Multiparameter MRI-based radiomics analysis for preoperative prediction of type II endometrial cancer.

Objectives: This study aimed to develop and validate a radiomics nomogram based on multiparameter MRI for preoperative differentiation of type II and type I endometrial carcinoma (EC). Methods: A total of 403 EC patients from two centers were retrospectively recruited (training cohort, 70 %; validation cohort, 30 %). Radiomics features were extracted from T2-weighted imaging, dynamic contrast-enhanced T1-weighted imaging at delayed phase(DCE4), and apparent diffusion coefficient (ADC) maps. Following dimensionality reduction, radiomics models were developed by logistic regression (LR), random forest (RF), bootstrap aggregating (Bagging), support vector machine (SVM), artificial neural network (ANN), and naive bayes (NB) algorithms. The diagnostic performance of each radiomics model was evaluated using the ROC curve. A nomogram was constructed by incorporating the optimal radiomics signatures with significant clinical-radiological features and immunohistochemistry (IHC) markers obtained from preoperative curettage specimens. The diagnostic performance and clinical value of the nomogram were evaluated using ROC curves, calibration curves, and decision curve analysis (DCA). Results: Among the radiomics models, the NB model, developed from 12 radiomics features derived from ADC and DCE4 sequences, exhibited strong performance in both training and validation sets, with the AUC values of 0.927 and 0.869, respectively. The nomogram, incorporating the radiomics model with significant clinical-radiological features and IHC markers, demonstrated superior performance in both the training (AUC = 0.951) and the validation sets (AUC = 0.915). Additionally, it exhibited excellent calibration and clinical utility. Conclusions: The radiomics nomogram has great potential to differentiate type II from type I EC, which may be an effective tool to guide clinical decision-making for EC patients.

Epidermal growth factor receptor­targeted antibody nimotuzumab combined with chemoradiotherapy improves survival in patients with locally advanced head and neck squamous cell carcinoma: a propensity score matching real-world study.

Patients with locally advanced head and neck squamous cell carcinoma (LA-HNSCC) have poor survival outcomes. The real-world efficacy of nimotuzumab plus intensity modulated radiotherapy (IMRT)-based chemoradiotherapy in patients with LA-HNSCC remains unclear. A total of 25,442 HNSCC patients were screened, and 612 patients were matched by propensity score matching (PSM) (1:1). PSM was utilized to balance known confounding factors. Patients who completed at least five doses of nimotuzumab were identified as study group. The primary end point was 3-year overall survival (OS) rate. Log-rank test examined the difference between two survival curves and Cloglog transformation test was performed to compare survival at a fixed time point. The median follow-up time was 54.2 (95% confidence interval [CI]: 52.7-55.9) months. The study group was associated with improved OS (hazard ratio [HR] = 0.75, 95% CI: 0.57-0.99, p = 0.038) and progression-free survival (PFS) (HR = 0.74, 95% CI: 0.58-0.96, p = 0.021). Subgroup analysis revealed that aged 50-60 year, IV, N2, radiotherapy dose ≥ 60 Gy, without previous surgery, and neoadjuvant therapy have a trend of survival benefit with nimotuzumab. Nimotuzumab showed favorable safety, only 0.2% had nimotuzumab-related severe adverse events. Our study indicated the nimotuzumab plus chemoradiotherapy provides survival benefits and safety for LA-HNSCC patients in an IMRT era.

Comparison between solid pseudopapillary neoplasms of the pancreas and pancreatic ductal adenocarcinoma with cystic changes using computed tomography.

BACKGROUND: Solid pseudopapillary neoplasms of the pancreas (SPN) share similar imaging findings with pancreatic ductal adenocarcinoma with cystic changes (PDAC with cystic changes), which may result in unnecessary surgery. AIM: To investigate the value of computed tomography (CT) in differentiation of SPN from PDAC with cystic changes. METHODS: This study retrospectively analyzed the clinical and imaging findings of 32 patients diagnosed with SPN and 14 patients diagnosed with PDAC exhibiting cystic changes, confirmed through pathological diagnosis. Quantitative and qualitative analysis was performed, including assessment of age, sex, tumor size, shape, margin, density, enhancement pattern, CT values of tumors, CT contrast enhancement ratios, "floating cloud sign," calcification, main pancreatic duct dilatation, pancreatic atrophy, and peripancreatic invasion or distal metastasis. Multivariate logistic regression analysis was used to identify relevant features to differentiate between SPN and PDAC with cystic changes, and receiver operating characteristic curves were obtained to evaluate the diagnostic performance of each variable and their combination. RESULTS: When compared to PDAC with cystic changes, SPN had a lower age (32 years vs 64 years, P < 0.05) and a slightly larger size (5.41 cm vs 3.90 cm, P < 0.05). SPN had a higher frequency of "floating cloud sign" and peripancreatic invasion or distal metastasis than PDAC with cystic changes (both P < 0.05). No significant difference was found with respect to sex, tumor location, shape, margin, density, main pancreatic duct dilatation, calcification, pancreatic atrophy, enhancement pattern, CT values of tumors, or CT contrast enhancement ratios between the two groups (all P > 0.05). The area under the receiver operating characteristic curve of the combination was 0.833 (95% confidence interval: 0.708-0.957) with 78.6% sensitivity, 81.3% specificity, and 80.4% accuracy in differentiation of SPN from PDAC with cystic changes. CONCLUSION: A larger tumor size, "floating cloud sign," and peripancreatic invasion or distal metastasis are useful CT imaging features that are more common in SPN and may help discriminate SPN from PDAC with cystic changes.

The association between non-HDL cholesterol and high-grade pancreatic neuroendocrine neoplasms.

PURPOSE: High-density lipoprotein cholesterol (HDL-c) plays an important role in tumorigenesis in several endocrine-related cancers. Few studies have shown the effect of non-HDL-c in malignant tumors. The present study aimed to identify the association between non-HDL-c and high-grade pancreatic neuroendocrine neoplasms (PNENs). METHODS: A total of 197 PNEN patients who underwent surgery were analyzed retrospectively. Clinical and histopathological features, such as patients' age and sex, tumor location and size, tumor grade, the level of serum total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c) and fasting plasma-glucose levels were obtained. Non-HDL-c was calculated as total cholesterol - HDL-c. The relationships between those features and high-grade PNENs were identified using logistic regression analysis. RESULTS: Among the 197 patients with PNENs, a lower HDL-c level was more common seen in patients with poorly differentiated PNENs than in those with well-differentiated PNENs (P < 0.05). The non-HDL-c/HDL-c ratio was greater in patients with poorly differentiated PNENs than in those with well-differentiated PNENs (P < 0.01). Similarly, a greater proportion of patients with a non-HDL-c/HDL-c ratio larger than 5 was found in patients with poorly differentiated PNENs than in those with well-differentiation PNENs (P < 0.01). Multivariate logistic analysis showed that the non-HDL-c/HDL-c ratio was positively associated with poorly differentiated PNENs (odds ratio (OR) = 1.45, 95% conference interval (CI):1.13-1.87). Similarly, the risk of poorly differentiated PNENs increased significantly in patients with a non-HDL-c/HDL-c greater than 5 (OR = 14.13, 95%CI: 2.98-66.89). The risk of high-grade PNENs increased in patients with a high non-HDL-c/HDL-c ratio (OR = 1.27, 95% CI: 1.04-1.55), and the risk also increased markedly when the ratio was greater than 5 (OR = 5.00, 95%CI: 1.28-19.49). CONCLUSIONS: A high ratio of non-HDL-c/HDL-c was associated with high-grade PNENs or poorly differentiated PNENs.

Thoracic high resolution computed tomography evaluation of imaging abnormalities of 108 lung cancer patients with different pulmonary function.

PURPOSE: Preserved ratio impaired spirometry (PRISm) and chronic obstructive pulmonary disease (COPD) belong to lung function injury. PRISm is a precursor to COPD. We compared and evaluated the different basic information, imaging findings and survival curves of 108 lung cancer patients with different pulmonary function based on high resolution computed tomography (HRCT). METHODS: This retrospective study was performed on 108 lung cancer patients who did pulmonary function test (PFT) and thoracic HRCT. The basic information was evaluated: gender, age, body mass index (BMI), smoke, smoking index (SI). The following pulmonary function findings were evaluated: forced expiratory volume in 1s (FEV1), forced vital capacity (FVC), FEV1/FVC ratio. The following computed tomography (CT) findings were evaluated: appearance (bronchiectasis, pneumonectasis, atelectasis, ground-glass opacities [GGO], interstitial inflammation, thickened bronchial wall), diameter (aortic diameter, pulmonary artery diameter, MPAD/AD ratio, inferior vena cava diameter [IVCD]), tumor (volume, classification, distribution, staging [I, II, III, IV]). Mortality rates were calculated and survival curves were estimated using the Kaplan-Meier method. RESULTS: Compared with normal pulmonary function group, PRISm group and COPD group were predominantly male, older, smoked more, poorer lung function and had shorter survival time after diagnosis. There were more abnormal images in PRISm group and COPD group than in normal lung function group (N-C group). In PRISm group and COPD group, lung cancer was found late, and the tumor volume was larger, mainly central squamous carcinoma. But the opposite was true for the N-C group. The PRISm group and COPD group had significant poor survival probability compared with the normal lung function group. CONCLUSIONS: Considerable differences regarding basic information, pulmonary function, imaging findings and survival curves are found between normal lung function group and lung function injury group. Lung function injury (PRISm and COPD) should be taken into account in future lung cancer screening studies.

MRI-Based Clinical-Imaging-Radiomics Nomogram Model for Discriminating Between Benign and Malignant Solid Pulmonary Nodules or Masses.

RATIONALE AND OBJECTIVES: Pulmonary nodules or masses are highly prevalent worldwide, and differential diagnosis of benign and malignant lesions remains difficult. Magnetic resonance imaging (MRI) can provide functional and metabolic information of pulmonary lesions. This study aimed to establish a nomogram model based on clinical features, imaging features, and multi-sequence MRI radiomics to identify benign and malignant solid pulmonary nodules or masses. MATERIALS AND METHODS: A total of 145 eligible patients (76 male; mean age, 58.4 years ± 13.7 [SD]) with solid pulmonary nodules or masses were retrospectively analyzed. The patients were randomized into two groups (training cohort, n = 102; validation cohort, n = 43). The nomogram was used for predicting malignant pulmonary lesions. The diagnostic performance of different models was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS: Of these patients, 95 patients were diagnosed with benign lesions and 50 with malignant lesions. Multivariate analysis showed that age, DWI value, LSR value, and ADC value were independent predictors of malignant lesions. Among the radiomics models, the multi-sequence MRI-based model (T1WI+T2WI+ADC) achieved the best diagnosis performance with AUCs of 0.858 (95%CI: 0.775, 0.919) and 0.774 (95%CI: 0.621, 0.887) for the training and validation cohorts, respectively. Combining multi-sequence radiomics, clinical and imaging features, the predictive efficacy of the clinical-imaging-radiomics model was significantly better than the clinical model, imaging model and radiomics model (all P < 0.05). CONCLUSION: The MRI-based clinical-imaging-radiomics model is helpful to differentiate benign and malignant solid pulmonary nodules or masses, and may be useful for precision medicine of pulmonary diseases.

Development and external validation of a nomogram for predicting overall survival of patients with non-endometrioid endometrial cancer: A population-based analysis.

Objectives: The main objective of this study was to identify the key predictors and construct a nomogram that can be used to predict the overall survival of individuals with non-endometrioid endometrial cancer. Methods: A total of 2686 non-endometrioid endometrial cancer patients confirmed between 1988 and 2018 were selected from the Surveillance, Epidemiology, and End Results database. They were divided into a training cohort and an internal validation cohort. Independent risk factors were chosen by Cox regression analyses. A predictive nomogram model for overall survival was constructed based on above factors. A Chinese cohort of 41 patients was collected to be an external validation cohort. Results: Eight variables were estimated as independent predictors for overall survival. A nomogram was established using these factors. The C-index for predicting the overall survival of patients with non-endometrioid endometrial cancer from the nomogram was 0.734, 0.700, and 0.767 in training, internal, and external validation cohort, respectively. Calibration plots and decision curve analysis showed that the nomogram was valuable for further clinical application. Conclusion: We constructed a nomogram which can be used as an effective tool to predict the 3- and 5-year overall survival of Non-endometrioid endometrial cancer patients.

Computed tomography-based radiomics diagnostic approach for differential diagnosis between early- and late-stage pancreatic ductal adenocarcinoma.

BACKGROUND: One of the primary reasons for the dismal survival rates in pancreatic ductal adenocarcinoma (PDAC) is that most patients are usually diagnosed at late stages. There is an urgent unmet clinical need to identify and develop diagnostic methods that could precisely detect PDAC at its earliest stages. AIM: To evaluate the potential value of radiomics analysis in the differentiation of early-stage PDAC from late-stage PDAC. METHODS: A total of 71 patients with pathologically proved PDAC based on surgical resection who underwent contrast-enhanced computed tomography (CT) within 30 d prior to surgery were included in the study. Tumor staging was performed in accordance with the 8th edition of the American Joint Committee on Cancer staging system. Radiomics features were extracted from the region of interest (ROI) for each patient using Analysis Kit software. The most important and predictive radiomics features were selected using Mann-Whitney U test, univariate logistic regression analysis, and minimum redundancy maximum relevance (MRMR) method. Random forest (RF) method was used to construct the radiomics model, and 10-times leave group out cross-validation (LGOCV) method was used to validate the robustness and reproducibility of the model. RESULTS: A total of 792 radiomics features (396 from late arterial phase and 396 from portal venous phase) were extracted from the ROI for each patient using Analysis Kit software. Nine most important and predictive features were selected using Mann-Whitney U test, univariate logistic regression analysis, and MRMR method. RF method was used to construct the radiomics model with the nine most predictive radiomics features, which showed a high discriminative ability with 97.7% accuracy, 97.6% sensitivity, 97.8% specificity, 98.4% positive predictive value, and 96.8% negative predictive value. The radiomics model was proved to be robust and reproducible using 10-times LGOCV method with an average area under the curve of 0.75 by the average performance of the 10 newly built models. CONCLUSION: The radiomics model based on CT could serve as a promising non-invasive method in differential diagnosis between early and late stage PDAC.

Characterization of a novel VenusX orthogonal dual-layer multileaf collimator.

PURPOSE: To investigate and characterize the performance of a novel orthogonal dual-layer alpha multileaf collimator (αMLC) mounted on the LinaTech VenusX linac. METHODS: We evaluated leaf positioning accuracy and reproducibility using an electronic portal imaging device through the picket fence test. The average, interleaf, intraleaf, and leaf tip transmissions of the single and dual layers were measured using an ionization chamber. Square and rhombus fields were used to evaluate the leaf penumbra of αMLC. To investigate the advantages of the orthogonal dual-layer multileaf collimator (MLC) in field shaping, right triangular and circular pattern fields were formed using both the dual layers and single layers of the αMLC. RESULTS: The average maximum positioning deviations of the upper and lower αMLC over 1 year were 0.76 ± 0.09 mm and 0.62 ± 0.07 mm, respectively. The average transmissions were 1.87%, 1.83%, and 0.03% for the upper-, lower- and dual-layer αMLC, respectively. The maximum interleaf transmissions of the lower- and dual-layer were 2.43% and 0.17%, respectively. The leaf tip transmissions were 9.34% and 0.25%, respectively. The penumbra of the square field was 6.2 mm in the X direction and 8.0 mm in the Y direction. The average penumbras of the rhombus fields with side lengths of 5 and 10 cm were 3.6 and 4.9 mm, respectively. For the right triangular and circular fields, the fields shaped by the dual-layer leaves were much closer to the set field than those shaped by single-layer leaves. The dose undulation amplitude of the 50% isodose lines and leaf stepping angle change of the dual-layer leaves were smaller than those of the single-layer leaves. CONCLUSIONS: The αMLC benefits from its orthogonal dual-layer design. Leaf transmission, dose undulations at the field edge, and MLC field dependence of the leaf stepping angle of the dual-layer αMLC were remarkably reduced.

The association between hemoglobin level and sarcopenia in Chinese patients with Crohn's disease.

Sarcopenia and anemia are common complications in patients with Crohn's Disease (CD). However, few studies have shown the association between sarcopenia and hemoglobin levels in CD patients. This retrospective study aimed to explore such association in Chinese patients with CD. Two hundred and twelve adult CD inpatients who underwent computed tomography (CT) or magnetic resonance imaging (MRI) examinations from July 2019 to December 2021 were included in the study. Sarcopenia was defined according to the cutoff value of skeletal muscle index of lumbar spine 3 (SMI-L3) (< 44.77cm2/m2 for males and < 32.5cm2/m2 for females). The CD patients were divided into two groups based on the presence or absence of sarcopenia. Clinical data, hemoglobin levels, and other laboratory data were retrospectively collected. The association between hemoglobin levels and sarcopenia was analyzed by univariable and multivariable logistic regression analysis. Sarcopenia occurred in 114 CD patients (53.8%). Compared to patients without sarcopenia, patients with sarcopenia had a lower proportion of L1 (30.7% vs. 45.8%, p = 0.032) and B1 classification (58.8% vs. 72.4%, p = 0.037). Patients with sarcopenia had significantly lower levels of hemoglobin (Hb) (116.5 ± 22.8 vs. 128.1 ± 21.0, p < 0.001). The prevalence of sarcopenia increased with the decrease in hemoglobin level (p for trend < 0.05). Linear regression analysis showed that hemoglobin levels were associated with SMI-L3 (ß = 0.091, p = 0.001). Multivariable logistic regression analysis found that higher hemoglobin levels (OR:0.944; 95% CI: 0.947,0.998; p = 0.036) were independent protective factors for sarcopenia. Lower hemoglobin levels are independently associated factors of sarcopenia in adult Chinese patients with CD.

Bone mineral density in lower thoracic vertebra for osteoporosis diagnosis in older adults during CT lung cancer screening.

BACKGROUND: Quantitative computed tomography (QCT)-based lumbar bone mineral density (LBMD) has been used to diagnose osteoporosis. This study explored the value of lower thoracic BMD (TBMD) in diagnosing osteoporosis in older adults during CT lung cancer screening. METHODS: This study included 751 subjects who underwent QCT scans with both LBMD and TBMD. 141 of them was selected for a validation. Osteoporosis was diagnosed based on LBMD using the ACR criteria (gold standard). TBMD thresholds were obtained using receiver operating characteristic curve. TBMD was also translated into LBMD (TTBMD) and osteoporosis was defined based on TTBMD using ACR criteria. The performance of TBMD and TTBMD in identifying osteoporosis was determined by Kappa test. The associations between TBMD- and TTBMD-based osteoporosis and fracture were tested in 227 subjects with followed up status of spine fracture. RESULTS: The performance of TBMD in identifying osteoporosis was low (kappa = 0.66) if using the ACR criteria. Two thresholds of TBMD for identifying osteopenia (128 mg/cm3) and osteoporosis (91 mg/cm3) were obtained with areas under the curve of 0.97 and 0.99, respectively. The performance of the identification of osteoporosis/osteopenia using the two thresholds or TTBMD both had good agreement with the gold standard (kappa = 0.78, 0.86). Similar results were observed in validation population. Osteoporosis identified using the thresholds (adjusted hazard ratio (HR) = 18.72, 95% confidence interval (CI): 5.13-68.36) or TTBMD (adjusted HR = 10.28, 95% CI: 4.22-25.08) were also associated with fractures. CONCLUSION: Calculating the threshold of TBMD or normalizing TBMD to LBMD are both useful in identifying osteoporosis in older adults during CT lung cancer screening.

Multimodal Imaging-Guided Photoimmunotherapy of Pancreatic Cancer by Organosilica Nanomedicine.

Immune checkpoint blockade (ICB) treatments have contributed to substantial clinical progress. However, challenges persist, including inefficient drug delivery and penetration into deep tumor areas, inadequate response to ICB treatments, and potential risk of inflammation due to over-activation of immune cells and uncontrolled release of cytokines following immunotherapy. In response, this study, for the first time, presents a multimodal imaging-guided organosilica nanomedicine (DCCGP) for photoimmunotherapy of pancreatic cancer. The novel DCCGP nanoplatform integrates fluorescence, magnetic resonance, and real-time infrared photothermal imaging, thereby enhancing diagnostic precision and treatment efficacy for pancreatic cancer. In addition, the incorporated copper sulfide nanoparticles (CuS NPs) lead to improved tumor penetration and provide external regulation of immunotherapy via photothermal stimulation. The synergistic immunotherapy effect is realized through the photothermal behavior of CuS NPs, inducing immunogenic cell death and relieving the immunosuppressive tumor microenvironment. Coupling photothermal stimulation with αPD-L1-induced ICB, the platform amplifies the clearance efficiency of tumor cells, achieving an optimized synergistic photoimmunotherapy effect. This study offers a promising strategy for the clinical application of ICB-based combined immunotherapy and presents valuable insights for applications of organosilica in precise tumor immunotherapy and theranostics.

A Transformable Supramolecular Bispecific Cell Engager for Augmenting Natural Killer and T Cell-Based Cancer Immunotherapy.

Immune cells are pivotal in cancer immunotherapy, yet their therapeutic effectiveness is often hampered by limited tumor infiltration and inhibitory tumor microenvironments. An alkaline phosphatase (ALP)-responsive and transformable supramolecular bis-specific cell engager (Supra-BiCE) to harness natural killer (NK)/T cells for effective cancer immunotherapy is introduced here. The Supra-BiCE, consisting of both SA-P (a phosphorylated peptide targeting and blocking programmed cell death ligand 1 (PD-L1)) and SA-T (a phosphorylated peptide targeting and blocking T cell immunoglobulin and ITIM domain (TIGIT)) is constructed by a simple co-assembling strategy. Upon intravenous administration, Supra-BiCE self-assembles into nanoribbons and interacts with NK/T cells via TIGIT. Notably, these nanoribbons undergo transformation into long nanofibrils within ALP-overexpressing tumor regions, resulting in enhanced binding affinities of Supra-BiCE to both PD-L1 and TIGIT. Consequently, this leads to the accumulation and retention of NK/T cells within tumor regions. Furthermore, the combinatorial blockade of checkpoints by Supra-BiCE activates infiltrating NK/T cells. Moreover, the adjustable peptide ratio in Supra-BiCE enables customization for optimal therapeutic effects against distinct tumor types. Particularly, Supra-BiCE (T:P = 1:3) achieved 98.27% tumor suppression rate against colon carcinoma model. Overall, this study offers a promising tool for engaging NK and T cells for cancer immunotherapy.

A Weakly Supervised Segmentation Network Embedding Cross-Scale Attention Guidance and Noise-Sensitive Constraint for Detecting Tertiary Lymphoid Structures of Pancreatic Tumors.

The presence of tertiary lymphoid structures (TLSs) on pancreatic pathological images is an important prognostic indicator of pancreatic tumors. Therefore, TLSs detection on pancreatic pathological images plays a crucial role in diagnosis and treatment for patients with pancreatic tumors. However, fully supervised detection algorithms based on deep learning usually require a large number of manual annotations, which is time-consuming and labor-intensive. In this paper, we aim to detect the TLSs in a manner of few-shot learning by proposing a weakly supervised segmentation network. We firstly obtain the lymphocyte density maps by combining a pretrained model for nuclei segmentation and a domain adversarial network for lymphocyte nuclei recognition. Then, we establish a cross-scale attention guidance mechanism by jointly learning the coarse-scale features from the original histopathology images and fine-scale features from our designed lymphocyte density attention. A noise-sensitive constraint is introduced by an embedding signed distance function loss in the training procedure to reduce tiny prediction errors. Experimental results on two collected datasets demonstrate that our proposed method significantly outperforms the state-of-the-art segmentation-based algorithms in terms of TLSs detection accuracy. Additionally, we apply our method to study the congruent relationship between the density of TLSs and peripancreatic vascular invasion and obtain some clinically statistical results.

The association between jaundice and poorly differentiated pancreatic neuroendocrine neoplasms (Ki67 index > 55.0%).

BACKGROUND: Jaundice occurs in some pancreatic disease. However, its occurrences and role in pancreatic neuroendocrine neoplasms (PNENs) has not been well studied. In this study we showed the association between jaundice and the risk of high grade and poorly differentiated PNENs. METHODS: Ninety-three patients with head-neck PNENs were included. Poorly differentiated pancreatic neuroendocrine neoplasms were defined by a ki67 index > 55.0%. Logistic regression was used to show the association between demographic information, clinical signs and symptoms and the risk of poorly differentiated tumors. A nomogram model was developed to predict poorly differentiated tumor. RESULTS: Eight of 93 PNEN patients (8.6%) had jaundice. The age and ki67 index in patients with jaundice were significantly higher than those patients without jaundice. All jaundice occurred in patients with grade 3 PNENs. Mutivariable regression analysis showed that age (odds ratio(OR) = 1.10, 95% confidence interval (CI):1.02-1.19), tumor size (OR = 1.42, 95%CI:1.01-2.00) and jaundice (OR = 14.98, 95%CI: 1.22-184.09) were associated with the risk of poorly differentiated PNENs. The age and size combination showed a good performance in predicting poorly differentiated PNENs (area under the curve (AUC) = 0.81, 95% CI: 0.71-0.90). The addition of jaundice further improved the age- and size-based model (AUC = 0.86, 95% CI: 0.78-0.91). A nomogram was developed based on age, tumor size and jaundice. CONCLUSION: Our data showed that jaundice was associated with the risk of high grade PNENs and poorly differentiated PNENs.

Imaging Findings and Misdiagnosis of Bronchogenic Cysts: A Study of 83 Cases.

Objective: We characterize computed tomography (CT) and magnetic resonance imaging (MRI) features of bronchogenic cysts (BCs) and analyze misdiagnosis. Methods: The retrospective study consisted of 83 patients with BCs. CT and MRI images were assessed for mass location, maximum diameter, density, calcification, signal intensity, and enhancement pattern. Eighty-three patients underwent plain CT in which 53 underwent enhanced CT. Thirteen patients received both plain and enhanced MR, and only one received just a plain MR. Results: Eighty-three masses were all solitary, with 71 having a roundish morphology, and twelve having a lobulated or irregular morphology. Sixty-six masses are mediastinal type, four are intrapulmonary type, and 13 are ectopic type. Calcification occurred in 14 lesions. On plain CT, 13 lesions displayed water-like attenuation (-20-20 Hu), and 70 showed soft-tissue attenuation (≥21 Hu). On T1WI, eight masses were hyperintense, three were isointense, and three were hypointense. Fourteen masses were hyperintense on T2WI and (Apparent Diffusion Coefficient) ADC sequence. On (Diffusion Weighted Imaging) DWI, six masses were hypointense and eight were hyperintense. Enhanced T1WI showed seven cases were unenhanced, while six were marginally enhanced. Twenty cases were misdiagnosed as thymomas, eleven as neurogenic tumors, six as lymphangiomas, and two as lung cancer. Five cases were misdiagnosed as other diseases. Patients with BCs underwent MR (42.9%) had a lower rate of misdiagnosis than those who underwent CT alone (53.0%). Conclusion: The imaging findings of BCs in the chest are generally consistent. Misdiagnosis occurs frequently when CT attenuation values exceed 20 Hu. Diagnostic accuracy of BCs tends to improve with preoperative MR examination.

Characterization of immune microenvironment in patients with HPV-positive and negative head and neck cancer.

Human papillomavirus (HPV) status strongly predicts positive clinical outcomes in patients with head and neck squamous cell cancer (HNSCC); however, the potential reasons have not been fully elucidated. Here, we characterized the immune context in HPV+ and HPV- HNSCC by integrating scRNA-seq and bulk RNA-seq data. In scRNA-seq data, HPV + HNSCC displayed increased B cells, plasma cells, CD4+ effector T cells, and decreased macrophages and mast cells. This finding was validated using bulk-cell data. Plasma cells predicted improved survival, and macrophages were associated with survival disadvantage. 1403 upregulated and 1877 downregulated differential expressed genes (DEGs) were obtained. Gene Ontology and KEGG enrichment analysis showed these DEGs focused on cytokine-related activity. Transcriptional analysis of B and plasma cells revealed associations between B-cell surface marker FCER2 and improved survival. In vitro assays confirmed the ability of FCER2 to suppress cellular proliferation and migration of HPV + tumors. In conclusion, our analysis revealed a heterogeneous tumor immune environment (TME) for HPV+ and HPV- HNSCC. Further, FCER2+ B cells contribute to antitumor immunity.

Predicting mixed venous oxygen saturation (SvO2) impairment in COPD patients using clinical-CT radiomics data: A preliminary study.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is one of the most common chronic airway diseases in the world. OBJECTIVE: To predict the degree of mixed venous oxygen saturation (SvO2) impairment in patients with COPD by modeling using clinical-CT radiomics data and to provide reference for clinical decision-making. METHODS: A total of 236 patients with COPD diagnosed by CT and clinical data at Xiangyang No. 1 People's Hospital (n= 157) and Xiangyang Central Hospital (n= 79) from June 2018 to September 2021 were retrospectively analyzed. The patients were divided into group A (SvO⩾2 62%, N= 107) and group B (SvO<2 62%, N= 129). We set up training set and test set at a ratio of 7/3 and time cutoff spot; In training set, Logistic regression was conducted to analyze the differences in general data (e.g. height, weight, systolic blood pressure), laboratory indicators (e.g. arterial oxygen saturation and pulmonary artery systolic pressure), and CT radiomics (radscore generated using chest CT texture parameters from 3D slicer software and LASSO regression) between these two groups. Further the risk factors screened by the above method were used to establish models for predicting the degree of hypoxia in COPD, conduct verification in test set and create a nomogram. RESULTS: Univariate analysis demonstrated that age, smoking history, drinking history, systemic systolic pressure, digestive symptoms, right ventricular diameter (RV), mean systolic pulmonary artery pressure (sPAP), cardiac index (CI), pulmonary vascular resistance (PVR), 6-min walking distance (6MWD), WHO functional classification of pulmonary hypertension (WHOPHFC), the ratio of forced expiratory volume in the first second to the forced vital capacity (FEV1%), and radscore in group B were all significantly different from those in group A (P< 0.05). Multivariate regression demonstrated that age, smoking history, digestive symptoms, 6MWD, and radscore were independent risk factors for SvO2 impairment. The combined model established based on the abovementioned indicators exhibited a good prediction effect [AUC: 0.903; 95%CI (0.858-0.937)], higher than the general clinical model [AUC: 0.760; 95%CI (0.701-0.813), P< 0.05] and laboratory examination-radiomics model [AUC: 0.868; 95%CI (0.818-0.908), P= 0.012]. The newly created nomogram may be helpful for clinical decision-making and benefit COPD patients. CONCLUSION: SvO2 is an important indicator of hypoxia in COPD, and it is highly related to age, 6MWD, and radscore. The combined model is helpful for early identification of SvO2 impairment and adjustment of COPD treatment strategies.