Dual-Region Computed Tomography Radiomics-Based Machine Learning Predicts Subcarinal Lymph Node Metastasis in Patients with Non-small Cell Lung Cancer.

BACKGROUND: Noninvasively and accurately predicting subcarinal lymph node metastasis (SLNM) for patients with non-small cell lung cancer (NSCLC) remains challenging. This study was designed to develop and validate a tumor and subcarinal lymph nodes (tumor-SLNs) dual-region computed tomography (CT) radiomics model for predicting SLNM in NSCLC. METHODS: This retrospective study included NSCLC patients who underwent lung resection and SLNs dissection between January 2017 and December 2020. The radiomic features of the tumor and SLNs were extracted from preoperative CT, respectively. Ninety machine learning (ML) models were developed based on tumor region, SLNs region, and tumor-SLNs dual-region. The model performance was assessed by the area under the curve (AUC) and validated internally by fivefold cross-validation. RESULTS: In total, 202 patients were included in this study. ML models based on dual-region radiomics showed good performance for SLNM prediction, with a median AUC of 0.794 (range, 0.686-0.880), which was superior to those of models based on tumor region (median AUC, 0.746; range, 0.630-0.811) and SLNs region (median AUC, 0.700; range, 0.610-0.842). The ML model, which is developed by using the naive Bayes algorithm and dual-region features, had the highest AUC of 0.880 (range of cross-validation, 0.825-0.937) among all ML models. The optimal logistic regression model was inferior to the optimal ML model for predicting SLNM, with an AUC of 0.727. CONCLUSIONS: The CT radiomics showed the potential for accurately predicting SLNM in NSCLC patients. The ML model with dual-region radiomic features has better performance than the logistic regression or single-region models.

Machine learning model predicts airway stenosis requiring clinical intervention in patients after lung transplantation: a retrospective case-controlled study.

BACKGROUND: Patients with airway stenosis (AS) are associated with considerable morbidity and mortality after lung transplantation (LTx). This study aims to develop and validate machine learning (ML) models to predict AS requiring clinical intervention in patients after LTx. METHODS: Patients who underwent LTx between January 2017 and December 2019 were reviewed. The conventional logistic regression (LR) model was fitted by the independent risk factors which were determined by multivariate LR. The optimal ML model was determined based on 7 feature selection methods and 8 ML algorithms. Model performance was assessed by the area under the curve (AUC) and brier score, which were internally validated by the bootstrap method. RESULTS: A total of 381 LTx patients were included, and 40 (10.5%) patients developed AS. Multivariate analysis indicated that male, pulmonary arterial hypertension, and postoperative 6-min walking test were significantly associated with AS (all P < 0.001). The conventional LR model showed performance with an AUC of 0.689 and brier score of 0.091. In total, 56 ML models were developed and the optimal ML model was the model fitted using a random forest algorithm with a determination coefficient feature selection method. The optimal model exhibited the highest AUC and brier score values of 0.760 (95% confidence interval [CI], 0.666-0.864) and 0.085 (95% CI, 0.058-0.117) among all ML models, which was superior to the conventional LR model. CONCLUSIONS: The optimal ML model, which was developed by clinical characteristics, allows for the satisfactory prediction of AS in patients after LTx.

Pararespiratory and paradigestive lymph node metastases in esophageal squamous cell carcinoma: predicting survival and refining the N staging system.

BACKGROUND: The site of lymph node metastasis (LNM) may affect the prognosis of patients with esophageal squamous cell carcinoma (ESCC). To investigate the prognoses of pararespiratory and paradigestive LNM and to propose a novel N (nN) staging system that integrates both the LNM site and count. METHODS: This study was a multicenter, large-sample, retrospective cohort study that included ESCC patients with LNM between January 2014 and December 2019 from three Chinese institutes. Patients were set into training (two institutes) and external validation (one institute) cohorts. The primary outcomes were survival differences in LNM site and the development of novel nodal staging system. The overall survival (OS) of patients with pararespiratory LNM only (Group A), paradigestive LNM only (Group B), and both sites (Group C) was evaluated by Kaplan-Meier. Cox proportional hazards models were used to identify the independent prognostic factors. An nN staging system considering both the LNM site and count was developed and evaluated by the area under the receiver operating characteristic curve (AUC). RESULTS: In total, 1313 patients were included and split into training (n = 1033) and external validation (n = 280) cohorts. There were 342 (26.0%), 568 (43.3%) and 403 (30.7%) patients in groups A, B and C, respectively. The OS of patients with pararespiratory and patients with paradigestive LNM presented significant differences in the training and validation cohorts (P < 0.050). In the training cohort, LNM site was an independent prognostic factor (hazard ratio: 1.58, 95% confidence intervals: 1.41-1.77, P < 0.001). The nN staging definition: nN1 (1-2 positive pararespiratory/paradigestive LNs), nN2 (3-6 pararespiratory LNs or 1 pararespiratory with 1paradigestive LN), nN3 (3-6 LNs with ≥ 1 paradigestive LN), nN4 (≥ 7 LNs). Subsets of patients with different nN stages showed significant differences in OS (P < 0.050). The prognostic model of the nN staging system presented higher performance in the training and validation cohorts at 3-year OS (AUC, 0.725 and 0.751, respectively) and 5-year OS (AUC, 0.740 and 0.793, respectively) than the current N staging systems. CONCLUSIONS: Compared to pararespiratory LNM, the presence of paradigestive LNM is associated with worse OS. The nN staging system revealed superior prognostic ability than current N staging systems.

Double-lung versus heart-lung transplantation for end-stage cardiopulmonary disease: a systematic review and meta-analysis.

We compared posttransplant outcomes following double-lung transplantation (DLTx) and heart-lung transplantation (HLTx), based on a search of PubMed, Cochrane Library, and Embase, from inception to March 8, 2022, for studies that report outcomes of these procedures. We then performed a meta-analysis of baseline characteristics and posttransplant outcomes. Subgroup analyses were implemented according to indication, publication year, and center. This study was registered on PROSPERO (number CRD42020223493). Ten studies were included in this meta-analysis, involving 1230 DLTx patients and 1022 HLTx patients. The DLTx group was characterized by older donors (P = 0.04) and a longer allograft ischemia time (P < 0.001) than the HLTx group. The two groups had comparable 1-year, 3-year, 5-year, 10-year survival rates (all P > 0.05), with similar results identified in subgroup analyses. We found no significant differences in 1-year, 5-year, and 10-year chronic lung allograft dysfunction (CLAD)-free survival, length of intensive care unit stay and hospital stay, length of postoperative ventilation, in-hospital mortality, or surgical complications between the groups (all P > 0.05). Thus, DLTx provides similar posttransplant survival to HLTx for end-stage cardiopulmonary disease. These two procedures have a comparable risk of CLAD and other posttransplant outcomes.

Salvage surgery and conversion surgery for patients with non-small cell lung cancer: A narrative review.

Non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related deaths. With the development of screening, patient selection and treatment strategies, patients' survival outcomes and living quality significantly improved. However, some patients still have local recurrence or residual tumors after receiving definitive therapies. Salvage surgery has been regarded as an effective option for recurrent or residual NSCLC, but its effectiveness remains undetermined. Furthermore, conversion surgery is a special type of salvage surgery for tumors converted from "initially unresectable" to "potentially resectable" status due to a favorable response to systemic treatments. Although conversion surgery is a promising curative procedure for advanced NSCLC, its concept and clinical value remain unfamiliar to clinicians. In this narrative review, we provided an overview of the safety and efficacy of salvage surgery, especially salvage surgery after sublobar resection in early-stage NSCLC. More importantly, we highlighted the concept and value of conversion surgery after systemic treatment in advanced NSCLC to gain some insights into its role in the treatment of lung cancer.

Machine learning-based radiomic computed tomography phenotyping of thymic epithelial tumors: Predicting pathological and survival outcomes.

OBJECTIVE: For patients with thymic epithelial tumors, accurately predicting clinicopathological outcomes remains challenging. We aimed to investigate the performance of machine learning-based radiomic computed tomography phenotyping for predicting pathological (World Health Organization [WHO] type and TNM stage) and survival outcomes (overall and progression-free survival) in patients with thymic epithelial tumors. METHODS: This retrospective study included patients with thymic epithelial tumors between January 2001 and January 2022. The radiomic features were extracted from preoperative unenhanced computed tomography images. After strict feature selection, random forest and random survival forest models were fitted to predict pathological and survival outcomes, respectively. The model performance was assessed by the area under the curve (AUC) and validated internally by the bootstrap method. RESULTS: In total, 124 patients with a median age of 61 years were included. The radiomics random forest models of WHO type and TNM stage showed satisfactory performance with an AUCWHO of 0.898 (95% CI, 0.753-1.000) and an AUCTNM of 0.766 (95% CI, 0.642-0.886). For overall survival and progression-free survival prediction, the radiomics random survival forest models showed good performance (integrated AUCs, 0.923; 95% CI, 0.691-1.000 and 0.702; 95% CI, 0.513-0.875, respectively), and the integrated AUCs increased to 0.935 (95% CI, 0.705-1.000) and 0.811 (95% CI, 0.647-0.942), respectively, when combined with clinicopathological features. CONCLUSIONS: Machine learning-based radiomic computed tomography phenotyping might allow for the satisfactory prediction of pathological and survival outcomes and further improve prognostic performance when integrated with clinicopathological features in patients with thymic epithelial tumors.

Machine Learning-Based Prognostic Model for Patients After Lung Transplantation.

Importance: Although numerous prognostic factors have been found for patients after lung transplantation (LTx) over the years, an accurate prognostic tool for LTx recipients remains unavailable. Objective: To develop and validate a prognostic model for predicting overall survival in patients after LTx using random survival forests (RSF), a machine learning algorithm. Design, Setting, and Participants: This retrospective prognostic study included patients who underwent LTx between January 2017 and December 2020. The LTx recipients were randomly assigned to training and test sets in accordance with a ratio of 7:3. Feature selection was performed using variable importance with bootstrapping resampling. The prognostic model was fitted using the RSF algorithm, and a Cox regression model was set as a benchmark. The integrated area under the curve (iAUC) and integrated Brier score (iBS) were applied to assess model performance in the test set. Data were analyzed from January 2017 to December 2019. Main Outcomes And Measures: Overall survival in patients after LTx. Results: A total of 504 patients were eligible for this study, consisting of 353 patients in the training set (mean [SD] age, 55.03 [12.78] years; 235 [66.6%] male patients) and 151 patients in the test set (mean [SD] age, 56.79 [10.95] years; 99 [65.6%] male patients). According to the variable importance of each factor, 16 were selected for the final RSF model, and postoperative extracorporeal membrane oxygenation time was identified as the most valuable factor. The RSF model had excellent performance with an iAUC of 0.879 (95% CI, 0.832-0.921) and an iBS of 0.130 (95% CI, 0.106-0.154). The Cox regression model fitted by the same modeling factors to the RSF model was significantly inferior to the RSF model with an iAUC of 0.658 (95% CI, 0.572-0.747; P < .001) and an iBS of 0.205 (95% CI, 0.176-0.233; P < .001). According to the RSF model predictions, the patients after LTx were stratified into 2 prognostic groups displaying significant difference, with mean overall survival of 52.91 months (95% CI, 48.51-57.32) and 14.83 months (95% CI, 9.44-20.22; log-rank P < .001), respectively. Conclusions and relevance: In this prognostic study, the findings first demonstrated that RSF could provide more accurate overall survival prediction and remarkable prognostic stratification than the Cox regression model for patients after LTx.

The application of radiomics in esophageal cancer: Predicting the response after neoadjuvant therapy.

Esophageal cancer (EC) is one of the fatal malignant neoplasms worldwide. Neoadjuvant therapy (NAT) combined with surgery has become the standard treatment for locally advanced EC. However, the treatment efficacy for patients with EC who received NAT varies from patient to patient. Currently, the evaluation of efficacy after NAT for EC lacks accurate and uniform criteria. Radiomics is a multi-parameter quantitative approach for developing medical imaging in the era of precision medicine and has provided a novel view of medical images. As a non-invasive image analysis method, radiomics is an inevitable trend in NAT efficacy prediction and prognosis classification of EC by analyzing the high-throughput imaging features of lesions extracted from medical images. In this literature review, we discuss the definition and workflow of radiomics, the advances in efficacy prediction after NAT, and the current application of radiomics for predicting efficacy after NAT.

Pathological tumor long-to-short axis ratio as a prognostic factor in patients with thymic epithelial tumors.

BACKGROUND: Thymic epithelial tumors (TETs) exhibit irregular shapes reflective of the heterogeneity in tumor growth and invasive properties. We aimed to identify the prognostic value of the pathological tumor long-to-short axis (L/S) ratio in TETs. METHODS: A retrospective study was performed on patients with TETs who underwent extended thymectomy between January 1999 and December 2019 in our institute. Patients were divided into two groups according to the threshold of the L/S ratio. Overall survival (OS) and progression-free survival (PFS) were evaluated by Kaplan-Meier analysis. The independent prognostic factors of TETs were identified by multivariate analysis. The performance of prediction models for the above survival outcomes with and without the L/S ratio was evaluated using an integrated time-dependent area under the curve (iAUC). RESULTS: Eligible patients were divided into two groups based on higher (n = 42) and lower (n = 94) L/S ratios according to a threshold value of 1.39. A significant difference was found between the two groups only in disease progression (p = 0.001). Poorer survival outcomes were found from Kaplan-Meier curves in the higher L/S ratio group (p < 0.05). In the multivariable analysis, the L/S ratio showed significant effects on OS and PFS (p < 0.05). The performance of models with the L/S ratio was better than that without the L/S ratio in predicting survival outcomes. CONCLUSIONS: The pathological tumor L/S ratio is an independent prognostic factor for OS and PFS in patients with TETs, and an L/S ratio >1.39 is associated with worse survival outcomes.

Right lung transplantation with a left-to-right inverted anastomosis in a rat model.

Objective: Right lung transplantation in rats has been attempted occasionally, but the technical complexity makes it challenging to apply routinely. Additionally, basic research on inverted lobar lung transplantation is scarce because of the lack of a cost-effective experimental model. We first reported right lung transplantation in a rat model using left-to-right inverted anastomosis to imitate the principle of clinically inverted lung transplantation. Methods: Right lung transplantation was performed in 10 consecutive rats. By using a 3-cuff technique, the left lung of the donor rat was implanted into the right thoracic cavity of the recipient rat. The rat lung graft was rotated 180° along the vertical axis to achieve anatomic matching of right hilar structures. Another 10 consecutive rats had received orthotopic left lung transplantation as a control. Results: All lung transplantation procedures were technically successful without intraoperative failure. One rat (10%) died of full pulmonary atelectasis after right lung transplantation, whereas all rats survived after left lung transplantation. No significant difference was observed in heart-lung block retrieval (8.6 ± 0.8 vs 8.4 ± 0.9 minutes), cuff preparation (8.3 ± 0.9 vs 8.7 ± 0.9 minutes), or total procedure time (58.2 ± 2.6 vs 56.6 ± 2.1 minutes) between the right lung transplantation and standard left lung transplantation groups (P > .05), although the cold ischemia time (14.2 ± 0.9 vs 25.5 ± 1.7 minutes) and warm ischemia time (19.8 ± 1.5 vs 13.7 ± 1.8 minutes) were different (P < .001). Conclusions: Right lung transplantation with a left-to-right inverted anastomosis in a rat model is technically easy to master, expeditious, and reproducible. It can potentially imitate the principle of clinically inverted lung transplantation and become an alternative to standard left lung transplantation.

Noninvasive monitoring of allograft rejection in a rat lung transplant model: Application of machine learning-based 18F-fluorodeoxyglucose positron emission tomography radiomics.

BACKGROUND: Standardized uptake values (SUVs) derived from 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) are valuable but insufficient for detecting lung allograft rejection (AR). Using a rat lung transplantation (LTx) model, we investigated correlations of AR with the SUVmax and PET-derived radiomics and further evaluated the performance of machine learning (ML)-based radiomics for monitoring AR. METHODS: LTx was performed on 4 groups of rats: isograft, allograft-cyclosporinecontinuous (CsAcont), allograft-CsAdelayed, and allograft-CsA1week. Each rat underwent 18F-FDG PET at week 3 or 6. The SUVmax and radiomic features were extracted from the PET images. Least absolute shrinkage and selection operator regression was used to construct a radiomics score (Rad-score). Ten modeling algorithms with 7 feature selection methods were performed to develop 70 radiomics models (49 ML models and 21 logistic regression models) for monitoring AR, validated using the bootstrap method. RESULTS: In total, 837 radiomic features were extracted from each PET image. The SUVmax and Rad-score showed significant positive correlations with histopathology (p < .05). The area under the curve (AUC) of SUVmax for detecting AR was 0.783. The median AUC of ML models was 0.921, which was superior to that of logistic regression models (median AUC, 0.721). The optimal ML model using a random forest modeling algorithm with random forest feature selection method exhibited the highest AUC of 0.982 (95% confidence interval, 0.875-1.000) in all models. CONCLUSIONS: SUVmax provided a good correlation with AR, but ML-based PET radiomics further strengthened the power of 18F-FDG PET functional imaging for monitoring AR in LTx.

Machine learning models predict lymph node metastasis in patients with stage T1-T2 esophageal squamous cell carcinoma.

Background: For patients with stage T1-T2 esophageal squamous cell carcinoma (ESCC), accurately predicting lymph node metastasis (LNM) remains challenging. We aimed to investigate the performance of machine learning (ML) models for predicting LNM in patients with stage T1-T2 ESCC. Methods: Patients with T1-T2 ESCC at three centers between January 2014 and December 2019 were included in this retrospective study and divided into training and external test sets. All patients underwent esophagectomy and were pathologically examined to determine the LNM status. Thirty-six ML models were developed using six modeling algorithms and six feature selection techniques. The optimal model was determined by the bootstrap method. An external test set was used to further assess the model's generalizability and effectiveness. To evaluate prediction performance, the area under the receiver operating characteristic curve (AUC) was applied. Results: Of the 1097 included patients, 294 (26.8%) had LNM. The ML models based on clinical features showed good predictive performance for LNM status, with a median bootstrapped AUC of 0.659 (range: 0.592, 0.715). The optimal model using the naive Bayes algorithm with feature selection by determination coefficient had the highest AUC of 0.715 (95% CI: 0.671, 0.763). In the external test set, the optimal ML model achieved an AUC of 0.752 (95% CI: 0.674, 0.829), which was superior to that of T stage (0.624, 95% CI: 0.547, 0.701). Conclusions: ML models provide good LNM prediction value for stage T1-T2 ESCC patients, and the naive Bayes algorithm with feature selection by determination coefficient performed best.

A novel web-based dynamic nomogram for recurrent laryngeal nerve lymph node metastasis in esophageal squamous cell carcinoma.

Background: Patients with esophageal squamous cell carcinoma (ESCC) are liable to develop recurrent laryngeal nerve (RLN) lymph node metastasis (LNM). We aimed to assess the predictive value of the long diameter (LD) and short diameter (SD) of RLN lymph node (LN) and construct a web-based dynamic nomogram for RLN LNM prediction. Methods: We reviewed 186 ESCC patients who underwent RLN LN dissection from January 2016 to December 2018 in the Affiliated Hospital of North Sichuan Medical College. Risk factors for left and right RLN LNM were determined by univariate and multivariate analyses. A web-based dynamic nomogram was constructed by using logistic regression. The performance was assessed by the area under the curve (AUC) and Brier score. Models were internally validated by performing five-fold cross-validation. Results: Patients who underwent left and right RLN LN dissection were categorized as left cohort (n = 132) and right cohort (n = 159), with RLN LNM rates of 15.9% (21/132) and 21.4% (34/159), respectively. The AUCs of the LD (SD) of RLN LN were 0.663 (0.688) in the left cohort and 0.696 (0.705) in the right cohort. The multivariate analysis showed that age, the SD of RLN LN, and clinical T stage were significant risk factors for left RLN LNM (all P < 0.05), while tumor location, the SD of RLN LN, and clinical T stage were significant risk factors for right RLN LNM (all P < 0.05). The dynamic nomograms showed reliable performance after five-fold cross-validation [(left (right), mean AUC: 0.814, range: 0.614-0.891 (0.775, range: 0.084-0.126); mean Brier score: 0.103, range: 0.084-0.126 (0.145, range: 0.105-0.206)], available at https://mpthtw.shinyapps.io/leftnomo/ and https://mpthtw.shinyapps.io/rightnomo/. Conclusion: The LD and SD of RLN LN are inadequate to predict RLN LNM accurately, but online dynamic nomograms by combined risk factors show better prediction performance and convenient clinical application.

The minimum number of examined lymph nodes for accurate nodal staging and optimal survival of stage T1-2 esophageal squamous cell carcinoma: A retrospective multicenter cohort with SEER database validation.

BACKGROUND: The extent of lymphadenectomy during esophagectomy remains controversial for patients with T1-2 ESCC. The aim of this study was to identify the minimum number of examined lymph node (ELN) for accurate nodal staging and overall survival (OS) of patients with T1-2 esophageal squamous cell carcinoma (ESCC). MATERIALS AND METHODS: Patients with T1-2 ESCC from three institutes between January 2011 and December 2020 were retrospectively reviewed. The associations of ELN count with nodal migration and OS were evaluated using multivariable models, and visualized by using locally weighted scatterplot smoothing (LOWESS). Chow test was used to determine the structural breakpoints of ELN count. External validation in the SEER database was performed. RESULTS: In total, 1537 patients were included. Increased ELNs was associated with an increased likelihood of having positive nodal disease and incremental OS. The minimum numbers of ELNs for accurate nodal staging and optimal survival were 14 and 18 with validation in the SEER database (n = 519), respectively. The prognostic prediction ability of N stage was improved in the group with ≥14 ELNs compared with those with fewer ELNs (iAUC, 0.70 (95%CI 0.66-0.74) versus 0.61(95%CI 0.57-0.65)). The higher prognostic value was found for patients with ≥18 ELNs than those with <18 ELNs (iAUC, 0.78 (95%CI 0.74-0.82) versus 0.73 (95%CI 0.7-0.77)). CONCLUSION: The minimum numbers of ELNs for accurate nodal staging and optimal survival of stage T1-2 ESCC patients were 14 and 18, respectively.

Genetic effect of ischemia-reperfusion injury upon primary graft dysfunction and chronic lung allograft dysfunction in lung transplantation: evidence based on transcriptome data.

The unclear mechanism that ischemia-reperfusion injury (IRI) contributes to the development of primary graft dysfunction (PGD) and chronic lung allograft dysfunction (CLAD) remains a major issue in lung transplantation. Differentially expressed PGD-related genes and CLAD-related genes during IRI (IRI-PGD common genes and IRI-CLAD common genes) were identified using GEO datasets (GSE127003, GSE8021, GSE9102) and GeneCards datasets. Enrichment analysis and four network analyses, namely, protein-protein interaction, microRNA (miRNA)-gene, transcription factor (TF)-gene, and drug-gene networks, were then performed. Moreover, GSE161520 was analyzed to identify the differentially expressed core miRNAs during IRI in rats. Finally, Pearson correlation analysis and ROC analysis were performed. Eight IRI-PGD common genes (IL6, TNF, IL1A, IL1B, CSF3, CXCL8, SERPINE1, and PADI4) and 10 IRI-CLAD common genes (IL1A, ICAM1, CCL20, CCL2, IL1B, TNF, PADI4, CXCL8, GZMB, and IL6) were identified. Enrichment analysis showed that both IRI-PGD and IRI-CLAD common genes were significantly enriched in "AGE-RAGE signaling pathway in diabetic complication" and "IL-17 signaling pathway". Among the core miRNAs, miR-1-3p and miR-335 were differentially expressed in IRI rats. Among core TFs, CEBPB expression had a significant negative correlation with P/F ratio (r = -0.33, P = 0.021). In the reperfused lung allografts, the strongest positive correlation was exhibited between PADI4 expression and neutrophil proportion (r = 0.76, P < 0.001), and the strongest negative correlation was between PADI4 expression and M2 macrophage proportion (r = -0.74, P < 0.001). In lung allografts of PGD recipients, IL6 expression correlated with activated dendritic cells proportion (r = 0.86, P < 0.01), and IL1B expression correlated with the neutrophils proportion(r = 0.84, P < 0.01). In whole blood of CLAD recipients, GZMB expression correlated with activated CD4+ memory T cells proportion (r = 0.76, P < 0.001).Our study provides the novel insights into the molecular mechanisms by which IRI contributes to PGD and CLAD and potential targets for therapeutic intervention.

Preoperative Clinical Characteristics Predict Recurrent Laryngeal Nerve Lymph Node Metastasis and Overall Survival in Esophageal Squamous Cell Carcinoma: A Retrospective Study With External Validation.

Background: Recurrent laryngeal nerve (RLN) lymph node metastasis (LNM) is not rare in patients with esophageal squamous cell carcinoma (ESCC). We aimed to develop and externally validate a preoperative nomogram using clinical characteristics to predict RLN LNM in patients with ESCC and evaluate its prognostic value. Methods: A total of 430 patients with ESCC who underwent esophagectomy with lymphadenectomy of RLN LNs at two centers between May 2015 and June 2019 were reviewed and divided into training (center 1, n = 283) and external validation cohorts (center 2, n = 147). Independent risk factors for RLN LNM were determined by multivariate logistic regression, and a nomogram was developed. The performance of the nomogram was assessed in terms of discrimination, calibration, clinical usefulness, and prognostic value. The nomogram was internally validated by the bootstrap method and externally validated by the external validation cohort. Results: Multivariate analysis indicated that clinical T stage (P <0.001), endoscopic tumor length (P = 0.003), bioptic tumor differentiation (P = 0.004), and preoperative carcinoembryonic antigen level (P = 0.001) were significantly associated with RLN LNM. The nomogram had good discrimination with the area under the curve of 0.770 and 0.832 after internal and external validations. The calibration curves and decision curve analysis confirmed the good calibration and clinical usefulness of this model. High-risk of RLN LNM predicted by the nomogram was associated with worse overall survival in the external validation cohort (P <0.001). Conclusion: A nomogram developed by preoperative clinical characteristics demonstrated a good performance to predict RLN LNM and prognosis for patients with ESCC.

Development and validation of an early predictive nomogram for delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage.

BACKGROUND: Previous studies have shown that platelet is involved in the occurrence and progression of delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH), but the relationship between platelet and DCI is not completely clear. Here, we aimed to screen the early platelet parameters associated with DCI after aSAH and develop an early predictive nomogram for DCI after aSAH. METHODS: The study was carried out in the neurosurgery department of Affiliated Hospital of North Sichuan Medical College. A total of 285 consecutive aSAH patients admitted within 24 hours after onset were analyzed retrospectively. Univariate and multivariate analyses were used to identify risk factors for DCI. A predictive nomogram was developed and validated with R software. RESULTS: Sixty-six (23.16%) of the 285 patients with aSAH exhibited DCI during hospitalization. The DCI group and the non-DCI group showed statistically significant differences in red blood cell count (RBC), platelet count (PLT), mean platelet volume (MPV), modified Fisher grade and platelet distribution width (PDW). Multivariable logistic regression analysis showed that modified Fisher grade [odds ratio (OR) =1.354; 95% confidence interval (CI): 1.034-1.773; P=0.028] and mean MPV [OR =1.825; 95% CI: 1.429-2.331; P<0.001] were independent risk factors for DCI. Modified Fisher grade, RBC, PLT, MPV, and PDW were used to develop a predictive nomogram for DCI. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.799 (95% CI: 0.737-0.861) in the training set and 0.783 (95% CI: 0.616-0.949) in the validation set. The calibration curve showed that the predicted probability concurred with the actual probability. Decision curve analysis indicated that this nomogram had good clinical application value and could be used for clinical decision making. CONCLUSIONS: Our study found that MPV was an early predictor of DCI after aSAH. The nomogram incorporating early MPV had greater value in predicting DCI after aSAH.

Risk factors for lymph node metastasis in T1 esophageal squamous cell carcinoma: A systematic review and meta-analysis.

BACKGROUND: Lymph node metastasis (LNM) affects the application and outcomes of endoscopic resection in T1 esophageal squamous cell carcinoma (ESCC). However, reports of the risk factors for LNM have been controversial. AIM: To evaluate risk factors for LNM in T1 ESCC. METHODS: We searched Embase, PubMed and Cochrane Library to select studies related to LNM in patients with T1 ESCC. Included studies were divided into LNM and non-LNM groups. We performed a meta-analysis to examine the relationship between LNM and clinicopathologic features. Odds ratio (OR), mean differences and 95% confidence interval (CI) were assessed using a fixed-effects or random-effects model. RESULTS: Seventeen studies involving a total of 3775 patients with T1 ESCC met the inclusion criteria. After excluding studies with heterogeneity based on influence analysis, tumor size (OR = 1.93, 95%CI = 1.49-2.50, P < 0.001), tumor location (OR = 1.46, 95%CI = 1.17-1.82, P < 0.001), macroscopic type (OR = 3.17, 95%CI = 2.33-4.31, P < 0.001), T1 substage (OR = 6.28, 95%CI = 4.93-8.00, P < 0.001), differentiation (OR = 2.11, 95%CI = 1.64-2.72, P < 0.001) and lymphovascular invasion (OR = 5.86, 95%CI = 4.60-7.48, P < 0.001) were found to be significantly associated with LNM. Conversely, sex, age and infiltrative growth pattern were not identified as risk factors for LNM. CONCLUSION: A tumor size > 2 cm, lower location, nonflat macroscopic type, T1b stage, poor differentiation and lymphovascular invasion were associated with LNM in patients with T1 ESCC.