Predictor Of Recurrence-free Survival Research Articles

Recurrence-Free Survival Prediction for Anal Squamous Cell Carcinoma After Chemoradiotherapy using Planning CT-based Radiomics Model.

Approximately 30% of non-metastatic anal squamous cell carcinoma (ASCC) patients will experience recurrence after chemoradiotherapy (CRT), and currently available clinical variables are poor predictors of treatment response. We aimed to develop a model leveraging information extracted from radiation pretreatment planning CT to predict recurrence-free survival (RFS) in ASCC patients after CRT. Radiomics features were extracted from planning CT images of 96 ASCC patients. Following pre-feature selection, the optimal feature set was selected via step-forward feature selection with a multivariate Cox proportional hazard model. The RFS prediction was generated from a radiomics-clinical combined model based on an optimal feature set with five repeats of nested five-fold cross validation. The risk stratification ability of the proposed model was evaluated with Kaplan-Meier analysis. Shape- and texture-based radiomics features significantly predicted RFS. Compared to a clinical-only model, radiomics-clinical combined model achieves better performance in the testing cohort with higher C-index (0.80 vs 0.73) and AUC (0.84 vs 0.78 for 1-year RFS, 0.84 vs 0.79 for 2-year RFS, and 0.85 vs 0.81 for 3-year RFS), leading to distinctive high- and low-risk of recurrence groups (p < 0.001). A treatment planning CT based radiomics and clinical combined model had improved prognostic performance in predicting RFS for ASCC patients treated with CRT as compared to a model using clinical features only. The use of radiomics from planning CT is promising in assisting in personalized management in ASCC. The study outcomes support the role of planning CT-based radiomics as potential imaging biomarker.

Prediction of recurrence-free survival and risk factors of sinonasal inverted papilloma after surgery by machine learning models

ObjectivesOur research aims to construct machine learning prediction models to identify patients proned to recurrence after inverted papilloma (IP) surgery and guide their follow-up treatment.MethodsThis study collected 210 patients underwent IP resection surgery at a university hospital from January 2010 to December 2023. Six machine learning algorithms including ExtraSurvivalTrees (EST), GradientBoostingSurvivalAnalysis (GBSA), RandomSurvivalForest (RSF), SurvivalSVM, Coxnet and Coxph, were used to construct the prediction models. Shapley Additive Explanations (SHAP) values were used to explain the importance of various features in predicting IP recurrence.ResultsWe found that the recurrence rate of IP patients is 20.00%, with a median recurrence time of 35.5 months. Multivariate Cox regression analysis identified mild or moderate dysplasia as an independent risk factor for recurrence. The EST model performs the best in predicting postoperative recurrence of IP, with C-index of 0.968 and 0.878 in the training and testing sets. SHAP emphasizes five important predictive factors for recurrence, including bone defects, orbital involvement, smoking, no processing of tumor attachment sites and drinking.ConclusionsTo our knowledge, this is the first study to use multiple ML models to predict postoperative recurrence of IP. The EST model has the best predictive performance, with SHAP emphasizing several key predictive factors for IP recurrence. This study emphasizes the practicality of machine learning algorithms in predicting IP clinical outcomes, providing valuable insights into the potential for improving clinical decision-making.

Tumor Spread Through Air Spaces Predicts Survival in Resected Pulmonary Lymphoepithelial Carcinoma.

Tumor spread through air spaces (STAS) has been recognized as a prognostic factor for several types of lung cancers. However, information regarding its clinical significance in pulmonary lymphoepithelial carcinoma is limited. Therefore, this study investigated effects of STAS on the clinical outcomes for patients with pulmonary lymphoepithelial carcinoma. This study retrospectively reviewed 56 surgically resected pulmonary lymphoepithelial carcinomas. The study defined STAS as the presence of tumor cells within air spaces in lung parenchyma beyond the tumor edge. Artifacts were excluded. Recurrence-free survival (RFS) was analyzed using the log-rank test and Cox proportional hazards model. In 18 patients (32.1%), STAS was observed and found to be associated with larger tumor size (>3 cm) (p = 0.009), higher pathologic stage (p = 0.026), and tumor necrosis (p = 0.046). Patients with STAS had a significantly lower 5-year RFS rate (p = 0.025). Multivariate analysis showed that STAS was an independent predictor of worse RFS (hazard ratio, 3.395; p = 0.038). Patients with STAS had a significantly increased risk of locoregional recurrence (p = 0.049). The study findings suggest that STAS is an independent predictor of poor RFS. Based on these findings, a new three-tier grading system based on the patterns of tumor border and STAS was proposed for effective prediction of 5-year RFS in pulmonary lymphoepithelial carcinoma.

New prognostic model for liver transplantation outcomes in hepatocellular carcinoma

Background. Liver transplantation remains a priority treatment option for hepatocellular carcinoma in the presence of liver cirrhosis; yet precise outcome prediction post-operation continues to be a complex challenge. Existing prognostic model often overlook patient age and donor type. Enhanced models that incorporate these parameters can improve prediction accuracy and treatment efficacy, which is critically important in the dynamically evolving field of transplantation.Objective. The aim of this study is to develop a prognostic model for liver transplantation outcomes in patients with hepatocellular carcinoma and liver cirrhosis.Material and methods. This retrospective study included 69 patients with hepatocellular carcinoma on the background of liver cirrhosis who underwent liver transplantation between May 2010 and December 2022. Of these, 42 patients (61%) received organs from living donors, and 27 (39%) from deceased donors. The study involved analysis of alpha-fetoprotein levels in blood, as well as assessment of radiological (maximum tumor nodule size, number of nodules) and histological parameters (maximum tumor nodule size, number of nodules, presence of vascular invasion). Cox regression model was used to predict recurrence-free survival, and the results for five-year recurrence-free survival, recipient age, and donor type were reused in the Cox model to predict overall survival.Results. Four models for predicting recurrence-free survival and overall survival based on histological and radiological data were developed, demonstrating high prognostic value with C-indexes on training/test data of 0.76/1; 0.73/1; 0.78/0.8; 0.6/0.8 respectively. All models showed recurrence-free survival prediction accuracy comparable to the Milan criteria. The model outcomes are available as a calculator on the website https://nadit.ru/calculate_HCC.Conclusion. The developed prognostic models are vital tools for personalized outcome prediction after liver transplantation for hepatocellular carcinoma. To enhance the accuracy of these models, further amalgamation and validation of data from various medical centers, as well as open scientific collaboration, are necessary.

Enhancing Predictive Accuracy for Recurrence-Free Survival in Head and Neck Tumor: A Comparative Study of Weighted Fusion Radiomic Analysis.

Despite advancements in oncology, predicting recurrence-free survival (RFS) in head and neck (H&N) cancer remains challenging due to the heterogeneity of tumor biology and treatment responses. This study aims to address the research gap in the prognostic efficacy of traditional clinical predictors versus advanced radiomics features and to explore the potential of weighted fusion techniques for enhancing RFS prediction. We utilized clinical data, radiomic features from CT and PET scans, and various weighted fusion algorithms to stratify patients into low- and high-risk groups for RFS. The predictive performance of each model was evaluated using Kaplan-Meier survival analysis, and the significance of differences in RFS rates was assessed using confidence interval (CI) tests. The weighted fusion model with a 90% emphasis on PET features significantly outperformed individual modalities, yielding the highest C-index. Additionally, the incorporation of contextual information by varying peritumoral radii did not substantially improve prediction accuracy. While the clinical model and the radiomics model, individually, did not achieve statistical significance in survival differentiation, the combined feature set showed improved performance. The integration of radiomic features with clinical data through weighted fusion algorithms enhances the predictive accuracy of RFS outcomes in head and neck cancer. Our findings suggest that the utilization of multi-modal data helps in developing more reliable predictive models and underscore the potential of PET imaging in refining prognostic assessments. This study propels the discussion forward, indicating a pivotal step toward the adoption of precision medicine in cancer care.

903P Patterns and predictors of recurrence-free survival (RFS) in salivary gland cancers (SGCs): A Spanish multicenter study

903P Patterns and predictors of recurrence-free survival (RFS) in salivary gland cancers (SGCs): A Spanish multicenter study

Evaluation of the novel International Association for the Study of Lung Cancer grading system in adenocarcinoma with spread through air space.

The International Association for the Study of Lung Cancer (IASLC) pathology panel has proposed a new grading system for invasive lung adenocarcinoma (LADC). This study aims to validate this novel grading system for invasive LADC using propensity score matching (PSM), with a specific focus on patients exhibiting spread through air space (STAS). We retrospectively analyzed the clinicopathologic features of a large cohort of 910 non-mucinous LADCs with STAS from 2017 to 2020 and classified them according to the novel grading system. We applied PSM to adjust for potential confounders between the grading groups. Kaplan-Meier and Cox proportional hazards models were adopted for prognostic evaluation. The results showed that the IASLC grading system (grades 2 and 3) stratified well in terms of recurrence-free survival (RFS) and overall survival (OS) (P=0.02 and P=0.02, respectively) after matching, with Grade 3 being an independent predictor of RFS [hazard ratio (HR), 1.533; P=0.02] and OS (HR, 2.765; P=0.02) in multivariable models. The concordance index (C-index) and area under the curve (AUC) of the IASLC system were 0.719 and 0.754 for recurrence and 0.844 and 0.891 for death, respectively. In addition, anaplastic lymphoma kinase (ALK) fusion and tumor protein p53 (TP53) mutations were detected more frequently in grade 3 tumors, while epidermal growth factor receptor (EGFR) mutations were more prevalent in grade 2 tumors. The IASLC grade did not predict the benefit of adjuvant chemotherapy (ACT). This study suggests that the new IASLC grading system is a valuable prognostic tool for patients with STAS-positive LADC.

Prognostic nutritional index predicts lateral lymph node metastasis and recurrence free survival in papillary thyroid carcinoma

BackgroundPreoperative hematological parameters are predictors of pathological features and recurrence-free survival (RFS) in various malignancies. However, comprehensive studies of preoperative indicators associated with papillary thyroid carcinoma (PTC) are scarce. The present study investigated the association between preoperative indicators and RFS in patients with PTC. Accordingly, we explored the clinical impact of the prognostic nutritional index (PNI) on lymph node metastasis and RFS in patients with PTC.MethodsA total of 619 PTC patients were retrospectively reviewed between Jan 2013 and Dec 2017. Laboratory values were measured and calculated. Receiver operating characteristic curves were generated to calculate the cutoff value. Univariate and multivariate analyses using the COX proportional hazard model were performed for RFS. The effects of PNI and age on RFS were investigated by the Kaplan-Meier method. Clinical characteristics and PNI were tested with the chi-square test. Univariate and multivariate logistic analyses were conducted to evaluate the predictive value of PNI for lymph node metastasis.ResultsIn the multivariate Cox analysis, age, PNI and lymph node metastasis were independent prognostic indicators for RFS. The Kaplan-Meier method showed that the lower PNI group and age older than 55 years group displayed poor RFS. A low preoperative PNI was remarkably correlated with age, sex, extrathyroidal invasion, T stage, N stage and TNM stage. PNI was the only preoperative hematological indicator for lateral lymph node metastasis.ConclusionsAmong the preoperative hematological indicators, PNI may serve as a promising and effective predictor for RFS and lateral lymph node metastasis in PTC patients.

Impact of Surgical and Anesthetic Procedures after Colorectal Cancer Surgery: A Propensity Score-Matched Cohort Study (The PROCOL Study).

Background: Surgical inflammatory pain decreases the innate and adaptive immune antitumor response and favors residual circulating tumor cells. Objectives: This study investigated whether minimally invasive surgeries (laparoscopic and robotic procedures), which are less painful and inflammatory, improved oncological outcomes after colorectal resection compared to laparotomy. Methods: This research was a single-center propensity score-matched study involving patients who underwent colectomy and rectum resection from July 2017 to December 2019. Results: Seventy-four laparotomies and 211 minimally invasive procedures were included. Minimally invasive procedures were associated with less blood loss (0 mL vs. 75 mL, p < 0.001), shorter length of stay (8 days vs. 12 days, p < 0.001), and fewer complications at 3 months (11.8% vs. 29.4%, p = 0.02) compared to laparotomies. No difference in overall survival (OS) and recurrence-free survival (RFS) at 3 years between groups was observed. Univariate Cox regression analyses demonstrated that age and ASA > 3 can negatively impact OS, while adjuvant chemotherapy can positively influence OS. pT3-T4 stage and postoperative pain could negatively influence RFS. Multivariate Cox regression analyses concluded that age (HR 1.08, p < 0.01) and epidural analgesia (HR 0.12, p = 0.03) were predictors for OS. Lidocaine infusion (HR 0.39, p = 0.04) was a positive predictor for RFS. Conclusions: Minimally invasive procedures reduce postoperative complications and shorten the length of hospital stay compared to major surgeries without improving prognosis. However, the administration of local anesthetics through neuraxial anesthesia or intravenous infusion could improve survival and decrease the occurrence of relapses.

M2BPGi Correlated with Immunological Biomarkers and Further Stratified Recurrence Risk in Patients with Hepatocellular Carcinoma

Introduction Novel biomarkers reflecting liver fibrosis and the immune microenvironment may correlate with the risk of hepatocellular carcinoma (HCC) recurrence. This study aimed to evaluate the prognostic value of serum biomarkers in predicting HCC recurrence. Methods Serum biomarkers, including M2BPGi, IL-6, IL-10, CCL5, VEGF-A, soluble PD-1, PD-L1, TIM-3, and LAG-3, were measured in 247 patients with HCC undergoing surgical resection. Factors associated with recurrence-free survival (RFS) and overall survival (OS) were evaluated. The ERASL-post model and IMbrave050 criteria were used to define HCC recurrence risk groups. Results Serum M2BPGi levels significantly correlated with FIB-4 score, APRI, ALBI score, AFP, ALT, AST, IL-10, CCL5, VEGF-A, soluble PD-1, PD-L1, TIM-3, and LAG-3 levels. M2BPGi, VEGF-A, soluble PD-1, and TIM-3 levels significantly correlated with RFS. In multivariate analysis, M2BPGi &amp;gt;1.5 COI (hazard ratio (HR) = 2.100, p &amp;lt; 0.001), tumor size &amp;gt;5 cm (HR = 1.859, p = 0.002), multiple tumors (HR = 2.562, p &amp;lt; 0.001), AFP &amp;gt;20 ng/mL (HR = 2.141, p &amp;lt; 0.001), and microvascular invasion (HR = 1.954, p = 0.004) were independent predictors of RFS. M2BPGi levels significantly stratified the recurrence risk in ERASL-post and IMbrave050 risk groups. An M2BPGi-based model could significantly discriminate RFS in the overall cohort as well as in the IMbrave050 low- and high-risk groups. M2BPGi &amp;gt;1.5 COI was also an independent predictor of OS after resection (HR = 2.707, p &amp;lt; 0.001). Conclusion Serum M2BPGi levels significantly correlated with surrogate markers of liver fibrosis, liver function, and immunology. M2BPGi is a significant predictor of HCC recurrence and survival after resection and could be incorporated into recurrence-prediction models.

Relationships between postoperative recurrences and standardized uptake value on 18F-fluorodeoxyglucose-positron emission tomography in patients with resectable and borderline resectable pancreatic ductal adenocarcinoma who underwent curative pancreatic resection after neoadjuvant chemoradiotherapy

BackgroundThis study aimed to examine postoperative recurrence after curative pancreatic resection following neoadjuvant chemoradiotherapy (NACRT) in patients with resectable (R-) and borderline resectable (BR-) pancreatic ductal adenocarcinoma (PDAC), focusing on its relationship with the standardized uptake value (SUV) on 18F-fluorodeoxyglucose-positron emission tomography (FDG-PET). MethodThe postoperative initial recurrence patterns were examined in patients with R- and BR-PDAC who underwent NACRT followed by curative pancreatic resection. Data collected from three prospective clinical trials were retrospectively analysed. ResultsAfter a median follow-up of 29 months, 91 (60 %) of 151 patients experienced postoperative recurrence. The median recurrence-free survival (RFS) for all patients was 18 months. The sites of first recurrence were lung-only in 24 (26 %) patients, liver-only in 23 (25 %), local-only in 11 (12 %), peritoneum-only in 10 (11 %), other single site in 5 (5 %), and multiple sites in 19 (21 %) patients. Multivariate analysis identified the maximum standardized uptake value (SUVmax) on FDG-PET at diagnoses ≥5.40 (hazard ratio [HR], 1.62; 95 % confidence interval [CI], 1.01–2.61; p = 0.045) and node-positive pathology (HR, 2.01; 95 % CI, 1.32–3.08; p = 0.001) as significant predictors of RFS. Furthermore, the SUVmax at initial diagnosis and after NACRT correlated with liver metastasis. ConclusionR- and BR-PDACs with high SUV on FDG-PET at diagnosis are risk factors for postoperative recurrence. Among patients who undergo surgery after NACRT, those with a high SUVmax at diagnosis or post-NACRT require careful attention for postoperative liver recurrence.

Development and Validation of a Nomogram for Predicting Postoperative Recurrence-Free Survival in Patients With Nonmetastatic Pathological T3a Stage Renal Cell Carcinoma

BackgroundTo establish a nomogram predicting postoperative recurrence-free survival (RFS) in patients with nonmetastatic renal cell carcinoma (RCC) of pathological T3a (pT3a) stage undergoing nephrectomy. Materials and MethodsA retrospective review included 668 patients with pT3a RCC between 2008 and 2019, randomly divided into training and validation groups (7:3 ratio). Cox regression analysis established the RFS-predicting nomogram in the training group. Nomogram performance was assessed using Harrell's concordance index (C-index), time-dependent receiver operating characteristic curve, decision curve analysis, and Kaplan-Meier survival analysis. ResultsOf the 668 patients with pT3a RCC, 167 patients experienced local recurrence or distant metastasis. Using multivariable Cox regression analysis, tumor size, ISUP grade, necrosis, capsular invasion, pT3a invasion pattern were identified as the significant predictors for RFS to establish the nomogram. The C-index of the nomogram was 0.753 (95% CI, 0.710-0.796) and 0.762 (95% CI, 0.701-0.822) for the training and validating group, respectively. The areas under the 1-year, 3-year and 5-year RFS receiver operating characteristic curves were 0.814, 0.769 and 0.768, respectively. Decision curve analysis showed the optimal application of the model in clinical decision-making. Patients with low risk T3a RCC have better RFS than those with high risk T3a RCC. ConclusionTumor size, ISUP grade, necrosis, capsular invasion and T3a invasion patterns were independent risk factors for worse RFS in patients with nonmetastatic pT3a RCC. The current nomogram could effectively predict the RFS of patients with nonmetastatic pT3a RCC.

Comprehensive assessment of postoperative recurrence and survival in patients with cervical cancer

BackgroundThe prediction of postoperative recurrence and survival in cervical cancer patients has been a major clinical challenge. The combination of clinical parameters, inflammatory markers, intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI), and MRI-derived radiomics is expected to support the prediction of recurrence-free survival (RFS), disease-free survival (DFS), tumor-specific survival (CSS), and overall survival (OS) of cervical cancer patients after surgery. MethodsA retrospective analysis of 181 cervical cancer patients with continuous follow-up was completed. The parameters of IVIM-DWI and radiomics were measured, analyzed, and screened. The LASSO regularization was used to calculate the radiomics score (Rad-score). Multivariate Cox regression analysis was used to construct nomogram models for predicting postoperative RFS, DFS, CSS, and OS in cervical cancer patients, with internal and external validation. ResultsClinical stage, parametrial infiltration, internal irradiation, D-value, and Rad-score were independent prognostic factors for RFS; Squamous cell carcinoma antigen, internal irradiation, D-value, f-value and Rad-score were independent prognostic factors for DFS; Maximum tumor diameter, lymph node metastasis, platelets, D-value and Rad-score were independent prognostic factors for CSS; Lymph node metastasis, systemic inflammation response index, D-value and Rad-score were independent prognostic factors for OS. The AUCs of each model predicting RFS, DFS, CSS, and OS at 1, 3, and 5 years were 0.985, 0.929, 0.910 and 0.833, 0.818, 0.816 and 0.832, 0.863, 0.891 and 0.804, 0.812, 0.870, respectively. ConclusionsNomograms based on clinical and imaging parameters showed high clinical value in predicting postoperative RFS, DFS, CSS, and OS of cervical cancer patients and can be used as prognostic markers.

Enhancing Two-Year Recurrence-Free Survival Prediction in Non-Small Cell Lung Cancer (NSCLC) Patients Using Tumor-Centric Attention Network (TCA-Net)

Enhancing Two-Year Recurrence-Free Survival Prediction in Non-Small Cell Lung Cancer (NSCLC) Patients Using Tumor-Centric Attention Network (TCA-Net)

Development and validation of a nomogram to predict recurrence for clinical T1/2 clear cell renal cell carcinoma patients after nephrectomy

ObjectiveTo develop and validate a nomogram for predicting recurrence-free survival (RFS) for clinical T1/2 (cT1/2) clear cell renal cell carcinoma (ccRCC) patients after nephrectomy.MethodsClinicopathological and survival data from 1289 cT1/2 ccRCC patients treated at the Second Hospital of Tianjin Medical University between 2017 and 2020 were included. Cox regression analysis was used to identify independent risk factors in 902 and 387 ccRCC patients in the training and validation cohorts, respectively, and construct the nomogram. The performance of the nomogram was assessed through calibration plots, time-dependent receiver operating characteristic (ROC) curves, C-index (concordance-index), and decision curve analysis (DCA). Kaplan-Meier curves were used to evaluate the probability of RFS in patients with different recurrence risks.ResultsAge, tumor size, surgical approach, Fuhrman grade, and pT3a upstage were identified as independent predictors of RFS. The area under the curve (AUC) for the 3-year and 5-year RFS ROC curves were 0.791 and 0.835 in the training cohort, and 0.860 and 0.880 in the validation cohort. The DCA and calibration plots demonstrated the optimal application and excellent accuracy of the nomogram for predicting 3-year and 5-year RFS. Kaplan-Meier curves revealed significant differences in RFS among the three risk groups in both the training and validation cohorts. Clinically, the developed nomogram provides a more precise tool for risk stratification, enabling tailored postoperative management and surveillance strategies, ultimately aiming to improve patient outcomes.ConclusionsWe developed a nomogram for predicting RFS in cT1/2 ccRCC patients after nephrectomy with high accuracy. The clinical implementation of this nomogram can significantly enhance clinical decision-making, leading to improved patient outcomes and optimized resource utilization in the management of ccRCC.

Preoperative neutrophil-to-lymphocyte ratio predicts recurrence of esophageal squamous cell carcinoma after neoadjuvant triplet chemotherapy.

Neoadjuvant chemotherapy followed by esophagectomy is the standard treatment for resectable advanced esophageal squamous cell carcinoma (ESCC) in Japan. Triplet chemotherapy is the standard neoadjuvant regimen. Inflammatory markers such as neutrophil-to-lymphocyte ratio (NLR) are well-known prognostic factors for esophageal cancer. However, their usefulness in patients with resectable advanced disease undergoing esophagectomy after neoadjuvant triplet chemotherapy is unknown. We examined 144 ESCC patients who underwent neoadjuvant triplet chemotherapy followed by esophagectomy between January 2015 and December 2020 to investigate the relationship between inflammatory markers and recurrence-free survival (RFS). Optimal marker cutoff values for RFS were determined using receiver operating characteristic curve analysis. Patients were divided into high and low NLR groups (NLR cutoff, 3.0). NLR was high in 61 patients and low in 83. Univariate analyses demonstrated that low NLR was significantly associated with worse RFS (p = 0.049). Multivariate analyses demonstrated that high NLR was an independent predictor of RFS (odds ratio, 1.911; 95% confidence interval, 1.098-3.327; p = 0.022). RFS significantly differed between the low and high NLR groups. RFS did not significantly differ between the patients when stratified according to the other inflammatory markers. Preoperative NLR is an easily obtained and useful predictor of RFS in patients with resectable advanced ESCC treated with neoadjuvant triplet chemotherapy followed by esophagectomy.

New online dynamic nomograms to predict recurrence-free and overall survival after resection of endometrial cancer: a single-institution retrospective cohort study

PurposeThe significant global burden of endometrial cancer (EC) and the challenges associated with predicting EC recurrence indicate the need for a dynamic prediction model. This study aimed to propose nomograms based on clinicopathological variables to predict recurrence-free survival (RFS) and overall survival (OS) after surgical resection for EC.MethodsThis single-institution retrospective cohort study included patients who underwent surgical resection for EC. Web-based nomograms were developed to predict RFS and OS following resection for EC, and their discriminative and calibration abilities were assessed.ResultsThis study included 289 patients (median age, 56 years). At a median follow-up of 51.1 (range, 4.1–128.3) months, 13.5% (39/289) of patients showed relapse or died, and 10.7% (31/289) had non-endometrioid tumors (median size: 2.8 cm). Positive peritoneal cytology result (hazard ratio [HR], 35.06; 95% confidence interval [CI], 1.12–1095.64; P = 0.0428), age-adjusted Charlson comorbidity index (AACCI) (HR, 52.08; 95% CI, 12.35–219.61; P < 0.001), and FIGO (Federation of Gynecology and Obstetrics) stage IV (HR, 138.33; 95% CI, 17.38–1101.05; P < 0.001) were predictors of RFS. Similarly, depth of myometrial invasion ≥ 1/2 (HR, 1; 95% CI, 0.46–2.19; P = 0.995), AACCI (HR, 93.63; 95% CI, 14.87–589.44; P < 0.001), and FIGO stage IV (HR, 608.26; 95% CI, 73.41–5039.66; P < 0.001) were predictors of OS. The nomograms showed good predictive capability, positive discriminative ability, and calibration (RFS: 0.895 and OS: 0.891).ConclusionThe nomograms performed well in internal validation when patients were stratified into prognostic groups, offering a personalized approach for risk stratification and treatment decision-making.

The prognostic significance of preoperative platelet-to-lymphocyte ratio and interleukin-6 level in non-muscle invasive bladder cancer.

Non-muscle invasive bladder cancer (NMIBC) is the most prevalent type of bladder cancer, typically associated with a favorable prognosis and a risk of recurrence during the follow-up period. Inflammatory markers have been used to predict prognosis in various cancer types. The aim of this study was to explore the prognostic value of the readily accessible inflammatory markers, platelet-to-lymphocyte ratio (PLR) and interleukin-6 (IL-6), in NMIBC. The study comprised a retrospective analysis of clinical data collected from NMIBC patients diagnosed between October 2018 and October 2020. PLR was calculated using the routine preoperative blood test results, and preoperative IL-6 levels were recorded. Receiver operating characteristic (ROC) curves were generated for PLR and IL-6 level and the optimal cut-off values were determined using Youden's index. Survival curves were generated to evaluate the association between PLR and IL-6, and recurrence-free survival (RFS), and univariate and multivariate analysis were performed using the Cox proportional hazards regression model. A nomogram and calibration curve were generated to assess the clinical significance of the model. The ROC curves demonstrated that PLR and IL-6 levels were significantly associated with tumor pathology grade, with area under the curve (AUC) values of 0.833 (95% CI 0.757, 0.910) for PLR and 0.724 (95% CI 0.622, 0.825) for IL-6 levels. PLR and IL-6 levels were also positively associated with tumor recurrence, with AUC values of 0.647 (95% CI 0.538, 0.756) and 0.846 (95% CI 0.769, 0.924), respectively. The survival curves indicated that patients with high PLR and high IL-6 levels had shorter RFS than those with low PLR and low IL-6 level (P < 0.01). Univariate Cox proportional hazards regression analysis showed that age, tumor size, tumor number, pathological grade, PLR and IL-6 were potential risk factors for NMIBC recurrence. Multivariate analysis further revealed that tumor number, smoking, PLR, and IL-6 were independent risk factors for NMIBC recurrence (P < 0.05). Preoperative peripheral blood inflammatory markers (PLR and IL-6) are useful predictors of RFS in NMIBC patients at the time of initial diagnosis. High PLR and high IL-6 were identified as independent risk factors for tumor recurrence and could serve as potential biological markers for prediction of NMIBC recurrence.

PET/CT based transformer model for multi-outcome prediction in oropharyngeal cancer

Background and purposeTo optimize our previously proposed TransRP, a model integrating CNN (convolutional neural network) and ViT (Vision Transformer) designed for recurrence-free survival prediction in oropharyngeal cancer and to extend its application to the prediction of multiple clinical outcomes, including locoregional control (LRC), Distant metastasis-free survival (DMFS) and overall survival (OS). Materials and MethodsData was collected from 400 patients (300 for training and 100 for testing) diagnosed with oropharyngeal squamous cell carcinoma (OPSCC) who underwent (chemo)radiotherapy at University Medical Center Groningen. Each patient’s data comprised pre-treatment PET/CT scans, clinical parameters, and clinical outcome endpoints, namely LRC, DMFS and OS. The prediction performance of TransRP was compared with CNNs when inputting image data only. Additionally, three distinct methods (m1-3) of incorporating clinical predictors into TransRP training and one method (m4) that uses TransRP prediction as one parameter in a clinical Cox model were compared. ResultsTransRP achieved higher test C-index values of 0.61, 0.84 and 0.70 than CNNs for LRC, DMFS and OS, respectively. Furthermore, when incorporating TransRP’s prediction into a clinical Cox model (m4), a higher C-index of 0.77 for OS was obtained. Compared with a clinical routine risk stratification model of OS, our model, using clinical variables, radiomics and TransRP prediction as predictors, achieved larger separations of survival curves between low, intermediate and high risk groups. ConclusionTransRP outperformed CNN models for all endpoints. Combining clinical data and TransRP prediction in a Cox model achieved better OS prediction.

Survival outcomes with adjuvant immunotherapy after hepatic resection in patients with intermediate/advanced (BCLC stage B/C) hepatocellular carcinoma: Insights from a propensity-matched multicenter study.

4096 Background: With the growing interest in adjuvant immunotherapy for reducing recurrence and improving prognosis after hepatic resection in hepatocellular carcinoma (HCC), particularly in high-risk patients, this study aims to evaluate the impact of adjuvant immunotherapy on long-term recurrence and survival in patients with intermediate/advanced HCC. Methods: A prospective multicenter database was utilized to identify patients who underwent curative-intent resection for Barcelona Clinic Liver Cancer (BCLC) stage B/C HCC. Propensity score matching (PSM) was employed to equate recurrence-free survival (RFS) and overall survival (OS) between patients receiving adjuvant immune checkpoint inhibitors (ICIs) and those who did not. Multivariate Cox-regression analysis was used to identify independent predictors of RFS and OS. Results: Out of 627 patients, 109 (23.3%) received adjuvant immunotherapy, with most ICI-related adverse reactions being grade I-II. The ICI agents used among these 109 patients were tislelizumab (51.4%, n=56), sintilimab (29.3%, n=32), camrelizumab (9.2%, n=10), pembrolizumab (6.4%, n=7), and toripalimab (3.7%, n=4). All patients receiving adjuvant immunotherapy completed at least 3 months of ICI treatment, with no patients discontinuing the treatment due to irAEs. PSM resulted in 99 matched pairs with comparable baseline characteristics. In this cohort, patients receiving adjuvant immunotherapy showed significantly improved median RFS (29.6 vs. 19.3 months, P=0.031) and OS (35.1 vs. 27.8 months, P=0.036) compared to those who did not. Multivariable analyses confirmed adjuvant immunotherapy as an independent factor for better RFS (HR: 0.630; 95% CI: 0.435-0.914; P=0.015) and OS (HR: 0.601; 95% CI: 0.401-0.898; P=0.013). Subgroup analyses suggested prognostic benefits of adjuvant immunotherapy in both intermediate and advanced-stage HCC. Conclusions: This real-world observational study indicates that adjuvant immunotherapy is associated with improved RFS and OS in patients undergoing curative-intent resection for intermediate/advanced HCC. These findings support the need for future randomized controlled trials to establish definitive evidence for this high-risk population.