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.

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.

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.

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.

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.

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.

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.

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 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.

A pilot study of radiomics technology based on X-ray mammography in patients with triple-negative breast cancer.

PURPOSE: To explore the radiomics features of triple negative breast cancer (TNBC) and non-triple negative breast cancer (non-TNBC) based on X-ray mammography, and to differentiate the two groups of cases. MATERIALS AND METHODS: Preoperative mammograms of 120 patients with breast ductal carcinoma confirmed by surgical pathology were retrospectively analyzed, which include 30 TNBC and 90 non-TNBC patients. The manual segmentation of breast lesions was performed by ITK-SNAP software and 12 radiomics features were extracted by Omni-Kinetics software. The differences of these radiomics features between TNBC and non-TNBC groups were compared, and the receiver operating characteristic (ROC) curve was used to determine the optimal cutoff value of each radiomics parameter for differentiating TNBC from non-TNBC, and the corresponding area under the curve (AUC), sensitivity and specificity were obtained. RESULTS: There were statistically significant differences for 4 radiomics features between TNBC and non-TNBC datasets (P < 0.05). They were the roundness, concavity, gray average and skewness of breast lesions. Compared with non-TNBC, TNBC cases have following characteristics of (1) more round with the roundness of 0.621 vs. 0.413 (P < 0.001), (2) more regular with the concavity of 0.087 vs. 0.141 (P < 0.01), (3) higher density or gray average (67.261 vs. 56.842, P < 0.05), and (4) lower skewness (- 0.837 vs.- 0.671, P = 0.034). AUCs of ROC curves computed using features of the roundness and concavity were both larger than 0.70. CONCLUSION: Radiomics features based on X-ray mammography may be helpful to distinguish between TNBC and non-TNBC, which were associated with breast tumor histology.

A pilot study of low-dose CT perfusion imaging (LDCTPI) technology in patients with triple-negative breast cancer.

PURPOSE: To investigate associations between the clinicopathologic features and CT perfusion parameters of triple-negative breast cancer (TNBC) and non-TNBC using low-dose computed tomography perfusion imaging (LDCTPI), and to find potential clinical applications in the prognosis assessment of TNBC. MATERIALS AND METHODS: A total of 60 patients with breast cancer confirmed by pathological examination were studied prospectively using LDCTPI on a 64-slice spiral CT scanner. The acquired volume data were used for calculations, mapping, and analysis by using a tumor perfusion protocol in the CT perfusion software package to measure 2 parameters namely, blood flow (BF), and permeability surface (PS) area product. Patients were grouped into TNBC (n = 27) and non-TNBC (n = 33) subtypes. Associations between these two subtypes and clinicopathologic characteristics were evaluated by both univariate and multivariate logistic regression. CT perfusion parameters values were compared for clinicopathologic characteristics using independent 2-sample t test. RESULTS: TNBC displayed higher CT perfusion parameters values (BF: 57.56±10.94 vs 52.70±7.79 mL/100 g/min, p = 0.006; PS: 38.98±9.46 vs 33.39±8.07 mL/100 g/min, p = 0.001) than non-TNBC. In addition, breast cancer with poorly histologic grade or positive Ki-67 expression showed higher BF and PS values than those with well and moderately histologic grade or negative Ki-67 expression (p < 0.05). TNBC had poorer histologic grade (P = 0.032) and higher Ki-67 expression (P = 0.013) than non-TNBC. CONCLUSION: LDCTPI is a functional imaging technology from the perspective of hemodynamics with potential of clinical applications. The BF and PS values were higher in TNBC patient group than non-TNBC group. TNBC patients also have poorer clinicopathologic outcome.

An application study of low-dose computed tomography perfusion imaging for evaluation of the efficacy of neoadjuvant chemotherapy for advanced gastric adenocarcinoma.

BACKGROUND: This study used low-dose computed tomography (CT) perfusion imaging technology to evaluate the efficacy of neoadjuvant chemotherapy in patients with advanced gastric adenocarcinoma and to determine whether any of the perfusion parameters could predict tumor response to chemotherapy. METHODS: Forty patients with gastric adenocarcinoma (T3-4NxM0) received three cycles of neoadjuvant chemotherapy and low-dose spiral CT perfusion imaging prior to and after the first and third series of chemotherapy. We calculated tissue blood flow (BF) and blood volume (BV) using commercial software. One-way analysis of variance (ANOVA) was used to detect any significant variation of the tested parameters between different times of scanning. Spearman's test was used to evaluate the correlation among perfusion parameters, tumor size and pathological efficacy grade, and clinical response after chemotherapy, respectively. A receiver-operating characteristic analysis was used to determine the optimal diagnostic cutoff value for changes in perfusion parameters and tumor size. RESULTS: One-way ANOVA showed significant differences in BF and BV values between those before and after chemotherapy (p < 0.01). The BF, BV and size reduction rate after three series of chemotherapy were significantly correlated with pathological efficacy grade. BF and BV values after the first and third series of chemotherapy were also significantly correlated with clinical response (p < 0.01, respectively). The diagnostic sensitivity and specificity of the BV reduction rate were higher than those of size reduction rate. CONCLUSIONS: Low-dose CT perfusion imaging is a valuable tool that permits microcirculation evaluation and therefore can evaluate the efficacy of neoadjuvant chemotherapy in patients with advanced gastric adenocarcinoma.

A clinical application of dynamic contrast-enhanced CT (DCE-CT) in patients with variously differentiated breast ductal carcinoma.

PURPOSE: To explore the hemodynamic characteristics of variously differentiated breast ductal carcinoma (BDC) using the dynamic contrast-enhanced CT (DCE-CT) based CT perfusion imaging (CTPI), including the specific perfusion parameter values, and to identify potential clinical applications in the cell differentiation degree of BDC. MATERIALS AND METHODS: Forty patients with breast ductal carcinoma confirmed by needle puncture biopsy were studied prospectively using CTPI on a 64-slice spiral CT scanner. The acquired volume data were used for calculations, mapping, and analysis by using a tumor perfusion protocol in the CT perfusion software package to measure 4 parameters namely, blood flow (BF), blood volume (BV), mean transit time (MTT), and the permeability surface (PS) area product. The different differentiated BDC with CT perfusion parameters were divided into 3 groups of high, moderate and poor differentiation. The comparison among these groups were then made using statistical data analysis software. RESULTS: The patients were categorized into three groups of 12, 13, and 15 highly, moderately and poorly differentiated ductal carcinoma cases, respectively. Comparing the perfusion parameters values of the three groups, BF, BV, and PS values increased from highly to poorly differentiated BDC cases. Differences between the highly and moderately or poorly differentiated groups were all statistically significant for BF, BV, and PS values (p < 0.05), while MTT value showed no statistical difference among the three groups (p > 0.05). CONCLUSION: CTPI is a functional imaging technology from the perspective of hemodynamics with potential clinical applications. Three parameters of BF, BV and PS values have potential to serve as indicators of the cell differentiation degree of the breast ductal carcinoma.

An application study of low-dose computed tomography perfusion imaging (LDCTPI) in breast cancer and breast fibroadenoma.

PURPOSE: To explore the characteristics of breast cancer and breast fibroadenoma using low-dose computed tomography perfusion imaging (LDCTPI) including specific perfusion parameter values, and seek the potential clinical applications in cancer prognosis assessment. MATERIALS AND METHODS: Fifty patients including 30 diagnosed with breast cancer and 20 with breast fibroadenoma, as well as 15 control subjects with normal breasts were studied prospectively using LDCTPI examinations. The acquired volumetric imaging data were used for calculation, mapping and analysis by using a body tumor perfusion protocol in the CT perfusion software to measure 4 parameters: blood flow (BF), blood volume (BV), mean transit time (MTT), and the permeability surface (PS) area product. Statistical data analysis was then performed to distinguish the difference of the 4 parameter values among normal control, breast cancer and breast fibroadenoma cases. RESULTS: The mean perfusion values of 15 normal controls were as follows: BF, 20.03±4.08 mL/100 g/min; BV, 4.53±0.95 mL/100 g; MTT, 5.90±0.82 s; and PS, 9.25±1.18 mL/100 g/min. The mean perfusion values of 30 cancer patients were as follows: BF, 56.67±6.59 mL/100 g/min; BV, 5.82±0.68 mL/100 g; MTT, 6.01±0.82 s; and PS, 24.95±5.05 mL/100 g/min. The mean perfusion values of 20 patients with breast fibroadenoma were as follows: BF, 46.24±6.65 mL/100 g/min; BV, 5.07±0.73 mL/100 g; MTT, 7.51±0.62 s; and PS, 16.73±6.48 mL/100 g/min. Comparing the 3 groups, differences were all statistically significant for BF, BV, MTT and PS values (p < 0.05, respectively); The BF, BV, PS values were highest in group of cancer patients, while the MTT value was highest in group of patients diagnosed with breast fibroadenoma. CONCLUSION: Breast CT perfusion imaging is a promising functional imaging technology in breast cancer diagnosis, which can provide valuable quantitative imaging markers to assist evaluation of breast tumors.

Can low-dose CT perfusion imaging accurately assess response of advanced gastric cancer with neoadjuvant chemotherapy?

PURPOSE: To explore the value of low-dose CT perfusion imaging (LDCTPI) technology and its perfusion parameters in assessing response of neoadjuvant chemotherapy (NAC) in patients with advanced gastric cancer (AGC). METHODS: Thirty patients with AGC were studied prospectively by LDCTPI to measure two parameters including blood flow (BF) and blood volume (BV) of tumor area before and after chemotherapy, respectively. All of the patients received two courses of NAC and surgical resection of gastric tumor within one week after chemotherapy, and then obtained the result of postoperative pathology response for chemotherapy. The comparisons of BF and BV values of AGC before and after chemotherapy were analyzed by paired-samples t-test, respectively; and the correlations between BF as well as BV decrease rates after NAC and the pathology response grade were analyzed by Spearman statistical test. Thirty patients were divided into effective and ineffective groups according to different pathology response grade. Comparisons of BF as well as BV decrease rates between effective and ineffective groups were analyzed by independent-samples t-test, respectively. Receiver operating characteristic (ROC) curves were used to determine the cutoff values of BF and BV decrease rates as evaluation indicators of AGC after NAC and calculate area under the curve (AUC). RESULTS: There were significant differences in BF and BV values of AGC between before and after NAC (p < 0.001), respectively, and there were obvious correlations between BF as well as BV decrease rates and pathology response grade (r = 0.660, p < 0.001; r = 0.706, p < 0.001), respectively. There were also significant differences in BF and BV decrease rates of AGC between effective and ineffective groups (P = 0.001), respectively. If BF decrease rate of 12.1% (AUC was 0.816, P = 0.005) was used as the cutoff value for chemotherapy effectiveness of AGC, the sensitivity of 82% and specificity of 84% were achieved, and if BV decrease rate of 32.8% (AUC was 0.844, P = 0.002) was used as the cutoff value for chemotherapy effectiveness of AGC, the sensitivity of 82% and specificity of 89% were achieved. CONCLUSIONS: BF and BV decrease rates have potential to be used as effective indicators to assess chemotherapy efficacy of AGC from the hemodynamics.

The feasibility of low-dose CT perfusion imaging in gastric cancer.

PURPOSE: To investigate feasibility of applying low-dose CT perfusion imaging (CTPI) to diagnose gastric cancer. MATERIALS AND METHODS: Twenty patients with gastric cancer confirmed by endoscopic biopsy were undergone routine dose (120 kV, 100 mA) and low-dose (120 kV, 50 mA) CTPI examination, respectively. The original data were processed by body perfusion software, and the perfusion parameters values including blood flow (BF), blood volume (BV) and permeability surface (PS) of gastric cancer were measured. Statistical data analyses including paired-samples t test, Pearson correlation analysis and Bland-Altman consistency test were used to compare the perfusion parameters values between the routine dose and low-dose CTPI examinations. Radiation dosage, which the patients received during two CTPI examinations, was also calculated and compared. RESULTS: There were no statistical differences in the BF, BV and PS values between routine dose group and low-dose group (P > 0.05), and there were significant correlation in the BF, BV and PS values between two groups (P <  0.01). The consistency of BF and BV values between the two groups was preferable to that of PS value. The radiation dosage of the low-dose group was much less than that of routine dose group, and the CTDIvol and DLP values of low-dose CTPI were decreased by 50%, respectively. CONCLUSION: The parameters BF and BV values may play a valuable role in the diagnosis and assessment of gastric cancer in low-dose CTPI examination.

Role of CT perfusion imaging in patients with variously differentiated gastric adenocarcinoma.

PURPOSE: To explore the characteristics of variously differentiated gastric cancers on computed tomography (CT) perfusion imaging, including specific perfusion parameter values, and potential clinical applications in the prognosis assessment of gastric cancer. MATERIALS AND METHODS: Fifty patients with gastric cancer confirmed by gastroscope pathology were studied prospectively using CT perfusion imaging examinations on a 64-slice spiral CT scanner. The acquired volume data were used for calculations, mapping, and analysis by using an abdominal tumor perfusion protocol (deconvolution method) in the CT perfusion software package to measure 4 parameters: blood flow (BF), blood volume (BV), mean transit time (MTT), and the permeability surface (PS) area product. The different differentiated Gastric cancers with CT perfusion values were divided into 3 groups: well-differentiated, moderately differentiated and poorly differentiated gastric adenocarcinoma, and compared statistically with one another by statistical software. RESULTS: The mean perfusion values of 10 patients with well-differentiated gastric adenocarcinoma were as follows: BF, 75.28 ± 6.81 mL/100 g/min; BV, 9.01 ± 0.94 mL/100 g; MTT, 9.89 ± 1.65 s; and PS, 10.05 ± 0.71 mL/100 g/min. The mean perfusion values of 24 patients with moderately differentiated gastric adenocarcinoma were as follows: BF, 110.01 ± 31.90 mL/100 g/min; BV, 18.18 ± 5.62 mL/100 g; MTT, 9.81 ± 3.69 s; and PS, 40.08 ± 15.82 mL/100 g/min. The mean perfusion values of 16 patients with poorly differentiated gastric adenocarcinoma were as follows: BF, 138.59 ± 38.09 mL/100 g/min; BV, 21.08 ± 4.11 mL/100 g; MTT, 9.47 ± 1.80 s; and PS, 57.50 ± 13.28 mL/100 g/min. Comparing the 3 groups, differences between the well-differentiated group and the moderate differentiation group were all statistically significant for BF, BV, and PS (p < 0.05, respectively), differences between the well-differentiated group and the poor differentiation group were all statistically significant for BF, BV, and PS (p < 0.05,respectively) as well; While MTT value showed no statistical difference among the 3 groups (p > 0.05). CONCLUSION: Stomach CT perfusion imaging is a functional imaging technology from the perspective of hemodynamics with potential clinical applications. The BF, BV and PS values could serve as indicators of the degree of malignancy and aid in prognostic assessment of gastric cancer.

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.

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.