ISI was 134.5 ±7.5 Gy. Treatment intervention for eleven patients was performed using a 5-ml syringe as a guide during CT-ISI. There were no differences in total dose and number of implanted seeds between the two groups, but the number of punctures per lesion was lower in the syringe group than in the no-syringe group (1.9 ±0.5 vs. 2.9 ±0.6, p < 0.001), suggesting a higher puncture accuracy. The total number of SMTIL was 50 (median, 2; range, 1-10), and the median size was 1.9 cm (range, 0.8-2.4 cm). All SMTIL were well-controlled at 6-months follow-up (response rate [RR] = 100%). One patient in the no-syringe group experienced grade 2 chest tightness, chest pain, intraoperative needle tract bleeding, and post-operative blood in sputum. CONCLUSIONS: Puncture with a 5-ml syringe as a guide during CT-ISI seems to be a more accurate option for patients with small (< 2.5 cm) lung metastasis.