Effect of initial recurrence site on the prognosis of different tissue types of non-small cell lung cancer: a retrospective cohort study.

PURPOSE: To explore the correlation between the initial recurrence site and survival after recurrence (PRS) in non-small cell lung cancer (NSCLC). METHODS: We collected 588 stages I-III NSCLC patients with recurrence after radical resection in Yunnan Cancer Hospital from January 2013 to December 2018. We used Kaplan-Meier survival curves to compare PRS in patients with different site recurrences. The univariate and multivariate Cox proportional hazard models were used to analyze the impact of the initial recurrence site on PRS. RESULTS: The recurrence site included the lung (n = 109), brain (n = 113), bone (n = 79), abdomen (n = 28), pleura (n = 24), lymph node (n = 81), and multisite (n = 154). In the total population, patients with multisite recurrence had substantially worse PRS (24.8 months, 95% confidence interval [CI]: 17.46-32.20) than that of patients without multiple sites recurrence (42.2 months, 95% CI 32.24-52.10) (P = 0.026). However, patients with lung recurrence had better RFS (63.1 months, 95% CI 51.13-74.00) than those who did not (31.0 months, 95% CI 25.10-36.96) (P < 0.001). In adenocarcinoma, patients with pleural recurrence had substantially worse PRS (21.3 months, 95% CI 15.07-27.46) than that of patients without pleural recurrence (46.9 months, 95% CI 35.07-58.80) (P = 0.031). Multivariate Cox proportional hazards regression analysis revealed that lung recurrence (HR 0.58, 95% CI 0.40-0.82; P = 0.003) was independent protective prognostic factor for PRS in the total population, while pleural recurrence (HR 2.18, 95% CI 1.14-4.17; P = 0.018) was independent adverse prognostic factors for PRS in adenocarcinoma patients. CONCLUSION: The initial recurrence site was associated with PRS in NSCLC patients. Identification of recurrence sites could guide the subsequent treatment.

A nomogram for predicting liver metastasis in patients with gastric gastrointestinal stromal tumor.

BACKGROUND: Liver metastasis (LIM) is an important factor in the diagnosis, treatment, follow-up, and prognosis of patients with gastric gastrointestinal stromal tumor (GIST). There is no simple tool to assess the risk of LIM in patients with gastric GIST. Our aim was to develop and validate a nomogram to identify patients with gastric GIST at high risk of LIM. METHODS: Patient data diagnosed as having gastric GIST between 2010 and 2019 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training cohort and internal validation cohort in a 7:3 ratio. For external validation, retrospective data collection was performed on patients diagnosed as having gastric GIST at Yunnan Cancer Center (YNCC) between January 2015 and May 2023. Univariate and multivariate logistic regression analyses were used to identify independent risk factors associated with LIM in patients with gastric GIST. An individualized LIM nomogram specific for gastric GIST was formulated based on the multivariate logistic model; its discriminative performance, calibration, and clinical utility were evaluated. RESULTS: In the SEER database, a cohort of 2341 patients with gastric GIST was analyzed, of which 173 cases (7.39%) were found to have LIM; 239 patients with gastric GIST from the YNCC database were included, of which 25 (10.46%) had LIM. Multivariate analysis showed tumor size, tumor site, and sex were independent risk factors for LIM (P < .05). The nomogram based on the basic clinical characteristics of tumor size, tumor site, sex, and age demonstrated significant discrimination, with an area under the curve of 0.753 (95% CI, 0.692-0.814) and 0.836 (95% CI, 0.743-0.930) in the internal and external validation cohort, respectively. The Hosmer-Lemeshow test showed that the nomogram was well calibrated, whereas the decision curve analysis and the clinical impact plot demonstrated its clinical utility. CONCLUSION: Tumor size, tumor subsite, and sex were significantly correlated with the risk of LIM in gastric GIST. The nomogram for patients with GIST can effectively predict the individualized risk of LIM and contribute to the planning and decision making related to metastasis management in clinical practice.

Machine Learning-Based Prediction of Pathological Responses and Prognosis After Neoadjuvant Chemotherapy for Non-Small-Cell Lung Cancer: A Retrospective Study.

BACKGROUND: Neoadjuvant chemotherapy has variable efficacy in patients with non-small-cell lung cancer (NSCLC), yet reliable noninvasive predictive markers are lacking. This study aimed to develop a radiomics model predicting pathological complete response and postneoadjuvant chemotherapy survival in NSCLC. MATERIALS AND METHODS: Retrospective data collection involved 130 patients with NSCLC who underwent neoadjuvant chemotherapy and surgery. Patients were randomly divided into training and independent testing sets. Nine radiomics features from prechemotherapy computed tomography (CT) images were extracted from intratumoral and peritumoral regions. An auto-encoder model was constructed, and its performance was evaluated. X-tile software classified patients into high and low-risk groups based on their predicted probabilities. survival of patients in different risk groups and the role of postoperative adjuvant chemotherapy were examined. RESULTS: The model demonstrated area under the receiver operating characteristic (ROC) curve of 0.874 (training set) and 0.876 (testing set). The larger the area under curve (AUC), the better the model performance. Calibration curve and decision curve analysis indicated excellent model calibration (Hosmer-Lemeshow test, P = .763, the higher the P-value, the better the model fit) and potential clinical applicability. Survival analysis revealed significant differences in overall survival (P = .011) and disease-free survival (P = .017) between different risk groups. Adjuvant chemotherapy significantly improved survival in the low-risk group (P = .041) but not high-risk group (P = 0.56). CONCLUSION: This study represents the first successful prediction of pathological complete response achievement after neoadjuvant chemotherapy for NSCLC, as well as the patients' survival, utilizing intratumoral and peritumoral radiomics features.

[Characteristics of Vegetation and Soil of Degraded Grasslands and Their Relationships in Xizang].

To investigate the characteristics of grassland degradation on a regional scale in Xizang, data on grassland degradation from the second grassland survey of Xizang and 12 vegetation and soil indicators from the National Tibetan Plateau Data Center were collected. Using ArcMap, 10 000 random sample points were selected on raster data （excluding non-grassland, desertification, and salinization data, leaving 7 949 valid sample points）. The multi-value extraction to-point method was applied to extract degradation and indicator data for each sample point. The characteristics of degraded grassland vegetation and soil and their relationships were analyzed in Xizang. Moreover, random forest modeling was conducted to predict the trend of grassland ecosystem changes. The results indicated that： ① The grasslands in Xizang were primarily composed of alpine steppe and alpine meadow types, accounting for 45.83% and 41.15% of the valid sample points, respectively. ② With the intensification of grassland degradation, the number of steppe-type species among the 17 grassland types gradually decreased, and the proportion of steppe dominated by species such as Stipa purpurea and Carex moorcroftii decreased, whereas the proportion of miscellaneous grasses and Dasiphora fruticosa increased. ③ As the degree of degradation increased, vegetation indicators generally showed a declining trend, with soil total nitrogen, total phosphorus, total potassium, and organic carbon decreasing, whereas soil pH and bulk density increased, and soil moisture content was not significant. ④ A positive correlation exists between soil moisture content, total nitrogen, total phosphorus, total potassium, organic carbon, vegetation cover, net primary productivity of vegetation, normalized difference vegetation index, aboveground biomass, and habitat quality. However, there was a negative correlation between pH and soil bulk density, and the correlation coefficients among various indicators decreased with the intensification of degradation. ⑤ The random forest simulation results showed that during the degradation process, the contribution rates of soil bulk density and habitat quality both exceeded 12%, with the model prediction accuracy reaching 78%. The study revealed that grassland degradation in Xizang was closely related to soil bulk density and habitat quality, indicating that higher soil bulk density or lower habitat quality may correspond to more severe grassland degradation. This provides a scientific basis for future grassland conservation and management.

Association of Perioperative Skeletal Muscle Index Change With Outcome in Colorectal Cancer Patients.

BACKGROUND: The association between perioperative changes in the skeletal muscle index (SMI) and colorectal cancer (CRC) outcomes remains unclear. We aim to explore perioperative change patterns of SMI and evaluate their effects on long-term outcomes in CRC patients. METHODS: This retrospective cohort study included Stage I-III CRC patients who underwent curative resection between 2012 and 2019. SMI at the third lumbar vertebra level was calculated using computed tomography scans. Optimal cut-off values for SMI were defined separately for males and females and classified as high or low preoperatively and at 3, 6, 9 and 12 months postoperatively. SMI status was further categorized into different perioperative SMI change patterns: highpre-highpost, highpre-lowpost, lowpre-highpost and lowpre-lowpost. The association with recurrence-free survival (RFS) and overall survival (OS) was examined using Cox proportional hazards models. RESULTS: A total of 2222 patients (median [interquartile range] age, 60.00 [51.00-68.00] years; 1302 (58.60%) men; 222 (9.99%) with preoperative low SMI) were evaluated. During a median follow-up of 60 months, 375 patients (16.88%) died, and 617 patients (27.77%) experienced a recurrence. Multivariate Cox model analysis showed that, compared to patients with highpre-highpost, those with highpre-lowpost (HR = 3.32, 95% CI: 1.60-6.51; HR = 2.54, 95% CI: 1.03-6.26; HR = 2.93, 95% CI: 1.19-7.19, all p < 0.05) had significantly worse RFS and OS (HR = 4.07, 95% CI: 1.55-10.69; HR = 4.78, 95% CI: 1.40-16.29; HR = 9.69, 95% CI: 2.53-37.05, all p < 0.05), at postoperative 6, 9 and 12 months, respectively. Patients with lowpre-lowpost were an independent prognostic factor for worse OS at postoperative 12 months (HR = 3.20, 95% CI: 1.06-9.71, p = 0.040). Patients with lowpre-highpost had similar risk of RFS compared to those with highpre-highpost at postoperative 3, 6 and 12 months (HR = 1.49, 95% CI: 0.75-2.98; HR = 1.05, 95% CI: 0.45-2.43; HR = 1.36, 95% CI: 0.31-6.06, all p > 0.05) and similar risk of OS at postoperative 3, 6, 9 and 12 months (all p > 0.05). CONCLUSIONS: Patients with a high preoperative SMI that decline postoperatively have poor RFS and OS. Consistently low SMI also correlates with worse OS. Patients with low SMI but increased after resection are not an indicator of better prognosis. Routine measurement of postoperative, rather than preoperative, SMI is warranted. Patients with low SMI are at an increased risk for recurrence and death, especially within the first year after surgery.