PER1 promotes functional recovery of mice with hindlimb ischemia by inducing anti-inflammatory macrophage polarization.

Hindlimb ischemia (HLI) is an arterial occlusive disease that exposes the patients to the risk of limb gangrene and loss. Polarization of macrophages is related to HLI-induced inflammation. Period circadian regulator 1 (PER1) is a core component of the circadian clock. We first showed, based upon bioinformatics analysis of microarray data, that PER1 expression was reduced in monocytes from patients with critical limb ischemia. The proximal femoral artery in the left hindlimb of male mice was ligated and then the femoral artery and its collateral branches were removed to establish the HLI mouse model. After modeling, a single intramuscular injection of 1 × 109 pfu Ad-PER1 was performed at the adductor and gastrocnemius muscles. The gastrocnemius muscle tissues were collected at day 0, 3, 7, 14, 21 post-HLI. There was obvious pathological necrosis, accompanied with reduced expression of PER1 in the muscle tissues of HLI mice. Expression of CD68 and CD31 seemed to be corresponded to PER1 in gastrocnemius muscle, implying the potential of PER1 in regulating macrophage-related inflammation and angiogenesis. PER1 overexpression diminished myocyte damage, promoted blood flow restoration and improved behavioral scores of HLI mice. Immunostaining of CD31 and α-SMA revealed that PER1 upregulation reversed HLI-induced decreases in capillary and arteriole density. In vitro, RAW264.7 cells were cultured in hypoxia (1% O2) for 24 h. The percentage of pro-inflammatory CD86+ macrophages (M1 type) was decreased and that of anti-inflammatory CD206+ macrophages (M2 type) was increased when PER1 was overexpressed. Moreover, the expression levels of TNF-α, IL-6 and M1-type marker iNOS were decreased, and levels of IL-10 and M2-type marker Arg-1 were increased by PER1 in gastrocnemius muscle of HLI mice and hypoxia-treated RAW264.7 cells. PER1 might reduce M1 macrophage polarization and promote M2 macrophage polarization, and thus exert anti-inflammatory and pro-angiogenic actions. Our findings suggest that PER1 overexpression promotes functional recovery of mice with HLI through regulating macrophage polarization.

Deep learning predicts resistance to neoadjuvant chemotherapy for locally advanced gastric cancer: a multicenter study.

BACKGROUND: Accurate pre-treatment prediction of neoadjuvant chemotherapy (NACT) resistance in patients with locally advanced gastric cancer (LAGC) is essential for timely surgeries and optimized treatments. We aim to evaluate the effectiveness of deep learning (DL) on computed tomography (CT) images in predicting NACT resistance in LAGC patients. METHODS: A total of 633 LAGC patients receiving NACT from three hospitals were included in this retrospective study. The training and internal validation cohorts were randomly selected from center 1, comprising 242 and 104 patients, respectively. The external validation cohort 1 comprised 128 patients from center 2, and the external validation cohort 2 comprised 159 patients from center 3. First, a DL model was developed using ResNet-50 to predict NACT resistance in LAGC patients, and the gradient-weighted class activation mapping (Grad-CAM) was assessed for visualization. Then, an integrated model was constructed by combing the DL signature and clinical characteristics. Finally, the performance was tested in internal and external validation cohorts using area under the receiver operating characteristic (ROC) curves (AUC). RESULTS: The DL model achieved AUCs of 0.808 (95% CI 0.724-0.893), 0.755 (95% CI 0.660-0.850), and 0.752 (95% CI 0.678-0.825) in validation cohorts, respectively, which were higher than those of the clinical model. Furthermore, the integrated model performed significantly better than the clinical model (P < 0.05). CONCLUSIONS: A CT-based model using DL showed promising performance for predicting NACT resistance in LAGC patients, which could provide valuable information in terms of individualized treatment.

A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study.

Background: Accurate prediction of treatment response to neoadjuvant chemotherapy (NACT) in individual patients with locally advanced gastric cancer (LAGC) is essential for personalized medicine. We aimed to develop and validate a deep learning radiomics nomogram (DLRN) based on pretreatment contrast-enhanced computed tomography (CT) images and clinical features to predict the response to NACT in patients with LAGC. Methods: 719 patients with LAGC were retrospectively recruited from four Chinese hospitals between Dec 1st, 2014 and Nov 30th, 2020. The training cohort and internal validation cohort (IVC), comprising 243 and 103 patients, respectively, were randomly selected from center I; the external validation cohort1 (EVC1) comprised 207 patients from center II; and EVC2 comprised 166 patients from another two hospitals. Two imaging signatures, reflecting the phenotypes of the deep learning and handcrafted radiomics features, were constructed from the pretreatment portal venous-phase CT images. A four-step procedure, including reproducibility evaluation, the univariable analysis, the LASSO method, and the multivariable logistic regression analysis, was applied for feature selection and signature building. The integrated DLRN was then developed for the added value of the imaging signatures to independent clinicopathological factors for predicting the response to NACT. The prediction performance was assessed with respect to discrimination, calibration, and clinical usefulness. Kaplan-Meier survival curves based on the DLRN were used to estimate the disease-free survival (DFS) in the follow-up cohort (n = 300). Findings: The DLRN showed satisfactory discrimination of good response to NACT and yielded the areas under the receiver operating curve (AUCs) of 0.829 (95% CI, 0.739-0.920), 0.804 (95% CI, 0.732-0.877), and 0.827 (95% CI, 0.755-0.900) in the internal and two external validation cohorts, respectively, with good calibration in all cohorts (p > 0.05). Furthermore, the DLRN performed significantly better than the clinical model (p < 0.001). Decision curve analysis confirmed that the DLRN was clinically useful. Besides, DLRN was significantly associated with the DFS of patients with LAGC (p < 0.05). Interpretation: A deep learning-based radiomics nomogram exhibited a promising performance for predicting therapeutic response and clinical outcomes in patients with LAGC, which could provide valuable information for individualized treatment.

Tumor response as defined by iRECIST in gastrointestinal malignancies treated with PD-1 and PD-L1 inhibitors and correlation with survival.

BACKGROUND: Atypical tumor response patterns during immune checkpoint inhibitor therapy pose a challenge to clinicians and investigators in immuno-oncology practice. This study evaluated tumor burden dynamics to identify imaging biomarkers for treatment response and overall survival (OS) in advanced gastrointestinal malignancies treated with PD-1/PD-L1 inhibitors. METHODS: This retrospective study enrolled a total of 198 target lesions in 75 patients with advanced gastrointestinal malignancies treated with PD-1/PD-L1 inhibitors between January 2017 and March 2021. Tumor diameter changes as defined by immunotherapy Response Evaluation Criteria in Solid Tumors (iRECIST) were studied to determine treatment response and association with OS. RESULTS: Based on the best overall response, the tumor diameter ranged from - 100 to + 135.3% (median: - 9.6%). The overall response rate was 32.0% (24/75), and the rate of durable disease control for at least 6 months was 30.7% (23/75, one (iCR, immune complete response) or 20 iPR (immune partial response), or 2iSD (immune stable disease). Using univariate analysis, patients with a tumor diameter maintaining a < 20% increase (48/75, 64.0%) from baseline had longer OS than those with ≥20% increase (27/75, 36.0%) and, a reduced risk of death (median OS: 80 months vs. 48 months, HR = 0.22, P = 0.034). The differences in age (HR = 1.09, P = 0.01), combined surgery (HR = 0.15, P = 0.01) and cancer type (HR = 0.23, P = 0.001) were significant. In multivariable analysis, patients with a tumor diameter with a < 20% increase had notably reduced hazards of death (HR = 0.15, P = 0.01) after adjusting for age, combined surgery, KRAS status, cancer type, mismatch repair (MMR) status, treatment course and cancer differentiation. Two patients (2.7%) showed pseudoprogression. CONCLUSIONS: Tumor diameter with a < 20% increase from baseline during therapy in gastrointestinal malignancies was associated with therapeutic benefit and longer OS and may serve as a practical imaging marker for treatment response, clinical outcome and treatment decision making.

A Machine Learning Model for Predicting a Major Response to Neoadjuvant Chemotherapy in Advanced Gastric Cancer.

AIMS: To develop and validate a model for predicting major pathological response to neoadjuvant chemotherapy (NAC) in advanced gastric cancer (AGC) based on a machine learning algorithm. METHOD: A total of 221 patients who underwent NAC and radical gastrectomy between February 2013 and September 2020 were enrolled in this study. A total of 144 patients were assigned to the training cohort for model building, and 77 patients were assigned to the validation cohort. A major pathological response was defined as primary tumor regressing to ypT0 or T1. Radiomic features extracted from venous-phase computed tomography (CT) images were selected by machine learning algorithms to calculate a radscore. Together with other clinical variables selected by univariate analysis, the radscores were included in a binary logistic regression analysis to construct an integrated prediction model. The data obtained for the validation cohort were used to test the predictive accuracy of the model. RESULT: A total of 27.6% (61/221) patients achieved a major pathological response. Five features of 572 radiomic features were selected to calculate the radscores. The final established model incorporates adenocarcinoma differentiation and radscores. The model showed satisfactory predictive accuracy with a C-index of 0.763 and good fitting between the validation data and the model in the calibration curve. CONCLUSION: A prediction model incorporating adenocarcinoma differentiation and radscores was developed and validated. The model helps stratify patients according to their potential sensitivity to NAC and could serve as an individualized treatment strategy-making tool for AGC patients.

Multidisciplinary team consultation for resectable Gastric Cancer: A propensity score matching analysis.

Purpose: Previous studies proposed that the multidisciplinary team (MDT) consultation could improve tumor staging accuracy and outcomes of patients with gastric malignancy. However, evidence-based reports remain limited. This study aimed to determine the effectiveness of MDT for tumor staging accuracy and outcomes of patients with resectable gastric cancer, and to explore the potential factors affecting its effectiveness. Methods: This retrospective study enrolled 719 gastric cancer patients who underwent gastrectomy in our hospital. After propensity score matching, 378 patients were selected, including 189 in the non-MDT group and 189 in the MDT group. Data regarding baseline characteristics, staging, treatments, and survival were analyzed. Results: The data showed that the staging accuracy in the MDT group and non-MDT group was comparable (53% vs 61% for T stage, 46.1% vs 35.3% for N stage, and 78.3% vs 78.7% for M stage). The MDT group had a higher proportion of preoperative chemotherapy (39.2% vs 28%, p=0.03) and laparoscopic surgery (82.5% vs 72%, p=0.02) than the non-MDT group. However, the achievement of R0 resection was similar in the two groups (93.7% vs 88.9%, p=0.73). There was no significant difference in the 1-year and 3-year overall survival rates between the two groups. Moreover, we observed poor patient compliance when the MDT recommended further examinations, radiotherapy, or chemotherapy before surgical interventions. Conclusion: MDT consultation has limited effects on improving the staging accuracy and treatment outcomes including survival of patients with resectable gastric cancer. Poor patient compliance may be a factor affecting the effectiveness of MDT consultation.

Quantitative study of liver hemodynamic changes in rats with small-for-size syndrome by the 4D-CT perfusion technique.

OBJECTIVE: The microcirculatory hemodynamic changes of small-for-size syndrome (SFSS) are still unclear. In this study, they were investigated by four-dimensional CT perfusion (4D-CTP) technique. METHODS: The sham group, 50, 60, 70 and 80 % partial hepatectomy (PH) rat groups were established. At 1 hour (1 h), 1 day (1 d), 3 days (3 d) and 7 days (7 d) post-operation, serological examination, 4D-CTP scan and histopathological examination were performed. One-way analysis of variance and the Kruskal-Wallis test were used for the comparison. RESULTS: Based on the diagnostic criteria of SFSS, the 80 % group was considered to be a successful model. In all the PH groups, portal vein perfusion and total liver perfusion peaked at 1 h and declined at 1d and 3d. Both portal vein perfusion and total liver perfusion were significantly higher in the 80 % group than the sham group, 50 and 60% groups at 1 h (p < 0.05), and 80 % group at 3d and 7d (p < 0.05). In the 50 and 60 % groups, hepatic artery perfusion decreased at 1 h and maintained at a lower level until at 7 d; whereas, in the 70 and 80% groups, it increased at 1 h, then decreased and reached the lowest level at 7 d. No significant difference appeared in hepatic artery perfusion between any two groups at any time points. At all time points, hepatic perfusion index was lower in all the PH groups than the sham group. Significant differences in hepatic perfusion index appeared between the 80% group and the sham group at 1 h and 1 d (p < 0.05). CONCLUSIONS: The CTP parameters quantitatively revealed the microcirculatory hemodynamic changes in SFSS, which were further confirmed to be associated with histopathological injury. It is suggested that the hemodynamic changes in SFSS remnant liver can provide useful information for further revealing the mechanism of SFSS and may help for guiding the treatments. ADVANCES IN KNOWLEDGE: By using the 4D-CTP technique, the hepatic microcirculatory hemodynamic changes could be quantitatively measured in vivo for small animal research.

Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI.

OBJECTIVES: Preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular cancer (HCC) is important for surgery strategy making. We aimed to develop and validate a combined intratumoural and peritumoural radiomics model based on gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for preoperative prediction of MVI in primary HCC patients. METHODS: This study included a training cohort of 110 HCC patients and a validating cohort of 50 HCC patients. All the patients underwent preoperative Gd-EOB-DTPA-enhanced MRI examination and curative hepatectomy. The volumes of interest (VOIs) around the hepatic lesions including intratumoural and peritumoural regions were manually delineated in the hepatobiliary phase of MRI images, from which quantitative features were extracted and analysed. In the training cohort, machine-learning method was applied for dimensionality reduction and selection of the extracted features. RESULTS: The proportion of MVI-positive patients was 38.2% and 40.0% in the training and validation cohort, respectively. Supervised machine learning selected ten features to establish a predictive model for MVI. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity of the combined intratumoural and peritumoural radiomics model in the training and validation cohort were 0.85 (95% confidence interval (CI), 0.77-0.93), 88.2%, 76.2%, and 0.83 (95% CI, 0.71-0.95), 90.0%, 75.0%, respectively. CONCLUSIONS: We evaluate quantitative Gd-EOB-DTPA-enhanced MRI image features of both intratumoural and peritumoural regions and provide an effective radiomics-based model for the prediction of MVI in HCC patients, and may therefore help clinicians make precise decisions regarding treatment before the surgery. KEY POINTS: • An effective radiomics model for prediction of microvascular invasion in HCC patients is established. • The radiomics model is superior to the radiologist in prediction of MVI. • The radiomics model can help clinicians in pretreatment decision making.

Potential Application of Radiomics for Differentiating Solitary Pulmonary Nodules.

A solitary pulmonary nodule is defined as radiographic lesion with diameters no more than 3 cm and completely surrounded by normal lung tissue. It is commonly encountered in clinical practice and its diagnosis is a big challenge. Medical imaging, as a non-invasive approach, plays a crucial role in the diagnosis of solitary pulmonary nodules since the potential morbidity of surgery and the limits of biopsy. Advanced hardware, image acquisition and analysis technologies have led to the utilization of imaging towards quantitative imaging. With the aim of mining more useful information from image data, radiomics with high-throughput extraction can play a useful role. This article is to introduce the current state of radiomics studies and describe the general procedures. Another objective of this paper is to discover the feasibility and potential of radiomics methods on differentiating solitary pulmonary nodules and to look into the future direction of radiomics in this area.