PET-based radiomic feature based on the cross-combination method for predicting the mid-term efficacy and prognosis in high-risk diffuse large B-cell lymphoma patients.

Objectives: This study aims to develop 7×7 machine-learning cross-combinatorial methods for selecting and classifying radiomic features used to construct Radiomics Score (RadScore) of predicting the mid-term efficacy and prognosis in high-risk patients with diffuse large B-cell lymphoma (DLBCL). Methods: Retrospectively, we recruited 177 high-risk DLBCL patients from two medical centers between October 2012 and September 2022 and randomly divided them into a training cohort (n=123) and a validation cohort (n=54). We finally extracted 110 radiomic features along with SUVmax, MTV, and TLG from the baseline PET. The 49 features selection-classification pairs were used to obtain the optimal LASSO-LASSO model with 11 key radiomic features for RadScore. Logistic regression was employed to identify independent RadScore, clinical and PET factors. These models were evaluated using receiver operating characteristic (ROC) curves and calibration curves. Decision curve analysis (DCA) was conducted to assess the predictive power of the models. The prognostic power of RadScore was assessed using cox regression (COX) and Kaplan-Meier plots (KM). Results: 177 patients (mean age, 63 ± 13 years,129 men) were evaluated. Multivariate analyses showed that gender (OR,2.760; 95%CI:1.196,6.368); p=0.017), B symptoms (OR,4.065; 95%CI:1.837,8.955; p=0.001), SUVmax (OR,2.619; 95%CI:1.107,6.194; p=0.028), and RadScore (OR,7.167; 95%CI:2.815,18.248; p<0.001) independently contributed to the risk factors for predicting mid-term outcome. The AUC values of the combined models in the training and validation groups were 0.846 and 0.724 respectively, outperformed the clinical model (0.714;0.556), PET based model (0.664; 0.589), NCCN-IPI model (0.523;0.406) and IPI model (0.510;0.412) in predicting mid-term treatment outcome. DCA showed that the combined model incorporating RadScore, clinical risk factors, and PET metabolic metrics has optimal net clinical benefit. COX indicated that the high RadScore group had worse prognosis and survival in progression-free survival (PFS) (HR, 2.1737,95%CI: 1.2983, 3.6392) and overall survival (OS) (HR,2.1356,95%CI: 1.2561, 3.6309) compared to the low RadScore group. KM survival analysis also showed the same prognosis prediction as Cox results. Conclusion: The combined model incorporating RadScore, sex, B symptoms and SUVmax demonstrates a significant enhancement in predicting medium-term efficacy and prognosis in high-risk DLBCL patients. RadScore using 7×7 machine learning cross-combinatorial methods for selection and classification holds promise as a potential method for evaluating medium-term treatment outcome and prognosis in high-risk DLBCL patients.

TRIANGLE operation, combined with adequate adjuvant chemotherapy, can improve the prognosis of pancreatic head cancer: A retrospective study.

BACKGROUND: The TRIANGLE operation involves the removal of all tissues within the triangle bounded by the portal vein-superior mesenteric vein, celiac axis-common hepatic artery, and superior mesenteric artery to improve patient prognosis. Although previously promising in patients with locally advanced pancreatic ductal adenocarcinoma (PDAC), data are limited regarding the long-term oncological outcomes of the TRIANGLE operation among resectable PDAC patients undergoing pancreaticoduodenectomy (PD). AIM: To evaluate the safety of the TRIANGLE operation during PD and the prognosis in patients with resectable PDAC. METHODS: This retrospective cohort study included patients who underwent PD for pancreatic head cancer between January 2017 and April 2023, with or without the TRIANGLE operation. Patients were divided into the PDTRIANGLE and PDnon-TRIANGLE groups. Surgical and survival outcomes were compared between the two groups. Adequate adjuvant chemotherapy was defined as adjuvant chemotherapy ≥ 6 months. RESULTS: The PDTRIANGLE and PDnon-TRIANGLE groups included 52 and 55 patients, respectively. There were no significant differences in the baseline characteristics or perioperative indexes between the two groups. Furthermore, the recurrence rate was lower in the PDTRIANGLE group than in the PDnon-TRIANGLE group (48.1% vs 81.8%, P < 0.001), and the local recurrence rate of PDAC decreased from 37.8% to 16.0%. Multivariate Cox regression analysis revealed that PDTRIANGLE (HR = 0.424; 95%CI: 0.256-0.702; P = 0.001), adequate adjuvant chemotherapy ≥ 6 months (HR = 0.370; 95%CI: 0.222-0.618; P < 0.001) and margin status (HR = 2.255; 95%CI: 1.252-4.064; P = 0.007) were found to be independent factors for the recurrence rate. CONCLUSION: The TRIANGLE operation is safe for PDAC patients undergoing PD. Moreover, it reduces the local recurrence rate of PDAC and may improve survival in patients who receive adequate adjuvant chemotherapy.

Prognosis Prediction of Diffuse Large B-Cell Lymphoma in 18F-FDG PET Images Based on Multi-Deep-Learning Models.

Diffuse large B-cell lymphoma (DLBCL), a cancer of B cells, has been one of the most challenging and complicated diseases because of its considerable variation in clinical behavior, response to therapy, and prognosis. Radiomic features from medical images, such as PET images, have become one of the most valuable features for disease classification or prognosis prediction using learning-based methods. In this paper, a new flexible ensemble deep learning model is proposed for the prognosis prediction of the DLBCL in 18F-FDG PET images. This study proposes the multi-R-signature construction through selected pre-trained deep learning models for predicting progression-free survival (PFS) and overall survival (OS). The proposed method is trained and validated on two datasets from different imaging centers. Through analyzing and comparing the results, the prediction models, including Age, Ann abor stage, Bulky disease, SUVmax, TMTV, and multi-R-signature, achieve the almost best PFS prediction performance (C-index: 0.770, 95% CI: 0.705-0.834, with feature adding fusion method and C-index: 0.764, 95% CI: 0.695-0.832, with feature concatenate fusion method) and OS prediction (C-index: 0.770 (0.692-0.848) and 0.771 (0.694-0.849)) on the validation dataset. The developed multiparametric model could achieve accurate survival risk stratification of DLBCL patients. The outcomes of this study will be helpful for the early identification of high-risk DLBCL patients with refractory relapses and for guiding individualized treatment strategies.

Comparison of Lugano Criteria Versus RECIL and PERCIST as Prognostic Factors in Diffuse Large B-Cell Lymphoma.

OBJECTIVE: This study aimed to compare the criteria of the Lugano, RECIL, and PERCIST for prognosis in patients with diffuse large B-cell lymphoma. PATIENTS AND METHODS: We retrospectively evaluated 335 patients with diffuse large B-cell lymphoma. All patients underwent baseline 18 F-FDG PET/CT. Among them, 252 and 213 patients underwent interim PET/CT (I-PET/CT) and end-of-treatment PET/CT (EoT-PET/CT), respectively. Scans were interpreted by 2 nuclear medicine physicians using Lugano, RECIL, and PERCIST. RECIL and PERCIST were compared with Lugano for predicting progression-free survival (PFS) and overall survival (OS). RESULTS: All 3 response criteria could be used to predict PFS and OS. In I-PET/CT, the concordance index of Lugano in predicting PFS and OS was higher than that of RECIL (both P = 0.043) or PERCIST ( P = 0.008 and P = 0.034, respectively). In EoT-PET/CT, the concordance index of Lugano for predicting PFS and OS was similar to RECIL and not significantly different from PERCIST ( P = 0.597 and P = 0.231, respectively). CONCLUSIONS: For I-PET/CT, using the Lugano criteria is more accurate than RECIL or PERCIST in predicting PFS and OS. However, for EoT-PET/CT, the PERCIST criteria are minimally better.

M3S-Net: multi-modality multi-branch multi-self-attention network with structure-promoting loss for low-dose PET/CT enhancement.

Objective.PET (Positron Emission Tomography) inherently involves radiotracer injections and long scanning time, which raises concerns about the risk of radiation exposure and patient comfort. Reductions in radiotracer dosage and acquisition time can lower the potential risk and improve patient comfort, respectively, but both will also reduce photon counts and hence degrade the image quality. Therefore, it is of interest to improve the quality of low-dose PET images.Approach.A supervised multi-modality deep learning model, named M3S-Net, was proposed to generate standard-dose PET images (60 s per bed position) from low-dose ones (10 s per bed position) and the corresponding CT images. Specifically, we designed a multi-branch convolutional neural network with multi-self-attention mechanisms, which first extracted features from PET and CT images in two separate branches and then fused the features to generate the final generated PET images. Moreover, a novel multi-modality structure-promoting term was proposed in the loss function to learn the anatomical information contained in CT images.Main results.We conducted extensive numerical experiments on real clinical data collected from local hospitals. Compared with state-of-the-art methods, the proposed M3S-Net not only achieved higher objective metrics and better generated tumors, but also performed better in preserving edges and suppressing noise and artifacts.Significance.The experimental results of quantitative metrics and qualitative displays demonstrate that the proposed M3S-Net can generate high-quality PET images from low-dose ones, which are competable to standard-dose PET images. This is valuable in reducing PET acquisition time and has potential applications in dynamic PET imaging.

Robust deep learning-based PET prognostic imaging biomarker for DLBCL patients: a multicenter study.

OBJECTIVE: To develop and independently externally validate robust prognostic imaging biomarkers distilled from PET images using deep learning techniques for precise survival prediction in patients with diffuse large B cell lymphoma (DLBCL). METHODS: A total of 684 DLBCL patients from three independent medical centers were included in this retrospective study. Deep learning scores (DLS) were generated from PET images using deep convolutional neural network architecture known as VGG19 and DenseNet121. These DLSs were utilized to predict progression-free survival (PFS) and overall survival (OS). Furthermore, multiparametric models were designed based on results from the Cox proportional hazards model and assessed through calibration curves, concordance index (C-index), and decision curve analysis (DCA) in the training and validation cohorts. RESULTS: The DLSPFS and DLSOS exhibited significant associations with PFS and OS, respectively (P<0.05) in the training and validation cohorts. The multiparametric models that incorporated DLSs demonstrated superior efficacy in predicting PFS (C-index: 0.866) and OS (C-index: 0.835) compared to competing models in training cohorts. In external validation cohorts, the C-indices for PFS and OS were 0.760 and. 0.770 and 0.748 and 0.766, respectively, indicating the reliable validity of the multiparametric models. The calibration curves displayed good consistency, and the decision curve analysis (DCA) confirmed that the multiparametric models offered more net clinical benefits. CONCLUSIONS: The DLSs were identified as robust prognostic imaging biomarkers for survival in DLBCL patients. Moreover, the multiparametric models developed in this study exhibited promising potential in accurately stratifying patients based on their survival risk.

Prognostic value of 18F-FDG lesion dissemination features in patients with peripheral T-cell lymphoma (PTCL).

PURPOSE: To explore the prognostic value of the distance between the two lesions that were farthest apart (Dmax) on baseline 18F-FDG PET/CT in peripheral T lymphoma (PTCL) and establish a new prognostic model for predicting the survival outcomes of patients with PTCL. METHODS: In this study, a retrospective analysis of 95 patients with PTCL who underwent baseline 18F-FDG PET/CT was performed to assess the predictive value of Dmax. The total metabolic tumour volume (TMTV), total lesion glycolysis (TLG), standardized uptake value (SUV), and Dmax were calculated with LIFEx software. Progression-free survival (PFS) and overall survival (OS) were used as endpoints. The prognostic model was developed based on the results of the multivariate analysis. The time-dependent area under the ROC curve (tdAUC), calibration curves, Harrell C-index, and decision curve analysis (DCA) were used to assess the model. RESULTS: Patients were followed up for a median of 17.0 months. Multivariate analysis showed that bone marrow biopsy (BMB) and Dmax were independent predictors of PFS (HR: 1.889, P = 0.039; HR: 1.965, P = 0.047) and OS (HR: 1.923, P = 0.031; HR: 1.982, P = 0.034). The model consisting of Dmax, TMTV, and BMB had substantial prognostic value for survival outcomes of PTCL and could successfully identify four groups of patients with significantly different prognoses (χ2 = 13.731, P = 0.003 for PFS; χ2 = 11.841, P = 0.008 for OS). The tdAUC, C-index, calibration curves, and DCA supported that the model was superior to the prognostic index for T-cell lymphoma (PIT) and International Prognostic Index (IPI) scores. CONCLUSION: BMB and Dmax were independent predictors of PTCL in our study. Moreover, a prognostic model based on the Dmax, TMTV, and BMB could be useful for predicting the survival outcomes of patients with PTCL.

Effect of Dexmedetomidine on Cardiac Surgery Patients.

ABSTRACT: Dexmedetomidine, an alpha-2 adrenoreceptor agonist that is widely used as a sedative medication, is becoming more and more attractive in clinical application on cardiac surgery patients. In this review, we aim to summarize and discuss both retrospective studies and clinical trials regarding the effect of dexmedetomidine on patients who underwent cardiac surgery (including coronary artery bypass grafting, valve surgery, aortic surgery, percutaneous coronary intervention, and so on), which illustrates that the clinical effects of dexmedetomidine could effectively reduce mortality, major complications, and the intensive care unit and hospital length of stay without comprising safety. In addition, inconsistent results from both retrospective studies and clinical trials have also been demonstrated. Although the effectiveness and safety of dexmedetomidine on cardiac surgery patients is suggested, high-quality clinical trials are needed for further verification.

Longitudinal genomic alternations and clonal dynamics analysis of primary malignant melanoma of the esophagus.

Primary malignant melanoma of the esophagus (PMME) is a rare gastrointestinal melanoma with a high rate of recurrence and metastasis. The standard of care for PMME has not been established yet due to a lack of understanding of its clinical and molecular pathogenesis. Thus, we performed genomic profiling on a recurrent PMME case to seek novel opportunities for the management of this rare disease. Between 2013 and 2016, 6 tissue samples including 3 from the primary tumors, 2 from the relapsed tumors, and 1 from a normal control were collected from a patient diagnosed with PMME and were subjected to whole-exome sequencing to track the dynamic genetic changes. Additionally, we also analyzed a cohort of 398 samples obtained from the TCGA skin cutanesous melanoma (TCGA-SKCM) dataset to assess the frequency and determine the clinical implications of genomic events found in the presented study. ARHGAP35 (p.L1022M) was the only mutation shared across temporal PMME lesions. The PMME samples showed higher levels of genetic instability and intra-tumor heterogeneity. They also shared several concordant copy number variations (CNV). All lesions were concordant with the evolution trajectory, and shrinkage of the founding clone caused the subclonal population to become dominant in PT1c, which was likely the reason behind metastatic seeding. ARHGAP35 mutations were found in 6% of the TCGA-SKCM cohort samples. The presence of the mutations was associated with poor progression-free survival (PFS) by both univariate and multivariate Cox regression analyses. Our study showed that the primary tumor clone disseminates earlier in PMME. This highlights the need to understand the mechanism involved in the early PMME recurrence to optimize treatment.

Application of omental interposition to reduce pancreatic fistula and related complications in pancreaticoduodenectomy: A propensity score-matched study.

BACKGROUND: The life-threatening complications following pancreatoduodenectomy (PD), intra-abdominal hemorrhage, and postoperative infection, are associated with leaks from the anastomosis of pancreaticoduodenectomy. Although several methods have attempted to reduce the postoperative pancreatic fistula (POPF) rate after PD, few have been considered effective. The safety and short-term clinical benefits of omental interposition remain controversial. AIM: To investigate the safety and feasibility of omental interposition to reduce the POPF rate and related complications in pancreaticoduodenectomy. METHODS: In total, 196 consecutive patients underwent PD performed by the same surgical team. The patients were divided into two groups: An omental interposition group (127, 64.8%) and a non-omental interposition group (69, 35.2%). Propensity score-matched (PSM) analyses were performed to compare the severe complication rates and mortality between the two groups. RESULTS: Following PSM, the clinically relevant POPF (CR-POPF, 10.1% vs 24.6%; P = 0.025) and delayed postpancreatectomy hemorrhage (1.4% vs 11.6%; P = 0.016) rates were significantly lower in the omental interposition group. The omental interposition technique was associated with a shorter time to resume food intake (7 d vs 8 d; P = 0.048) and shorter hospitalization period (16 d vs 21 d; P = 0.031). Multivariate analyses showed that a high body mass index, nonapplication of omental interposition, and a main pancreatic duct diameter < 3 mm were independent risk factors for CR-POPF. CONCLUSION: The application of omental interposition is an effective and safe approach to reduce the CR-POPF rate and related complications after PD.

The C-Terminal Transmembrane Domain of Cowpea Mild Mottle Virus TGBp2 Is Critical for Plasmodesmata Localization and for Its Interaction With TGBp1 and TGBp3.

The movement of some plant RNA viruses is mediated by triple gene block (TGB) proteins, which cooperate to transfer the viral genome from cell to cell through plasmodesmata. Here, we investigated the function of the TGB proteins of cowpea mild mottle virus (CPMMV; genus Carlavirus, family Betaflexiviridae), which causes severe damage to soybean production. Subcellular localization experiments demonstrated that TGBp1 and TGBp3 were localized to the endoplasmic reticulum (ER), plasmodesmata (PD) and nucleus in Nicotiana benthamiana leaves. TGBp2 was unusually localized to PD. In protein interaction assays TGBp2 significantly enhanced the interaction between TGBp3 and TGBp1. Interaction assays using deletion mutants showed that the C-terminal transmembrane (TM) domain of TGBp2 is critical for its localization to PD and for its interaction with TGBp1 and TGBp3.

Radiomics signature from [18F]FDG PET images for prognosis predication of primary gastrointestinal diffuse large B cell lymphoma.

OBJECTIVES: To investigate the prognostic value of PET radiomics feature in the prognosis of patients with primary gastrointestinal diffuse large B cell lymphoma (PGI-DLBCL) treated with R-CHOP-like regimen. METHODS: A total of 140 PGI-DLBCL patients who underwent pre-therapy [18F] FDG PET/CT were enrolled in this retrospective analysis. PET radiomics features obtained from patients in the training cohort were subjected to three machine learning methods and Pearson's correlation test for feature selection. Support vector machine (SVM) was used to build a radiomics signature classifier associated with progression-free survival (PFS) and overall survival (OS). A multivariate Cox proportional hazards regression model was established to predict survival outcomes. RESULTS: A total of 1421 PET radiomics features were extracted and reduced to 5 features to build a radiomics signature which was significantly associated with PFS and OS (p < 0.05). The combined model incorporating radiomics signatures, metabolic metrics, and clinical risk factors showed high C-indices in both the training (PFS: 0.825, OS: 0.834) and validation sets (PFS: 0.831, OS: 0.877). Decision curve analysis (DCA) demonstrated that the combined models achieved the most net benefit across a wider reasonable range of threshold probabilities for predicting PFS and OS. CONCLUSION: The newly developed radiomics signatures obtained by the ensemble strategy were independent predictors of PFS and OS for PGI-DLBCL patients. Moreover, the combined model with clinical and metabolic factors was able to predict patient prognosis and may enable personalized treatment decision-making. KEY POINTS: • Radiomics signatures generated from the optimal radiomics feature set from the [18F]FDG PET images can predict the survival of PGI-DLBCL patients. • The optimal radiomics feature set is constructed by integrating the feature selection outputs of LASSO, RF, Xgboost, and PC methods. • Combined models incorporating radiomics signatures from18F-FDG PET images, metabolic parameters, and clinical factors outperformed clinical models, and NCCN-IPI.

Optimal PET-based radiomic signature construction based on the cross-combination method for predicting the survival of patients with diffuse large B-cell lymphoma.

PURPOSE: To develop and externally validate models incorporating a PET radiomics signature (R-signature) obtained by the cross-combination method for predicting the survival of patients with diffuse large B-cell lymphoma (DLBCL). METHODS: A total of 383 patients with DLBCL from two medical centres between 2011 and 2019 were included. The cross-combination method was used on three types of PET radiomics features from the training cohort to generate 49 feature selection-classification candidates based on 7 different machine learning models. The R-signature was then built by selecting the optimal candidates based on their progression-free survival (PFS) and overall survival (OS). Cox regression analysis was used to develop the survival prediction models. The calibration, discrimination, and clinical utility of the models were assessed and externally validated. RESULTS: The R-signatures determined by 12 and 31 radiomics features were significantly associated with PFS and OS, respectively (P<0.05). The combined models that incorporated R-signatures, metabolic metrics, and clinical risk factors exhibited significant prognostic superiority over the clinical models, PET-based models, and the National Comprehensive Cancer Network International Prognostic Index in terms of both PFS (C-index: 0.801 vs. 0.732 vs. 0.785 vs. 0.720, respectively) and OS (C-index: 0.807 vs. 0.740 vs. 0.773 vs. 0.726, respectively). For external validation, the C-indices were 0.758 vs. 0.621 vs. 0.732 vs. 0.673 and 0.794 vs. 0.696 vs. 0.781 vs. 0.708 in the PFS and OS analyses, respectively. The calibration curves showed good consistency, and the decision curve analysis supported the clinical utility of the combined model. CONCLUSION: The R-signature could be used as a survival predictor for DLBCL, and its combination with clinical factors may allow for accurate risk stratification.

Deep learning-based tumour segmentation and total metabolic tumour volume prediction in the prognosis of diffuse large B-cell lymphoma patients in 3D FDG-PET images.

OBJECTIVES: To demonstrate the effectiveness of automatic segmentation of diffuse large B-cell lymphoma (DLBCL) in 3D FDG-PET scans using a deep learning approach and validate its value in prognosis in an external validation cohort. METHODS: Two PET datasets were retrospectively analysed: 297 patients from a local centre for training and 117 patients from an external centre for validation. A 3D U-Net architecture was trained on patches randomly sampled within the PET images. Segmentation performance was evaluated by six metrics, including the Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), sensitivity (Se), positive predictive value (PPV), Hausdorff distance 95 (HD 95), and average symmetric surface distance (ASSD). Finally, the prognostic value of predictive total metabolic tumour volume (pTMTV) was validated in real clinical applications. RESULTS: The mean DSC, JSC, Se, PPV, HD 95, and ASSD (with standard deviation) for the validation cohort were 0.78 ± 0.25, 0.69 ± 0.26, 0.81 ± 0.27, 0.82 ± 0.25, 24.58 ± 35.18, and 4.46 ± 8.92, respectively. The mean ground truth TMTV (gtTMTV) and pTMTV were 276.6 ± 393.5 cm3 and 301.9 ± 510.5 cm3 in the validation cohort, respectively. Perfect homogeneity in the Bland-Altman analysis and a strong positive correlation in the linear regression analysis (R2 linear = 0.874, p < 0.001) were demonstrated between gtTMTV and pTMTV. pTMTV (≥ 201.2 cm3) (PFS: HR = 3.097, p = 0.001; OS: HR = 6.601, p < 0.001) was shown to be an independent factor of PFS and OS. CONCLUSIONS: The FCN model with a U-Net architecture can accurately segment lymphoma lesions and allow fully automatic assessment of TMTV on PET scans for DLBCL patients. Furthermore, pTMTV is an independent prognostic factor of survival in DLBCL patients. KEY POINTS: •The segmentation model based on a U-Net architecture shows high performance in the segmentation of DLBCL patients on FDG-PET images. •The proposed method can provide quantitative information as a predictive TMTV for predicting the prognosis of DLBCL patients.

Low preoperative skeletal muscle index increases the risk of mortality among resectable pancreatic cancer patients: A retrospective study.

BACKGROUND: The only potential curative treatment for patients with pancreatic cancer is surgery; however, the prognosis remains poor. Measures of body composition based on computed tomography (CT) have been established as a reliable predictor of the prognosis of cancer patients after surgery. AIM: To elucidate the associations of body composition measures derived from preoperative CT scans with the prognosis of patients with pancreatic cancer. METHODS: One hundred fifteen patients undergoing pancreatic resection with curative intent for pancreatic cancer were retrospectively enrolled. A preoperative CT scan at the third lumbar vertebral level was performed to measure the skeletal muscle index (SMI), mean skeletal muscle radiodensity, subcutaneous adipose tissue index, and visceral to subcutaneous adipose tissue area ratio. The clinical and pathological data were collected. The effects of these factors on long-term survival were evaluated. RESULTS: Among the five body composition measures, only low SMI independently predicted overall survival (OS) [hazard ratio (HR): 2.307; 95% confidence interval (CI): 1.210-4.402] and recurrence-free survival (HR: 1.907; 95%CI: 1.147-3.171). Furthermore, patients with low SMI (vs high SMI) were older (68.8 ± 9.3 years vs 63.3 ± 8.4 years); low SMI was present in 27 of 56 patients (48.2%) aged 65 years and older and in 11 of 59 younger patients (18.6%). In addition, subgroup analyses revealed that the correlation between low SMI and OS was observed only in patients aged 65 years and older. CONCLUSION: Low preoperative SMI was more prevalent in elderly patients and was associated with a poor prognosis among pancreatic cancer patients, especially elderly patients.

High-Throughput Functional Analysis of CFTR and Other Apically Localized Proteins in iPSC-Derived Human Intestinal Organoids.

Induced Pluripotent Stem Cells (iPSCs) can be differentiated into epithelial organoids that recapitulate the relevant context for CFTR and enable testing of therapies targeting Cystic Fibrosis (CF)-causing mutant proteins. However, to date, CF-iPSC-derived organoids have only been used to study pharmacological modulation of mutant CFTR channel activity and not the activity of other disease-relevant membrane protein constituents. In the current work, we describe a high-throughput, fluorescence-based assay of CFTR channel activity in iPSC-derived intestinal organoids and describe how this method can be adapted to study other apical membrane proteins. Specifically, we show how this assay can be employed to study CFTR and ENaC channels and an electrogenic acid transporter in the same iPSC-derived intestinal tissue. This phenotypic platform promises to expand CF therapy discovery to include strategies that target multiple determinants of epithelial fluid transport.

Effects of Laser Spot Size on the Mechanical Properties of AISI 420 Stainless Steel Fabricated by Selective Laser Melting.

The purpose of this study is to investigate the effects of laser spot size on the mechanical properties of AISI 420 stainless steel, fabricated by selective laser melting (SLM), process. Tensile specimens were built directly via the SLM process, using various laser spot diameters, namely 0.1, 0.2, 0.3, and 0.4 mm. The corresponding volumetric energy density (EV) is 80, 40, 26.7, and 20 J/mm3, respectively. Experimental results indicate that laser spot size is an important process parameter and has significant effects on the surface roughness, hardness, density, tensile strength, and microstructure of the SLM AISI 420 builds. A large laser spot with low volumetric energy density results in balling, un-overlapped defects, a large re-heated zone, and a large sub-grain size. As a result, SLM specimens fabricated by the largest laser spot diameter of 0.4 mm exhibit the roughest surface, lowest densification, and lowest ultimate tensile strength. To ensure complete melting of the powder and melt pool stability, EV of 80 J/mm3 proves to be a suitable laser energy density value for the given SLM processing and material system.

Value of total lesion glycolysis and cell-of-origin subtypes for prognostic stratification of diffuse large B-cell lymphoma patients.

BACKGROUND: This study aimed to explore the added prognostic value of baseline metabolic volumetric parameters and cell of origin subtypes to the National Comprehensive Cancer Network International Prognostic Index (NCCN-IPI) in nodal diffuse large B-cell lymphoma (DLBCL) patients. METHODS: A total of 184 consecutive de novo nodal DLBCL patients who underwent baseline positron emission tomography/computed tomography (PET/CT) were included in this study. Kaplan-Meier estimates were generated to evaluate the clinical, biological, and PET/CT parameters' prognostic value. The Cox proportional hazards model was performed to examine the potential independent predictors for progression-free survival (PFS) and overall survival (OS). RESULTS: With a median follow-up of 35 months, the 3-year PFS and OS were 65.2% and 73.0%, respectively. In univariate analysis, total lesion glycolysis (TLG), cell-of-origin subtypes, and NCCN-IPI were both PFS and OS predictors. High TLG (≥1,852), non-germinal center B (non-GCB), as well as high NCCN-IPI (≥4), were shown to be independently significantly associated with inferior PFS and OS after multivariate analysis. Based on the number of risk factors (high TLG, non-GCB, and high NCCN-IPI), a revised risk model was designed, and the participants were divided into four risk groups with very different outcomes, in which the PFS rates were 89.7%, 66.2%, 51.7%, and 26.7% (χ2=30.179, P<0.001), and OS rates were 93.1%, 73.8%, 56.7%, and 43.3%, respectively (χ2=23.649, P<0.001), respectively. Compared with the NCCN-IPI alone, the revised risk model showed a stronger ability to reveal further discrimination among subgroups, especially for participants with very unfavorable survival outcomes (PFS: χ2=9.963, P=0.002; OS: χ2=4.166, P=0.041, respectively). CONCLUSIONS: The TLG, cell-of-origin subtypes, and NCCN-IPI are independent prognostic survival factors in DLBCL patients. Moreover, the revised risk model composed of the number of risk factors (high TLG, non-GCB, and high NCCN-IPI) can stratify patients better than the NCCN-IPI, especially for patients at high risk, which suggests its potential integration into decision making for personalized medicine.

Sustained remission of multi-line relapsed extranodal NK/T-cell lymphoma, nasal type, following sintilimab and chidamide: A case report.

INTRODUCTION: There is currently no optimal treatment modality for refractory or relapsed Extranodal NK/T-cell lymphoma, nasal type (ENKTL). In recent years, programmed cell death protein 1 (PD-1)/programmed cell - ligand 1 pathway blockade and histone deacetylase inhibitors have emerged as promising strategies for refractory or relapsed ENKTL. Accumulating evidence has shown that therapeutic effects of anti-PD-1 antibody could be enhanced by histone deacetylase inhibitors. PATIENT CONCERNS: A 52-year-old male patient was diagnosed with stage I ENKTL by biopsy on February 2010. DIAGNOSIS: positron emission tomography-computed tomography (PET-CT) and biopsy were used to diagnose relapsed ENKTL in 2014. INTERVENTIONS: The patient was treated with radiotherapy and six cycles of etoposide, prednisone, vincristine (Oncovin), cyclophosphamide and doxorubicin hydrochloride and achieved complete remission (CR) by PET-CT in August 2010. In November 2014, the patient was diagnosed with relapsed stage IV ENKTL and was treated with six cycles of alternative chemotherapy with the regimen of steroid (dexamethasone), methotrexate, ifosfamide, L-asparaginase, and etoposide and pegaspargase plus Gemcitabine, Oxaliplatin along with radiotherapy. The patient achieved remission and was placed on thalidomide maintenance treatment. Upon suspicion of relapse suggested by PET-CT, Autologous stem cell transplant was performed after BCNU, etoposide, Ara-C, and melphalan preconditioning on February 2016. Following relapse again in December 2016, the lesions of left femur were treated with radiotherapy and he received anti-PD-1 antibody. He was treated with 4 cycles of pegaspargase plus Gemcitabine, Oxaliplatin on August 2017. The patient's condition improved. He received maintenance and consolidation therapy including lenalidomide, radiotherapy of the right nasal cavity and paranasal sinuses and antigen-specific reactive T cell infusions. PET-CT imaging showed there was high metabolic activity signal in the distal end of right femoral on August 2018 and the treatment regimen was adjusted to radiotherapy of the distal end of right femoral and systemic treatment of PD-1 antibody Sintilimab and chidamide 30 mg. After 5 months post-treatment, biopsy of nasopharynx showed no lymphoma cells. The patient continued the treatment of Sintilimab and chidamide 20 mg. OUTCOMES: PET-CT imaging showed his lesions obtained remission after 8 months post-treatment. CONCLUSION: Thus, combination of sintilimab and chidamide can be used to treat relapsed ENKTL following treatment failure from chemo-, radio-, and immuno-therapy. A clinical trial has been launched.

The anatomical features of dorsal pancreatic artery in the pancreatic head and its clinical significance in laparoscopic pancreatoduodenectomy.

BACKGROUND: Bleeding control as one of the major challenges in laparoscopic pancreaticoduodenectomy (LPD) necessitates a considerable anatomical knowledge of the blood supply to the pancreatic head so as to improve the safety of this surgery. This study aimed towards a better understanding of the anatomical features of the dorsal pancreatic artery (DPA), as well as its clinical significance in LPD. METHOD: Thirteen Chinese cadaveric specimens were used to study the blood supply of the pancreatic head. Twelve of them were perfused with latex, and the other fresh one was used to build the intraorganic structure model of the pancreas by mold casting. Between July 2018 and June 2019, a total of thirty-five consecutive patients without vascular encasement, who underwent LPD in our institute, were performed with computed tomography as a preoperative detection of the DPA. The DPA was ligated prior to uncinate process dissection in seventeen patients ("early DPA ligation" group), as the others were assigned into the control group. RESULTS: In the thirteen cadaveric specimens, the DPA originates, respectively, from the splenic artery (46.1%), superior mesenteric artery (38.5%), common hepatic artery (7.7%) and right gastroepiploic artery (7.7%). The right branch of the DPA gives off terminal arteries to form an "inner ring" in the pancreatic head, which communicates with the pancreaticoduodenal arterial arches by plenty of collateral arteries. As compared to the control group, the "early DPA ligation" group showed a significantly lower mean blood loss (218 ± 111 vs 320 ± 162, P = 0.038), as well as shorter mean resection time (121 ± 23 vs 136 ± 22, P = 0.049). CONCLUSION: The DPA is one of the major blood supplies to the pancreatic head. A ligation of DPA prior to dissection of the uncinate process can help to completely block the blood supply to the pancreatic head, and therefore improve surgical outcome and safety in LPD.