Deregulation of immune response contributing to fulminant hepatitis in HEV infected pregnant women.

Hepatitis E virus (HEV) infection in pregnant women is associated with a wide spectrum of adverse consequences for both mother and fetus. The high mortality in this population appears to be associated with hormonal changes and consequent immunological changes. This study conducted an analysis of immune responses in pregnant women infected with HEV manifesting varying severity. Data mining analysis of the GSE79197 was utilized to examine differentially biological functions in pregnant women with HEV infection (P-HEV) versus without HEV infection (P-nHEV), P-HEV progressing to ALF (P-ALF) versus P-HEV, and P-HEV versus non-pregnant women with HEV infection (nP-HEV). We found cellular response to interleukin and immune response-regulating signalings were activated in P-HEV compared with P-nHEV. However, there was a significant decrease of immune responses, such as T cell activation, leukocyte cell-cell adhesion, regulation of lymphocyte activation, and immune response-regulating signaling pathway in P-ALF patient than P-HEV patient. Compared with nP-HEV, MHC protein complex binding function was inhibited in P-HEV. Further microRNA enrichment analysis showed that MAPK and T cell receptor signaling pathways were inhibited in P-HEV compared with nP-HEV. In summary, immune responses were activated during HEV infection while being suppressed when developing ALF during pregnancy, heightening the importance of immune mediation in the pathogenesis of severe outcome in HEV infected pregnant women.

Spectral CT assists differentiation of osteoblastic bone metastasis from bone island in newly diagnosed cancer patients.

OBJECTIVES: To investigate measurements derived from plain and enhanced spectral CT in differentiating osteoblastic bone metastasis (OBM) from bone island (BI). MATERIALS AND METHODS: From January to November 2020, 73 newly diagnosed cancer patients with 201 bone lesions (OBM = 92, BI = 109) having received spectral CT were retrospectively enrolled. Measurements including CT values of 40-140 keV, slope of the spectral curve, effective atomic number (Zeff), water (calcium) density, calcium (water) density, and Iodine (calcium) density were derived from manually segmented lesions on plain and enhanced spectral CT, and then analyzed using Student t-test and Pearson's correlation. Multivariate analysis was performed to build models (plain spectral model, enhanced spectral CT model, and combined model) for the discrimination of OBM and BI with performance evaluated using receiver operator characteristics curve and DeLong test. RESULTS: All features were significantly different between the BI group and OBM group (all p < 0.05), highly correlated with the corresponding features between plain and enhanced spectral CT both in OBM (r: 0.392-0.763) and BI (r: 0.430-0.544). As for the model performance, the combined model achieved the best performance (AUC = 0.925, 95% CI: 0.879 to 0.957), which significantly outperformed the plain spectral CT model (AUC = 0.815, 95% CI: 0.754 to 0.866, p < 0.001) and enhanced spectral CT model (AUC = 0.901, 95% CI: 0.852 to 0.939, p = 0.024) in differentiating OBM and BI. CONCLUSION: In addition to plain spectral CT measurements, enhanced spectral CT measurements would further significantly benefit the differential diagnosis. CLINICAL RELEVANCE STATEMENT: Measurements derived either from plain or enhanced spectral CT could provide additional valuable information to improve the differential diagnosis between OBM and BI in newly diagnosed cancer patients. KEY POINTS: • We intend to investigate plain and enhanced spectral CT measurements in differentiating OBM from BI. • Both plain and enhanced spectral CT help in discriminating OBM and BI in newly diagnosed cancer patients. • Enhanced spectral CT measurements further improve plain spectral CT measurements-based differential diagnosis.

Voxel-wise mapping of DCE-MRI time-intensity-curve profiles enables visualizing and quantifying hemodynamic heterogeneity in breast lesions.

OBJECTIVES: To propose a novel model-free data-driven approach based on the voxel-wise mapping of DCE-MRI time-intensity-curve (TIC) profiles for quantifying and visualizing hemodynamic heterogeneity and to validate its potential clinical applications. MATERIALS AND METHODS: From December 2018 to July 2022, 259 patients with 325 pathologically confirmed breast lesions who underwent breast DCE-MRI were retrospectively enrolled. Based on the manually segmented breast lesions, the TIC of each voxel within the 3D whole lesion was classified into 19 subtypes based on wash-in rate (nonenhanced, slow, medium, and fast), wash-out enhancement (persistent, plateau, and decline), and wash-out stability (steady and unsteady), and the composition ratio of these 19 subtypes for each lesion was calculated as a new feature set (type-19). The three-type TIC classification, semiquantitative parameters, and type-19 features were used to build machine learning models for identifying lesion malignancy and classifying histologic grades, proliferation status, and molecular subtypes. RESULTS: The type-19 feature-based model significantly outperformed models based on the three-type TIC method and semiquantitative parameters both in distinguishing lesion malignancy (respectively; AUC = 0.875 vs. 0.831, p = 0.01 and 0.875vs. 0.804, p = 0.03), predicting tumor proliferation status (AUC = 0.890 vs. 0.548, p = 0.006 and 0.890 vs. 0.596, p = 0.020), but not in predicting histologic grades (p = 0.820 and 0.970). CONCLUSION: In addition to conventional methods, the proposed computational approach provides a novel, model-free, data-driven approach to quantify and visualize hemodynamic heterogeneity. CLINICAL RELEVANCE STATEMENT: Voxel-wise intra-lesion mapping of TIC profiles allows for visualization of hemodynamic heterogeneity and its composition ratio for differentiation of malignant and benign breast lesions. KEY POINTS: • Voxel-wise TIC profiles were mapped, and their composition ratio was compared between various breast lesions. • The model based on the composition ratio of voxel-wise TIC profiles significantly outperformed the three-type TIC classification model and the semiquantitative parameters model in lesion malignancy differentiation and tumor proliferation status prediction in breast lesions. • This novel, data-driven approach allows the intuitive visualization and quantification of the hemodynamic heterogeneity of breast lesions.

Breast Amide Proton Transfer Imaging at 3 T: Diagnostic Performance and Association With Pathologic Characteristics.

BACKGROUND: Amide proton transfer (APT) imaging has been increasingly applied in tumor characterization. However, its value in evaluating breast cancer remains undetermined. PURPOSE: To assess the diagnostic performance of APT imaging in breast cancer and its association with prognostic histopathologic characteristics. STUDY TYPE: Prospective. SUBJECTS: Eighty-four patients with breast lesions. FIELD STRENGTH/SEQUENCE: A 3.0 T/single-shot fast spin echo APT imaging. ASSESSMENT: APTw signal in breast lesion was quantified. Lesion malignancy, T stage, grades, Ki-67 index, molecular biomarkers (estrogen receptor [ER] expression, progesterone receptor [PR] expression, human epidermal growth factor receptor [HER-2] expression), molecular subtypes (luminal A, luminal B, triple negative, and HER-2 enriched) were determined. STATISTICAL TESTS: Student t-test, one-way analysis of variance, receiver operating characteristic analysis, and Pearson's correlation with P < 0.05 as statistical significance. RESULTS: APTw signal was significantly higher in malignant lesions (1.55% ± 1.24%) than in benign lesions (0.54% ± 1.13%), and in grade III lesions than in grade II lesions (1.65% ± 0.84% vs. 0.96% ± 0.96%), and in T2- (1.57% ± 0.64%) and T3-stage lesions (1.54% ± 0.63%) than in T1-stage lesions (0.81% ± 0.64%) for invasive breast carcinoma of no special type. APTw signal significantly correlated with Ki-67 index (r = 0.364) but showed no significant difference in groups of ER (P = 0.069), PR (P = 0.069), HER-2 (P = 0.961), and among molecular subtypes (P = 0.073). DATA CONCLUSION: APT imaging shows potential in differentiating breast lesion malignancy and associates with prognosis-related tumor grade, T stage, and proliferative activity. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Demonstration of fast and equilibrium human muscle creatine CEST imaging at 3 T.

PURPOSE: Creatine chemical exchange saturation transfer (CrCEST) MRI is used increasingly in muscle imaging. However, the CrCEST measurement depends on the RF saturation duration (Ts) and relaxation delay (Td), and it is challenging to compare the results of different scan parameters. Therefore, this study aims to evaluate the quasi-steady-state (QUASS) CrCEST MRI on clinical 3T scanners. METHODS: T1 and CEST MRI scans of Ts/Td of 1 s/1 s and 2 s/2 s were obtained from a multi-compartment creatine phantom and 5 healthy volunteers. The CrCEST effect was quantified with asymmetry analysis in the phantom, whereas 5-pool Lorentzian fitting was applied to isolate creatine from phosphocreatine, amide proton transfer, combined magnetization transfer and nuclear Overhauser enhancement effects, and direct water saturation in four major muscle groups of the lower leg. The routine and QUASS CrCEST measurements were compared under two different imaging conditions. Paired Student's t-test was performed with p-values less than 0.05 considered statistically significant. RESULTS: The phantom study showed a substantial influence of Ts/Td on the routine CrCEST quantification (p = 0.02), and such impact was mitigated with the QUASS algorithm (p = 0.20). The volunteer experiment showed that the routine CrCEST, amide proton transfer, and combined magnetization transfer and nuclear Overhauser enhancement effects increased significantly with Ts and Td (p < 0.05) and were significantly smaller than the corresponding QUASS indices (p < 0.01). In comparison, the QUASS CrCEST MRI showed little dependence on Ts and Td, indicating its robustness and accuracy. CONCLUSION: The QUASS CrCEST MRI is feasible to provide fast and accurate muscle creatine imaging.

Induction TPF followed by concurrent chemoradiotherapy versus concurrent chemoradiotherapy alone in locally advanced hypopharyngeal cancer: a preliminary analysis of a randomized phase 2 trial.

PURPOSE: Concurrent chemoradiotherapy (CCRT) is a standard treatment choice for locally advanced hypopharyngeal carcinoma. The aim of this study was to investigate whether induction chemotherapy (IC) followed by CCRT is superior to CCRT alone to treat locally advanced hypopharyngeal carcinoma. METHODS AND MATERIALS: Patients (n = 142) were randomized to receive two cycles of paclitaxel/cisplatin/5-fluorouracil (TPF) IC followed by CCRT or CCRT alone. The primary end point was overall survival (OS). The secondary end points included the larynx-preservation rate, progression-free survival (PFS), distant metastasis-free survival (DMFS), and toxicities. RESULTS: Ultimately, 113 of the 142 patients were analyzed. With a median follow-up of 45.6 months (interquartile range 26.8-57.8 months), the 3-year OS was 53.1% in the IC + CCRT group compared with 54.8% in the CCRT group (hazard ratio, 1.004; 95% confidence interval, 0.573-1.761; P = 0.988). There were no statistically significant differences in PFS, DMFS, and the larynx-preservation rate between the two groups. The incidence of grade 3-4 hematological toxicity was much higher in the IC+ CCRT group than in the CCRT group (54.7% vs. 10%, P < 0.001). CONCLUSIONS: Adding induction TPF to CCRT did not improve survival and the larynx-preservation rate in locally advanced hypopharyngeal cancer, but caused a higher incidence of acute hematological toxicities. TRIAL REGISTRATION: ClinicalTrials.gov , number NCT03558035. Date of first registration, 15/06/2018.

A MRI-based radiomics model predicting radiation-induced temporal lobe injury in nasopharyngeal carcinoma.

OBJECTIVES: To develop and validate a radiomics-based model for predicting radiation-induced temporal lobe injury (RTLI) in nasopharyngeal carcinoma (NPC) by pretreatment MRI of the temporal lobe. METHODS: A total of 216 patients with diagnosed NPC were retrospectively reviewed. Patients were randomly allocated to the training (n = 136) and the validation cohort (n = 80). Radiomics features were extracted from pretreatment contrast-enhanced T1- or fat-suppressed T2 weighted MRI. A radiomics signature was generated by the least absolute shrinkage and selection operator (LASSO) regression algorithm, Pearson correlation analysis, and univariable logistic analysis. Clinical features were selected with logistic regression analysis. Multivariable logistic regression analysis was conducted to develop three models for RTLI prediction in the training cohort: namely radiomics signature, clinical variables, and clinical-radiomics parameters. A radiomics nomogram was used and assessed with respect to calibration, discrimination, reclassification, and clinical application. RESULTS: The radiomics signature, composed of two radiomics features, was significantly associated with RTLI. The proposed radiomics model demonstrated favorable discrimination in both the training (AUC, 0.89) and the validation cohort (AUC, 0.92), outperforming the clinical prediction model (p < 0.05). Combining radiomics and clinical features, higher AUCs were achieved (AUC, 0.93 and 0.95), as well as a better calibration and improved accuracy of the prediction of RTLI. The clinical-radiomics model showed also excellent performance in predicting RTLI in different clinical-pathologic subgroups. CONCLUSION: A radiomics model derived from pretreatment MRI of the temporal lobe showed persuasive performance for predicting radiation-induced temporal lobe injury in nasopharyngeal carcinoma. KEY POINTS: • Radiomics features from pretreatment MRI are associated with radiation-induced temporal lobe injury in nasopharyngeal carcinoma. • The radiomics model shows better predictive performance than a clinical model and was similar to a clinical-radiomics model. • A clinical-radiomics model shows excellent performance in the prediction of radiation-induced temporal lobe injury in different clinical-pathologic subgroups.

ICAM3 mediates tumor metastasis via a LFA-1-ICAM3-ERM dependent manner.

ICAM3 was reported to promote metastasis in tumors. However, the underlying mechanism remains elusive. Here, we disclosed that the expression of ICAM3 was closely correlated with the TNM stage of human breast and lung cancer, as well as the dominant overexpression in high aggressive tumor cell lines (231 and A549 cells). Moreover, the knockdown of ICAM3 inhibited tumor metastasis whereas the ectopic expression of ICAM3 promoted tumor metastasis both in vitro and in vivo. In addition, exploration of the underlying mechanism demonstrated that ICAM3 not only binds to LFA-1 with its extracellular domain and structure protein ERM but also to lamellipodia with its intracellular domain which causes a tension that pulls cells apart (metastasis). Furthermore, ICAM3 extracellular or intracellular mutants alternatively abolished ICAM3 mediated tumor metastasis in vitro and in vivo. As a therapy strategy, LFA-1 antibody or Lifitegrast restrained tumor metastasis via targeting ICAM3-LFA-1 interaction. In summary, the aforementioned findings suggest a model of ICAM3 in mediating tumor metastasis. This may provide a promising target or strategy for the prevention of tumor metastasis.

Low dosage of arsenic trioxide (As2O3) inhibits angiogenesis in epithelial ovarian cancer without cell apoptosis.

Arsenic trioxide (As2O3) induces cell apoptosis and reduces the invasive and metastatic activities in various cancer types. However, the role of As2O3 in ovarian cancer angiogenesis remains unclear. In this study, we investigated the role of As2O3 in ovarian cancer angiogenesis and found that a low concentration of As2O3 causes no effects on epithelial ovarian cancer cell viability or apoptosis. Moreover, we found that As2O3-treated epithelial ovarian cancer cells demonstrate a reduced tube formation of endothelial cells in Matrigel. In addition, As2O3-treated epithelial ovarian cancer cells show a decreased VEGFA, VEGFR2 and CD31 mRNA expression. As per the underlying mechanisms involved in As2O3 treatment, we found that As2O3 inhibits VEGFA and VEGFR2 expression that thereby inhibits the VEGFA-VEGFR2-PI3K/ERK signaling pathway. This leads to a suppression in both VEGFA synthesis and angiogenesis-related gene expression. A decreased VEGFA synthesis and secretion also inhibits the VEGFA-VEGFR2-PI3K/ERK signaling pathway in human umbilical vein endothelial cells (HUVECs). In summary, our results may provide strategies for the use of As2O3 in the prevention of tumor angiogenesis.

Diagnostic value of single-source dual-energy spectral computed tomography for papillary thyroid microcarcinomas.

BACKGROUND: Ultrasound (US) and computed tomography (CT) are common diagnostic imaging methods for detecting and diagnosing papillary thyroid microcarcinoma (PTMC). However, single-source dual-energy spectral computed tomography (spectral CT) reduces beam hardening artefacts and optimizes contrast, which may add value in detecting PTMC. OBJECTIVE: To investigate values of applying single-source dual-energy spectral CT for diagnosing PTMCs, in comparison with high frequency ultrasound and conventional polychromatic images. METHODS: Thirty-one patients with suspected PTMC underwent contrast-enhanced dual-energy spectral CT. The images were analyzed by two experienced radiologists. Noise and contrast-noise-ratio (CNR) were compared between conventional CT and spectral CT. Ultrasonography was also performed by an experienced radiologist with a 7 to 12-MHz linear array transducer. Detection and diagnostic sensitivity were determined and compared. RESULTS: Forty-six pathologically-confirmed PTMC lesions were detected in 31 patients. Spectral CT had lower noise and higher CNR than conventional CT (P < 0.05). US detected more tumors (45/46 [97.8%] than conventional CT images (40/46 [87.0%]) or spectral CT images (44/46 [95.7%]). Among them, 30 (65.2%), 36 (78.3%), and 40 (87.0%) lesions were diagnosed correctly by conventional CT, spectral CT and US, respectively. Spectral CT had higher sensitivity than conventional CT (P = 0.031). However, there was no significant difference between spectral CT and US diagnostic sensitivities (P = 0.125). CONCLUSION: Single-source dual-energy spectral CT was superior to conventional polychromatic images and similar to high frequency ultrasound in detecting and diagnosing for PTMCs. CT had advantages in detecting level VI and VII lymph nodes. Spectral CT and US provided good results for PTMC, and aid preoperative diagnosis.

[Analysis of the effectiveness of radiotherapy in postoperative residual tumors in patients with thyroid papillary carcinoma].

OBJECTIVE: The aim of this study was to assess the impact of radiotherapy on patients with postoperative residual or recurrent papillary thyroid cancer (PTC). METHODS: We retrospectively reviewed the medical records of 34 patients with PTC, who underwent surgery and radiotherapy in other hospitals, and treated at the Department of Head and Neck Surgery at Cancer Institute & Hospital CAMS from January 2011 to January 2014. Among the 34 cases, 22 were in stage I, 5 in stage II and 7 in stage IVa. The 34 patients received 1.5 times of surgery before radiotherapy in average. All the cases received radiotherapy (mean, 56 Gy; range, 50-70 Gy). The patients were re-operated in our hospital, and the specimens were examined by pathology. The pre- and post-radiotherapy images (CT and B-ultrasound) were compared, and the changes of tumor volume were examined. The objective effect of treatment on the tumor residual focus was evaluated using RECIST, and analyzed by t-test (SPSS 17.0). RESULTS: All the re-resected lesions after radiotherapy were proved by pathology to be papillary thyroid cancer (PTC) or metastatic PTC in cervical lymph nodes. Among the 34 patients, 22 cases showed mild or moderate cell degeneration and the other 12 cases showed no obvious degeneration. The largest tumor diameter was 27.18 mm before radiotherapy and 27.76 mm after radiotherapy, with a non-significant difference between them (t=-1.618, P>0.05). Among the 34 patients, only 3 patients received reoperation, all other 31 cases had complete resection, and no severe complications were observed except recurrent laryngeal nerve injury in one case. CONCLUSIONS: Radiotherapy has few therapeutic benefit to PTC patients after surgery with residual tumor or local recurrence. It should be used in the PTC patients, in which the tumor invasion involves important organ tissues and is difficult for a single operation to achieve safe resection margin, or in patients who can't bear a surgery because of severe coronary heart disease or others.

[Application of single-source dual-energy spectral CT in differentiating lymphoma and metastatic lymph nodes in the head and neck].

OBJECTIVE: To investigate the feasibility of differentiation of lymphoma, metastatic lymph nodes of squamous cell carcinoma (SCC) and papillary thyroid carcinoma (PTC) in the head and neck by single-source dual-energy spectral CT. METHODS: 25 cases of non-Hodgkin lymphoma (NHL) with 236 lymph nodes, 3 cases of Hodgkin's lymphoma (HL) with 32 lymph nodes, 21 cases of SCC with 86 lymph nodes and 19 cases of PTC with 92 lymph nodes were evaluated by enhanced GSI. CT attenuation of lymph nodes in the monochromatic images at different keV levels and the iodine and water contents of these lymph nodes were measured. The slope of spectral curve was calculated using CT value at 40 keVand 90 keV. All results were analyzed with ANOVA and t test. RESULTS: 70 keV had the best single energy images. Normalized Hounsfield unit (NHU) of diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), T lymphoblastic lymphoma (T-LBL), HL, PTC and SCC was 0.32 ± 0.10, 0.46 ± 0.08, 0.41 ± 0.11, 0.41 ± 0.11, 0.56 ± 0.15 and 0.34 ± 0.16, respectively. Normalized iodine concentration (NIC) of them was 0.20 ± 0.08, 0.32 ± 0.08, 0.25 ± 0.09, 0.30 ± 0.12, 0.49 ± 0.18 and 0.23 ± 0.18, respectively. The slope of spectral curve (k) of them was -1.92 ± 0.55, -2.45 ± 0.60, -1.82 ± 0.57, -2.57 ± 0.54, -5.44 ± 2.41 and -1.97 ± 0.81, respectively. Compared with the NHU, there was a statistically significant difference in each pair except DLBCL and SCC, and T-LBL and HL. Compared with the NIC, there was a statistically significant difference in each pair except DLBCL and SCC, FL and HL, T-LBL and SCC, and T-LBL and HL. Compared with the slope of spectral curve, there was statistically significant difference in each pair except DLBCL and T-LBL, DLBCL and SCC, FL and HL, and T-LBL and SCC. CONCLUSIONS: Malignant lymph nodes of different types of diseases have certain different values of quantitative parameters in spectral CT imaging. By using CT attenuation, the shape and slope of spectral curve and the iodine content, single-source dual-energy CT may potentially provide a quantitative analysis tool for the diagnosis and differential diagnosis of lymph node alterations.

[Value of MRI in preoperative evaluation of carotid body tumor].

OBJECTIVE: To evaluate the value of MRI in preoperative evaluation of carotid body tumor. METHODS: A retrospective study was including 32 CBT of 28 patients of carotid body tumor with complete clinical, imaging and pathological data in our hospital. The MRI images of vascular adjacent length, no enhancement vascular wall, carotid displacement, wrapping angle and carotid stenosis were analyzed respectively in tumor resection group, tumor dissection and artery repair group, and tumor and artery resection group. The results were compared with surgery. RESULTS: The indexes of vascular adjacent average length were (3.2 ± 0.8), (3.4 ± 0.7) and (3.8 ± 1.0) cm, respectively.The indexes of vascular adjacent average length and vascular displacement, which all showed no significant difference between each operation group (P=0.577, 0.859). The indexes of no enhancement vascular wall, wrapping angle and carotid stenosis, which all showed significant difference between each operation group (all P<0.01). Compared with the surgical and pathological findings, with no enhancement vascular wall <2/3 circumference as the index of carotid artery repair or resection, the sensitivity, specificity and accuracy were 86.4%, 90%, 87.5% respectively. With wrapping angle >1/3 circumference as the index of carotid artery repair or resection, the sensitivity, specificity and accuracy were 86.4%, 90%, 87.5% respectively. With carotid stenosis as the index of carotid artery resection, the sensitivity, specificity and accuracy were 80%, 100%, 93.7% respectively. CONCLUSION: The no enhancement vascular wall, wrapping angle and carotid stenosis have a correlation with carotid artery intervention.The no enhancement vascular wall <2/3 circumference, wrapping angle >1/3 circumference and carotid stenosis have a certain value in preoperative evaluation of carotid body tumor, although vascular adjacent average length and vascular displacement have limited value.

An interactive nuclei segmentation framework with Voronoi diagrams and weighted convex difference for cervical cancer pathology images.

Objective.Nuclei segmentation is crucial for pathologists to accurately classify and grade cancer. However, this process faces significant challenges, such as the complex background structures in pathological images, the high-density distribution of nuclei, and cell adhesion.Approach.In this paper, we present an interactive nuclei segmentation framework that increases the precision of nuclei segmentation. Our framework incorporates expert monitoring to gather as much prior information as possible and accurately segment complex nucleus images through limited pathologist interaction, where only a small portion of the nucleus locations in each image are labeled. The initial contour is determined by the Voronoi diagram generated from the labeled points, which is then input into an optimized weighted convex difference model to regularize partition boundaries in an image. Specifically, we provide theoretical proof of the mathematical model, stating that the objective function monotonically decreases. Furthermore, we explore a postprocessing stage that incorporates histograms, which are simple and easy to handle and prevent arbitrariness and subjectivity in individual choices.Main results.To evaluate our approach, we conduct experiments on both a cervical cancer dataset and a nasopharyngeal cancer dataset. The experimental results demonstrate that our approach achieves competitive performance compared to other methods.Significance.The Voronoi diagram in the paper serves as prior information for the active contour, providing positional information for individual cells. Moreover, the active contour model achieves precise segmentation results while offering mathematical interpretability.

A two-stage deep-learning framework for CT denoising based on a clinically structure-unaligned paired data set.

Background: In low-dose computed tomography (LDCT) lung cancer screening, soft tissue is hardly appreciable due to high noise levels. While deep learning-based LDCT denoising methods have shown promise, they typically rely on structurally aligned synthesized paired data, which lack consideration of the clinical reality that there are no aligned LDCT and normal-dose CT (NDCT) images available. This study introduces an LDCT denoising method using clinically structure-unaligned but paired data sets (LDCT and NDCT scans from the same patients) to improve lesion detection during LDCT lung cancer screening. Methods: A cohort of 64 patients undergoing both LDCT and NDCT was randomly divided into training (n=46) and testing (n=18) sets. A two-stage training approach was adopted. First, Gaussian noise was added to NDCT data to create simulated LDCT data for generator training. Then, the model was trained on a clinically structure-unaligned paired data set using a Wasserstein generative adversarial network (WGAN) framework with the initial generator weights obtained during the first stage of training. An attention mechanism was also incorporated into the network. Results: Validated on a clinical CT data set, our proposed method outperformed other available methods [CycleGAN, Pixel2Pixel, block-matching and three-dimensional filtering (BM3D)] in noise removal and detail retention tasks in terms of the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and root mean square error (RMSE) metrics. Compared with the results produced by BM3D, our method yielded an average improvement of approximately 7% in terms of the three evaluation indicators. The probability density profile of the denoised CT output produced using our method best fit the reference NDCT scan. Additionally, our two-stage model outperformed the one-stage WGAN-based model in both objective and subjective evaluations, further demonstrating the higher effectiveness of our two-stage training approach. Conclusions: The proposed method performed the best in removing noise from LDCT scans and exhibited good detail retention, which could potentially enhance the lesion detection and characterization effects obtained for soft tissues in the scanning scope of LDCT lung cancer screening.

Utility of mono-exponential, bi-exponential, and stretched exponential signal models of intravoxel incoherent motion (IVIM) to predict prognosis and survival risk in laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) patients after chemoradiotherapy.

PURPOSE: To investigate the predictive power of mono-exponential, bi-exponential, and stretched exponential signal models of intravoxel incoherent motion (IVIM) in prognosis and survival risk of laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) patients after chemoradiotherapy. MATERIALS AND METHODS: Forty-five patients with laryngeal or hypopharyngeal squamous cell carcinoma were retrospectively enrolled. All patients had undergone pretreatment IVIM examination, subsequently, mean apparent diffusion coefficient (ADCmean), maximum ADC (ADCmax), minimum ADC (ADCmin) and ADCrange (ADCmax - ADCmean) by mono-exponential model, true diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f) by bi-exponential model, distributed diffusion coefficient (DDC), and diffusion heterogeneity index (α) by stretched exponential model were measured. Survival data were collected for 5 years. RESULTS: Thirty-one cases were in the treatment failure group and fourteen cases were in the local control group. Significantly lower ADCmean, ADCmax, ADCmin, D, f, and higher D* values were observed in the treatment failure group than in the local control group (p < 0.05). D* had the greatest AUC of 0.802, with sensitivity and specificity of 77.4 and 85.7% when D* was 38.85 × 10-3 mm2/s. Kaplan-Meier survival analysis showed that the curves of N stage, ADCmean, ADCmax, ADCmin, D, D*, f, DDC, and α values were significant. Multivariate Cox regression analysis showed ADCmean and D* were independently correlated with progression-free survival (PFS) (hazard ratio [HR] = 0.125, p = 0.001; HR = 1.008, p = 0.002, respectively). CONCLUSION: The pretreatment parameters of mono-exponential and bi-exponential models were significantly correlated with prognosis of LHSCC, ADCmean and D* values were independent factors for survival risk prediction.

Analysis of preoperative computed tomography radiomics and clinical factors for predicting postsurgical recurrence of papillary thyroid carcinoma.

BACKGROUND: Postsurgical recurrence is of great concern for papillary thyroid carcinoma (PTC). We aim to investigate the value of computed tomography (CT)-based radiomics features and conventional clinical factors in predicting the recurrence of PTC. METHODS: Two-hundred and eighty patients with PTC were retrospectively enrolled and divided into training and validation cohorts at a 6:4 ratio. Recurrence was defined as cytology/pathology-proven disease or morphological evidence of lesions on imaging examinations within 5 years after surgery. Radiomics features were extracted from manually segmented tumor on CT images and were then selected using four different feature selection methods sequentially. Multivariate logistic regression analysis was conducted to identify clinical features associated with recurrence. Radiomics, clinical, and combined models were constructed separately using logistic regression (LR), support vector machine (SVM), k-nearest neighbor (KNN), and neural network (NN), respectively. Receiver operating characteristic analysis was performed to evaluate the model performance in predicting recurrence. A nomogram was established based on all relevant features, with its reliability and reproducibility verified using calibration curves and decision curve analysis (DCA). RESULTS: Eighty-nine patients with PTC experienced recurrence. A total of 1218 radiomics features were extracted from each segmentation. Five radiomics and six clinical features were related to recurrence. Among the 4 radiomics models, the LR-based and SVM-based radiomics models outperformed the NN-based radiomics model (P = 0.032 and 0.026, respectively). Among the 4 clinical models, only the difference between the area under the curve (AUC) of the LR-based and NN-based clinical model was statistically significant (P = 0.035). The combined models had higher AUCs than the corresponding radiomics and clinical models based on the same classifier, although most differences were not statistically significant. In the validation cohort, the combined models based on the LR, SVM, KNN, and NN classifiers had AUCs of 0.746, 0.754, 0.669, and 0.711, respectively. However, the AUCs of these combined models had no significant differences (all P > 0.05). Calibration curves and DCA indicated that the nomogram have potential clinical utility. CONCLUSIONS: The combined model may have potential for better prediction of PTC recurrence than radiomics and clinical models alone. Further testing with larger cohort may help reach statistical significance.

A deep learning model based on the attention mechanism for automatic segmentation of abdominal muscle and fat for body composition assessment.

Background: Quantitative muscle and fat data obtained through body composition analysis are expected to be a new stable biomarker for the early and accurate prediction of treatment-related toxicity, treatment response, and prognosis in patients with lung cancer. The use of these biomarkers can enable the adjustment of individualized treatment regimens in a timely manner, which is critical to further improving patient prognosis and quality of life. We aimed to develop a deep learning model based on attention for fully automated segmentation of the abdomen from computed tomography (CT) to quantify body composition. Methods: A fully automatic segmentation deep learning model was designed based on the attention mechanism and using U-Net as the framework. Subcutaneous fat, skeletal muscle, and visceral fat were manually segmented by two experts to serve as ground truth labels. The performance of the model was evaluated using Dice similarity coefficients (DSCs) and Hausdorff distance at 95th percentile (HD95). Results: The mean DSC for subcutaneous fat and skeletal muscle were high for both the enhanced CT test set (0.93±0.06 and 0.96±0.02, respectively) and the plain CT test set (0.90±0.09 and 0.95±0.01, respectively). Nevertheless, the model did not perform well in the segmentation performance of visceral fat, especially for the enhanced CT test set. The mean DSC for the enhanced CT test set was 0.87±0.11, while the mean DSC for the plain CT test set was 0.92±0.03. We discuss the reasons for this result. Conclusions: This work demonstrates a method for the automatic outlining of subcutaneous fat, skeletal muscle, and visceral fat areas at L3.

A classifier-combined method for grading breast cancer based on Dempster-Shafer evidence theory.

Background: Preoperative non-invasive histologic grading of breast cancer is essential. This study aimed to explore the effectiveness of a machine learning classification method based on Dempster-Shafer (D-S) evidence theory for the histologic grading of breast cancer. Methods: A total of 489 contrast-enhanced magnetic resonance imaging (MRI) slices with breast cancer lesions (including 171 grade Ⅰ, 140 grade Ⅱ, and 178 grade Ⅲ lesions) were used for analysis. All the lesions were segmented by two radiologists in consensus. For each slice, the quantitative pharmacokinetic parameters based on a modified Tofts model and the textural features of the segmented lesion on the image were extracted. Principal component analysis was then used to reduce feature dimensionality and obtain new features from the pharmacokinetic parameters and texture features. The basic confidence assignments of different classifiers were combined using D-S evidence theory based on the accuracy of three classifiers: support vector machine (SVM), Random Forest, and k-nearest neighbor (KNN). The performance of the machine learning techniques was evaluated in terms of accuracy, sensitivity, specificity, and the area under the curve. Results: The three classifiers showed varying accuracy across different categories. The accuracy of using D-S evidence theory in combination with multiple classifiers reached 92.86%, which was higher than that of using SVM (82.76%), Random Forest (78.85%), or KNN (87.82%) individually. The average area under the curve of using the D-S evidence theory combined with multiple classifiers reached 0.896, which was larger than that of using SVM (0.829), Random Forest (0.727), or KNN (0.835) individually. Conclusions: Multiple classifiers can be effectively combined based on D-S evidence theory to improve the prediction of histologic grade in breast cancer.

Prediction model based on preoperative CT findings for carotid artery invasion in patients with head and neck masses.

Objectives: To investigate the performance of a model in predicting carotid artery (CA) invasion in patients with head and neck masses using computed tomography (CT). Methods: This retrospective study included patients with head and neck masses who underwent CT and surgery between January 2013 and July 2021. Patient characteristics and ten CT features were assessed by two radiologists. The patients were randomly allocated to a training cohort (n=106) and a validation cohort (n=109). Independent risk factors for CA invasion were assessed by univariate and multivariate logistic regression analyses. The predictive model was established as a nomogram using the training cohort. In addition, the calibration, discrimination, reclassification, and clinical application of the model were assessed in the validation cohort. Results: A total of 215 patients were evaluated, including 54 patients with CA invasion. Vascular wall deformation (odds ratio [OR], 7.17; p=0.02) and the extent of encasement to the CA (OR, 1.02; p<0.001) were independent predictors of CA invasion in the multivariable analysis in the training cohort. The performance of the model was similar between the training and validation cohort, with an area under the receiver operating characteristic curve of 0.93 (95% confidence intervals [CI], 0.88-0.98) and 0.88 (95% CI, 0.80-0.96) (p=0.07), respectively. The calibration curve showed a good agreement between the predicted and actual probabilities. Conclusion: A predictive model for carotid artery invasion can be defined based on features that come from patient characteristics and CT data to help in improve surgical planning and invasion evaluation.