Influence of abdominal fat distribution and inflammatory status on post-operative prognosis in non-small cell lung cancer patients: a retrospective cohort study.

PURPOSE: To evaluate the influence of visceral fat area (VFA), subcutaneous fat area (SFA), the systemic immune-inflammation index (SII) and total inflammation-based systemic index (AISI) on the postoperative prognosis of non-small cell lung cancers (NSCLC) patients. METHODS: 266 NSCLC patients received surgery from two academic medical centers were included. To assess the effect of abdominal fat measured by computed tomography (CT) imaging and inflammatory indicators on patients' overall survival (OS) and progression-free survival (PFS), Kaplan-Meier survival analysis and Cox proportional hazards models were used. RESULTS: Kaplan-Meier analysis showed the OS and PFS of patients in high-VFA group was better than low-VFA group (p < 0.05). AISI and SII were shown to be risk factors for OS and PFS (p < 0.05) after additional adjustment for BMI (Cox regression model II). After further adjustment for VFA (Cox regression model III), low-SFA group had longer OS (p < 0.05). Among the four subgroups based on VFA (high/low) and SFA (high/low) (p < 0.05), the high-VFA & low-SFA group had the longest median OS (108 months; 95% CI 74-117 months) and PFS (85 months; 95% CI 65-117 months), as well as the lowest SII and AISI (p < 0.05). Low-SFA was a protective factor for OS with different VFA stratification (p < 0.05). CONCLUSION: VFA, SFA, SII and AISI may be employed as significant prognostic markers of postoperative survival in NSCLC patients. Moreover, excessive SFA levels may encourage systemic inflammation decreasing the protective impact of VFA, which may help to provide targeted nutritional support and interventions for postoperative NSCLC patients with poor prognosis.

CT enterography-based radiomics combined with body composition to predict infliximab treatment failure in Crohn's disease.

PURPOSE: Accurately predicting the treatment response in patients with Crohn's disease (CD) receiving infliximab therapy is crucial for clinical decision-making. We aimed to construct a prediction model incorporating radiomics and body composition features derived from computed tomography (CT) enterography for identifying individuals at high risk for infliximab treatment failure. METHODS: This retrospective study included 137 patients with CD between 2015 and 2021, who were divided into a training cohort and a validation cohort with a ratio of 7:3. Patients underwent CT enterography examinations within 1 month before infliximab initiation. Radiomic features of the intestinal segments involved were extracted, and body composition features were measured at the level of the L3 lumbar vertebra. A model that combined radiomics with body composition was constructed. The primary outcome was the occurrence of infliximab treatment failure within 1 year. The model performance was evaluated using discrimination, calibration, and decision curves. RESULTS: Fifty-two patients (38.0%) showed infliximab treatment failure. Eight significant radiomic features were used to develop the radiomics model. The model incorporating radiomics model score, skeletal muscle index (SMI), and creeping fat showed good discrimination for predicting infliximab treatment failure, with an area under the curve (AUC) of 0.88 (95% CI 0.81, 0.95) in the training cohort and 0.83 (95% CI 0.66, 1.00) in the validation cohort. The favorable clinical application was observed using decision curve analysis. CONCLUSIONS: We constructed a comprehensive model incorporating radiomics and muscle volume, which could potentially be used to facilitate the individualized prediction of infliximab treatment response in patients with CD.

Preoperative Body Composition Combined with Tumor Metabolism Analysis by PET/CT Is Associated with Disease-Free Survival in Patients with NSCLC.

Objective: To evaluate the relationship between preoperative primary tumor metabolism and body composition in patients with NSCLC and analyze their effects on DFS. Method: A retrospective study was conducted on 154 patients with NSCLC. All patients were scanned by baseline 18F-FDG PET/CT. SUVmax (maximum standard uptake value) of primary tumor, liver SUVmean (mean standard uptake value), and spleen SUVmean were measured by AW workstation. The skeletal muscle area (SMA), skeletal muscle mass index (SMI), skeletal muscle radiation density (SMD), visceral fat area (VFA), visceral adipose tissue index (VATI), and skeletal muscle visceral fat ratio (SVR) were measured by ImageJ software. Kaplan-Meier survival analysis was used to evaluate the impact of the above parameters on DFS. Results: Compared with the low SUVmax group of primary tumors, the mean values of SMA, VFA, and VATI in the high SUVmax group were significantly higher. In addition, there were obvious differences in histopathological type, pathological differentiation, AJCC stage, and T stage between the two groups. Univariate analysis of DFS showed that VFA, VATI, pathological differentiation, tumor SUVmax, AJCC stage, tumor T stage, and N stage all affected the DFS of patients except for the parameters reflecting skeletal muscle content. Multivariate regression analysis showed that only VFA and SUVmax were associated with DFS. Kaplan-Meier survival analysis showed that high SUVmax, low VFA, high T stage, and high N stage were related to the decrease of DFS. Conclusion: ：Preoperative 18F-FDG PET/CT could comprehensively evaluate the primary tumor SUVmax, skeletal muscle, and visceral fat in patients with NSCLC. The combination of primary tumor SUVmax and visceral fat area can well evaluate the prognosis of patients with NSCLC.

Glucose Metabolism Reprogramming of Primary Tumor and the Liver Is Associated With Disease-Free Survival in Patients With Early NSCLC.

PURPOSE: Tumor promote disease progression by reprogramming their metabolism and that of distal organs, so it is of great clinical significance to study the changes in glucose metabolism at different tumor stages and their effect on glucose metabolism in other organs. METHODS: A retrospective single-centre study was conducted on 253 NSCLC (non-small cell lung cancer) patients with negative lymph nodes and no distant metastasis. According to the AJCC criteria, the patients were divided into different groups based on tumor size: stage IA, less than 3 cm (group 1, n = 121); stage IB, greater than 3-4 cm (group 2, n = 64); stage IIA, greater than 4-5 cm (group 3, n = 36); and stage IIB, greater than 5-7 cm (group 4, n = 32). All of the patients underwent baseline 18F-FDG PET/CT scans, and the primary lesion SUVmax (maximum standardized uptake value), liver SUVmean (mean standardized uptake value), spleen SUVmean, TLR (Tumor-to-liver SUV ratio) and TSR (Tumor-to-spleen SUV ratio) were included in the study, combined with clinical examination indicators to evaluate DFS (disease free survival). RESULTS: In NSCLC patients, with the increase in the maximum diameter of the tumor, the SUVmax of the primary lesion gradually increased, and the SUVmean of the liver gradually decreased. The primary lesion SUVmax, liver SUVmean, TLR and TSR were related to disease recurrence or death. The best predictive parameters were different when the tumor size differed. SUVmax had the highest efficiency when the tumor size was less than 4 cm (AUC:0.707 (95% CI, 0.430-0.984) tumor size < 3 cm), (AUC:0.726 (95% CI, 0.539-0.912) tumor size 3-4 cm), liver SUVmean had the highest efficiency when the tumor size was 4-5 cm (AUC:0.712 (95% CI, 0.535-0.889)), and TLR had the highest efficiency when the tumor size was 5-7 cm [AUC:0.925 (95%CI, 0.820-1.000)]. CONCLUSIONS: In patients with early NSCLC, glucose metabolism reprogramming occurs in the primary lesion and liver. With the increase in tumor size, different metabolic parameters should be selected to evaluate the prognosis of patients.

Early prediction of disease progression in COVID-19 pneumonia patients with chest CT and clinical characteristics.

The outbreak of coronavirus disease 2019 (COVID-19) has rapidly spread to become a worldwide emergency. Early identification of patients at risk of progression may facilitate more individually aligned treatment plans and optimized utilization of medical resource. Here we conducted a multicenter retrospective study involving patients with moderate COVID-19 pneumonia to investigate the utility of chest computed tomography (CT) and clinical characteristics to risk-stratify the patients. Our results show that CT severity score is associated with inflammatory levels and that older age, higher neutrophil-to-lymphocyte ratio (NLR), and CT severity score on admission are independent risk factors for short-term progression. The nomogram based on these risk factors shows good calibration and discrimination in the derivation and validation cohorts. These findings have implications for predicting the progression risk of COVID-19 pneumonia patients at the time of admission. CT examination may help risk-stratification and guide the timing of admission.

Radiomics in predicting tumor molecular marker P63 for non-small cell lung cancer.

OBJECTIVE: To establish a radiomics signature based on CT images of non-small cell lung cancer (NSCLC) to predict the expression of molecular marker P63.
 Methods: A total of 245 NSCLC patients who underwent CT scans were retrospectively included. All patients were confirmed by histopathological examinations and P63 expression were examined within 2 weeks after CT examination. Radiomics features were extracted by MaZda software and subjective image features were defined from original non-enhanced CT images. The Lasso-logistic regression model was used to select features and develop radiomics signature, subjective image features model, and combined diagnostic model. The predictive performance of each model was evaluated by the receiver operating characteristic (ROC) curve, and compared with Delong test.
 Results: Of the 245 patients, 96 were P63 positive and 149 were P63 negative. The subjective image feature model consisted of 6 image features. Through feature selection, the radiomics signature consisted of 8 radiomics features. The area under the ROC curves of the subjective image feature model and the radiomics signature in predicting P63 expression statue were 0.700 and 0.755, respectively, without a significant difference (P>0.05). The combined diagnostic model showed the best predictive power (AUC=0.817, P<0.01).
 Conclusion: The radiomics-based CT scan images can predict the expression status of NSCLC molecular marker P63. The combination of the radiomics features and subjective image features can significantly improve the predictive performance of the predictive model, which may be helpful to provide a non-invasive way for understanding the molecular information for lung cancer cells.

Salmonella-Mediated Cancer Therapy: An Innovative Therapeutic Strategy.

Bacterial-mediated cancer therapy (BMCT) has become a hot topic in the area of antitumor treatment. Salmonella has been recommended to specifically colonize and proliferate inside tumors and even inhibit tumor growth. Salmonella typhimurium (S. typhimurium) is one of the most promising mediators, which can be easily manipulated. S. typhimurium has been engineered and designed as cancer-targeting therapeutics, and can be improved by combining with other therapeutic methods, e.g. chemotherapy and radiotherapy, which regulate the tumor microenvironment synergistically. In view of all these strengths, the engineered attenuated strains have significant advantages for tumor diagnosis and treatment. This treatment has also been approved by the FDA for clinical trial. In this review, we summarized the recent progress and research in the field of Salmonella -mediated cancer therapy.

Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer.

PURPOSE: To explore the feasibility and performance of machine learning-based radiomics classifier to predict the cell proliferation(Ki-67)in non-small cell lung cancer (NSCLC). METHODS: 245 histopathological confirmed NSCLC patients who underwent CT scans were retrospectively included. The Ki-67 proliferation index (Ki-67 PI) were measured within 2 weeks after CT scans. A lesion volume of interest (VOI) was manually delineated and radiomics features were extracted by MaZda software from CT images. A random forest feature selection algorithm (RFFS) was used to reduce features. Six kinds of machine learning methods were used to establish radiomics classifiers, subjective imaging feature classifiers and combined classifiers, respectively. The performance of these classifiers was evaluated by the receiver operating characteristic curve (ROC) and compared with Delong test. RESULTS: 103 radiomics features were extracted and 20 optimal features were selected using RFFS. Among the radiomics classifiers established by six machine learning methods, random forest-based radiomics classifier achieved the best performance (AUC = 0.776) in predicting the Ki-67 expression level with sensitivity and specificity of 0.726 and 0.661, which was better than that of subjective imaging classifiers (AUC = 0.625, P < 0.05). However, the combined classifiers did not improve the predictive performance (AUC = 0.780, P > 0.05), with sensitivity and specificity of 0.752 and 0.633. CONCLUSIONS: The machine learning-based CT radiomics classifier in NSCLC can facilitate the prediction of the expression level of Ki-67 and provide a novel non-invasive strategy for assessing the cell proliferation.

Radiomics and its advances in hepatocellular carcinoma.

Liver cancer is the second leading cause of cancer-related death worldwide, so early detection and prediction for response to treatment is of great benefit to hepatocellular carcinoma (HCC) patients. Currently, needle biopsy and conventional medical imaging play a significant and basic role in HCC patients' management, while those two approaches are limited in sample error and observer-dependence. Radiomics can make up for this deficiency because it is an emerging non-invasive technic that is capable of getting comprehensive information relevant to tumor situation across spatial-temporal limitation. The basic procedure for radiomics includes image acquisition, region of interest segmentation and reconstruction, feature extraction, selection and classification, and model building and performance evaluation. The current advances and potential prospect of radiomics in HCC studies are involved in diagnosis, prediction for response to treatment, prognosis evaluation and radiogenomics.

Alkyne- and Nitrile-Anchored Gold Nanoparticles for Multiplex SERS Imaging of Biomarkers in Cancer Cells and Tissues.

Surface-enhanced Raman spectroscopy (SERS) has proven a powerful tool for multiplex detection and imaging due to its narrow peak width and high sensitivity. However, conventional SERS reporters are limited to thiolated compounds, which have limitations such as chemical stability and spectral overlap. Here, we used alkyne- and nitrile-bearing molecules to directly fabricate a set of SERS tags for multiplex imaging. The alkyne and nitrile groups act as both the anchoring points to interact with gold nanoparticle (AuNP) surfaces and the reporters exhibiting strong and nonoverlapping signals in the cellular Raman-silent region. The SERS tags were subsequently modified with different antibodies for multicolor imaging of cancer cells and human breast cancer tissues. The reporters have a simple and readily accessible structure, hence providing new opportunities to prepare SERS nanoprobes.