Lipid-Encapsulated Engineered Bacterial Living Materials Inhibit Cyclooxygenase II to Enhance Doxorubicin Toxicity.

Recently, there has been increasing interest in the use of bacteria for cancer therapy due to their ability to selectively target tumor sites and inhibit tumor growth. However, the complexity of the interaction between bacteria and tumor cells evokes unpredictable therapeutic risk, which induces inflammation, stimulates the up-regulation of cyclooxygenase II (COX-2) protein, and stimulates downstream antiapoptotic gene expression in the tumor microenvironment to reduce the antitumor efficacy of chemotherapy and immunotherapy. In this study, we encapsulated celecoxib (CXB), a specific COX-2 inhibitor, in liposomes anchored to the surface of Escherichia coli Nissle 1917 (ECN) through electrostatic absorption (C@ECN) to suppress ECN-induced COX-2 up-regulation and enhance the synergistic antitumor effect of doxorubicin (DOX). C@ECN improved the antitumor effect of DOX by restraining COX-2 expression. In addition, local T lymphocyte infiltration was induced by the ECN to enhance immunotherapy efficacy in the tumor microenvironment. Considering the biosafety of C@ECN, a hypoxia-induced lysis circuit, pGEX-Pvhb-Lysis, was introduced into the ECN to limit the number of ECNs in vivo. Our results indicate that this system has the potential to enhance the synergistic effect of ECN with chemical drugs to inhibit tumor progression in medical oncology.

A chest CT-based nomogram for predicting survival in acute myeloid leukemia.

BACKGROUND: The identification of survival predictors is crucial for early intervention to improve outcome in acute myeloid leukemia (AML). This study aim to identify chest computed tomography (CT)-derived features to predict prognosis for acute myeloid leukemia (AML). METHODS: 952 patients with pathologically-confirmed AML were retrospectively enrolled between 2010 and 2020. CT-derived features (including body composition and subcutaneous fat features), were obtained from the initial chest CT images and were used to build models to predict the prognosis. A CT-derived MSF nomogram was constructed using multivariate Cox regression incorporating CT-based features. The performance of the prediction models was assessed with discrimination, calibration, decision curves and improvements. RESULTS: Three CT-derived features, including myosarcopenia, spleen_CTV, and SF_CTV (MSF) were identified as the independent predictors for prognosis in AML (P < 0.01). A CT-MSF nomogram showed a performance with AUCs of 0.717, 0.794, 0.796 and 0.792 for predicting the 1-, 2-, 3-, and 5-year overall survival (OS) probabilities in the validation cohort, which were significantly higher than the ELN risk model. Moreover, a new MSN stratification system (MSF nomogram plus ELN risk model) could stratify patients into new high, intermediate and low risk group. Patients with high MSN risk may benefit from intensive treatment (P = 0.0011). CONCLUSIONS: In summary, the chest CT-MSF nomogram, integrating myosarcopenia, spleen_CTV, and SF_CTV features, could be used to predict prognosis of AML.

Nomogram incorporating preoperative MRI-VASARI features for differentiating intracranial extraventricular ependymoma from glioblastoma.

Background: Intracranial extraventricular ependymoma (IEE) and glioblastoma (GBM) may have similar imaging findings but different prognosis. This study aimed to develop and validate a nomogram based on magnetic resonance imaging (MRI) Visually AcceSAble Rembrandt Images (VASARI) features for preoperatively differentiating IEE from GBM. Methods: The clinical data and the MRI-VASARI features of patients with confirmed IEE (n=114) and confirmed GBM (n=258) in a multicenter cohort were retrospectively analyzed. Predictive models for differentiating IEE from GBM were built using a multivariate logistic regression method. A nomogram was generated and the performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Results: The predictors identified in this study consisted of six VASARI features and four clinical features. Compared with the individual models, the combined model incorporating clinical and VASARI features had the highest area under the curve (AUC) value [training set: 0.99, 95% confidence interval (CI): 0.98-1.00; validation set: 0.97, 95% CI: 0.94-1.00] in comparison to the clinical model. The nomogram was well calibrated with significant clinical benefit according to the calibration curve and decision curve analyses. Conclusions: The nomogram combining clinical and MRI-VASARI characteristics was robust for differentiating IEE from GBM preoperatively and may potentially assist in diagnosis and treatment of brain tumors.

Ensemble Machine Learning Model Incorporating Radiomics and Body Composition for Predicting Intraoperative HDI in PPGL.

CONTEXT: Intraoperative hemodynamic instability (HDI) can lead to cardiovascular and cerebrovascular complications during surgery for pheochromocytoma/paraganglioma (PPGL). OBJECTIVES: We aimed to assess the risk of intraoperative HDI in patients with PPGL to improve surgical outcome. METHODS: A total of 199 consecutive patients with PPGL confirmed by surgical pathology were retrospectively included in this study. This cohort was separated into 2 groups according to intraoperative systolic blood pressure, the HDI group (n = 101) and the hemodynamic stability (HDS) group (n = 98). It was also divided into 2 subcohorts for predictive modeling: the training cohort (n = 140) and the validation cohort (n = 59). Prediction models were developed with both the ensemble machine learning method (EL model) and the multivariate logistic regression model using body composition parameters on computed tomography, tumor radiomics, and clinical data. The efficiency of the models was evaluated with discrimination, calibration, and decision curves. RESULTS: The EL model showed good discrimination between the HDI group and HDS group, with an area under the curve of (AUC) of 96.2% (95% CI, 93.5%-99.0%) in the training cohort, and an AUC of 93.7% (95% CI, 88.0%-99.4%) in the validation cohort. The AUC values from the EL model were significantly higher than the logistic regression model, which had an AUC of 74.4% (95% CI, 66.1%-82.6%) in the training cohort and an AUC of 74.2% (95% CI, 61.1%-87.3%) in the validation cohort. Favorable calibration performance and clinical applicability of the EL model were observed. CONCLUSION: The EL model combining preoperative computed tomography-based body composition, tumor radiomics, and clinical data could potentially help predict intraoperative HDI in patients with PPGL.

[Effects of manipulating lactate dehydrogenase gene on metabolism of HEK-293 and production of human adenovirus].

Reducing lactate accumulation has always been a goal of the mammalian cell biotechnology industry. When animal cells are cultured in vitro, the accumulation of lactate is mainly the combined result of two metabolic pathways. On one hand, glucose generates lactate under the function of lactate dehydrogenase A (LDHA); on the other hand, lactate can be oxidized to pyruvate by LDHB or LDHC and re-enter the TCA cycle. This study comprehensively evaluated the effects of LDH manipulation on the growth, metabolism and human adenovirus (HAdV) production of human embryonic kidney 293 (HEK-293) cells, providing a theoretical basis for engineering the lactate metabolism in mammalian cells. By knocking out ldha gene and overexpression of ldhb and ldhc genes, the metabolic efficiency of HEK-293 cells was effectively improved, and HAdV production was significantly increased. Compared with the control cell, LDH manipulation promoted cell growth, reduced the accumulation of lactate and ammonia, significantly enhanced the efficiency of substrate and energy metabolism of cells, and significantly increased the HAdV production capacity of HEK-293 cells. Among these LDH manipulation measures, ldhc gene overexpression performed the best, with the maximum cell density increased by about 38.7%. The yield of lactate to glucose and ammonia to glutamine decreased by 33.8% and 63.3%, respectively; and HAdV titer increased by at least 16 times. In addition, the ATP production rate, ATP/O2 ratio, ATP/ADP ratio and NADH content of the modified cell lines were increased to varying degrees, and the energy metabolic efficiency was significantly improved.

Surformer: An interpretable pattern-perceptive survival transformer for cancer survival prediction from histopathology whole slide images.

BACKGROUND AND OBJECTIVE: High-resolution histopathology whole slide images (WSIs) contain abundant valuable information for cancer prognosis. However, most computational pathology methods for survival prediction have weak interpretability and cannot explain the decision-making processes reasonably. To address this issue, we propose a highly interpretable neural network termed pattern-perceptive survival transformer (Surformer) for cancer survival prediction from WSIs. METHODS: Notably, Surformer can quantify specific histological patterns through bag-level labels without any patch/cell-level auxiliary information. Specifically, the proposed ratio-reserved cross-attention module (RRCA) generates global and local features with the learnable prototypes (pglobal, plocals) as detectors and quantifies the patches correlative to each plocal in the form of ratio factors (rfs). Afterward, multi-head self&cross-attention modules proceed with the computation for feature enhancement against noise. Eventually, the designed disentangling loss function guides multiple local features to focus on distinct patterns, thereby assisting rfs from RRCA in achieving more explicit histological feature quantification. RESULTS: Extensive experiments on five TCGA datasets illustrate that Surformer outperforms existing state-of-the-art methods. In addition, we highlight its interpretation by visualizing rfs distribution across high-risk and low-risk cohorts and retrieving and analyzing critical histological patterns contributing to the survival prediction. CONCLUSIONS: Surformer is expected to be exploited as a useful tool for performing histopathology image data-driven analysis and gaining new insights for interpreting the associations between such images and patient survival states.

Prediction of clinically significant prostate cancer using a novel 68Ga-PSMA PET-CT and multiparametric MRI-based model.

Background: There are some limitations in the commonly used methods for the detection of prostate cancer. There is a lack of nomograms based on multiparametric magnetic resonance imaging (mpMRI) and 68Ga-prostate-specific membrane antigen (PSMA) positron emission tomography-computed tomography (PET-CT) for the prediction of prostate cancer. The study seeks to compare the performance of mpMRI and 68Ga-PSMA PET-CT, and design a novel predictive model capable of predicting clinically significant prostate cancer (csPCa) before biopsy based on a combination of 68Ga-PSMA PET-CT, mpMRI, and patient clinical parameters. Methods: From September 2020 to June 2021, we prospectively enrolled 112 consecutive patients with no prior history of prostate cancer who underwent both 68Ga-PSMA PET-CT and mpMRI prior to biopsy at our clinical center. Univariate and multivariate regression analyses were used to identify predictors of csPCa, with a predictive model and its nomogram incorporating 68Ga-PSMA PET-CT, mpMRI, and the clinical predictors then being generated. The constructed model was evaluated using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis, and further validated with the internal and external cohorts. Results: The model incorporated prostate-specific antigen density (PSAd), Prostate Imaging Reporting and Data System (PI-RADS) category, and maximum standardized uptake value (SUVmax), and it exhibited excellent predictive efficacy when applying to evaluate both training and validation cohorts [area under the curve (AUC): 0.936 and 0.940, respectively]. Compared with SUVmax alone, the model demonstrated excellent diagnostic performance with improved specificity (0.910, 95% CI: 0.824-0.963) and positive predictive values (0.811, 95% CI: 0.648-0.920). Calibration curve and decision curve analysis further confirmed that the model exhibited a high degree of clinical net benefit and low error rate. Conclusions: The constructed model in this study was capable of accurately predicting csPCa prior to biopsy with excellent discriminative ability. As such, this model has the potential to be an effective non-invasive approach for the diagnosis of csPCa.

Magnetic resonance imaging findings of intracranial extraventricular ependymoma: A retrospective multi-center cohort study of 114 cases.

BACKGROUND: Intracranial extraventricular ependymoma (IEE) is an ependymoma located in the brain parenchyma outside the ventricles. IEE has overlapping clinical and imaging characteristics with glioblastoma multiforme (GBM) but different treatment strategy and prognosis. Therefore, an accurate preoperative diagnosis is necessary for optimizing therapy for IEE. METHODS: A retrospective multicenter cohort of IEE and GBM was identified. MR imaging characteristics assessed with the Visually Accessible Rembrandt Images (VASARI) feature set and clinicopathological findings were recorded. Independent predictors for IEE were identified using multivariate logistic regression, which was used to construct a diagnostic score for differentiating IEE from GBM. RESULTS: Compared to GBM, IEE tended to occur in younger patients. Multivariate logistic regression analysis identified seven independent predictors for IEE. Among them, 3 predictors including tumor necrosis rate (F7), age, and tumor-enhancing margin thickness (F11), demonstrated higher diagnostic performance with an Area Under Curve (AUC) of more than 70% in distinguishing IEE from GBM. The AUC was 0.85, 0.78, and 0.70, with sensitivity of 92.98%, 72.81%, and 96.49%, and specificity of 65.50%, 73.64%, and 43.41%, for F7, age, and F11, respectively. CONCLUSION: We identified specific MR imaging features such as tumor necrosis and thickness of enhancing tumor margins that could help to differentiate IEE from GBM. Our study results should be helpful to assist in diagnosis and clinical management of this rare brain tumor.

A Phase 1/2 Multicenter Randomized Trial of Local Ablation plus Toripalimab versus Toripalimab Alone for Previously Treated Unresectable Hepatocellular Carcinoma.

PURPOSE: To assess the safety and efficacy of local ablation plus PD-1 inhibitor toripalimab in previously treated unresectable hepatocellular carcinoma (HCC). PATIENTS AND METHODS: In the multicenter, two-stage, and randomized phase 1/2 trial, patients were randomly assigned to receive toripalimab alone (240 mg, every 3 weeks), subtotal local ablation followed by toripalimab starting on post-ablation day 3 (Schedule D3), or on post-ablation day 14 (Schedule D14). The first endpoint of stage 1 was to determine which combination schedule could continue and progression-free survival (PFS) as the primary endpoint for stage 1/2. RESULTS: A total of 146 patients were recruited. During stage 1, Schedule D3 achieved numerically higher objective response rate (ORR) than Schedule D14 for non-ablation lesions (37.5% vs. 31.3%), and was chosen for stage 2 evaluation. For the entire cohort of both stages, patients with Schedule D3 had a significantly higher ORR than with toripalimab alone (33.8% vs. 16.9%; P = 0.027). Moreover, patients with Schedule D3 had improved median PFS (7.1 vs. 3.8 months; P < 0.001) and median overall survival (18.4 vs. 13.2 months; P = 0.005), as compared with toripalimab alone. In addition, six (9%) patients with toripalimab, eight (12%) with Schedule D3, and 4 (25%) with Schedule D14 developed grade 3 or 4 adverse events, and one patient (2%) with Schedule D3 manifested grade 5 treatment-related pneumonitis. CONCLUSIONS: In patients with previously treated unresectable HCC, subtotal ablation plus toripalimab improved the clinical efficacy as compared with toripalimab alone, with an acceptable safety profile.

CT-based assessment of sarcopenia for differentiating wild-type from mutant-type gastrointestinal stromal tumor.

Non-invasive prediction for KIT/PDGFRA status in GIST is a challenging problem. This study aims to evaluate whether CT based sarcopenia could differentiate KIT/PDGFRA wild-type gastrointestinal stromal tumor (wt-GIST) from the mutant-type GIST (mu-GIST), and to evaluate genetic features of GIST. A total of 174 patients with GIST (wt-GIST = 52) were retrospectively identified between January 2011 to October 2019. A sarcopenia nomogram was constructed by multivariate logistic regression. The performance of the nomogram was evaluated by discrimination, calibration curve, and decision curve. Genomic data was obtained from our own specimens and also from the open databases cBioPortal. Data was analyzed by R version 3.6.1 and clusterProfiler ( http://cbioportal.org/msk-impact ). There were significantly higher incidence (75.0% vs. 48.4%) and more severe sarcopenia in patients with wt-GIST than in patients with mu-GIST. Multivariate logistic regression analysis showed that sarcopenia score (fitted based on age, gender and skeletal muscle index), and muscle fat index were independent predictors for higher risk of wt-GIST (P < 0.05 for both the training and validation cohorts). Our sarcopenia nomogram achieved a promising efficiency with an AUC of 0.879 for the training cohort, and 0.9099 for the validation cohort with a satisfying consistency in the calibration curve. Favorable clinical usefulness was observed using decision curve analysis. The additional gene sequencing analysis based on both our data and the external data demonstrated aberrant signal pathways being closely associated with sarcopenia in the wt-GIST. Our study supported the use of CT-based assessment of sarcopenia in differentiating the wt-GIST from the mu-GIST preoperatively.

Incorporating ultrasound-based lymph node staging significantly improves the performance of a clinical nomogram for predicting preoperative axillary lymph node metastasis in breast cancer.

Models for predicting axillary lymph node metastasis (ALNM) in breast cancer patients are lacking. We aimed to develop an efficient model to accurately predict ALNM. Three hundred fifty-five breast cancer patients were recruited and randomly divided into the training and validation sets. Univariate and multivariate logistic regressions were applied to identify predictors of ALNM. We developed nomograms based on these variables to predict ALNM. The performance of the nomograms was tested using the receiver operating characteristic curve and calibration curve, and a decision curve analysis was performed to assess the clinical utility of the prediction models. The nomograms that included clinical N stage (cN), pathological grade (pathGrade), and hemoglobin accurately predicted ALNM in the training and validation sets (area under the curve [AUC] 0.80 and 0.80, respectively). We then explored the importance of the cN and pathGradesignatures used in the integrated model and developed new nomograms by removing the two variables. The results suggested that the combine-pathGrade nomogram also accurately predicted ALNM in the training and validation sets (AUC 0.78 and 0.78, respectively), but the combine-cN nomogram did not (AUC 0.64 and 0.60, in training and validation sets, respectively). We described a cN-based ALNM prediction model in breast cancer patients, presenting a novel efficient clinical decision nomogram for predicting ALNM.

CT radiomics identifying non-responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer.

BACKGROUND AND PURPOSE: Early detection of non-response to neoadjuvant chemoradiotherapy (nCRT) for locally advanced colorectal cancer (LARC) remains challenging. We aimed to assess whether pretreatment radiotherapy planning computed tomography (CT) radiomics could distinguish the patients with no response or no downstaging after nCRT from those with response and downstaging after nCRT. MATERIALS AND METHODS: Patients with LARC who were treated with nCRT were retrospectively enrolled between March 2009 and March 2019. Traditional radiological characteristics were analyzed by visual inspection and radiomic features were analyzed through computational methods from the pretreatment radiotherapy planning CT images. Differentiation models were constructed using radiomic methods and clinicopathological characteristics for predicting non-response to nCRT. Model performance was assessed for classification efficiency, calibration, discrimination, and clinical application. RESULTS: This study enrolled a total of 215 patients, including 151 patients in the training cohort (50 non-responders and 101 responders) and 64 patients in the validation cohort (21 non-responders and 43 responders). For predicting non-response, the model constructed with an ensemble machine learning method had higher performance with area under the curve (AUC) values of 0.92 and 0.89 as compared to the model constructed with the logistic regression method (AUC: 0.72 and 0.71 for the training and validation cohorts, respectively). Both decision curve and calibration curve analyses confirmed that the ensemble machine learning model had higher prediction performance. CONCLUSION: Pretreatment CT radiomics achieved satisfying performance in predicting non-response to nCRT and could be helpful to assist in treatment planning for patients with LARC.

Application of Radiomics in Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Strategies and Challenges.

Neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision is the standard treatment for locally advanced rectal cancer (LARC). A noninvasive preoperative prediction method should greatly assist in the evaluation of response to nCRT and for the development of a personalized strategy for patients with LARC. Assessment of nCRT relies on imaging and radiomics can extract valuable quantitative data from medical images. In this review, we examined the status of radiomic application for assessing response to nCRT in patients with LARC and indicated a potential direction for future research.

Preoperative computed tomography-determined sarcopenia is a reliable prognostic factor in patients with gastric cancer after radical gastrectomy: A sex-specific analysis.

Background: The predictive role of sarcopenia in cancer prognosis is an area of increasing concern. However, the influence of sex difference on the predictive role of sarcopenia in cancer prognosis has not been clearly defined. This retrospective cohort study investigated the effect of preoperative sarcopenia on the long-term outcomes of patients with gastric cancer (GC) based on sexual dimorphism. Methods: Preoperative abdominal computed tomography (CT) scans from 379 GC patients who underwent radical gastrectomy were carefully analyzed. The patients were categorized into sarcopenia and non-sarcopenia groups according to the L3 skeletal muscle index (L3 SMI) measured on CT scans. Moreover, other indexes which can be used to evaluate the muscle area or the muscle quality, including skeletal muscle area (SMA), visceral fat area (VFA), subcutaneous fat area (SFA), skeletal muscle radiation attenuation (SM-RA), visceral fat index (VFI), subcutaneous fat index (SFI), and subcutaneous and visceral ratio (SV), were obtained from CT scans. Results: There were 254 men and 125 women included in our study. After calculation, we defined sex-specific SMI-related mortality cutoff as 39.73 and 32.97 cm2/m2 for men and women. Univariable analysis showed that pathological tumor-node-metastasis (pTNM), depth of invasion, lymph node metastasis, differentiation degree, preoperative sarcopenia (for men), SMA (for men), L3 SMI, SFA (for women), SFI (for women), SV (for women), and SM-RA (especially for men) were significant independent predictors of overall survival (OS). Multivariable analysis showed that pTNM, depth of invasion, poor differentiation, and SM-RA were significantly associated with 5-year OS in GC patients. However, CT-determined sarcopenia was associated with significantly worse OS only in men, and SFA was significantly associated with 5-year OS only in women. Conclusion: SM-RA is a reliable prognostic factor in patients with GC after radical gastrectomy. The impact of indexes mentioned above on survival outcomes is dependent on sex. CT-determined preoperative sarcopenia, a muscle-related indicator, was associated with outcomes in men. Adipose-related indicator (SFA), instead, was associated with outcomes in women.

Application of radiomics in adrenal incidentaloma: a literature review.

Assessment of adrenal incidentaloma relies on imaging analysis and evaluation of adrenal function. Radiomics as a tool for quantitative image analysis is useful for evaluation of adrenal incidentaloma. In this review, we examined radiomic literature on adrenal incidentaloma including both adrenal functional assessment and structural differentiation of benign versus malignant adrenal tumors. In this review, we summarized the status of radiomic application on adrenal incidentaloma and suggested potential direction for future research.

Preoperative serum CA19-9 should be routinely measured in the colorectal patients with preoperative normal serum CEA: a multicenter retrospective cohort study.

OBJECTIVE: Whether preoperative serum carbohydrate antigen 19-9 (CA19-9) is an independent prognostic factor and there are interactions of serum CA19-9 with carcinoembryonic antigen (CEA) on the risk of recurrence in colorectal cancer (CRC) patients are still not clarified. METHODS: Consecutive patients with CRC who underwent curative resection for stage II-III colorectal adenocarcinoma at five hospitals were collected. Based on Cox models, associations of preoperative CA19-9 with recurrence-free survival (RFS) and overall survival (OS) were evaluated in patients with or without elevated CEA, and interactions between CEA and CA19-9 were also calculated. Restricted cubic spline (RCS) curves were used to evaluate the associations between preoperative CA19-9 and CRC outcomes on a continuous scale. RESULTS: A total of 5048 patients (3029 [60.0%] men; median [interquartile range, IQR] age, 61.0 [51.0, 68.0] years; median [IQR] follow-up duration 46.8 [36.5-62.4] months) were included. The risk of recurrence increased with the elevated level of preoperative CA19-9, with the slope steeper in patients with normal CEA than those with elevated CEA. Worse RFS was observed for elevated preoperative CA19-9 (> 37 U/mL) (n = 738) versus normal preoperative CA19-9 (≤ 37 U/mL) (n = 4310) (3-year RFS rate: 59.4% versus 78.0%; unadjusted hazard ratio [HR]: 2.02; 95% confidence interval [CI]:1.79 to 2.28), and significant interaction was found between CA19-9 and CEA (P for interaction = 0.001). Increased risk and interaction with CEA were also observed for OS. In the Cox multivariable analysis, elevated CA19-9 was associated with shorter RFS and OS regardless of preoperative CEA level, even after adjustment for other prognostic factors (HR: 2.08, 95% CI:1.75 to 2.47; HR: 2.25, 95% CI:1.80 to 2.81). Subgroup analyses and sensitivity analyses yielded largely similar results. These associations were maintained in patients with stage II disease (n = 2724). CONCLUSIONS: Preoperative CA19-9 is an independent prognostic factor in CRC patients. Preoperative CA19-9 can be clinically used as a routine biomarker for CRC patients, especially with preoperative normal serum CEA.

Impact of body composition and genotype on haemodynamics during surgery for pheochromocytoma and paraganglioma.

BACKGROUND: Maintaining intraoperative haemodynamic stability can reduce cardiovascular complications during surgery for pheochromocytoma and paraganglioma (PPGL). Risk factors such as tumour size and catecholamine levels are reported to predict haemodynamic responses during surgery for PPGL. We hypothesized that additional factors including body composition and genetic information could further improve prediction. METHODS: Consecutive patients with PPGL confirmed by surgical pathology between June 2010 and June 2019 were retrospectively included. Cross-sectional computed tomography images at the L3 level were used to assess body composition parameters including skeletal muscle area and visceral fat area. Next-generation sequencing was performed using a panel containing susceptibility genes of PPGL. Differences in clinical-genetic characteristics and body composition parameters were analysed and compared in patients with and without intraoperative haemodynamic instability (HDI). RESULTS: We included 221 patients with PPGL (median age 47 [38-56] years, and 52% male). Among them, 49.8% had Cluster 2 mutations (related to kinase signalling pathways), 44.8% had sarcopenia, and 52.9% experienced intraoperative HDI. Compared with patients without HDI, more patients with HDI had Cluster 2 mutations (59.8% vs. 38.5%, P = 0.002) and less had sarcopenia (35.9% vs. 54.8%, P = 0.005). Multivariate analysis showed that urine vanillylmandelic acid ≥ 58 µmol/day (adjusted odds ratio [OR] = 1.840, 95% confidence interval [CI] = 1.012-3.347, P = 0.046), tumour size ≥ 4 cm (adjusted OR = 2.278, 95% CI = 1.242-4.180, P = 0.008), and Cluster 2 mutations (adjusted OR = 2.199, 95% CI = 1.128-4.285, P = 0.021) were independent risk factors for intraoperative HDI, while sarcopenia (adjusted OR = 0.475, 95% CI = 0.266-0.846, P = 0.012) decreased the risk. CONCLUSIONS: Body composition and genotype were associated with intraoperative haemodynamics in patients with PPGL. Our results indicated that inclusion of body composition and genotype in the overall assessment of patients with PPGL helped to predict HDI during surgery, which could assist in implementing preoperative and intraoperative measures to reduce perioperative complications.

Myosteatosis can Predict Unfavorable Outcomes in Advanced Hepatocellular Carcinoma Patients Treated With Hepatic Artery Infusion Chemotherapy and Anti-PD-1 Immunotherapy.

Aim: To evaluate the feasibility of computed tomography (CT) - derived measurements of body composition parameters to predict the risk factor of non-objective response (non-OR) in patients with hepatocellular carcinoma (HCC) undergoing anti-PD-1 immunotherapy and hepatic artery infusion chemotherapy (immune-HAIC). Methods: Patients with histologically confirmed HCC and treated with the immune-HAIC were retrospectively recruited between June 30, 2019, and July 31, 2021. CT-based estimations of body composition parameters were acquired from the baseline unenhanced abdominal CT images at the level of the third lumbar vertebra (L3) and were applied to develop models predicting the probability of OR. A myosteatosis nomogram was built using the multivariate logistic regression incorporating both myosteatosis measurements and clinical variables. Receiver operating characteristic (ROC) curves assessed the performance of prediction models, including the area under the curve (AUC). The nomogram's performance was assessed by the calibration, discrimination, and decision curve analyses. Associations among predictors and gene mutations were also examined by correlation matrix analysis. Results: Fifty-two patients were recruited to this study cohort, with 30 patients having a OR status after immune-HAIC treatment. Estimations of myosteatosis parameters, like SM-RA (skeletal muscle radiation attenuation), were significantly associated with the probability of predicting OR (P=0.007). The SM-RA combined nomogram model, including serum red blood cell, hemoglobin, creatinine, and the mean CT value of visceral fat (VFmean) improved the prediction probability for OR disease with an AUC of 0.713 (95% CI, 0.75 to 0.95) than the clinical model nomogram with AUC of 0.62 using a 5-fold cross-validation methodology. Favorable clinical potentials were observed in the decision curve analysis. Conclusions: The CT-based estimations of myosteatosis could be used as an indicator to predict a higher risk of transition to the Non-OR disease state in HCC patients treated with immune-HAIC therapy. This study demonstrated the therapeutic relevance of skeletal muscle composition assessments in the overall prediction of treatment response and prognosis in HCC patients.

Pre-Treatment Computed Tomography Radiomics for Predicting the Response to Neoadjuvant Chemoradiation in Locally Advanced Rectal Cancer: A Retrospective Study.

Background and Purpose: Computerized tomography (CT) scans are commonly performed to assist in diagnosis and treatment of locally advanced rectal cancer (LARC). This study assessed the usefulness of pretreatment CT-based radiomics for predicting pathological complete response (pCR) of LARC to neoadjuvant chemoradiotherapy (nCRT). Materials and Methods: Patients with LARC who underwent nCRT followed by total mesorectal excision surgery from July 2010 to December 2018 were enrolled in this retrospective study. A total of 340 radiomic features were extracted from pretreatment contrast-enhanced CT images. The most relevant features to pCR were selected using the least absolute shrinkage and selection operator (LASSO) method and a radiomic signature was generated. Predictive models were built with radiomic features and clinico-pathological variables. Model performance was assessed with decision curve analysis and was validated in an independent cohort. Results: The pCR was achieved in 44 of the 216 consecutive patients (20.4%) in this study. The model with the best performance used both radiomics and clinical variables including radiomic signatures, distance to anal verge, lymphocyte-to-monocyte ratio, and carcinoembryonic antigen. This combined model discriminated between patients with and without pCR with an area under the curve of 0.926 and 0.872 in the training and the validation cohorts, respectively. The combined model also showed better performance than models built with radiomic or clinical variables alone. Conclusion: Our combined predictive model was robust in differentiating patients with and without response to nCRT.