Postoperative Survival Of Patients Research Articles

Prognostic factors and predictive model construction in patients with non-small cell lung cancer: a retrospective study

ObjectiveThe purpose of this study was to construct a nomogram model based on the general characteristics, histological features, pathological and immunohistochemical results, and inflammatory and nutritional indicators of patients so as to effectively predict the overall survival (OS) and progression-free survival (PFS) of patients with non-small cell lung cancer (NSCLC) after surgery.MethodsPatients with NSCLC who received surgical treatment in our hospital from January 2017 to June 2021 were selected as the study subjects. The predictors of OS and PFS were evaluated by univariate and multivariable Cox regression analysis using the Cox proportional risk model. Based on the results of multi-factor Cox proportional risk regression analysis, a nomogram model was established using the R survival package. The bootstrap method (repeated sampling for 1 000 times) was used to internally verify the nomogram model, and C-index was used to represent the prediction performance of the nomogram model. The calibration graph method was used to visually represent its prediction compliance, and decision curve analysis (DCA) was used to evaluate the application value of the model.ResultsUnivariate and multivariate analyses were used to identify independent prognostic factors and to construct a nomogram of postoperative survival and disease progression in operable NSCLC patients, with C-index values of 0.927 (907–0.947) and 0.944 (0.922–0.966), respectively. The results showed that the model had high predictive performance. Calibration curves for 1-year, 2-year, and 3-year OS and PFS show a high degree of agreement between the predicted probability and the actual observed probability. In addition, the results of the DCA curve show that the model has good clinical application value.ConclusionWe established a predictive model of survival prognosis and disease progression in patients with non-small cell lung cancer after surgery, which has good predictive performance and can guide clinicians to make the best clinical decision.

Analysis of causes and prognostic impact of tube occlusion during hyperthermic intraperitoneal chemotherapy for appendiceal pseudomyxoma peritonei

BackgroundAppendiceal pseudomyxoma peritonei (PMP), a rare tumor from mucinous appendiceal origins, is treated with Cytoreductive Surgery (CRS) and Hyperthermic Intraperitoneal Chemotherapy (HIPEC). However, tubing blockages during HIPEC treatment pose a common challenge, impeding the smooth progression of therapy. Few studies to date have explored the incidence and risk factors of tube occlusion during HIPEC in patients with appendiceal PMP, as well as its adverse impact on postoperative complications.MethodsFrom October 2017 to June 2023, a total of 80 patients with appendiceal PMP undergoing combined CRS and HIPEC were included in this study. Tubing blockage events were strictly defined, with patients experiencing blockages during HIPEC treatment allocated to the study group, while those with unobstructed perfusion were assigned to the control group. A comparative analysis was conducted between the two groups regarding post-HIPEC health assessments and occurrence of complications. Risk factors for luminal occlusion during closed HIPEC procedures were identified through univariate and multivariate analysis of data from 303 HIPEC treatments.ResultsTubing blockages occurred in 41 patients (51.3%). The study group experienced prolonged gastrointestinal decompression time (4.1 ± 3.0 vs. 2.5 ± 1.7 days, P = 0.003) and prolonged time to bowel movement (6.1 ± 2.3 vs. 5.1 ± 1.8 days, P = 0.022) compared to the control group. There was no significant difference in the incidence of complications between the two groups. The 1-year survival rate postoperatively was 97%, and the 3-year survival rate was 81%, with no association found between tubing blockage and poorer survival. Additionally, In 303 instances of HIPEC treatment among these 80 patients, tube occlusion occurred in 89 cases (89/303, 29.4%). Multivariable logistic regression analysis revealed age, diabetes, hypertension, and pathology as independent risk factors for tube occlusion.ConclusionTubing blockages are a common occurrence during HIPEC treatment, leading to prolonged postoperative gastrointestinal functional recovery time. When patients are elderly and have concomitant hypertension and diabetes, along with a histological type of low-grade mucinous tumor, the risk of tube occlusion increases. However, this study did not find a significant correlation between tubing blockage and the incidence of postoperative complications or overall patient survival.

Development and internal validation of machine-learning models for predicting survival in patients who underwent surgery for spinal metastases.

A retrospective study. This study aimed to develop machine-learning algorithms for predicting survival in patients who underwent surgery for spinal metastasis. This study develops machine-learning models to predict postoperative survival in spinal metastasis patients, filling the gaps of traditional prognostic systems. Utilizing data from 389 patients, the study highlights XGBoost and CatBoost algorithms̓ effectiveness for 90, 180, and 365-day survival predictions, with preoperative serum albumin as a key predictor. These models offer a promising approach for enhancing clinical decision-making and personalized patient care. A registry of patients who underwent surgery (instrumentation, decompression, or fusion) for spinal metastases between 2004 and 2018 was used. The outcome measure was survival at postoperative days 90, 180, and 365. Preoperative variables were used to develop machine-learning algorithms to predict survival chance in each period. The performance of the algorithms was measured using the area under the receiver operating characteristic curve (AUC). A total of 389 patients were identified, with 90-, 180-, and 365-day mortality rates of 18%, 41%, and 45% postoperatively, respectively. The XGBoost algorithm showed the best performance for predicting 180-day and 365-day survival (AUCs of 0.744 and 0.693, respectively). The CatBoost algorithm demonstrated the best performance for predicting 90-day survival (AUC of 0.758). Serum albumin had the highest positive correlation with survival after surgery. These machine-learning algorithms showed promising results in predicting survival in patients who underwent spinal palliative surgery for spinal metastasis, which may assist surgeons in choosing appropriate treatment and increasing awareness of mortality-related factors before surgery.

Surgical Outcomes of da Vinci Xi™ and da Vinci SP™ for Early-Stage Endometrial Cancer in Patients Undergoing Hysterectomy.

Objectives: This study aimed to evaluate and compare the feasibility and outcomes of two robotic hysterectomy (da Vinci Xi™ vs. da Vinci SP™) systems without lymph node dissection in patients with early-stage endometrial cancer, and assess the postoperative recurrence rate and overall survival of patients. Methods: A retrospective review of 84 patients who underwent robotic hysterectomy for endometrial cancer (stage 1A) was conducted. Surgical procedures, patient characteristics, intraoperative measures, and postoperative outcomes were statistically analyzed. A single gynecologist performed all surgeries. Results: Patient characteristics, average age, and body mass index showed no significant differences between the two models. The total operative time was significantly shorter with da Vinci SP™. Recurrence was identified in only one patient operated on with da Vinci Xi™. All patients were alive during analysis, with a median overall survival of 38 and 9 months for da Vinci Xi™ and da Vinci SP™, respectively. Conclusions: Robotic hysterectomy without lymph node dissection appears to be a safe and effective approach for patients with early-stage endometrial cancer. The da Vinci SP offers the advantage of shorter operative times than the da Vinci Xi™. These findings support the consideration of robotic surgery as a viable option for selected patients.

The effect of CXCL12 on survival outcomes of patients with viral hepatitis-associated hepatocellular carcinoma after hepatectomy

BackgroundThe CXCL12-CXCR4/CXCR7 axis is garnering growing attention. But the comprehension of its function in the progression of HCC remains controversial. The purpose of this study was to investigate the effects of CXCL12 and its receptor on the prognosis of patients with viral hepatitis-associated HCC after hepatectomy. MethodsA total of 86 patients had been enrolled who had undergone hepatectomy for HCC and followed up to July 31, 2019, and their clinicopathological and follow-up data were recorded. Tumor and peritumoral tissues were obtained to detect the expression of CXCL12, CXCR4, and CXCR7 using immunohistochemistry. Real-time polymerase chain reaction was utilized to detect hepatitis B or C virus loads, while survival analysis was performed using the Kaplan–Meier method. Furthermore, the Cox proportional hazards regression model was employed to analyze the factors affecting the prognosis. ResultsThe results revealed that the CXCL12, CXCR4, and CXCR7 expression in tumor tissues was lower than in the corresponding non-tumor tissues in 20.93 %, 22.09 %, and 23.26 % of the patients, respectively, and that only CXCL12 was found to be related to the extrahepatic invasion of HCC. The survival analysis and Cox regression showed that only CXCL12 was associated with the postoperative survival of patients with HCC, and that it was an independent prognostic risk factor in the CXCL12-CXCR4/CXCR7 axis. The CXCL12low group represented shorter progression-free survival and lower overall survival rates. However, the subgroup analysis displayed that the survival difference associated with CXCL12 was only manifested in patients with higher expression of CXCR4 or CXCR7 in HCC, as compared to the surrounding tissues. ConclusionsOur findings suggest that, when assessing the prognostic significance of CXCL12 in HCC, it is essential to consider the expression level of its receptor. Nevertheless, CXCL12 can potentially serve as a promising prognostic marker for HCC.

Factors associated with one-year survival in post-operative glioblastoma patients

Factors associated with one-year survival in post-operative glioblastoma patients

Impact of Surgery-Induced Myeloid-derived Suppressor Cells and the NOX2/ROS Axis on Postoperative Survival in Human Pancreatic Cancer.

Pancreatic cancer surgery triggered pronounced accumulation of NOX2+ myeloid-derived suppressor cells that inhibited NK cell function and negatively prognosticated postoperative patient survival. We propose the targeting of M-MDSC as a conceivable strategy to reduce postoperative immunosuppression in pancreatic cancer.

Conditional survival analysis and real-time prognosis prediction in stage III T3–T4 colon cancer patients after surgical resection: a SEER database analysis

BackgroundConditional survival (CS) takes into consideration the duration of survival post-surgery and can provide valuable additional insights. The aim of this study was to investigate the risk factors associated with reduced one-year postoperative conditional survival in patients diagnosed with stage III T3–T4 colon cancer and real-time prognosis prediction. Furthermore, we aim to develop pertinent nomograms and predictive models.MethodsClinical data and survival outcomes of patients diagnosed with stage III T3–T4 colon cancer were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, covering the period from 2010 to 2019. Patients were divided into training and validation cohorts at a ratio of 7:3. The training set consisted of a total of 11,386 patients for conditional overall survival (cOS) and 11,800 patients for conditional cancer-specific survival (cCSS), while the validation set comprised 4876 patients for cOS and 5055 patients for cCSS. Univariate and multivariate Cox regression analyses were employed to identify independent risk factors influencing one-year postoperative cOS and cCSS. Subsequently, predictive nomograms for cOS and cCSS at 2-year, 3-year, 4-year, and 5-year intervals were constructed based on the identified prognostic factors. The performance of these nomograms was rigorously assessed through metrics including the concordance index (C-index), calibration curves, and the area under curve (AUC) derived from the receiver operating characteristic (ROC) analysis. Clinical utility was further evaluated using decision curve analysis (DCA).ResultsA total of 18,190 patients diagnosed with stage III T3–T4 colon cancer were included in this study. Independent risk factors for one-year postoperative cOS and cCSS included age, pT stage, pN stage, pretreatment carcinoembryonic antigen (CEA) levels, receipt of chemotherapy, perineural invasion (PNI), presence of tumor deposits, the number of harvested lymph nodes, and marital status. Sex and tumor site were significantly associated with one-year postoperative cOS, while radiation therapy was notably associated with one-year postoperative cCSS. In the training cohort, the developed nomogram demonstrated a C-index of 0.701 (95% CI, 0.711–0.691) for predicting one-year postoperative cOS and 0.701 (95% CI, 0.713–0.689) for one-year postoperative cCSS. Following validation, the C-index remained robust at 0.707 (95% CI, 0.721–0.693) for one-year postoperative cOS and 0.700 (95% CI, 0.716–0.684) for one-year postoperative cCSS. ROC and calibration curves provided evidence of the model's stability and reliability. Furthermore, DCA underscored the nomogram’s superior clinical utility.ConclusionsOur study developed nomograms and predictive models for postoperative stage III survival in T3–T4 colon cancer with the aim of accurately estimating conditional survival. Survival bias in our analyses may lead to overestimation of survival outcomes, which may limit the applicability of our findings.

Deep learning model for predicting postoperative survival of patients with gastric cancer.

Prognostic prediction for surgical treatment of gastric cancer remains valuable in clinical practice. This study aimed to develop survival models for postoperative gastric cancer patients. Eleven thousand seventy-five patients from the Surveillance, Epidemiology, and End Results (SEER) database were included, and 122 patients from the Chinese database were used for external validation. The training cohort was created to create three separate models, including Cox regression, RSF, and DeepSurv, using data from the SEER database split into training and test cohorts with a 7:3 ratio. Test cohort was used to evaluate model performance using c-index, Brier scores, calibration, and the area under the curve (AUC). The new risk stratification based on the best model will be compared with the AJCC stage on the test and Chinese cohorts using decision curve analysis (DCA), the net reclassification index (NRI), and integrated discrimination improvement (IDI). It was discovered that the DeepSurv model predicted postoperative gastric cancer patients' overall survival (OS) with a c-index of 0.787; the area under the curve reached 0.781, 0.798, 0.868 at 1-, 3- and 5- years, respectively; the Brier score was below 0.25 at different time points; showing an advantage over the Cox and RSF models. The results are also validated in the China cohort. The calibration plots demonstrated good agreement between the DeepSurv model's forecast and actual results. The NRI values (test cohort: 0.399, 0.288, 0.267 for 1-, 3- and 5-year OS prediction; China cohort:0.399, 0.288 for 1- and 3-year OS prediction) and IDI (test cohort: 0.188, 0.169, 0.157 for 1-, 3- and 5-year OS prediction; China cohort: 0.189, 0.169 for 1- and 3-year OS prediction) indicated that the risk score stratification performed significantly better than the AJCC staging alone (P < 0.05). DCA showed that the risk score stratification was clinically useful and had better discriminative ability than the AJCC staging. Finally, an interactive native web-based prediction tool was constructed for the survival prediction of patients with postoperative gastric cancer. In this study, a high-performance prediction model for the postoperative prognosis of gastric cancer was developed using DeepSurv, which offers essential benefits for risk stratification and prognosis prediction for each patient.

Gastric cancer and metabolic syndrome

Gastric cancer (GC), a prevalent disease in Asian countries, presents a substantial global health challenge. The risk factors for GC include Helicobacter pylori infection, diet, smoking, alcohol, and metabolic syndrome (MetS). This review meticulously examines the intricate connections between MetS and GC, focusing on visceral adipocytes, hormonal factors, obesity, and their impact on survival outcomes. Visceral adipocytes, which secrete inflammatory cytokines and hormones, play a pivotal role in influencing cancer development. Hormonal factors demonstrate nuanced associations with specific GC subtypes, underscoring the complexity of their impact. Large-scale studies exploring obesity-related factors reveal sex-specific nuances and underscore the importance of considering overall weight and body composition. Furthermore, the review explores the impact of eradication therapy for H. pylori infection, which is the most significant factor in the onset of GC, on the components of MetS. Additionally, the influence of MetS on postoperative outcomes and survival in GC patients highlights the interplay between therapeutic interventions and lifestyle factors. This comprehensive exploration sheds light on the multifaceted relationship between MetS and GC, providing valuable insights for future research and preventive strategies.

Treatment patterns and survival outcomes in patients with non-metastatic early-onset pancreatic cancer.

The incidence of patients with early-onset pancreatic cancer (EOPC; age ≤ 50 years at diagnosis) is on the rise, placing a heavy burden on individuals, families, and society. The role of combination therapy including surgery, radiotherapy, and chemotherapy in non-metastatic EOPC is not well-defined. To investigate the treatment patterns and survival outcomes in patients with non-metastatic EOPC. A total of 277 patients with non-metastatic EOPC who were treated at our institution between 2017 and 2021 were investigated retrospectively. Overall survival (OS), disease-free survival, and progression-free survival were estimated using the Kaplan-Meier method. Univariate and multivariate analyses with the Cox proportional hazards model were used to identify prognostic factors. With a median follow-up time of 34.6 months, the 1-year, 2-year, and 3-year OS rates for the entire cohort were 84.3%, 51.5%, and 27.6%, respectively. The median OS of patients with localized disease who received surgery alone and adjuvant therapy (AT) were 21.2 months and 28.8 months, respectively (P = 0.007). The median OS of patients with locally advanced disease who received radiotherapy-based combination therapy (RCT), surgery after neoadjuvant therapy (NAT), and chemotherapy were 28.5 months, 25.6 months, and 14.0 months, respectively (P = 0.002). The median OS after regional recurrence were 16.0 months, 13.4 months, and 8.9 months in the RCT, chemotherapy, and supportive therapy groups, respectively (P = 0.035). Multivariate analysis demonstrated that carbohydrate antigen 19-9 level, pathological grade, T-stage, N-stage, and resection were independent prognostic factors for non-metastatic EOPC. AT improves postoperative survival in localized patients. Surgery after NAT and RCT are the preferred therapeutic options for patients with locally advanced EOPC.

High-risk Patients With Colorectal Liver Metastases Assessed by the Beppu Score Can Have Excellent Survival Through Multidisciplinary Treatment Including Local Ablation.

The Beppu score assessed by the Japanese Society of Hepato-Biliary-Pancreatic Surgery nomogram helps predict postoperative disease-free survival for patients with resectable colorectal liver metastases (CRLM). Using the Beppu score, patients with resectable CRLM were divided into three groups according to recurrence risk: low (≤6 points), moderate (7-10 points), and high-risk (≥11 points). Hepatectomy following preoperative chemotherapy is recommended for high-risk patients. The surgical outcome, local recurrence rates, and long-term survival were assessed, focusing on local ablation. Twenty high-risk and unresectable CRLM patients were enrolled between April 2016 and April 2022. Hepatectomy with or without local ablation was performed after induction chemotherapy. Local ablation was permissive for patients with effective chemotherapy (partial response and stable disease) with CRLM ≤2 cm and ≥5 mm distant from major vessels. The median diameters and numbers of CRLM were 26 (10-150) mm and 9 (1-46). All 18 patients who received preoperative chemotherapy were disease controls. Local ablation was performed simultaneously on hepatectomy in 14 patients. The median diameters and numbers of the ablated nodules were 12 (5-17) mm and 3 (1-21). Local recurrence was 8.5% per 82 ablative nodules. Three-year disease-free and five-year overall survival was 57.4% and 56.2%, respectively. There was no significant difference in patients with or without local ablation. Our treatment strategy for high-risk CRLM patients is feasible and can provide an excellent long-term prognosis regardless of adding local ablation to hepatectomy.

Postoperative chemotherapy relative dose intensity and overall survival in patients with colon cancer.

Quantifying the association of chemotherapy relative dose intensity (RDI) with overall survival may enable supportive care interventions that improve chemotherapy RDI to estimate their magnitude of potential clinical benefit. This cohort study included 533 patients with stage II-III colon cancer who initiated a planned regimen of 12 cycles of 5-fluorouracil, leucovorin, and oxaliplatin (FOLFOX) chemotherapy. The primary exposure was chemotherapy RDI. The primary outcome was overall survival. Restricted cubic splines estimated hazard ratios (HR). Chemotherapy regimen RDI was associated with overall survival in an L-shaped pattern (linear P = 0.006; nonlinear P = 0.057); the risk of death was flat above 85% but increased linearly below 85%. For example, a decrease in RDI from 85 to 75% was associated with an increased risk of death [HR: 1.20 (95% CI: 1.08, 1.52)], whereas an increase in RDI from 85 to 95% was not associated with the risk of death [HR: 1.06 (95% CI: 0.82, 1.38)]. If chemotherapy RDI is considered a potential surrogate of overall survival, supportive care interventions that improve chemotherapy RDI might confer a potential clinical benefit in this population.

Comparison between the effects of epidural and intravenous patient-controlled analgesia on postoperative disability-free survival in patients undergoing thoracic and abdominal surgery: A post hoc analysis.

Thoracic epidural analgesia (TEA) and intravenous patient-controlled analgesia (IV-PCA) are widely used to mitigate immediate postoperative pain; however, their effects on long-term disability-free survival are poorly documented. This study aimed to compare the effects of postoperative TEA and IV-PCA on disability-free survival in patients who underwent thoracic or abdominal surgery. This post hoc analysis of a prospective observational study included 845 inpatients aged ≥55 years that underwent elective thoracic and abdominal surgery between 1 April 2016 and 28 December 2018 in a tertiary care hospital. Inverse probability of treatment weighted (IPTW) using stabilized inverse propensity scores was adopted to minimize bias. The primary outcome in this study was disability-free survival, defined as survival with a 12-item World Health Organization Disability Assessment Schedule 2.0 score of <16%, assessed at 3 months and 1 year after surgery. The final analysis included 601 patients who received TEA and 244 who received IV-PCA. After IPTW, the weighted incidence of disability-free survival at 3 months and 1 year was 60.5% and 61.4% in the TEA group and 78.3% and 66.2% in the IV-PCA group, respectively. The adjusted OR for disability-free survival at 3 months and 1 year was 0.84 (95% confidence interval [CI]: 0.50-1.39) and 1.21 (95% CI: 0.72-2.05), respectively, for the TEA group. No significant differences were observed in the disability-free survival at 3 months and 1 year after elective thoracic and abdominal surgery in patients aged ≥55 years who received TEA or IV-PCA. This study is the first in our setting to document the long-term effects of patient-controlled analgesia. In a post hoc analysis of our prospective cohort study, we show that although differences in chronic postsurgical pain exist at 3 months post-surgery, disability-free survival rates at 1 year do not differ irrespective of the choice of patient-controlled analgesia. The findings of this study highlight the need for shared decision-making between clinicians and patients.

Association Between Long‑Term Exposure to Air Pollution and the Rate of Mortality After Hip Fracture Surgery in Patients Older Than 60 Years: Nationwide Cohort Study in Taiwan.

To enhance postoperative patient survival, particularly in older adults, understanding the predictors of mortality following hip fracture becomes paramount. Air pollution, a prominent global environmental issue, has been linked to heightened morbidity and mortality across a spectrum of diseases. Nevertheless, the precise impact of air pollution on hip fracture outcomes remains elusive. This retrospective study aims to comprehensively investigate the profound influence of a decade-long exposure to 12 diverse air pollutants on the risk of post-hip fracture mortality among older Taiwanese patients (older than 60 years). We hypothesized that enduring long-term exposure to air pollution would significantly elevate the 1-year mortality rate following hip fracture surgery. From Taiwan's National Health Insurance Research Database, we obtained the data of patients who underwent hip fracture surgery between July 1, 2003, and December 31, 2013. Using patients' insurance registration data, we estimated their cumulative exposure levels to sulfur dioxide (SO2), carbon dioxide (CO2), carbon monoxide (CO), ozone (O3), particulate matter having a size of <10 μm (PM10), particulate matter having a size of <2.5 μm (PM2.5), nitrogen oxides (NOX), nitrogen monoxide (NO), nitrogen dioxide (NO2), total hydrocarbons (THC), nonmethane hydrocarbons (NMHC), and methane (CH4). We quantified the dose-response relationship between these air pollutants and the risk of mortality by calculating hazard ratios associated with a 1 SD increase in exposure levels over a decade. Long-term exposure to SO2, CO, PM10, PM2.5, NOX, NO, NO2, THC, NMHC, and CH4 demonstrated significant associations with heightened all-cause mortality risk within 1 year post hip fracture surgery among older adults. For older adults, each 1 SD increment in the average exposure levels of SO2, CO, PM10, PM2.5, NOX, NO, NO2, THC, NMHC, and CH4 corresponded to a substantial escalation in mortality risk, with increments of 14%, 49%, 18%, 12%, 41%, 33%, 38%, 20%, 9%, and 26%, respectively. We further noted a 35% reduction in the hazard ratio for O3 exposure suggesting a potential protective effect, along with a trend of potentially protective effects of CO2. This comprehensive nationwide retrospective study, grounded in a population-based approach, demonstrated that long-term exposure to specific air pollutants significantly increased the risk of all-cause mortality within 1 year after hip fracture surgery in older Taiwanese adults. A reduction in the levels of SO2, CO, PM10, PM2.5, NOX, NO, NO2, THC, NMHC, and CH4 may reduce the risk of mortality after hip fracture surgery. This study provides robust evidence and highlights the substantial impact of air pollution on the outcomes of hip fractures.

Prognostic factors to predict postoperative survival in patients with recurrent glioblastoma

BackgroundThere are no generally accepted criteria for selecting patients with recurrent glioblastoma for surgery. This retrospective study in a Danish population-based cohort aimed to identify prognostic factors affecting postoperative survival after repeated surgery for recurrent glioblastoma and to test if the preoperative New Scale for Recurrent Glioblastoma Surgery (NSGS) developed by Park CK et al could assist in the selection of patients for repeat glioblastoma surgery. MethodsClinical data from 66 patients with recurrent glioblastoma and repeated surgery were analyzed. Kaplan–Meier plots were produced to illustrate survival in each of the three NSGS prognostic groups, and Cox proportional hazard regression was used to identify prognostic variables. Multivariable analysis was used to identify differences in survival in the three prognostic groups. ResultsSix variables significantly affected postoperative survival: preoperative Karnofsky Performance Status (KPS) < 70 (p = 0.002), decreased KPS after second surgery (p = 0.012), ependymal involvement (p = 0.002), tumor volume ≧ 50 cm3 (p = 0.021), age (p = 0.033) and Ki-67 (p = 0.005). Retrospective application of the criteria previously published by Park CK et al showed that median postoperative survival for the three prognostic groups was 390 days (0 points), 279 days (1 point), and 80 days (2 points), respectively. ConclusionSeveral prognostic variables to predict postoperative survival in patients with recurrent glioblastoma were identified and should be considered when selecting patient for repeat surgery. The NSGS scoring system was useful as there were significant differences in postoperative survival between its three prognostic groups.

Prediction of Postoperative Survival Time of Hepatocellular Carcinoma Patients Based on Machine Learning

The survival rate of patients with primary liver cancer after operation is very low. Accurate prediction of postoperative survival of cancer patients plays a key role in the whole treatment period, both for patients and medical staff. For cancer patients, accurate prediction of survival time can make patients have a positive and optimistic attitude when fighting against the disease, and can also reasonably plan the rest of life to realize some wishes. For medical staff, doctors can only predict the survival time of patients through their experience before, and patients' expectations for curing diseases are generally high. Accurate survival prediction can avoid the contradiction between doctors and patients. According to the difference of prognosis of patients with clinical data, medical staff can make individualized postoperative treatment plan for patients to prolong the survival time of patients. To sum up, it is necessary to accurately predict the postoperative survival time of patients through clinical data. In this paper, survival time of 437 hepatocellular carcinoma patients was divided into 1, 3, 5 years and the unbalanced data set is oversampled and shuffled. Then 784 samples are divided into training set and test set by ten fold cross validation. Six machine learning algorithms, were used to predict the postoperative survival time of patients. The performance of the model is evaluated by calculating the accuracy. The optimal machine learning model is selected and the XGboost algorithm will be further optimized by Grid Search. Finally, the survival time of hepatocellular carcinoma patients will be well predicted.

Development and validation of a deep learning model for predicting postoperative survival of patients with gastric cancer

BackgroundDeep learning (DL), a specialized form of machine learning (ML), is valuable for forecasting survival in various diseases. Its clinical applicability in real-world patients with gastric cancer (GC) has yet to be extensively validated.MethodsA combined cohort of 11,414 GC patients from the Surveillance, Epidemiology and End Results (SEER) database and 2,846 patients from a Chinese dataset were utilized. The internal validation of different algorithms, including DL model, traditional ML models, and American Joint Committee on Cancer (AJCC) stage model, was conducted by training and testing sets on the SEER database, followed by external validation on the Chinese dataset. The performance of the algorithms was assessed using the area under the receiver operating characteristic curve, decision curve, and calibration curve.ResultsDL model demonstrated superior performance in terms of the area under the curve (AUC) at 1, 3, and, 5 years post-surgery across both datasets, surpassing other ML models and AJCC stage model, with AUCs of 0.77, 0.80, and 0.82 in the SEER dataset and 0.77, 0.76, and 0.75 in the Chinese dataset, respectively. Furthermore, decision curve analysis revealed that the DL model yielded greater net gains at 3 years than other ML models and AJCC stage model, and calibration plots at 3 years indicated a favorable level of consistency between the ML and actual observations during external validation.ConclusionsDL-based model was established to accurately predict the survival rate of postoperative patients with GC.

The effect of spread through air spaces on postoperative recurrence-free survival in patients with multiple primary lung cancers

PurposeThe purpose of the study was to investigate the effect of spread through air spaces (STAS) on the postoperative prognosis of patients with multiple primary lung cancers staged from IA to IB based on tumor size.MethodsClinicopathological and follow-up data of 122 patients with multiple primary lung cancers diagnosed at stages IA-IB and surgically treated at the Department of Thoracic Surgery, Shenzhen people’s Hospital from January 2019 to December 2021 were retrospectively analyzed. The study involved 42 males and 80 females. STAS status was used to divide them into two groups (87 cases in STAS (-) and 35 cases in STAS (+)). A logistic regression analysis, univariate and multivariate Cox regression analysis, and Kaplan-Meier curves (K-M) were used to determine how STAS affected recurrence-free survival (RFS) in patients.ResultsSTAS (+) had a significantly higher recurrence rate than STAS (-). STAS was predicted by smoking history (P = 0.044), main tumor diameter (P = 0.02), and solid nodules on chest CT (P = 0.02). STAS incidence was not significantly different between lobectomy and sublobar resection groups (P = 0.17). Solid nodules on CT, tumor diameter, vascular invasion, pleural invasion, and STAS were significant predictors of recurrence in the univariate Cox regression analysis. Tumor diameter, pleural invasion and STAS were significant prognostic factors for recurrence in the multivariate Cox regression analysis. Furthermore, STAS (+) group was at greater risk of recurrence than STAS (-) group (34% vs. 0%, P < 0.05)。.ConclusionStage IA-IB multiple primary lung cancer patients with STAS (+) had a higher recurrence rate and a shorter overall survival rate.

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

PurposeTo 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.Methods266 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.ResultsKaplan–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).ConclusionVFA, 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.